Disruption of the adaptor protein SH2B1 (SH2-B, PSM) is associated with severe obesity, insulin resistance, and neurobehavioral abnormalities in mice and humans. Here, we identify 15 SH2B1 variants in severely obese children. Four obesity-associated human SH2B1 variants lie in the Pleckstrin homology (PH) domain, suggesting that the PH domain is essential for SH2B1’s function. We generated a mouse model of a human variant in this domain (P322S). P322S/P322S mice exhibited substantial prenatal lethality. Examination of the P322S/+ metabolic phenotype revealed late-onset glucose intolerance. To circumvent P322S/P322S lethality, mice containing a two-amino acid deletion within the SH2B1 PH domain (ΔP317, R318 [ΔPR]) were studied. Mice homozygous for ΔPR were born at the expected Mendelian ratio and exhibited obesity plus insulin resistance and glucose intolerance beyond that attributable to their increased adiposity. These studies demonstrate that the PH domain plays a crucial role in how SH2B1 controls energy balance and glucose homeostasis.
Hyperphagia, severe obesity, insulin resistance, and neurobehavioral abnormalities have been reported in individuals with rare coding variants in the gene encoding SH2B1 (SH2-B, PSM) (1,2). Consistently, mice null for Sh2b1 exhibit obesity, impaired glucose homeostasis, and often, aggressive behavior (3–5). Transgenic expression of the β-isoform of SH2B1 (SH2B1β) in the brain largely corrects the obesity and glucose intolerance of otherwise Sh2b1-null mice (6), suggesting the importance of brain SH2B1 for the control of energy balance and glucose homeostasis.
At the cellular level, SH2B1 is an intracellular adaptor protein that is recruited to phosphorylated tyrosine residues on specific membrane receptor tyrosine kinases (e.g., receptors for brain-derived neurotrophic factor [BDNF], nerve growth factor [NGF], insulin) and cytokine receptor/Janus kinase (JAK) complexes (e.g., leptin receptor/JAK2) and enhances the function of these receptors (7–13). The exact mechanism(s) by which it does so is unclear, although a variety of mechanisms have been proposed. These include enhanced dimerization causing increased activation of the kinase itself (14), stabilization of the active state of the kinase (15), decreased dephosphorylation or increased complex formation of insulin receptor substrate (IRS) proteins bound to receptors or receptor/JAK2 (16,17), regulation of the actin cytoskeleton (18), and activation of specific pathways, including extracellular signal–regulated kinases (ERKs), Akt, and/or phospholipase Cγ (10,19). Some of these receptors, including the leptin, BDNF, and insulin receptors, play important roles in the central control of energy expenditure and/or glucose homeostasis (20). SH2B1β has been shown to enhance BDNF- and NGF-stimulated neurite outgrowth in PC12 cells (13,21).
The four isoforms of SH2B1 (α, β, γ, δ), which differ only in their COOH termini, share 631 NH2-terminal amino acids. These amino acids possess a dimerization domain, Pleckstrin homology (PH) domain, src-homology 2 (SH2) domain, nuclear localization sequence (NLS), and nuclear export sequence (NES) (22–24) (Fig. 1A). The SH2 domain enables SH2B1 recruitment to specific phosphorylated tyrosine residues in activated tyrosine kinases (25). The NLS and NES are essential for SH2B1 to shuttle among the nucleus, the cytosol, and the plasma membrane (22,23). The NLS combined with the dimerization domain enables SH2B1 to associate with the plasma membrane (26). However, the function and importance of the SH2B1 PH domain remains largely unknown. Four human obesity-associated variants lie in the SH2B1 PH domain (Fig. 1A), suggesting the importance of the PH domain in SH2B1 function. The PH domains of some proteins bind inositol phospholipids to mediate membrane localization (27,28). However, 90–95% of all human PH domains do not bind strongly to phosphoinositides and presumably mediate other functions (29). Indeed, the PH domain of SH2B1 neither localizes to the plasma membrane nor is required to localize SH2B1β to the plasma membrane (23,30). Here, we tested the importance of the PH domain of SH2B1 in vivo by generating and studying mice containing human obesity-associated (P322S) or engineered (in-frame deletion of P317 and R318 [ΔPR]) mutations in the SH2B1 PH domain. Our results demonstrate that the SH2B1 PH domain plays multiple crucial roles in vivo, including for the control of energy balance and glucose homeostasis, and in in vitro studies, changes the subcellular distribution of SH2B1β and enhances NGF-stimulated neurite outgrowth in PC12 cells.
Identification of SH2B1 variants and generation of P322S mice. A: Human SH2B1 protein (NP_001139268). Amino acid residues for newly characterized human obesity-associated SH2B1 variants and the previously characterized variant P322S are shown. DD, dimerization domain; P, proline-rich region. B: SH2B1 mutations impair the ability of SH2B1 to enhance neurite outgrowth. PC12 cells transiently coexpressing GFP and either empty pcDNA3.1(+) vector (−), human SH2B1β WT, or SH2B1β mutant were treated with 20 ng/mL rat NGF for 3 days, after which neurite outgrowth was assessed. R555E lacking an intact SH2 domain and the human mutation P322S reported previously were included as positive controls. GFP-positive cells were scored for the presence of neurites two times the length of the cell body (≥400 cells/condition/experiment). The percentage of cells with neurites was determined by dividing the number of GFP-positive cells with neurites by the total number of GFP-positive cells. Data are mean ± SEM (n = 5). Each construct was compared with WT using a two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. C: CRISPR/Cas9 schematic for Sh2b1 P322S gene editing. RNA guide sequence, PAM sequence, and cut site for Cas9 are shown for guide 2. The region of the 180-nucleotide oligo donor template used to direct homology-directed repair in the vicinity of the ΔPR deletion and P322 is shown. Mutations in donor template that introduce the C>T mutation to code for P322S and silent mutations to create a diagnostic XbaI site and disrupt guide RNA binding following repair are highlighted in red. aa seq., amino acid sequence. D: Proteins in brain tissue lysates from Sh2b1 WT, P322S/P322S, and KO male mice were immunoblotted with αSH2B1 or αβ-tubulin. Migration of the 100-kDa protein standard and the four isoforms of SH2B1 are shown. IB, immunoblot. E: P322S/P322S mice from intercrosses of heterozygous mice were born at approximately one-half of the expected Mendelian ratio. P < 0.05, χ2 test. n = 179 mice.
Research Design and Methods
The Genetics of Obesity Study (GOOS) is a cohort of >7,000 individuals with severe obesity with age of onset of <10 years (31,32). Severe obesity is defined as a BMI (kg/m2) SD score >3 (U.K. reference population). Whole-exome sequencing and targeted resequencing were performed as in Hendricks et al. (33). All variants were confirmed by Sanger sequencing (1). HOMA of insulin resistance (HOMA-IR) was calculated using the equation HOMA-IR score = [(fasting insulin in μU/mL) × (fasting glucose in mg/dL)] / 405, which estimates steady-state β-cell function and insulin sensitivity (34,35). All human studies were approved by the Cambridge local research ethics committee. Each subject (or parent for those <16 years of age) provided written informed consent; minors provided oral consent.
Animal procedures were approved by the University of Michigan Committee on the Use and Care of Animals in accordance with Association for Assessment and Accreditation of Laboratory Animal Care and National Institutes of Health guidelines. Mice were bred at the University of Michigan and housed in ventilated cages at 23°C on a 12-h light (0600–1800 h)/12-h dark cycle with ad libitum access to food and tap water except as noted. Mice were fed standard chow (20% protein, 9% fat [PicoLab Mouse Diet 20 5058, #0007689]) or, as described in Fig. 2H–M and Supplementary Fig. 2F–K, a high-fat diet (HFD) (20% protein, 20% carbohydrate, 60% fat [D12492; Research Diets]).
The P322S mutation in SH2B1 leads to impaired glucose homeostasis in male mice challenged with an HFD. A–G: Male mice fed standard chow. A: Body weight was assessed at weeks 4–29 (n = 8 WT, 16 P322S/+). B: Food intake was assessed at weeks 5–25 and cumulative food intake graphed (n = 7 WT, 8 P322S/+). C: Body fat mass was determined at week 30. Percent fat mass was determined by dividing fat mass by body weight (n = 9 WT, 16 P322S/+). D: GTT was assessed in 28-week-old mice. After a 4-h fast, mice were injected intraperitoneally with d-glucose (2 mg/kg of body weight). Blood glucose was monitored at indicated times (n = 8 WT, 14 P322S/+). E: ITT was assessed in 29-week-old mice. After a 6-h fast, mice were injected intraperitoneally with insulin (1 IU/kg of body weight). Blood glucose was monitored at indicated times (n = 8 WT, 15 P322S/+). F: At week 30, serum from P322S/+ and WT mice was assayed for leptin (n = 5 WT, 8 P322S/+). G: Thirteen-week-old mice were fasted overnight (1800–0900 h), and insulin levels were determined (n = 5). H–M: Male mice fed an HFD. H: Starting at week 6, body weight of mice was assessed weekly (n = 7 WT, 12 P322S/+). I: Food intake in mice was measured during week 27 (n = 7 WT, 12 P322S/+). J: Body fat mass was determined at week 30 (n = 7 WT, 11 P322S/+). K: GTT was assessed as in D at 28 weeks and blood glucose monitored at the times indicated (n = 7 WT, 12 P322S/+). L: ITT was assessed as in C at 29 weeks and blood glucose monitored at the times indicated (n = 6 WT, 10 P322S/+). M: At week 30, mice were fasted overnight, and insulin levels were determined (n = 6 WT, 7 P322S/+). For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01. n.s., not significant.
Mouse Models, Genotyping, and Gene Expression
CRISPR/Cas9 genome editing was used to insert the P322S mutation into mice. The reverse complement of the genomic Sh2b1 sequence in C57BL/6J mice (accession number NC_000073, GRC m38) was used to design the reagents for CRISPR. The guides were designed using the website described in Ran et al. (36). The mutations in the donor are summarized in Fig. 1C (details in the Supplementary Data). After testing, each guide/donor combination was injected into C57BL/6J oocytes by the University of Michigan Transgenic Animal Model Core. P322S and ΔPR founders were backcrossed to C57BL/6J mice. The mice were genotyped as described in Truett et al. (37) using primers listed in Supplementary Table 1. The P322S and ΔPR PCR products were digested with XbaI or purified and sequenced. Sh2b1 knockout (KO) mice were obtained from Dr. Liangyou Rui (University of Michigan) and genotyped according to Duan et al. (3). C57BL/6J mice used to invigorate our C57BL/6J colony came from The Jackson Laboratory. Relative levels of Sh2b1 gene expression were determined using RT-PCR (details in the Supplementary Data).
Mouse Body Weight and Food Intake
Mice were individually housed, and body weight and food consumption were assessed weekly.
Mouse Glucose Tolerance Tests, Insulin Tolerance Tests, and Hormone Levels
Mice were fasted 0900–1300 h for glucose tolerance tests (GTTs) or 0800–1400 h for insulin tolerance tests (ITTs). Glucose or human insulin was injected intraperitoneally, and blood was collected from the tail vein. Blood glucose levels were assessed using a Bayer Contour glucometer. For plasma insulin levels, mice were fasted 0800–1400 h, and tail blood was assayed using an Ultra Sensitive Mouse Insulin ELISA kit (#90080; Crystal Chem). Tail blood (0900–1000 h) (Figs. 2F and 4G) or trunk blood after sacrifice (1000–1300 h) (Fig. 6B) from fed mice was tested for leptin using a Mouse Leptin ELISA kit (#90030; Crystal Chem).
Mouse Body Composition, Metabolic Assessment, and Tissue Collection
Body composition was measured at room temperature in the morning or evening (Fig. 6A) using a Minispec LF90 II Bruker Optics nuclear magnetic resonance (NMR) analyzer (University of Michigan Animal Phenotyping Core). To assess metabolic state, mice were single-housed for 3 days and then tested for 72 h using a Comprehensive Lab Animal Monitoring System (Columbus Instruments). O2 consumption (VO2), CO2 production (VCO2), X activity, and Z activity were collected in 20-min bins. The final 24 h of recordings are presented. Mice were sacrificed (1000–1300 h) using decapitation under isoflurane. Trunk blood was collected and the serum stored at −80°C. Tissues were collected, weighed, cryopreserved in liquid nitrogen, and stored at −80°C.
Frozen tissues were lysed in L-RIPA lysis buffer (50 mM Tris, 150 mM NaCl, 2 mM EGTA, 0.1% Triton X-100, pH 7.2 containing 1 mM Na3V04, 1 mM PMSF, 10 μg/mL aprotinin, 1 μg/mL leupeptin). Equal amounts of protein were immunoblotted with antibody to SH2B1 (αSH2B1) (sc-136065, RRID:AB_2301871; Santa Cruz Biotechnology) (1:1,000 dilution) or β-tubulin (sc-55529, RRID:AB_2210962; Santa Cruz Biotechnology) (1:1,000 dilution) as described in Joe et al. (19). For immunoprecipitations, tissue lysates containing equal amounts of protein were incubated with αSH2B1 (1:100) and immunoprecipitated and immunoblotted as in Joe et al. PC12 cells (ATCC) were cultured and treated as in Joe et al. Briefly, the cells were grown in PC12 medium A (RPMI medium, 5% FBS, 10% heparan sulfate) in 10-cm dishes coated with rat tail type I collagen (#354236; Corning). Cells were transfected and, 24 h later, incubated overnight in deprivation medium (RPMI medium, 2% heparan sulfate, 1% FBS) before being lysed and immunoblotted with αSH2B1.
Live Cell Imaging
The indicated construct was transiently transfected into 293T cells or PC12 cells. Cells were treated and live cell images captured by confocal microscopy using an Olympus FV500 laser scanning microscope and FluoView version 5.0 software, as in Joe et al. (19).
For Fig. 3D, PC12 cells were plated in six-well collagen-coated dishes, transiently transfected as indicated for 24 h, and incubated overnight in deprivation medium. Cells were treated, and neurite outgrowth was determined as in Joe et al. (19). For Fig. 1B, PC12 cells were treated as in Joe et al., with modifications described in the Supplementary Data.
Disruption of the PH domain changes the subcellular localization of SH2B1 and impairs the ability of SH2B1 to enhance NGF-induced neurite outgrowth. A: Proteins in whole-cell lysates from PC12 cells transiently expressing the indicated GFP-SH2B1β were immunoblotted with αSH2B1. Migration of molecular weight standards are on the left. IB, immunoblot. B and C: Live 293T cells and PC12 cells transiently expressing GFP-SH2B1β WT or GFP-SH2B1β ΔPR were imaged by confocal microscopy. D: PC12 cells transiently expressing GFP, GFP-SH2B1β WT, or GFP-SH2B1β ΔPR were treated with 25 ng/mL mouse NGF for 2 days, after which neurite outgrowth was assessed. GFP-positive cells were scored for the presence of neurites more than two times the length of the cell body (total of 300 cells/condition/experiment). The percentage of cells with neurites was determined by dividing the number of GFP-positive cells with neurites by the total number of GFP-positive cells counted. Data are mean ± SEM (n = 3). *P < 0.05.
Structural Modeling and ClustalW Analysis
A structural model for human SH2B1 was created by overlaying the PH domain sequence of human SH2B1 onto the mouse NMR structure of APS (Protein Data Bank ID 1V5M) using the PyMOL Molecular Graphics System version 2.2.3. ClustalW alignments were performed using LaserGene version 14.0.0 (DNASTAR, Madison, WI). Functional homology was defined as residues that match the consensus within 1 distance unit using the PAM250 mutation probability matrix.
All nonhuman analyses were carried out using GraphPad Prism software. Body weight, GTTs, and ITTs were analyzed by two-way ANOVA followed by Fisher least significant difference posttest. Food intake was analyzed by linear regression. Significance of the deviation of birth rate from the expected Mendelian ratio was assessed using χ2 test. For other physiological parameters, experimental animals were compared with their wild-type (WT) littermates by two-tailed Student t test. Neurite outgrowth was analyzed by a two-tailed Student t test. For all comparisons, P < 0.05 was considered significant.
Data and Resource Availability
Any raw data sets generated during the current study are available from the corresponding author on reasonable request, with all reagent and analytical details included in the published article (and its Supplementary Data). The mouse models generated and analyzed during the current study are available from the corresponding author upon reasonable request.
Identification and Characterization of 15 Rare Human Variants in SH2B1
Using exome sequencing, targeted resequencing, and Sanger sequencing of 3,000 individuals exhibiting severe obesity before the age of 10 years (33), we identified 15 rare variants in SH2B1 in 16 unrelated individuals (Table 1 and Fig. 1A). Eleven variants are newly identified, while four (R227C, R270W, E299G, V209I) have been previously reported in other obese individuals but not well characterized (4,38,39). Fourteen of these variants are in the first 631 amino acids shared by all isoforms of SH2B1. The mean ± SD BMI SD score of variant carriers was 4.0 ± 0.6. The 15th variant causes the G638R mutation in the COOH-terminal tail unique to the β-isoform of SH2B1. A number of the SH2B1 variant carriers had HOMA-IR (34,35) scores of >1.9, indicating insulin resistance and increased risk of type 2 diabetes (40). Some of the HOMA-IR scores, including those for people carrying three variants in or near the PH domain (G238C, R270Q, and M388V), were particularly high. A spectrum of neurobehavioral abnormalities, including learning difficulties, dyspraxia, hyperactivity/inattention, aggression/emotional lability, anxiety, and autistic traits, were detected in all the individuals for whom behavioral information was available (Table 1). In the neurite outgrowth assay, 7 of these 15 rare variants impaired the ability of SH2B1β to stimulate NGF-induced neurite outgrowth (Fig. 1B), suggesting that many of the variants negatively affect the neuronal function of SH2B1. Because the variants are found in multiple domains in SH2B1 and throughout the SH2B1 sequence, it is not surprising that individuals with different SH2B1 variants have different phenotypes. These newly characterized variants add support to SH2B1 being an important regulator of human body weight, insulin sensitivity, and behavior. Interestingly, four of the human obesity-associated SH2B1 variants lie in the PH domain of SH2B1, suggesting the importance of the PH domain in the ability of SH2B1 to regulate energy balance, glucose metabolism, and behavior.
Phenotypes seen in carriers of rare variants in SH2B1
Developmental Lethality in Mice Homozygous for the SH2B1 P322S Mutation
To gain insight into the role of the PH domain of SH2B1 in energy balance and glucose metabolism, we studied the effect of the P322S human obesity-associated SH2B1 PH domain variant in mice. We chose the P322S variant because of its strong association with obesity in the proband family (1), the conservation of P322 across mammals and with the SH2B1 family member SH2B2/APS, its predicted disruptive effect on PH domain function by Provean (RRID:SCR_002182) and PolyPhen (RRID:SCR_013189) analysis, and P322S-dependent deficiencies in SH2B1 function observed in cultured cells (1). We used CRISPR/Cas9-based genome editing to introduce the P322S variant into Sh2b1 in C57BL/6J mice (Fig. 1C). DNA sequencing confirmed germline transmission of the P322S edit (Supplementary Fig. 1A). The P322S mutation neither affects the mRNA levels for any of the Sh2b1 isoforms in the examined tissues (brain, liver, and heart) (Supplementary Fig. 1B and C) nor alters SH2B1 protein levels or isoform selection in brain tissue (Fig. 1D and Supplementary Fig. 1D). However, we found that homozygous (P322S/P322S) mice are born at much less than the expected Mendelian ratio (Fig. 1E), suggesting that the P322S mutation disrupts SH2B1 PH domain function in a manner that interferes with embryo implantation and/or development. Consistent with this, preliminary data from timed pregnancies reveal that at embryonic day 17, the homozygous embryos (P322S/P322S) are also present at less than the expected Mendelian ratio.
Mice Heterozygous for P322S Exhibit Altered Glucose Tolerance, but Not Altered Energy Balance
In addition to the difficulty of producing sufficient P322S/P322S mice for study, the high rate of embryonic lethality in P322S/P322S mice suggested that the surviving P322S/P322S mice might have underlying poor health, which could interfere with the analysis of their metabolic phenotype. For these reasons, and because human obesity is linked with heterozygosity for P322S (1), we studied energy balance and glucose homeostasis in heterozygous (P322S/+) male (Fig. 2) and female (Supplementary Fig. 2) mice. We found no difference in food intake, body weight, or adiposity between WT and P322S/+ mice fed standard chow (9% fat) or an HFD (60% fat). However, in contrast to their WT littermates, 28-week-old HFD-fed P322S/+ male and female mice displayed glucose intolerance in an intraperitoneal GTT. Neither insulin concentrations nor the response to an ITT were altered in the P322S/+ animals compared with littermate controls, however. These findings suggest that the PH domain of SH2B1 is important for SH2B1 function, including for the control of glucose homeostasis, but that the resultant metabolic phenotype is less penetrant in the heterozygous state in mice than it is in humans. We thus sought to study mice homozygous for mutations in the SH2B1 PH domain.
Deletion of P317 and R318 in the PH Domain Alters the Subcellular Localization of SH2B1
Because of the early lethality of P322S/P322S mice, we examined the function of another SH2B1 mutation containing a two-amino acid deletion (ΔPR) within the PH domain of SH2B1 (Fig. 1C and Supplementary Fig. 1E). This mutation arose as a separate line during the generation of the P322S mice.
When transiently expressed as green fluorescent protein (GFP) fusion proteins in PC12 cells, SH2B1β and SH2B1β ΔPR demonstrated similar expression levels (Fig. 3A), suggesting that ΔPR does not destabilize the protein. However, while GFP-SH2B1β localizes primarily to the plasma membrane and cytoplasm in 293T and PC12 cells (as previously shown [22,26]), SH2B1β ΔPR localizes primarily to the nucleus (Fig. 3B and C). The nuclear localization of SH2B1β ΔPR suggests that the ΔPR mutation alters SH2B1 nuclear cycling to favor retention in the nucleus. We predicted that this altered localization would change the cellular function of SH2B1β ΔPR. Indeed, SH2B1β-dependent NGF-stimulated neurite outgrowth in PC12 cells was decreased in cells expressing SH2B1β ΔPR (Fig. 3D). Thus, disruption of the PH domain by the ΔPR mutation alters the subcellular distribution of SH2B1β and impairs the ability of SH2B1β to enhance neurotrophic factor–induced neurite outgrowth.
Obesity, Hyperphagia, and Disrupted Glucose Homeostasis in Mice Homozygous for the SH2B1 ΔPR Mutation
We examined the phenotype of the mice containing the ΔPR mutation with the hope that this mutation might produce a less dramatic reproductive phenotype than that observed with P322S in the homozygous state, allowing us to examine the effects of the ΔPR mutation on energy balance and glucose homeostasis in homozygous mice. As with the P322S mutation, ΔPR did not affect the mRNA levels for any of the Sh2b1 isoforms in the tissues tested (brain or heart) (Fig. 4A). At the protein level, the ΔPR mutation did not alter the relative levels of the different isoforms in brain tissue, although levels of SH2B1 protein were somewhat reduced (Fig. 4B). Importantly, in contrast to P322S/P322S mice, ΔPR/ΔPR homozygous mice were born and survived at the expected Mendelian frequency (Supplementary Fig. 1F), permitting us to examine the effect of this SH2B1 PH domain mutation in the homozygous state.
Disruption of the PH domain in SH2B1 results in obesity. A: mRNA was extracted from brain and heart tissue of WT and ΔPR/ΔPR male mice. The migration of DNA standards (left) and isoform-specific PCR products (right) are shown. bp, base pair. B: Proteins in brain lysates from Sh2b1 WT, ΔPR/ΔPR, and KO male mice were immunoblotted with αSH2B1 and αβ-tubulin. The migration of the 100-kDa protein standard (left) and the four known isoforms of SH2B1 and β-tubulin (right) are shown. IB, immunoblot. C: Body weight was assessed weekly starting at week 4 (males: n = 9 WT, 14 ΔPR/+, 9 ΔPR/ΔPR; females: n = 7 WT, 16 ΔPR/+, 11 ΔPR/ΔPR). D: Representative Sh2b1 WT and ΔPR/ΔPR male mice (6 months). E: Perigonadal fat of representative Sh2b1 WT and ΔPR/ΔPR male littermates (6 months). F and H: Body fat and lean mass was determined at weeks 24–26. Percent fat or lean mass was determined by dividing by body weight (males: n = 5 WT, ΔPR/+, and ΔPR/ΔPR; females: n = 5 WT, 11 ΔPR/+, 8 ΔPR/ΔPR). G: At weeks 24–26, serum from Sh2b1 WT, ΔPR/+, and ΔPR/ΔPR male and female mice was assayed for leptin (males: n = 8 WT, ΔPR/+, and ΔPR/ΔPR; females: n = 7 WT, 6 ΔPR/+, 9 ΔPR/ΔPR). For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001. n.s., not significant.
ΔPR/ΔPR mice fed standard chow exhibit significantly increased body weight compared with their WT littermates (Fig. 4C and D). By 20 weeks of age (Fig. 4C), ΔPR/ΔPR male mice were 15 g (>40%) heavier than their WT littermates, while female ΔPR/ΔPR mice were ∼9 g (∼35%) heavier than their WT littermates. It should be noted that we do not believe that the reduced levels of SH2B1 protein in the ΔPR/ΔPR mice can account for the increased obesity detected in ΔPR/ΔPR mice because heterozygote Sh2b1−/+ mice are not obese (5). Body length was not significantly different in preliminary studies (Supplementary Fig. 1G and H). Overall adiposity (Fig. 4E and F) as well as circulating leptin concentrations (Fig. 4G) were increased in ΔPR/ΔPR homozygotes but not lean body mass (Fig. 4H). The heterozygous (ΔPR/+) male and female mice showed no significant increase in adiposity (Fig. 4F). However, ΔPR/+ males had a slight increase in circulating leptin levels (Fig. 4G), suggesting that in males, even a single copy of the ΔPR mutation may be sufficient to produce a minor effect on energy balance.
Increased food intake (assessed at 18–20 weeks) is observed in ΔPR/ΔPR male and female mice compared with their WT and ΔPR/+ littermates (Fig. 5A), while VO2 (at 11–12 weeks of age) (Fig. 5B), respiratory exchange ratio (data not shown), and locomotor activity (data not shown) were not altered. On the basis of these data and the previous finding that Sh2b1 KO mice are obese primarily as a consequence of increased food intake (5), we believe it most likely that the ΔPR mutation caused obesity in the mice primarily as a consequence of increasing food intake rather than decreasing energy expenditure.
The ΔPR mice exhibit increased food intake and reduced glucose tolerance and insulin sensitivity. A: Food intake was measured for weeks 18–20 and cumulative food intake graphed (males: n = 11 WT, 10 ΔPR/+, 7 ΔPR/ΔPR; females: n = 6 WT, 9 ΔPR/+, and ΔPR/ΔPR). Same cohort of mice as in Fig. 4. B: Energy expenditure was assessed at 11–12 weeks using a Comprehensive Lab Animal Monitoring System. VO2 was normalized to lean body mass (LBM) (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 11 WT, 15 ΔPR/+, 13 ΔPR/ΔPR). C: At week 18, mice were fasted for 4 h, and blood glucose was measured (males: n = 8 WT, 12 ΔPR/+, 10 ΔPR/ΔPR; females: n = 7 WT, 14 ΔPR/+, 10 ΔPR/ΔPR). D: GTT was assessed at 18 weeks as in Fig. 2D and blood glucose monitored at times indicated (males: n = 8 WT, 12 ΔPR/+, 10 ΔPR/ΔPR; females: n = 7 WT, 14 ΔPR/+, 10 ΔPR/ΔPR). E: ITT was assessed at 19 weeks as in Fig. 2E and blood glucose monitored at the times indicated (males: n = 9 WT, 14 ΔPR/+, 11 ΔPR/ΔPR; females: n = 6 WT, 13 ΔPR/+, 11 ΔPR/ΔPR). Same cohort of mice as Fig. 4. For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 compared with WT littermates. n.s., not significant.
Glucose Tolerance and Insulin Resistance of ΔPR/ΔPR Mice
We initially examined parameters of glycemic control in ΔPR mice at 18–19 weeks of age. In homozygous ΔPR/ΔPR mice, hyperglycemia at baseline was evident (Fig. 5C) as well as impaired glucose tolerance (male and female mice) and insulin resistance (male mice) in intraperitoneal GTT and ITT, respectively (Fig. 5D and E). Male heterozygous ΔPR/+ mice (like P322S/+ mice) also displayed impaired glucose tolerance (Fig. 5D), although other parameters of glucose homeostasis were not different from WT littermates.
Because the disruption of glucose homeostasis in the aged ΔPR/ΔPR mice presumably resulted (at least in part) from their increased adiposity, we examined glucose homeostasis in young preobese mice to define any adiposity-independent effects of SH2B1 ΔPR on glucose homeostasis. We examined the adiposity of younger ΔPR/ΔPR mice to determine an age at which we might examine glucose homeostasis without it being confounded by increased adiposity. At 11–12 weeks of age, adiposity was already increased in male ΔPR/ΔPR mice but not detectably increased in female ΔPR/ΔPR mice (Fig. 6A). By 7 weeks, leptin levels were increased in male, but not female, ΔPR/ΔPR mice (Fig. 6B). We thus examined glucose homeostasis in ΔPR/ΔPR mice at 8 weeks of age, revealing hyperinsulinemia and glucose intolerance (with unchanged insulin tolerance) in both male and female ΔPR/ΔPR mice (Fig. 6C–F). The hyperinsulinemia and glucose intolerance in the presence of unchanged leptin and adiposity in young preobese females suggest that the ΔPR mutation interferes with glucose homeostasis independently of adiposity. Taken together, our results suggest that in obese ΔPR mice, the ΔPR mutation likely interferes with glucose homeostasis both independently of adiposity and secondary to the effects of adiposity on energy balance.
ΔPR female mice exhibit reduced glucose tolerance before the onset of obesity. A: Body fat mass was determined at weeks 11–12. Percent fat mass was determined by dividing the mass by body weight (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 11 WT, 15 ΔPR/+, 13 ΔPR/ΔPR). B: At week 7, serum from WT, ΔPR/+, and ΔPR/ΔPR male mice was assayed for leptin (males: n = 8 WT and ΔPR/ΔPR, 7 ΔPR/+; females: n = 8 WT, 5 ΔPR/+, 12 ΔPR/ΔPR). C: Eight-week-old mice were fasted for 6 h, and insulin levels were determined (males: n = 11 WT, 9 ΔPR/+, 10 ΔPR/ΔPR; females: n = 9 WT, 11 ΔPR/+, 13 ΔPR/ΔPR). D: At week 8, mice were fasted for 4 h, and blood glucose was measured (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 10 WT, 13 ΔPR/+, 12 ΔPR/ΔPR). E: In a separate study, GTT was assessed at 8 weeks as in Fig. 2D and blood glucose monitored at the times indicated (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 10 WT, 13 ΔPR/+, 12 ΔPR/ΔPR). F: ITT was assessed at 9 weeks as in Fig. 2E and blood glucose monitored at the times indicated (males: n = 11 WT, 9 ΔPR/+, 10 ΔPR/ΔPR; females: n = 9 WT, 11 ΔPR/+, 13 ΔPR/ΔPR). Same cohort of mice as Fig. 5B. For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 compared with WT littermates. n.s., not significant.
The identification of four human obesity-associated variants in the PH domain-encoding region of SH2B1, the fact that the three individuals with PH domain variants whose behavior has been documented all displayed behavioral abnormalities (1,2, and the present study), and the fact that the three individuals with variants in or near the PH domain had HOMA-IR scores suggesting severe insulin resistance and risk of type 2 diabetes, highlight the importance of the PH domain in SH2B1 function. The lethality of the human obesity-associated P322S mutation in the PH domain of SH2B1 in homozygous P322S/P322S mice demonstrates the importance of this mutation for SH2B1 function in vivo. Similarly, the obesity and diabetes observed in ΔPR/ΔPR mice highlight the importance of the SH2B1 PH domain for SH2B1-mediated metabolic control. The adiposity-independent glucose intolerance of young ΔPR/ΔPR female mice before the onset of obesity as well as of P322S/+ and ΔPR/+ mice also reveals the importance of SH2B1 and its PH domain for the control of glucose homeostasis independent of body weight, as previously suggested from the phenotype of humans bearing mutations in SH2B1 (1,2).
On the basis of the increased food intake of ΔPR/ΔPR mice and the findings in the Sh2b1 KO mice (5), we believe it likely that the increased body weight of the ΔPR/ΔPR mice is due to impaired function of SH2B1 in the hypothalamus. However, SH2B1 is also expressed in the periphery. The increased insulin concentrations with glucose intolerance in young, nonobese female ΔPR/ΔPR mice suggest alterations in tissues that control glucose uptake. However, the presence of glucose intolerance despite the increased insulin levels is consistent with the islets of ΔPR/ΔPR mice having an impaired ability to fully compensate for those alterations (41,42).
While humans heterozygous for P322S exhibit severe obesity, P322S/+ mice display mild glucose intolerance only in aged, HFD-fed animals. Because the region surrounding P322 is conserved between mice and humans (Fig. 7), it is unlikely that the more modest phenotype of P322S/+ mice compared with humans reflects species differences that result in structural changes in the SH2B1 PH domain, per se, but rather that PH domain binding partners may have different tolerances for P322S in mice and humans and/or that human physiology adapts more poorly to the resultant alterations in SH2B1 function. Consistent with the importance of the PH domain for the function of SH2B family members, at least nine point mutations (E208Q/E, A215V, G220V/R, A223V, G229S, D234N, F287S) have been identified in the PH domain of the SH2B1 ortholog SH2B3/Lnk in patients with myeloproliferative neoplasms (43–48) (Fig. 7).
ClustalW analysis and modeling of the three-dimensional structure of the PH domain of SH2B1. ClustalW of SH2B1, SH2B2/APS, and SH2B3/Lnk in the region included in the NMR structure of SH2B2/APS. Homologous residues are highlighted in black, and functionally homologous residues are cyan. The PH domain is indicated by the blue line below the sequences. P317, R318 in SH2B1 and the residues in SH2B1 for which variants are associated with obesity are indicated by magenta ovals. The variants in Lnk associated with myeloproliferative neoplasms are indicated by orange ovals. The variants are noted above the ClustalW. Residues within 8 Å of P317 in SH2B1 (P240 in SH2B3/Lnk) are denoted by taupe ovals. Residues within 8 Å of R318 in SH2B1 (K241 in SH2B3/Lnk) in the 3-dimensional structure (Video 1) are denoted by purple ovals. Residues within 8 Å of P322 in SH2B1 (P245 in SH2B3/Lnk) are denoted by green ovals. hum, human; mus, mouse.
This image is from a video available online at https://bcove.video/2lNxwhM. Modeling of the three-dimensional structure of the PH domain of SH2B1. A model of human SH2B1 was created by overlaying the sequence of the PH domain of human SH2B1 onto the mouse structure of APS. The two PH domain sequences are 75% similar (55.3% identical). The SH2B1 model is missing a single amino acid insertion at residue 296 and the 16-residue insertion at residue 262 which contains the R270W/Q human variant in SH2B1. The amino acid sequence of residues surrounding the P322 site is highly conserved between the two proteins. P322 is shown as yellow sticks. The other human variants in SH2B1 as well as residues P317 and R318 are magenta sticks. The human variants in Lnk are orange sticks. Differences between mouse and human SH2B1 are brown (no sticks). Oxygen atoms are red and nitrogen blue. β-Pleated sheets, α-helices, connecting loops, and hydrogen bonds in the region between the N-terminal end of β-strand 2 and the loop containing P317, R318, and P322 are indicated. The surface of the PH domain is tinted gray. P322 in SH2B1 is within 8 Å of the site of the D234N variant in SH2B3/Lnk. The turn that contains D234N also contains the G229S variant in SH2B3/Lnk and the E299G variant in SH2B1. P317 and R318 in SH2B1 are within 8 Å of the site of G220 R/V and A223V in SH2B3/Lnk. This region is stabilized by π-π stacking between P317 and F309 in SH2B1 (P248 and F240 in Lnk) and a network of hydrogen bonds.
To gain insight into how the SH2B1 P322S mutation or deletion of residues P317 and R318 in SH2B1 might regulate the function of the PH domain in SH2B1, we performed ClustalW analysis of SH2B family members and analyzed a model of SH2B1 that was based on the NMR structure of the PH domain of the SH2B1 family member SH2B2/APS (49). ClustalW analysis of the PH domains of the SH2B family members reveals that the PH domains are highly conserved (Fig. 7). In the model, residues P317, R318, and P322S in SH2B1 are on an exterior surface of the PH domain (Video 1). This surface is presumably a binding interface that interacts with either another region in SH2B1 or another protein. Another human obesity-associated variant in SH2B1 (E299G) as well as five of the human myeloproliferative neoplasm-associated variants in SH2B3/Lnk (G220V/R, A223V, G229S, and D234N) are in proximity to P317, R318, and P322 in SH2B1. In addition, eight of the human variants (E208Q, A215V, G220 V/R, G229S, D234N in SH2B3/Lnk and E299G, P322S in SH2B1) as well as P317 and R318 in SH2B1 are on or in proximity to this putative protein-binding interface (Fig. 7 and Video 1).
The number of human variants in SH2B1 and SH2B3 in this region of the PH domain suggests that small structural changes in this region as a result of mutation or other modification have the potential to produce substantial functional consequences. Because the residues corresponding to P317 and R318 in SH2B1 are on the surface of the PH domain and do not substantially change the direction of the loop, the P317, R318 deletion in SH2B1 would shorten the loop but not severely damage the overall structure. However, the deletion would be expected to diminish stabilization of the turn provided by the predicted π-π stacking between residues P317 and F309 in SH2B1. In addition, the deletion would be expected to alter the shape and electrostatics of the interface surface in the region of P317 and R318 in SH2B1.
Because SH2B1 from humans and mice share 95% sequence identity, with only one conservative difference (S325T) near P322 (Fig. 7), the structures in mouse and human are expected to be nearly identical. Therefore, the more modest phenotypes of P322S/+ mice compared with humans may be due to differences in the affinity of PH domain binding partners. The mouse binding partners may be able to accommodate the P322S mutation better than human binding partners, and/or human physiology adapts more poorly to the resultant alterations in SH2B1 function. Given the different phenotypes produced by the P322S and ΔPR mutations in mice, we postulate that the two mutations alter the structure of the SH2B1 PH domain in different ways to produce distinct changes in cell physiology. That relatively small changes in the PH domain, predicted to have only minor effects on PH domain structure, cause a rather profound effect on SH2B1β localization at the cellular level and energy balance and glucose homeostasis at the whole-animal level provides some of the first real evidence of the importance of the PH domain in SH2B1 function. While SH2B1β has been shown to cycle through the nucleus, it is generally found at the plasma membrane and in the cytoplasm (22,23,26). The accumulation of SH2B1β ΔPR in the nucleus indicates that the ΔPR deletion greatly alters the ratio between nuclear import and nuclear export of SH2B1β. Consistently, the ΔPR mutation as well as many of the other human obesity-associated SH2B1 variants impair the ability of SH2B1β to promote neurotrophic factor–induced neurite outgrowth of PC12 cells. Because neurite outgrowth in PC12 cells shares many properties with the formation of axons and/or dendrites (50), and because the Sh2b1 KO mice have impaired leptin signaling, it will be important in the future to examine the impact of SH2B1 PH domain changes on the structure of neurons that control energy balance.
Acknowledgments. The authors thank Drs. Malcolm Low, Miriam Meisler, Lei Yin, Xin Tong, Liangyou Rui, and Stephanie Bielas (University of Michigan) for helpful discussions and Dr. Rui (University of Michigan) for the gift of the Sh2b1 KO strain. The authors acknowledge the Wellcome-MRC Institute of Metabolic Science Translational Research Facility and Imaging Core Facility, both supported by a Wellcome Strategic Award (100574/Z/12/Z); the University of Michigan DNA Sequencing Core for DNA sequencing; and Dr. Thomas Saunders, Galina Gavrilina, and Dr. Wanda Filipiak of the University of Michigan Transgenic Animal Model Core as well as the Michigan Diabetes Research Center Molecular Genetics Core for help making the mouse models. The authors are indebted to the patients and their families for their participation and to the physicians involved in the Genetics of Obesity Study (www.goos.org.uk).
Funding. This work was supported by National Institutes of Health (NIH) grants R01-DK-54222 and R01-DK-107730 (to C.C.-S.). A.F. was supported by predoctoral fellowships from the Horace H. Rackham School of Graduate Studies, University of Michigan (Rackham Merit Fellowship); the Systems and Integrative Biology Training Program (NIH T32-GM-8322); and the Howard Hughes Medical Institute (Gilliam Fellowship for Advanced Study). Mouse body composition measurements were partially supported by the NIH-funded Michigan Diabetes Research Center (P30-DK-020572), Michigan Nutrition Obesity Research Center (P30-DK-089503), and Michigan Mouse Metabolic Phenotyping Center (U2C-DK-110678). Generation of the CRISPR mice was partially supported by the Molecular Genetics Core of the Michigan Diabetes Research Center (P30-DK-020572). Studies in humans were supported by the Wellcome Trust (207462/Z/17/Z to I.S.F. and WT206194 to I.B.); National Institute for Health Research Cambridge Biomedical Research Centre (to I.S.F.); and Bernard Wolfe Health Neuroscience Endowment (to I.S.F.).
The views expressed are those of the authors and not necessarily those of the NHS, National Institute for Health Research, or NIH.
Duality of Interest. No potential conflicts of interest relevant to this article were reported.
Author Contributions. A.F. directed and conducted experiments, analyzed data, and prepared the manuscript. A.F. and L.S.A. designed and generated the mice. A.F., L.S.A., I.S.F, M.G.M., and C.C.-S. developed the concept, designed experiments, and interpreted the data. L.S.A. and J.S. analyzed the model of the PH domain (Fig. 7 and Video 1). L.S.A., I.S.F., M.G.M., and C.C.-S. made revisions to the manuscript. L.K.J.S. and E.M.d.O. characterized the human mutations in cells (Fig. 1B). A.E.M. helped to regenotype the mice. A.E.M., L.C.D., G.C., and Y.H. helped to measure body weight and food intake (Figs. 2A, B, H, and I, 4C, and 5A and Supplementary Fig. 2A, B, F, and G). P.B.V. conducted neurite outgrowth experiments (Fig. 3D) and helped with experiments for Fig. 3A and C. R.M.J. conducted preliminary experiments for Fig. 3B. J.M.C. made the GFP-SH2B1β WT and GFP-SH2B1β ΔPR constructs (Fig. 3). J.M.K., E.H., and I.S.F. performed the clinical studies in mutation carriers (Fig. 1A and Table 1). I.B. and I.S.F. performed the genetic studies (Fig. 1A and Table 1). E.S.C. maintained mouse colonies and helped to genotype mice and collect blood samples (Figs. 4G and 6B). All authors approved the final content. I.S.F. and C.C.-S. 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.
Prior Presentation. Parts of this work were presented in poster form or as short oral presentations at the 2015 Neurotrophic Factors Gordon Research Conference, Newport, RI, 31 May–5 June 2015; 2016 Keystone Symposium on Molecular and Cellular Biology—Axons: From Cell Biology to Pathology, Santa Fe, NM, 24–27 January 2016; 2017 Keystone Symposium on Molecular and Cellular Biology: Neuronal Control of Appetite, Metabolism and Weight, Copenhagen, Denmark, 9–13 May 2017; 2017 Society for Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS) National Diversity in STEM Conference, Salt Lake City, UT, 19–21 October 2017; 2018 Molecular and Cellular Neurobiology Gordon Research Conference, Hong Kong, China, 1–6 July 2018; 2018 SACNAS National Diversity in STEM Conference, San Antonio, TX, 11–13 October 2018; Experimental Biology 2018, San Diego, CA, 21–25 April 2018; and 2019 Keystone Symposium on Molecular and Cellular Biology: Functional Neurocircuitry of Feeding Disorders, Banff, AB, Canada, 10–14 February 2019.
Have you been tempted by meal delivery services like Blue Apron and Sun Basket, but afraid the food wouldn’t fit your diabetes meal plan? Curious, I tried Diet-to-Go’s Balance Diabetes program for three days. Here’s what happened.
Let me say upfront that, in general, I’m not a huge fan of prepared food. I rarely eat frozen dinners or premade meals from containers. I’m too much of a control freak (or a food snob), I guess. However, Diet-to-Go offered me some meals to try while we were remodeling a bathroom and the house was complete chaos. Not cooking amid the mess for a few days before heading off to Washington, DC for Diabetes Patient Advocacy Coalition (DPAC) training sounded pretty good.
The Diet-to-Go Box Arrives
FedEx delivered my box of Balance Diabetes (Balance-D) meals on the date I requested. Lined with bubble wrap and filled with ice packs, the cardboard box contained three breakfasts, three lunches, and three dinners. It also had information about how to contact Diet-to-Go’s health coaches, how to join their Facebook group, and how to reheat the meals. Also included was a Snack Smart card with ideas for savory and sweet between-meal snacks. So far, so good.
I decided to check my blood sugar before each meal and two hours afterward to see the impact of the meal on my diabetes management.
The first day of Diet-to-Go meals featured:
Breakfast: Cinnamon Walnut Cereal and Chobani Less Sugar Greek Yogurt (Gili Cherry)
Lunch: Blackened Salmon on Spinach Cauliflower Purée and Brussels Sprouts
Dinner: Turkey Picadillo, Brown Rice Pilaf, and Apricot-Glazed Asparagus
You could serve the breakfast cereal hot or cold; I opted for cold given the 90+°F temperature outside. I found the cereal to be too sweet but I was excited to discover a fruit-flavored “less sugar” yogurt that didn’t involve a sugar substitute.
Lunch came with a lemon wedge to squeeze over the salmon after heating. Unfortunately, the lemon was bitter and I wished I hadn’t used it. I wouldn’t have called the salmon “blackened,” but it was moist (not overcooked) and I would definitely eat it again (without the lemon). The Brussels sprouts tasted fresh and I loved the purée.
For dinner, I combined the turkey together with the rice pilaf. The result was a sloppy joe-type mixture with raisins providing a hint of sweetness. The bright green asparagus was delicious, but I trimmed off the ends because they were a bit woody.
At the end of Day 1, I was pleasantly surprised that the food had been better than I expected. I ate some pistachios in the late afternoon and had a few walnuts and a couple of prunes after dinner. My bedtime BG was towards the lower end of my range.
The second day of Diet-to-Go meals featured:
Breakfast: Kale and Swiss Frittata and Chicken Sausage
Lunch: Turkey Salisbury Steak, Mashed Potatoes and Gravy, and Green Beans
Dinner: Chicken Fontina, Green Peppers and Onions, and Broccoli with Almonds
The breakfast frittata was heavy on the kale, but that’s a good thing. The sausage didn’t look very appetizing but had good flavor. I felt like the meal needed some fruit, so I ate a few apple slices too (which are not reflected in the nutritional information below).
After microwaving the lunch meal, the turkey wasn’t quite hot all the way through. I heated it more and the green beans overcooked. Oh well. It was still tasty and I’d eat it all again. The mashed potatoes did make my blood sugar spike, but I still stayed within my range.
I didn’t eat all of the Chicken Fontina because it featured mushrooms (which I don’t like). The broccoli, however, was perfectly crunchy and I inhaled it. There’s nothing worse than mushy broccoli. (Except mushrooms.)
Breakfast: Sunny Breakfast Box with Chobani Less Sugar Greek Yogurt (Gili Cherry), Peaches, and Almonds
Lunch: Chicken Pesto Salad and Whole Wheat Roll
Dinner: Thai Turkey Tenderloin, Stir Fry Rice, and Green Bean Medley
The “Less Sugar” Chobani yogurt was featured a second time for breakfast, so I’m glad I liked it. The peaches tasted like grade-school fruit cocktail (minus the maraschino cherries) and added unnecessary carbs in my opinion. I would have been perfectly happy to sprinkle the almonds on the yogurt and call it breakfast, skipping the fruit entirely. My blood sugar spiked afterward and I blame the peaches.
I loved the chicken salad at lunchtime. Featuring olives and peas, which I didn’t expect, it provided a nice change of pace from my go-to Pesto Chicken Salad with Grapes. I would have preferred a few crunchy Nut-Thin crackers to the soft roll.
My husband ate the Thai Turkey Tenderloin meal because I wasn’t home for dinner. He said it was much spicier than he expected.
My afternoon snack on Day 3 was string cheese plus a few walnuts.
* Difference between BG measured before the meal and two hours afterward. ** Meal had ingredients I didn’t like, so I did not eat all of it or test my BG afterward. *** I had a business dinner and my husband ate the meal.
While the Diet-to-Go meals were better than I expected them to be, I don’t see myself becoming a prepared meal junkie. I like to cook and prefer to have more control over what I eat. I also tend to eat more fiber than these meals provide. However, I will say if you eat a lot of frozen dinners anyway, you might want to give meal delivery a shot. Not having to think about what to eat can alleviate stress, that’s for sure. Also, if you don’t cook or hate grocery shopping, delivered meals might work well for you.
Disclosure: I received nine free meals from Diet-to-Go’s Balance Diabetes program for review purposes. All opinions are my own.
Hello! I’ve missed you. I can’t believe it’s been well over two weeks since I’ve done a proper post.
Well, one reason was that my old laptop wasn’t working right. I couldn’t log onto the internet, pictures wouldn’t post, etc. I got a new laptop when I was with my brother and I am not sure why I was dragging my feet for so long – it’s so fast! My old laptop was actually Jacob’s laptop and he got it in 2010.
Only one tiny problem, I can’t seem to find the lightening cord that connects my phone to the laptop to download my pictures. Huh.
I had a great week though at my brother’s house with his family. Got to hang out with my sister-in-law, and my niece Rachel. My other niece Sarah came with her husband the day after Thanksgiving – so good to see them!
And I got to spend the night at my stepson and his wife’s house. So great catching up with them.
December 2 marked the five year anniversary of my husband’s death, and it was a good day. I went out to dinner with Hannah and Jacob and Jacob’s Mom Val – their Mom/Grandma died on December 2 as well two years ago.
Here is what is different about this year than any other year on this date. I usually eat and drink like an asshole. Monday night? I drank iced tea and ate a reasonable dinner. I tracked my points. Didn’t eat a bag of gummies by myself in bed.
This year? I forgave myself. You may be asking for what – and I basically decided that this year to really FULLY move on and not dwell on the past, was to forgive myself for the medical decisions I made for my husband when he was sick.
I’ve been wearing an invisible guilt cape on for five years as I relive the decisions and things that went on five years ago, and this year I decided to take the cape off and throw it in the garbage.
That doesn’t mean I am just forgetting my husband altogether – that will never happen. But this year I was able to remember all the things that I love/miss about him.
he told me every.single.day that I was beautiful, no matter what I weighed
hanging out with him in the summer while I grilled and he smoked his cigar and we listened to Frank Sinatra
his Sunday morning breakfasts he used to make me
me asking stupid questions during football games while I made snacks 😀
All the good stuff rises to the top after a loss. He was also verytimes opinionated and always thought he was right no matter your argument. He could be stubborn and an asshole at times, but in the end we just went together like peas and carrots.
I’ll be back tomorrow with a 2 point macaroni and cheese recipe made with Alouette cheese – it’s amazing!
Hope you all had a wonderful holiday week and don’t get too stressed over the holiday season. Remember it’s the people around the tree that matter more than the presents underneath.
A habit of binge-eating during low blood sugars can wreak havoc on your blood sugar levels, your energy, your weight, and your daily life.
The blood sugar roller coaster that often accompanies over-eating during hypoglycemia is exhausting.
In this article, we’ll discuss how to stop the cycle and habit of binge-eating during low blood sugars.
What is hypoglycemia?
In the human body, a blood sugar level below 70 mg/dL will interfere with your brain and your entire body’s ability to function properly. Even the most basic tasks, like walking or speaking, can become extremely difficult the lower your blood sugar drops below 70 mg/dL.
Your brain relies on a second-by-second delivery of glucose from your bloodstream in order to function. Without enough glucose (sugar) in your bloodstream, your brain and entire body will struggle to function.
The intense food cravings during a low blood sugar are really coming from your brain, pleading, “Feed me! Feed me!”
Left untreated, low blood sugars can lead to seizures and death.
Here are 5 steps to stopping the habit of binge-eating during low blood sugars.
Identify the symptoms of your low blood sugar
Acknowledge your current habit (and its consequences) around lows and food
Choose 3 specific fast-acting carbohydrates as your primary treatment for lows
Distract yourself with something else…
Reinforce this mantra: “I do have control over what I eat when I’m low.”
Let’s take a closer look.
Identify the symptoms of that specific low blood sugar
Hunger is one of the many telltale signs that your blood sugar has dropped below a safe level, but it’s important to identify other symptoms of low blood sugar to help reinforce good habits around how you treat low blood sugars in general.
By identifying the symptoms that you personally experience during a low blood sugar, you’re reminding yourself that those intense cravings for food aren’t random or normal hunger, they are directly tied to your blood sugar, and they aren’t rational.
When you do notice your blood sugar is low, take just 20 seconds to identify your symptoms — including those severe cravings — and remind yourself they are an irrational aspect of hypoglycemia.
Yes, you need some food, but you don’t need all the food.
Acknowledge your current habit (and its consequences) around low blood sugars and food
Routinely binge-eating during low blood sugars comes with some not-so-subtle consequences. And a big part of breaking that binge-eating habit comes down to fully acknowledging it.
You could write it down, make a YouTube video, or record a voice-memo about that you send to your mom — whatever works for you! The goal is to simply look at the habit and the vicious cycle it creates from a distance.
Things to consider…
How many extra calories do you consume every day or week because of binge-eating during lows?
that you’re always prepared (Gummy Lifesavers, for example, survive in hot and cold temperatures, only require 3 or 4 to treat the average low, and one bag can treat dozens of lows).
that you can control exactly how many grams of carbohydrates you consume. Mild lows may only need 6 to 8 grams of carbohydrate, while severe lows may need 15 to 20 grams.
that you’re consuming something that digests easily and quickly. Using high-fat or high-protein foods to treat lows will only make your symptoms and cravings last longer.
that you’re consuming “medicine food.” Whichever 3 foods you choose (for example: jelly beans, juice box, fruit snacks), you can think of those things as “medicine foods.”
you won’t use lows an excuse to binge-eat yummy treats like brownies or ice cream. Instead, give yourself permission to eat those treats in a more controlled environment when your blood sugar and your appetite are rational and stable.
When you choose fast-acting carbohydrates like fruit snacks, jelly beans, gummy Lifesavers, Smarties, glucose tabs, etc. to treat lows, you can prepare by storing them in your purse, desk, backpack, nightstand, gym bag, jacket pocket, and glove compartment in your car.
The more you are prepared for treating lows properly, the more easily you’ll be able to manage them properly, too!
Distract yourself with something else…
Of course, we all know the symptoms of low blood sugar can persist long after your blood sugar has risen to a safe level. And of course, extreme hunger is one of the most persistent symptoms (followed by the desperate desire for a cozy nap!)
This means that after you treat the low, you need to distract yourself with something until those intense cravings for more food quiet down and disappear.
You may find that simply noshing on something like gum or carrots can provide that satisfying feeling to your brain because you’re chewing without consuming starchy carbohydrates.
Here are a few distraction techniques:
Chug some ice-cold water
Chew on carrots or celery
Pop some gum or TicTacs
Record a voice memo to yourself about your ability to control what you eat
Clean your kitchen
Squeeze a stress ball
Lie down in bed and close your eyes
The goal is to either distract your mouth or your hands or both!
Reinforce this mantra: “I do have control 0ver what I eat when I’m low.”
Truly, the biggest contributor to your habit of binge-eating during low blood sugars is the belief that you have no control over what you’re doing. That you are helplessly putting more food in your mouth because your brain is desperately begging for more
But you do have control. You do have rational thought and logic and reason at your disposal, you just have to make the choice to use those things.
Sure, I’d like to eat 3 bowls of Honeycombs mixed with Peanut Butter Captain Crunch every time my blood sugar dips below 50 mg/dL, but I know what that will do to the rest of my day, to my blood sugar, to my energy, and how guilty and regretful I’ll feel because of those things.
If you continue to tell yourself you have no control over how much you eat during lows or that you deserve to eat as much as you want during lows, then you’ll continue to feed that belief and that vicious habit.
If you take a moment to remind yourself, “I do have control over what I eat when I’m low,” you will be taking responsibility for how you treat lows and for your actions in general.
It’s actually pretty darn empowering. And quite rewarding, because treating a low with a reasonable amount of fast-acting carbohydrates means bringing your blood sugar up to a reasonable blood sugar level.
This means you don’t have to deal with a blood sugar roller coaster and the guilt of overeating. Instead, you get to continue with your day, proud of your ability to exert control over your own behavior.
Self-discipline produces…pride. Low blood sugars could become a source of pride and integrity in how you manage your health. And that could easily start to spill-over into other areas of your health, too.
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To identify the factors mediating the progression of diabetic nephropathy (DN), we performed RNA sequencing of kidney biopsy samples from patients with early DN, advanced DN, and normal kidney tissue from nephrectomy samples. A set of genes that were upregulated at early but downregulated in late DN were shown to be largely renoprotective, which included genes in the retinoic acid pathway and glucagon-like peptide 1 receptor. Another group of genes that were downregulated at early but highly upregulated in advanced DN consisted mostly of genes associated with kidney disease pathogenesis, such as those related to immune response and fibrosis. Correlation with estimated glomerular filtration rate (eGFR) identified genes in the pathways of iron transport and cell differentiation to be positively associated with eGFR, while those in the immune response and fibrosis pathways were negatively associated. Correlation with various histopathological features also identified the association with the distinct gene ontological pathways. Deconvolution analysis of the RNA sequencing data set indicated a significant increase in monocytes, fibroblasts, and myofibroblasts in advanced DN kidneys. Our study thus provides potential molecular mechanisms for DN progression and association of differential gene expression with the functional and structural changes observed in patients with early and advanced DN.
Diabetic nephropathy (DN) is the most common cause of end-stage renal disease in the U.S., and its incidence is rising worldwide despite glycemic and blood pressure control regimens (1). Therefore, elucidating mechanisms that mediate the early stage of DN may help us to identify new targets for better preventive and therapeutic measures.
Genome-wide gene expression profiling can be useful in providing a global picture of the disease pathogenesis and to identify potential new biomarkers and drug targets for DN. Several previous studies examined the transcriptomes of human diabetic kidney samples. The European Renal cDNA Bank (ERCB) consortium examined the gene expression of the tubulointerstitial compartments of European Caucasian patients with DN compared with those from pretransplant donors and minimal change disease by microarray analysis, which identified the expression of genes related to the nuclear factor-κB–driven inflammatory pathway to be highly associated with the progression of DN (2). The ERCB consortium also found that vascular endothelial growth factor A (VEGFA) expression was downregulated in the tubulointerstitial compartment of DN, which is associated with interstitial vascular rarefaction (3). Berthier et al. (4) analyzed the transcriptome of glomerular and tubulointerstitial compartments in patients with early DN from a Pima Indian cohort, patients with progressive DN from ERCB, and control subjects without diabetes and identified the Janus kinase-STAT pathway to be upregulated in both glomerular and tubulointerstitial compartments in DN. Woroniecka et al. (5) analyzed the glomerular and tubular microarray data from kidney biopsy samples of patients with DN compared with samples from living allograft donors and surgical nephrectomies, which showed the upregulation of RhoA, Cdc42, integrin, and VEGF signaling in the glomerular compartment and inflammation-related pathways in the tubulointerstitial compartment in DN. Nair et al. (6) examined the microarray analysis of the tubulointerstitial compartments samples from the Pima Indian cohort, which included patients with diabetes with normoalbuminuria, microalbuminuria, and macroalbuminuria, and found that the cortical interstitial fractional volume, an index of tubulointerstitial damage, correlated significantly with the transcripts enriched for pathways associated with mitochondrial dysfunction, inflammation, migratory mechanisms, and tubular metabolic functions. In another recent study by Pan et al. (7), microarray analysis of kidney samples from patients with advanced DN and surgical nephrectomies were performed for glomerular transcriptome profiling, which identified SLIT-ROBO GTPase-activating protein 2a (SRGAP2a) as a key gene associated with proteinuria and estimated glomerular filtration rate (eGFR) in patients with DN. Thus, the transcriptomic analyses are of microarray analyses in samples from patients with advanced DN, with the exception of the Pima Indian study. In most of these previous studies, the transcriptomes were profiled in glomerular and tubulointerstitial compartments separately.
Here, we report an RNA sequencing (RNA-seq) analysis of the whole-kidney biopsy samples of patients with early and advanced DN compared with nephrectomy sample tissues from patients without diabetes. We compared gene expression profiles among normal samples, early DN, and advanced DN. We also performed a correlation analysis of genes with renal function (eGFR) and histological parameters in patients with DN. We took advantage of the recently published single-cell RNA-seq (scRNA-seq) data to perform a computational deconvolution analysis to identify the different cell types present in normal and diseased kidneys. Finally, we validated some of the key findings by immunostaining of the kidney tissues from these patients.
Research Design and Methods
Human Kidney Biopsy Sample Collection
A total of 28 patients with biopsy-proven DN hospitalized from January 2015 to December 2016 in Shanghai Jiao Tong University Affiliated Sixth People’s Hospital were enrolled in the study. Kidney tissues were collected through ultrasound-guided kidney biopsy after informed consent was obtained, according to the guidelines of the local ethics committee. Samples were quickly frozen in liquid nitrogen and stored at −80°C before use. Patients were divided into two groups—early DN (n = 6) and advanced DN (n = 22)—on the basis of urinary albumin-to-creatinine ratio (UACR) and renal function (calculated using the MDRD equation) by at least two randomized measurements. Early DN was defined as UACR between 30 and 300 mg/g, eGFR >90 mL/min/1.73 m2, whereas advanced DN was defined as UACR >300 mg/g, eGFR <90 mL/min/1.73 m2. Nine control human kidney samples were obtained from the unaffected portion of tumor nephrectomies. Demographic, blood biochemical characteristic, urine albumin excretion, and kidney function data were collected. The study was approved by the institutional review board at Shanghai Jiao Tong University Affiliated Sixth People’s Hospital.
RNA-Seq and Bioinformatics Analysis
RNA-seq was performed on 28 DN and 9 control samples. Total RNA was extracted using TRIzol (Thermo Fisher Scientific). The RNA quality was evaluated by an Agilent 2100 Bioanalyzer. The total RNA sample was treated using a Ribo-Zero Magnetic Gold Kit to deplete rRNA, and cDNA libraries were prepared and sequenced at BGI using a HiSeq 4000 system. The reads with good quality were first aligned to human reference databases including hg19 human genome, exon, and splicing junction segment and a contamination database including ribosome and mitochondria sequences using the STAR alignment algorithm (8). After filtering reads mapped to the contamination database, the reads that were uniquely aligned to the exon and splicing junction segments with a maximal two mismatches for each transcript were then counted as the expression level for the corresponding transcript. After filtering out the transcripts with low reads (<100) across all samples, the read counts were log2 transformed and quantile normalized at an equal global median value to compare the transcription level across samples. Gene expression data were first adjusted by demographic confounders such as age and sex by extracting residuals from the linear regression model. Principal component analysis was then performed to assess the overall sample distribution using adjusted expression data of all the genes. The differential analysis by limma test (9) was carried out to identify significantly dysregulated genes at P < 0.05 (for advanced DN vs. early DN, we used a false discovery rate [FDR] <0.05 because of a large amount of differentially expressed genes [DEGs]) and 1.5-fold change, which was then subjected to Gene Ontology (GO) function (10) and pathway (Kyoto Encyclopedia of Genes and Genomes, Ingenuity Pathway Analysis, BioCarta, Naba, Panther, Pathway Interaction Database, Reactome, WikiPathways) enrichment analysis by Fisher exact test. Correlation of gene expression and eGFR and histology scores were done on all DN samples. We determined eGFR-related genes at an FDR <0.05 by Pearson correlation test. Correlation with histology scores was determined by multivariate analysis using linear regression model at P < 0.05 for each score. As for deconvolution analysis, CIBERSORT (11) was used to estimate the percentage of each cell type on the basis of the expression value of markers in bulk sequencing samples. A human kidney allograft biopsy single-cell data set was obtained from the Gene Expression Omnibus (GEO) database (GSE109564) and used to identify 16 cell types by Seurat with parameters described in the original article (12). For each cell type, gene expression values were compared against other cell types. Genes with t test P < 0.01 and log2(fold change) >0.5 were selected as markers for corresponding cell types. For each marker, we calculated the mean expression value in each cell type to construct the base matrix for deconvolution analysis. The same strategy for deconvolution analysis was also performed on the basis of mouse single-cell sequencing data obtained from the GEO database (GSE107585) (13).
Histological analysis of all patients with DN was performed by investigators blinded to the experimental groups. A renal pathologist scored detailed histological features from whole-slide images of kidney biopsy samples stained with hematoxylin-eosin, periodic acid Schiff, and Masson trichrome as described previously (14).
Immunostaining of Kidney Sections
Human kidney biopsy samples from these patients were collected as described under protocols approved by the institutional review board. Biopsy samples included 5 cases of early DN, 44 of advanced DN, and 7 of normal tissue adjacent to tumor nephrectomy samples. Immunostaining was performed in all five samples of early DN, five randomly selected samples of advanced DN, and five normal samples from nephrectomies using specific primary antibodies and biotinylated secondary antibodies (Vector Laboratories Inc.). Staining was revealed with avidin-peroxidase (VECTASTAIN Elite; Vector Laboratories Inc.). Slides were mounted in Aqua Poly/Mount (Polysciences, Inc.) and photographed under an Olympus BX60 microscope with a digital camera. The following antibodies were used: retinol-binding protein 4 (RBP4) (ab133530; Abcam) and GRP1 (ab166987; Abcam). The extent of kidney staining in human biopsy samples was semiquantitatively scored on the basis of the percentage of positive staining area divided by total kidney cortex area in the kidney section for each patient. Immunostaining for immune cells was performed with anti-Mrc1 for macrophages (ab64693; Abcam), CD3 for T cells (A0452; Dako), and CD20 (M0755; Dako) for B cells.
Data and Resource Availability
The RNA-seq data set generated and analyzed in the current study is available from the GEO repository (GSE128736). All other data sets generated during the current study are available from the corresponding authors upon reasonable request. No applicable resources were generated during the current study.
RNA-Seq of Kidney Biopsy Samples From Patients With DN
Kidney biopsy samples from 28 patients with DN (22 advanced and 6 early) and 9 normal nephrectomy samples adjacent to tumors were used for RNA-seq analysis. Baseline characteristics are presented in Supplementary Table 1. Patients with early DN had an eGFR >90 mL/min/1.73 m2 and microalbuminuria (UACR <300 mg/g). Patients with advanced DN had either an eGFR <90 mL/min/1.73 m2 or UACR >300 mg/g. Patients with advanced DN had significantly lower eGFR and serum albumin but higher UACR, serum creatinine, and blood urea nitrogen levels than patients with early DN. Patients with early DN had a similar duration of diabetes and other clinical parameters to patients with advanced DN, suggesting that they were likely to be nonprogressors. In addition, their UACR and renal function in the 2–3-year follow-up from the time of collection of kidney biopsy samples remained stable (data not shown). To minimize the tissue processing (i.e., without any dissections or digestions) that may change the gene expression profiles (15), RNA-seq was performed using the whole-kidney biopsy samples, which contain primary kidney cortices. The principal component analysis of the RNA-seq data showed three distinct clusters (Supplementary Fig. 1), indicating large changes in the overall gene signatures among the three groups.
Comparison of RNA-Seq Data of Kidney Biopsy Samples Between Patients With Early DN and Control Patients
We first analyzed the DEGs in the kidney cells of patients with early DN compared with control patients without diabetes. Figure 1A shows a heatmap of the top 25 upregulated and top 25 downregulated genes in early DN, and Fig. 1B shows a volcano plot of the top 50 DEGs (limma P < 0.05 and fold change >1.5 or less than −1.5) (a complete list of DEGs is included in Supplementary Excel File 1). Of note, among the top 25 downregulated genes were transcription factors that are implicated in the pathogenesis of DN (16), such as EGR1, EGR2, EGR3, JUNB, FOS, FOSB, and ATF3. IL6 and CXCL2, inflammatory markers associated with kidney injury in DN (17,18), were also included in the top 25 downregulated genes. GO analysis of DEGs between early DN and control samples is shown in Fig. 2. The top GO terms of the upregulated genes in early DN were related to cellular contraction, containing several myosin and actin genes, and to hormonal regulation and visual perception, containing several genes in the retinoic acid pathway (e.g., RDH8, RDH12, and RBP4). Retinoic acid pathway is shown to be renoprotective in several animal models of kidney disease (19,20). Interestingly, glucagon-like peptide 1 receptor (GLP1R) expression was also highly upregulated in early DN. The pathway enrichment analysis of combined upregulated and downregulated DEGs is shown in Supplementary Fig. 2. Many of the downregulated genes were related to immune response, suggesting that the inflammatory pathway is suppressed in early and nonprogressive DN compared with control nephrectomy samples. This could be due to low inflammation status in the kidney of the patients with early DN but may be a result of mildly increased inflammation in the nephrectomy samples from the normal tissues surrounding tumor.
Differential gene expression analysis for early DN vs. nondiabetic control. A: Heatmap of the top 50 dysregulated genes (25 upregulated genes and 25 downregulated genes) in early DN samples. Data are z normalized for heatmap visualization. Each column represents an individual sample from the control or early DN group. B: Volcano plot of dysregulated genes at limma P < 0.05 and fold change >1.5 or less than −1.5. The top 50 DEGs are labeled on the plot. Log2Rat, log2 ratio; −log10(p), −log10P value.
GO pathway analysis for DEGs for early DN vs. control. The top 20 enriched GO functions for upregulated (Top_Up, pink) and downregulated (Top_Dn, blue) DEGs are shown. −log10(P), −log10P value.
Comparison of RNA-Seq Data of Kidney Biopsy Samples Between Advanced and Early DN
We next examined the gene expression between early DN and advanced DN. Figure 3 shows a heatmap and volcano plot that indicate the top 50 dysregulated genes in advanced DN versus early DN (a complete list of DEGs is included in Supplementary Excel File 1). Consistent with previous findings, many matrix- and inflammation-related genes were upregulated in advanced DN. GO analysis indicated that the top GO terms of upregulated genes are related to the immune response, whereas the downregulated DEGs are largely of transport and metabolic processes (Fig. 4). The pathway enrichment analysis for all DEGs showed that changes in the pathways were related to matrisome, fibrosis, inflammation, and metabolism (Supplementary Fig. 3). These findings are consistent with the high level of inflammation, tubular dysfunction, and metabolic dysregulation observed in the kidneys of patients with advanced DN.
Differential gene expression analysis for advanced DN vs. early DN. A: Heatmap of the top 50 dysregulated genes (25 upregulated genes and 25 downregulated genes) in advanced DN samples. Data are z normalized for heatmap visualization. Each column represents an individual sample from the early DN or the advanced DN group. B: Volcano plot of dysregulated genes at limma FDR <0.05 and fold change >1.5 or less than −1.5. The top 50 DEGs are labeled on the plot. Log2Rat, log2 ratio; −log10(p), −log10P value.
GO pathway analysis for DEGs for advanced DN vs. early DN. Top 20 enriched GO functions for upregulated (Top_Up, pink) and downregulated (Top_Dn, blue) DEGs are shown. −log10(P), −log10P value.
Comparison of RNA-Seq Data Among Control, Early, and Advanced DN Biopsy Samples
We then compared the gene expression in all three groups. Only a moderate number of genes changed in their expression progressively from control to early DN to advanced DN (five increased and seven decreased genes) (Fig. 5A and B and Supplementary Excel File 1). The analysis further identified 148 genes that increased in early DN versus control but decreased in advanced DN versus early DN (Fig. 5C and Supplementary Excel File 1). Interestingly, many of these genes were of the retinoic acid pathway, such as RDH8, RDH12, and RBP4; furthermore, GLP1R was among these genes. These data suggest that retinoic acid and GLP1R agonists might have protective effects in early DN, thus preventing patients with diabetes from the progression of DN. The top GO terms of the 148 genes are shown in Fig. 5D and a full list is found in Supplementary Excel File 1. We also identified 270 genes that were downregulated in patients with early DN versus control subjects but were significantly increased in advanced DN versus early DN (Fig. 5E and Supplementary Excel File 1). These genes were mostly related to immune response (Fig. 5F and Supplementary Excel File 1), suggesting that these genes may be involved in the promotion of DN through enhanced inflammation.
Gene expression change from control to DN states. Mean expression (black line) of genes and the SD of the mean (gray band) are shown for the control, early DN, and advanced DN groups. A and B: Mean expression of five upregulated (A) and seven downregulated (B) genes. C: Mean expression of 148 genes that were upregulated in early DN but downregulated in advanced DN. D: The top 10 GO enrichment terms for 148 DEGs in panel C. E: Mean expression of 270 genes that were downregulated in early DN but upregulated in advanced DN. F: The top 10 GO enrichment terms for 270 DEGs in panel E. −log10(P), −log10P value.
Association of Genes With eGFR in Patients With DN
Additionally, we examined the association of genes in DN with eGFR in all patients with diabetes. GO terms related to iron transport and cell differentiation were positively associated with eGFR, while the immune response was at the top of the list of GO terms that were negatively associated with eGFR (Fig. 6A). Pathway enrichment analysis of combined DEGs showed phagocytosis regulation as one of the top pathways associated with eGFR (Supplementary Fig. 4). Small G-protein regulation (RhoA and Rac1), fibrosis, and inflammatory pathways were also highly associated with eGFR. The individual list of genes associated with eGFR is included in Supplementary Excel File 2.
Top enriched GO functions for eGFR and histological parameters in all DN samples. A: The bar chart shows the top enriched functions for DEGs that correlated positively (pink) or negatively (blue) with eGFR. B: A summary heatmap of the top enriched functions shows pathways that are correlated with each histological parameter. Scale bar indicates significance (shown as −log10P value [−log10(P)]). IF, interstitial fibrosis; TA, tubular atrophy; Top_Dn, top downregulated DEGs; Top_Up, top upregulated DEGs; tub, tubular.
Association of Genes With Kidney Histological Scores in All Patients With DN
We also examined the correlation between genes and histological scores in all patients with DN. Supplementary Table 2 shows the scoring of the individual histological parameters in patients with early or advanced DN. The most enriched GO pathways that correlated with histology scores in all DN samples are summarized in Fig. 6B. Segmental glomerulosclerosis (GS), tubular atrophy, and fibrosis had several shared GO pathways, such as Ras protein signaling, cell death, and several metabolic pathways. Global GS and arteriosclerosis also shared several pathways, such as chromatin remodeling and calcium-dependent cell-cell adhesion. Regulation of microtubule cytoskeleton was a common pathway between acute tubular injury and arteriosclerosis. However, each histological parameter also had distinct GO terms. For example, segmental GS had a unique feature on Rho protein signaling, which may be related to podocyte dysfunction. Mitochondrial dysfunction and fatty acid disturbance were associated more with global GS. Multiple inflammation-related pathways were associated with tubular atrophy/interstitial fibrosis. However, the acute tubular injury was associated with actin filament, iron transport, and secretion. Arteriosclerosis had specific GO terms on blood vessel remodeling, hormonal regulation of systemic arterial blood pressure, activation of adenylate cycle activity, and regulation of osteoclast differentiation. The latter might be related to the vascular calcification. Overall, these pathway analyses highlight the potential underlying molecular mechanisms for the different pathological changes observed in DN. The full list of DEGs associated with each histological parameter is included in Supplementary Excel File 3.
Deconvolution of RNA-Seq Data for the Representation of Kidney Cell Types
Because the biopsied tissue samples used in this study contain a heterogeneous mixture of kidney cell types, we used an R-based algorithm, CIBERSORT (11), to deconvolve the current data set to estimate the relative fractions of diverse kidney cell types. For this, we used the recently published scRNA-seq data set from human kidneys as a reference from Wu et al. (12). Enumeration of the data showed distinct clusters of kidney cells in control, early DN, and advanced DN kidneys (Fig. 7A). We found a significant reduction of proximal tubular and collecting cells in advanced DN compared with early DN and control samples. Interestingly, endothelial cells appeared to be decreased in both early and advanced DN compared with control, suggesting that kidney (glomerular and peritubular) endothelial cell injury may occur in early DN (Fig. 7A). There was an increase of monocytes, fibroblasts, myofibroblasts, B cells, and plasma cells in advanced DN compared with early DN and control samples. Since the scRNA-seq data used as a reference by Wu et al. did not include macrophages as a cell cluster, we could not determine whether there was a change in the macrophage population in DN. Therefore, we used the scRNA-seq data from normal mouse kidneys from Park et al. (13) as a reference, which showed a significant increase of macrophages and a small but significant increase in both T and B cells in advanced DN compared with early DN and control (Fig. 7B). These analyses are consistent with previous histological observations of increased inflammation, fibrosis, and tubular cell loss in the kidneys of patients with advanced DN compared with those from patients with early DN and control patients.
Estimates of cell components by deconvolution analysis in control, early, and advanced DN samples. A: Box plots of deconvoluted cell population across all cell types estimated by Wu et al. (12). B: Box plots of deconvoluted cell population across immune cells estimated by Park et al. (13). CD, collecting duct; EC, endothelial cell; LOH_AL, loop of Henle, ascending limb; LOH_DL, loop of Henle, distal limb; Mono1, monocyte type 1; Mono2, monocyte type 2; NK, natural killer; PT, proximal tubule.
The above deconvolution analysis showed a significant reduction of tubular cells in advanced DN. However, we could not appreciate the changes of podocytes because of the small portion of podocytes in the kidney cortices and the limitations of the analysis. Therefore, we compared the expression of both glomerular and tubular cell–specific markers and found that there was a reduction of both podocyte- and tubular cell–specific markers in advanced DN. The full list of kidney cell–specific DEGs is presented in Supplementary Excel File 4.
Since the changes of kidney cell population may affect the data analysis, we rechecked the DEGs and the GO pathways from early DN versus control and advanced DN to early DN after adjusting for the differences in cell population, as estimated from the deconvolution analysis. The adjusted analysis (Supplementary Figs. 5 and 6) largely showed similar findings to the unadjusted analysis (Figs. 2 and 4). In addition, we compared the DEGs from this study with the previously published transcriptomic data sets from Pan et al. (7) (GSE96804), a microarray analysis of glomerular transcriptome in DN, and Woroniecka et al. (5) (GSE30122), a microarray analysis of glomerular and tubular transcriptomes. As shown in Supplementary Table 3, there was an ∼30% overlap in DEGs and >60% overlap in most of the enriched pathways compared with both sets. Given that these published data sets are of glomerular or tubular compartment–specific microarray analyses and that our data set is of RNA-seq data of biopsied whole-cortex samples, the overlap is quite significant between our and these two studies.
Validation of the Findings From Our RNA-Seq Data
We next confirmed by immunohistochemical analysis the change in expression of several of the DEGs identified. We were particularly interested in the genes that were increased in early DN but decreased in advanced DN because they may represent genes that may have a protective role against the progression of DN. Among these, we selected RBP4 and GLP1R for further validation by immunostaining in the kidney sections of patients with DN because their expression has not been well characterized in diabetic kidneys. As shown in Fig. 8A, we confirmed that RBP4 and GLP1R increased in early DN but decreased in advanced DN. RBP4 staining localized mostly in renal tubular cells in early DN, which largely colocalized with proximal tubule marker AQP1 (Supplementary Fig. 7), while the staining was very weak in the kidneys with advanced DN (Fig. 8A and Supplementary Fig. 8A). GLP1R localized in both glomerular and proximal tubular cells in early DN kidneys and colocalized with AQP1 in the tubular compartment (Supplementary Fig. 7), but the staining was also very weak in advanced DN (Fig. 8A and Supplementary Fig. 8A). For genes that are increased in advanced DN but not in early DN, we selected two immune response genes for validation by immunostaining. We found that expression of both interleukin 6 (IL-6) and IL-1B was significantly increased in advanced DN kidneys but not in early DN kidneys (Fig. 8B and Supplementary Fig. 8B).
Immunostaining of genes that are altered in DN. A: Representative images of RBP4 and GLP1R immunostaining in control and DN kidneys. B: Representative images of IL-6 and IL-1B immunostaining in control and DN kidneys. C: Representative images of CD20, CD3, and MRC1 immunostaining in control and DN kidneys. Semiquantification of immunostaining is shown in Supplementary Fig. 8.
We also performed immunostaining for the markers of immune cells to validate the findings from the deconvolution data. We found that the staining for M2-macrophage marker MRC1 increased significantly in advanced DN compared with early DN (Fig. 8C). There was also a significant increase of staining for T- and B-cell markers CD3 and CD20, respectively, in advanced DN compared with early DN kidneys (Fig. 8C and Supplementary Fig. 8C). These data are consistent with our deconvolution analysis and support an important role of these immune cells in the progression of DN.
In the current study, we performed transcriptomic studies of whole-kidney biopsy samples from patients with either early or advanced DN compared with control nephrectomy samples from patients without diabetes obtained from Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. For this study, we chose to use the whole-kidney biopsy samples for analysis. The rationale for this was mainly because of the limited amount of tissue that is typically available in biopsy samples and, importantly, because the dissection and digestion processes could alter the transcriptomic profiles by induction of stress-related gene expression as described in a previous study (15). The digestion process in particular may also have differential effects between nephrectomy and native kidney biopsy samples as well as between normal and diseased kidneys, thereby creating further artificial differences in gene expression between experimental groups. However, the limitation to our approach is that since kidney cortices contain mostly a tubulointerstitial compartment, the transcriptomic data will reflect mostly the mRNA expression of cells in this compartment. Indeed, the largest population of cell types identified by deconvolution analysis was proximal tubules. While the isolation of glomeruli would yield more specific information on glomerular cells, the data will nevertheless represent those of heterogeneous glomerular cell types. To circumvent this issue, we had performed isolation of specific glomerular cells from fluorescently labeled cells in mice (21,22), which cannot be done in human tissue samples. More recently, scRNA-seq has emerged as a powerful tool for observing gene expression at a single-cell level (23,24). However, the depth and number of the genes detected by scRNA-seq are still limited compared with the bulk RNA-seq (25). As mentioned above, the scRNA-seq data can be significantly affected by the digestion process because the procedure requires more stringent digestion of kidney tissues into the single cells. In addition, digestion of the human kidney biopsy samples for scRNA-seq is challenging to perform because of the limited amount of available material (26,27). Therefore, each technical approach has distinct advantages and disadvantages but together can provide complementary information to better understand the pathogenesis of DN.
There are a few advantages of the transcriptomic analysis described in this study. First, we used the RNA-seq approach, rather than microarray analyses used in most previous studies, to study the transcriptome of diabetic kidneys. The limitation of microarray analysis is that not all genes can be detected with currently available chips. In addition, our study provides data on the changes of noncoding RNAs in the diabetic kidney. For example, we found several noncoding RNAs among the DEGs between early and advanced DN that included LOC101926964, LOC101927136, LOC100507537, LINC00417, and LINC01255. We found that miR-3189 was upregulated in early DN compared with control, and it is known that miR-3189 is a potent regulator of cell apoptosis through the p53-dependent pathway (28). Therefore, we will confirm whether mature miR-3189 is indeed upregulated in patients with early DN compared with control patients in future investigations.
Another strength of our study is that we were able to include several samples from patients with early DN in addition to samples from patients with advanced DN. Although we only had samples from six patients with clinical and biopsy-proven early DN, the transcriptomic data from these patients provide useful insight into the early disease process of DN. These six patients have the same duration of diabetes and other clinical parameters as those with advanced DN and, therefore, are likely to be nonprogressors. To further support this, we have followed these six patients for 2–3 years since their enrollment and found that they had stable renal function and UACR. Previous studies included only patients with advanced DN (4,5), and the only other kidney transcriptome of early DN was performed in the Pima Indian study (6). Because our study is of patients of Asian origin, it would be informative to compare the transcriptomic data of patients with DN from different ethnic backgrounds.
Also, by using the recently published scRNA-seq data sets from normal human and mouse kidneys, we were able to deconvolve the bulk RNA-seq data from DN samples to estimate the changes occurring in specific kidney cell types in DN. Because of the variations of methodology and analysis among the currently available scRNA-seq data sets, we used two scRNA-seq data sets as a reference: human (26) and mouse (13) whole-kidney cortices. Even though the deconvolution data generated from the two data sets had some differences, the overall results suggest that there is an increase of monocytes/macrophages, fibroblasts, and myofibroblasts in DN kidneys. We further confirmed this by immunostaining of the kidney sections from the same patients. Thus, our data further support the critical role of macrophages in the pathogenesis of human DN. We are aware of the limitations of this analysis, but we believe that this approach will be significantly improved when we have more reliable scRNA-seq data from patients with DN.
When we examined the DEGs between early and advanced DN and control, we identified a group of genes that was highly expressed in early DN but suppressed in advanced DN, such as genes in the retinoic acid pathway and GLP1R. These likely represent a group of genes that have renoprotective effects in the early stage of DN, consistent with the nonprogressive nature of these patients. In addition, we were able to validate the expression of RBP4 and GLP1R in the kidney tissues of these patients. The role of RBP4 is to deliver retinol to the target tissue (29) and has been shown to be associated with renal function in patients with diabetes (30). Retinoic acid has been shown to have protective effects in vitro and in animal models of kidney disease (19,20). Recent studies suggested that local synthesis of retinoic acid appears impaired in the diseased kidney and contributes to the progression of kidney disease (31). Interestingly, genetic variations of RBP4 are associated with early DN but not advanced DN (32). Our study suggests that local expression of RBP4 might be elevated in early DN and contribute to local retinoic acid synthesis and protects patients from the progression of DN. Future studies are required to further examine the role of RBP4 in the progression of DN. GLP1R agonists have been shown to have renal protective effects in animal models and humans with DN (33–35). Our study suggests that GLP1R expression was increased locally in the kidney to protect the kidney from injury in early DN. Immunostaining of GLP1R indicated that its expression is indeed increased in both glomerular and tubular cells in early DN. How GLP1R mediates the effects of its agonists in local kidney cells requires further studies.
Interestingly, we found that expression of the immune response or inflammatory genes were suppressed in early DN but highly upregulated in advanced DN. The suppression of immune response genes in early DN could be partially due to mildly elevated inflammatory status of control nephrectomy samples. But more likely, these patients with early DN are nonprogressors and resistant to the progression of DN, and therefore, the inflammation is suppressed. The marked increase in immune response and inflammatory genes in patients with advanced DN confirms a critical role of inflammation in the progression of DN and is consistent with the previous transcriptomic data showing that the Janus kinase-STAT and nuclear factor-κB pathways are highly enriched in diabetic kidneys (2,4) and with the findings in Woroniecka et al. (5). Consistently, our deconvolution data suggest an increase in immune cells in the diabetic kidney, such as macrophages. Together, these data support a critical role of inflammation in the progression of DN.
We also performed correlation studies of gene transcripts with renal function (eGFR). Interestingly, DEGs related to phagocytosis and inflammatory pathways were negatively correlated with eGFR, suggesting again the role of macrophages and immune response in the progression of DN. DEGs related to iron transports were positively correlated with eGFR, indicating that tubular cell injury (loss of iron transports) contributes to the progression of DN.
DN has classic pathological features, with injury in the glomeruli, tubule/interstitium, and blood vessels (14,36). Different patients may present more injury in one of these compartments than others. However, the underlying molecular mechanisms of each pathological feature in DN remain unclear. Therefore, we studied the association of renal transcripts with the histological scores of individual pathological features obtained from patients with both early and advanced DN. Interestingly, we found that the segmental GS, global GS, and arteriosclerosis share some but have their own unique GO terms. Also, tubular fibrosis score was highly associated with the DEGs related to the immune response and inflammatory pathways. Since tubular fibrosis is known to be tightly associated with eGFR, our data show that the DEGs related to inflammation pathways correlate with eGFR. Interestingly, we also found several specific GO terms associated with arteriosclerosis, such as blood vessel remodeling and hormonal regulation of blood pressure. Several genes involved in bone metabolism were also associated with arteriosclerosis, and they may be involved in vascular calcification in patients with chronic kidney disease. A similar study was performed recently in a Pima Indian cohort with DN (6). The authors reported that the cortical interstitial fractional volume, an index of tubulointerstitial damage, correlated significantly with the transcripts enriched for pathways associated with mitochondrial dysfunction, inflammation, migratory mechanisms, and tubular metabolic functions. Further studies are required to determine how these DEGs contribute to the specific pathological changes observed in DN.
In conclusion, our study provides the transcriptomic data of patients with early and advanced DN compared with normal tissues from control patients from nephrectomy samples in a Chinese population of patients with diabetes. The correlation of renal transcripts with renal function and pathological changes will help us to further understand the underlying molecular mechanisms contributing to the progression of DN. We believe that whole-kidney transcriptomic data and scRNA-seq data will be complementary, and future sophisticated computational programs could help to better dissect the mechanisms of individual kidney cell injury by combining these two data sets. Finally, we believe that the data generated here could be an important resource for the renal community to further dissect the pathogenesis of DN.
Funding. Y.F. is supported by the National Natural Science Foundation of China (81870468) and the Medical and Engineering Cross Fund of Shanghai Jiao Tong University (YG2017MS10). K.L. is supported by National Institute of Diabetes and Digestive and Kidney Diseases grant R01-DK-117913. J.C.H. is supported by a Veterans Administration Merit Award and National Institute of Diabetes and Digestive and Kidney Diseases grants 1R01-DK-078897, 1R01-DK-088541, and P01-DK-56492. N.W. is supported by the National Natural Science Foundation of China (81670657 and 81870504).
Duality of Interest. No potential conflicts of interest relevant to this article were reported.
Author Contributions. Y.F., Z.Y., F.Z., J.W., T.Z., Z.L., L.H., and Q.Z. performed the experiments and histological scoring. Y.F., Z.Y., Z.S., W.Z., K.L., and J.C.H. analyzed the data. Y.F., K.L., J.C.H., and N.W. designed the research project. Y.F., K.L., J.C.H., and N.W. drafted and revised the manuscript. V.D.D’A. performed the histopathological scoring. Y.F., J.C.H., and N.W. 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.
It’s officially Brussels sprouts season! I can’t think about sprouts without thinking of my BFF Wendy. She was the only kid I knew who loved Brussels Sprouts. The rest of us wouldn’t go near them. Boy, how taste buds change! (Not Wendy’s, though.) Celebrate the season by making Roasted Brussels Sprouts with Grapes.
My husband has been on a mission to eat more cruciferous vegetables ever since he starting reading How Not to Die by Michael Greger, MD.
Cruciferous vegetables include cabbage, cauliflower, and Brussels sprouts. They contain tons of nutrients and fiber, help you feel full longer, and reduce that dreaded inflammation. Ease into eating these nutritional powerhouses with recipes like the one below. Brussels sprouts, grapes, and shallots are roasted in the oven, then combined with a touch of balsamic vinegar for a hint of sweetness.
How to Trim Brussels Sprouts
Are you intimidated by prepping Brussels sprouts? It does take a little bit of time, but it’s really very easy. All you need are a sharp knife, a cutting board, and a colander. Most markets sell sprouts individually, but some, especially farmers’ markets, sell fresh ones still on the stalk. If yours are on the stalk, simply cut them off and then proceed:
Trim a slice off the stem end. (This may not be necessary if you just cut yours off the stalk.)
Remove any unattractive outer leaves.
Cut each in half lengthwise (through the stem end).
Rinse and drain.
Hosting a holiday meal? Roasted Brussels Sprouts with Grapes would be a great vegan and gluten-free dish for your table.
Other Recipes You Might Like
Like Brussels sprouts? Check out these other recipes:
Roasted Brussels Sprouts with Grapes
Brussels sprouts with a hint of sweetness from grapes and balsamic vinegar
Author:Adapted from Prevention
Course: Side Dishes
Keyword: brussels sprouts, roasted brussels sprouts, thanksgiving side
8 teaspoons extra-virgin olive oil divided
1 pound Brussels sprouts trimmed and cut in half
1/4 teaspoon kosher salt
1/8 teaspoon freshly ground black pepper
2 cups seedless red grapes
2 shallots sliced
1/2 tablespoon balsamic vinegar
2 tablespoons sliced almonds toasted (see Notes)
Preheat oven to 425°F. Brush two baking sheets with 1 teaspoon of olive oil each.
In a large bowl, toss together the Brussels sprouts, 1 tablespoon olive oil, salt, and pepper. Spread the Brussels sprouts in a single layer on one of the prepared baking sheets, cut side down.
In the same bowl, toss together the grapes, shallots, and remaining 1 tablespoon olive oil. Transfer to the second baking sheet.
Place both sheets in the oven and roast. After 15 minutes, remove the pan with the grapes from the oven and stir. Return it to the oven and remove the pan with the Brussels sprouts and stir. Return it to the oven and continue roasting until the sprouts are golden and can be easily pierced with a fork, about 10 more minutes (25 minutes total). Remove both pans from the oven.
In a small bowl, combine the balsamic vinegar with 1 tablespoon water. Pour it over the grapes (still in the pan), then stir and scrape up any browned bits.
In a large serving bowl, combine the Brussels sprouts and the grapes with their juices. Top with the almonds and serve.
How to toast almonds: Place almonds in a nonstick skillet over medium heat. Cook, tossing frequently, until the almonds are aromatic and golden in color, about 5 to 6 minutes. They will brown quickly once the pan gets hot.
Hey guys! I’ve missed you! It was a much longer blog break than I anticipated, and well, my new laptop has taken me a while to get used to. I would download pics to my laptop, start a post and then had no idea HOW to find the pictures that I knew were on my laptop somewhere.
Not surprisingly, my brother Charlie and his family who are in town this weekend, helped me figure it out. I’ve also got a new theme to my blog, although it will be a work in progress. Many things to fix, but hopefully in the next month it will be where I want it to be. My recipes are so hard to find, and that’s number one priority.
So while I am back to posting, don’t worry if the blog looks a bit jenky for a bit!
Last week I made the most delish cheese danishes. The star of them was Safe + Fair’s chocolate candy cane granola. Holy balls this quickly shot to the top of my favorite granola. I love that I don’t get the candy cane in every bite, so it’s a surprise when I get it!
One of my high school breakfasts when Suzy-Q’s were unavailable at 7-11 (along with a Dr. Pepper!) was a Hostess cherry pie. That cherry filling, the crackly frosting on the outside – pure heaven!
These are my version of a grown up Hostess cherry pie – made with the Safe + Fair Candy Cane granola kneaded into the danish dough, filled with brie and no sugar added cherry pie filling. Then finished off with a glaze. It’s delicious. You don’t get the candy cane in every bite, so it’s a nice surprise when you get that bite. The slightly saltiness of the brie mixed with sweet/tart cherry pie filling. Well, let’s just say I am glad I only made three otherwise I’d eat a dozen.
Going forward, I think I will ditch the 1/2 teaspoon of butter that I kneaded into the dough – not sure it made that much of a difference, which would make them 4 points, not 5.
No matter what @ww plan you chose, these are each 5 points. Leave out the 1/2 teaspoon of butter brings it down to 4 points.
It’s been a busy few weeks. I was supposed to go to LA this past weekend, but the owner of Nyrvana had a family emergency, so it’s been postponed to the end of January. I was happy though since my brother and his family were in town this weekend, and I got to spend more time with him, even though I just got back from a week of visiting him. We laugh so much!
I am nearly done with my Christmas shopping. I’ve learned to kind of wait until the weekend before because otherwise I tend to spend too much money.
I’ll be back tomorrow with another recipe – my sugar free chocolate cupcakes with a sugar free vanilla frosting that’s shaped into a Christmas tree – it’s so pretty!
Happy Monday friends – hope you have an amazing day.
What’s your strategy for managing your diabetes during the holidays?
If your plan is to eat yourself into a food coma, you are not alone. Most gatherings this time of year revolve around food and drinks, and usually not the healthy stuff. And I absolutely think there should be room for that!
There should be room for indulgence, throwing your diet to the wind for a few hours and just enjoying being with family and friends.
And yes, of course we can do that even though we live with diabetes. If you are insulin-dependent like me, you just need a solid game plan and you’ll be able to get through the festivities without wreaking havoc on your diabetes management.
My plan for successfully managing my diabetes during the holidays
My plan is fairly straightforward, so it should work for you too:
Know your carb ratios (how much insulin to take to a gram of carbs) – if you don’t know them yet, get a piece of paper and take notes for a few days to get it right. Knowing your daily carb ratios will help you gain good blood sugar control year-round, not just for the holidays. You can read my post about carb ratios and sensitivities to learn more.
Know what you are eating – if you didn’t make it yourself, ask the host. You can’t always see what’s in a dish. Mashed potatoes are usually not just potatoes but also include a lot of butter, milk (or cream) and maybe even sugar.
Bring a carb counting cheat sheet – It might not be exact, but it’s better than nothing. If you go to your mom’s house during the holidays, you probably know a lot of the food she will be cooking because you have had it since childhood.
Do a little research upfront and find out how many carbs are in a serving of your favorite Christmas foods. Maybe you can even get mom’s recipe and calculate the exact carbs? You can find different cheat sheets online or simply make your own.
Work out in the morning – hit the weights before you go out. It will improve your insulin sensitivity and make it easier to manage your blood sugars during the afternoon/night. Not only will you feel better, but your muscles will love the additional carbs and proteins you’ll enjoy later in the day. Why not use all those holiday calories to build some muscles?
Consider increasing your basal rate – if you are sitting down eating for hours, your basal insulin may need to be turned up. Always consult your healthcare professional before making changes to your insulin dosage, but it might be an idea to increase your basal rate during dinner and throughout the night.
Go for a walk after dinner (or a snowball fight) – it will not only help digestion but also your blood sugar. I’m not even sure this one needs more explanation. Just be sure your sugars don’t crash!
Test, test, test and keep track of active insulin so you don’t overdose – I bring my meter to the table and rely heavily on my CGM. My sugars will have some spikes, and that’s ok, as long as they come down as planned.
Remember to bolus for alcohol – Bolusing for alcohol can be tricky because alcohol can make your sugars drop. My rule of thumb is that I bolus for everything except hard liquor (which I never drink anyway). Especially if your alcohol is mixed with juice or other carb-heavy drinks, you’ll need insulin with that.
And please drink responsibly. It’s really hard to manage anything, and especially your diabetes, if you are out of your mind drunk. If you for some reason aren’t eating but only drinking, be very careful with dosing for alcohol since you’ll have a high likelihood of going low during the night.
Alcohol is tricky, so if you’re drinking and unsure how your blood sugars will react, I suggest having a bedtime snack just to be safe.
I’ve successfully managed my diabetes during the holidays if I’ve enjoyed myself without having to worry about my diabetes too much, if I’ve been able to manage my sugars so that I don’t wake up the next morning in the 200 mg/dL (11 mmol/L) range, and if I haven’t had too many low sugars.
I doubt Santa is going to bring me a new pancreas for Christmas, so it’s a good thing I have learned to manage my diabetes on my own.
Suggested next posts
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Obesity and type 2 diabetes are emerging as global epidemics and impose huge burdens on patient families as well as society. They are leading causal factors for cardiovascular disease, neurological disease, cancer, and kidney disease and negatively impact health span and life span. However, treatment options are limited, and there is an unmet need for developing effective and safe medications. In this issue of Diabetes, Jiang et al. (1) report a new treatment strategy and a new antidiabetes agent.
Obesity arises from energy imbalance, and excessive energy is stored as triacylglycerol in fat (2). Mitochondria are an essential organelle responsible for cellular respiration and energy production. Mitochondria oxidize free fatty acids (FFAs) and glucose to produce ATP (referred to as oxidative phosphorylation). The inner mitochondrial membrane (IMM) contains an electron transport chain composed of the complexes I, II, III, IV, and V. Burning of FFAs and glucose produces high-energy electrons that are transported from complex I or II to complexes III and IV, where electron-carried energy is released to pump out protons, generating proton gradients across the IMM (Fig. 1). The IMM proton gradients drive ATP synthesis by complex V. The IMM also contains uncoupling proteins (UCPs) that mediate proton influx, thus suppressing ATP synthesis. UCPs release free energy as heat (thermogenesis) and are engaged in body temperature homeostasis. Brown adipose tissue UCP1 plays a pivotal role in the maintenance of body temperature homeostasis, particularly in rodents (3). UCP-mediated thermogenesis increases energy expenditure, thus protecting against obesity (3). In the 1930s, chemical uncoupler 2,4-dinitrophenol (DNP) was developed for obesity treatment; unfortunately, it had severe adverse effects (e.g., hyperthermia, hyperlactacidemia, death) and was stopped for clinical use (4,5). Nonetheless, the diabetes community continues to search for safe, therapeutic mitochondrial uncouplers for the treatments of obesity and type 2 diabetes. Here, Jiang et al. identified a promising one called 6j.
Protection against diabetes by dual mechanisms of mitochondrial uncoupling and pyruvate oxidation. 6j possesses dual properties of stimulating uncoupling and PDH-mediated pyruvate oxidation. Uncoupling increases mitochondrial respiration and burning of glucose and fatty acids. PDH activation reroutes glycolysis-derived pyruvate from lactate production to tricarboxylic acid cycle (TCA)-mediated catabolism, preventing hyperlactacidemia toxicity. I, II, and IV: complexes I, II, and IV.
DNP-induced uncoupling lowers ATP synthesis and ATP content, thereby increasing glycolysis to compensate for ATP deficiency. Increase in anaerobic glycolysis leads to pyruvate and lactate overproduction and hyperlactacidemia following DNP treatment (5). Of note, mitochondrial pyruvate dehydrogenase (PDH), which is tightly regulated through phosphorylation, catalyzes the first reaction of pyruvate catabolism (6). Pyruvate dehydrogenase kinases (PDKs) phosphorylate PDH at phospho-Ser293 and potently inhibit PDH (6). Several PDK inhibitors, including dichloroacetic acid (DCA), have been developed to study the function of the PDK/PDH pathway. To combat hyperlactacidemia toxicity, Jiang et al. reasoned that DCA-stimulated PDH activation might reroute pyruvate from lactate production to PDH-mediated oxidation, thereby ameliorating DNP-associated hyperlactacidemia.
Jiang et al. first established cell culture systems and validated DNP and DCA actions in vitro. As expected, DNP potently stimulates mitochondrial uncoupling, as revealed by marked increases in both oxygen consumption rate and ADP/ATP ratio. DCA inhibits PDK-induced phosphorylation of PDH at phospho-Ser293, leading to PDH activation and suppression of lactate synthesis (i.e., decrease in extracellular acidification rate) due to PDH-mediated pyruvate oxidation. Jiang et al. then tested DNP and DCA in mice. In line with previous reports, acute DNP administration induces hyperthermia, hyperlactatemia, and death at high doses. A chronic DCA treatment decreases both PDK-mediated phosphorylation of PDH (i.e., increases PDH activity) and lactate production. Notably, DCA also induces ectopic lipid accumulations in the liver and skeletal muscle. As an adaptive response to DCA-stimulated pyruvate catabolism and glucose oxidation, FFA oxidation is suppressed, contributing to muscle and liver steatosis. Additionally, pyruvate-derived acetyl-CoA may serve as a lipogenic precursor to increase de novo lipogenesis. Intracellular lipid species are known to promote insulin resistance and type 2 diabetes. For instance, diacylglycerol activates protein kinase C-θ (PKC-θ) in skeletal muscle and PKC-ε in the liver, which in turn inhibit insulin signaling and induce insulin resistance (7,8). Accordingly, the authors observed that DCA treatment activates muscle PKC-θ and liver PKC-ε and impairs insulin signal transduction in these two tissues. Next, Jiang et al. tested an innovative idea that DNP and DCA dual treatments may preserve the antidiabetes effect while eliminating the adverse consequences. The authors elegantly demonstrated that in cell cultures, DCA-stimulated pyruvate oxidation abrogates DNP-induced lactate overproduction while DNP maintains its ability to stimulate mitochondrial uncoupling. In mice, DCA pretreatment (i.e., increased pyruvate oxidation) reverses DNP-induced hyperthermia, hyperlactacidemia, and death. In mice with dietary obesity, remarkably, DNP/DCA dual treatments considerately improve hyperglycemia, insulin resistance, and glucose intolerance. Furthermore, DNP decreases DCA-induced ectopic lipid accumulations and insulin resistance in the muscle and liver. These exciting results provide proof-of-concept evidence that concomitant stimulation of both mitochondrial uncoupling and pyruvate catabolism is a viable strategy for type 2 diabetes treatment. These observations also raise an intriguing possibility that mitochondrial uncoupling and pyruvate oxidation act coordinately to regulate mitochondrial selection of fuel substrates and metabolic flexibility. Next, Jiang et al. wondered, cleverly, whether they can design an agent that possesses dual properties of stimulating both mitochondrial uncoupling and PDH activation—two birds with one stone. They successfully engineered compound 6j (Fig. 1), using high-throughput screenings and structure/activity-based chemical modifications. Remarkably, chronic 6j administrations, like DNP/DCA dual treatments, considerably improve insulin resistance, glucose intolerance, and liver steatosis in mice with either dietary or genetic (db/db) obesity. Thus, 6j and related agents hold great promise as new medications for the treatment of type 2 diabetes.
This work, like many other great studies, raises several interesting questions. ATP deficiency, due to mitochondrial uncoupling, potentially has adverse consequences on energy-demanding, ATP-sensitive cells (e.g., cardiomyocytes, neuronal subpopulations). Hence, cell type–specific 6j-related agents are expected to have additional safety properties. In line with this notion, recent studies highlight the translational significance of cell type–specific mitochondrial uncouplers (9,10). Aberrant mitochondrial uncoupling and depolarization may cause mitochondrial injury, mitochondria-originated cell death and inflammation, and/or mitophagy (11). Therefore, the potential effects of long-term 6j treatments on mitochondrial integrity, function, and mitochondrial diseases need to be further assessed. Given the paramount role of mitochondrial energy expenditure in body weight control, the impact of 6j and related agents on protection against obesity needs additional investigation. Interestingly, DCA suppresses DNP-induced hyperthermia, but the underlying mechanism is not fully understood. Finally, the direct molecular targets of 6j linking to uncoupling and PDH activation remain elusive, impeding structure/activity-based optimizations to further improve specificity and efficacy.
Funding. This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases grants RO1 DK115646 and RO1 DK114220.
Duality of Interest. No other potential conflicts of interest relevant to this article were reported.
Sprinkle the chicken with the cumin and pepper. Place the water and a steamer basket in the Instant Pot. Arrange the chicken in the steamer basket. Seal the lid, close the valve, and set the Manual/Pressure Cook button to 6 minutes.
Use a natural pressure release for 5 minutes, followed by a quick pressure release. When the valve drops, carefully remove the lid. Remove the chicken and place it on a cutting board. Let stand for 5 minutes before shredding. Set aside.
Meanwhile, whisk together the yogurt, mayonnaise, sugar, curry, and salt in a medium bowl and set aside.
Place the asparagus and edamame in the steamer basket in the pot. Seal the lid, close the valve, press the Cancel button, and reset to Manual/Pressure Cook for 1 minute.
Use a quick pressure release. Transfer the asparagus mixture to a colander and run it under cold water to stop the cooking process and cool quickly; drain well.
Place equal amounts of the kale mix on each of 4 dinner plates. Top with equal amounts of the asparagus mixture.
Add the chicken and onions to the yogurt mixture and toss until well coated. Spoon equal amounts on top of each serving of the asparagus mixture, and sprinkle with cilantro.
A serving is approximately 1/2 cup of the chicken mixture, 1/2 cup of the asparagus mixture, and 1 cup of greens.