Butternut Squash Twice Baked Potatoes

By electricdiet / December 12, 2020





Butternut Squash Twice Baked Potatoes – My Bizzy Kitchen


























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Skinny Peppermint Mocha | Diabetes Strong

By electricdiet / December 10, 2020


Want to add a little holiday cheer to your morning coffee? This rich, delicious, skinny peppermint mocha is easy to make, low in carbs, and full of festive flavor!

Latte in a white coffee mug topped with whipped cream

Does anyone else feel like coffee shops are in charge of the seasons these days?

We all know that fall starts when Dunkin Donuts or Starbucks release their pumpkin spice latte. And a few months later, they offer drinks like peppermint mocha and eggnog lattes to kick off the holiday season.

One morning, I found myself craving something festive, but didn’t feel like leaving the house. That’s when I decided to whip up this delicious skinny peppermint mocha.

I may never buy the coffee-shop version again! The creamy, chocolate flavor is so rich and decadent, and the kick of peppermint really brings it all together.

Plus, this homemade recipe is so much better for you. For comparison, a grande peppermint mocha from Starbucks made with 2% milk and whipped cream on top has a whopping 63 grams of carbs and 54 grams of sugar!

This skinny version, on the other hand, has 5.3 grams of carbs and 3 grams of fiber per serving. That’s only 2.3 net carbs! And there’s only 1 gram of sugar.

So whether you’re in the holiday spirit or feel like celebrating Christmas in July, this decadent yet healthy peppermint mocha latte is just what you need.

How to make a skinny peppermint mocha

This simple recipe is so luxurious yet easy to make. Just heat the ingredients, blend until frothy, and enjoy!

Ingredients for peppermint mocha in separate ramekins, as seen from above

Step 1: Add the coffee and half the milk to a saucepan over medium heat.

Step 2: Heat for about 5 minutes, then add the cocoa powder, peppermint extract, vanilla extract, and stevia. Mix well.

Ingredients simmering in a saucepan

Step 3: Transfer the mixture to a blender. Add the other half of the milk.

Mixture for beverage in a blender

Step 4: Blend until frothy.

Step 5: Pour into mugs and serve immediately.

Peppermint mocha latte in a white coffee mug

You can enjoy your peppermint mocha as-is, or feel free to finish with your favorite toppings!

Toppings for your drink

Don’t get me wrong, this coffee creation is absolutely delicious all on its own. I would drink it straight out of the blender!

But sometimes, a few toppings really add to the festivity for me. If you feel the same way, then have some fun with your peppermint mocha.

Personally, I like to add a little whipped cream, a sprinkle of cocoa powder, and some sugar-free mini marshmallows to mine. It’s like hot cocoa meets coffee with a peppermint twist, and it is amazing!

Skinny peppermint mocha latte topped with whipped cream on a wooden serving board

Storage

This drink is best served immediately. You want it to be hot and frothy!

If you find yourself with any extra, you can store it in the refrigerator in an airtight container for up to 3 days. When you’re ready to enjoy, I recommend reheating gently on the stove, then re-blending until frothy.

Other tasty ways to start the day

Whether you’re looking for caffeine, something yummy, or a combination of the two, there are so many options for starting your day the right way! Jumpstart your morning with one of these delicious recipes:

For even more inspiration, check out this roundup I created of low-carb smoothies for diabetics!

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

Recipe Card

Skinny Peppermint Mocha

Want to add a little holiday cheer to your morning coffee? This rich, delicious, skinny peppermint mocha is easy to make, low in carbs, and full of festive flavor!

Prep Time:10 minutes

Total Time:10 minutes

Servings:2

Skinny peppermint mocha latte topped with whipped cream on a wooden serving board

Instructions

  • Add the coffee and half of the milk to a saucepan over medium heat.

  • Heat for about 5 minutes, then add the cocoa powder, peppermint extract, vanilla extract, and stevia. Mix well.

  • Transfer the mixture to a blender. Add the other half of the milk.

  • Blend until frothy.

  • Pour into mugs and serve immediately.

Recipe Notes

This recipe is for 2 servings of peppermint mocha.
This drink is best enjoyed immediately.
Leftovers can be stored in an airtight container in the refrigerator for up to 3 days. I recommend reheating gently on the stove, then blending until frothy.

Nutrition Info Per Serving

Nutrition Facts

Skinny Peppermint Mocha

Amount Per Serving (1 serving)

Calories 66
Calories from Fat 29

% Daily Value*

Fat 3.2g5%

Saturated Fat 0.7g4%

Trans Fat 0g

Polyunsaturated Fat 1.4g

Monounsaturated Fat 0.7g

Cholesterol 2mg1%

Sodium 45mg2%

Potassium 505.4mg14%

Carbohydrates 5.3g2%

Fiber 3g12%

Sugar 1g1%

Protein 4.8g10%

Net carbs 2.3g

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

Course: Breakfast, Drinks

Keyword: coffee, gluten-free, keto, latte, low carb, peppermint mocha



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Cranberry White Chocolate Bars Top Best Cookie Swap Cookies for Christmas

By electricdiet / December 8, 2020


Best Cookie Swap Cookies For Christmas: Cranberry White Chocolate Bars!

Seasonal ingredients are the best and Holly’s Cranberry White Chocolate Bars make the best Christmas cookies. If you are invited to a holiday party or need some new cookie swap ideas look no further? You probably don’t have an extra minute in the day so here is the holiday cookie solution with Holly’s best cookie swap cookies. They are also called Magic Cranberry Bar cookies and that’s because they disappear off the plate!  With dried cranberries, white chocolate chips and pecans, it doesn’t get much better.  Except, they take only about 5 minutes to make!  These festive cranberry white chocolate recipe is from Holly’s fun cookbook, Too Hot in the Kitchen with trendy, simple healthy easy recipes. Love to make homemade Christmas gifts.

Cranberry White Chocolate Bars
Holiday ingredients for easy festive cranberry bar cookies. Tart cranberries, sweet white chocolate, the spice of ginger and nuts pack this delicious dessert with wholesome vitamins and minerals – perfect to indulge in while staying fit this holiday season. And, this recipe is also diabetic-friendly!! I like bar cookies as they are made in one pan and you are done! In fact, I think I make so many pans of this recipe during the holiday season because they are the perfect holiday bars!

    Servings48 servings

    Ingredients

    • 1 1/2cups


      gingersnap crumbs

    • 6tablespoons


      buttermelted

    • 1teaspoon


      vanilla extract

    • 1/2cup


      dried cranberries or craisins

    • 1/3cup


      white chocolate chips

    • 1/3cup


      chopped pecans

    • 2/3(14-ounce) can


      fat-free sweetened condensed milk

    Instructions
    1. Preheat oven 350° F. Coat 13x9x2-inch pan with nonstick cooking spray.


    2. In prepared pan, mix gingersnaps, butter, and vanilla; press into pan.


    3. Sprinkle cranberries, white chocolate chips, and pecans evenly over gingersnap crust. Drizzle sweetened condensed milk over top. Bake 15-20 minutes or until bubbly and light brown.

    Recipe Notes

    Per Serving: Calories 57 Calories from fat 42% Fat 3g Saturated Fat 1g Cholesterol 4mg Sodium 34mg Carbohydrate 8g Dietary Fiber 0g Sugars 6g Protein 1g Dietary Exchanges: 1/2 other carbohydrate, 1/2 fat

    Simple To Make with Holiday Ingredients for Best Cranberry White Chocolate Cookies Recipe

    gingersnap crust for cranberry white chocolate bars-my favorite cranberry cookies

    Start with gingersnaps which are easy to find this time of year. Crush them in the food processor but you can do it however you want. These gingerbread snaps form your crust.

    Next step is to combine the ginger snap crumbs with butter and then sprinkle with the cranberries, white chocolate, and pecans. Then, drizzle the fat-free sweetened condensed milk on top and you’re ready to bake.

    Layer ingredients in the pan and drizzle with sweetened condensed milk.  Pop in the oven and that is it!

    Best Cookie Swap Cookies Recipe Also Makes Perfect Holiday Homemade Gifts

    Turn to this favorite cranberry white chocolate bar recipe for friends and family this time of year. For a quick and delicious gift, just cut the cranberry cookie bars into squares, wrap with plastic wrap and tie with a holiday ribbon. From teachers to coaches, neighbors to doctors, give the delicious gift of nutrition this holiday season! If you have had Hello Dollies, then these cranberry bar cookies are the holiday version with cranberries, white chocolate, pecans and gingersnaps.

    Too Hot in the Kitchen Has So Many Simple Sassy Recipes

    Holly has lot of cookbooks but honestly, people who have Too Hot in the Kitchen cookbook say it is their favorite cookbook. Probably because the recipes are a little more trendy and the chapters are just so great! From Easy Entertaining to Quickies!

    These fabulous Cranberry White Chocolate Bars are from the Easy Entertaining Chapter. The flavor and ingredients are the essence of this time of year.  You can literally find all kinds of simple entertaining recipes in this chapter and you probably already have the ingredients in your pantry.

    Excited To Find Reduced Sugar Craisins for Cranberry White Chocolate Bars

    These Ocean Spray reduced sugar craisins (dried cranberries) are fabulous!! Best all, you cannot taste any difference so they were just as tasty but better for you.  In all of Holly’s recipes that call for dried cranberries, use the reduced sugar craisins.  Why not? You should be able to find them in any grocery store.  They still provide 25% of your daily recommended fruit needs and are an excellent source of fiber. You’ll love these cranberry bar cookies with these craisins and besides, this is a diabetic cranberry cookie!  Amazing, simple to make, festive and diabetic make them the overwhelming best cookie swap cookie recipe.

    Freeze Fresh Cranberries when in Season – You Can Always Substitute Dried Cranberries

    Buy fresh cranberries when in season and freeze in freezable plastic bag for one year to have fresh cranberries year round.  If a recipe calls for fresh cranberries, dried cranberries may be used.  Two top seasonal recipes taking advantage of fresh cranberries are the simple Cranberry Lemon Bundt cake and Cranberry Orange Muffins . Both make great gifts or to keep around your house during the holiday season.

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

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

    The Best Kitchen Gadgets List!

    Have you started making you holiday to-do list but it has you wondering what to give for a gift? Look no further than Holly’s Christmas wish list of favorite and 12 top unique kitchen gadgets!

    From an inexpensive mini spatula perfect for bar cookies to my pricey coffee maker which truly makes the best coffee, the research is done for you. LOVE the silicon bakeware and kitchen tools. Once you use them, you will understand why.

    Another Favorite Bar Cookie For Best Cookie Swap Cookies

    White Chocolate Recipes Make Sensational Seasonal Holiday Recipes

    Who doesn’t like a dessert that is made with white chocolate?  Hard to beat a white chocolate dessert! If you like this cranberry white chocolate holiday treats, wait until you try Holly’s fabulous White Chocolate Cheesecake from Gulf Coast Favorites cookbook or Chocolate Truffles with White Chocolate.

    Favorite Mini Spatula Perfect For Bar Cookies

    Favorite mini spatula because it is the perfect size for bar cookies.  Holly’s Blonde Brownies made with Holiday M&M’s are another great Christmas bar cookie.  Perfect for the spatula!  Holly discovered this amazing little kitchen tool while doing The 700 Club on her Cancer cookbook. In the make up room, someone was selling Pampered Chef so she wanted to see what everyone was buying. She bought this miniature spatula and it’s the perfect size to get bar cookies.

    Get All Holly’s Healthy Easy Cookbooks

    The post Cranberry White Chocolate Bars Top Best Cookie Swap Cookies for Christmas appeared first on The Healthy Cooking Blog.



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    In Vivo Reporter Assays Uncover Changes in Enhancer Activity Caused by Type 2 Diabetes–Associated Single Nucleotide Polymorphisms

    By electricdiet / December 5, 2020


    Abstract

    Many single nucleotide polymorphisms (SNPs) associated with type 2 diabetes overlap with putative endocrine pancreatic enhancers, suggesting that these SNPs modulate enhancer activity and, consequently, gene expression. We performed in vivo mosaic transgenesis assays in zebrafish to quantitatively test the enhancer activity of type 2 diabetes–associated loci. Six out of 10 tested sequences are endocrine pancreatic enhancers. The risk variant of two sequences decreased enhancer activity, while in another two incremented it. One of the latter (rs13266634) locates in an SLC30A8 exon, encoding a tryptophan-to-arginine substitution that decreases SLC30A8 function, which is the canonical explanation for type 2 diabetes risk association. However, other type 2 diabetes–associated SNPs that truncate SLC30A8 confer protection from this disease, contradicting this explanation. Here, we clarify this incongruence, showing that rs13266634 boosts the activity of an overlapping enhancer and suggesting an SLC30A8 gain of function as the cause for the increased risk for the disease. We further dissected the functionality of this enhancer, finding a single nucleotide mutation sufficient to impair its activity. Overall, this work assesses in vivo the importance of disease-associated SNPs in the activity of endocrine pancreatic enhancers, including a poorly explored case where a coding SNP modulates the activity of an enhancer.

    Introduction

    Type 2 diabetes affects >300 million people, causing severe complications and premature death (1), yet the underlying molecular mechanisms are largely unknown. This disease is highly complex, multifactorial, and partially characterized by endocrine pancreatic dysfunction, leading to insufficient insulin production (1). Genome-wide association studies (GWASs) have identified several single nucleotide polymorphisms (SNPs) associated with an increased risk of type 2 diabetes (24). Part of these variants are located in noncoding sequences with epigenetic marks associated to enhancer activity known to regulate the expression of their target genes by interacting with their promoters (5) and some overlap with transcription factor (TF) binding sites (TFBSs) required for proper endocrine pancreatic function (610). In this way, type 2 diabetes–associated SNPs may ultimately translate into transcriptional changes of the target genes (610). Methods to predict enhancers include profiling chromatin accessibility (11) and histone modifications (e.g., H3K27ac, H3K4me1) (12). The majority of the enhancer testing assays are performed in vitro in specific cell lines, missing cellular diversity and physiological contexts. To overcome these limitations, animal models have been used (13,14). The zebrafish has been successfully used for the study of pancreas development and function (15), having an endocrine compartment with the same cell types (α-, β-, δ-, and ε-cells) and functions as in mammal pancreas (16,17). Additionally, orthologous TFs operate in the zebrafish pancreas during early development, some of which are also important for adult pancreas maintenance. As in humans, Pdx1 plays a crucial role in zebrafish pancreas development and β-cell maturation and function (18), and Nkx6.1 is required for the identity of endocrine pancreatic progenitors (19,20).

    Some coding mutations are associated with the development of type 2 diabetes, while others might confer a protective effect (2,21). Interestingly, SLC30A8, a zinc transporter–encoding gene, shows contradicting results. The coding SNP rs13266634 located in the SLC30A8 gene is associated with an increased risk for type 2 diabetes (3) because of a tryptophan-to-arginine switch at protein position 325, causing reduced zinc transport activity (22,23). Zinc is essential for insulin packaging and maturation and secretion in β-cells (24); thus, the decrease of SLC30A8 activity is the simplest explanation for the increased risk for the disease. Surprisingly, recently identified protein-truncating SNPs in SLC30A8 have been associated with a protective effect (2,25) by enhanced insulin secretion (26). Further work is needed to clarify the type 2 diabetes association with SLC30A8 SNPs. An unexplored explanation for these apparently contradicting results could be that the coding SNP rs13266634 exerts a specific impact on adjacent or overlapping cis-regulatory sequences.

    In this work, we investigated the impact that SNPs located in putative enhancer regions have in enhancer activity, using an in vivo approach. Ten sequences that overlap with type 2 diabetes–associated loci and with marks for enhancer activity were tested, one overlapping with an exon of SLC30A8 (seq132wt). To test sequences for enhancer activity in the endocrine pancreas, we performed in vivo mosaic transgenesis assays in zebrafish embryos. We show that this strategy is sensitive, has low noise, and can be quantitative to address enhancer activity. Using this method, we observed that 6 out of 10 tested sequences are endocrine pancreatic enhancers, including the SLC30A8 exon-containing sequence. In addition, two sequences were found to be pancreatic progenitor enhancers. We also found that the type 2 diabetes–associated SNP (henceforth referred to as risk allele) decreased the enhancer activity of two enhancers, while the risk allele of two sequences resulted in a gain of enhancer activity. Interestingly, the SLC30A8 coding risk allele (seq132risk) showed an increase in enhancer activity, demonstrating that coding SNPs have the potential to modulate the target gene activity at both the transcriptional and the protein level. To better understand how SNPs can affect enhancer activity, we focused on the seq132 enhancer. We divided seq132wt into different fragments, observing that all are necessary for a robust pancreatic enhancer activity. We also show that common SNPs in seq132 modulate its pancreatic enhancer activity and that a single nucleotide mutation ablates completely the endocrine pancreatic enhancer activity of seq132. Additionally, we observed a chromatin interaction between seq132 and the promoter of Slc30a8 gene in murine cells and noted that targeting transcriptional modulators to seq132 using CRISPR affects the transcription of Slc30a8, strongly suggesting that the seq132 enhancer belongs to the regulatory landscape of Slc30a8. Overall, in this work, we use an in vivo system to validate enhancers that overlap with type 2 diabetes–associated SNPs, showing several cases where nucleotide variations result in complex changes in enhancer activity, including a classical and poorly understood coding SNP.

    Research Design and Methods

    Zebrafish Husbandry and Embryo Culture

    Zebrafish (Danio rerio) were handled according to European animal welfare regulations and standard protocols. Embryos were cultured at 28°C in Petri dishes containing E3 medium supplemented with 1-phenyl-2-thiourea to delay pigmentation formation (27).

    Putative Enhancer Selection

    Putative enhancer sequences were selected on the basis of GWAS data that uncovered 163 SNPs associated with type 2 diabetes or glycemic traits (P < 5e-8), considering all variants in high linkage disequilibrium (1000 Genomes Project Utah residents with ancestry from northern and western Europe r2 > 0.8), with lead GWAS SNPs being the exception for rs735949 (P < 3.70e-6) (6,2830). Risk alleles associated with seq132, seq117, and seq790 are part of the previously described GWAS credible set SNPs (31). Sequences were analyzed using the Islet Regulome Browser (32), which identifies active enhancers by the presence of H3K4me1, H3K27ac, and H2A.Z epigenetic marks in adult human endocrine pancreatic samples. The analysis was further refined by the presence of PDX1, MAFB, NKX6.1, FOXA2, and NKX2.2 binding obtained by chromatin immunoprecipitation sequencing (ChIP-seq) profiles (6,28), resulting in a list of 10 putative enhancer sequences, each overlapping with one SNP associated with type 2 diabetes (Supplementary Table 1).

    In Vivo Mosaic Transgenesis Assays

    Zebrafish transgenesis was performed using the Tol2 transposon system (33). One-cell embryos from the Tg(sst:mCherry) zebrafish reporter line were microinjected with 3 nL containing 25 ng/μL Tol2 transposase mRNA and 25 ng/μL phenol/chloroform-purified reporter vector. Injections were performed at least two times. Ins:GFP reporter was built by isolating the insulin promoter from the ins-CFP-NTR vector (SacI and BamHI) (34), cloning it in a pEM-MCS vector (35) and recombining it to a Tol2-based transposon containing a Gateway cassette and a GFP reporter gene.

    Human sequences were PCR amplified from human genomic DNA using specific primers (Supplementary Table 2), cloned in TOPO vector (pCR8/GW/TOPO TA Cloning KIT; Invitrogen) and recombined in vitro to the Z48 transgenesis vector (36) by the Gateway system. Risk SNPs from seq58, seq68, seq73, seq219, and seq460 were inserted by site-directed mutagenesis using specific primers containing the risk variant (Supplementary Table 2). Injected embryos showing expression of GFP in the midbrain were selected for immunohistochemistry at 48 hours post fertilization (hpf).

    sst:mCherry Reporter Line

    To identify the zebrafish endocrine pancreatic domain, we developed an in vivo reporter line that drives expression of mCherry in δ-cells. Primers for the somatostatin (sst) promoter amplification were designed (Supplementary Table 2) and the amplified fragment cloned in a Tol2 transposon containing mCherry as reporter gene. The vector was microinjected in one-cell embryos using the Tol2 transposon system. Embryos were selected for sst:mCherry-positive cells and raised until adulthood and a stable transgenic line was isolated.

    Immunohistochemistry

    The 48-hpf microinjected embryos were dechorionated and fixed in 4% formaldehyde in PBS (PBS1×) overnight at 4°C and then washed in PBS-T (0.5% Triton X-100 in PBS1×) with 1% Triton X-100 in PBS1× (2 h) and 5% BSA in PBS-T (0.1%). Embryos were incubated with anti-Nkx6.1 (1:75) (F55A12; Developmental Studies Hybridoma Bank) and anti-insulin (1:50) (ab210560; Abcam) diluted in 5% BSA-PBS-T followed by washing. Embryos were then incubated with anti-mouse Alexa Fluor 647 (1:800), anti-rabbit Alexa Fluor 647 (1:800) (Thermo Fisher Scientific), and DAPI (1:1,000) (Invitrogen) diluted in 5% BSA-PBS-T. Embryos were washed and stored in 50% glycerol in PBS1×. Microscopy slides were prepared using 50% glycerol in PBS1×. Confocal imaging was performed using a Leica SP5II confocal microscope.

    Assessment of Enhancer Activity

    Embryos were analyzed, using confocal microscopy, for the presence of GFP-positive cells in the endocrine pancreatic domain (sst:mCherry reporter domain) or in the endocrine progenitor domain (anti-Nkx6.1). One embryo was considered positive if at least one GFP-positive cell was detected within the endocrine pancreatic domain or progenitor domain. Quantifications are presented as percentages of positive embryos to ensure the quantification of different transposon integrations.

    Prediction of TFBSs Affected by Type 2 Diabetes Risk Variants

    The wild-type (wt) and risk variant sequences were analyzed using 719 specific position weight matrices for vertebrate TFs using JASPAR software (37). TFs were ranked by position-specific score matrix. The relative score is a threshold score between 0 and 1 and is calculated by (score − minimum score) / (maximum score − minimum score), meaning 1 is the highest affinity and 0 is no affinity of binding (37). TFs that showed differential binding affinity in wt and risk variants were selected and filtered by presence of H3K4me3 at their promoters (6,32).

    ChIP on Plasmid

    Seq119wt and risk were cloned into a pLVX lentiviral backbone (#125839; Addgene) as KpnI-ApaI (Anza) fragments. Lentiviral particles were produced in HEK-293 cells (packaging plasmids psPAX2, #12260, and pCMV-VSV-G, #8454; Addgene) and used to infect MIN6 cells (a gift from Lorenzo Pasquali) according to standard procedures. Infected cells were selected with puromycin (1 μg/mL; Sigma-Aldrich) for 12 days, starting 48 h after infection. Three to 10 million cells were used for ChIP (12) with 4 μg of Nkx6.1 antibody (F5510; Developmental Studies Hybridoma Bank) and magnetic Dynabeads (Thermo Fisher Scientific). Eluted chromatin was purified with a MiniElute Kit (QIAGEN). Immunoprecipitated DNA was dissolved in water and further analyzed by real-time PCR (iTaq Universal SYBR Green Supermix, CFX 384; Bio-Rad).

    Real-time Quantitative Expression Analysis

    MIN6 cells were harvested for RNA extraction with TRIzol (Ambion) and treated with DNase (Thermo Fisher Scientific). Five hundred nanograms to 1 μg of DNA-free RNA was retrotranscribed with iScript cDNA Synthesis Kit (Bio-Rad). cDNA was used for quantitative PCR (iTaq Universal SYBR Green Supermix, CFX 384). Slc30a8 expression was calculated by the ΔCt method to actb housekeeping mRNA.

    4C Sequencing

    The 4C sequencing (4C-seq) was performed on 10 million MIN6 cells using sequential DpnII and Csp6I as previously described (38), with minor modifications. The 4C template was purified using an Amicon Ultra-15 Centrifugal Filter Unit (Millipore). Two libraries were independently prepared with the Expand Long Template PCR System (Roche) using specific primers (Supplementary Table 2). Libraries were purified with QIAquick PCR Purification Kit (QIAGEN) followed by the Agencourt AMPure XP reagent (Beckman Coulter). Libraries were sequenced on an Ion S5 XL System (Ion 540 Chip, Ion Torrent; Thermo Fisher Scientific). Previously described processing (39,40) was used with a custom Perl script. More than 3.5 million reads were aligned to the mouse genome (mm10) using Bowtie2 (default parameters, global alignment mode) (41). Reads within fragments flanked by restriction sites of the same enzyme (checked with bedtools) or fragments <40 base pairs (bp) were filtered out. Mapped reads were then converted to reads-per-first-enzyme-fragment-end units and smoothed using a 30-fragment mean running window algorithm.

    CRISPR Inactivation and CRISPR Activation Targeting

    Twenty-nucleotide single guide RNAs (sgRNAs) targeting the murine Slc30a8 enhancer with high predicted cleavage were selected from the UCSC Genome Browser CRISPR target track and cloned into the lentiviral backbone for enhancer inactivation (CRISPRi) [in Lenti-(BB)-hPGK-KRAB-dCas9-2A-BlastR, #118155; Addgene] or activation (CRISPRa) (in lentiSAMv2, #75112; Addgene) as previously described (42). Lentiviral particles were produced in HEK-293 cells (packaging plasmids: pRSV-rev, #12253; pMDLg/pRRE, #12251; and pMD2G, #12259; Addgene) and used to infect MIN6 cells according to standard procedures. Infected cells were selected by blasticidin (8 μg/mL) (Sigma-Aldrich) for 12 days, starting 48 h after infection.

    Statistical Analysis

    Statistical analyses were performed by using the χ2 test with Fisher correction and unpaired t test, applying a significance level of P ≤ 0.05. For real-time expression experiments, statistical analysis was performed with the Mann-Whitney test.

    Data and Resource Availability

    All data generated or analyzed during this study are included in the published article (and its Supplementary Material), with the exception of 4C-seq sequencing data. The 4C-seq data sets have been deposited in the European Nucleotide Archive at EMBL-EBI under accession number PRJEB39688 (https://www.ebi.ac.uk/ena/browser/view/PRJEB39688). The sst:mCherry reporter line generated during the current study is available from the corresponding author upon reasonable request.

    Results

    Identification of Endocrine Pancreatic Enhancers In Vivo by Mosaic Transgenesis in Zebrafish

    In vivo enhancer reporter assays can be performed in zebrafish either by generating stable transgenic lines, a time-consuming approach, or by mosaic transgenesis (43) on the basis of the analysis of many independent integration events. To test sequences for endocrine pancreatic enhancer activity, we used a Tol2 transposon (44) containing a minimal promoter, a GFP reporter gene, and a midbrain-specific enhancer (Z48) acting as an internal control of transgenesis (36) (Fig. 1A). As an endocrine marker, we developed an in vivo reporter line that drives expression of mCherry in δ-cells (sst:mCherry). To validate sst:mCherry as an endocrine pancreatic reporter, we generated double-positive embryos for the sst:mCherry and insulin (ins:GFP) reporter transgenes (Fig. 1B). At 48 hpf, all GFP-positive cells (ins:GFP) were located within the sst:mCherry expression pattern (Fig. 1C), indicating that the sst:mCherry reporter line can be used to define the endocrine pancreatic domain. Next, to understand whether the mosaic strategy to identify endocrine pancreatic regulatory elements was sensitive enough, we mobilized a Tol2 transposon containing the insulin promoter upstream of GFP. The mosaic analysis of 48-hpf–injected embryos, using confocal microscopy, revealed that 69% (n = 23) showed GFP expression in the endocrine pancreatic domain (Fig. 1D and Supplementary Fig. 1). Random integrations of the Z48 transgenesis vector can generate noise as a result of the influence of regulatory elements located in the genomic landscapes of each integration, termed position effect (43). To test whether the Z48 transgenesis vector was prone to position effect, we mobilized this vector without a sequence to test (negative control [NC]). These injected embryos did not show expression of GFP in the endocrine pancreas (0%, n = 43) at 48 hpf. In total, these results show that the use of mosaic transgenic embryos is sensitive enough to identify endocrine pancreatic regulatory sequences and that the associated noise as a result of the position effect is very low (Fig. 1D and Supplementary Fig. 1). Because endocrine pancreatic enhancers might also be active in pancreatic progenitor cells, we asked whether mosaic transgenesis assays could also be applied for these cell types. To test this hypothesis, we used an anti-Nkx6.1 antibody to define the endocrine pancreatic progenitor domain (Fig. 1E), and we mobilized the Z48 vector containing a known progenitor enhancer from the human SOX9 locus (36). Twenty-seven percent (n = 11) of embryos showed expression of GFP within the pancreatic progenitor domain labeled by Nkx6.1, contrasting with the NC, for which GFP was not detected (0%, n = 23) (Fig. 1F and Supplementary Fig. 1).

    Figure 1
    Figure 1

    A: Schematic representation of the Z48 vector (top) and a representative image of a Z48-injected embryo (bottom) showing GFP expression in the midbrain, mediated by the Z48 enhancer (blue), at 48 hpf. The expression of GFP in the midbrain functions as an internal control of transgenesis. Scale bar = 200 μm. B: Representative images of sst:mCherry (top) and ins:GFP (bottom) reporter lines at 48 hpf, showing mCherry and GFP expression in δ- and β-cells, respectively. Scale bars = 50 μm. C: Confocal images showing the endocrine pancreatic domain (dashed line), defined by the cross of the sst:mCherry and ins:GFP reporter lines. The 48-hpf embryos were counterstained with the nuclear marker DAPI. Scale bars = 10 μm. D: Percentage of embryos showing GFP-positive cells within the endocrine domain when injected with a vector containing GFP as reporter gene under the control of the insulin promoter (ins:GFP) (69%, n = 23) or with the Z48 vector without an endocrine enhancer (NC). E: Confocal images from 48-hpf embryos stained with anti-Nkx6.1 antibody (purple) to define the progenitor pancreatic domain (dashed line) in the sst:mCherry reporter line and the nuclear marker DAPI. Scale bars = 10 μm. F: Graph representing the percentage of embryos with GFP expression in progenitor cells when injected with a pancreatic progenitor enhancer (SOX9_PPE) (27%, n = 11) or the NC (0%, n = 43). *P < 0.05, by χ2 test.

    Identification of Endocrine Pancreatic Enhancers Overlapping With Type 2 Diabetes–Associated SNPs

    We selected 10 sequences (Supplementary Table 1) that overlap with SNPs previously associated with type 2 diabetes that are enriched for enhancer marks (H3K4me1, H3K27ac, and H2A.Z) and for TFBSs of endocrine pancreas TFs (FOXA2, NKX2.2, NKX6.1, MAFB, and PDX1) (6) (Fig. 2A and Supplementary Fig. 2AH). Nine of these sequences are noncoding while one, seq132, partially overlaps with a coding exon of the SLC30A8 gene. The respective sequences that do not contain the type 2 diabetes–associated variant (wt alleles) were cloned in Z48 transgenesis vector, and in vivo enhancer assays were performed by mosaic transgenesis in zebrafish embryos (36). Out of the 10 tested sequences, 6 showed a consistent expression of GFP in the endocrine pancreatic domain, therefore being endocrine pancreatic enhancers (seq58wt, seq68wt, seq73wt, seq132wt, seq219wt, and seq460wt) (Fig. 2B and D and Supplementary Figs. 35). Stable transgenic lines were generated for three of these sequences (seq132wt, seq460wt, and seq58wt) to confirm their endocrine pancreatic enhancer activity (Supplementary Fig. 6). Interestingly, for at least three of the tested sequences (seq58, seq68, and seq73), the GFP signal was also detected adjacent to the endocrine pancreatic domain (Supplementary Fig. 7), suggesting that these sequences may be pancreatic progenitor enhancers. To address this hypothesis, we labeled embryos injected with reporters of seq58, seq68, and seq73 with anti-Nkx6.1 antibody, showing that seq68 and seq73 drive GFP expression in the Nkx6.1-positive progenitor domain (45% [n = 13] and 25% [n = 12], respectively) (Fig. 2C and E), being therefore identified as pancreatic progenitor enhancers. To further characterize the identified enhancers, we determined in which endocrine pancreatic cell types they drive expression. For that, we injected the Z48 transgenesis vector containing the respective enhancers in a gcga:mCherry reporter line (α-cells), counterstaining these embryos with anti-insulin to label β-cells. We found that the majority of tested enhancers were able to drive expression in β-cells and that most of them were able to drive expression in more than one cell type (Supplementary Fig. 8).

    Figure 2
    Figure 2

    A: Genomic landscapes of the putative enhancer seq58. Tracks represent H3K27ac, H3K4me1, histone variant H2A.Z, and TF binding (PDX1, NKX2.2, FOXA2, and NKX6.1) from ChIP-seq data of human endocrine pancreatic samples. Human ZFAND3 is the nearest gene to the putative enhancer (blue). The location of the type 2 diabetes–associated SNP (rs58692659) is represented as a vertical black line. B: In vivo reporter assay for endocrine pancreatic enhancers. Top panels show a representative confocal image a sst:mCherry zebrafish embryo injected with the Z48 enhancer reporter vector containing the seq73wt sequence, showing GFP-positive cells within the endocrine pancreatic domain (dashed line), contrasting that absent in embryos injected with NC (bottom). Scale bars = 10 μm. C: In vivo reporter assay for pancreatic progenitor enhancers. Confocal analysis of seq68 reporter assay shows colocalization of GFP-positive cells with Nkx6.1 progenitor marker, contrasting with NC for the pancreatic progenitor domain (dashed line) that did not show GFP-positive cells. All vectors were injected in the sst:mCherry reporter line and embryos analyzed at 48 hpf and stained with DAPI. Scale bars = 10 μm. D: Graph representing the percentage of embryos with GFP expression in endocrine pancreatic domain at 48 hpf for each sequence analyzed: seq58wt (36%, n = 56), seq68wt (13%, n = 47), seq73wt (28%, n = 47), seq132wt (23%, n = 34), seq219wt (24%, n = 38), seq460wt (27%, n = 36), seq72wt (0%, n = 27), seq117wt (0%, n = 21), seq119wt (4%, n = 27), seq790wt (0%, n = 20), and NC (0%, n = 43). E: Graph representing the number of embryos with GFP expression in pancreatic progenitor domain, defined by Nkx6.1 staining at 48 hpf, for each sequence analyzed: seq58wt (0%, n = 12), seq68wt (46%, n = 13), seq73wt (25%, n = 12), and NC (0%, n = 43). *P < 0.05, by χ2 test.

    SNPs Associated With Increased Risk of Type 2 Diabetes Modulate Enhancer Activity

    To address the possible impact that type 2 diabetes–associated SNPs have in overlapping enhancers, we tested the corresponding variants (risk alleles), performing enhancer assays for endocrine pancreas. Of the six previously identified endocrine pancreatic enhancers, two (seq58risk and seq219risk) showed a decreased enhancer activity for the respective risk allele and two an increase (seq68 and seq132) compared with the wt allele (Fig. 3A and B and Supplementary Figs. 9 and 10). Strikingly, for seq132, the risk allele is in a coding exon of SLC30A8 (seq132risk 56%, n = 36; seq132wt 23%, n = 34) (Fig. 3B). For one sequence, the risk allele was able to drive GFP expression in the endocrine pancreas above the established threshold, while the wt allele did not (seq119risk 14%, n = 28; seq119wt 4%, n = 27) (Fig. 3B). Overall, these results demonstrate that type 2 diabetes–associated SNPs have the potential to modulate the activity of enhancers in a sequence-specific manner.

    Figure 3
    Figure 3

    A: Representative confocal images for seq219wt, seq219risk, and NC. Seq219wt showed GFP expression in endocrine pancreatic domain (dashed line), defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 µm. B: Graph showing the total percentage of positive embryos in wt and risk alleles for each of the 10 sequences analyzed: seq58wt/risk (36%, n = 56; 12%, n = 43, respectively), seq68wt/risk (13%, n = 47; 50%, n = 32), seq73wt/risk (28%, n = 47; 22%, n = 36), seq132wt/risk (23%, n = 34; 56%, n = 36), seq219wt/risk (24%, n = 38; 6%, n = 35), seq119wt/risk (4%, n = 27; 14%, n = 28), seq72wt/risk (0%, n = 27; 6%, n = 30), seq117wt/risk (0%, n = 21; 0%, n = 8), seq460wt/risk (27%, n = 36; 27%, n = 30), and seq790wt/risk (0%, n = 20; 0%, n = 14). Six sequences showed differential enhancer activity between wt and risk allele. *P < 0.05, by χ2 test.

    Differential binding of TFs to wt and risk alleles could explain the observed differential enhancer activity. To test this hypothesis, we generated a stable mouse MIN6 β-cell line containing human wt and risk sequences of the seq119 enhancer. Seq119 risk showed both an increased predicted affinity to Nkx6.1 binding (Supplementary Fig. 11A) and increased enhancer activity (Fig. 3B). Performing ChIP-PCR, we demonstrated that Nkx6.1 binds with higher affinity to the seq119 risk variant (Supplementary Fig. 11B). Additionally, we predicted bioinformatically TFBSs for wt and risk alleles of each sequence (Supplementary Table 3). Sequences were then clustered in two groups: sequences that had shown differential enhancer activity between wt and risk alleles (seq58, seq68, seq119, seq132, and seq219) and sequences that did not (seq72, seq73, seq117, seq460, and seq790). Although both groups showed a similar number of predicted TFBSs in the wt allele, the differential activity enhancers group showed a higher number of predicted differential binding between wt and risk alleles (Supplementary Fig. 11C). These results suggest that differential binding of TFs might control the regulatory output of wt and risk variants.

    The Enhancer Seq132mm Belongs to the Slc30a8 Regulatory Landscape

    Among the detected enhancers, we found that seq132, a sequence that partially overlaps with an exon of SLC30A8, is an endocrine pancreatic enhancer. Additionally, we showed that the type 2 diabetes–associated risk allele (seq132risk), which encodes a tryptophan-to-arginine substitution causing a decrease in the function of SLC30A8 (24), has increased enhancer activity compared with the wt allele (seq132wt). To determine whether seq132 belongs to the regulatory landscape of SLC30A8, we used the MIN6 cell line to detect chromatin interaction points, since enhancers contact the promotor of the genes that they control. First, we performed enhancer assays in zebrafish, demonstrating that the mouse orthologous sequence (seq132mm) of the seq132 human enhancer is also an endocrine pancreas enhancer (Fig. 4A). Then, using 4C-seq (38), we observed the existence of an interaction between the Slc30a8 promoter and seq132mm (Fig. 4B and Supplementary Fig. 12). To further validate that seq132mm belongs to the regulatory landscape of Slc30a8, we targeted seq132mm using the CRISPR/Cas9 system with a dCas9 fused to a transcriptional activation domain (CRISPRa) and another to a repressor domain (CRISPRi), observing a significant increase and decrease of Slc30a8 expression levels, respectively (Fig. 4C). These results strongly suggest that seq132mm belongs to the regulatory landscape of Slc30a8, and because of the remarkable conservation in the activity and sequence of this enhancer, we propose that this regulatory mechanism is conserved in humans.

    Figure 4
    Figure 4

    A: Representative confocal images for the mouse seq132 (seq132mm enhancer chr15:52334298 + 52335281). Seq132mm enhancer showed GFP-positive cells within the endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 μm. Representative graph showing the total percentage of positive embryos for the sequence analyzed (13%, n = 35). *P < 0.05, by χ2 test. B: Genomic landscape of the mouse Slc30a8 gene (blue), showing 4C-seq profiles (black), with view point in Slc30a8 promoter (pink asterisk) in the Min6 cell line; zoom-out (top) and zoom-in (bottom) of the Slc30a8 gene and seq132mm enhancer. The targets line represents the regions where the interaction is significant. C: sgRNAs targeting murine Slc30a8 enhancer in CRISPRa or CRISPR assay in Min6 cells. Slc30a8 expression was calculated relative to the β-actin housekeeping gene by quantitative PCR. Dot blots represent the 10–90% quantile of six biological replicates. *P < 0.05, **P < 0.01. ctrl, control.

    The SLC30A8 Seq132 Enhancer Is Divided Into Different Functional Domains

    Next, we wanted to understand whether seq132 is divided into different functional domains. For that, we divided seq132wt into four fragments (Fig. 5A) and performed enhancer assays for each (Fig. 5B). Fragments seq132wt1 (872 bp) and seq132wt2 (967 bp) showed a milder endocrine pancreatic enhancer activity than the seq132wt total fragment (seq132wt 23%, n = 34; seq132wt1 9.8%, n = 41; seq132wt2 6.5%, n = 31). We also tested another fragment, seq132wt3 (899 bp), that contains seq132wt1 and extends to the end of the coding sequence of SLC30A8. Seq132wt3 was able to drive GFP expression in endocrine pancreatic cells (10%, n = 30), as was the remaining fragment seq132wt4 (788 bp), although with a decreased efficiency (4%, n = 23) (Fig. 5C). From these results, we conclude that seq132 has several functional domains spread through this sequence, and the sum of these parts is necessary for this enhancer to be fully functional. These results also suggest that other SNPs could potentially affect the output of this enhancer. To test this, we have performed enhancer assays with seq132risk, which contains three other common SNPs with no known association to type 2 diabetes (seq132risk#: rs2466296, rs2466295, and rs2466294) (Fig. 6A). Interestingly, seq132risk# was a less active enhancer than seq132risk, showing an activity similar to seq132wt (Fig. 6B and C) and demonstrating that the impact of disease risk alleles in the activity of enhancers might be modulated by other adjacent polymorphisms.

    Figure 5
    Figure 5

    A: Schematic representation of the four analyzed fragments derived from seq132wt: seq132wt1, seq132wt2, seq132wt3, and seq132wt4. The gene SLC30A08 is represented in blue. The wt allele is discriminated in black boxes. B: Representative confocal images for the total sequence, seq132wt, and the four analyzed fragments showing GFP-positive cells in the endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 µm. C: Graph representing the total percentages of positive embryos for seq132wt (23%, n = 34), seq132wt1 (9.8%, n = 41), seq132wt2 (6.5%, n = 31), seq132wt3 (10%, n = 30), and seq132wt4 (4%, n = 23). *P < 0.05, by χ2 test.

    Figure 6
    Figure 6

    A: Schematic representation of the three different analyzed versions of seq132: seq132wt, seq132risk, and seq132risk#. The gene SLC30A08 is represented in blue. The symbol # represents common variants with no association with type 2 diabetes. The wt and risk alleles are represented in black boxes. B: Representative confocal images for the seq132risk#, showing GFP expression in the endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 μm. C: Graph representing the total percentages of positive embryos for the seq132wt (23%, n = 34), seq132risk (56%, n = 36), and seq132risk# (20%, n = 20). *P < 0.05, by χ2 test.

    A Single Nucleotide Mutation Impairs Seq132 Enhancer

    Focusing on seq132, we wanted to further determine whether a single nucleotide mutation could lead to the complete ablation of the activity of this enhancer. Previous results have shown that Pdx1, an important TF required for proper pancreatic function, controls the activity of one endocrine pancreatic enhancer located in the second intron of the mouse Slc30a8 gene (45). On the basis of this, we hypothesized that seq132 could also be controlled by PDX1 binding. After performing TFBS analysis (Supplementary Table 3), we found that within seq132, there is a high score–predicted binding site for PDX1 (JASPAR score 0.9654) (Fig. 7A). To test whether this binding site is required for enhancer activity, we performed transgenesis assays using seq132wt containing a mutation in the predicted binding site of PDX1 (seq132wtPDX1) (Fig. 7B and C). This is an adenine-to-guanine substitution in the PDX1 consensus binding site, resulting in a predicted ablation of the binding of PDX1 (PDX1 predicted binding score: seq132wt 0.9654; seq132wtPDX1 0). Comparing endocrine pancreatic enhancer activity between seq132wt and seq132wtPDX1, we found that the seq132wtPDX1 sequence is unable to drive GFP expression in the endocrine pancreas (0%, n = 20) (Fig. 7D). Because we previously observed that seq132risk had increased endocrine pancreatic enhancer activity, we explored the possibility of the risk SNP rescuing the loss of function observed for the seq132wtPDX1 sequence. We observed that seq132riskPDX1 also showed no endocrine pancreatic enhancer activity (0%, n = 23); thus, the risk SNP is not sufficient to rescue the loss of the PDX1 binding site (Fig. 7D).

    Figure 7
    Figure 7

    A: PDX1 binding site prediction by JASPAR software. The human SLC30A8 is represented in blue. The vertical black line represents the single nucleotide mutation that overlaps with a PDX1 putative binding site. B: Schematic representation of the analyzed sequences: seq132wt, seq132wtPDX1, seq132risk, and seq132riskPDX1. The wt and risk alleles are discriminated in black boxes and the mutation in red boxes. The green boxes represent the binding site for PDX1. C: Representative confocal images for the analyzed sequence containing the risk variant in the absence (top) of the mutation in the putative binding site for PDX1, showing GFP expression in endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The sequence containing the wt allele and the mutation (bottom) was not able to drive GFP expression. The 48-hpf embryos were stained with DAPI. Scale bars = 10 μm. D: Graph representing the total percentages of positive embryos for the different sequences represented in panel B: seq132wt (23%, n = 34), seq132wtPDX1 (0%, n = 21), seq132risk (56%, n = 36), and seq132riskPDX1 (0%, n = 20). *P < 0.05, by χ2 test.

    Discussion

    In this work, we demonstrate the feasibility to perform enhancer assays using mosaic transgenesis in zebrafish. The zebrafish pancreas, as its mammal counterpart, is composed mainly by α-, β-, and δ-cells that secrete the hormones glucagon, insulin, and somatostatin, respectively. The malfunction of these cell types can contribute to type 2 diabetes development. Therefore, enhancer assays evaluating type 2 diabetes–associated alleles should consider all these cell types. In the current work, we used an sst:mCherry reporter construct to determine the zebrafish endocrine pancreatic domain in vivo, making available the inherent cellular complexity of a fully functional pancreas to define the activity of enhancers. In contrast, most of in vitro assays are limited to only one endocrine pancreatic cell type, in many cases not fully functional (46). In vivo assays have, however, their own limitations. This is particularly important when studying SNPs that might modulate the activity of enhancers and, therefore, transcriptional levels rather than binary activation or inactivation states. In this work, we demonstrate that a mosaic transgenesis method is sensitive enough and has low levels of noise, making it possible to assay quantitatively the activity of enhancers. In the current study, variation of enhancer activity comprises at least three parameters predicted to have an impact on the number of embryos with GFP expression in the endocrine pancreas: 1) transcriptional output, affecting the amount of GFP expression per cell and its detection potential defined by the GFP detection threshold; 2) expression domain, defined by the potential of GFP expression in different endocrine pancreatic cell types; and 3) robustness, defined by the stability of GFP expression. Although the present assay does not discriminate these three sources of differential enhancer activity, the introduction of further improvements can offer this possibility. Expression domain can be discriminated if specific endocrine pancreatic cell markers are used, as shown for wt alleles (Supplementary Fig. 7), and differences in transcriptional output can be observed if an internal control is used and GFP expression per cell is quantified.

    Using mosaic transgenesis in zebrafish, we validated 6 out of 10 tested sequences as human endocrine pancreatic enhancers, two of which are also pancreatic progenitor enhancers, underlining the accuracy of enhancers prediction. Although species-specific regulatory outputs cannot be completely excluded, genetic networks and TFs that operate in enhancers are usually highly conserved between distantly related vertebrates as zebrafish and human, making interspecies enhancers assays reliable.

    Exploring the impact of the risk-associated SNPs in enhancer activity, we found that for seq58 and seq219, the risk allele dramatically decreased the enhancer activity while for seq68, seq119, and seq132 the opposite result was observed, suggesting that disease-associated SNPs have the potential to be translated into a loss or gain of function of target genes (47). For one case, seq119, we further demonstrated the increased binding affinity of Nkx6.1 in the risk allele, suggesting that the enhancer activity outputs might be explained by the differential binding of TFs to wt and risk alleles, consistent with previous works (48,49).

    Seq132 overlaps with exon 8 of SLC30A8. Strikingly, the risk allele rs13266634 (4), which results in an amino acid substitution impairing the function of SLC30A8 (22), showed a significantly higher level of enhancer activity than the wt sequence. These results demonstrate that coding SNPs have the potential to modulate the activity of overlapping enhancers, a mechanism poorly explored but compatible with described overlapping exon/enhancer functions (50). SLC30A8 encodes a zinc transporter, an ion essential for insulin maturation and secretion in β-cells (22,23). Therefore, the rs13266634-associated increased risk for type 2 diabetes is commonly attributed to the decrease of the zinc transporter activity. In opposition to this hypothesis, the loss of function of SLC30A8 was shown to enhance insulin secretion (26), and 12 identified truncating SNPs in the SLC30A8 gene have been associated with a protective effect against type 2 diabetes (2,25). This incongruence could be explained if the rs13266634 association with type 2 diabetes results, not from the reduced SLC30A8 zinc transporter activity, but from an increase of its transcription caused by a more active enhancer.

    Further exploring the seq132 enhancer, we observed that the increase of its activity when containing the risk allele can be reverted by the presence of three other common SNPs, highlighting how combinatorial variations in single nucleotides can alter enhancer activity. Furthermore, a mutation that disrupts the predicted binding site of PDX1 in seq132 results in the complete ablation of the enhancer. This suggests that the loss of the binding of PDX1 might coincide with the gain of a transcriptional repressor, since this mutation ablates the activity of nonoverlapping seq132 subfragments that have mild autonomous enhancer activity (Fig. 8). Overall, in this work, we open new avenues on the understanding of the code embedded in noncoding regulatory sequences and show the complexity of effects that one single nucleotide variation might have in the activity of enhancers and its possible impact on human disease.

    Figure 8
    Figure 8

    Theoretical model of the functional domains of seq132. Schematic representation from the three possible sequence versions and the respective associated enhancer activity level: high (A), low (B), and no enhancer activity (C). The gene SLC30A08 is represented in blue. The symbol # represents common variants with no association with type 2 diabetes. The wt and risk alleles are represented in black boxes, the putative repressor in red, and the binding site for PDX1 in green. The dashed line shows interdependence.

    Article Information

    Acknowledgments. The authors thank Lorenzo Pasquali (Josep Carreras Leukaemia Research Institute, Barcelona, Spain) for helpful suggestions and critical reading of the manuscript. The authors acknowledge the contribution of Joana Teixeira (i3S–Instituto de Investigação e Inovação em Saúde, Universidade do Porto, and IBMC–Instituto de Biologia Celular e Molecular, Porto, Portugal) for the ChIP-seq data plotting, Silvia Naranjo (CABD – Centro Andaluz de Biología del Desarrollo, Universidad Pablo de Olavide, Seville, Spain) for the sst:mCherry vector, Ana Maia (i3S–Instituto de Investigação e Inovação em Saúde, Universidade do Porto, and IBMC–Instituto de Biologia Celular e Molecular, Porto, Portugal) for the ins:GFP construct, the support of i3S Scientific Platform Advanced Light Microscopy, members of the national infrastructure Portuguese Platform of BioImaging (supported by POCI010145FEDER022122), and the assistance of the Genomics i3S Scientific Platform (supported by POCI-01-0145-FEDER-022184).

    Funding. This study was supported by the H2020 European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. ERC-2015-StG-680156-ZPR), the Fundação para a Ciência e a Tecnologia (FCT) (IF/00654/2013), and the European Regional Development Fund (Norte-01-0145-FEDER-000029). A.E., M.D., and F.J.F. are PhD fellows from FCT (grants SFRH/BD/147762/2019 to A.E., SFRH/BD/135957/2018 to M.D., and PD/BD/105745/2014 to F.J.F.). J.B. acknowledges FCT for a scientific stimulus grant (CEECIND/03482/2018).

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

    Author Contributions. A.E. carried out the experiments. A.E., M.D., and J.B. wrote the manuscript. A.E. and J.B. conceived, designed, and analyzed the data. C.P. performed the ChIP-seq experiment and CRISPRa and CRISPRi assays. F.J.F. performed the 4C-seq assay. M.D. contributed to the development of the sst:mCherry reporter line. M.G. performed the bioinformatic analysis of the 4C-seq experiment. J.B. designed and supervised the study. All authors revised the manuscript. J.B. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    • Received October 17, 2019.
    • Accepted September 2, 2020.



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    Black Bean and Sausage Stew

    By electricdiet / December 3, 2020





    Black Bean and Sausage Stew – My Bizzy Kitchen

























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    Low Carb Stuffed Mushrooms | Diabetes Strong

    By electricdiet / December 1, 2020


    These low carb stuffed mushrooms are easy to make and packed with melted cheesy goodness! They’re perfect any time you’re looking for a crowd-pleasing appetizer.

    low carb stuffed mushrooms garnished with fresh chives on a baking tray

    I love finding new ways to enjoy vegetables. Oftentimes, it’s also a new way to enjoy cheese!

    These low carb stuffed mushrooms are a great example. Tender baked mushrooms are filled with a creamy mixture of onions, garlic, cream cheese, and mozzarella, then baked to rich and melty perfection.

    Cheese? Check. Vegetables? Check. Easy to throw together in about 10 minutes? Check!

    So the next time you’re looking for an appetizer that will please a crowd, these low carb and meatless stuffed mushrooms are a great option. Who can resist all that melty goodness?

    How to make low carb stuffed mushrooms

    This recipe only takes about 10 minutes to prep and 20 minutes to bake, so you can be ready to serve in about half an hour!

    Ingredients for recipe separated into different ramekins, as seen from above

    Step 1: Preheat your oven to 350 F (180 C).

    Step 2: Remove the thick stems from the mushrooms, then chop the stems roughly.

    Step 3: Place the mushrooms caps into a baking dish.

    Step 4: Heat a large frying pan over high heat. Add the olive oil. Once the oil is hot, add the onions.

    Step 5: Once the onions are translucent (about 5 minutes), add the chopped mushroom stems and cook for another 3 minutes.

    Step 6: Add the garlic and cook until fragrant, about 1 minute.

    Cooked onions and mushroom stems in a frying pan

    Step 7: In a large bowl, mix together the cooked stems and onions, cream cheese, mozzarella, salt, and pepper.

    Cheese mixture combined in a large glass bowl with a spoon

    Step 8: Stuff the mushrooms caps with the mixture.

    Mushroom caps filled with the cheese mixture in the baking tray

    Step 9: Place the mushrooms in the oven and bake for 20 minutes or until the cheese is melted and golden.

    I recommend giving the mushrooms a few minutes to cool. They’re so tempting fresh out of the oven, but the cheese mixture is very hot, and you don’t want to burn your mouth!

    Finally, garnish with fresh chives before serving.

    Overhead view of baked mushrooms stuffed with cheese on a baking tray next to a ramekin of fresh chives

    What kind of mushrooms are best for this recipe?

    I like to use larger brown mushrooms for this recipe. Six of them definitely fill my baking dish!

    So when you’re at the grocery store, look for any good-sized brown mushrooms. You don’t want them to be bite-sized, but you don’t want the biggest portobello mushrooms either. Something right in the middle is perfect!

    Storage

    These mushrooms are best enjoyed immediately. That way, the cheese mixture will be hot and bubbly and the mushrooms will be tender!

    If you do have leftovers, you can store them covered in the refrigerator. I recommend eating within 3-4 days.

    Mushrooms stuffed with cheese and garnished with chives, as seen from above

    Other low-carb meatless recipes

    I love diving into low-carb meals that are packed with healthy veggies! Especially when they involve lots of cheese. If you feel the same way, then here are a few more meatless recipes that I know you’ll enjoy:

    You can also read this roundup I created of 9 Low-Carb Vegan Recipes for even more low-carb plant-based recipe ideas.

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

    Recipe Card

    Low Carb Stuffed Mushrooms

    These low carb stuffed mushrooms are easy to make and packed with melted cheesy goodness! They’re perfect any time you’re looking for a crowd-pleasing appetizer.

    Prep Time:10 minutes

    Cook Time:20 minutes

    Total Time:30 minutes

    Servings:6

    Low carb stuffed mushrooms garnished with chives on a baking tray

    Instructions

    • Preheat your oven to 350 F (180 C).

    • Remove the thick stems from the mushrooms, then chop the stems roughly.

    • Place the mushrooms caps into a baking dish.

    • Heat a large frying pan over high heat. Add the olive oil. Once the oil is hot, add the onions.

    • Once the onions are translucent (about 5 minutes), add the chopped mushroom stems and cook for another 3 minutes.

    • Add the garlic and cook until fragrant, about 1 minute.

    • In a large bowl, mix together the cooked stems and onions, cream cheese, mozzarella, salt, and pepper.

    • Stuff the mushrooms caps with the mixture.

    • Place the mushrooms in the oven and bake for 20 minutes or until the cheese is melted and golden.

    Recipe Notes

    This recipe is for 6 servings of stuffed mushrooms.
    You can use any mid-sized brown mushroom for this recipe.
    These mushrooms are best served immediately. If you have any leftovers, you can store them covered in the refrigerator for 3-4 days.

    Nutrition Info Per Serving

    Nutrition Facts

    Low Carb Stuffed Mushrooms

    Amount Per Serving (1 stuffed mushroom)

    Calories 250
    Calories from Fat 195

    % Daily Value*

    Fat 21.7g33%

    Saturated Fat 11.7g59%

    Trans Fat 0g

    Polyunsaturated Fat 1g

    Monounsaturated Fat 5.4g

    Cholesterol 61.3mg20%

    Sodium 411.3mg17%

    Potassium 66mg2%

    Carbohydrates 5.2g2%

    Fiber 1g4%

    Sugar 3.1g3%

    Protein 8.8g18%

    Net carbs 4.2g

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

    Course: Appetizer, Side Dish

    Cuisine: American

    Keyword: low carb, Mushroom



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    Shrimp Remoulade Sauce – How To Make Healthy and Simple Remoulade Recipe

    By electricdiet / November 29, 2020


    Shrimp Remoulade Sauce is an all-time favorite!  However, if you order it out at restaurants it is usually high in fat with lots of mayo. To satisfy your cravings, Holly created a simple remoulade sauce recipe that’s healthy.  This Shrimp Remoulade recipe in Guy’s Guide To Eating Well cookbook is actually from the Obesity & Diabetes Chapter!  How about that! A simple remoulade sauce that you can enjoy eating without any guilt! So, this fantastic shrimp remoulade salad you can whip up in seconds and you’ll have a satisfying and impressive wonderful meal.

    simple remoulade sauce

    Shrimp Remoulade Sauce
    Show off your culinary skills with this dimple remoulade sauce recipe.  Serve on a bed of lettuce for a light lunch or fabulous first course. A healthy diabetic remoulade sauce that’s simple and also gluten free!

      Servings8 (1/4 cup) servings

      Ingredients

      • 1pound


        medium peeled shrimpseasoned and cooked

      • 2tablespoons


        light mayonnaise

      • 2tablespoons


        Creole or grainy mustard

      • 1tablespoon


        ketchup

      • 1tablespoon


        lemon juice



      • Dash hot sauce

      • 1/4cup


        chopped green onions

      • 2tablespoons


        finely chopped red onion

      • 2tablespoons


        chopped fresh parsley

      Instructions
      1. Place shrimp in bowl.  In another small bowl, mix together the remaining ingredients and toss with shrimp. Refrigerate until serving.

      Recipe Notes

      Per Serving:  Calories 74, Calories from fat (%) 22, Fat (g) 2, Saturated Fat (g) 0, Cholesterol (mg) 112, Sodium (mg) 243, Carbohydrate (g) 1, Dietary Fiber (g) 0, Sugars (g) 1, Protein (g) 12, Diabetic Exchanges: 2 very lean meat

      Guy’s Guide To Eating Well Includes Obesity & Diabetes Chapter!

      You might be surprised to find this Louisiana Shrimp Remoulade recipe in this man’s cookbook plus in the Obesity & Diabetes Chapter!  First, you won’t find an easier remoulade sauce recipe so anyone can whip it up.  This simple remoulade sauce is made with everyday ingredients that you probably already have at home. This book is about simplicity and good food! You’ll find a “D” to highlight diabetic recipes like this simple remoulade sauce recipe. It is easy to eat healthy with Holly Clegg’s recipes!

      You’ll find lots of your favorite easy Cajun recipes and southern recipes throughout the book. Cook for your man or get the man in the kitchen for preventive health with these healthy easy recipes!

      Don’t Turn Up Your Nose To Entree Salads When You Taste This Shrimp Remoulade Salad

      shrimp remoulade salad with simple remoulade recipe

      You can use the shrimp remoulade sauce as a dip and serve the shrimp around it. Or, you can serve in martini glasses for an appetizer. Holly’s favorite way to enjoy this recipe is a Shrimp Remoulade salad. Add all your favorite fresh salad ingredients to enjoy this wonderful, delicious sauce for the ultimate Shrimp Remoulade Salad that will fill you up and not out! Super-satisfying and simple to create and include whatever fresh vegetables you like.

      Get Creative With Shrimp Remoulade Recipe

      easy shrimp remoulade sauce recipe for crawfish remoulade

      Try Holly’s Easy Blackened Fish Recipe with 6 Ingredient Blackened Seasoning and serve it with Shrimp remoulade sauce. Talk about a great combination! Spicy blackened shrimp complements the light tasty remoulade sauce.

      Also, who doesn’t like Louisiana crawfish recipes? Holly created a Crawfish Remoulade. Perfect for leftover crawfish tails during crawfish season. An easy and fabulous pairing!

      Get Fabulous Easily With Simple Remoulade Sauce In Unbreakable Martini Glasses

      How fun is it to use martini glasses to serve a shrimp remoulade salad for fun appetizers!?! Keep it casual with unbreakable, good quality martini glasses. Fill the glasses with mixed greens and top with this Shrimp Remoulade recipe or turn into Crawfish Remoulade for the EASIEST but most impressive appetizer!

      Of course, these are dishwasher safe.  Start collecting and make your gathering a smashing success!

      12 Ideas For Christmas Foodies: Buy Now

      Christmas recipes

       

      Team Holly is excited to share with you Holly Clegg’s 12 Ideas for Christmas Foodies. From evening appetizers to teacher gifts, even – what to cook Christmas morning, these festive favorite recipes will be your go-to dishes that will get you through all of the parties and last-minute family get-togethers this December.  No need to stress with what to make this holiday season! Your Holiday Needs 12 Ideas For Christmas Foodies Downloadable Only $1.99!

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      The post Shrimp Remoulade Sauce – How To Make Healthy and Simple Remoulade Recipe appeared first on The Healthy Cooking Blog.



      Sell Unused Diabetic Strips Today!

      Generation and Characterization of a Novel Mouse Model That Allows Spatiotemporal Quantification of Pancreatic β-Cell Proliferation

      By electricdiet / November 27, 2020


      Abstract

      Pancreatic β-cell proliferation has been gaining much attention as a therapeutic target for the prevention and treatment of diabetes. In order to evaluate potential β-cell mitogens, accurate and reliable methods for the detection and quantification of the β-cell proliferation rate are indispensable. In this study, we developed a novel tool that specifically labels replicating β-cells as mVenus+ cells by using RIP-Cre; R26Fucci2aR mice expressing the fluorescent ubiquitination-based cell cycle indicator Fucci2a in β-cells. In response to β-cell proliferation stimuli, such as insulin receptor antagonist S961 and diet-induced obesity (DIO), the number of 5-ethynyl-2′-deoxyuridine-positive insulin+ cells per insulin+ cells and the number of mVenus+ cells per mCherry+ mVenus cells + mCherry mVenus+ cells were similarly increased in these mice. Three-dimensional imaging of optically cleared pancreas tissue from these mice enabled quantification of replicating β-cells in the islets and morphometric analysis of the islets after known mitogenic interventions such as S961, DIO, pregnancy, and partial pancreatectomy. Thus, this novel mouse line is a powerful tool for spatiotemporal analysis and quantification of β-cell proliferation in response to mitogenic stimulation.

      Introduction

      Diabetes is caused by β-cell dysfunction as well as increased insulin resistance. Stimulation of β-cell proliferation is therefore a promising strategy for the prevention and treatment of diabetes. In an attempt to evaluate potential β-cell mitogens, accurate and reliable methods for the detection and quantification of β-cell proliferation are indispensable. So far, determination of the β-cell proliferation rate has relied on immunohistochemical detection of cell cycle markers such as nucleotide analogs (BrdU and 5-ethynyl-2′-deoxyuridine [EdU]) or replication proteins (proliferating cell nuclear antigen and Ki-67). However, the β-cell proliferation rates obtained by immunohistochemical analysis are not always accurate and reproducible (1,2), and methodological differences in immunolabeling and image acquisition techniques can cause interlaboratory variability of results (2). In addition, three-dimensional (3D) analysis of whole islets has not been possible, and replicating non–β-cells overlying quiescent β-cells within islets can confound results. Furthermore, the sampling size of β-cells is sometimes inadequate because the data are acquired from a certain number of pancreatic sections per condition. Thus, a new method for quantifying replicating β-cells that compensates for these limitations is required.

      The fluorescent ubiquitination-based cell cycle indicator (Fucci) reporter is a well- established probe for monitoring cell cycle status (3). The Fucci system relies on the expression of a pair of fluorescent proteins: mCherry-hCdt1 (30/120) (a fragment with degradation sequence [degron] of chromatin licensing and DNA replication factor [Cdt]1 fused to a fluorescent protein in the red spectrum) and mVenus-hGem (1/110) (a degron of Geminin fused to a fluorescent protein in the green spectrum). Reciprocal expression of these paired proteins labels cells in the G1 phase and those in the S/G2/M phase with red and green fluorescence, respectively. Thus, the Fucci system can be used to visualize the G1/S transition and thus quantify replicating β-cells.

      In this study, we generated and characterized a mouse line in which the Fucci probe is expressed in β-cells to monitor their cell cycle phase. Using this model, we evaluated β-cell proliferation induced by administration of the insulin receptor antagonist S961, a reported β-cell mitogen (4), diet-induced obesity (DIO) (5), pregnancy (6,7) and partial pancreatectomy (PPTX) (8). In addition, we performed 3D analyses of whole islets by observing optically cleared pancreata of these mice and found a strong and significant correlation between islet size and the number of replicating β-cells per islet. These results demonstrate the usefulness of this mouse model for the study of β-cell proliferation.

      Research Design and Methods

      Animals

      To establish the mouse model for studying β-cell proliferation, we used R26Fucci2aR mice in which a single copy of the Fucci2a transgene under the control of the cytomegalovirus early enhancer/chicken β-actin promoter was inserted into the Rosa26 locus by homologous recombination (RIKEN BRC06511) (9). This newer Fucci2a reporter is a bicistronic Cre-inducible probe consisting of two fluorescent proteins: truncated Cdt1 fused to mCherry and truncated Geminin fused to mVenus. The two fusion proteins are always alternately expressed according to the cell cycle phase in the same ratio, making it possible to detect and quantify the number of labeled cells. By crossing rat insulin promoter (RIP)-Cre mice (mixed C57BL/6 and CBA/J background) (10) and R26Fucci2aR mice (mixed C57BL/6 and 129 background), we generated RIP-Cre; R26Fucci2aR mice expressing the Fucci2a reporter in a β-cell–specific manner. In these mice, mCherry-hCdt1 (red fluorescence) and mVenus-hGem (green fluorescence) are expressed in β-cell nuclei during the G0/G1 and S/G2/M phase, respectively. The mice had free access to standard rodent chow and water and were housed in a temperature-controlled environment under a 14:10-h light/dark cycle. Animal care and protocols were reviewed and approved by the Kyoto University Graduate School of Medicine Animal Care and Use Committee (MedKyo15298), Kyoto, Japan.

      Animal Experiments

      S961 was obtained from Novo Nordisk (Bagsværd, Denmark). Vehicle (PBS) or 10 nmol S961 was loaded into an osmotic pump (Alzet 2001; DURECT Corp., Cupertino, CA) subcutaneously implanted into the back of RIP-Cre; R26Fucci2aR mice at 8 weeks of age. Mice were euthanized, and the pancreata were harvested 7 days after S961 or vehicle treatment. Blood glucose levels were measured daily. Plasma was collected on days 0 and 7 to measure insulin level. For a model of DIO, 6-week-old RIP-Cre; R26Fucci2aR mice were fed a high-fat diet (HFD; fat content, 60 kcal%) (cat. no. D12492; Research Diets) or a control diet (cat. no. D12450J; Research Diets) for 13 weeks, and body weight was measured weekly. For pregnancy studies, 8-week-old RIP-Cre; R26Fucci2aR mice were interbred, and the pancreas was harvested at 14.5 days of gestation. A 50% PPTX was performed in 8-week-old RIP-Cre; R26Fucci2aR mice. The mice were anesthetized with isoflurane. The splenic portion of the pancreas was removed by gentle abrasion with cotton applicators and by partially breaking the mesenteric connections to the stomach, small bowel, and retroperitoneum. Mice in the sham group underwent laparotomy, and the pancreas was left intact. For the EdU-labeling assay, mice were intraperitoneally injected with EdU (50 mg/kg) 6 h before sacrifice. For the oral glucose tolerance test, mice were fasted for 16 h and then orally administered a 20% glucose solution (2 g/kg body weight). Blood samples were collected from the tail vein of mice 0, 15, and 30 min after glucose loading using heparinized calibrated glass capillary tubes (cat. no. 2-000-044-H; Drummond Scientific Co., Broomall, PA). Blood glucose level was measured using the Glutest Neo Sensor (Sanwa Kagaku Kenkyusho, Nagoya, Japan). Plasma samples were prepared by centrifuging the blood samples at 9,000g for 10 min, and the insulin level was measured using the Ultra Sensitive PLUS Mouse Insulin ELISA kit (cat. no. 49170-53; Morinaga, Tokyo, Japan).

      Immunohistochemical Analysis of Pancreas

      Mice were anesthetized by an intraperitoneal injection of pentobarbital sodium (10 mg/kg), a 26-gauge needle was inserted into the left ventricle through the apex, and the mice were transcardially perfused with ice-cold PBS, followed by ice-cold 4% paraformaldehyde (Wako Pure Chemical Industries, Osaka, Japan). The harvested pancreas was immediately immersed in paraformaldehyde 4°C with gentle shaking for <24 h and then embedded in optimal cutting temperature compound. Frozen samples were cut into 8-µm sections. After air drying, the frozen sections were incubated with blocking buffer composed of PBS with 10% (v/v) goat serum and 0.2% (v/v) Triton-X100 for 30 min at room temperature. The sections were then incubated overnight at room temperature in blocking buffer supplemented with rabbit anti-insulin (200-fold dilution) (cat. no. ab181547), mouse anti-glucagon (2000-fold dilution) (cat. no. ab10988), rat anti-somatostatin (100-fold dilution) (cat. no. ab30788), or rabbit anti-Nkx 6.1 (100-fold dilution) (cat. no. ab221549) antibody (all from Abcam, Cambridge, MA), followed by Alexa Fluor 647-conjugated goat anti-rabbit IgG (H+L) (200-fold dilution) (cat. no. A-21245; Thermo Fisher Scientific, Waltham, MA), Alexa Fluor 647-conjugated goat anti-mouse IgG (H+L) (200-fold dilution) (cat. no. ab150115; Abcam), or Alexa Fluor 647-conjugated goat anti-rat IgG (H+L) (200-fold dilution) (cat. no. ab150159; Abcam) for 1 h at room temperature. The sections were incubated in PBS containing DAPI (final concentration: 0.01 mg/mL) for 15 min at room temperature and mounted with Vectashield (Vector Laboratories, Burlingame, CA) on 24- × 40-mm coverslips (cat. no. C024401; Matsunami Glass, Osaka, Japan). Immunolabeled tissue sections were observed with an inverted fluorescence microscope (BZ-X710; Keyence, Osaka, Japan). The Click-iT EdU Cell Proliferation Kit for Imaging (cat. no. C10340) and the Click-iT Plus TUNEL Assay for In Situ Apoptosis Detection (cat. no. C10619) were obtained from Thermo Fisher Scientific and used according to the manufacturer’s protocol.

      Tissue Clearing and 3D Imaging of Pancreas

      Pancreas tissue samples were collected and fixed as described above and washed three times for more than 2 h each time in PBS at room temperature with gentle shaking. CUBIC reagents were obtained from Tokyo Chemical Industry Co., Ltd (cat. nos. T3740 and T3741). For delipidation and permeabilization, the samples were immersed in 50% (v/v) CUBIC-L clearing reagent for at least 6 h, followed by 100% (v/v) CUBIC-L at 37°C with gentle shaking for 3 days. The CUBIC-L reagent was refreshed daily during this period. After clearing, samples were immersed in 50% (v/v) CUBIC-R for at least 6 h and in 100% (v/v) CUBIC-R at room temperature with gentle shaking for at least 2 days. 3D images of optically cleared pancreas tissue were acquired with a light-sheet microscope (Lightsheet Z.1; Carl Zeiss, Oberkochen, Germany) equipped with a 5×/0.16 numerical aperture objective lens. For mCherry-hCdt1 imaging, 22% laser power (561-nm laser) and a 28-ms exposure time were used. For mVenus-hGem imaging, 90% laser power (488-nm laser) and a 70-ms exposure time were used. The z-stack images (1,920 × 1,920 pixel, 16-bit) were acquired at 4.63-µm intervals.

      Image Processing

      Acquired images were analyzed with the 3D reconstruction software Imaris (Bitplane AG, Zurich, Switzerland). A whole series of consecutive two-dimensional cross-sectional images was reconstructed into a 3D structure using the “Volume rendering” function. Each islet was then isolated using the “Crop 3D function,” and a Gaussian filter was applied for background noise reduction. The Imaris “Spot” algorithm was used to identify β-cells from mCherry or mVenus signals with “Background subtraction.” The estimated detection diameter was set to 10 μm, and the “Quality” filter with the threshold value adjusted automatically (for mCherry) and manually (for mVenus) was applied. The diameter of the β-cell clusters (i.e., mCherry-signal cluster) was calculated by the Imaris “Surface” function, rendering the mCherry-signal clusters into 3D objects and measuring their maximum diameter automatically.

      Intravital Imaging

      After S961 treatment for 40 h, RIP-Cre; Fucci2aR mice were anesthetized by 1.5–2% isoflurane (Wako Pure Chemical Industries) inhalation. The hair on the abdominal area was removed and the skin disinfected with 70% ethanol. A small oblique incision running parallel to the last left rib was made to expose the pancreas on the left side of the abdominal wall. The mice were then placed supine on an electric heating pad maintained at 37°C. The pancreas was immobilized using a suction imaging device (Supplementary Fig. 2), and time-lapse imaging was performed with a two-photon excitation microscope (FV1200MPE-BX61WI; Olympus, Tokyo, Japan) equipped with a 25×/1.05 numerical aperture water-immersion objective lens (XLPLN 25XWMP; Olympus) and an In-Sight DeepSee Ultrafast laser (Spectra Physics, Santa Clara, CA). Images were acquired every 5 min for ∼10 h in 5-µm steps at a scan speed of 20 µs/pixel. Mice were euthanized after imaging.

      Quantification and Statistical Analysis

      The Mann-Whitney U test was performed to evaluate the difference between two sets of data. P values <0.05 were considered statistically significant. No statistical method was used to predetermine sample size. Statistical analyses were performed using GraphPad Prism (GraphPad Software, La Jolla, CA).

      Data and Resource Availability

      The data sets generated during the current study are available from the corresponding author upon reasonable request. No applicable resources were generated or analyzed during the current study.

      Results

      Generation and Characterization of RIP-Cre; R26Fucci2aR Mice

      To distinguish β-cells in the G0/G1 phase from those in S/G2/M phase, we used Fucci technology, which is a proven tool for detecting actively proliferating cells (3). The R26Fucci2aR mouse line harboring the Fucci2a reporter was recently generated, in which Cre/loxP-mediated conditional expression of the Fucci2a transgene at the Rosa26 locus is driven by the cytomegalovirus early enhancer/chicken β-actin promoter (9). By crossing rat RIP-Cre and R26Fucci2aR mice, we generated the RIP-Cre; R26Fucci2aR line, in which the Fucci2a probe is specifically expressed in β-cells (Fig. 1A). RIP-Cre; R26Fucci2aR mice showed similar body weight and random-fed blood glucose levels compared with R26Fucci2aR littermates (Fig. 1B and C), and there was no significant difference in blood glucose and insulin levels during the oral glucose tolerance test (2 g/kg) between them (Fig. 1D and E), indicating that RIP-Cre; R26Fucci2aR mice have normal glucose tolerance and insulin secretion.

      Figure 1
      Figure 1

      Genotype and metabolic phenotype of RIP-Cre; Fucci2aR mice. A: Breeding scheme for the generation of RIP-Cre; Fucci2aR mice. RIP-Cre and Fucci2aR mouse lines were crossed to obtain RIP-Cre; Fucci2aR mice. After Cre-mediated recombination, the Fucci2a transgene was expressed specifically in β-cells. Body weight (B) and random-fed blood glucose levels of RIP-Cre (+/−); Fucci2aR (+/+) (n = 7) and control littermates (RIP-Cre (−/−); Fucci2aR (+/+) (n = 7) mice during postnatal growth (C). D and E: Oral glucose tolerance test (2 g/kg body wt) performed on RIP-Cre; Fucci2aR (n = 7) and control littermates (n = 7) mice at 8 weeks. Data are expressed as mean ± SEM. *P < 0.05 (Mann-Whitney U test).

      We then investigated the expression pattern of the Fucci2a probe in RIP-Cre; R26Fucci2aR mice. To characterize not only mCherry+ but also mVenus+ cells, we induced β-cell proliferation in RIP-Cre; R26Fucci2aR mice by continuous infusion of the vehicle PBS or insulin receptor antagonist S961 for 7 days using an osmotic pump. At the end of the treatment, frozen sections were prepared from the dissected pancreas and immunostained for insulin, glucagon, somatostatin, and Nkx-6.1, and the fluorescent signals of the Fucci2a probe were directly observed. In both vehicle and S961-treated RIP-Cre; R26Fucci2aR mice, mCherry and mVenus were expressed specifically in insulin+ and Nkx 6.1+ cells (Fig. 2A and B and Supplementary Fig. 1A and B), but not in glucagon+ or somatostatin+ cells (Fig. 2C and D and Supplementary Fig. 1C and D). To ensure that replicating β-cells could be quantified, we compared the results of the EdU assay and the β-cell proliferation assay performed using RIP-Cre; R26Fucci2aR mice treated by S961. The fluorescence images demonstrated that only mCherry mVenus+ cells were labeled by EdU, but not mCherry+ mVenus+ cells (Fig. 2E and Supplementary Fig. 1E). MetaMorph software (Molecular Devices) was used to count mVenus+ cells (Fig. 3A) or EdU+ insulin+ DAPI+ cells (Fig. 3B) in frozen sections. We confirmed that the numbers of EdU+ insulin+ cells per insulin+ cells and the numbers of mVenus+ cells per mCherry+ mVenus cells + mCherry mVenus+ cells in the S961-treated group were similarly higher than those in control group. We also evaluated β-cell proliferation in RIP-Cre; R26Fucci2aR mice in response to DIO and confirmed that the numbers of EdU+ insulin+ cells per insulin+ cells and the numbers of mVenus+ cells per mCherry+ mVenus cells + mCherry mVenus+ cells in DIO group were similarly higher than those in control group (Fig. 3C and D).

      Figure 2
      Figure 2

      β-Cell–specific expression of Fucci2a in RIP-Cre; R26Fucci2aR mice. AE: Frozen sections of pancreas tissues from RIP-Cre; R26Fucci2aR mice treated with S961 or vehicle at 8 weeks of age immunostained for islet hormones, Nkx 6.1, and EdU (original magnification ×40). Representative fluorescence images of mCherry+ (red) and mVenus+ (yellow, arrowheads) cells and immunofluorescence for islet hormones (green): insulin (A), glucagon (C), and somatostatin (D). While 269–280 islets from eight pancreata (four vehicle-treated mice and four S961-treated mice) were analyzed, no glucagon+ areas and somatostatin+ areas were merged with mCherry+ and/or mVenus+ cells. B: All mCherry+ (red) and mVenus+ (green, arrowheads) cells were Nkx 6.1+ (yellow). Nuclei were stained with DAPI (blue). E: Frozen sections were labeled by EdU. Note that only mCherry mVenus+ cells (green, arrowhead) were labeled by EdU (yellow). Scale bars, 100 µm.

      Figure 3
      Figure 3

      Comparison of replicating β-cell quantification by Fucci2a probes with EdU assay. A: Quantification of mVenus+ cells in the islets of RIP-Cre; R26Fucci2aR mice treated with PBS (vehicle; n = 4) or S961 (10 nmol/week; n = 4). B: Quantification of EdU+ insulin+ cells in islets of RIP-Cre; R26Fucci2aR mice treated with PBS (vehicle; n = 4) or S961 (10 nmol/week; n = 4). C: Quantification of mVenus+ cells in the islets of RIP-Cre; R26Fucci2aR mice fed the control diet (n = 4) or HFD (n = 4). D: Quantification of EdU+ insulin+ cells in islets of RIP-Cre; R26Fucci2aR mice fed the control diet (n = 4) or HFD (n = 4). In vehicle- and S961-treated group, 1,386–8,368 insulin+ cells and 1,291–2,837 mCherry+ mVenus cells + mCherry mVenus+ cells were counted per mouse. In the control and HFD groups, 2,411–6,976 insulin+ cells and 1,922–4,919 mCherry+ mVenus cells + mCherry mVenus+ cells were counted per mouse. Data are expressed as mean ± SEM. *P < 0.05.

      3D Imaging of Islets in RIP-Cre; R26Fucci2aR Mice

      Because each islet is densely packed with various cell types, replicating β-cells can be misidentified in histological sections labeled for insulin and replication markers. In order to detect and quantify replicating β-cells in 3D in whole islets of RIP-Cre; R26Fucci2aR mice, CUBIC clearing reagent (11) was applied to pancreatic tissue samples from vehicle- or S961-treated RIP-Cre; R26Fucci2aR mice, and 3D images of the optically cleared tissue were obtained with a light-sheet microscope equipped with a 5× objective lens. The spatial distributions of mVenus+ and mCherry+ cells were simultaneously visualized (Fig. 4AF and Supplementary Video 1). Islets contained more mVenus+ cells after S961 treatment (Fig. 4C and D). Spot objects corresponding to mVenus+ or mCherry+ cells were reconstructed using Imaris Spot Detection and quantified by an automated process to determine the number of replicating β-cells in each islet (Fig. 4G and H). The Imaris Surface tool was used to measure the diameter of β-cell clusters in each islet. Thus, RIP-Cre; R26Fucci2aR mice are amenable to cross-sectional analyses of the number and spatial distribution of proliferating β-cells.

      Figure 4
      Figure 4

      Representative 3D imaging of islets in vehicle- and S961-treated RIP-Cre; R26Fucci2aR mice after treatment for 1 week. Representative fluorescence images of mCherry+ (red) (A and B) and mVenus+ (green) cells (C and D), and overlay (E and F). G and H: Morphological 3D reconstruction of mCherry+ (red) and mVenus+ (green) cells for automated cell counting. Images were obtained with a light-sheet microscope. Scale bars, 50 µm.

      Given the utility of the Fucci2a probe for real-time monitoring of the cell cycle, we performed real-time in vivo imaging in S961-treated RIP-Cre; R26Fucci2aR mice using a two-photon microscope equipped with a 25× water objective lens. This intravital imaging of an islet in a RIP-Cre; R26Fucci2aR mouse initiated 40 h after S961 treatment revealed the S-phase progression of β-cells (Supplementary Fig. 2 and Supplementary Video 2). We also detected one mCherry+ mVenus+ cell for >8 h (Supplementary Video 3).

      The Number of Replicating β-Cells per Islet Is Positively Correlated With Islet Size

      The relationship between the number of replicating β-cells per islet and the morphological characteristics of the islets is unclear. We addressed this issue by analyzing 3D images obtained from RIP-Cre; R26Fucci2aR mice using the 3D reconstruction software Imaris. Blood glucose and insulin levels were higher in mice treated with S961 than in those treated with vehicle (Fig. 5A and B). When we examined all islets with β-cell cluster diameter >100 µm, the β-cell cluster diameter (i.e., mCherry-signal cluster diameter) and β-cell number per islet (i.e., the number of mCherry signals and mVenus signals per islet) were greater in S961-treated RIP-Cre; R26Fucci2aR mice (Fig. 5CE). In addition, the proportion of mVenus+ cells per islet was higher in S961-treated mice compared with that in control mice (Fig. 5F). Moreover, the mVenus+ cell number per islet was positively correlated with the β-cell number per islet in both vehicle-treated (Fig. 5G) (r = 0.77, P < 0.0001) and S961-treated (Fig. 5G) (r = 0.87, P < 0.0001) mice. As an exploratory research, we also tested whether S961-induced β-cell proliferation is due to hyperglycemia using a sodium–glucose cotransporter 2 inhibitor that enhances urinary glucose excretion and normalizes hyperglycemia-associated S961 administration. While it has been shown that hyperglycemia induces β-cell proliferation (12,13), near-normalization of hyperglycemia associated with S961 administration by sodium–glucose cotransporter 2 inhibitor treatment increased the β-cell number per islet (Supplementary Fig. 3). These results suggest that hyperglycemia itself has limited effects on S961-induced β-cell proliferation.

      Figure 5
      Figure 5

      Quantification of replicating β-cells in RIP-Cre; R26Fucci2aR mice after S961 treatment. A and B: RIP-Cre; R26Fucci2aR mice were treated with S961 (10 nmol/week) (n = 4) or PBS (vehicle; n = 4) for 7 days. Random-fed blood glucose (A) and serum insulin (B) levels at the end of the 7-day PBS and S961 treatment. CG: Morphometric analysis was performed on islets harboring β-cell clusters with diameter >100 µm (S961, 454 islets from four mice; and vehicle, 348 islets from four mice). C: Histogram of β-cell cluster diameter. D: β-Cell cluster diameter. E: Number of β-cells per islet. F: Percentage of mVenus+ cells per islet. G: Correlation between number of mVenus+ cells and number of β-cells per islet. mVenus+ cell number and β-cell number per islet was strongly correlated in both groups (S961, r = 0.87, P < 0.0001; vehicle, r = 0.77, P < 0.0001). Data are presented as mean ± SEM. *P < 0.05 and ****P < 0.0001.

      We then investigated whether this positive correlation also exists under other physiological conditions such as DIO, pregnancy, and PPTX. The RIP-Cre; R26Fucci2aR mice were divided into two groups: one fed the HFD and the other fed the control diet for 13 weeks. The HFD group gained significantly more body weight than control diet group during the 13-week period (Fig. 6A). Compared with the control diet group, the HFD group showed larger β-cell cluster diameter (Fig. 6B and C), β-cell number per islet (Fig. 6D), and proportion of mVenus+ cells per islet (Fig. 6E). Although the pregnant group showed no significant difference in β-cell number per islet (Fig. 7C) compared with the virgin group, its β-cell cluster diameter (Fig. 7A and B) was larger, and its proportion of mVenus+ cells per islet (Fig. 7D) was higher. Finally, the positive correlation between the mVenus+ cell number per islet and the β-cell number per islet was also found in the HFD group (Fig. 6F) (r = 0.81, P < 0.0001) and in the pregnant group (Fig. 7E) (r = 0.90, P < 0.0001). Regarding PPTX, although β-cell cluster diameter in the PPTX group was as large as that in sham group 2 days after the operation (Fig. 8A and B), the PPTX group showed fewer β-cell numbers per islet (Fig. 8C). However, the number of mVenus+ cells per islet was significantly higher (Fig. 8D) and was positively correlated with the β-cell number per islet in the PPTX group (Fig. 8E) (r = 0.49, P < 0.0001). These data indicate that islets with a larger population of β-cells have more replicating β-cells.

      Figure 6
      Figure 6

      Quantification of replicating β-cells in RIP-Cre; R26Fucci2aR mice fed the control diet or the HFD. A: Body weight of RIP-Cre; R26Fucci2aR mice fed the HFD (n = 7) or control diet (n = 7) for 13 weeks. BF: Morphometric analysis was performed on islets harboring β-cell clusters with diameter >100 µm (HFD, 407 islets from four mice; control, 432 islets from four mice). B: Histogram of β-cell cluster diameter. C: β-Cell cluster diameter. D: Number of β-cells per islet. E: Percentage of mVenus+ cells per islet. F: Correlation between number of mVenus+ cells and number of β-cells per islet. mVenus+ cell number and β-cell number per islet were strongly correlated in both groups (HFD, r = 0.81, P < 0.0001; control diet, r = 0.60, P < 0.0001). Data are presented as mean ± SEM. ****P < 0.0001.

      Figure 7
      Figure 7

      Quantification of replicating β-cells in pregnant (gestational days 14.5 of pregnancy) or virgin RIP-Cre; R26Fucci2aR mice. AE: Morphometric analysis was performed on islets harboring β-cell clusters with diameter >100 µm (pregnant, 290 islets from four mice; virgin, 294 islets from four mice). A: Histogram of β-cell cluster diameter. B: β-Cell cluster diameter. C: Number of β-cells per islet. D: Percentage of mVenus+ cells per islet. E: Correlation between number of mVenus+ cells and number of β-cells per islet. mVenus+ cell number and β-cell number per islet were strongly correlated in both groups (pregnant, r = 0.90, P < 0.0001; virgin, r = 0.64, P < 0.0001). Data are presented as mean ± SEM. *P < 0.05 and ****P < 0.0001.

      Figure 8
      Figure 8

      Quantification of replicating β-cells in RIP-Cre; R26Fucci2aR mice 2 days after 50% PPTX or sham operation. AE: Morphometric analysis was performed on islets harboring β-cell clusters with diameter >100 µm (PPTX, 226 islets from four mice; sham, 191 islets from four mice). A: Histogram of β-cell cluster diameter. B: β-Cell cluster diameter. C: Number of β-cells per islet. D: Percentage of mVenus+ cells per islet. E: Correlation between number of mVenus+ cells and number of β-cells per islet. mVenus+ cell number and β-cell number per islet were strongly correlated in both groups (PPTX, r = 0.49, P < 0.0001; sham, r = 0.33, P < 0.0001). Data are presented as mean ± SEM. **P < 0.01 and ****P < 0.0001.

      Discussion

      In the current study, we generated and characterized a novel mouse line (i.e., RIP-Cre; R26Fucci2aR mice) that enables a quantitative analysis of replicating β-cells in a spatiotemporal manner. β-Cell proliferation analysis has been traditionally performed by immunohistochemical assay using BrdU or EdU labeling. However, these results are occasionally inaccurate and unreproducible due to the inherent limitations. A recent study reported interlaboratory variability in the immunohistochemical detection of Ki-67 for identification of β-cells as well as quantification of their replication; the authors concluded that the discrepancy among laboratories was due to the misidentification of replicating non–β-cells within islets as β-cells (2). Because several different cell types are densely packed in the sphere-like islets, analysis of two-dimensional immunohistochemical data might well lead to inaccuracies in detection of β-cells. Moreover, the nucleotide analog BrdU, which is often used as a cell cycle marker in traditional immunohistochemical assays, has unfavorable effects on the cell cycle of β-cells (14), especially in causing underestimation the β-cell proliferation rate. The aim of our study is to establish an alternative method that overcomes these weaknesses.

      Fucci2a, a single fluorescence probe for monitoring cell cycle transition, differentiates cells in G0/G1 from those in the S/G2/M phase based on mCherry-hCdt1 and mVenus-hGem expression (3). By establishing RIP-Cre; R26Fucci2aR mice in which Fucci2a is expressed specifically in β-cells, we have established an alternative and more accurate assay for proliferating β-cells. In high magnification images (Supplementary Fig. 1A), we observed several mCherry mVenus insulin+ cells which could be either β-cells without Fucci probe expression or β-cells immediately after mitosis. If mCherry mVenus insulin+ cells are composed mainly of the latter, their proportion among total β-cells should be higher in the S961-treated group. However, no significant difference in their proportion was found between the vehicle- and S961-treated groups (data not shown). This might suggest that such mCherry mVenus insulin+ cells were rarely detected because mCherry mVenus duration was the shortest among other phases of the cell cycle (3).

      In frozen sections, we also found that many mVenus+ cells were mCherry+. Because these cells were not labeled by EdU (Fig. 2E and Supplementary Fig. 1E), they might be β-cells during G1-S transition. These mCherry+ mVenus+ cells could suggest prolonged G1-S transition or an unknown cell cycle restriction in response to S961. This is also supported by the finding that one β-cell remained mCherry+ mVenus+ for >8 h according to the intravital imaging data (Supplementary Video 3). While the number of replicating β-cells that can be analyzed in our intravital imaging is presently limited and it is difficult to draw definitive conclusions, these findings suggest the utility of our tool to investigate cell cycle regulation and kinetics of β-cells in the future.

      Since RIP-Cre; R26Fucci2aR mice label a proliferative β-cell pool, 3D analysis of optically cleared pancreas of these mice enables measurement of the β-cell proliferation rate unaffected by replicating non–β-cells. In addition, 3D analysis allows sampling of β-cells from whole islets, which increases the sample size and provides spatial information on the replicating β-cells within an islet. As a result, the correlation between the β-cell proliferative capacity and the morphological characteristics of each islet can be established. Although some improvements to our system are required to keep the animals alive long enough to monitor the entire time course of β-cell proliferation, our intravital imaging data demonstrates that longitudinal spatiotemporal data on β-cell proliferation can be obtained from RIP-Cre; R26Fucci2aR mice (Supplementary Videos 2 and 3).

      The 3D analysis of the pancreas of RIP-Cre; R26Fucci2aR mice revealed differing rates of β-cell proliferation within each islet under various interventions such as S961 treatment, DIO, pregnancy, and PPTX. By comparing these four models, we found that in our experimental conditions, S961 treatment induces the highest β-cell proliferation rate, followed by pregnancy, PPTX, and DIO (Supplementary Fig. 4). When we counted all mCherry+ and mVenus+ cells as β-cells, we found that the total number of β-cells per islet was increased by S961 treatment and DIO. This is consistent with previous reports on S961 and DIO-induced β-cell proliferation and mass expansion (4,5). However, we found the difference in β-cell cluster diameter and the number of β-cells per islet between the vehicle and S961 groups was surprisingly small. Our TUNEL assay found that S961 treatment caused significantly more β-cell apoptosis (Supplementary Fig. 5). The intravital imaging also captured mVenus+ cells undergoing apoptosis in S961-treated mice (Supplementary Video 3). Considering these findings together, the small difference may be due to apoptosis of β-cells including mVenus+ cells caused by profound hyperglycemia concomitant with S961 treatment. On the other hand, there was no significant difference in the total number of β-cells per islet between the pregnant and virgin groups. This is possibly because the pancreata were harvested at ∼14.5 days of gestation when β-cell proliferation reaches its peak, while β-cell mass does so at later stages during pregnancy (15). The similar scenario could be applicable to our PPTX experiments. Samples were analyzed 2 days after the operation, which might be too soon to detect an increase in the total number of β-cells per islet. In addition, the strong positive correlation between mVenus+ cells and the total β-cell number per islet suggests that islets comprising a greater number of β-cells contain more replicating β-cells.

      Our study has several limitations. First, because only β-cells were labeled by the Fucci2a probe, other endocrine cells within islets could not be detected in RIP-Cre; R26Fucci2aR mice. Therefore, mitogenic effects on non–β-cells must be investigated using other methods. Second, the attenuation of fluorescence by light scattering limited the observation depth from the pancreas surface. Such signal attenuation is unavoidable despite the optical clearing process. Under the light-sheet microscope, only islets near (∼2.0 mm from) the surface were clearly detected for quantification of fluorescent cells. Although this restricts the size of islet population, the sample size is still larger using our method compared with a conventional immunohistochemical assay because it is based on 3D analysis of whole pancreas.

      In summary, the current mouse line expressing the Fucci2a probe in β-cells serves as a new tool that allows spatiotemporal analysis in a quantitative manner of β-cell proliferation in response to mitogen stimulation.

      Article Information

      Acknowledgments. The authors thank Asako Sakaue-Sawano and Atsushi Miyawaki (RIKEN Center for Brain Science, Wako, Saitama, Japan) and Tomonobu Hatoko (Department of Diabetes, Endocrinology and Nutrition, Kyoto University) for their scientific discussion; Saki Kanda and Sara Yasui (Department of Diabetes, Endocrinology and Nutrition, Kyoto University) for technical assistance; and Yukiko Inokuchi, Yukiko Tanaka, and Fumiko Uwamori (Department of Diabetes, Endocrinology and Nutrition, Kyoto University) for secretarial assistance.

      Funding. This work was supported by grants from Japan Society for the Promotion of Sciences (KAKENHI grant numbers 26111004, 17K09825, and 17K19654). This work was also supported by the Kyoto University Live Imaging Center and in part by Grants-in-Aid KAKENHI 16H06280 “ABiS.”

      Duality of Interest. D.Y. received consulting or speaking fees from MSD K.K., Nippon Boehringer Ingelheim Co. Ltd., and Novo Nordisk Pharma Ltd., and received clinically commissioned/joint research grants from Taisho Toyama Pharmaceutical Co. Ltd., MSD K.K., Ono Pharmaceutical Co. Ltd., Novo Nordisk Pharma Ltd., Arklay Co. Ltd., Terumo Co. Ltd., and Takeda Pharmaceutical Co. Ltd. N.I. received clinical commissioned/joint research grants from Mitsubishi Tanabe, AstraZeneca, Astellas, and Novartis Pharma, and scholarship grants from Takeda, MSD, Ono, Sanofi, Japan Tobacco Inc., Mitsubishi Tanabe, Novartis, Boehringer Ingelheim, Kyowa Kirin, Astellas, and Daiichi-Sankyo. No other potential conflicts of interest relevant to this article were reported.

      Author Contributions. S.T analyzed the data. S.T., D.Y., and N.I. designed the study. S.T., D.Y., and N.I. wrote the manuscript. S.T. and A.B. performed experiments. H.T., R.U., M.F., H.G., P.L.H., and M.O. contributed to the analysis and interpretation of data and critical revisions of the manuscript for important intellectual content. All authors approved the version to be published. D.Y. and N.I. 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 study were presented in abstract form at the 79th Scientific Sessions of the American Diabetes Association, San Francisco, CA, 7–11 June 2019, and at the 55th European Association for the Study of Diabetes Annual Meeting, Barcelona, Spain, 16–20 September 2019. A non–peer-reviewed version of this article was posted on the bioRxiv preprint server (https://www.biorxiv.org/content/10.1101/659904v1.full) on 4 June 2019.

      • Received March 24, 2020.
      • Accepted August 2, 2020.



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      Watermelon Salad with Goat Cheese & Basil

      By electricdiet / November 25, 2020


      This watermelon salad with goat cheese and basil is bursting with flavor! You’ll love the sweet fruit, tangy cheese, fresh herbs, and red onion with the bright vinaigrette.

      Watermelon salad with goat cheese and basil in a white bowl with silver serving spoons, as seen from above

      Watermelon is delicious to eat on its own or mixed in with a few other fruits. I’ve always thought of it as a nice, refreshing summer treat with a mild taste.

      But have you ever tried combining it with big, bold flavors? This watermelon salad with goat cheese and basil does exactly that, and it may be my new favorite summer salad!

      Crisp fruit, tangy goat cheese, vibrant red onion, and fresh basil tossed in a bright lemon vinaigrette is a symphony of tastes. Seriously, the flavors are amazing together!

      And the entire recipe takes about 15 minutes or less to make. Just mix the vinaigrette ingredients, chop your watermelon and onion, then combine everything in a bowl and dig in. It’s incredibly simple, but so delicious.

      How to make watermelon salad with goat cheese and basil

      Ready to see how this easy salad comes together in just 4 steps?

      Salad ingredients in separate ramekins, as seen from above

      Step 1: In a small bowl, mix together the vinaigrette ingredients until smooth. Set aside.

      Step 2: Add the cubed watermelon to a large salad bowl.

      Step 3: Crumble the goat cheese over the watermelon, add the sliced red onion, then sprinkle with the torn basil leaves.

      Step 4: Just before serving, drizzle the vinaigrette over the salad and toss well to combine.

      That’s it! Your salad is ready to enjoy.

      Close up of salad with watermelon, onion, goat cheese, and basil

      Variations for your salad

      Want to get creative with this recipe? You can easily substitute ingredients to try out different flavor combinations!

      Instead of goat cheese, you could use feta to add even more tanginess. Or, for a milder flavor, fresh mozzarella would be delicious with the watermelon and basil.

      Don’t love red onion? Use a milder onion, or simply omit it altogether.

      Want to try out some different herbs? Fresh mint, cilantro, or dill would each be a fun twist on the flavor profile.

      Finally, feel free to swap out the lemon vinaigrette for another dressing of your choice. This easy balsamic vinaigrette or citrus vinaigrette would both be great options.

      Have some fun with the flavors of this dish!

      Storage

      Once you slice fruit, it’s best to serve as soon as possible. If you want to prep this dish in advance, you can chop and combine all the ingredients except the vinaigrette, then store in separate airtight containers in the refrigerator.

      When you’re ready to serve, add the vinaigrette and mix to combine.

      Leftover salad can be stored covered in the refrigerator for a day or two, but keep in mind that the vinaigrette will soften the watermelon.

      Salad with watermelon, onion, goat cheese, and basil in a white bowl next to a ramekin with salad dressing

      Other delicious watermelon recipes

      Looking for more ways to use this refreshing summer fruit? I love finding new options to incorporate it into easy recipes! Here are a few of my favorites that I know you’ll enjoy:

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

      Recipe Card

      Watermelon salad with goat cheese and basil

      Watermelon Salad with Goats Cheese and Basil

      This watermelon salad with goat cheese and basil is bursting with flavor! You’ll love the sweet fruit, tangy cheese, fresh herbs, and red onion with the bright vinaigrette.

      Prep Time:15 minutes

      Total Time:15 minutes

      Author:Shelby Kinnaird

      Servings:8

      Instructions

      • In a small bowl, mix together the vinaigrette ingredients until smooth. Set aside.

      • Add the cubed watermelon to a large salad bowl.

      • Crumble the goat cheese over the watermelon, add the sliced red onion, then sprinkle with the torn basil leaves.

      • Just before serving, drizzle the vinaigrette over the salad and toss well to combine.

      Recipe Notes

      This recipe is for 10 servings of watermelon salad. Each serving is a ½ cup.
      This salad can be prepped a few hours in advance. Chop and combine the watermelon, onion, cheese, and basil, then store covered in the refrigerator. Mix the vinaigrette, then store separately in the refrigerator.
      Once mixed, leftovers can be stored covered in the refrigerator for a day or two. Keep in mind that the vinaigrette will soften the watermelon.

      Nutrition Info Per Serving

      Nutrition Facts

      Watermelon Salad with Goats Cheese and Basil

      Amount Per Serving (0.5 cup)

      Calories 102
      Calories from Fat 64

      % Daily Value*

      Fat 7.1g11%

      Saturated Fat 3g19%

      Trans Fat 0g

      Polyunsaturated Fat 0.4g

      Monounsaturated Fat 2.5g

      Cholesterol 1.6mg1%

      Sodium 149.7mg7%

      Potassium 136.8mg4%

      Carbohydrates 7.2g2%

      Fiber 0.7g3%

      Sugar 5.2g6%

      Protein 3.4g7%

      Net carbs 6.5g

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

      Course: Salad, Salads & Dressings

      Cuisine: American

      Diet: Diabetic

      Keyword: fruit salad, Watermelon salad



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      Quick Tomato Soup – My Bizzy Kitchen

      By electricdiet / November 23, 2020





      Quick Tomato Soup – My Bizzy Kitchen

























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