Temporal Transcriptomic and Proteomic Landscapes ofDeteriorating Pancreatic Islets in Type 2 Diabetic RatsJunjie Hou,1 Zonghong Li,1,2 Wen Zhong,1,3 Qiang Hao,1 Lei Lei,1 Linlin Wang,1,4 Dongyu Zhao,1

Pingyong Xu,1 Yifa Zhou,2 You Wang,1 and Tao Xu1,3,4

Diabetes 2017;66:2188–2200 | https://doi.org/10.2337/db16-1305

Progressive reduction in b-cell mass and function com-prise the core of the pathogenesis mechanism of type 2diabetes. The process of deteriorating pancreatic islets,in which a complex network of molecular events is in-volved, is not yet fully characterized. We used RNA sequenc-ing and tandem mass tag–based quantitative proteomicstechnology to measure the temporal mRNA and proteinexpression changes of pancreatic islets in Goto-Kakizaki(GK) rats from 4 to 24 weeks of age. Our omics data setoutlines the dynamics of the molecular network during thedeterioration of GK islets as two stages: The early stage(4–6 weeks) is characterized by anaerobic glycolysis, in-flammation priming, and compensation for insulin synthe-sis, and the late stage (8–24 weeks) is characterized byinflammation amplification and compensation failure. Fur-ther time course analysis allowed us to reveal 5,551 dif-ferentially expressed genes, a large portion of which havenot been reported before. Our comprehensive and tem-poral transcriptome and proteome data offer a valuableresource for the diabetes research community and forquantitative biology.

Type 2 diabetes (T2D) is a major public health issuecharacterized by pancreatic islet b-cell failure in the pres-ence of insulin resistance. Accumulating evidence suggeststhat progressive deterioration of pancreatic b-cell functionand gradual loss of b-cell mass could be the core eventsduring T2D development, regardless of therapy status(1–4). Genome-wide association and sequencing studieshave identified multiple risk variants for T2D, the majority

of which appear to play a primary role in b-cell functionrather than to affect insulin resistance, further highlightingthe importance of b-cells in the pathogenesis of T2D (5).

T2D is a complex disease, and b-cell failure is likely causedby altered expression of many genes and their products.Therefore, the use of system-oriented strategies is criticalto investigate the complex changes that occur in b-cells orpancreatic islets, which primarily comprise b-cells. Hence,large-scale and unbiased omics technologies, particularlymicroarray-based transcriptomics and mass spectrometry(MS)–based proteomics, have been used to analyze isletsisolated from various T2D animal models and human ca-daver donors to elucidate the mechanisms underlying b-cellfailure (summarized in Supplementary Table 1). b-Cell fail-ure during diabetes progression is a gradual process thatundergoes various stages (6,7) wherein different moleculeevents occur in chronological order. However, current stud-ies have focused primarily on single time points at relativelylate stages of the disease, so mapping the order in whichthese events occur and distinguishing causal molecularevents (leading to diabetes) from those that occur as aconsequence of glucolipotoxicity associated with diabeticconditions are impossible. For this reason, prospectivestudies investigating the evolution of molecular eventsin islet b-cells at various stages of T2D are of interest.

The study of b-cells in humans with T2D often hasbeen hindered by the limited accessibility of human isletsand by ethical considerations. In this context, appropriaterodent models are essential for the identification of diabeticmechanisms (8). The Goto-Kakizaki (GK) rat, one of the

1National Laboratory of Biomacromolecules, CAS Center for Excellence in Bio-macromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing,China2School of Life Sciences, Northeast Normal University, Changchun, China3College of Life Science and Technology, HuaZhong University of Science andTechnology, Wuhan, China4College of Life Sciences, University of Chinese Academy of Sciences, Beijing,China

Corresponding author: You Wang, wangyou51@126.com, and Tao Xu, xutao@ibp.ac.cn.

Received 18 November 2016 and accepted 17 May 2017.

This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-1305/-/DC1.

J.H., Z.L., and W.Z. contributed equally to this work.

© 2017 by the American Diabetes Association. Readers may use this article aslong as the work is properly cited, the use is educational and not for profit, and thework is not altered. More information is available at http://www.diabetesjournals.org/content/license.

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ISLETSTUDIES

best-characterized animal models of spontaneous T2D (9),shares many characteristics with human patients with di-abetes (10). Similar to human T2D, the core cause under-lying hyperglycemia in GK rats is b-cell failure (11,12).

In the current study, to understand the process ofdeteriorating pancreatic islets at the molecular level, weused RNA sequencing (RNA-seq) and tandem mass tag(TMT)–based quantitative proteomics technology to gener-ate integrated transcriptomic and proteomic profiles ofpancreatic islets in GK rats after the establishment of hyper-glycemia (from 4 to 24 weeks). Subsequent bioinformaticsanalysis in a time course fashion revealed the chronologicalorder of T2D-related molecular events during the deterio-ration of pancreatic islets. This large quantitative data setrepresents a valuable resource that provides a comprehen-sive picture of the mechanisms responsible for islet dysfunc-tion and will allow us to identify potential interventions toprevent b-cell failure and deterioration in human T2D.

RESEARCH DESIGN AND METHODS

Brief descriptions of key experimental procedures areprovided below. More details are provided in the Supple-mentary Data.

AnimalsFounders of GK/Jcl diabetic rats were purchased from RIKENBioResource Center (Ibaraki, Japan). All GK/Jcl diabetic ratsand Wistar (WST) rats were maintained under specificpathogen-free conditions and were used between 4 and24 weeks of age in accordance with the animal experimentalguidelines set forth by the Institutional Animal Care andUse Committee of the Institute of Biophysics, ChineseAcademy of Sciences.

Preparation of Pancreatic Islets From GK and WST RatsPancreatic islets from male GK and age-matched controlWST rats were isolated through collagenase digestion. Afterseparation on a Ficoll density gradient, the islets werehandpicked in Hanks’ buffer under a dissection microscope.

N-Acetyl-L-Cysteine Treatment ExperimentsSixteen male GK rats were used in the following experi-ments. Littermates of GK rats were randomly divided intoN-acetyl-L-cysteine (NAC) and control groups. Four-week-old rats were orally administered NAC 200 mg/kg of bodyweight (616-91-1; Sigma) or drinking water by gavage oncea day for 12 weeks. Random blood glucose assay and glucosetolerance, insulin tolerance, and glucose-stimulated insulinsecretion (GSIS) tests were performed as described in theSupplementary Data.

RNA-Seq AnalysisRNA-seq was performed by using a multiplex analysis ofpolyA-linked sequences (MAPS) approach as previouslydescribed (13).

TMT-Based Proteomics AnalysisProteins extracted from isolated islets were digestedand labeled by 6-plex TMT reagents (14) according to the

instructions from the manufacture (Thermo Fisher Scien-tific). TMT-labeled peptide mixtures were equally pooled,separated by offline high pH reversed-phase chromatogra-phy, and repeatedly analyzed using a nano–liquid chro-matography-tandem MS technique (Supplementary Fig.1A). The raw MS data were processed with Proteome Dis-coverer 1.4 software. The peptide confidence value was setas 0.01. At the protein level, a precursor intensity fractionof 50% was selected as an optimal trade-off value for bothidentification and quantification (Supplementary Fig. 1B). Apseudocount representing relative protein abundance wascalculated by using the TMT ratio and the normalized spec-tral abundance factor (15).

Bioinformatics AnalysisBoth mRNA raw counts and protein pseudocounts werenormalized by using the remove unwanted variation approach(Supplementary Fig. 1C) (16). Differentially expressed (DE)genes were assessed by ANOVA with a false discovery rateof ,0.01. The Database for Annotation, Visualization andIntegrated Discovery (DAVID) Web service API Perl Client(17,18) was used to perform gene ontology (GO) functionalenrichment analysis (with a false discovery rate of ,0.05).The k-means clustering algorithm was used to classify dy-namic gene expression patterns. Kyoto Encyclopedia ofGenes and Genomes (KEGG) signaling pathway enrichmentanalysis was carried out by using the Generally ApplicableGene Set Enrichment (GAGE) package in R software (withan adjusted P , 0.05) (19).

Data ResourcesFor this work, 92.4 gigabyte sequencing data were gener-ated. All RNA-seq data were deposited in the NationalCenter for Biotechnology Information Gene ExpressionOmnibus under accession number GSE 81811. The MS datawere deposited in the ProteomeXchange Consortium throughthe PRIDE (Proteomics Identifications) (20) partner reposi-tory with the data set identifier PXD004709.

RESULTS

Transcriptomic and Proteomic Profiles of RatPancreatic Islets Over TimeTo investigate the global molecular dynamics of T2D islets,we analyzed the transcriptomes and proteomes of pancre-atic islets isolated from male GK rats and age- and sex-matched control WST rats at five consecutive time points(weeks 4, 6, 8, 16, and 24) (Fig. 1A). Transcriptomes andproteomes of islets were measured by using the MAPS-based RNA-seq technique and TMT labeling–based proteo-mic method, respectively. Combined analysis of all samplesyielded the identification of 15,101 mRNAs and 8,362 pro-teins, of which 7,395 overlapped (Fig. 1B). Furthermore,13,866 mRNAs (minimal counts of 10 detected in at leastthree samples) and 5,631 proteins (identified in at least twobiological replicates by peptides of precursor intensityfraction #50%) were considered a quantifiable data set,of which 5,015 overlapped (information for all identifiedgenes is provided in Supplementary Table 2). We estimated

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relative protein abundance, which was noted as the proteinpseudocount in this study, on the basis of TMT ratio andnormalized spectral abundance factor. Compared with TMTratios, protein pseudocounts resulted in the generation ofmore-reasonable clusters that matched the experiments(Supplementary Fig. 2A). Protein pseudocounts positivelycorrelated with mRNA counts, with a mean Spearman cor-relation coefficient of 0.37 (Supplementary Fig. 2B), whichis similar to reported values in other biological systems(21,22). These results demonstrate that our method ofestimating protein pseudocounts was reasonable andunbiased.

We first carried out an unsupervised hierarchical clus-tering analysis of the transcriptomes and proteomes fromGK and WST islets (Fig. 1C). At the mRNA level, WST isletsclustered together and clearly separated from GK islets asexpected, representing different pathophysiological statesof islets in control rats versus diseased rats. Overall, thetranscriptomes of GK and WST rats demonstrated appro-priate clustering at different time points, representing thedevelopmental stages of islets. Of note, GK islets at 4, 6,and 8 weeks formed one branch that was distinct from thebranch formed at 16 and 24 weeks, likely reflecting twodifferent diabetic stages of islets in GK rats. Upon proteomeclustering examination, GK islets at 4 and 6 weeks were

separated from other GK islets and instead clustered withWST islets, suggesting minimal changes in protein expres-sion at the early stages of T2D, even when there are signif-icant changes at the mRNA level.

We next performed a principal component (PC) analysisto investigate transcriptome and proteome dynamics overtime (Fig. 1D). PC2 primarily reflected age-related develop-mental changes, whereas PC1 represented the differencesbetween normal WST and diabetic GK rats. Similar to theabove results obtained from unsupervised hierarchical clus-tering, PC1 highlighted two notable diabetic stages in GKislets. At the mRNA level, islets at 4, 6, and 8 weeks clus-tered as one stage, and islets at 16 and 24 weeks repre-sented another stage. However, at the protein level, islets at4 and 6 weeks clustered as one stage, and the remainingtime points clustered as a separate stage. GK islets progres-sively develop into disorganized structures exhibiting pro-nounced fibrosis separating strands of endocrine cells (23).Of note, these changes were not present or rare in isletsat 4–6 weeks but became prominent at older ages (8–24 weeks) (Supplementary Fig. 2C), correlating well withour proteome-defined two stages (Fig. 1C and D). Takentogether, global profiling at the mRNA and protein levelroughly characterizes the deterioration of islets in GK ratsfrom 4 to 24 weeks of age into two stages: an early stage at

Figure 1—Transcriptomic and proteomic analysis of pancreatic islets in diabetic GK rats over time. A: Experimental workflow. Pancreatic isletsisolated from GK and age-matched control WST rats of five different ages (4, 6, 8, 16, and 24 weeks) were subjected to MAPS-based RNA-seqand TMT labeling–based proteomics analysis. After performing data quality control and normalization, differentially expressed mRNAs andproteins were analyzed by ANOVA followed by integrated bioinformatics analysis and biological validation. B: Venn diagram of identifiable andquantifiable mRNAs and proteins in this study. C and D: Unsupervised hierarchical clustering and PC analysis of all quantifiable mRNAsand proteins from GK and WST islets indicated the reproducibility of biological replicates; however, islet gene expression in both GK andWST rats was highly variable between time points at both the mRNA and the protein level.

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4–6 weeks and a late stage at 8–24 weeks, with a turningpoint at ;8 weeks.

Analysis of DE mRNAs and ProteinsDE genes between GK and WST rats at each time pointwere first assessed by one-way ANOVA (P , 0.05) (Supple-mentary Table 2). The results revealed a remarkable increaseof DE mRNAs over time, whereas only a mild increase wasfound in the number of DE proteins, suggesting a moredynamic regulation of gene expression at the mRNA levelthan at the protein level during the development of GKdiabetes (Supplementary Fig. 3A). When we compared thefold changes in mRNA and protein levels, the Pearson cor-relation value (0.11) was relatively poor at 4 weeks but increasedto 0.23–0.28 at later time points (Supplementary Fig. 3B).

From our time-resolved expression data set, we analyzedthe temporal significance of gene expression changesby using two-way ANOVA, which considered weeks(five different time points) and rats (GK vs. WST) as thetwo statistical factors. In total, we identified 5,551 DEgenes, including 3,910 mRNAs and 2,387 proteins (Supple-mentary Table 2), of which 746 were identified at both themRNA and the protein level. The correlations among these746 DE genes varied from anticorrelation to full accordance,with an average Pearson coefficient of 0.39 (Fig. 2A andSupplementary Table 3), which is larger than the correlationcoefficients at individual time points. DAVID functionalclustering analysis indicated carbon metabolism and ribo-some enrichment among genes with concordant mRNA andprotein levels (Fig. 2B), potentially representing the set ofgenes exhibiting stable mRNA and protein expression (24).In contrast, no significant functional clustering was identi-fied for negatively correlated DE genes.

For the confirmation of expression data, we randomlyselected six DE genes to measure their mRNA expression byquantitative RT-PCR (qRT-PCR) in independent GK/WSTislet samples. The expression patterns of these genesmeasured by RNA-seq and qRT-PCR were similar (Supple-mentary Fig. 3C). Moreover, by comparing our GK/WSTdata set with the published data set of islets from individ-uals with and without T2D (Supplementary Table 1) (10),we found that on average, 68.9% of DE genes were consis-tent between GK rats and humans with diabetes (Supple-mentary Fig. 3D), indicating high relevance of the currentstudy in GK rat to human islets in T2D.

Dynamic mRNA and Protein Expression PatternsOver Time in GK IsletsThe primary purpose for the generation of the time coursedata set in this study is to reveal the temporal properties ofbiological pathways relevant to the development of GKdiabetes at the system level. Therefore, we performed timecourse pattern analysis for all DE genes by using thek-means clustering method and successfully identified12 mRNA expression patterns (m1–m12) and 9 proteinexpression patterns (p1–p9) (Fig. 2C). To explore thebiological functions of these expression patterns, we carriedout a DAVID analysis and organized the identified networks

with enriched functions by using EnrichmentMap software(Supplementary Table 4) (25). On the basis of the time-course expression patterns of clustered genes, we classifiedthe biological events into the following categories:

1. Constant up (m2, p8) and down (p2, m3, m10) genes,which were either up- or downregulated at all the timepoints. The constant up genes were highly enriched forsuch functions as cell redox homeostasis, translation elon-gation, cytoskeleton organization, antiapoptosis, and soforth. In contrast, the constant down genes were mainlyassociated with mitochondrion, metabolism, lysosome,protein transport, and so forth.

2. Up early genes (p1, p6, m11) that were upregulated at4–8 weeks, which include those participating in theglucose metabolism and innate immune response. Inaddition, many proteasome proteins were dramaticallyupregulated at 4 weeks.

3. Up late genes (p9, m8, p7, m1, m12) that were upregu-lated at the late stage of 8–24 weeks. These genes arehighly enriched for cell adhesion and cytoskeleton orga-nization at the protein level, probably associating withthe development of islets fibrosis, whereas at themRNA level, apoptosis was significantly upregulatedat 24 weeks.

4. Down early genes (p3, m6) that were downregulated at4–8 weeks. The most noticeable feature is the downregu-lation of oxidative phosphorylation (OXPHOS) and tri-carboxylic acid (TCA) cycle at the protein level, probablysuggesting the insufficient energy supply and oxidativestress in GK islets at the early stage (26–28). At themRNA level, the genes associated with cell cycle andnuclear lumen were highly enriched, likely contributingto the loss of b-cells in GK islets.

5. Down late genes (p5, m9, p4, p7, m5, m4) that weredownregulated at 8–16 weeks. The GO functions of in-sulin secretion, lysosome, and secretory granule wererepresentatively enriched.

Such a time course clustering analysis from genes tobiological functions suggests that the progression of GKislet deterioration was stage based and dynamically regu-lated. Furthermore, because genes exhibiting similar ex-pression patterns generally share functional relationships,clustering analyses also allow the prediction of genes thatshare similar temporal expression patterns, with previouslyvalidated diabetes-related genes as potential new candidatesfor further investigation.

Pathway Dynamics in GK Islets During DiabetesProgressionTo gain a deeper understanding of temporal pathwaysequences during the deterioration of GK islets, weperformed GAGE analysis with the quantitative transcrip-tomic and proteomic data sets and identified 161 KEGGpathways significantly enriched for at least one time point(Benjamini-Hochberg–adjusted P , 0.05) (SupplementaryTable 5). Consistent with the above gene expression

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Figure 2—Temporal gene expression patterns during GK diabetes progression. A: Pearson correlation analysis of the temporal mRNA andprotein expression of 746 overlapping DE genes. In total, 76.8% of genes were positively correlated, of which 41.3%were significantly correlated(adjusted P < 0.01). The mean Pearson correlation coefficient was 0.388. B: DAVID analysis of positively correlated DE genes revealed twoenriched functional annotations primarily associated with carbon metabolism and ribosomes. No functional annotation was enriched fornegatively correlated DE genes. C: Time course dynamic expression clustering analysis of DE genes. First, fold changes in DE genes weretransformed into z scores. Next, the k-means clustering method was used to classify the genes into 12 mRNA and 9 protein patterns, displayedas a Circos figure. Functional enrichment analyses of the genes within each pattern were carried out by using EnrichmentMap software. Theenriched GO functional groups are selectively highlighted with transparent pink (upregulated) and blue (downregulated) circles. ER, endoplasmicreticulum; ETC, electron transport chain; FA, fatty acid; IKK, IkB kinase; MAPKKK, mitogen-activated protein kinase kinase kinase; w, week.

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clustering analysis results, these KEGG pathways alsoroughly comprised down- and upregulation-dominated tem-poral classes (Fig. 3). For example, insulin secretion, andSNARE interactions in vesicular transport were downregu-lated at the late stage with the same temporal pattern(representative genes illustrated in Supplementary Fig. 4).These two pathways were partially downregulated at 4 and6 weeks with no significant change in statistics and becamesignificantly downregulated after 8 weeks, clearly demon-strating the dynamic features of various pathways involvedin insulin secretion during the progression of T2D. We alsonoticed that the pathway of glycolysis/gluconeogenesis wasgradually upregulated since 6 weeks, probably indicatingthat in GK islets, the anaerobic metabolism is the dominantapproach for supplying the energy because of the defect inOXPHOS and TCA cycle.

In addition, we found that signaling pathways associatedwith inflammation were upregulated at the mRNA level,including the nucleotide oligomerization domain (NOD)–like receptor, tumor necrosis factor (TNF), and nuclearfactor-kB (NF-kB) signaling pathways. This finding is con-sistent with previous reports that islet inflammation playsan important role in the pathogenesis of GK diabetes(26,29) and provides more comprehensive details of path-way dynamics at both the mRNA and the protein level.

Mitochondrial Signatures in GK IsletsWe identified 311 DE genes as mitochondria relatedby using GO terms for cellular components that containthe keywords mitochondrion or mitochondrial and dividedthem into four groups on the basis of the unsupervisedhierarchical clustering analysis of their temporal profiles(Supplementary Table 6). Further manual annotationrevealed the details regarding mitochondrial dysfunctionduring the progression of T2D (Fig. 4). We found thatOXPHOS complexes, mitochondrial ribosome proteins, trans-locase outer/inner membrane complex (TOM/TIM), andsome metabolite transporters were downregulated early atthe protein level, and conversely, most of correspondingmRNAs were upregulated at 4 and 6 weeks, indicating tran-scription compensation at the early stage of T2D. However,this compensation ability was eventually lost at the laterstage during b-cell deterioration. In addition, several pro-teins responsible for protein assembly and quality control,mitochondrial biogenesis, and mitochondrial DNA tran-scription were downregulated, such as Hspd1, Grpel1,Lonp1, Letm1 and Pmpcb, Tfam, Mtfr1l, Mfn2, the transferRNA ligases (Rars2, Nars2, Dars2, and Vars2), and 12 mito-chondrial RNAs (Supplementary Fig. 5A). Taken together,mitochondrial dysfunction was considered one of earliestpathogenic events in islets of GK rats.

Overview of Metabolism in GK IsletsTo gain a deeper understanding about how the metabolismin GK islets changed with the development of diabetes, wemapped our quantitative omics data to KEGG metabolicpathways (Fig. 5). The most notable change of metabolismin GK islets was the upregulation of glycolysis metabolism

(26,27) and the downregulation of the TCA cycle, OXPHOS,and fatty acid metabolism, which suggests that the primarymetabolism of GK islets switches from aerobic metabolism to

Figure 3—Heat map of KEGG pathway enrichment analysis. Normal-ized counts/pseudocounts of the DE genes were subjected to GAGEanalysis by using the Bioconductor package gage. Pathways withadjusted P values (Benjamini-Hochberg procedure) of <0.05 are in-dicated by asterisks. The Stat.mean values represent the averagedmagnitude and direction of fold changes at the gene set level corre-sponding to the color-coded upregulated (red) and downregulated(blue) changes. KEGG pathway maps were used to perform classifi-cations. Akt, protein kinase B; ECM, extracellular matrix; HIF-1, hyp-oxia-inducible factor 1; Jak, Janus kinase; MAPK, mitogen-activatedkinase; PI3K, phosphatidylinositol 3-kinase; TGF, transforming growthfactor; tRNA, transfer RNA.

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anaerobic metabolism (the so-called Warburg-like effect). Fur-thermore, we found that besides FAD-dependent glycerol-3-phosphate dehydrogenase (Gpd2) reported previously(30), Got1, Got2, Mdh1, and Mdh2 were also downregu-lated, representing a comprehensive picture of defectivemalate-aspartate shuttle and glycerol phosphate shuttle inGK islets (Supplementary Fig. 5B). Such a defect could causean increase of the cytoplasmic NADH/NAD+ ratio, enhancethe formation of lactate from pyruvate, and further disruptthe link between cytosolic glycolysis and mitochondrialmetabolism.

In addition, some genes participating in amino acidmetabolism were also downregulated, partially contributingto a defect in GSIS (31–33). However, several enzymes in-volved in glutathione metabolism were upregulated at boththe mRNA and the protein level in GK islets, including Gclc,Ggct, Ggt1, Gsta3, and Gsto1, which may reflect an adaptivemechanism to combat increased reactive oxygen species(ROS) in GK islets (34,35).

Reduced Neogenesis and b-Cell ProliferationInsufficient insulin secretion is caused by either impairedGSIS or reduced b-cell mass. b-Cell mass is regulated by abalance among b-cell neogenesis, proliferation, and apopto-sis. Consistent with previous studies (26,36), the current

data show that several genes functionally associated withneogenesis are downregulated in GK islets, including Pdx1,Nkx2-2, Nkx6-1,Mafa, and Fev, at the mRNA and/or proteinlevel (Fig. 6). Moreover, several genes specific for a-cells andpancreatic polypeptide cells (i.e., Arx, Isl1, Pax6, Pou3f4,Ppy) were downregulated at the early stage. Certain genesrequired for the differentiation of pancreatic progenitorsinto endocrine progenitors (i.e., Foxa1, Gata6, Sox9, Onecut1)were significantly upregulated at the late stage of GKdiabetes.

Low levels of b-cell proliferation in GK rats constituteanother factor contributing to decreased b-cell mass(11,26,36). In the current data set, 38 genes among clusterm6, including Aurkb, Ccna2, and Kifc1, were associated withcell cycle and nuclear lumen and exhibited similar ex-pression patterns at the early stage (Supplementary Fig.6A). These genes gradually decreased over time in WST rats,reflecting an aging-dependent reduction in proliferationover time (38). In contrast, all these genes were dramati-cally downregulated at 4 weeks in GK islets and then pro-gressively decreased to even lower levels at 24 weeks(Supplementary Fig. 6B). Consistently, Ki67-positiveb-cells were significantly decreased in GK islets at 4, 6,and 8 weeks (Supplementary Fig. 6C), suggesting reducedb-cell proliferation (11,12,26). Conversely, the apoptosis

Figure 4—Mitochondrial signatures in GK islets. On the basis of the locations or biological functions of GO annotations, the mitochondria-related DE genes were mapped to the outer and inner membrane, membrane transporter, OXPHOS complex, ribosomal proteins, protein qualitycontrol, transcription, translation, biogenesis, and antioxidant. The heat maps for mRNA and protein expression at the five time points are color-coded according to the log2 fold-changes for GK vs. WST.

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pathway was only elevated at 24 weeks at the mRNA level,implying that apoptosis was not responsible for b-cell lossduring the early phase of the disease.

Two-Stage Inflammation in GK IsletsChronic inflammation in GK islets has been demonstratedand considered as a pathophysiological contributor inT2D (29,39). In the current study, temporal expression of

proinflammatory cytokines revealed two distinct stages:early priming and late amplification. During the primingstage, interleukin-1b (IL-1b) and IL-6 were only elevatedto low levels between 4 and 8 weeks followed by a rapidincrease to much higher levels (84-fold increase in GK at24 weeks for IL-6) during the late amplification stage (be-tween 16 and 24 weeks) (Fig. 7). Because immune cell in-filtration was hardly detectable in GK islets before 8 weeks

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Gck Pfkm Gpi Aldoa Aldob Gapdh Eno1 Pklr Ldha Aco2 Cs Dhtkd1 Dlat Fh Idh2 Idh3a Idh3B Mdh2 Ogdhl Pdhb Sdha Sdhb Sdhd Suclg1 Suclg2 Glutathione metabolism

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Figure 5—Metabolic overview of GK islets. DE enzymes were mapped on the KEGG global metabolism map. Gold lines represent differentiallyexpressed metabolic enzymes. Metabolic pathways are selectively highlighted with pink and green lines representing upregulation and down-regulation, respectively. The heat maps for mRNA and protein expression at five time points are displayed and grouped as glycolysis, TCA cycle,fatty acid biosynthesis, fatty acid degradation, amino acid metabolism, and glutathione metabolism. ACOOA, acetyl CoA; Ala, alanine; Arg,arginine; Asp, aspartic acid; Cys, cysteine; FA, fatty acid; GABA, g-aminobutyric acid; GLC, glucose; Gln, glutamine; Glu, glutamic acid; GSH,glutathione; Gly, glysine; Ile, isoleucine; LA, lactate; Leu, leucine; Lys, lysine; Met, methionine; PYR, pyruvate; Ser, serine; Val, valine.

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(39), the early priming stage is likely induced by metabolicdysfunction in GK islets. The identity of specific sensorsthat are triggered to produce this priming of inflammationis not fully understood.

The NLRP3 (NLR family, pyrin domain containing 3)inflammasome activates both NF-kB (to induce pro-IL-1bproduction) and caspase-1 (to process pro-IL-1b into itsmature active form). ASC (apoptosis-associated speck-like

Figure 6—b-Cell neogenesis defects in GK rats. The illustration depicts an overview of pancreatic endocrine cell development, which wasreconstructed by adapting a figure from a reference article (37) with minor modifications. The master transcription factors within each type of cellare listed; upregulation and downregulation are presented in red and green, respectively. Gene expression line chart data are mean 6 SEM ofrepeated experiments (n = 3 [except for WST week 4 mRNA data where n = 2]). *P< 0.01, **P< 0.001, ***P< 0.0001 by adjusted ANOVA. ns, nosignificant difference between GK and WST; PP, pancreatic polypeptide.

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Figure 7—ROS and inflammation contribute to the pathogenesis of islet dysfunction in GK rats. The illustration depicts ROS signaling flux as acore hub linking metabolic dysfunction with islet inflammation and fibrosis. The time course gene expression data for several key genes involvedin the generation of ROS, antioxidants, inflammation, and fibrosis are displayed as a line chart. Gene expression line chart data are mean6 SEMof repeated experiments (n = 3 [except for WST week 4 mRNA data where n = 2]). *P < 0.01, **P < 0.001, ***P < 0.0001 by adjusted ANOVA.ECM, extracellular matrix; ER, endoplasmic reticulum; HIF-1a, hypoxia-inducible factor 1a; Mito, mitochondrion; ns, no significant differencebetween GK and WST.

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protein containing a CARD, also called PYCARD), whichinteracts with NLRP3 during inflammasome assembly,was upregulated in GK islets, indicating possible activationof the NLRP3 inflammasome during the initiation of sterileinflammation. The NLRP3 inflammasome is also activatedby TXNIP (thioredoxin-interacting protein), a key node link-ing glucotoxicity and endoplasmic reticulum stress toNLRP3 inflammasome activation (40). We observed thatTXNIP was gradually upregulated in GK islets after 6 weeks.

Invasive ROS Contributing to the Deteriorationof Islets in GKOxidative stress is a pathogenic factor caused by chronichyperglycemia in GK pancreatic islets (41). However, sour-ces of ROS in T2D have not been clearly defined (42). Thecurrent data suggest multiple sources of ROS accumulationin GK islets at the early stage, including dysfunctional mi-tochondria, nitric oxide synthase (Nos2/iNOS), NADPH ox-idase (Nox4), cyclo-oxygenase (Ptgs1/Cox1 and Ptgs2/Cox2),and cytochrome P450 mono-oxygenase (Cyp2s1, Cyp7b1,and Cyp4f5) (Fig. 7). We also observed increased expressionof several antioxidants, such as Sod1, Prdx4, and Gpx2 (Fig.7), in GK islets, consistent with previous reports (27,35,43).

To validate the involvement of ROS in the pathogen-esis of T2D, we treated 4-week-old GK rats with anantioxidant, NAC, for 10 weeks. NAC treatment signif-icantly reduced random blood glucose of GK-NAC rats(n = 8, NAC 200 mg/kg) compared with that of age-matchedGK-control rats (n = 8, sham) (Supplementary Fig. 7A–D),and GK-NAC rats exhibited greater tolerance to high glu-cose in the oral glucose tolerance test. No significantdifferences in insulin sensitivity between GK-NAC andGK-control rats were observed. The islets in GK-NACgroups had higher GSIS than those in GK-control groups(Supplementary Fig. 7E). Furthermore, we measured themRNA expressions in islets between GK-NAC and GK-controlby using RNA-seq. The results show that the insulin secretionpathway was upregulated, whereas ROS and inflammation-related genes (Tnf, Ggt1, Pycard, Cxcl1, etc.) and signalingpathways (NOD-like receptor signaling pathway, TNF signal-ing pathway, metabolism of xenobiotics by cytochrome P450,etc.) were downregulated in GK islets after NAC treatment(Supplementary Fig. 7F). Thus, the neutralization of in-creased ROS by NAC ameliorates its impact on GSIS andinflammation and protects GK b-cell function.

DISCUSSION

We carried out a large-scale analysis of gene and proteindynamics in pancreatic islets of GK rats at various stagesof T2D. Combined transcriptome and proteome analysisrevealed sufficient depth of coverage and quantitativeaccuracy to generate functional portraits of healthy anddiseased pancreatic islets with unprecedented detail. ManyDE genes and proteins identified previously were confirmedin this study, such as those associated with OXPHOS,mitochondrial function, metabolism, insulin secretion, oxi-dative stress, and inflammation, thus validating the analytic

methods used here. More importantly, the construction oftime course–based gene expression and protein profilesallowed us to identify the chronological order of biologicalevents contributing to the pathogenesis of T2D.

The data suggest two early events that likely contributeto T2D in GK islets: a reduction in b-cell mass and a shift inmetabolism. Many transcription factors required for thespecification of endocrine cells were downregulated earlyin GK islets, whereas those required for trunk and exocrinecells were not altered at 4 weeks but increased at later timepoints (Fig. 6). Indeed, GK rats from the Paris colony ex-hibit a significant reduction in b-cell mass at the fetal stagethat precedes the onset of hyperglycemia at ;4 weeks afterbirth (11), similar to our GK colony. Besides neogenesis,defective proliferation also causes a reduction in b-cellmass, as has been suggested for GK rats from the Pariscolony (11,26). In our GK rats, many genes required forthe cell cycle and proliferation were significantly downregu-lated at the early stage (4–6 weeks) (Supplementary Fig. 6Cand D), suggesting an early defect in proliferation.

Another notable feature of the current data is the obser-vation of an early shift in metabolism. As early as 4 weeks,the primary metabolism in the islets of GK rats switchedfrom aerobic metabolism to anaerobic metabolism (Warburg-like effect). Although metabolism switch has been proposedin previous research (34), our time course–based quantita-tive data allowed us to detect this metabolic shift as an earlyevent of T2D in GK rats and permitted us to gain a deeperinsight into the mechanism underlying this shift. Insuffi-cient reduction in b-cell mass alone may not necessarilycause T2D because autopsy studies of patients with T2Dhave revealed an ;50% decrease in b-cell mass comparedwith BMI-matched control patients (44,45). Furthermore, areduction in b-cell mass of ;50% is required for dogs andrats to develop diabetes (46). This metabolic shift is likelycaused by mitochondrial dysfunction. Many proteins asso-ciated with the OXPHOS system, metabolite transporters,and the TCA cycle were downregulated starting at 4 weeks(Fig. 3). Thus, the mitochondrial limitation of glucose oxi-dation in GK islets occurred during the early stage. To com-pensate for this energy deficiency, GK islets improved therate of glycolysis and upregulated the expression of Ldha,which converted pyruvate to lactate and further disruptedthe link between cytosolic glycolysis and mitochondrial me-tabolism (Fig. 4). Anaerobic glucose metabolism with NADHaccumulation in the b-cell of mitochondrial diabetes causedby ethidium bromide treatment can impair the transcription ofmitochondria DNA, halt the TCA cycle, and affect GSIS (47).Therefore, this Warburg-like metabolic shift may contributeto the early impairment of GSIS in GK b-cells because GSISrequires the production of both a triggering signal (ATP)and amplifying signals (i.e., cAMP, short-chain acyl-CoA com-pounds, NADPH) produced during aerobic metabolism (48).

Despite the early reduction in b-cell mass, insulin andthe enzymes required for proinsulin processing were com-parable or even higher at both the mRNA and the proteinlevel at 4–6 weeks, suggesting compensation during the

2198 Deteriorating Pancreatic Islets in T2D Diabetes Volume 66, August 2017

early phase in response to hyperglycemia. Absolute insulininsufficiency only occurred during the late stage of T2D.Consistently, we also observed compensation for OXPHOScomplexes and mitochondrial machinery at the mRNA levelat 4–8 weeks, despite reduced protein levels. Thus, the pro-gression of T2D in GK rats from 4 to 24 weeks can be dividedinto two stages: compensation (4–6 weeks) and compensationfailure (8–24 weeks). Further data mining to examine tem-poral expression patterns will help to elucidate the mecha-nisms underlying compensation and decompensation.

Mitochondrial dysfunction and the Warburg effect gen-erate greater ROS invasion, which in turn induces chroniclow-grade inflammation (49). Of note, proinflammatorycytokines also exhibit two distinct stages (Fig. 7): a primingstage at 4–6 weeks and an amplification stage after 8 weeks.Given that no inflammatory cell infiltration was observed inGK islets before 8 weeks (39), the priming stage of inflam-mation was likely induced by intracellular signals generatedby metabolic stress, such as the presence of increased ROSor free fatty acids. However, the second stage of inflamma-tion amplification may be induced by a complex combina-tion of intracellular and extracellular (i.e., macrophageinfiltration) inducers. The current data provide clues tounravel the mechanism underlying the initiation and am-plification of sterile inflammation; understanding this mech-anism is necessary to developing novel anti-inflammationtherapies to treat T2D. Islet inflammation is undoubtedlyan early event during T2D pathogenesis, but it is not likely acausal event because IL-1b and TXNIP were not significantlyexpressed at 4 weeks, although they gradually increasedduring later stages of the disease.

In summary, the data reveal two stages during theprogression of T2D in GK islets. The early stage (4–6 weeks) ischaracterized by anaerobic glycolysis, inflammation priming,and compensation for insulin synthesis, whereas the late stage(8–24 weeks) is characterized by inflammation amplificationand compensation failure (Supplementary Fig. 8). We didnot observe significant apoptosis during the early stage.The apoptosis pathway was only significantly elevated at24 weeks at the mRNA level. The time course transcriptomeand proteome data sets for GK rat islets depict a compre-hensive landscape of dynamic changes in gene expression atvarious stages of diabetes, representing a valuable resourcefor the research community to further explore the molecu-lar etiology and progression of diabetes. In-depth explora-tion of this resource will aid in the discovery of potentialdiagnostic and therapeutic targets for human T2D.

Acknowledgments. The authors thank the staff of the Institute of BiophysicsCore Facilities, in particular, Yan Teng for technical support with confocal imaging,Dr. Jifeng Wang for MS operation, and Zhen Fan and Xiaowei Chen for RNA-seqdesign and data collection.Funding. This work was supported by grants from the National Key BasicResearch Project of China (2014CB910503 and 2015CB910303), the National KeyResearch and Development Program of China (2016YFC0903301), the StrategicPriority Research Program of the Chinese Academy of Sciences (XDA12030101), andthe National Science Foundation of China (31421002, 31400703, and 31400658).

Duality of Interest. No potential conflicts of interest relevant to this articlewere reported.Author Contributions. J.H. performed the proteomic experiments and MSdata analysis. J.H. and W.Z. prepared the figures. J.H., W.Z., and D.Z. carried out thebioinformatics analyses. J.H., Y.W., and T.X. wrote the manuscript with help from theother authors. Z.L. performed the RNA-seq experiments and immunohistochemistryimaging. Q.H. carried out rat breeding. L.L. and L.W. performed the qRT-PCR andGSIS experiments. Y.W. carried out the islet preparation and animal experiments.Y.W. and T.X. conceived the project. All authors read the manuscript and discussedthe interpretation of results. T.X. is the guarantor of this work and, as such, had fullaccess to all the data in the study and takes responsibility for the integrity of the dataand the accuracy of the data analysis.

References1. Saisho Y. b-Cell dysfunction: its critical role in prevention and management oftype 2 diabetes. World J Diabetes 2015;6:109–1242. Turner RC. The U.K. Prospective Diabetes Study. A review. Diabetes Care 1998;21(Suppl. 3):C35–C383. Cnop M, Welsh N, Jonas JC, Jorns A, Lenzen S, Eizirik DL. Mechanisms ofpancreatic beta-cell death in type 1 and type 2 diabetes: many differences, fewsimilarities. Diabetes 2005;54(Suppl. 2):S97–S1074. Del Prato S, Bianchi C, Marchetti P. b-Cell function and anti-diabetic phar-macotherapy. Diabetes Metab Res Rev 2007;23:518–5275. Brun T, Li N, Jourdain AA, et al. Diabetogenic milieus induce specific changesin mitochondrial transcriptome and differentiation of human pancreatic islets. HumMol Genet 2015;24:5270–52846. Fonseca VA. Defining and characterizing the progression of type 2 diabetes.Diabetes Care 2009;32(Suppl. 2):S151–S1567. Weir GC, Bonner-Weir S. Five stages of evolving beta-cell dysfunction duringprogression to diabetes. Diabetes 2004;53(Suppl. 3):S16–S218. King AJ. The use of animal models in diabetes research. Br J Pharmacol 2012;166:877–8949. Goto YKM, Masaki N. Spontaneous diabetes produced by selective breeding ofnormal Wistar rats. Proc Jpn Acad 1975;51:510. Akash MS, Rehman K, Chen S. Goto-Kakizaki rats: its suitability as non-obesediabetic animal model for spontaneous type 2 diabetes mellitus. Curr Diabetes Rev2013;9:387–39611. Movassat J, Saulnier C, Serradas P, Portha B. Impaired development of pan-creatic beta-cell mass is a primary event during the progression to diabetes in the GKrat. Diabetologia 1997;40:916–92512. Plachot C, Movassat J, Portha B. Impaired beta-cell regeneration after partialpancreatectomy in the adult Goto-Kakizaki rat, a spontaneous model of type II di-abetes. Histochem Cell Biol 2001;116:131–13913. Fox-Walsh K, Davis-Turak J, Zhou Y, Li H, Fu XD. A multiplex RNA-seq strategyto profile poly(A+) RNA: application to analysis of transcription response and 39 endformation. Genomics 2011;98:266–27114. Dayon L, Hainard A, Licker V, et al. Relative quantification of proteins in humancerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal Chem 2008;80:2921–293115. Zhang Y, Wen Z, Washburn MP, Florens L. Refinements to label free proteomequantitation: how to deal with peptides shared by multiple proteins. Anal Chem 2010;82:2272–228116. Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA-seq data usingfactor analysis of control genes or samples. Nat Biotechnol 2014;32:896–90217. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis oflarge gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44–5718. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: pathstoward the comprehensive functional analysis of large gene lists. Nucleic Acids Res2009;37:1–1319. Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ. GAGE: generallyapplicable gene set enrichment for pathway analysis. BMC Bioinformatics 2009;10:161

diabetes.diabetesjournals.org Hou and Associates 2199

20. Vizcaino JA, Csordas A, del-Toro N, et al. 2016 update of the PRIDE databaseand its related tools. Nucleic Acids Res 2016;44:D447–D45621. Zhang B, Wang J, Wang X, et al. Proteogenomic characterization of humancolon and rectal cancer. Nature 2014;513:382–38722. Gry M, Rimini R, Stromberg S, et al. Correlations between RNA and proteinexpression profiles in 23 human cell lines. BMC Genomics 2009;10:36523. Guenifi A, Abdel-Halim SM, Hoog A, Falkmer S, Ostenson CG. Preserved beta-cell density in the endocrine pancreas of young, spontaneously diabetic Goto-Kakizaki (GK) rats. Pancreas 1995;10:148–15324. Schwanhausser B, Busse D, Li N, et al. Global quantification of mammaliangene expression control. Nature 2011;473:337–34225. Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One2010;5:e1398426. Portha B, Giroix MH, Tourrel-Cuzin C, Le-Stunff H, Movassat J. The GK rat: aprototype for the study of non-overweight type 2 diabetes. Methods Mol Biol 2012;933:125–15927. Portha B, Lacraz G, Chavey A, et al. Islet structure and function in the GK rat. InIslets of Langerhans. Islam MS, Ed. Dordrecht, Springer Netherlands, 2015, p. 743–76528. Giroix MH, Saulnier C, Portha B. Decreased pancreatic islet response toL-leucine in the spontaneously diabetic GK rat: enzymatic, metabolic and secretorydata. Diabetologia 1999;42:965–97729. Homo-Delarche F, Calderari S, Irminger JC, et al. Islet inflammation and fibrosisin a spontaneous model of type 2 diabetes, the GK rat. Diabetes 2006;55:1625–163330. Ostenson CG, Abdel-Halim SM, Rasschaert J, et al. Deficient activity of FAD-linkedglycerophosphate dehydrogenase in islets of GK rats. Diabetologia 1993;36:722–72631. Gheni G, Ogura M, Iwasaki M, et al. Glutamate acts as a key signal linkingglucose metabolism to incretin/cAMP action to amplify insulin secretion. Cell Reports2014;9:661–67332. Sener A, Malaisse WJ. The stimulus-secretion coupling of amino acid-inducedinsulin release. Insulinotropic action of L-alanine. Biochim Biophys Acta 2002;1573:100–10433. Yang J, Dolinger M, Ritaccio G, et al. Leucine stimulates insulin secretion viadown-regulation of surface expression of adrenergic alpha2A receptor through themTOR (mammalian target of rapamycin) pathway: implication in new-onset diabetesin renal transplantation. J Biol Chem 2012;287:24795–2480634. Sasaki M, Fujimoto S, Sato Y, et al. Reduction of reactive oxygen speciesameliorates metabolism-secretion coupling in islets of diabetic GK rats by sup-pressing lactate overproduction. Diabetes 2013;62:1996–200335. Lacraz G, Figeac F, Movassat J, et al. Diabetic beta-cells can achieve self-protection against oxidative stress through an adaptive up-regulation of their anti-oxidant defenses. PLoS One 2009;4:e6500

36. Chavey A, Bailbe D, Maulny L, Renard JP, Movassat J, Portha B. Aeuglycaemic/non-diabetic perinatal environment does not alleviate early beta cellmaldevelopment and type 2 diabetes risk in the GK/Par rat model. Diabetologia2013;56:194–20337. Khadra A, Schnell S. Development, growth and maintenance of beta-cell mass:models are also part of the story. Mol Aspects Med 2015;42:78–9038. Wang P, Fiaschi-Taesch NM, Vasavada RC, Scott DK, Garcia-Ocana A, StewartAF. Diabetes mellitus–advances and challenges in human beta-cell proliferation.Nat Rev Endocrinol 2015;11:201–21239. Calderari S, Irminger JC, Giroix MH, et al. Regenerating 1 and 3b gene ex-pression in the pancreas of type 2 diabetic Goto-Kakizaki (GK) rats. PLoS One2014;9:e9004540. Devi TS, Lee I, Huttemann M, Kumar A, Nantwi KD, Singh LP. TXNIP linksinnate host defense mechanisms to oxidative stress and inflammation in retinalMuller glia under chronic hyperglycemia: implications for diabetic retinopathy. ExpDiabetes Res 2012;2012:43823841. Ihara Y, Toyokuni S, Uchida K, et al. Hyperglycemia causes oxidative stress inpancreatic beta-cells of GK rats, a model of type 2 diabetes. Diabetes 1999;48:927–93242. Cantley J, Biden TJ. Sweet and sour beta-cells: ROS and Hif1alpha induceWarburg-like lactate production during type 2 diabetes. Diabetes 2013;62:1823–182543. Ehses JA, Lacraz G, Giroix MH, et al. IL-1 antagonism reduces hyperglycemiaand tissue inflammation in the type 2 diabetic GK rat. Proc Natl Acad Sci U S A2009;106:13998–1400344. Yoon KH, Ko SH, Cho JH, et al. Selective beta-cell loss and alpha-cell expansionin patients with type 2 diabetes mellitus in Korea. J Clin Endocrinol Metab 2003;88:2300–230845. Butler AE, Janson J, Bonner-Weir S, Ritzel R, Rizza RA, Butler PC. Beta-celldeficit and increased beta-cell apoptosis in humans with type 2 diabetes. Diabetes2003;52:102–11046. Matveyenko AV, Veldhuis JD, Butler PC. Mechanisms of impaired fasting glu-cose and glucose intolerance induced by an approximate 50% pancreatectomy.Diabetes 2006;55:2347–235647. Noda M, Yamashita S, Takahashi N, et al. Switch to anaerobic glucose me-tabolism with NADH accumulation in the beta-cell model of mitochondrial diabetes.Characteristics of betaHC9 cells deficient in mitochondrial DNA transcription. J BiolChem 2002;277:41817–4182648. Prentki M, Matschinsky FM, Madiraju SR. Metabolic signaling in fuel-inducedinsulin secretion. Cell Metab 2013;18:162–18549. Talero E, Garcia-Maurino S, Avila-Roman J, Rodriguez-Luna A, Alcaide A,Motilva V. Bioactive compounds isolated from microalgae in chronic inflammationand cancer. Mar Drugs 2015;13:6152–6209

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