Keto Shrimp Stir Fry | Diabetes Strong

By electricdiet / January 7, 2021


When you need an easy weeknight meal, this tasty keto shrimp stir fry takes less than half an hour to make. Plus, it’s packed with healthy protein and nutrients!

Stir fry served in two white bowls with forks, as seen from above

Looking for an easy weeknight meal that’s low in carbs, packed with lean protein and veggies, and takes less than half an hour to make?

Then you have to try this keto shrimp stir fry! Savory shrimp with crisp snow peas, bell peppers, and thinly-sliced carrots come together for a bowl that is simply irresistible.

Not to mention, the entire dish is made in one pan! So cooking is simple and clean-up is a breeze. Just what everyone needs on a busy weeknight, right?

So when you want something healthy that’s bursting with flavor and can be ready in a snap, I highly recommend giving this low carb dish a try.

How to make keto shrimp stir fry

This tasty one-pan meal comes together in just 8 simple steps.

Stir fry ingredients in separate ramekins, as seen from above

Step 1: In a large pan over medium heat, heat the butter until melted.

Step 2: Add the shrimp, salt, and pepper, then stir. Cook for 5 minutes, stirring occasionally, until the shrimp are cooked through and light pink.

shrimp cooking in the skillet

Step 3: Remove the shrimp from the pan.

Step 4: In the same pan over medium-high heat, add the sesame oil. Once hot, add the vegetables, tamari, and rice vinegar.

Step 5: Cook for another 7 – 10 minutes until the vegetables are cooked.

Step 6: Add the garlic and ginger, then mix well. Add salt to taste if desired.

Step 7: Add the shrimp back to the pan and stir to combine with the other ingredients.

Stir fry in skillet, as seen from above

Step 8: Garnish with chopped green onions and sesame seeds before serving.

That’s it! Your healthy low carb dinner is ready to enjoy.

Close-up of stir fry in a white bowl with a fork

Is shrimp good for keto?

Shrimp, like most seafood, is a great source of lean protein. But is it a good ingredient for someone following a keto way of eating?

In fact, there is only 1 gram of carbs per cup of cooked shrimp. So I would say YES, this shellfish is a great option for anyone watching their carbs!

That same serving size also packs in 24 grams of protein. Not to mention, shrimp are rich in nutrients like zinc, magnesium, iron, and more.

Thanks to its nutritional profile and very low carb content, shrimp makes a great protein for any keto or low carb meal.

Storage

Stir fry is always best when it’s hot out of the pan (or wok). But if you find yourself with leftovers, don’t let them go to waste!

Simply store them in an airtight container in the refrigerator. I recommend enjoying your leftovers within 3 days.

Close-up of stir fry in a white bowl with a fork

Other low carb seafood recipes

Seafood is such a great option for high-protein, low-carb meals. If you’re looking for more ideas, here are a few of my favorite keto seafood recipes I know you’ll enjoy:

You can also check out this roundup of healthy low-carb seafood recipes for even more options!

When you’ve tried this stir fry recipe, please don’t forget to let me know how you liked it and rate the recipe in the comments below!

Recipe Card

Keto Shrimp Stir Fry

When you need an easy weeknight meal, this tasty keto shrimp stir fry takes less than half an hour to make. Plus, it’s packed with healthy protein and nutrients!

Prep Time:10 minutes

Cook Time:15 minutes

Total Time:25 minutes

Servings:4

Keto shrimp stir fry in a white bowl with a spoon

Instructions

  • In a large pan over medium heat, heat the butter until melted.

  • Add the shrimp, salt, and pepper, then stir. Cook for 5 minutes, stirring occasionally, until the shrimp are cooked through and light pink.

  • Remove the shrimp from the pan.

  • In the same pan over medium-high heat, add the sesame oil. Once hot, add the vegetables, tamari, and rice vinegar.

  • Cook for another 7 – 10 minutes until the vegetables are cooked.

  • Add the garlic and ginger, then mix well. Add salt to taste if desired.

  • Add the shrimp back to the pan and stir to combine with the other ingredients.

  • Garnish with chopped green onions and sesame seeds before serving.

Recipe Notes

This recipe is for 4 servings of stir fry.
To lower the carbs, reduce or omit the carrots.
Leftovers can be stored in an airtight container in the refrigerator for up to 3 days.

Nutrition Info Per Serving

Nutrition Facts

Keto Shrimp Stir Fry

Amount Per Serving

Calories 231
Calories from Fat 95

% Daily Value*

Fat 10.6g16%

Saturated Fat 3.7g19%

Trans Fat 0g

Polyunsaturated Fat 1.8g

Monounsaturated Fat 2.1g

Cholesterol 172.5mg58%

Sodium 1006.9mg42%

Potassium 469.2mg13%

Carbohydrates 14g5%

Fiber 7.3g29%

Sugar 6g7%

Protein 18.7g37%

Net carbs 6.7g

* Percent Daily Values are based on a 2000 calorie diet.

Course: Main Course

Cuisine: American

Keyword: gluten-free, Grilled shrimp, low carb, shrimp stir fry, stir-fry



Sell Unused Diabetic Strips Today!

Linking Kidney and Cardiovascular Complications in Diabetes—Impact on Prognostication and Treatment: The 2019 Edwin Bierman Award Lecture

By electricdiet / January 5, 2021


Abstract

In diabetes, increasing albuminuria and decreasing glomerular filtration rate are hallmarks of chronic kidney disease in diabetes and increase the risk of atherosclerotic cardiovascular events and mortality as well as the risk for end-stage kidney disease. For two decades, standard of care has been controlling risk factors, such as glucose, blood pressure, lipids, and lifestyle factors, and specifically use of agents blocking the renin-angiotensin system. This has improved outcome, but a large unmet need has been obvious. After many failed attempts to advance the therapeutic options, the past few years have provided several new promising treatment options such as sodium–glucose cotransporter 2 inhibitors, endothelin receptor antagonists, glucagon-like peptide 1 agonists, and nonsteroidal mineralocorticoid receptor antagonists. The benefits and side effects of these agents demonstrate the link between kidney and heart; some have beneficial effects on both, whereas for other potentially renoprotective agents, development of heart failure has been a limiting factor. They work on different pathways such as hemodynamic, metabolic, inflammatory, and fibrotic targets. We propose that treatment may be personalized if biomarkers or physiological investigations assessing activity in these pathways are applied. This could potentially pave the way for precision medicine, where treatment is optimized for maximal benefit and minimal adverse outcomes. At least it may help prioritizing agents for an individual subject.

Introduction

The global burden of diabetes is currently estimated to affect 463 million individuals, or 1 in 11, according to the International Diabetes Federation, and projections suggest a 48% increase in the prevalence to 700 million people by 2045 (1). Diabetes is associated with a two- to fourfold increased risk for atherosclerotic cardiovascular disease (CVD) compared with the background population, and 30–40% with diabetes are affected by chronic kidney disease characterized by increased albuminuria or decreased glomerular filtration rate (GFR) (or diabetic kidney disease [DKD]). The presence of kidney disease increases the risk of CVD, and the combination is a deadly cocktail. Increasing albuminuria or decreasing GFR increases the risk of CVD and mortality (2) (see Fig. 1) as well as the risk for end-stage kidney disease. Furthermore, albuminuria and GFR levels form the basis on which chronic kidney disease is staged according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines (3). Early-onset DKD may shorten life expectancy by 14–16 years (4), and excess mortality in diabetes is primarily due to mortality in DKD (5), with a 6-fold increased risk for mortality with albuminuria and 15-fold increased risk with albuminuria and reduced GFR (5).

Figure 1
Figure 1

Declining eGFR and increasing albuminuria are associated with mortality in individuals with diabetes. ACR, albumin-to-creatinine ratio (2).

The aim of this review is to discuss the link between kidney and heart in diabetes, as it is important to understand for optimal treatment and prevention of late complications. Deckert et al. (6) formulated the Steno hypothesis, suggesting that albuminuria reflects widespread vascular damage and proposing a linkage between DKD and CVD. Here, we will discuss recent investigations of functional links showing connections between kidney and heart damage. We will evaluate biomarkers ranging from albuminuria to omics, which could pave the way to a personalized medicine approach in kidney and heart diseases. Finally, we will describe how these biomarkers can be used when applying new therapies such as sodium–glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide 1 receptor agonists (GLP-1RA), and mineralocorticoid receptor antagonists. These agents have different mechanisms of action, and the biomarkers can help tailoring treatment to the pathophysiology. The cardio-renal link is stressed by the fact that some of these agents may work on the kidney to “save” the heart and others protect the kidney but with a risk for heart failure.

Investigations of Functional Links—Connections Between the Kidney and Heart

A chronic cardio-renal syndrome has been described where impaired renal function with retention of uremic solutes, hypertension, fluid retention, and anemia affect the heart. On the other hand, a failing heart with low cardiac output with hypoperfusion and atherosclerosis has detrimental impact on renal function (7). In diabetes, the coexistence of microvascular and macrovascular complications increases mortality, and we aimed to investigate the associations between albuminuria and vascular and ventricular function of the heart.

Major advances in noninvasive imaging enable the investigation of new aspects of the cardiac microcirculation. Among these methods is quantitative cardiac positron emission tomography (PET), which allows measurement of myocardial blood flow at rest and during pharmacologically induced hyperemic conditions. The ratio between flow in the two situations is termed the myocardial flow reserve and mirrors the function of the large epicardial arteries and the microcirculation of the myocardium. Thus, in individuals without epicardial coronary stenosis, cardiac PET can be used to assess the function of the microcirculation, including the combined function of cells in the vascular smooth muscle and endothelial cells. A higher myocardial flow reserve represents enhanced ability to increase the myocardial blood flow under stress.

A hybrid scanner can combine cardiac PET with computed tomography (CT), enabling the simultaneous estimation of the coronary artery calcium score. A high coronary artery calcium score can identify asymptomatic individuals who are at higher risk of coronary heart disease and mortality (8) and is a specific marker of atherosclerosis, independent of its etiology.

In the past, clinical use of cardiac PET/CT was limited by the requirement of an expensive PET/CT scanner and an on-site cyclotron for radioisotope production. The development of less expensive PET/CT scanners has resulted in a wider clinical application of cardiac PET/CT. Moreover, rubidium-82 (82Rb) is a PET myocardial perfusion tracer produced with a strontium-82 (82Sr)/82Rb generator and therefore can be used in centers without immediate access to an on-site cyclotron. PET myocardial perfusion imaging with 82Rb has several other advantages including high image quality and low radiation dose as well as rapid examination time. Therefore, cardiac 82Rb PET/CT has replaced the classical myocardial scintigraphy with single-photon emission CT as routine examination for individuals with suspected cardiac ischemia.

Taking advantage of cardiac 82Rb PET/CT, we have conducted two cross-sectional studies. We aimed to gain information on the prevalence of reduced myocardial flow reserve and increased coronary artery calcium score in individuals with type 1 diabetes and type 2 diabetes (with or without albuminuria as a measure of renal and microvascular damage) while comparing them with healthy control subjects. Moreover, we wanted to examine the association between the myocardial flow reserve and the coronary artery calcium score.

The first study included 60 individuals with type 2 diabetes, but free of overt CVD, stratified by presence/history of albuminuria (≥30 mg/24 h) (n = 30) or normoalbuminuria (<30 mg/24 h) (n = 30), and 30 age- and sex-matched healthy control subjects (9). The second study comprised 60 individuals with type 1 diabetes stratified by presence/history of macroalbuminuria (≥300 mg/g; n = 30) or normoalbuminuria (<30 mg/g; n = 30) (10). Different cutoff values to define reduced myocardial flow reserve have been applied depending on the characteristic of the study population, and a cutoff of 2.5 has been suggested in individuals without obstructive coronary artery disease (11). We therefore prespecified a cutoff of 2.5. An elevated coronary artery calcium score was defined as an Agatston score >300.

Cardiac PET/CT in Control Subjects and in Participants With Type 1 or Type 2 Diabetes Stratified by Urinary Albumin Excretion

Table 1 summarizes the sex, age, level of albuminuria, and main results from the cardiac PET/CT scans in the participants, grouped into control subjects and individuals with type 1 or type 2 diabetes (stratified by urinary albumin excretion level).

Table 1

Myocardial flow rate reduced and coronary artery calcium score elevated in type 1 and type 2 diabetes subjects with albuminuria compared with subjects with normoalbuminuria or control subjects

The main findings in type 1 diabetes were as follows:

  • 1) Myocardial microvascular function was comparable in the healthy control subjects and the individuals with type 1 diabetes and normoalbuminuria but impaired in the presence of macroalbuminuria (see Fig. 2). This indicates that there is a separate microvascular injury in the heart of individuals with type 1 diabetes and albuminuria compared with normoalbuminuria.

  • 2) Coronary calcification was higher in individuals with type 1 diabetes compared with healthy control subjects.

  • 3) Coronary calcification was comparable in normo- and macroalbuminuric individuals with type 1 diabetes; however, when coronary artery calcium score was dichotomized, the frequency of elevated coronary artery calcium score (>300) was higher in individuals with macro- compared with normoalbuminuria. Therefore, it might be that there is an association between coronary artery calcium score and albuminuria in type 1 diabetes, but that we had limited power to detect it because of a skewed distribution of the coronary artery calcium scores.

Figure 2
Figure 2

Myocardial flow reserve (MFR) associated with albuminuria (urinary albumin-to-creatinine ratio [UACR]) in type 1 diabetes (10).

The main findings in type 2 diabetes were as follows:

  • 1) Myocardial microvascular function was impaired in individuals with type 2 diabetes and normoalbuminuria compared with healthy control subjects.

  • 2) Myocardial microvascular function was impaired in the presence of albuminuria compared with normoalbuminuria, and 83% of individuals with albuminuria had impaired myocardial flow reserve (<2.5) compared with 40% with normoalbuminuria.

  • 3) Coronary calcification was higher in individuals with type 2 diabetes compared with healthy control subjects and in individuals with type 2 diabetes and albuminuria compared with normoalbuminuria.

The Relationship Between Cardiac Vascular Function and Atherosclerosis

For both type 1 and type 2 diabetes, we demonstrated a significant, but modest, negative correlation between myocardial flow reserve and coronary artery calcium score (R2 = 0.20, P < 0.001 [10], and R2 = 0.24, P < 0.001 [9], respectively). Thus, the relationship between functional changes (myocardial flow reserve) and anatomical features of atherosclerosis (coronary artery calcium score) may not be straightforward and these measures might expose different pathophysiological processes and differences in time course. This implies that a coronary artery calcium score of 0 cannot solely be used as a gatekeeper, as we measured myocardial flow reserves ranging from 1.8 to 4.9 in individuals with a coronary artery calcium score of 0 (9).

The Relationship Between Cardiac Vascular Function and Cardiac Autonomic Dysfunction

Cardiac autonomic neuropathy is a severe and often overlooked complication in diabetes associated with kidney disease, increased mortality, and silent myocardial ischemia. Cardiac autonomic dysfunction, including loss of cardiac sympathetic integrity, may contribute to impaired myocardial blood flow regulation.

The cardiac autonomic function can be evaluated with simple bedside tests using heart rate variability indices or response in heart rate to standing, slow breathing, or the Valsalva maneuver (cardiovascular autonomic reflex tests). These indirect tests can reveal altered sympathetic and parasympathetic activity. Cardiac radionuclide imaging using the nonmetabolized norepinephrine analog metaiodobenzylguanidine (MIBG) allows a direct assessment of the integrity of the adrenergic cardiac innervation and may be more reliable for evaluation of cardiac autonomic function and might also diagnose cardiovascular autonomic neuropathy in early clinical stages before it can be detected by the indirect tests.

In the two cross-sectional studies described above, we evaluated the association between the cardiac autonomic function and the cardiac vascular function (assessed as the myocardial flow reserve). The cardiac autonomic function was evaluated with use of cardiac MIBG imaging, heart rate variability indices, and cardiovascular autonomic reflex tests (12,13).

In both studies, we demonstrated that impaired function of the cardiac autonomic system correlated with lower myocardial flow reserve. The association was strongest when the cardiac autonomic function was evaluated with cardiac MIBG imaging, as it persisted after comprehensive adjustment (12,13).

Cardiac Autonomic Function in Control Subjects and in Participants With Type 1 or Type 2 Diabetes Stratified by Urinary Albumin Excretion

We demonstrated a general impaired function of the cardiac autonomic system in individuals with type 1 or type 2 diabetes and normoalbuminuria as compared with healthy control subjects (12,13). Compared with participants with normoalbuminuria, individuals with albuminuria had similar cardiac autonomic function assessed by the cardiac MIBG imaging and the heart rate variability indices in type 1 and type 2 diabetes. However, cardiac autonomic neuropathy was more frequent in the participants with albuminuria compared with in those with normoalbuminuria when evaluated by the cardiac autonomic reflex tests.

Ongoing Study: Myocardial Flow Reserve as Risk Marker

As the myocardial flow reserve estimates the microvascular function of the heart, it may provide unique risk information beyond the extent of coronary atherosclerosis, and identification of early stages of coronary microvascular disease may provide new prospects for risk stratification.

The predictive value of the myocardial flow reserve has been evaluated for mortality outcomes in individuals with diabetes (mix of type 1 and type 2) referred for cardiac 82Rb PET/CT due to chest pain or dyspnea. More than 60% had previous CVD and the baseline examination was in 2006–2010, before modern multifactorial treatment, including SGLT2 inhibitors and GLP-1RA, was established (14). The study demonstrated that impaired myocardial flow reserve (<1.6) was associated with a higher rate of cardiac death (14). However, the predictive value of myocardial flow reserve for CVD and mortality in asymptomatic individuals with diabetes remains to be investigated. Therefore, we are conducting a prospective study with the primary aim of identifying subpopulations at high risk of developing CVD during follow-up, among asymptomatic individuals with type 2 diabetes, using myocardial flow reserve and coronary artery calcium score.

Heart Failure and Association With Microvascular Damage

In recent years, there has been increasing focus on heart failure in diabetes, as this is associated with a poor prognosis. Heart failure is particularly common in subjects with type 2 diabetes and kidney disease (15) but is also a concern in type 1 diabetes (16). To study whether systolic dysfunction could be detected in individuals with type 1 diabetes without known ischemic heart disease, we conducted a study with echocardiography measuring global longitudinal strain (GLS) as a sensitive measure of systolic function in 1,065 individuals with type 1 diabetes (17). Compared with 198 healthy control subjects, there was a significantly impaired systolic function (GLS) in diabetes. However, for subjects with normoalbuminuria there were no difference when compared with healthy control subjects, whereas presence of micro- or macroalbuminuria was associated with increasingly impaired GLS, again highlighting a link between the kidney (albuminuria) and heart. In evaluations after 7.5 years of follow-up, measures of systolic and diastolic function predicted cardiovascular events independently of guideline-recommended clinical risk factors alone (18).

In a cohort of 1,030 subjects with type 2 diabetes with or without previous CVD, we also found albuminuria to be related to echocardiographic abnormalities (19). When this cohort was followed for 4.8 years, a range of echocardiographic parameters predicted CVD in this cohort; however, in multivariable analyses, mean E/e′ (a measure of diastolic dysfunction) was the strongest predictor and had the highest model performance. We observed a hitherto undescribed sex interaction, as mean E/e′ performed best in men, whereas in women GLS was best (20).

Markers From Different Pathways Predict Heart and Kidney Outcomes

As an alternative description of the cardio-renal syndrome, Zannad and Rossignol described risk factors such as diabetes and hypertension, activating pathways such as inflammation, oxidative stress leading to fibrosis, and inflammation affecting both the kidney and heart (21). This model can be used for precision medicine in using biomarkers related to the different activated pathways to guide therapy (Fig. 3). After many years with renin-angiotensin system (RAS) blocking agents as the only therapy for DKD, we are currently in the fortunate situation of having a number of potential therapies for DKD that have been or are being evaluated in phase 3 studies with cardiovascular or renal primary end points. Combination therapy including all agents is probably neither feasible nor safe, and assuming pathophysiological heterogeneity between people with DKD, application of therapies based on relevant biomarkers may thus be a way forward to optimize benefit and minimize adverse events.

Figure 3
Figure 3

Potential risk factors, pathological pathways, and corresponding markers on the path to heart and kidney complications. B-Glucose, blood glucose; BNP, brain natriuretic peptide; U-CKD273, urinary proteomic marker of chronic kidney disease; u-CAD238, urinary proteomic marker of coronary heart disease; proC6, serum PRO-C6 (marker of fibrosis); 8-oxodG, 8-oxo-7,8-dihydro-2′-deoxyguanosine (see text for details).

Vascular Damage

As discussed initially, elevated urinary albumin excretion reflects widespread vascular damage and predicts development of renal failure and cardiovascular events. In addition, treatment-induced reductions are associated with improved renal and cardiac prognosis as initially demonstrated in smaller studies (22,23) and recently documented in meta-analyses of observational (24) and intervention (25) studies. Thus, albuminuria has been an inclusion criterion in most renal outcome studies in diabetes and a surrogate outcome in many phase 2 studies. For several decades, elevated albuminuria has been clinically used as an indicator for cardioprotective therapy with RAS-blocking agents.

Troponin T has, in addition to its use in acute settings as a marker of myocardial damage, been used to demonstrate vascular, cardiac, and renal risk in both type 1 and type 2 diabetes and could be a marker of increased risk for atherosclerosis (26,27). Trimethylamine-N-oxide (TMAO), is a metabolite of phosphatidylcholine, choline, and carnitine produced by the gut microbiota from ingested animal food sources (meat, eggs, and fish). A higher level of TMAO has been suggested as an independent risk factor for renal impairment and CVD. First, simply as a biomarker of recurrent CVD in people with known CVD (28), but TMAO might be mechanistically involved in the pathogenesis of CVD, as it has been shown to be associated with higher levels of cholesterol in macrophages and it has been shown to enhance the risk of thrombosis by promoting platelet hyperactivity. We demonstrated that higher TMAO was associated with renal and cardiac events during follow-up in type 1 diabetes (29), although not independently of renal function, maybe because it is a marker of filtration or because the effect is mediated by impaired renal function. In type 2 diabetes, it was also predictive of cardiovascular damage (30).

Fibrosis

We studied, as a marker of fibrosis, serum and urine PRO-C6, a product specifically generated during collagen VI formation. We tested whether it is prognostic for adverse outcomes in individuals with type 2 diabetes and microalbuminuria. We found a doubling of serum PRO-C6 increased hazards for cardiovascular events (hazard ratio [HR] 3.06 [95% CI 1.31–7.14]) and all-cause mortality (6.91 [2.96–16.11]) and reduction of estimated GFR (eGFR) of >30% (4.81 [1.92–12.01]) (see Fig. 4). We also tested this in type 1 diabetes and found similar results (31), although in individuals with type 1 diabetes the association with cardiovascular events was lost after adjustment for other risk factors.

Figure 4
Figure 4

Serum PRO-C6 (marker of fibrosis) associated with kidney disease progression (defined as a decline of eGFR of >30% from baseline) (A) and cardiovascular events (cardiovascular mortality, stroke, ischemic CVD, and heart failure) (B) in subjects with type 2 diabetes (n = 200) (60). Dotted line, tertile 1 (T1); dashed line, tertile 2 (T2); solid line, tertile 3 (T3).

Applying urinary proteomic analysis with capillary electrophoresis coupled to mass spectrometry, Good et al. (32) described a high dimensional urinary biomarker pattern composed of 273 peptides associated with overt kidney disease: CKD273. The original studies included people with chronic kidney disease on a mixed background compared with healthy control subjects. The components of CKD273 include collagen fragments and are assumed to relate to early fibrosis in the kidney. In retrospective studies, this proteomic classifier identified subjects at risk for DKD and progression in albuminuria class earlier than the indices currently used in clinical practice (33). We tested, in a prospective study including people with type 2 diabetes and normoalbuminuria, whether CKD273 was associated with development of microalbuminuria and whether progression to microalbuminuria could be prevented with the mineralocorticoid receptor antagonist (MRA) spironolactone (34) (see Fig. 5). We chose spironolactone, as this MRA had been proposed to prevent fibrosis and had been demonstrated to reduce albuminuria in DKD (35). We followed 1,775 participants; 12% (n = 216) had a high-risk urinary proteomic pattern, of whom 209 were included in the trial and assigned spironolactone (n = 102) or placebo (n = 107). Median follow-up time was 2.51 years. Progression to microalbuminuria was seen in 28.2% of high-risk and 8.9% of low-risk participants (P < 0.001) (HR 2.48 [95% CI 1.80–3.42], P < 0.001). There was no significant effect of spironolactone on development of microalbuminuria (HR 0.81 [95% CI 0.49–1.34] P = 0.41), which may reflect lack of power, or alternatively it only works in established chronic kidney disease. Based on the same urinary proteomic technology, different signatures associated with heart failure (HF1) (36) or atherosclerotic CVD (CAD238) (37) have been developed but less thoroughly evaluated.

Figure 5
Figure 5

Design of the PRIORITY study, testing a urinary proteomic biomarker, CKD273, of risk for DKD and the potential for mitigating risk for progression to microalbuminuria in normoalbuminuric subjects with type 2 diabetes with spironolactone (34).

Inflammation

Multiple markers have been investigated related to inflammation. These include fibrinogen, interleukin 6, and TNFα, which were found to be associated with risk of chronic kidney disease progression (38). Some of the most widely studied markers have been tumor necrosis factor receptor (TNFR)1 and 2. Recently, the Kidney Risk Inflammatory Signature (KRIS) was developed with 17 inflammatory markers including TNF receptor superfamily members (39). The signature was tested in two cohorts as a marker of end-stage kidney disease in both type 1 and type 2 diabetes. All components of the signature had a systemic, nonkidney source and may guide therapy to new targets. Interestingly, the signature was improved with the anti-inflammatory agent baricitinib but not with RAS blockade (39).

Soluble urokinase plasminogen activator receptor (suPAR) is considered an important inflammatory marker implicated in endothelial and podocyte dysfunction. We tested suPAR in type 1 diabetes and found it to be an independent risk marker of cardiovascular events, kidney function decline, and mortality. We observed an adjusted HR per doubling of suPAR for cardiovascular events (n = 94), progression in albuminuria (n = 36), eGFR decline (n = 93), end-stage kidney disease (n = 23), and mortality (n = 58) of 3.13 (95% CI 1.96–5.45), 1.27 (0.51–3.19), 2.93 (1.68–5.11), 2.82 (0.73–11.9), and 4.13 (1.96–8.69), respectively.

Oxidative Stress

It has been proposed that elevated levels of uric acid induce vascular and kidney damage, hypertension, and atherosclerosis due to inflammation and oxidative stress. We, and others, demonstrated elevated uric acid levels to be associated with cardiovascular events and progression of renal disease in type 1 diabetes (40). In the Prevention of Early RenaL function loss study (PERL), we tested whether lowering of uric acid with allopurinol in people with type 1 diabetes and early DKD with albuminuria or declining eGFR could prevent loss of measured GFR over 3 years. Mean serum urate level decreased from 6.1 to 3.9 mg/dL with allopurinol and remained at 6.1 mg/dL with placebo. Despite this, we found no evidence of a kidney-protective effect on albuminuria or decline in GFR (41). Although this suggests uric acid is not a target, in line with a Mendelian randomization study in type 1 diabetes (42), a study was presented in 2019 with larger reduction of uric acid in a small group of individuals with type 2 diabetes followed for 24 weeks with a urate reabsorption inhibitor, verinurad, and feboxustat in combination, giving a 49% reduction in urine albumin-to-creatinine ratio compared with placebo (43).

Other markers of oxidative stress are oxidatively modified guanine nucleosides 8-oxo-7,8-dihydro-2′-deoxyguanosine and 8-oxo-7,8-dihydroguanosine (8-oxoGuo) excreted in the urine. The level of 8-oxoGuo was associated with mortality and CVD in type 2 diabetes (44).

For clarification of whether the different markers from the diverse pathways are useful to guide selection of therapy, post hoc analyses of randomized controlled studies are useful, but ideally, there is a need for prospective studies designed to test the hypothesis that biomarker-guided therapy is better than standard of care. We have started investigating whether subjects with type 1 and type 2 diabetes have different responses to different interventions and whether these differences can be predicted by the biomarkers. Thus, participants are in random order receiving four different treatments targeting different pathways to test response and association with biomarkers before treatment.

New Treatment Options for Cardio-Renal Complications in Diabetes and Ideas for Personalized Selection of Agents

For more than 20 years, standard of care in people with diabetes and kidney disease has included treatment with RAS-blocking agents, either ACE inhibitors or angiotensin receptor blockers, in addition to control of lifestyle factors, blood glucose, lipids, and blood pressure (45). Thus, selecting treatment to protect the kidney was not complicated. Although this improved prognosis for people with DKD, there was a large unmet need. The effect on renal end points was significant but modest (HR 0.80), and for the heart there was benefit of controlling blood pressure and reducing heart failure, but RAS blockade did not provide benefit on mortality or CVD in these studies. New agents either increasing blockade of RAS or targeting other pathogenetic pathways were needed. Many strategies have been tested and failed during the past 20 years either due to side effects or due to lack of effects, such as ACE inhibitors plus angiotensin receptor blockers, renin inhibition, erythropoetin, avosentan, and bardoxolone. During the past couple of years, we have seen significant progress, with new agents showing benefit on renal and/or CVD end points in people with type 2 diabetes and chronic kidney disease. For most tested agents, the effects on heart and kidney have been linked but in different ways. For some, there were benefits on the kidney but side effects like heart failure; for others, there were benefits on kidney and heart. Although not all agents are on the market yet, we need to find out how to choose the best agent, or combination of agents, for an individual to provide optimal benefit for heart and kidney and minimal risk for side effects. We believe that the discussed physiological tests and biomarkers may be helpful in selecting between agents, although it should be stressed that this remains to be tested.

SGLT2 Inhibitors

The first, and so far, most marked, success has been with SGLT2 inhibitors, initially tested for safety in cardiovascular outcome trials, where not only a benefit on the primary end point major adverse cardiovascular events was demonstrated with empagliflozin (HR 0.86 [95% CI 0.74–0.99], P = 0.04 for superiority) (46). A significant benefit on hospitalization for heart failure was also observed. In addition, a reduction in incident or worsening nephropathy occurred (HR 0.61 [95% CI 0.53–0.70]) (47). These findings were confirmed in cardiovascular outcome trials with canagliflozin and dapagliflozin. The first study with hard renal end points (end-stage kidney disease, significant loss of renal function) as the primary end point using a SGLT2 inhibitor was Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE), showing a major benefit not only on renal outcome but also on heart failure and major adverse cardiovascular events in people with type 2 diabetes (urine albumin-to-creatinine ratio >300 mg/g and eGFR 30–90 mL/min/1.73 m2) (48). The primary outcome was a composite of end-stage kidney disease, a doubling of the serum creatinine level, or death from renal or cardiovascular causes. The study was stopped early showing a benefit of canagliflozin with HR 0.70 (95% CI 0.59–0.82). These data were confirmed and extended by Dapa-CKD (A Study to Evaluate the Effect of Dapagliflozin on Renal Outcomes and Cardiovascular Mortality in Patients With Chronic Kidney Disease) including subjects with chronic kidney disease with or without diabetes (49). Whereas SGLT2 inhibitors were introduced to treat hyperglycemia, they also provide organ protection in individuals without diabetes with heart failure and/or chronic kidney disease. The explanation for the renal and cardiac benefits is not clear but may involve interaction between the organs (50). Recently, neuropathy and renal innervation were implicated (51). It is now recommended that they be added to standard of care in type 2 diabetes with chronic kidney disease (52), and in addition to patients with albuminuria and eGFR criteria, patients at risk for or with heart failure would potentially benefit the most (Fig. 6).

Figure 6
Figure 6

Biomarker-guided treatment selection. A proposal for how biomarkers could guide selection of treatment among recently tested options with a precision medicine approach in diabetic kidney and heart disease (“Complication” in inner circle). Using available “Supporting Biomarkers” (green circle) reflecting underlying pathology and “Risk Biomarkers” or contraindications (red circle) to select optimal treatment (outer circle) in patients with type 2 diabetes. Thus, as an example in CKD: elevated urinary albumin-to-creatinine ratio (UACR) or fluid retention (brain natriuretic peptide [BNP]) would suggest an SGLT2 inhibitor (SGLT2i), whereas markers of inflammation and fibrosis would suggest nonsteroidal MRA (nsMRA) (currently not available), and suPAR would suggest endothelin receptor 1A antagonist (ET1Aant) (currently not available) unless there are signs of fluid retention (elevated NTproBNP). Aldo, aldosterone; ASCVD, atherosclerotic CVD; HF, heart failure; CKD, chronic kidney disease; Echo, echocardiography; CACS, coronary artery calcium score; K+, potassium; U-CKD273, urinary proteomic marker of chronic kidney disease; u-CAD238, urinary proteomic marker of coronary heart disease; HF1, urinary proteomic marker of heart failure; proC6, serum PRO-C6 (marker of fibrosis); TNT, troponin T; suPAR, soluble urokinase plasminogen activator receptor.

Atrasentan

The Study Of diabetic Nephropathy with AtRasentan (SONAR) was presented simultaneous with the presentation of CREDENCE (53). Although stopped early for concern of futility, the study eventually showed a renal benefit of the same magnitude as in CREDENCE, but with no effect on major adverse cardiovascular events and a tendency to increased risk of heart failure, which also stopped another ET1A receptor antagonist, avosentan. The mode of action may relate to an effect on inflammation, but also an effect on podocytes and glycocalyx has been proposed from experimental data (54). Thus, we propose that suPAR is used to identify subjects who would benefit, and markers of heart failure would exclude the use of atrasentan (Fig. 6).

GLP-1RA

For some of the long-acting GLP-1RA (liraglutide, semaglutide, dulaglutide), the cardiovascular outcome trials in type 2 diabetes demonstrated cardiovascular benefits in subjects with already existing atherosclerotic CVD (52). There were renal benefits as secondary end points, mostly driven by reductions in albuminuria, but also some potential effects on eGFR. This was supported by the AWARD-7 study with dulaglutide in individuals with DKD, although the primary end point was glycemic control (55). It remains to be demonstrated whether there will be benefits on hard renal end points in addition to cardiovascular benefits; this is currently being tested in the FLOW study (clinical trial reg. no. NCT03819153, ClinicalTrials.gov). It is suggested that albuminuria and BMI as well as markers related to vascular damage, including troponin T, CAD238, TMAO, and elevated coronary calcium score on CT imaging (or 82Rb PET/CT), could be used to identify relevant subjects, and as there is no impact on heart failure, heart failure was used as exclusion criteria (Fig. 6). Currently we are involved in investigations of the mode of action of the effect on atherosclerosis, and the study may provide insight into more specific markers or imaging techniques to guide therapy (clinical trial reg. no. NCT04032197, ClinicalTrials.gov).

Mineralocorticoid Receptor Antagonism

Short-term studies revealed reduction in proteinuria in DKD with the steroidal MRAs spironolactone and eplerenone (35)—an interesting strategy, as preventing overactivation of the mineralocorticoid receptor reduces inflammation and fibrosis, but due to potassium problems, diabetes and kidney disease became a contraindication for these agents. Nonsteroidal MRAs have been developed and may have the anti-inflammatory and antifibrotic effects with less potassium problems. Esaxerenone and finerenone were demonstrated to reduce microalbuminuria in type 2 diabetes (56,57). Two phase 3 trials with finerenone was started in type 2 diabetes with chronic kidney disease, and the first has been stopped and it has been announced that the primary renal and secondary cardiac outcomes were positive but not yet presented. Preclinical studies demonstrated improved effect on inflammation and fibrosis in the heart (58) and kidney (59), and thus depending on the data, nonsteroidal MRAs like finerenone may be preferred when inflammation (KRIS, TMAO) and fibrosis (CKD273, serum PRO-C6 [marker of fibrosis]) and perhaps aldosterone are elevated, whereas it remains to be seen whether potassium will be an issue (Fig. 6).

Conclusions

In diabetes, the heart and kidney are now doing better, thanks to recent advances in diagnosis and therapy. The next step is to convert this into fully individualized medicine, combining new possibilities in imaging and biomarker-based risk prediction with detailed knowledge of therapeutic avenues. This will ensure optimal treatment and prevent adverse events and unnecessary polypharmacy. A more detailed approach when choosing the right treatment for the right person may seem complicated and costly at first but has the potential to save both patients and the health care system considerable costs.

The amount of information supporting design of individualized treatment is expected to grow drastically soon. Studies of the kidney and the heart using functional MRI and kidney biopsy studies will lead to a better diagnostic discrimination. At the same time, genomics, epigenomics, and metabolomics studies increase our knowledge of physiological processes. All of this will increase the complexity of the diseases but holds promise for better understanding once we learn to interpret the large amount of data available. Hopefully, this will lead to a better prevention of renal and cardiovascular outcome in the future.

Article Information

Funding. The authors were supported by the Novo Nordisk Foundation grant PROTON – PeRsOnalizing Treatment Of diabetic Nephropathy (NNF14OC0013659).

Duality of Interest. P.R. has received honoraria to Steno Diabetes Center Copenhagen from teaching and consultancy for Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Gilead, Novo Nordisk, Merck, Merck Sharp & Dohme (MSD), Mundipharma, Sanofi, and Vifor. F.P. has served as a consultant on advisory boards or as an educator for AstraZeneca, Novo Nordisk, Sanofi, Mundipharma, MSD, Boehringer Ingelheim, Novartis, and Amgen and has received research grants to his institution from Novo Nordisk, Amgen, and AstraZeneca. No other potential conflicts of interest relevant to this article were reported.



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Homemade Taco Sauce –

By electricdiet / January 3, 2021





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Low-Carb Pound Cake | Diabetes Strong

By electricdiet / January 1, 2021


This rich and buttery low-carb pound cake has the perfect texture and tastes so indulgent! But with only 2.3 net carbs per slice, it’s a Keto-friendly treat you can enjoy any time.

Two slices of pound cake on a white plate next to a fork

Do you know where the dessert “pound cake” gets its name?

Traditionally, this cake was made with exactly one pound of each of the ingredients, which were flour, butter, eggs, and sugar. What a great, simple recipe.

However, if you’re looking for a Keto-friendly treat, you may not be too keen on that much flour and sugar. So why not whip up a wonderful loaf of this low-carb pound cake instead?

Just like the original, this recipe turns out so delicious, buttery, and rich. Plus, it has that classic soft texture that makes it oh-so-indulgent.

And it takes less than 10 ingredients to make! Okay, maybe that’s not QUITE as simple as 4 ingredients… but it doesn’t get much easier than mixing a few ingredients and throwing the loaf in the oven.

Not to mention, the result is SO worth it!

How to make low-carb pound cake

This simple recipe only takes about 15 minutes to prep. Then, all you have to do is throw it in the oven!

Pound cake ingredients in separate ramekins as seen from above

Step 1: Preheat your oven to 350 F (180 C) and line a small loaf tin with parchment paper.

Step 2: Add the butter and erythritol to a large mixing bowl. With an electric mixer, beat on high until light and fluffy.

Pound cake batter mixed with hand beaters in a glass bowl next to a ramekin with 3 eggs

Step 3: Beat in the eggs, two at a time, until well incorporated. Next, add the vanilla extract and beat on slow until mixed in.

Step 4: Add the almond flour, baking powder, salt, psyllium husk powder, and xanthan gum, then mix well. The dough will be quite thick but should not dry out.

Pound cake batter mixed with hand beaters in a glass bowl

Step 5: Spoon the dough into the prepared loaf pan and smooth over the top.

Pound cake batter in the loaf pan, as seen from above

Step 6: Bake for about 50 minutes or until a toothpick inserted into the middle comes out clean. After 30 minutes, if the top is browning too quickly, cover the loaf with foil to prevent burning.

Step 7: Remove from the oven and allow to cool completely.

Once the pound cake loaf has cooled, it’s ready to be sliced and served!

What holds this pound cake together?

When it comes to low-carb baking, texture is everything. You don’t want your baked goods to turn out dry or crumbly… ESPECIALLY not pound cake!

That’s why this recipe calls for both psyllium husk powder and xanthan gum. Together, these ingredients create the right texture and bind the batter together.

Keep in mind that xanthan gum is best added gradually to avoid clumping. I recommend sprinkling it into the batter while the electric mixer is running to evenly distribute it throughout the batter.

Close-up of two slices of pound cake of a white plate with a fork

Storage

This recipe is for 10 slices of pound cake. So unless you’re baking for a crowd or for family over the holidays, you’ll likely have some leftovers!

Simply place the loaf or slices in an airtight container and store in the refrigerator for up to 5 days. When you’re ready to enjoy, you can let the slices come up to room temperature or eat them chilled.

Other low-carb dessert recipes

Just because you’re watching your carb intake doesn’t meant you can’t enjoy the sweeter things in life! Thanks to a few ingredient swaps, it’s easy to make Keto-friendly desserts. Here are a few of my favorite recipes I think you’ll enjoy:

You can also check out my roundup of 10 delicious keto fat bomb recipes for more low-carb inspiration.

When you’ve tried this pound cake, please don’t forget to let me know how you liked it and rate the recipe in the comments below!

Recipe Card

Low-Carb Pound Cake

This rich and buttery low-carb pound cake has the perfect texture and tastes so indulgent! But with only 2.3 net carbs per slice, it’s a Keto-friendly treat you can enjoy any time.

Prep Time:15 minutes

Cook Time:50 minutes

Total Time:1 hour 5 minutes

Servings:10

Low-Carb pound cake loaf on a white serving tray with two slices cut

Instructions

  • Preheat your oven to 350 F (180 C) and line a small loaf tin with parchment paper.

  • Add the butter and erythritol to a large mixing bowl. With an electric mixer, beat on high until light and fluffy.

  • Beat in the eggs, two at a time, until well incorporated. Next, add the vanilla extract and beat on slow until mixed in.

  • Add the almond flour, baking powder, salt, psyllium husk powder, and xantahn gum, then mix well. The dough will be quite thick but should not dry out.

  • Spoon the dough into the prepared loaf pan and smooth over the top.

  • Bake for about 50 minutes or until a toothpick inserted into the middle comes out clean. After 30 minutes, if the top is browning too quickly, cover the loaf with foil to prevent burning.

  • Remove from the oven and allow to cool completely.

Recipe Notes

This recipe is for 10 servings. If you cut the loaf into 10 slices, each serving will be one slice.
Leftovers can be stored in an airtight container in the refrigerator for up to 5 days. You can eat them chilled from the refrigerator or let them warm to room temperature.

Nutrition Info Per Serving

Nutrition Facts

Low-Carb Pound Cake

Amount Per Serving (1 slice)

Calories 127
Calories from Fat 95

% Daily Value*

Fat 10.6g16%

Saturated Fat 3.5g18%

Trans Fat 0g

Polyunsaturated Fat 0g

Monounsaturated Fat 0g

Cholesterol 74mg25%

Sodium 410.9mg17%

Potassium 29.6mg1%

Carbohydrates 3.8g1%

Fiber 1.5g6%

Sugar 0.4g0%

Protein 4.7g9%

Net carbs 2.3g

* Percent Daily Values are based on a 2000 calorie diet.

Course: Dessert

Cuisine: American

Keyword: gluten-free, keto, keto pound cake, low carb, Low-carb pound cake, pound cake



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A Multifunctional Role of Leucine-Rich α-2-Glycoprotein 1 in Cutaneous Wound Healing Under Normal and Diabetic Conditions

By electricdiet / December 30, 2020


Abstract

Delayed wound healing is commonly associated with diabetes. It may lead to amputation and death if not treated in a timely fashion. Limited treatments are available partially due to the poor understanding of the complex disease pathophysiology. Here, we investigated the role of leucine-rich α-2-glycoprotein 1 (LRG1) in normal and diabetic wound healing. First, our data showed that LRG1 was significantly increased at the inflammation stage of murine wound healing, and bone marrow–derived cells served as a major source of LRG1. LRG1 deletion causes impaired immune cell infiltration, reepithelialization, and angiogenesis. As a consequence, there is a significant delay in wound closure. On the other hand, LRG1 was markedly induced in diabetic wounds in both humans and mice. LRG1-deficient mice were resistant to diabetes-induced delay in wound repair. We further demonstrated that this could be explained by the mitigation of increased neutrophil extracellular traps (NETs) in diabetic wounds. Mechanistically, LRG1 mediates NETosis in an Akt-dependent manner through TGFβ type I receptor kinase ALK5. Taken together, our studies demonstrated that LRG1 derived from bone marrow cells is required for normal wound healing, revealing a physiological role for this glycoprotein, but that excess LRG1 expression in diabetes is pathogenic and contributes to chronic wound formation.

Introduction

Wound healing is a natural reparative response to tissue injury. It proceeds through four continuous and overlapping phases: homeostasis, inflammation, proliferation, and tissue remodeling (1). Failure to progress through these phases in an orderly manner leads to impaired wound healing, which represents one of the common causes of morbidity associated with diabetes, affecting ∼25% of individuals with diabetes (2). These wounds frequently serve as portals of entry for bacterial infection that may lead to sepsis and lower-extremity amputation (3). Staggeringly, patients with lower-extremity amputation have a 5-year mortality rate of up to 50% (4). With the rising prevalence of diabetes, the incidence of wound complications is expected to increase substantially, posing a significant socioeconomic burden (5).

A plethora of factors contributes to delayed wound closure in patients with diabetes, such as excessive neutrophil infiltration and activation, impaired angiogenesis, and defective epithelial cell migration and proliferation (6). These defects lock the wound into a self-perpetuating inflammatory stage (7), which causes further tissue injury by increasing the production of inflammatory cytokines, reactive oxygen species, destructive enzymes, and cytotoxic extracellular traps in a process termed NETosis (8) (where NET is neutrophil extracellular trap). Thus, targeting inflammation serves as an attractive strategy to kick-start the proliferation phase of wound healing and promote repair. A number of anti-inflammatory agents have been developed over the last 20 years (9). Despite effectiveness in promotion of wound closure in rodent models, limited success has been achieved in clinical trials (10). This is likely due to the highly dynamic and complex interactions between different types of cell, extracellular matrix components, and soluble factors present in the wound microenvironment. A better understanding of the molecular mechanisms underlying diabetes-associated healing deficiency will guide the development of more effective therapeutics to treat wounds that do not respond sufficiently to good standard care.

Leucine-rich α-2-glycoprotein 1 (LRG1) is a secreted glycoprotein that was previously reported to regulate pathological neovascularization in the eye by switching the angiostatic TGFβ1-Smad2/3 signaling toward the proangiogenic TGFβ1-Smad1/5/8 signaling in endothelial cells (11). Besides its role in ocular angiogenesis, LRG1 is intimately associated with many inflammatory and autoimmune conditions (1214) and tumor malignancy (1517), which shares fundamental molecular mechanisms with chronic wound healing (18). Recently, elevated serum LRG1 levels were reported in patients with diabetes with peripheral arterial disease (19), a major risk factor for diabetic foot ulcers (DFU) (20). Paradoxically, exogenous LRG1 was reported to accelerate wound healing by promoting keratinocyte migration in animal models (21). Here, we characterized LRG1 expression level and pattern in wound tissue, investigated its contribution to wound healing under normal and diabetic condition using Lrg1-null mice, and explored its mechanism of action.

Research Design and Methods

Human Sample Analysis

This study was approved by Khoo Teck Puat Hospital Ethics Review Board (NHG Domain Specific Review Board). Adults between 21 and 90 years old with type 2 diabetes seen in Diabetes Centre of Khoo Teck Puat Hospital were enrolled into this study. Fasting blood and debrided tissues were collected from patients with ulcers during their podiatry assessment clinic. Fasting blood samples were centrifuged within 1 h after collection and kept at 4°C during this period. Thereafter, they were stored at −80°C in aliquots and used without additional freeze-thaw cycles. Devitalized tissue was obtained from desloughing and debridement performed as part of usual care. These samples were stored in liquid nitrogen until retrieval for assays described below. Control samples were obtained from patients without type 2 diabetes with venous ulcers in the same clinic. Serum level of LRG1 was measured using an ELISA kit (Immuno-Biological Laboratories, Hamburg, Germany) according to the manufacturer’s instructions.

Animals and Induction of Diabetes

C75BL/6J mice were purchased from InVivos (Singapore). Lrg1−/− mice were originally generated by the University of California, Davis, Knockout Mouse Project (KOMP) Repository Collection (https://www.komp.org) and were a generous gift from J. Greenwood and S.E. Moss (UCL Institute of Ophthalmology). Animal experiments were performed in compliance with the guidelines of the Institutional Animal Care and Use Committee (ARF-SBS/NIE-A0268/A19036) of Nanyang Technological University and the Guide for Care and Use of Laboratory Animals published by the National Institutes of Health. Diabetes was induced in 6- to 8-week-old male mice by intraperitoneal injection of 50 mg/kg streptozotocin (STZ) (50 mmol/L sodium citrate buffer, pH 4.5) for five consecutive days as previously described (22). Diabetes was confirmed when fasting blood glucose (FBG) was >200 mg/dL.

Creation of Full-Thickness Cutaneous Wounds

Six full-thickness cutaneous wounds were created on mouse dorsal skin using 4-mm Integra Miltex Standard Biopsy Punches (Thermo Fisher Scientific). Wounds were imaged daily with a digital camera. Wound size was quantified with use of ImageJ (National Institutes of Health). We used 6-mm Integra Miltex Standard Biopsy Punches (Thermo Fisher Scientific) for biopsy collection at different time points following injury. The mouse excisional wound splinting model was employed as previously described (23).

Bone Marrow Transplantation

Six-week old mice were irradiated at two doses of 5.5 Gy irradiation using a BIOBEAM GM γ irradiation device (Gamma-Service Medical, Leipzig, Germany). Bone marrow cells (BMCs) from female mice were harvested and filtrated through a 70-μm cell strainer (Falcon). BMCs (3 × 106) were intravenously injected into the irradiated recipient mice through the tail vein 24 h after the irradiation. Eight weeks after reconstitution, wounds were created at flanks of recipient mice.

Isolation and Flow Cytometry Analysis of Myeloid Cells From Wound Tissue

Wound tissues were digested in Iscove’s modified Dulbecco’s medium (Thermo Fisher Scientific) containing 2% FBS, 2 units/mL DNase I (Roche, Switzerland), and 1 mg/mL collagenase D (Roche) and passed through a 40-mm cell strainer for obtaining single-cell suspension. Red blood cells were lysed using 0.89% NH4Cl lysis buffer and removed by centrifugation. Cell pellet was resuspended and preincubated with anti–Fc receptor antibody (clone 2.4G2) followed by further incubation with anti-mouse BUV737-labeled CD45 (clone 30-F11), allophycocyanin-Cy7–labeled anti-mouse CD11b (clone M1/70), anti-mouse F4/80 (clone BM8), BUV395-labeled anti-mouse Ly6G (clone 1A8), BV605-labeled anti-mouse Ly6c (clone HK1.4), phycoerythrin-Cy7–labeled anti-mouse CD11c (clone N418), and BV421-labeled anti-mouse I-A/I-E clone (M5/114.15.2). Dead cells were labeled with fixable viability stain 510 (BD Biosciences, San Jose, CA). Cells were then fixed and permeabilized before being stained with anti–LRG-1 antibody (cat. no. 13224-1-AP; Proteintech) followed by FITC-labeled donkey anti-rabbit IgG (clone Poly4064). Cells were washed and subjected to analysis on a five-laser flow cytometer (BD LSRFortessa; BD Biosciences). Data were analyzed with FlowJo software (TreeStar, Ashland, OR).

Cells and Cell Culture

Primary mouse and human peripheral blood neutrophils (Institutional Review Board of Nanyang Technological University [IRB-2014-04/27]) were isolated, purified, and cultured as previously described (24,25). Human neutrophil-like cells (dHL-60) were derived from human promyelocytic leukemia cell line (HL-60; ATCC) by incubation with 1% DMSO (Sigma-Aldrich) for 7 days. Human dermal microvascular endothelial cells (HDMECs) (PromoCell), normal human dermal fibroblasts (PromoCell), human keratinocyte line HaCaT (ATCC), and Freestyle 293-F cells (Gibco, Thermo Fisher Scientific) were maintained according to the supplier’s instruction. Cells were treated with 20 μg mL−1 recombinant human LRG1 (rhLRG1), LDN193189 (100 nmol/L) (Sigma-Aldrich), SB431542 (10 μmol/L) (Sigma-Aldrich), and MK-2206 (10 μmol/L) (Selleck Chemicals) and as indicated.

Histology, Immunohistochemistry, and Immunofluorescence

Mice skin tissues were fixed in 4% paraformaldehyde and embedded in paraffin following a standard protocol. Paraffin sections (5 μm thick) were subjected to staining with hematoxylin-eosin or LRG1 antibody (cat. no. 13224-1-AP; Proteintech). For immunofluorescence staining, skin tissues were embedded in O.C.T. Compound (Thermo Fisher Scientific). Cryopreserved skin sections (5 μm thick) were stained with primary antibodies against LRG1 (13224-1-AP; Proteintech), F4/80 (MCA497; Bio-Rad), CD11b (130-113–235; Miltenyi Biotec), CD31 (550274; BD Biosciences), Myeloperoxidase (MPO) (ab9535; Abcam, Cambridge, U.K.), and Ki67 (ab15580; Abcam) followed by staining with Alexa 488 or Alexa 594 secondary antibodies (Thermo fisher Scientific). Images were captured using Leica DM5500 microscope (Leica Microsystems) or Carl Zeiss LSM 710 confocal microscopy (Zeiss, Berlin, Germany), processed using Adobe Photoshop CS6, and analyzed using ImageJ by investigators who were blinded to the identity of the experimental groups.

Quantitative Real-time PCR

Total RNA was extracted and purified with RNAzol RT (cat. no. 888-841-0900; Molecular Research Center) before being reverse transcribed to cDNA with qScript cDNA SuperMix (157031; Quanta Biosciences). PCR was conducted with PrecisionFAST qPCR Master Mix (FASR-LR-SY; Primerdesign Ltd, U.K.) with use of Applied Biosystems StepOnePlus Real-Time PCR System (Life Technologies). The expression levels of respective target genes were normalized to GAPDH, and relative gene expressions were calculated using standard 2−ΔΔCT method. Primers used in this study are listed in Table 1.

Table 1

qRT-PCR primer sequences

SDS-PAGE and Western Blotting

Cells or tissues were lysed on ice in radioimmunoprecipitation assay buffer containing 0.0037 mg/mL protease inhibitor (Roche), 1 mmol/L dithiothreitol (cat. no. D9779; Sigma-Aldrich), 1 mmol/L phenylmethylsulfonyl fluoride (P7626; Sigma-Aldrich), and 100 mmol/L phosphatase inhibitors (P0044 [for cell signaling experiments]; Sigma-Aldrich). Proteins were separated by 10% SDS-PAGE before being transferred onto an Immobilon-PSQ PVDF Membrane (ISEQ-00010; Merck Millipore). Blots were probed with LRG1 antibody (rabbit monoclonal, 13224-1-AP; Proteintech), phosphorylated (phospho-)Smad1/5 antibody (rabbit monoclonal, 9516; Cell Signaling Technology), anti-SMAD1+SMAD5 antibody (mouse monoclonal, ab75273; Abcam), histone H3 (citrulline R2 + R8 + R17) antibody (rabbit polyclonal, ab5103; Abcam), histone H3 antibody (rabbit polyclonal, ab1791; Abcam), phospho-Akt antibody (rabbit monoclonal, 4060; Cell Signaling Technology), Akt antibody (rabbit monoclonal, 9272; Cell Signaling Technology), cyclin D1 antibody (rabbit monoclonal, 2922; Cell Signaling Technology), or GAPDH antibody (rabbit polyclonal, sc-25778; Santa Cruz Biotechnology), followed by horseradish peroxidase–conjugated secondary antibodies (Santa Cruz Biotechnology). Densitometry was performed by use of ImageJ software.

Molecular Biological Methods

Human LRG1 (NM_052972) carrying a 6xHis tag expression vector, pcDNA3.1-LRG1, was generated as previously described (11). rhLRG1 protein was expressed in Freestyle 293 T cells (Invitrogen) and purified as previously described (11). siRNA oligonucleotides (cat. no. L-015179-01; Dharmacon) were used for LRG1 gene knockdown, while control siRNA (D-001810-10-20; Dharmacon) was used as a negative control. Transfection was performed using Lipofectamine 2000 (Invitrogen) for HaCaT cells and RNAiMAX (Invitrogen) for dHL-60 cells according to the manufacturer’s protocol.

Flow Cytometry

Cell-surface CD11b and L-selectin were measured using flow cytometry. dHL-60 cells were treated with rhLRG1 (20 μg/mL) for 1 h, 6 h, or 24 h before being washed with flow buffer (0.1% FBS/PBS). Cells were then incubated with L-selectin antibody (mouse monoclonal, cat. no. sc-390756; Santa Cruz Biotechnology), followed by staining with Alexa Fluor 488 goat anti-mouse (IgG) secondary antibody (Thermo Fisher Scientific) and CD11b-phycoerythrin (130-113-235; Miltenyi Biotec) and fixation in 1% paraformaldehyde. Cell acquisition (10,000 cells per sample) was carried out on BD LSRFortessa X-20 (BD Biosciences) and analyzed with use of FlowJo software (BD).

Neutrophil Adhesion Assay

Lrg1-knockdown dHL-60 cells or dHL-60 cells treated with rhLRG1 (20 μg/mL) cells were labeled with CellTracker Green CMFDA Dye (Invitrogen) before being seeded onto the confluent HDMEC monolayer. In the case of Lrg1-knockdown dHL-60 cells, HDMECs were pretreated with tumor necrosis factor (TNF)α (50 ng/mL; PeproTech). Two hours later, nonadherent neutrophils were subsequently removed by washing with prewarmed PBS. Adherent neutrophils were imaged with the Eclipse Ti-E Inverted Research Microscope (Nikon Instruments, Tokyo, Japan) and quantified by measurement of the fluorescence intensity with Synergy H1 microplate reader (BioTek) at the wavelength of 492 nm/517 nm.

Proliferation Assay

HDMECs were cultured in EGM-2MV media (Lonza, Basel, Switzerland) until 30% confluent and starved in EBM-2 medium (Lonza) containing 0.2% FBS for 16 h before being treated with rhLRG1 (20 μg/mL) for 48 h. Cells were then fixed and stained with Ki67 antibody (rabbit polyclonal antibody, cat. no. ab15580; Abcam) for detection of proliferating cells and DAPI (Thermo Fisher Scientific) for staining cell nuclei. Images were taken with Eclipse Ti-E Inverted Research Microscope and analyzed with ImageJ. Proliferation rate was calculated as the percentage of Ki67+ cells.

Transwell Migration Assay

HDMECs were pretreated with rhLRG1 for 24 h before being seeded onto rat tail collagen I (100 μg/mL; Corning)–coated Transwell Inserts (8 μm) (Corning). EBM2 medium containing 5% FBS served as a chemoattractant. After 5-h incubation, migrated cells were fixed and stained with DAPI (Thermo Fisher Scientific). Images were taken with Eclipse Ti-E Inverted Research Microscope and analyzed with Image J.

Matrigel Tube Formation Assay

Matrigel Growth Factor Reduced Basement Membrane Matrix (Corning) (60 μL) containing vehicle control or rhLRG1 (20 μg/mL) was added to each well of a 96-well plate and incubated for 30 min at 37°C for polymerization. HDMECs in 100 μL EBM-2 medium containing vehicle control or rhLRG1 (20 μg/mL) were seeded onto the polymerized Matrigel gel. HDMEC tube formation was imaged using phase-contrast mode on Eclipse Ti-E Inverted Research Microscope following overnight incubation, and tube formation was analyzed with ImageJ.

Trypan Blue Exclusion Assay

Transfected and rhLRG1 (2 μg/mL)–treated HaCaT cells were trypsinized and stained with Trypan blue before being counted with a hemocytometer under a phase-contrast microscope.

Scratch Wound Healing Assay

Confluent HaCaT cells were starved in DMEM medium containing 0.2% FBS (Thermo Fisher Scientific) for 24 h. A scratch was made to HaCaT cell monolayer with a sterile p200 pipette tip. The cells were washed and subsequently cultured in complete DMEM. Cells were imaged at 0 h and 24 h after scratching with Eclipse Ti-E Inverted Research Microscope 24 h after scratching. Images were analyzed with ImageJ.

SYTOX Green Assay

Primary mouse and human peripheral blood neutrophils were seeded onto each well of a 96-well black polystyrene microplate with a clear flat bottom (Corning). Cells were treated with 5 μmol/L calcium ionophore A23187 or rhLRG1 (100 μg/mL) for 4 h before incubation with the DNA dye SYTOX Green (1 μmol/L) (Invitrogen) for 15 min. NET formation was determined by measurement of the fluorescence intensity with Synergy H1 microplate reader (BioTek) at the wavelength of 504 nm/523 nm.

Induction of NETosis

Primary mouse neutrophils in RPMI-1640 medium containing 25 mmol/L HEPES were seeded onto each well of an eight-well chamber slide. Twenty minutes later, attached cells were incubated with 5 μmol/L calcium ionophore A23187 for 2 h before being fixed and stained with H3Cit antibody (1:100 dilution; Abcam) and DAPI (Thermo Fisher Scientific). Images were taken with Carl Zeiss LSM 710 confocal microscopy and analyzed with ImageJ.

Statistical Analysis

Data are represented as mean ± SEM. Statistical analyses were performed using unpaired, two-tailed Student t test or one-way/two-way ANOVA followed by Tukey/Bonferroni post-test analysis using Prism 5 (GraphPad Software Inc.).

Data and Resource Availability

Complete data sets generated and analyzed during the current study are available from X.W. on request.

Results

LRG1 Is Produced by Wound-Infiltrating Bone Marrow–Derived Cells Following Injury

To address the role of LRG1 in wound healing, we examined the expression of LRG1 in normal C56BL/6 mouse skin tissues by immunohistochemistry and revealed a very weak staining in the dermis (Fig. 1A). Western blot was used to evaluate LRG1 expression in wound tissues at various time points following injury (Fig. 1B). Our data showed that LRG1 was increased as early as 6 h post-injury and reached its highest level 24 h after wounding. LRG1 level then declined gradually and went back to basal level on day 5 following injury. It is worth noting that LRG1 expression in surrounding intact skin tissues remained low throughout the wound healing process (Supplementary Fig. 1).

Figure 1
Figure 1

LRG1 is elevated in cutaneous wounds. A: Immunohistochemical detection of LRG1 (brown) showed low expression of LRG1 in normal mouse skin. Scale bar: 100 μm. B: Representative Western blot (left) and densitometry analysis (right) of wounds harvested at different time points. C: Immunofluorescence staining detecting LRG1 (green), CD11b (red), or DAPI (blue) in day 1 mouse wounds. Scale bars: 120 μm and 20 μm. D: qRT-PCR analysis of day 1 wounds demonstrated reduced Lrg1 expression in irradiated wild-type mice with Lrg1−/− BMC transplantation in comparison with wild-type mice receiving wild-type BMCs. All images are representative; data are represented as mean (95% CI; P) of n ≥ 5 mice per group. Significance was determined by one- or two-way ANOVA followed by Tukey multiple comparisons test. *P < 0.05, ***P < 0.001. WB, Western blot; WT, wild type.

Immunofluorescence staining was used to identify the source of LRG1 during wound healing. We found that Lrg1+ cells were mainly present in the provisional matrix and were colocalized with CD11b+ myeloid cells in day 1 wound tissues (Fig. 1C). Flow cytometry analysis revealed that LRG1 is expressed by Ly6G+/CD11b+ neutrophils, Ly6C+/CD11b+ monocytes, Ly6C/F4/80+/CD11b+ macrophages, and Ly6C/F4/80+/MHC II+/CD11c+/CD11b+ dendritic cells (Supplementary Fig. 2), all of which are bone marrow–derived cells. For confirmation of this observation, allogenic bone marrow transplantation (BMT) study was carried out in irradiated wild-type mice with use of BMCs from Lrg1−/− mice and wild-type littermate controls. Similar to what we observed in unirradiated C56BL/6 mice, quantitative real-time-PCR (qRT-PCR) showed that Lrg1 transcript was significantly higher in day 1 wound tissues of wild-type mice transplanted with wild-type BMCs, whereas Lrg1 was not induced in those that received Lrg1−/− BMCs (Fig. 1D). These data suggest that wound-infiltrating bone marrow–derived cells (BMDCs) serve as a major source of LRG1 during cutaneous wound healing.

LRG1 Is Critical for Timely Wound Closure

For elucidation of the functional role of LRG1 in cutaneous would healing, 4-mm full-thickness wounds were created on the dorsal skin of wild-type and Lrg1−/− mice. Lrg1−/− mice demonstrated a significant delay in wound closure as compared with wild-type controls in both excisional wound model and excisional wound splinting model (Fig. 2A and Supplementary Fig. 3). As LRG1 is markedly induced at the inflammatory phase of wound healing, the number of wound-infiltrating immune cells was analyzed in the wound bed of wild-type and Lrg1−/− mice. Neutrophils are the first inflammatory cells to be recruited to the wound bed (26). After performing their functions, apoptotic neutrophils and tissue debris are cleared by macrophages, which eventually leads to the resolution of inflammation (27). Immunofluorescence staining showed a significant reduction in the number of wound-infiltrating MPO+ neutrophils (Fig. 2B) and F4/80+ macrophages (Fig. 2C) 1 day and 5 days post-injury, respectively. As BMDCs are major LRG1-producing cells, we next performed BMT between Lrg1−/− mice and wild-type controls. Mice subjected to irradiation were previously reported to show delayed wound repair (28). In this study, irradiated Lrg1−/− mice and wild-type control mice were intravenously transplanted with wild-type or Lrg1−/− BMCs, respectively (Fig. 2D). Wild-type mice transplanted with wild-type BMCs and Lrg1−/− mice transplanted with Lrg1−/− BMCs served as controls to exclude the impact of irradiation on wound healing. Consistent with what was observed in unirradiated control mice, wound closure was significantly delayed in Lrg1−/− recipients transplanted with Lrg1−/− BMCs as compared with that in wild-type recipients transplanted with wild-type BMCs. On the other hand, wound closure in wild-type recipients transplanted with Lrg1−/− BMCs was delayed substantially, whereas wild-type BMCs fully rescued the delayed wound healing in Lrg1−/− mice. Together, these data provide compelling evidence that LRG1-producing BMDCs are critical for timely wound closure.

Figure 2
Figure 2

Absence of Lrg1 leads to delayed wound healing. A: Quantification (left) and representative images (right) of wound size in wild-type and Lrg1−/− mice revealed a delayed wound closure in the absence of Lrg1. *P < 0.05. Scale bar: 1 mm. B: Representative immunofluorescence staining of MPO (green) and DAPI (blue) (top) and quantification of the presentation of MOP+ cells (bottom) in day 1 wounds of wild-type and Lrg1−/− mice; 5–10 fields per wound were analyzed. *P < 0.05. Scale bar: 30 μm. C: Representative immunofluorescence staining of F4/80 (green) and DAPI (blue) (top) and quantification of F4/80+ cells (bottom) of day 5 mouse wounds of wild-type and Lrg1−/− mice; 5–10 fields per wound were analyzed. *P < 0.05. Scale bar: 30 μm. D: Quantification (left) and representative images (right) of wound size in irradiated wild-type (WT) mice receiving BMCs from wild-type mice (WT to WT), irradiated Lrg1−/− mice receiving BMCs from Lrg1−/− mice (Lrg1−/− to Lrg1−/−), irradiated Lrg1−/− mice receiving BMCs from wild-type mice (WT to Lrg1−/− ), and irradiated wild-type mice receiving BMCs from Lrg1−/− mice (Lrg1−/− to WT). *P < 0.05: Lrg1−/− to Lrg1−/− vs. WT to WT; #P < 0.05: Lrg1−/− to WT vs. WT to WT; @P < 0.05, @@P < 0.01: WT to Lrg1−/− vs. Lrg1−/− to Lrg1−/−. Scale bar: 1 mm. All images are representative; data are represented as mean (95% CI; P) of n ≥ 6 mice per group. Significance was determined by unpaired, two-tailed Student t test between wild-type and Lrg1−/− or wound size at different time points.

LRG1 Promotes Neutrophil Adhesion via Inducing the Expression of L-Selectin

The ability of neutrophils to adhere to the endothelium is critical for their recruitment to the wound bed (29). To understand LRG1’s role in neutrophil infiltration, we investigated the ability of dHL-60 cells to adhere to a HDMEC monolayer in the presence and absence of rhLRG1. Our study showed that rhLRG1 significantly promoted dHL-60 cell adhesion to the HDMEC monolayer (Fig. 3A). As dHL-60 cells express higher levels of LRG1 compared with other skin cells (Supplementary Fig. 4), we went on investigating the consequence of siRNA-mediated LRG1 knockdown in neutrophil function and demonstrated reduced responsiveness to TNFα-induced adhesion to HDMECs (Fig. 3B). L-selectin, a cell adhesion molecule expressed on neutrophils, serves as a master regulator of neutrophil adhesion (30). We next examined whether LRG1 exerts its function through mediating the expression of L-selectin on neutrophil-like dHL-60 cells. Indeed, immunoblots showed a significant increase in L-selectin expression in dHL-60 cells subjected to 24-h treatment with rhLRG1 (Fig. 3C). Consistent with this, flow cytometry revealed a marked increase of the median of fluorescence intensity in dHL-60 following rhLRG1 treatment (Fig. 3D and Supplementary Fig. 5). However, rhLRG1 did not affect the expression of endothelial adhesion molecules including ICAM-1, VCAM-1, P-selectin, and E-selectin (Supplementary Fig. 6). Together, these data show that LRG1 promotes neutrophil adhesion, at least partially, by regulating the expression of L-selectin on neutrophils.

Figure 3
Figure 3

LRG1 mediates neutrophil adhesion. A: Representative images (left) and quantification (right) of neutrophil adhesion assay demonstrated rhLRG1 induced dH60 cell (labeled with CMFDA dye) adhesion to HDMECs. Scale bar: 200 μm. B: Representative images (left) and quantification (right) of TNFα-induced neutrophil adhesion by use of hHL-60 cells (labeled with CMFDA dye) subjected to siRNA-mediated LRG1 knockdown. Scale bar: 200 μm. C: Representative Western blot (left) and densitometry analysis (right) of L-selectin and GAPDH in rhLRG1-treated dHL-60 cells at different time points. D: Quantification of flow cytometry demonstrated an increase in L-selectinHigh population following rhLRG1treatment. All images are representative; data are presented as the mean (95% CI; P) of n ≥ 3 independent experiments per group. Significance was determined by one- or two-way ANOVA followed by Tukey multiple comparisons test or unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. WB, Western blot.

LRG1 Promotes Epithelial Cell Proliferation and Epithelial-to-Mesenchymal Transition

Besides inflammation, reepithelialization is essential to prompt wound repair. It is achieved by orchestrated migration and proliferation of epithelial cells adjacent to the wound (31). H-E analysis revealed delayed reepithelialization in day 4 wounds of Lrg1−/− mice (Fig. 4A). Although the denuded surface is completely covered by newly formed epithelium 5 days following injury, the reconstituted epidermis is significantly thinner in Lrg1−/− mice as compared with that in wild-type controls (Fig. 4B). It was previously reported that exogenous LRG1 promotes keratinocyte migration (21). Similarly, we showed that LRG1-overexpressing HaCaT cells migrated much faster as compared with control plasmid transfected cells, whereas the migration ability of the LRG1 siRNA-treated HaCaT was significantly compromised (Fig. 4C). Activation of the partial epithelial-to-mesenchymal transition (EMT) has been reported to drive keratinocyte migration (32). Our study demonstrated that rhLRG1 was able to cause a significant induction of key EMT markers, such as fibronectin (FN1) and N-cadherin (N-Cad) (Fig. 4D). To define LRG1’s role in reepithelialization further, we examined the keratinocyte proliferation as indicated by immunofluorescence staining with Ki67 in day 3 wounds of Lrg1−/− and wild-type control mice. Our study revealed a substantial reduction in the percentage of Ki67+ keratinocytes at the wound edge of Lrg1−/− mice (Fig. 4E). Consistent with this observation, the number of viable LRG1-overexpressing HaCaT cells was significantly higher than control cells, and HaCaT cells subjected to siRNA-mediated LRG1 knockdown showed reduced viability compared with control siRNA-treated HaCaT (Fig. 4F). This observation was supported by a marked increase in cell proliferation marker cyclin D1 in rhLRG1-treated HaCaT (Fig. 4G). Together, these data suggest that LRG1 facilitates reepithelialization by promoting keratinocyte proliferation and migration.

Figure 4
Figure 4

LRG1 regulates reepithelialization during wound healing. A: Representative H-E staining (left) and quantification of reepithelialization (right) of day 4 wounds of wild-type and Lrg1−/− mice. Scale bar: 125 μm. B: Representative H-E staining (left) and quantification of epithelium thickness (right) of day 5 wounds of wild-type and Lrg1−/− mice. Scale bar: 25 μm. C: Representative images (left) and quantification of wound gap (right) in scratch wound healing assay. Scale bar: 100 μm. D: Representative Western blot (left) and densitometry analysis (right) of FN1, N-cad, and GAPDH in rhLRG1-treated HaCaT cells. E: Representative immunofluorescence staining (top) and quantification (bottom) of Ki67 (red) and DAPI (blue) in day 3 wounds. Scale bar: 30 μm. F: Quantification of viable HaCaT cells in Trypan blue exclusion assay (G). Representative Western blot (top) and densitometry analysis (bottom) of cyclin D1 and GAPDH in rhLRG1-treated HaCaT cells. All images are representative, and data are represented as mean (95% CI; P) of n ≥ 5 mice or n ≥ 3 independent experiments per treatment group. Significance was determined by unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. WB, Western blot.

LRG1 Modulates Dermal Angiogenesis

Our previous study demonstrated an essential role of LRG1 in pathological neovascularization in the eye (11), and angiogenesis is required for the formation of granulation tissue during wound healing (1). To understand LRG1’s role in dermal angiogenesis during wound healing, day 7 wound tissues were subjected to immunofluorescence staining with an endothelial cell (EC)-specific marker, CD31. Although the total vessel area in the distal part of the skin remained unchanged (Supplementary Fig. 7), there was a significant reduction in total vessel area in the wound bed of Lrg1−/− mice (Fig. 5A). In line with the observations in macrovascular human umbilical vein endothelial cells (HUVECs) (11), rhLRG1 was able to induce HDMEC proliferation as visualized by Ki67 staining (Fig. 5B) and the ability of HDMECs to form tube-like structure in Matrigel (Fig. 5C). We also showed increased motility of rhLRG1-treated HDMECs (Fig. 5D). Mechanistically, we found that rhLRG1 significantly stimulated the phosphorylation of proangiogenic Smad1/5 in HDMECs, and blocking of TGFβ type I receptor activin-like kinase 1 (ALK1) and activin-like kinase 5 (ALK5) completely abrogated this activation (Fig. 5E). These data demonstrate an ALK1/5-dependent proangiogenic role of LRG1 in wound healing.

Figure 5
Figure 5

LRG1 modulates wound angiogenesis during wound healing. A: Representative immunofluorescence staining of CD31 (green) and DAPI (blue) (top) and quantification of vessel density (bottom) in day 7 wounds of wild-type and Lrg1−/− mice. Scale bar: 15 μm. B: Representative images of immunofluorescence staining detecting Ki67 (red) and DAPI (blue) (top) and quantification of percentage of Ki67+ cells (bottom) in HDMECs. Scale bar: 50 μm. C: Representative images (top) and quantification (bottom) of Matrigel tube formation. Scale bar: 125 μm. D: Representative images (top) and quantification (bottom) of Transwell migration assay. Scale bar: 100 μm. E: Representative Western blot (left) and densitometry analysis (right) of endothelial TGFβ-Smad1/5 signaling in HDMECs treated with rhLRG1 in the absence and presence of ALK1 inhibitor (LDN193189) or ALK5 inhibitor (SB431542). All images are representative, and data are represented as mean (95% CI; P) of n ≥ 6 mice or n ≥ 3 independent experiments per group. Significance was determined by unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. WB, Western blot.

LRG1 Is Highly Induced in Diabetic Mice and in Humans With Diabetes

Having established an important role for LRG1 in physiological wound healing, we next examined the association between LRG1 and chronic wound healing in mice and humans with diabetes. ELISA analysis revealed a significantly higher LRG1 level in the serum of DFU patients as compared with that in venous ulcer patients (Fig. 6A). Consistent with this, we showed that LRG1 expression in ulcer tissues of DFU patients was also significantly higher than that from venous ulcer patients (Fig. 6B). To support this observation, we analyzed wound tissues collected from C57BL/6 mice subjected to STZ-induced diabetes for the expression of LRG1. As observed in DFU patients, LRG1 levels were significantly higher in wounds of diabetic mice (Fig. 6C). Consistently, qRT-PCR analysis revealed a sustained high expression of Lrg1 transcript in the wound tissue of diabetic mice throughout the wound healing process (Fig. 6D). Wound closure was significantly impaired in diabetic mice as compared with that in nondiabetic control (Fig. 6E). These data show that in the skin, LRG1 expression is further increased in both humans and mice with diabetes.

Figure 6
Figure 6

Elevated LRG1 expression is observed in diabetic humans and mice. A: ELISA analysis of LRG1 in serum from venous ulcer patients and DFU patients. B: Representative Western blot (top) and densitometry analysis (bottom) of LRG1 and GAPDH in human patients with venous ulcer and DFU. C: Representative Western blot (top) and densitometry analysis (bottom) of LRG1 and GAPDH in normal and diabetic wounds of C57BL/6 mice. D: qRT-PCR analysis of normal and diabetic wounds of C57BL/6 mice. E: Representative images (left) and quantification (right) of wound size revealed a delayed wound closure in C57BL/6 mice with STZ-induced diabetes. Scale bar: 1 mm. All images are representative, and data are represented as mean (95% CI; P) of n ≥ 6 patients or mice per group. Significance was determined by unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01. WB, Western blot.

Deletion of the Lrg1 Gene Was Beneficial to Impaired Wound Healing in Diabetes

As our data thus far have demonstrated an elevated Lrg1 transcript level in wounds of both mice and humans with diabetes, we investigated whether wound closure in mice with STZ-induced diabetes is affected in the absence of Lrg1. Although STZ treatment led to a significant weight loss, there was no difference in body weight between STZ-treated wild-type and Lrg1−/− mice (Supplementary Fig. 8). Unlike what was observed in normoglycemic mice, mice with genetic deletion of Lrg1 were protected from the diabetes-induced delay in would closure (Fig. 7A). Recent studies highlighted the influence of diabetes on NET formation (8). Considering the role of LRG1 in neutrophil functions and its upregulation at the inflammatory phase of wound healing, we next studied whether LRG1 affects NETosis in mice subjected to STZ-induced diabetes. Western blot analysis showed a significant reduction in the expression of a NET marker, H3Cit, in day 3 wounds of diabetic Lrg1−/− mice (Fig. 7B). For confirmation of this observation, bone marrow–derived neutrophils were isolated from wild-type and Lrg1−/− mice and subjected to calcium ionophore–induced NETosis. Consistent with the earlier observation, Lrg1−/− neutrophils were resistant to calcium ionophore–induced expression of H3Cit (Fig. 7C). Similarly, immunofluorescence staining showed that calcium ionophore–induced NETs, as indicated by the presence of H3Cit+ neutrophils, were significantly reduced in Lrg1-deficient neutrophils (Fig. 7D). We also showed that Lrg1-deficient neutrophils formed fewer NETs in comparison with their wild-type counterparts upon calcium ionophore treatments (Fig. 7E). Complementing this observation, SYTOX Green assay showed that LRG1 supplementation significantly induced the formations of NETs in human peripheral blood neutrophils (Fig. 7F). Consistently, immunoblots demonstrated that rhLRG1 significantly induced citrullination of histone H3 in dHL-60 cells (Fig. 7G). Activation of Akt pathway was reported to mediate calcium ionophore–induced NETosis (33). We further showed that LRG1 was able to induce the phosphorylation of Akt in dHL-60 cells and the LRG1-induced expression of H3Cit and Akt phosphorylation were completely abolished in the presence of an allosteric Akt inhibitor, MK2206 (Fig. 7G). LRG1 was previously reported to signal through TGFβ type I receptor activin-like kinase 5 (ALK5) in non-ECs (34). For elucidation of whether LRG1-induced NETosis and Akt activation are dependent on ALK5, ALK5 was inhibited by SB431542, resulting in a complete abrogation of LRG1-induced phosphorylation of Akt and H3Cit (Fig. 7H). Together, our data demonstrate an important role of LRG1 in diabetic wounds and that LRG1 exerts its function through mediating NETosis in a TGFβ/ALK5/Αkt-dependent manner.

Figure 7
Figure 7

LRG1 mediates NETosis. A: Representative images (left) and quantification (right) of wound size in wild-type and Lrg1−/− mice with STZ-induced diabetes. Scale bar: 1 mm. B: Representative Western blot (top) and densitometry analysis (bottom) of H3Cit, histone H3 (H3), and GAPDH in day 3 wounds from wild-type and Lrg1−/− mice with STZ-induced diabetes. C: Representative Western blot (top) and densitometry analysis (bottom) of H3Cit, H3, and GAPDH in calcium ionophore–treated wild-type and Lrg1−/− neutrophils. D: Representative immunofluorescence staining detecting H3Cit (green) and DAPI (blue) (left) and quantification of percentage of H3Cit+ cells (right) in calcium ionophore–treated wild-type and Lrg1−/− neutrophils. Scale bar: 80 μm. E: SYTOX Green assay on calcium ionophore–treated wild-type and Lrg1−/− neutrophils. F: SYTOX Green assay on calcium ionophore–treated dHL-60 cells. G: Representative Western blot (left) and densitometry analysis (right) of H3Cit, H3, AKT, phospho-AKT (pAKT), and GAPDH in rhLRG1- and/or MK2206-treated dHL-60 cells. H: Representative Western blot (left) and densitometry analysis (right) of H3Cit, H3, AKT, phospho-AKT, and GAPDH in rhLRG1 with or without SB431542-treated dHL-60 cells. All images are representative, and data are represented as mean (95% CI; P) of n ≥ 5 mice or n ≥ 3 independent experiments per group. Significance was determined by one- or two-way ANOVA followed by Tukey multiple comparisons test or unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. CaI, calcium ionophore; WB, Western blot.

Discussion

Impaired wound healing and subsequent formation of foot ulcers is one of the most common complications found in patients with diabetes (2). Considering the important role of inflammation in DFU pathophysiology, multiple anti-inflammatory drugs have been developed but have shown limited success (10). LRG1 is a multifunctional protein that was previously linked to neutrophil activation (35), EMT (16), and angiogenesis (11), all of which are essential for effective wound closure. Here, we investigated the role of LRG1 in both physiological and pathological cutaneous wound healing.

Overwhelming evidence indicated the association between LRG1 and various inflammatory and autoimmune conditions (1214). Infiltrating myeloid cells have been reported to act as the key source of LRG1 in psoriatic skin lesions (13) and remodeling myocardium following infarction (36). In line with these observations, our study revealed that LRG1 is predominantly produced by the wound-infiltrating CD11b+ myeloid cells. Neutrophils are among the first inflammatory cells to reach the wound bed following injury. Both impaired neutrophil function and hyperactive neutrophils have been reported to compromise wound healing (26). Despite being induced during early neutrophil differentiation (37), the role of LRG1 in neutrophil function remains to be elucidated. Our study showed that LRG1 promotes neutrophil adhesion, likely by inducing the expression of neutrophil adhesion molecule, L-selectin. Consistent with this observation, the number of wound-infiltrating neutrophils was significantly reduced in the wound bed of Lrg1−/− mice. We further showed that wild-type recipients transplanted with Lrg1−/− BMCs showed a significant delay in wound closure as compared with wild-type recipients receiving wild-type BMCs. On the other hand, wild-type BMCs were sufficient to rescue the delayed wound closure in Lrg1−/− recipients. These data support the important role of BMDC-derived LRG1 in wound healing.

Reepithelialization plays an indispensable role in wound healing, and it is driven by the proliferation and migration of keratinocytes at the wound edge (31). A previous study using exogenous LRG1 showed that LRG1 does not affect keratinocyte proliferation as demonstrated by EdU+ staining (21). Here, we showed reduced number of proliferating keratinocytes as indicated by Ki67 staining at the wound edge of Lrg1-deficient mice. The discrepancy between the two studies is likely due to the use of different cellular markers for proliferating cell detection. Ki67 is a broad cell proliferation marker that is expressed throughout the active cell cycle (G1, S, G2, and M phases) (38), whereas EdU is only incorporated in nascent DNA during the S phase (39). Therefore, EdU may provide partial information regarding the extent of cell proliferation. Supporting LRG1’s role in keratinocyte proliferation, plasmid-mediated LRG1 overexpression and siRNA-mediated LRG1 knockdown treatment significantly affect the viability of keratinocytes in vitro. We also demonstrated a promoting role of LRG1 in the expression of cell-cycle marker cyclin D1. These results are in line with previous studies that LRG1 increases proliferation of different epithelial-derived cancer cells, such as colorectal cancer cells (40), pancreatic ductal adenocarcinoma (17), non-small-cell lung cancer cells (41), and gastric cancer cells (15). Consistent with the previous report (21), we showed that LRG1 overexpression and knockdown affect keratinocyte migration. To acquire migratory capacity, quiescent epithelial cells undergo phenotypic changes to gain mesenchymal characteristics (42). Our study discovered that rhLRG1 induces the expression of EMT markers, which is consistent with the promoting effect of LRG1 in EMT and colorectal cancer metastasis (16).

The increased metabolic demand of repairing triggers angiogenesis, and failure in forming functional new vessel leads to delayed wound closure (43). Although LRG1 has previously been implicated in ocular (11) and tumor (16) angiogenesis, its role in normal blood vessel formation during wound healing was largely unknown. In this study, we observed reduced blood vessel density in the wound bed of Lrg1−/− mice. We further showed that LRG1 promotes angiogenesis by mediating HDMEC proliferation, migration, and the ability to form tube-like structures. Unlike what was observed in HUVECs (11), both ALK1 and ALK5 are required for LRG1-induced Smad1/5/8 phosphorylation in HDMECs, which is not surprising, as ALK5 kinase activity is necessary for optimal TGFβ/ALK1 action (44).

Our study showed elevated LRG1 levels in serum and wound tissues of human patients with DFU and diabetic mice, which could be explained, at least partially, by the increased infiltration of immune cells, including neutrophils and macrophages, in diabetic wounds (45). While neutrophils are beneficial to normal wound repair, excessive neutrophil infiltration and NET formation are critical culprits in chronic inflammation and delayed wound closure in diabetes (8). Mechanistically, NETosis could be triggered in a NADPH oxidase (NOX)-dependent and -independent manner (33). Our study showed that there was a reduced NETosis in Lrg1−/− mice and Lrg1−/− neutrophils are resistant to calcium ionophore–induced NOX-independent NETosis. Akt is essential for NOX-independent NETosis (33). Furthermore, we demonstrated that LRG1-mediated NET formation is dependent on activation of the Akt pathway through TGFβ type I receptor ALK5, which is in agreement with LRG1-mediated TGFβ signaling in ECs (11), fibroblasts (34), glioma cells (46), and T-helper 17 cells (47). We further demonstrated that Lrg1−/− mice are resistant to diabetes-induced delay in wound closure, especially during the inflammatory phase, which is likely due to its role in NETosis. It is worth highlighting that global Lrg1−/− mice were used in this study, whereas prompt wound healing is achieved by collaborative interactions between multiple types of cells present in the wound microenvironment (1). Our BMT and in vitro experiments provided a direct evidence of BMDC-derived LRG1 on the behavior of other types of skin cells, However, this would not exclude the possible impacts of EC and keratinocyte-derived LRG1 on inflammation, reepithelialization, and angiogenesis during wound closure. To address these questions, we are now generating cell-specific knockout mice.

In conclusion, we define here a complex but critical role of LRG1 in normal and diabetic wound healing. Lrg1 deficiency leads to a significant delay in normal wound healing as a consequence of impaired inflammation, reepithelialization, and angiogenesis. On the other hand, there is a reduced NETosis in diabetic mice with ablation of Lrg1, which protects Lrg1−/− from the diabetes-induced delay in wound healing. Targeting LRG1 may represent an attractive strategy to suppress excessive NETosis, therefore accelerating wound closure in patients with diabetes.

Article Information

Acknowledgments. The authors thank David Becker, Lee Kong Chian School of Medicine, for advice on histology analysis.

Funding. This work was supported by Singapore Biomedical Research Council SPF grant (SIPRAD) to X.W. and W.H. and Singapore National Medical Research Council DYNAMO NMRC/OFLCG/001/2017 and TAPP NMRC/OFLCG/004/2018 to X.W. Singapore Study of Macroangiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D) is supported by the Singapore Ministry of Health’s National Medical Research Council under its Clinician Scientist Individual Research Grant (CS-IRG) (MOH-000066).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. C.L. designed the study, performed experiments, analyzed data, and wrote the manuscript. M.H.Y.T. and S.L.T.P. designed and performed experiments and discussed data. M.L.L. and H.M.T. performed experiments. H.W.H., C.R., S.E.M., J.G., and S.T. contributed to discussion and reviewed and edited the manuscript. W.H. secured funding, conceived the project, contributed to discussion, and reviewed and edited the manuscript. X.W. secured funding, conceived the project, designed the study, and wrote the manuscript. X.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in the abstract form at the 3rd Singapore International Conference on Skin Research, Singapore, 21 March 2018.

  • Received June 3, 2020.
  • Accepted August 24, 2020.



Sell Unused Diabetic Strips Today!

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By electricdiet / December 28, 2020





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Review: Low-Carb Cauliflower Pizza & Sandwich Thins from Outer Aisle

By electricdiet / December 26, 2020


I really enjoy a slice of pizza or bread, but the carb counting and potential blood sugar headache mean that eating pizza is not always worth it for me.

This is why I love that there are now some absolutely terrific low carb pizza options that make pizza night a little easier to maneuver.

I was super excited when Outer Aisle reached out and asked if I’d be up for trying their Cauliflower Pizza Crust as I have been buying their Cauliflower Sandwich Thins for well over a year now at my local Whole Foods market.

So let me walk you through why I love the Outer Aisle cauliflower products, how my blood sugars react after eating them, and where you can get yours.

Christel holding Outer Aisle pizza crust

Disclaimer: This is a sponsored post that contains affiliate links. All opinions in this post are my own and are based on my own tests of the products.

Why I love Outer Aisle Cauliflower products

What I like about the Outer Aisle products is that they are made with 63% fresh cauliflower, whole cage-free eggs, and Parmesan cheese. It’s as close as it comes to the way I would make pizza crust myself if I wanted to spend the time.

The cauliflower base means that the blood sugar impact is minimal, and the use of eggs and parmesan as binding agents means that the protein amount is higher than the fats.

That is something I appreciate a lot, as high-fat foods mean that the blood sugar impact is slowed down but that the blood sugar increase can “haunt” you for hours after you’ve finished your meal.

Another important thing about these products is that they don’t actually taste like cauliflower. Don’t get me wrong, I like cauliflower, but I don’t want my pizza to taste like a head of cauliflower.

Two Outer Aisle cauliflower pizza crusts in their packaging

How I eat the Outer Aisle products

I came across the Sandwich Thins at my local market when looking for low-carb bread substitutes. What I like about them is that they have only 2 grams of carbs (1 gram of net carb) and 50 calories per Sandwich Thin. They also taste great and the texture kinda reminds me of tortillas.

That means that they are great for scooping up a hot bowl of chili (how I’ve primarily been enjoying them) and are the perfect fit for taco night.

I don’t really eat sandwiches that often, but could also see them being great for that, as well as mini pizzas.

And talking about pizza, the Outer Aisle Pizza Crusts are made from the same base ingredients but are a little thicker and sturdier. They have to be able to hold all of the pizza toppings after all.

A pizza crust has 120 calories and 4 grams of carbs (3 grams of net carbs), so basically nothing.

The Outer Aisle products are really easy to prepare. I bake the Sandwich Thins under the broiler for a few minutes on each side until they get golden brown and firm up a bit.

The pizza I pre-bake at 425º degrees for 8 minutes then add my toppings and bake until the cheese is bubbly. Then I let it sit for a little bit to let the crust firm up. If you have a pizza stone, you probably don’t need to pre-bake it at all.

Cauliflower pizza on table with toppings

How Outer Aisle products impact my blood sugar

Since these products have so few carbs, you might not need any insulin at all. However, I am fairly sensitive to anything I eat so I do need to inject insulin even for a small amount of carbohydrates. I also load my pizzas high, so I have to take insulin for the sauce and cheese as well.

But these products are so gentle on my blood sugars! Let me share my blood sugar experiment from the last time I enjoyed one of the Outer Aisle Pizza crusts.

I was going to one of my girlfriend’s house, and asked her if she wanted me to bring sushi or cauliflower pizza crusts and toppings. She opted for the pizza…which tells you how good this stuff is.

Since I was at her house, and I guess slightly distracted with making the pizzas and catching up, I forgot to pre-bolus (dose 10-15 min before eating) and just took my insulin shot right before we sat down to eat.

We ate at 8 PM and as you can see from the graph below, there was no blood sugar spike after I ate. And that was even without a pre-bolus!

Blood sugar graph

I use MyFitnessPal to track my food and calculate my carb, protein, and fat intake. I bolus not just for the carbs but for protein as well. The pizza had 10 grams of carbs (3 from the pizza, and the rest from the pizza sauce and cheese) and 27 grams of protein, so I took 1 unit of rapid-acting insulin for the carbs and 0.5 units for the protein and that clearly worked out perfectly.

How much insulin you need of course depends on your individual insulin and carb sensitivities but I think it’s safe to say that this product won’t spike blood sugars and it’s low enough in total carbohydrates that it should work for most people living with diabetes, regardless of whether they manage with insulin or not.

Where to get it and pricing

Outer Aisle can be found in many stores across the US (Sprouts, Kroger Family Stores, Albertsons, Whole Foods, Meijer, Wegman’s, etc.). They have a convenient store finder where you can look up stores near you that carry the products.

You can also order it online and get it sent directly to your doorstep.

Prices online are $6.99 for 6 Sandwich Thins and $6.95 for 2 pizza crust (if bought through Instacart).

Some online stores only sell 4 packs at a time, but they freeze really well for up to six months, so you don’t have to worry about them going bad or needing to eat 24 sandwich thins very quickly.

If you would like to order online, you can order from their website here. Use the code DIABETESSTRONG at checkout to take 10% off your order.



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Food for Nausea – Gingerbread Muffins Recipe Make Easy Holiday Snack

By electricdiet / December 24, 2020


When Going Through Cancer Treatment Find Relief with Food for Nausea!

Acute side effects of chemotherapy can cause nausea, vomiting or loss of appetite due to the destruction of rapidly dividing cells lining the gastrointestinal tract when going through chemo. It is important to keep good nutrition for healing, however when not feeling that can be difficult but there is relief with food for nausea.  Holly Clegg’s best-selling Eating Well Through Cancer cookbook, was written to answer the often-asked question, “What can I eat when going through cancer treatment?” This diabetic Gingerbread Muffins recipe is found in the Tummy Troubles chapter because ginger can be soothing.  Also, these Gingerbread Muffins make the best holiday food for everyone.

Gingerbread muffins picmonkey

Gingerbread Muffins
This awesome muffin has all of the flavor of your favorite spiced cookie in a moist anytime snack or breakfast muffin. When you’re not feeling well, this is easy to eat and ginger is food for nausea. Muffins freeze well. These are also a diabetic gingerbread muffins recipe

    Servings20 muffins
    Prep Time15 minutes
    Cook Time25 minutes

    Ingredients

    • 1 1/2cups


      whole-wheat flour

    • 1cup


      all-purpose flour

    • 1teaspoon


      ground ginger

    • 1teaspoon


      ground cinnamon

    • 1/2teaspoon


      ground cloves

    • 1/2cup


      sugar

    • 1/3cup


      canola oil

    • 1cup


      light molasses

    • 2


      eggs

    • 1cup


      boiling water

    • 2teaspoons


      baking soda

    Instructions
    1. Preheat oven 325°F. Coat muffin pans with nonstick cooking spray or line with papers.


    2. In large bowl, combine both flours, ginger, cinnamon, and cloves. Set aside.


    3. In medium bowl, whisk together sugar and oil. Add molasses and eggs whisking until blended. In glass measuring cup combine water and baking soda. Stir to dissolve. Pour in egg mixture and whisk until blended. Add egg mixture to flour mixture, stirring just until combined.


    4. Spoon batter into paper lined tins, filling 1/2-3/4 full. Bake 20-25 minutes or until inserted toothpick comes out clean.

    Recipe Notes

    Per Serving: Calories 161, Calories from Fat 25%, Fat 5g, Saturated Fat 0g, Cholesterol 19mg, Sodium 140mg, Carbohydrates 28g, Dietary Fiber 1g, Total Sugars 14g, Protein 2g, Diabetic Exchanges: 2 starch, 1/2 fat

    Terrific Tip: Keep a few muffins in the freezer to pop out when not feeling well and need a boost. You can always use only all-purpose flour if that’s what you have.

    Nutrition Nugget: Ginger has been shown to help nausea symptoms so these muffins may be just the ticket to feeling better

    Gingerbread Muffins and Recipes For Cancer Patients In Easy Cancer Cookbook

    Eating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer PreventionEating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer PreventionEating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer Prevention

    This cancer cookbook is so helpful for people going through cancer treatment as they experience different side effects such as nausea. Each chapter addresses different symptoms and suggests foods that are best tolerated.

    As you can see from these diabetic Gingerbread Muffins in Eating Well Through Cancer, they really are a recipe the entire family and/or caregiver will also enjoy. All recipes for cancer patients are also for everyday cooking and cancer prevention. Just a practical healthy cookbook to guide you through cancer treatment! Easy diabetic recipes are highlighted in the cookbook just like these diabetic gingerbread muffins.

    When Feeling Queasy – Gingerbread Muffins Can Help With Nausea

    If you experience nausea and vomiting try to drink fluids throughout the day to prevent dehydration, such as sipping water, juices, and other clear, calorie- containing liquids. You may tolerate clear, cool liquids better than very hot or icy fluids. When you have stopped vomiting, try eating easy to digest foods such as crackers, gelatin, and plain toast. Ginger is food for nausea as its an herb recognized to help with nausea and sooth the stomach. Try drinking ginger tea, flat ginger ale, gingersnap cookies, ginger candy, pickled ginger or ginger flavored breads such as these Gingerbread Muffins. Find more recipes when going through cancer treatment on Team Holly’s blog.

    Love These Easy To Use Cupcake Liners and Muffin Molds for Diabetic Gingerbread Muffins

    How cute are these Silicone Cupcake Liners?! Make your muffins and cupcakes fun and bright with these BPA free, FDA approved 100% food grade silicone kitchen utensils reusable cupcake liners.

    These fun colors even make the Gingerbread muffins taste better and will brighten up a cancer patient’s day.

    Your Holiday Needs Holly’s 12 Ideas For Christmas Foodies Downloadable Only $1.99!

    The holidays are here and you need Holly’s 12 Ideas for Christmas Foodies. From evening appetizers to teacher gifts, even – what to cook Christmas morning, these festive favorite recipes are Holly’s go-to dishes that will get you through all of the parties and last-minute family get-togethers this December.  No need to stress with what to make this holiday season – let Holly do it for you with her December favorites!

    Get All of Holly’s Healthy Easy Cookbooks

    The post Food for Nausea – Gingerbread Muffins Recipe Make Easy Holiday Snack appeared first on The Healthy Cooking Blog.



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    VMAT2 Safeguards β-Cells Against Dopamine Cytotoxicity Under High-Fat Diet–Induced Stress

    By electricdiet / December 22, 2020


    Introduction

    Endocrine pancreatic β-cells are highly specialized for making insulin for the maintenance of glucose homeostasis in our bodies. Diabetes is a disease caused by the lack of (type 1) or dysfunction of (type 2) β-cells. Oversupply of nutrients and the subsequent overstimulation of β-cells contribute to insulin secretory failure in type 2 diabetes. Reactive oxygen species (ROS) are produced from mitochondrial respiration with stimulation with glucose and other fuels. In the pancreatic β-cells, glucose metabolism via the tricarboxylic acid cycle is central for triggering insulin secretion. Higher glucose-stimulated insulin secretion (GSIS) activity triggers more elevated levels of ROS production (1,2). In a healthy state, β-cells possess elaborate antioxidant mechanisms to adapt to the cytotoxicity of ROS. However, chronic overnutrition leads to progressive mitochondrial metabolic dysfunction and oxidative stress. Numerous studies have investigated the mechanisms involved in the progression of β-cell failure, in which ROS play an important role.

    There is uptake of monoamines by vesicular monoamine transporter 2 (VMAT2), a protein encoded by the Slc18a2 gene, from the cytoplasm into the vesicles. Cytoplasmic monoamines, namely, dopamine, serotonin, noradrenaline, adrenaline, and histamine, are transported by VMAT2 into cytosolic vesicles, where they are protected from degradation by monoamine oxidase (MAO) and stored for subsequent release (35). Adult β-cells possess the enzymes required to synthesize, interconvert, and catabolize monoamines and to store them in the vesicular granules. Of the two VMAT isoforms that transport monoamines, VMAT2 is the isomer expressed in the pancreas (69). Among the monoamines, dopamine is the most abundant monoamine in β-cells (10,11).

    During GSIS from pancreatic β-cells, dopamine modulates insulin release. Exogeneous dopamine inhibits GSIS in isolated islets through the Drd2 receptor, which is expressed on β-cells (12). Treatment of rat islets with the VMAT2-specific antagonist tetrabenazine (TBZ) significantly enhanced their insulin secretion (13). Dopamine and its precursor L-dopa inhibit GSIS (14). However, disruption of the dopamine D2 receptor results in impairment of insulin secretion and causes glucose intolerance (15). Furthermore, inhibition of MAO activity reduces insulin secretion in response to metabolic stimuli (16), which raises the possibility that dopamine is important for β-cell function. However, it remains unknown how dopamine affects the function of β-cells.

    Previously, we identified TBZ in a screening, searching for small molecular compounds that potentiate the differentiation of embryonic stem (ES) cells into insulin-expressing β-cells (11). We found that treatment with TBZ decreased dopamine content, thereby identifying VMAT2-dopamine signaling as a negative regulator for pancreatic β-cell differentiation. We also identified domperidone, an antagonist for dopamine D2 receptor (Drd2), in another screen as a compound that increases β-cell mass in adult islets (17). We found that the dopamine-Drd2 signal functions as a negative regulator for the maintenance of β-cell mass.

    In the current study, to understand the role of VMAT2 and dopamine signaling in the regulation of β-cell and glucose homeostasis, we generated a pancreatic β-cell–specific Vmat2 mutant mouse line using a rat insulin 2 promotor driving Cre recombinase (RIP-CreER) crossing with a conditional Vmat2 allele, Slc18a2tm1c. We found that VMAT2 plays an important role in protecting β-cells from cytotoxicity of ROS.

    Research Design and Methods

    Ethics Approval

    All studies involving animals were performed following local guidelines and regulations and were approved by the Institutional Committee for Animal Research in Tokyo Institute of Technology and Kumamoto University.

    Monoamine Content Assay

    The monoamine content assay was performed as previously described (11). Isolated islet cells were lysed with lysis buffer containing 1.0% Triton X-100 (Nacalai Tesque, Kyoto, Japan) in 0.1 mol/L PBS (pH 7.2) (Sigma-Aldrich) with protease inhibitor cocktail. Lysates were assayed for dopamine with a dopamine-specific ELISA kit (Labor Diagnostika Nord GmbH & Co. KG, Nordhorn, Germany).

    Generation of Slc18a2tm1a, Slc18a2tm1c, and Slc18a2tm1d Mouse Lines and a Conditional β-Cell–Specific Slc18a2 (Vmat2) Knockout Mouse Line, βVmat2KO

    An ES cell line bearing a targeted mutation at Slc18a2 (encoding VMAT2 protein) (Slc18a2tm1a(EUCOMM)Wtsi; number EPD0242_2_F06) (C57BL/6) was produced for the EUCOMM and EUCOMMTools projects by the Wellcome Trust Sanger Institute. The mutation details (Mouse Genome Informatics [MGI] identifier 4432865) are as follows: The L1L2_Pgk_P cassette was inserted at position 59262507 of chromosome 19 upstream of exon 3 (Build GRCm38). The cassette is composed of an FLP recombinase target (FRT) site followed by En2 SA, IRES and LacZ, SV40 polyA sequences, and a loxP site. This first loxP site is followed by a neomycin resistance (neo) gene under the control of the PGK promoter, SV40 polyA, a second FRT site, and a second loxP site. A third loxP site is inserted downstream of the targeted exon 3 at position 59263523. The exon 3 is thus flanked by loxP sites (Fig. 1). The tm1a allele was initially a Vmat2-nonexpressing form. Slc18a2tm1a/+ mice were generated by injection of the Slc18a2tm1a(EUCOMM)Wtsi ES cells into the perivitelline space of one-cell stage C57BL/6 mouse embryos. We have successfully produced mouse chimeras, 36 males and 15 females, in 200 injections. We then crossed the chimera mice to produce homozygous Slc18a2tm1a/tm1a, but all embryos died at embryonic day 11.5. Heterozygous Slc18a2tm1a/+ mice were alive and fertile. We then created a “conditional ready” Slc18a2tm1c mouse line (floxed) allele by crossing the Slc18a2tm1a/+ mice with a global Flp transgenic mouse strain [Gt(ROSA)26Sortm1(FLP1)Dym/J, stock no. 003946; The Jackson Laboratory, Bar Harbor, ME], so that subsequent cre expression results in a knockout mouse. β-cell–specific Vmat2 mutant mice were produced by crossing the homozygous Slc18a2tm1c/tm1c with RIP-Cre transgenic mice [B6.Cg-Tg(Ins2-cre)25Mgn/J, stock no. 003573; The Jackson Laboratory]. All mice used were maintained on a C57BL/6 background. PCR primers used for genotyping are listed in Supplementary Table 1.

    Figure 1
    Figure 1

    VMAT2 expression in the pancreatic islets and the generation of βVmat2KO mouse. A: Time-dependent VMAT2 expression in the pancreatic islets in response to glucose administration. Immunostaining of the pancreatic islets (left panel) showed the presence of VMAT2 (magenta) expression in insulin-expressing β-cells under high blood glucose (30 min) but not under low glucose (0 or 120 min) conditions, whereas localization of VMAT2 in glucagon-expressing α-cells was constantly observed. Blood glucose (BG) values of mice at 0, 30, and 120 min are shown above. The average intensity of the VMAT2 protein in β-cells or α-cells was plotted (right panel). B: Generation of the Slc18a2-deletion mutant mice. The Slc18a2 locus was inserted with a LacZ gene cassette to make the tm1a allele (Slc18a2tm1a). The tm1c allele (Slc18a2tm1c) was obtained by excising the sequence flanked by two FRT sites. βVmat2KO mice were then obtained by crossing homozygous Slc18a2tm1c with Ins-Cre transgenic mice. Yellow boxes: exons. Lower panel shows genomic PCR results for genotyping of Slc18a2tm1c. Genomic PCR products of the tm1c or WT Slc18a2. C: Immunostaining of control and βVmat2KO mouse islets at 10 weeks of age. VMAT2 expression was observed in control (Slc18a2tm1c) but not the βVmat2KO β-cells, whereas its expression in the α-cells was not affected. At the bottom of the panel, 0 or 30 minutes indicates the time after glucose injection. D: βVMAT2KO islets showed significantly lower dopamine content compared with that of the control islets. E: The effects of TBZ or DMSO treatments on insulin secretion in response to glucose stimulation in the isolated islets. A and C: VMAT2, magenta; INS, green; GCG, yellow; DAPI, blue. βKO, βVmat2KO. Scale bars = 50 μm. A, D, and E: Means ± SD are shown (n = 3). Significant differences vs. control, by one-way repeated-measures ANOVA and Dunnett multiple comparisons test. cont., control; w, weeks.

    SPiDER-βGal Staining of Slc18a2tm1a/+ Mouse Islets

    Slc18a2tm1a mouse genome bearing the LacZ gene was used to report Vmat2 gene expression (Supplementary Fig. 1). LacZ activity in the Slc18a2tm1a/+ pancreas sections was visualized by SPiDER-βGal staining solution (Dojindo Molecular Technologies, Inc., Rockville, MD) (18).

    Measurement of Glucose-Stimulated C-Peptide (Insulin) Secretion by ELISA

    For GSIS assays, mouse islets were preincubated for 30 min in low glucose (5.5 mmol/L) in Krebs-Ringer buffer (133.4 mmol/L NaCl, 4.7 mmol/L KCl, 1.2 mmol/L KH2PO4, 1.2 mmol/L MgSO4, 2.5 mmol/L CaCl2, 5.0 mmol/L NaHCO3, 2.8 mmol/L glucose, 10 mmol/L HEPES [pH 7.4], and 0.2% BSA). Islets were washed twice with PBS and then incubated for 1 h in low glucose (5.5 mmol/L) or high glucose (25.0 mmol/L). Insulin secretion into the buffer and insulin content of the cell lysates were measured using a mouse C-peptide ELISA kit (Shibayagi Co., Ltd., Gunma, Japan) and then normalized with the protein content of the cell lysates.

    High-Fat Diet Feeding

    Male mice were housed in a 12-h light-dark cycle. Feedings were switched from normal diet (ND) (AIN-93M; Oriental Yeast Co., Tokyo, Japan) to high-fat diet (HFD) (60% kcal from fat) (HFD-60; Oriental Yeast Co., Tokyo, Japan) at 6 weeks of age.

    Intraperitoneal Glucose Tolerance Test

    Mice that had been fasted for 16 h were used. Blood glucose levels were measured before (0 min) and at 15, 30, 60, 90, and 120 min after intraperitoneal administration of 25% glucose solution (Wako, Osaka, Japan) at 1.5 g/kg body wt.

    Intraperitoneal Insulin Tolerance Test

    Mice fasted for 6 h were administered with an intraperitoneal injection of insulin solution (0.4 units insulin/kg body wt HUMULIN R [regular human insulin injection]; Eli Lilly, Indianapolis, IN). Glucose levels were monitored.

    Blood Glucose Measurement

    Blood glucose levels were measured with OneTouch Ultra equipped with Life Check Sensor (Gunze, Kyoto, Japan) or blood glucose meter ANTSENSE III (Horiba, Kyoto, Japan).

    Insulin Measurements

    Blood samples were sampled 30 min after glucose intraperitoneal injection (2 mg/kg) and centrifuged to obtain plasma. Plasma insulin was measured using a Mouse Insulin ELISA Kit (Shibayagi Co., Ltd.).

    F-actin Staining and Immunohistochemistry Analysis

    Tissue samples were fixed with 4% formaldehyde, cryoprotected with 30% sucrose, and cut into 10-μm-thick sections. For F-actin staining, Alexa Fluor 555 Phalloidin (cat. no. 8953S, 1:20; Cell Signaling Technology, Tokyo, Japan) was used. For immunohistochemistry, the following antibodies were used: rabbit anti–chromogranin A (ab15160, 1:400; Abcam), mouse anti-glucagon (G2654, 1/1,000; Sigma-Aldrich), guinea pig anti-insulin (A0564, 1/500; Dako), and rabbit anti-VMAT2 (ab81855, 1:500; Abcam). Alexa Fluor 488 donkey anti-guinea pig IgG (706-546-148, 1:1,000; Jackson ImmunoResearch Laboratories, Inc., West Grove, PA), Alexa Fluor 568 donkey anti-rabbit IgG (A10037, 1:1,000; Invitrogen), and Alexa Fluor 647 donkey anti-mouse IgG (Jackson ImmunoResearch Laboratories, Inc.) were used. Tissue sections were counterstained with DAPI (Roche Diagnostics, Basel, Switzerland).

    TUNEL Assay

    The TUNEL assay was performed using the In Situ Cell Death Detection Kit, Fluorescein (cat. no. 11684795910; Roche Applied Science, Mannheim, Germany).

    Caspase-3/7 Detection

    Caspase-3/7–positive cells were stained with use of the CellEvent Caspase-3/7 Green Detection Reagent (Invitrogen Life Technologies Co., Carlsbad, CA).

    RNA Isolation, cDNA Synthesis, and Real-time PCR

    RNA was extracted using the RNeasy Mini Kit (QIAGEN, Hilden, Germany) and then treated with DNase I (QIAGEN). First-strand cDNA was synthesized using a SuperScript VILO cDNA Synthesis Kit (Invitrogen). Real-time PCR analysis was done using THUNDERBIRD SYBR qPCR Mix (Toyobo) with specific primers. All reactions were run on the StepOnePlus Real-Time PCR System (Applied Biosystems). β-Actin was used as an internal control. All primer sequences are listed in Supplementary Table 2.

    Islet Dissociation Culture or Whole Islet Culture

    Mouse islets from 13-week-old mutant or control (Slc18a2tm1c/tm1c) mice were isolated and handpicked as previously described (19). For dissociation culture, islets were incubated with 0.05% trypsin-EDTA (Invitrogen) for 5 min at 37°C, 5% CO2, and pipetted to dissociate into single cells. The dissociated cells were plated in DMEM (glucose concentration: 25 mmol/L) supplemented with 10% FBS, 100 μmol/L nonessential amid acids, 2 mmol/L l-glutamine, 50 units/mL penicillin, 50 μg/mL streptomycin, and 100 μmol/L 2-mercaptoethanol (Sumitomo Bakelite Co. Ltd., Tokyo, Japan). For whole islet culture, isolated islets were used directly for the culture without dissociation.

    ROS Exposure of the Whole Islets

    For testing of the vulnerability of whole islets to ROS, H2O2 was added to the medium and cultured for 6 h and then used for cell count or real-time PCR.

    ROS Staining

    Islet cells were plated at a density of 5,000 cells per well in 384-well polystyrene-coated plates. After 24 h, CellROX Green Reagent (Invitrogen) was added to each well at a concentration of 10 μmol/L and mixed vigorously. The fluorescence of CellROX was measured with a plate reader.

    H2O2 Content Assay

    Isolated islet cells were treated with chemical compounds for 1, 6, or 24 h and lysed with lysis buffer containing 3.0% Triton X-100 (Nacalai Tesque) in 0.1 mol/L PBS (pH 7.2) (Sigma-Aldrich) with protease inhibitor cocktail. Lysates were assayed for H2O2 contents in islet using Amplite Fluorimetric Hydrogen Peroxide Assay Kit (11502; AAT Bioquest, Sunnyvale, CA).

    Chemical Treatments

    The dissociated islet cells were seeded on 384-well plates (Sumitomo Bakelite Co. Ltd.) at a concentration at 2,000 cells/well. Next, 0.1 mmol/L TBZ (T284000; Toronto Research Chemicals) and 0.1 mmol/L pargyline (10007852; Cayman Chemical) were added into the medium on day 3. Both compounds were dissolved in DMSO. Therefore, all tests were performed under 0.1% DMSO (v/v) containing condition.

    Statistical Analyses

    Data were analyzed by one-way ANOVA and Dunnett multiple comparisons test, except the data for Fig. 5E, in which case unpaired Student t tests was used. All data are presented as mean ± SD.

    Data and Resource Availability

    All data generated or analyzed during this study are included here and in Supplementary Material.

    The mutant mouse generated during the current study is deposited in the Mouse Genome Database at the MGI website, The Jackson Laboratory, as Slc18a2<tm1c(EUCOMM)Wtsi> MGI:6386316. The resource will be available from the corresponding author upon reasonable request.

    Results

    β-Cell–Specific Vmat2 Deletion Results in Decreased Dopamine Content and Increased GSIS

    Of the two VMAT isoforms that transport monoamines, VMAT1 and VMAT2, VMAT2 is the isoform that is expressed in the β-cells in healthy adult pancreatic islets (6,9). There is debate regarding the cell types that express VMAT2 in rodents (7,8,20). We hypothesized that VMAT2 expression is regulated in a glucose-dependent manner. Therefore, we examined its expression in the mouse pancreatic islets in response to glucose administration. We administered glucose after fasting; then, we monitored blood glucose levels and harvested the pancreas at 0, 30, 60, 90, and 120 min after glucose administration. Immunohistochemistry revealed that VMAT2 expression in the β-cells was regulated in a glucose-dependent manner: VMAT2 expression was downregulated at low blood glucose levels (107 mg/dL) at 0 min, upregulated when blood glucose reached its highest level (348 mg/dL) at 30 min, and subsequently down-regulated with decreasing blood glucose levels at 60, 90, and 120 min after glucose administration. By contrast, VMAT2 was expressed constantly in α-cells in a blood glucose–independent manner (Fig. 1A). We used a mouse line, Slc18a2tm1a/+, in which a LacZ reporter cassette was inserted into Exon3 of the SLC18a2 gene. We confirmed a similar rapid transient upregulation of Vmat2 expression, by visualizing LacZ activity using SPiDER-βGal (18), at 30 min after exposure to high glucose (Supplementary Fig. 1). The result suggests that Vmat2 expression in response to high glucose is, in part, regulated at the transcription level.

    To study the function of VMAT2 in β-cells, we created a β-cell–specific VMAT2 mutant mouse line, through the crossing of heterozygous Slc18a2tm1a/+ mice with a mouse strain carrying global flp recombinase expression, to produce a “conditional ready” Slc18a2tm1c mouse line (Fig. 1B). We then crossed Slc18a2tm1c/tm1c with RIP-Cre (21) mice to obtain β-cell–specific Vmat2 knockout (βVmat2KO) mice (Fig. 1B). We confirmed that in the βVmat2KO mice, VMAT2 protein expression in β-cells was nonexistent at high blood glucose levels (30 min after glucose administration), while its expression in the α-cells was equivalent to that in wild-type (WT) mice (Fig. 1C).

    In pancreatic β-cells, the most abundant monoamine is dopamine (11). We isolated islets from the control (Slc18a2tm1c/tm1c) and βVmat2KO mice and measured dopamine content using an ELISA. We found that dopamine content was not affected at 5 weeks but was significantly reduced from 10 weeks, in the βVmat2KO islets compared with the control islets at the same age (Fig. 1D).

    We then isolated pancreatic islets from 13-week-old mice and examined GSIS and the effect of TBZ, a VMAT2-specific inhibitor, on βVmat2KO islets. βVmat2KO pancreatic islets showed a significantly higher GSIS activity compared with the control islets. We observed potentiation of GSIS by TBZ at low (5.5 mmol/L) and high (25.0 mmol/L) glucose in control, but not in βVmat2KO, islets (Fig. 1E [also refer to Fig. 3]).

    Our above results confirmed that in the βVmat2KO mice, VMAT2 expression was knocked out specifically in β-cells, which led to a decrease in dopamine content and a higher insulin secretion in response to low or high glucose stimulation.

    Impaired Glucose Tolerance and Insulin Tolerance in HFD-Fed βVmat2KO Mice

    To examine the phenotypic changes between the βVmat2KO and the control mice, we first compared body weight and blood glucose levels under ND conditions; however, no significant differences were found (Fig. 2A and B). We then assayed for glucose tolerance and insulin tolerance and again found no significant differences between the βVmat2KO and control (Slc18a2tm1c/m1c) mice (Fig. 2C and E). It is reported that RIP-Cre mice fed with ND could display glucose intolerance even at 2 months of age in a background of pure C57BL/6 or mixed 129xC57BL/6 background (22), while others reported that no significant glucose intolerance was observed (23). We initially used RIP-Cre+/−; Slc18a2tm1c/+ heterozygous mice as controls and found that neither glucose intolerance nor insulin intolerance was observed under ND-fed conditions (Supplementary Fig. 2). We then used Slc18a2tm1c/tm1c mice as controls for examining the RIP-Cre+/−; Slc18a2tm1c/tm1c homozygous mice in the subsequent experiments.

    Figure 2
    Figure 2

    βVmat2KO mice exhibited impaired glucose and insulin tolerance after prolonged HFD treatment. A and B: Age-dependent body weight (A) or blood glucose (B) of βVmat2KO (βKO) and control (Slc18a2tm1c/tm1c) mice under ND- or HFD-fed conditions was plotted. No significant difference between the βVmat2KO and control mice was observed under ND- or HFD-fed conditions, respectively. Body weight and blood glucose were elevated in the HFD-fed groups. C: Intraperitoneal glucose tolerance tests (IPGTTs) were performed in 8-, 12-, 15-, and 17-week-old βVmat2KO and control mice. The time dependence of blood glucose levels after glucose administration is shown. βVmat2KO but not control mice showed impaired glucose tolerance at 15 weeks and 17 weeks of age. D: AUC of the results at 15 weeks and 17 weeks shown in C reveals that HFD-fed βVmat2KO mice show impaired glucose tolerance compared with that of the ND-fed control mice. E: Insulin tolerance tests (ITTs) were performed with 14- and 16-week-old βVmat2KO and control mice. Plasma glucose levels were presented as % change from glucose level at time 0. F: AUC of the results shown in E reveals that HFD-fed βVmat2KO mice at 14 weeks or 16 weeks of age showed impaired insulin tolerance compared with that of the HFD-fed control mice of the same age (**P < 0.01). G: The plasma insulin levels of 13-, 15-, and 17-week-old mice were calculated. The results of βVmat2KO fed with ND or HFD were compared with those of control mice. The result of the control mouse at 6 weeks is displayed for comparison. Means ± SD are shown. (n = 5–10); significant differences vs. ND-fed control, §P < 0.05 and §§P < 0.01, or significant differences between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. cont., control; w, weeks.

    Since dopamine functions as a negative regulator for β-cell mass (17) and insulin secretion, we hypothesized that impaired VMAT2 function might be critical in conditions of high demand for insulin secretion. We then tested the effects of an HFD on βVmat2KO mice. We started HFD feeding at 5 weeks of age and compared the body weight and blood glucose levels of the mice with those of ND-fed mice. Both control and βVmat2KO mice fed with an HFD showed a rapid increase in body weight and nonfasting blood glucose compared with ND-fed mice (Fig. 2A and B). We then examined glucose tolerance in these mice. There were no significant differences in glucose tolerance in mice <15 weeks old. However, at 15 weeks of age and onward, HFD-fed βVmat2KO mice exhibited impaired glucose tolerance compared with ND-fed or HFD control mice (Fig. 2C). The area under the curve (AUC) of blood glucose concentration following glucose stimulation in 15- and 17-week-old HFD-fed βVmat2KO or control mice was significantly increased compared with that of the ND-fed control mice, with the AUC of the HFD-fed βVmat2KO even higher than that for control mice (Fig. 2D). The results indicate that HFD-fed βVmat2KO mice developed impaired glucose tolerance at increasing ages (>15 weeks). We then assessed the insulin tolerance of the mice at 14 and 16 weeks of age and found that βVmat2KO mice exhibited impaired insulin tolerance compared with ND-fed or HFD control mice (Fig. 2E). The AUC in HFD-fed βVmat2KO mice was significantly increased in 14- and 16-week-old mice (fed with HFD for 8 and 10 weeks, respectively) compared with that of the control mice (Fig. 2F). It was reported that C57BL/6 mice developed insulin resistance after > 11 weeks of HFD feeding (24). Our results show that HFD-fed βVmat2KO mice developed insulin intolerance before the control mice became overtly insulin intolerant. Our results demonstrate that prolonged HFD feeding led to impaired glucose tolerance and deteriorated insulin tolerance in βVmat2KO mice. We then measured plasma insulin levels. Plasma insulin gradually decreased in HFD-fed βVmat2KO but not in control mice. The result suggests that the impaired glucose tolerance observed in the HFD-fed βVmat2KO mice is due to the reduced plasma insulin level.

    Pancreatic Islets of HFD-Fed βVmat2KO Mice Showed an Initial Increase in β-Cell Mass Followed by Impaired GSIS and β-Cell Loss With Increasing Age

    To investigate the underlying molecular mechanism that triggers β-cell dysfunction in HFD-fed βVmat2KO mice, we performed an immunohistochemical analysis of the pancreatic islets harvested from mice at 30 min after glucose administration. A lack of VMAT2 expression was confirmed in the βVmat2KO β-cells but not the control β-cells under high blood glucose conditions, while expression of insulin and glucagon was not affected (Fig. 3A and B). We observed an increase in β-cell mass in young mice at 10 and 13 weeks of age in both βVmat2KO and control HFD-fed mice compared with ND-fed mice (Fig. 3A and B). The enlarged islets were present in control mice fed with an HFD throughout observation (up to 17 weeks of age) (Fig. 3B). In contrast, the β-cell mass decreased in HFD-fed βVmat2KO mice at 15 weeks of age and became much smaller at 17 weeks (Fig. 3A and B). The above results were confirmed by quantitative analysis of β-cell mass and islet mean size (Fig. 3C and D). On the other hand, the Vmat2 expression levels were not significantly different between ND- and HFD-fed control islets at all ages (Fig. 3E).

    Figure 3
    Figure 3

    HFD-fed βVmat2KO islets showed initial β-cell mass increase followed by β-cell loss. A and B: Immunostaining of βVmat2KO (βKO) (A) and control (Slc18a2tm1c/tm1c) (B) islets under ND- or HFD-fed conditions at 13 weeks, 15 weeks, and 17 weeks of age. In βVmat2KO islets, VMAT2 protein expression was not observed in β-cells but remained in α-cells. At 13 weeks of age, β-cell mass in HFD-fed βVmat2KO and control islets increased. At 15 weeks of age, β-cell mass in HFD-fed βVmat2KO but not control mice began to decrease, and β-cell mass further decreased at 17 weeks of age. VMAT2, red; INS, green; GCG, yellow. Scale bars = top panels, 100 μm; middle and lower panels, 50 μm. C and D: Quantitative analysis of the age-dependent changes in β-cell mass (C) and islet mean size (D) in the βVmat2KO and control mice fed with ND or HFD. E: Quantitative analysis of the intensity of anti-VMAT2 antibody staining shown in B. F: Isolated islets from ND- or HFD-fed mice at 6 weeks, 10 weeks, 13 weeks, or 15 weeks old were assayed for insulin secretion in response to glucose stimulation. CF: Means ± SD are shown (n = 3); significant differences between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. cont., control; w, weeks.

    We then isolated pancreatic islets from ND- and HFD-fed βVmat2KO and control mice and performed GSIS analysis in vitro. The islets of ND-fed βVmat2KO mice exhibited significantly elevated levels of insulin secretion compared with the control mice at all ages examined, in agreement with the above results (Fig. 1E). With HFD feeding, islets isolated from control mice showed elevated GSIS activity. In contrast, islets isolated from HFD-fed βVmat2KO mice showed elevated GSIS at 10 weeks of age, but GSIS activity decreased at 13 and 15 weeks of age in spite of the large β-cell mass at 13 weeks, suggesting that β-cell dysfunction occurred (Fig. 3F).

    These results suggest that the pancreatic islets of βVmat2KO mice secrete an elevated level of insulin even under low glucose conditions and respond to high glucose by secreting a higher level of insulin compared with the control mice. βVmat2KO β-cells can meet the metabolic requirements of mice and maintain blood glucose homeostasis under ND conditions. Under HFD conditions, a compensatory increase in β-cell mass occurred in control mice to meet the increased metabolic demands. However, in the βVmat2KO mice, β-cells could not overcome the increased metabolic demands, resulting in β-cell dysfunction and eventual β-cell loss.

    Dedifferentiation of β-Cells Is Accelerated in HFD-Fed βVmat2KO Islets

    β-cell dedifferentiation is known as a mechanism that underlies β-cell dysfunction in type 2 diabetes (25). We found that HFD feeding triggered insulin resistance in βVmat2KO mice. Therefore, we investigated the possibility that dedifferentiation might contribute to β-cell dysfunction by evaluating the expression of chromogranin A (CGA), whose expression is lost upon β-cell failure (26). We detected CGA expression in all ages of β-cells. While CGA expression was reduced in HFD-fed control β-cells at 15 weeks of age, CGA expression almost disappeared in HFD-fed βVmat2KO β-cells (Fig. 4A and B). Since actin remodeling functions during insulin secretion (27), we examined fiber-like F-actin and found a loss of F-actin (Fig. 4C and D) from the islets of HFD-fed βVmat2KO mice but not the islets of control mice at 15 weeks of age. We then examined the expression of other differentiation markers by real-time PCR in islets from ND- or HFD-fed control or βVmat2KO mice. To allow comparison across genotypes, we show expression levels as relative values versus those of ND-fed controls. It was reported that a reduction in the expression of differentiation markers was observed after HFD feeding for 12 weeks or 16 weeks (24). Here, even at early periods when expressions of most β-cell maturation markers were not yet observed in the HFD-fed control islets, decreased expression levels of Ins1, Ins2, Nkx6.1, Pdx1, Glut2, Glucokinase (Gck), and MafA were observed in the HFD-fed βVmat2KO mice. On the other hand, increased expression of MafB and Aldh1a3 was observed in the HFD-fed βVmat2KO and control islets at 13 or 15 weeks of age (HFD feeding for 7–9 weeks) (Fig. 4E), whereas no change in the expression of endocrine progenitor marker Neurog3 was observed (Fig. 4E). Our results revealed that accelerated dedifferentiation seemed to occur in the β-cells of HFD-fed βVmat2KO mice compared with the ND-fed control, which led to β-cell loss and β-cell dysfunction.

    Figure 4
    Figure 4

    Dedifferentiation of β-cells occurs in βVmat2KO mouse islets. AD: Immunostaining of ND- or HFD-fed βVmat2KO (βKO) and control Slc18a2tm1c islets isolated at 10 weeks (upper panels) and 15 weeks (lower panels) of age for CGA (magenta) (A and B) or F-actin (magenta) (C and D) is shown. B and D: Quantitative analyses of CGA (B) or F-actin (D) staining are shown. In 15-week-old βVmat2KO islets, CGA and F-actin staining in β-cells decreased. INS, green; GCG, yellow. Sections were counterstained with DAPI (blue). Scale bars = 50 μm. E: Real-time PCR analyses of age-dependent expression of endocrine maturation markers at 10 weeks, 13 weeks, and 15 weeks of age. The values of the HFD-fed WT or βVmat2KO mice are shown as fold expression vs. ND-fed control or βVmat2KO, respectively. B, D, E: Means ± SD are shown (n = 3); significant differences between βVmat2KO mice and their 10-week-old controls, §P < 0.05 and §§P < 0.01, or between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. Control, white bars; βVmat2KO, gray bars. cont., control; W, weeks.

    We then examined cell proliferation and β-cell death. The expression levels of cell cycle regulator genes, cyclin D1 (CcnD1), cyclin D2 (CcnD2), and proliferating cell nuclear antigen (Pcna), were upregulated in HFD-fed control and βVmat2KO islets from 10 or 13 weeks of age (HFD feeding for 4 or 7 weeks). However, their expression levels decreased at 15 weeks of age in HFD-fed βVmat2KO mice (Fig. 5A). Concurrently, TUNEL-positive cells increased significantly in the HFD-fed βVmat2KO mice (Fig. 5BD).

    Figure 5
    Figure 5

    β-cell–specific Vmat2 deletion increased the expression levels of cell-cycle regulator genes and induced apoptosis under HFD-fed conditions. A: Expression levels of Ccnd1, Ccnd2, and Pcna in the HFD-fed control Slc18a2tm1c or βVmat2KO islets. Values are shown as fold expression vs. in 10-week-old ND-fed control mice. Means ± SD are shown (n = 3); significant differences between βVmat2KO and control, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. BD: TUNEL staining followed by immunostaining of the pancreas tissue sections from HFD-fed control (B) or βVmat2KO (C) mice. C’: High magnification of the box shown in C. TUNEL, magenta; INS, green; DAPI, blue. Scale bars = 50 μm. D: The proportion of TUNEL-positive cells within insulin-expressing β-cells. Scattered plots with individual results together with mean ± SD are presented. Significant differences were analyzed by unpaired two-tailed Student t test and are shown as **P < 0.01. N = 8. βKO, βVmat2KO; cont., control; W, weeks.

    Our results confirmed that HFD triggered the dedifferentiation and adaptive proliferation of β-cells, as previously reported (24,28,29). In HFD-fed βVmat2KO islets, dedifferentiation and cell death in β-cells are accelerated compared with in controls, which seems to attribute to β-cell failure.

    βVmat2KO β-Cells Are Exposed to Elevated ROS Due to Cytoplasmic Dopamine Degradation and Are More Vulnerable to ROS-Induced Cytotoxicity

    We then attempted to reveal the underlying molecular mechanism that triggers the dedifferentiation of β-cells in the islets of βVmat2KO mice. The dopamine content in the islets of βVmat2KO mice decreased with age (Fig. 1D). The reduction in dopamine content was attributed to the MAO-mediated cytoplasmic degradation of dopamine, which contributes to ROS production through H2O2 synthesis during substrate degradation (30).

    We hypothesized that ROS production in the islets of βVmat2KO mice is higher, as VMAT2 depletion leads to increased cytoplasmic dopamine. To test our hypothesis, we used an islet dissociation culture system (Fig. 6AD), in which islets from ND-fed control or βVmat2KO mice were cultured in vitro. We visualized the production of ROS using a fluorogenic probe. We observed a significantly higher ROS intensity in the β-cells of βVmat2KO mice compared with controls (Fig. 6A and B). TBZ treatment increased ROS intensity in control but not βVmat2KO islets, and treatment with pargyline, an MAO inhibitor, reversed the increase in ROS. The number of β-cells in control islets was reduced by TBZ treatment (Fig. 6C). β-cell apoptosis (activated caspase-3/7+ within Ins+ cells) increased in high glucose conditions and under TBZ treatment and was rescued by pargyline treatment (Fig. 6D).

    Figure 6
    Figure 6

    ROS level is significantly higher in βVmat2KO compared with control mouse islets, which confer the vulnerability of βVmat2KO. AD: Islet dissociation culture. Images (A) and quantification (B) of ROS staining in dissociated islets cultured on day 5 under high glucose conditions (25 mmol/L glucose). Compared with control (Slc18a2tm1c/tm1c) islets, βVmat2KO isolated islets showed a higher ROS level, and both were reduced by treatment with 1 μmol/L pargyline, an MAOB inhibitor. Mice at 13 weeks of age were used. ROS, green; INS, magenta; DAPI, blue. C and D: Control or βVmat2KO β-cell numbers treated with chemicals or DMSO were quantified after 5 days of dissociation cultures. The number of β-cells in control islets was reduced by TBZ treatment. D: The proportion of β-cells that underwent apoptosis (caspase-3/7+) increased under high glucose conditions and was reduced by pargyline treatment, but this was reversed by TBZ + pargyline. CASP3, caspase-3/7. EH: Whole islet culture. E: H2O2 generation under low glucose (5.5 mmol/L [left panel]) or high glucose (25.0 mmol/L [middle panels]) conditions in the isolated islets from ND-fed control (left and middle panels) or βVmat2KO (right panel) mice, treated with chemicals. H2O2 was quantified at 1 h, 6 h, and 24 h after high glucose (25.0 mmol/L) stimulation. F: β-cell number in βVmat2KO or control islets after H2O2 treatment. H2O2 decreased βVmat2KO β-cell number compared with untreated control. G: Real-time PCR analyses of Nrf2 expression in whole islet culture treated with 5.5 or 25.0 mmol/L glucose at 10 weeks, 13 weeks, and 15 weeks of age. H: H2O2 contents in whole islets treated with chemicals, with or without cotreatment of 100 μmol/L oltiplaz for 6 h. I: A decrease in the expression of endocrine maturation markers by exposure to H2O2 in βVmat2KO islets compared with control from ND-fed isolated islets at 13 weeks of age. Values are shown as fold expression vs. controls. Means ± SD are shown (n = 3) (BI). Control, white bars; βVmat2KO, gray bars. Significant differences vs. 10-week-old controls, §P < 0.05 and §§P < 0.01, or between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. βKO, βVmat2KO; conc, concentration; cont., control; Parg, pargyline; w, weeks.

    We then used a whole islet culture system and measured the time-dependent generation of H2O2 by glucose stimulation using islets isolated from ND-fed control or βVmat2KO mice (Fig. 6E). In control islets, high glucose stimulation alone showed a slight increase in H2O2 level in whole islet culture. High glucose alone elevated ROS production (Fig. 6E [compare left and middle panels]), which is reported to be toxic in β-cells (31,32). However, in the presence of TBZ, high glucose stimulation induced a rapid and dramatic elevation in H2O2 levels, which decreased with time and returned to basal levels after 24 h and was rescued by the addition of pargyline (Fig. 6E, left and middle panels). In the islets of βVmat2KO mice, the H2O2 level was highest at 1 h after glucose stimulation without TBZ treatment. Pargyline treatment significantly lowered the H2O2 level in the islets of βVmat2KO mice (Fig. 6E, right panel). It has been reported that exogenous H2O2 treatment under basal glucose concentrations induced ROS generation up to a similar level with high glucose stimulation (33). We then examined β-cell number after exposure of the islets to different H2O2 concentrations and found that the islets of βVmat2KO had a significantly lower number of living β-cells than the control mice (Fig. 6F). The results suggested that βVmat2KO islets are more sensitive to ROS.

    It is reported that β-cells possess antioxidant mechanisms, such as the induction of the transcription factor nuclear factor erythroid 2p45-related factor 2 (Nrf2), which regulates the expression of several genes involved in redox metabolism (34). Nrf2 expression significantly increased in response to high glucose stimulation, to a greater extent in βVmat2KO islets compared with the control mice (Fig. 6G). The result suggests that βVmat2KO mice are exposed continuously to high ROS and therefore develop a protective mechanism in response to high glucose stimulation.

    Since ROS are mainly produced during mitochondrial respiration, we then assessed the proportion of MAO-mediated ROS generation, by treating islets with oltipraz, an antioxidant that exerts mitochondrial protective effects in β-cells (35). We found that approximately one-half of the H2O2 generated was reduced by oltipraz treatment in control islets (Fig. 6H). Pargyline treatment decreased ∼92% of the H2O2 produced by TBZ. Oltipraz treatment of the TBZ + pargyline islets further reduced the remaining <8% of the H2O2 triggered by TBZ. Similarly, oltipraz treatment did not reduce H2O2 in βVmat2KO islets. By contrast, pargyline treatment significantly reduced H2O2 in βVmat2KO islets. Therefore, our results suggest that a large proportion of ROS generated upon TBZ treatment or in βVmat2KO islets was derived from MAO-mediated generation of ROS, which plays a vital role in the progression of β-cell failure in these models.

    Real-time PCR analysis of the H2O2-treated islets revealed that the islets of βVmat2KO mice expressed mature markers such as Ins1, Ins2, MafA, Nkx6.1, Gck, and Pdx1 at significantly lower levels and expressed an immature marker, MafB, at significantly higher levels compared with control mice in response to H2O2 (Fig. 6I).

    Therefore, we infer that high glucose triggers dopamine secretion and insulin secretion simultaneously. In the presence of VMAT2, cytoplasmic dopamine is rapidly sequestered and stored. However, in the absence of VMAT2 function, cytoplasmic dopamine cannot be sequestered, and dopamine degradation by MAO results in rapid production of H2O2 upon high glucose stimulation. βVmat2KO β-cells show elevated insulin secretion even under low glucose conditions. β-cells are exposed continuously to ROS. They then develop antioxidative mechanisms to protect themselves. However, being placed under chronic exposure to ROS, βVmat2KO islets are more vulnerable to H2O2 toxicity than those of the controls. Under prolonged HFD feeding, where a high metabolic demand occurs, dedifferentiation, β-cell dysfunction, and β-cell loss are trigged in βVmat2KO β-cells.

    Discussion

    Pancreatic β-cells are susceptible to oxidative stress. Oversupply of nutrients, such as glucose and fatty acids, and overstimulation of β-cells are considered to contribute to β-cell failure in type 2 diabetes. Here, we found that VMAT2 acts to protect β-cells from the toxic effects of oxidative stress triggered by excessive insulin secretion through the compartmentalization of dopamine, which prevents its degradation. VMAT2 protein expression is regulated in a glucose-dependent manner so that β-cells in high glucose conditions show an upregulated VMAT2 expression. It is reported that there are tyrosine hydroxylase activities in the adult rat islets themselves (36). Therefore, β-cells synthesize dopamine themselves and store it in the vesicle via VMAT2 to prevent degradation by MAO. Upon insulin secretion in response to high glucose in normal control β-cells, dopamine is secreted into the extracellular space through exocytosis and acts as negative feedback for insulin secretion through binding to its receptor Drd2, which exists on the β-cell plasma membrane. Extracellular dopamine is cleared by reuptake into the β-cells through dopamine plasma membrane transporter DAT and stored in the vesicle via VMAT2 for subsequent release (Fig. 7). In this way, VMAT2 plays a significant regulatory role in the compartmentalization of dopamine. βVmat2KO β-cells (or control β-cells treated with VMAT2 inhibitor TBZ) cannot uptake dopamine into vesicles; thus, dopamine is subjected to degradation by MAO, leading to a reduced dopamine content and an increased generation of ROS. The decreased dopamine content leads to a reduction in the dopamine negative-feedback loop, which in turn leads to elevated insulin secretion. Under HFD conditions, where excess nutrient stress exists, insulin secretion frequently occurs, increasing β-cell exposure to ROS. In βVmat2KO β-cells, HFD triggers chronic exposure to MAO-derived ROS and leads to increased vulnerability and accelerated β-cell failure. βVmat2KO β-cells show an initial compensation via β-cell growth and increased β-cell mass followed by dedifferentiation and β-cell death, which is a characteristic of the progression of β-cell failure (Fig. 7).

    Figure 7
    Figure 7

    Schematic drawing of the molecular mechanism by which VMAT2 safeguards β-cell function under HFD from dopamine-mediated cytotoxicity. Dopamine is released at insulin secretion following high glucose stimulation and acts as a negative regulator for insulin secretion through dopamine receptor 2 (Drd2). Dopamine is normally taken up through dopamine transporter (DAT) and stored in VMAT2-regulated vesicles. Under ND, βVMAT2KO β-cells exhibit increased insulin release in response to glucose stimulation. Under HFD, where insulin secretion occurs frequently, β-cells are under long-term exposure to ROS and become vulnerable to damages by dopamine cytotoxicity, leading to accelerated dedifferentiation and cell death. Left, control Slc18a2tm1c/tm1c; right, βVMAT2KO β-cells.

    Dedifferentiation Is the Mechanism of β-Cell Failure

    HFD in rodents is a commonly studied model of a compensatory increase in insulin secretion and β-cell mass; β-cells eventually fail, leading to glucose intolerance and insulin intolerance. The HFD model shows an initial increased expression of β-cell functional genes, which is followed by a cessation of gene hyperexpression, endoplasmic reticulum stress, and β-cell functional failure (24). We observed an increase in β-cell mass in the islets of control mice and at early ages in the islets of βVmat2KO mice. However, in βVmat2KO islets, both β-cell mass and insulin secretion were impaired with increasing age. β-cell failure corresponded with a decrease in β-cell mass. Decreased expression of maturation markers, such as Ins1, Ins2, Glut2, Gck, Pdx1, Nkx6.1, and MafA, and increased expression of the dedifferentiation marker MafB occur in islets of βVmat2KO mice, which is in parallel with β-cell failure and precedes the decrease in β-cell mass. Our results agree with previous reports that dedifferentiation is one of the mechanisms of β-cell failure.

    Dopamine Actions in Neuronal Cells

    In neuronal cells, VMAT2 plays an important role in the compartmentation of dopamine to protect the cells from oxidative stress. Improper compartmentation of dopamine contributes to diseases in the neural system such as Parkinson disease. Accumulation of dopamine in the cytosolic space is toxic, inducing neuronal damage and apoptotic cell death (3740). Dopamine can be auto-oxidized to form ROS, including hydroxyl radicals, superoxide, and hydrogen peroxide. Oxidized dopamine can then be converted to highly toxic dopamine quinones and the protein function-altering cysteinyl adduct. Deamination by mitochondrial MAO converts cytosolic dopamine to hydroxyperoxide and a reactive aldehyde intermediate, which can be oxidized to create ROS (41,42). Genetic knockout of Vmat2 is reported to be lethal. Animals with Vmat2 knockouts are hypersensitive to the dopamine agonist apomorphine, and the psychostimulants cocaine and amphetamine, with animals dying a few days after birth (4). Animals with very low VMAT2 levels were reported to survive into adulthood but were more vulnerable to neural damage in the dopaminergic neurons (37,43). On the other hand, increasing dopamine stores by overexpression of VMAT2 attenuated cytosolic dopamine levels and enhanced dopaminergic cell survival by lowering dopamine-dependent oxidative stress (3,44).

    Dopamine as a Negative Regulator for Insulin Secretion in Pancreatic β-Cells

    In pancreatic β-cells, dopamine reportedly functions as a negative regulator for insulin secretion through Drd2. The knockdown of Drd2 in INS-1 cells (a β-cell line) resulted in increased insulin secretion (12). Dopamine treatment decreased insulin secretion in isolated islets (45). We previously reported that dopamine accelerated β-cell dedifferentiation, which could be rescued with the Drd2 antagonist domperidone (17). Long-term VMAT2 deficiency resulted in β-cell failure. This phenotype is in agreement with the previously reported mouse model with Drd2 disruption, which resulted in an impairment in glucose tolerance, diminished β-cell mass, and decreased β-cell replication (15). We interpret this to mean that loss of dopamine-negative signaling enhances insulin secretion. Insulin exocytosis accompanied dopamine exocytosis. Increased insulin secretion increases dopamine release extracellularly. Dopamine into the cytosol is degraded by MAO, which increases the ROS level, thereby contributing to β-cell dysfunction. Islet-specific MAO expression depends on the transcriptional activity of the mature endocrine β-cell marker MAFA. Therefore, mature β-cells develop a mechanism to degrade dopamine and increase the ROS level (16). Although βVmat2KO islets exhibit lower dopamine content compared with the control mice, a certain level of dopamine still exists, since the dopamine-synthesizing enzyme tyrosine hydroxylase is expressed in β-cells (46).

    Glucotoxicity and β-Cell Failure

    Chronic exposure to high glucose causes functional damage to pancreatic β-cells, which is known as glucose toxicity. One mechanism of glucose toxicity is oxidative stress (47) conferred by ROS (1,48). Administration of an antioxidant such as glutathione ameliorates glucotoxicity (49). ROS generation through dopamine degradation seems to play an important role in glucotoxicity. Here, we showed that an MAOB inhibitor, pargyline, reduced the level of ROS in βVmat2KO islets stimulated by glucose.

    Chronic hyperglycemia models are reported to lead to β-cell dysfunction in the long-term. Inducible mouse models selectively expressing gain-of-function KATP channel mutations (Kir6.2-V59M or K185QΔN30) in pancreatic β-cells show β-cell dysfunction due to β-cell dedifferentiation (50,51). Furthermore, loss-of-function mutants of the voltage-dependent K+ (Kv) channel KCNH6 plays a role in the modulation of insulin secretion. In a loss-of-function KCNH6 mutant in humans and mice, hyper–insulin secretion was observed initially, followed by subsequent hypo–insulin secretion as a result of overstimulation of insulin secretion; in the long-term, endoplasmic reticulum stress, apoptosis, loss of β-cell mass, and subsequent decreased insulin secretion were observed (52). These reports suggest that the overstimulation of insulin secretion causes β-cell failure in the long-term, which is in agreement with our results.

    VMAT2 as a Guardian for Maintenance of β-Cell Function

    β-cells are highly heterogeneous (5355), which protects β-cells themselves from overstimulation. Under normal conditions, β-cells secrete dopamine in response to high blood glucose. The secreted dopamine acts through Drd2 on their plasma membrane to negatively regulate insulin secretion. Dopamine is rapidly sequestered into the β-cells by DAT and stored in vesicles by VMAT2, thereby protecting β-cells from dopamine toxicity. However, miscompartmentalization of dopamine might occur with overstimulation of insulin secretion or low VMAT2 expression, increasing ROS accumulation and leading to β-cell failure. Future works on how the dopamine-VMAT2 signaling system works in the heterogenous β-cell population would be necessary to increase the understanding of VMAT2 in the maintenance of β-cell function.

    Article Information

    Acknowledgments. The authors thank the members of the Animal Centers and the Bio Resource Department at the Tokyo Institute of Technology and Kumamoto University for technical assistance. The authors thank Takeshi Nagura (Kumamoto University) for discussions and technical assistance.

    Funding. This work was supported by a grant from the Project for Realization of Regenerative Medicine from Japan Agency for Medical Research and Development (AMED) (grant number 17bm0704004h0101) and Grants-in-Aid from the Ministry of Education, Culture, Sports, Science and Technology, Japan (18H02861 to S.K. and 17K09455 to D.S.). This work was also supported in part by the Takeda Science Foundation and Japan Insulin Dependent Diabetes Mellitus (IDDM) Network.

    Duality of Interest. No potential conflicts of interest relevant to this article were reported.

    Author Contributions. D.S. designed the experiments and acquired, analyzed, and interpreted data. F.U., H.T., Y.S., and K.M. acquired and analyzed the data. N.T. performed blastocyst injection of the ES cells and generated the Slc18a2tm1a/+ mouse. N.N. provided technical advice and support for the maintenance of gene knockout mice. K.K. and N.S. discussed the data. S.K. provided conceptual input, discussion, writing, and revision of the manuscript; approved the final version of the manuscript; and obtained funding. D.S. and S.K. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.



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    Join the Fit With Diabetes Challenge

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    In this free 3-week challenge, Christel and a team of top diabetes experts will take you through some of the most important things you need to know to live a healthy life with any type of diabetes.

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