The G Protein-Coupled Receptor GPR31 Promotes Pro-inflammatory Responses in Pancreatic Islets and Macrophages

The G Protein-Coupled Receptor GPR31 Promotes Pro-inflammatory Responses in Pancreatic Islets and Macrophages | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The G Protein-Coupled Receptor GPR31 Promotes Pro-inflammatory Responses in Pancreatic Islets and Macrophages View ORCID Profile Kerim B. Kaylan , Christian Checkcinco , View ORCID Profile Jacob R. Enriquez , Titli Nargis , Emily Elliott , Armando A. Puente , Jiayi E. Wang , Melissa Walsh , Jennifer B. Nelson , Abhishek Kulkarni , Charanya Muralidharan , Sarah C. May , Ryan M. Anderson , View ORCID Profile Raghavendra G. Mirmira , View ORCID Profile Sarah A. Tersey doi: https://doi.org/10.1101/2025.10.02.680021 Kerim B. Kaylan 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kerim B. Kaylan Christian Checkcinco 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacob R. Enriquez 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jacob R. Enriquez Titli Nargis 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily Elliott 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Armando A. Puente 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jiayi E. Wang 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa Walsh 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer B. Nelson 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abhishek Kulkarni 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charanya Muralidharan 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah C. May 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ryan M. Anderson 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Raghavendra G. Mirmira 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raghavendra G. Mirmira For correspondence: stersey{at}uchicago.edu mirmira{at}uchicago.edu Sarah A. Tersey 1 Department of Medicine and the Kovler Diabetes Center, The University of Chicago , Chicago, IL 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sarah A. Tersey For correspondence: stersey{at}uchicago.edu mirmira{at}uchicago.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT In type 1 diabetes (T1D), the innate and adaptive immune systems attack and eventually destroy the insulin-secreting pancreatic β cells. During this process, β cells activate inflammatory signaling pathways that augment the dysfunction and destruction imposed by cellular autoimmunity. The 12-lipoxygenase (12-LOX) pathway produces the pro-inflammatory eicosanoid 12-HETE, which induces oxidative and endoplasmic reticulum stress and results in diminished insulin secretion and apoptosis. The G protein-coupled receptor GPR31 has been identified as a putative receptor for 12-HETE. In this study, we generated conventional GPR31 knockout (KO) mice on the C57BL/6J background. To interrogate the role of GPR31 in β cells, we treated islets from wildtype and Gpr31b KO mice with pro-inflammatory cytokines and subjected the islets to RNA sequencing. Differentially expressed pathways in Gpr31b KO islets included those pertaining to inflammation and oxidative stress, consistent with functional studies that demonstrated reduced cytokine-induced oxidative stress in Gpr31b KO islets compared to wildtype controls. Bone marrow-derived macrophages from Gpr31b KO mice showed reduced macrophage migration and decreased inflammatory IFN-α and IFN-γ signaling by RNA sequencing. To mimic islet and macrophage inflammation as seen in T1D, wildtype and Gpr31b KO mice were treated with the diabetogenic toxin streptozotocin. Compared to wildtype, Gpr31b KO mice had improved glucose tolerance and preserved β-cell mass. siRNA knockdown of Gpr31b in non-obese diabetic (NOD) mice reduced insulitis, macrophage infiltration, and oxidative stress. Collectively, these findings are consistent with previously published data using 12/15-LOX KO mice and suggest that GPR31 mediates the pro-inflammatory responses of 12-HETE in both β cells and macrophages. INTRODUCTION In the classical perspective of type 1 diabetes (T1D), loss of immune tolerance triggers a T cell-mediated immune response that destroys the insulin-producing pancreatic β cells. This immune-centric perspective has been challenged in recent years, and emerging evidence shows that other cell types within the pancreas, such as macrophages and β cells, actively participate in the pathogenesis of T1D [ 1 – 3 ]. For example, blockade of inflammatory signaling cascades within β cells alone prevents the emergence of adaptive immunity and β-cell loss in non-obese diabetic (NOD) mice [ 2 , 4 ]. Similarly, blockade of inflammatory responses in early infiltrating macrophages in NOD mice prevents adaptive immunity and overt T1D [ 1 , 5 , 6 ]. These findings are not limited to mouse models. Clinical studies employing the repurposed drugs verapamil and eflornithine suggest that reductions in β-cell oxidative and endoplasmic reticulum (ER) stress appear to preserve β-cell function in early T1D [ 7 , 8 ]. These and other studies emphasize the need to identify pathways and potential targets in β cells and macrophages that may contribute to the initiation or propagation of T1D. Lipoxygenase (LOX) enzymes are critical regulators of inflammation that catalyze the oxygenation of polyunsaturated fatty acids to form both pro- and anti-inflammatory mediators [ 9 , 10 ]. Among enzymes present in both β cells and macrophages, 12/15-LOX converts arachidonic acid (C20:4) to the pro-inflammatory eicosanoid 12( S )-hydroxyeicosatetraenoic acid (12( S )-HETE or simply 12-HETE) [ 11 , 12 ]. In individuals at risk for or with T1D, 12/15-LOX production is notably elevated in pancreatic islets [ 13 ], and levels of 12-HETE are elevated in subjects with new-onset T1D [ 14 ]. Chemical inhibition of 12/15-LOX decreases 12-HETE production in cytokine-exposed human islets and improves β-cell function [ 15 ]. In NOD mice, tissue-specific deletion of the gene encoding 12/15-LOX ( Alox15 ) in either β cells or macrophages results in reduced islet inflammation (insulitis) and protection from the development of diabetes in both sexes [ 1 , 4 ]. In NOD mice with the human ortholog replacing murine 12/15-LOX, inhibition with the selective inhibitor VLX-1005 delays diabetes onset through reduction in both β cell stress and inflammatory signaling in macrophages [ 16 ]. Although the signals transducing 12/15-LOX action have not been fully elucidated, it is known that its primary product, 12-HETE, can activate various pro-inflammatory signaling pathways, such as the mitogen-activated protein kinase (MAPK) pathways including p38 MAPK and c-JUN N-terminal kinase (JNK) [ 17 , 18 ], and can induce or exacerbate oxidative stress and ER stress [ 19 ]. As a lipid peroxide, 12-HETE may exert pro-oxidative effects in β cells and macrophages as it accumulates to critical levels intracellularly. However, since 12-HETE can be measured in circulation, its effects may not be limited to cell-autonomous functions, raising the possibility that an eicosanoid receptor may mediate systemic effects. The orphan G protein-coupled receptor GPR31 has been shown to function as a receptor for 12-HETE [ 20 ]. GPR31 displays high affinity (K d ∼4 nM) for 12-HETE and exhibits stereospecificity for its biologically relevant S- enantiomer. GPR31 is coupled to G αq , and its effects are mediated by phosphatidylinositol signaling and subsequent activation of MAPK, extracellular signal-regulated kinases 1/2 (ERK1/2), and nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB) [ 20 ]. Consistent with the idea that GPR31 functions as a receptor for 12-HETE, knockdown of its encoding gene in human hepatocytes diminished 12-HETE-induced activation of protein kinase C (PKC)–JNK signaling [ 21 ]. However, the role of GPR31 in macrophages and β cells remains speculative. In one study, the gene encoding GPR31 ( Gpr31b ) was found to be induced in islet-resident macrophages following high-fat diet feeding of mice [ 22 ]. Yet, its direct role in macrophage-mediated inflammation was not explored. Another study in zebrafish suggested that GPR31 lies downstream of 12/15-LOX in islets based on the developmental phenotype [ 23 ], but its role in β-cell inflammation was not examined. In this study, we generated mice with genetic deletion of Gpr31b to test the hypothesis that GPR31 promotes β-cell and macrophage inflammatory signaling and contributes to β-cell dysfunction in diabetes. RESEARCH DESIGN AND METHODS Mouse studies All animal experiments were approved by the University of Chicago Institutional Animal Care and Use Committee. All mice used in this study were housed under specific pathogen-free conditions with a standard 12 h light:dark cycle. Gpr31b Loxp/+ mice were generated using conditional long-arm bacterial artificial chromosome (BAC) technology to insert loxP sites around the single exon 1 and a Neo cassette for selection with flippase recombinase (FLP) sites for removal (Ingenious Targeting Labs, Ronkonkoma, NY). The Neo cassette was deleted via FLP recombination in vitro using FLP-expressing embryonic stem cells. Chimeric mice were then crossed to Sox2 -Cre mice (Jackson Laboratories #8454) to generate somatic germline whole-body Gpr31b -/- (herein referred to as Gpr31b KO) mice ( Figure 1B ). Gpr31b KO mice were backcrossed more than 10 generations onto the C57BL/6J background (Jackson Laboratories #664) using remote speed congenics services (Jackson Labs, Bar Harbor, ME). Download figure Open in new tab Figure 1: Mice with whole-body Gpr31b knockout (KO) exhibit normal body growth and glycemic control. (A) Relative expression of Gpr31b in tissues from C57BL/6J mice by quantitative polymerase reaction (PCR) (n=3 mice per condition). BMDM: bone-marrow derived macrophages; pMacs: peritoneal macrophages. (B) Schematic of approach to whole-body KO of Gpr31b . (C) Representative genotypes of wild type (WT) and Gpr31b KO mice by PCR. The WT Gpr31b allele generates an amplicon of 335 bp and the mutant allele generates an amplicon of 699 bp. Additionally, there is a WT band that includes pseudogenes with an amplicon of 3100 bp. (D) Relative expression of Gpr31b in WT, heterozygous, and Gpr31b KO mice by quantitative PCR (n=3 mice per condition). (E) Sequencing of PCR products of amplification of Gpr31b and its pseudogenes (n=3 mice per condition). (F) Body weight of 8-week-old males. (G) Glucose tolerance testing (GTT) of 8-week-old males ( left panel ) with area under the curve (AUC, right panel ). (H) GTT of 8-week-old males ( left panel ) with AUC (right panel). (I) Body weight of 8-week-old females. (J) GTT of 8-week-old females ( left panel ) with AUC ( right panel ). (K) GTT of 15-week-old females ( left panel ) with AUC ( right panel ). Data presented as mean with SEM. Single data points represent individual mice. Statistical significance determined by Student’s t-test or one-way analysis of variance (ANOVA). Allele-specific genotyping was performed by polymerase chain reaction (PCR) using the following primers: 5’–GCA GGA CGT GTC TTG TAA ACA GC–3’ and 5’–TTC CCT GGG GAC TCT ATG GG–3’; 5’–CAA ATT CTG CTG GCT ACT TGG–3’ and 5’–GGC TTC CAT ACT TTG AGA CG–3’. Using these primers, the wildtype (WT) Gpr31b allele generates an amplicon of 335 bp and the mutant allele generates an amplicon of 699 bp. Additionally, there is a WT band that includes pseudogenes with an amplicon of 3100 bp ( Figure 1C ). For multiple low-dose streptozotocin (mldSTZ) studies, mice were intraperitoneally (IP) injected with 55 mg/kg STZ (Sigma, St. Louis, MO) for 5 consecutive days. Blood glucose was monitored via tail vein using a glucometer (AlphaTrak, Zoetis, Parsippany-Troy Hills, NJ). For glucose tolerance testing at day 12 after the initial dose of STZ, mice were fasted for 16 h. Mice were IP injected with glucose (2 g/kg) and blood glucose was measured at 0, 10, 20, 30, 60, 90, and 120 min. For small interfering RNA (siRNA) studies, 6-week-old female NOD mice (Jackson Laboratories #1976) were IP injected with 1.7 mg/kg of siRNA against a non-targeting control pool (siNTC) or Gpr31b (si Gpr31b ) (Accell, Horizon Discovery, Cambridge, UK). Mice received three injections each spaced one day apart. The study ended 2 weeks after the initial dose of siRNA. At the end of each study, mice were euthanized and tissue and blood were collected. To isolate islets, collagenase was injected into the pancreatic bile duct to inflate the pancreas prior to removal as previously described [ 24 ]. Briefly, a Histopaque–Hank’s balanced salt solution gradient was applied to the dissociated pancreas and followed by centrifugation at 900 rcf for 18 min. The mouse islets were then removed from the center of the gradient and cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Islets were handpicked and allowed to recover overnight before experimentation. Zebrafish husbandry and maintenance Zebrafish were maintained in 2 L tanks at 28.5°C in a recirculating aquaculture system, in accordance with University of Chicago Institutional Animal Care and Use Committee regulations. The transgenic line used for experiments was Tg(mpeg:eGFP) gl22 [ 25 ]. Embryos were collected at spawning and placed into egg water (0.1% Instant Ocean salts, 0.0075% calcium sulfate, and 0.1% methylene blue). At 24 h post-fertilization, the media was replaced by egg water supplemented with 0.003% 1-phenyl-2-thiouria (PTU) to prevent pigmentation. Morpholinos (MO) were purchased from GeneTools (Philomath, OR) with the following sequences: gpr31 MO sequence: 5’–gtgca gattc ccagt ccgag atgac–3’; alox12 MO sequence: 5’–ccact gtcac tttgt actcc atctt–3’ [ 23 ]. For microinjections, MO were diluted with nuclease-free H 2 O and mixed with 0.1% phenol red marker. The injection volume was calibrated by determining the diameter of the injection droplet on mineral oil to inject 2 ng of the MO. Zygotes were collected at spawning and injected at the 1-cell stage. The embryos marked with phenol red were sorted for use in later experiments. Tailfin transection assay For the tailfin transection assay, larvae at 3 days post-fertilization (dpf) were paralyzed with 0.01% tricaine to prevent movement. The distal tips of the tailfins were cut with a sharp scalpel as previously described [ 1 ]. Following injury, embryos were placed in a 28.5°C incubator for 6 h with or without 12-HETE (3 µM; Cayman Chemicals, Ann Arbor, MI). At the end of the incubation period, the embryos were fixed with 3% formaldehyde in PEM buffer at 4°C for 18 h, washed, deyolked, and counterstained for nuclei with 4’,6-diamidino-2-phenylindole (DAPI; Thermo Fisher, Waltham, MA). The embryos were mounted on glass slides with antifade medium (Vector Labs, Newark, CA) and confocal imaging was performed with a Nikon A1 microscope (Nikon USA, Melville, NY). Macrophage number was determined by counting macrophages between the cut site and the distal tip of the notochord rod. Macrophage isolation and polarization Bone marrow-derived macrophages (BMDMs) were isolated as described previously [ 1 ] and cultured for 7 days in complete medium: RPMI containing 10% FBS, 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer, 100 U/ml penicillin/streptomycin, and 10 ng/ml M-CSF. On day 7, BMDMs were stimulated for 16 h with 10 ng/ml lipopolysaccharide (LPS) and 25 ng/ml IFN-γ for M1-like polarization and 10 ng/ml IL-4 for M2-like polarization. Polarized BMDMs were subsequently utilized for in vitro migration studies and RNA sequencing. Peritoneal macrophages were isolated from 8-10-week-old mice by injecting 10 ml ice cold phosphate-buffered saline (PBS) containing 3% FBS into the peritoneal cavity and removed using a 20-gauge needle as described previously [ 26 ]. Cells were lysed with red blood cell (RBC) lysis buffer to remove RBCs and washed with PBS. Isolated peritoneal macrophages were used for flow cytometry. In vitro macrophage migration studies For the migration assays, bone marrow-derived macrophages (2×10 5 ) were placed in the upper chamber of an 8 μm transparent polyethylene terephthalate (PET) membrane (Corning, Corning, NY) and RPMI 1640 medium with 10% FBS was loaded in the bottom chamber. Cells were incubated for 4 h at 37°C, fixed in formalin and stained with crystal violet. Migrated cells were mounted on a slide and imaged using a BZ-X810 fluorescence microscope (Keyence, Elmwood, NJ). The number of macrophages was quantified by manual counting. Flow cytometry To stain for surface antigens, peritoneal macrophages were incubated with F4/80 (Biolegend, San Diego, CA) and CD11b (Biolegend, San Diego, CA) antibodies for 20 min on ice. For intracellular staining, cells were permeabilized with Cytofix/Cytoperm (BD Pharmingen, Franklin Lakes, NJ) and incubated with CD206 (141706; Biolegend, San Diego, CA), iNOS (56-5920-82; Invitrogen, Carlsbad, CA), CD4 (100510; Biolegend), CD8 (100734; Biolegend), CD19 (152414, Biolegend), FoxP3 (560401, BD Biosciences), IFN-γ, (554412, BD Biosciences) or IL17a (560821, BD Biosciences) antibodies for 20 min on ice. All antibodies were used at 1:100 dilution. Cells were analyzed on the Attune NxT Flow Cytometer (BD Bioscience, Franklin Lakes, NJ). Data were analyzed by FlowJo software (FlowJo, BD, Ashland, OR). Immunofluorescence and immunohistochemistry Pancreata were fixed in 4% paraformaldehyde, paraffin embedded, and sectioned. Tissues were immunostained with anti-insulin (1:1000; 15848-1-AP, ProteinTech, Rosemont, IL) or anti-F4/80 (1:150; D2S9R, Cell Signaling, Danvers, MA) followed by conjugated anti-rabbit Ig (Vector Laboratories, Newark, CA), 3,3’-diaminobenzidine (DAB) peroxidase substrate kit (Vector Laboratories, Newark, CA), and counterstained with hematoxylin (Sigma, St. Louis, MO). Images were acquired using a BZ-X810 fluorescence microscope (Keyence, Elmwood, NJ). β-cell mass was calculated as previously described [ 27 ]. For fluorescence labeling, tissues were immunostained with anti-4-HNE (1:200; ab65545, Abcam, Waltham, MA), anti-insulin (1:4; IR002, Dako, Carpinteria, CA), followed by conjugated anti-rabbit Ig and anti-guinea pig Ig (1:500; ThermoFisher, Waltham, MA) and DAPI. Imaging was performed using a Nikon A1 confocal microscope (Nikon, Melville, NY). Intensity of 4-HNE was quantified using Fiji [ 28 ]. We quantified β-cell mass and scored insulitis as previously described [ 29 , 30 ]. Briefly, the insulitis score reflects the degree of immune cell infiltration within pancreatic islets. The score system we used is as follows: 1, no insulitis; 2, infiltrate 50% circumference; 4, infiltration within islet. Enzyme-linked immunosorbent assay CD5L protein levels in islet supernatants were quantified using a mouse CD5L enzyme-linked immunosorbent assay (ELISA) kit (orb565622; Biorbyt, Cambridge, UK) according to the manufacturer’s instructions. Briefly, islet culture supernatant was added to pre-coated wells and incubated for 90 min at 37°C, followed by sequential incubations with biotin-labeled detection antibody and horseradish peroxidase (HRP)-streptavidin conjugate. After incubation with 3,3′,5,5′-tetramethylbenzidine (TMB) substrate for no more than 30 minutes, optical density was measured at 450 nm and CD5L concentrations were calculated from a standard curve. Quantitative PCR RNA was isolated from mouse tissues and macrophages using an RNAeasy Mini kit (Qiagen, Germantown, MD) and cDNA synthesis was performed using a High-Capacity cDNA Reverse Transcription kit (Applied Biosystems, Waltham, MA) according to manufacturer’s instructions. For siRNA studies, MIN6 murine β cells were treated with and without pro-inflammatory cytokines (PIC; 10 ng/ml TNF-α, 100 ng/ml IFN-γ, 5 ng/ml IL-1β) and either 1 μM siNTC or si Gpr31b (Horizon Discovery, Cambridge, UK) for 24 h followed by RNA isolation. Quantitative PCR was performed using Bio-Rad CFX Opus with a predesigned Taqman assay probe for mouse genes Gpr31 (Mm02391728_g1) and Actb (Mm01205647_m1) (Invitrogen, Carlsbad, CA). Relative gene expression was calculated using the comparative threshold cycle value (C t ) normalized to Actb . In vitro oxidative stress measurement For assessment of oxidative stress in pancreatic islets isolated from WT and Gpr31b KO mice, we used a fluorescent dye-based free radical sensor (CellROX; Thermo Fisher Scientific, Waltham, MA). We followed the manufacturer’s instructions; briefly, islets were treated with PIC for 18 h. For the final 30 minutes of treatment with PIC, 5 µM CellROX reagent was added to the media. At endpoint, the islets were fixed and imaged as above. Intensity of CellROX was quantified using Fiji [ 28 ]. RNA sequencing Islets and BMDMs were isolated as described above. RNA was isolated using an RNAeasy Mini kit (Qiagen, Germantown, MD). Raw islet sequencing data was analyzed with Galaxy [ 31 ]. Reads were trimmed using trimmomatic (islets) [ 32 ] or cutadapt (macrophages) [ 33 ]. Quality scores were assessed using FastQC. Reads were aligned to the Mus musculus genome build GRCm38 (islets) or GRCm39 (macrophages) using STAR [ 34 ]. Individual sample reads were quantified using HTseq [ 35 ] (islets) or featureCounts [ 36 ] (macrophages). Genes with fewer than 5 counts in fewer than 2 samples per group were filtered out. Raw counts were normalized via relative log expression with DESeq2 [ 37 ] using R Statistical Software (version 4.5.1; R Core Team 2025). Prior to differential expression analysis, batch effects between experimental replicates were corrected using ComBat-Seq [ 38 ]. DEseq2 was used to calculate fold changes, adjust P-values for false discovery rate, and perform covariate correction. Gene set enrichment analysis was carried out using clusterProfiler [ 39 ] and the MSigDB Hallmark database [ 40 ], Gene Ontology knowledgebase [ 41 ], or Reactome [ 42 ] as indicated. Statistical analysis Data analyses were performed using the GraphPad Prism 10 software package (GraphPad, Boston, MA). Unless otherwise noted, statistical testing to determine significant differences was performed using two-tailed, unpaired Student’s t-test for comparing 2 means and one-way ANOVA followed by post-hoc Tukey’s test for comparing ≥3 means. P-values that were <0.05 were considered statistically significant. Data and resource availability RNA sequencing datasets have been deposited to the NIH Gene Expression Omnibus (GEO) with the accession numbers GSE284693 (islets) and GSE285039 (macrophages). RESULTS Mice with whole-body Gpr31b knockout exhibit normal body growth and glycemic control Prior work identified GPR31 as a receptor for 12-HETE [ 20 ]. Based on previous data showing 12-HETE is produced by pancreatic islet β cells and macrophages, we sought to determine if GPR31 is also expressed in islets and macrophages. In WT mice, we found Gpr31b mRNA to be expressed predominantly in the islets with lower expression in spleen, bone marrow derived macrophages (BMDMs), peritoneal macrophages, liver, and fat ( Figure 1A ) . To study the role of GPR31 in islet inflammation, we generated a whole-body knockout (KO) of GPR31 on the C57BL/6J background. In mice, GPR31 is encoded by the gene Gpr31b , but there are two non-functional Gpr31b pseudogenes ( Gpr31a and Gpr31c ) that do not encode protein. Because of the extensive homology between the three genes, we generated conditional Gpr31b Loxp/+ mice using long-arm bacterial artificial chromosome (BAC) technology. The Neo cassette was then deleted via FLP recombination in vitro using FLP-expressing embryonic stem cells. Chimeric mice were then crossed to Sox2 -Cre mice to generate somatic germline whole-body Gpr31b -/- (herein referred to as Gpr31b KO) mice ( Figure 1B ). To ensure that the Gpr31b KO mice are congenic with the background strain, they were backcrossed more than 10 generations onto the C57BL/6J background using remote speed congenics services (Jackson Labs). WT and Gpr31b KO mouse genotypes were confirmed by PCR genotyping ( Figure 1C ). To verify the deletion of Gpr31b in the Gpr31b KO mice, we performed quantitative PCR for Gpr31b mRNA in isolated pancreatic islets. Gpr31 mRNA expression is reduced by 40% in Gpr31b KO islets compared to islets isolated from WT littermates ( Figure 1D ). Because the TaqMan-based quantitative PCR analysis does not distinguish between Gpr31b and Gpr31a , we sequenced the PCR products to identify each gene individually. We found that WT islets express 70% Gpr31a and 30% Gpr31b , while Gpr31b KO islets only express Gpr31a , confirming that the appropriate gene has been deleted ( Figure 1E ). Gpr31b KO mice exhibit normal Mendelian inheritance and appear healthy. Both male and female KO mice have a comparable body weight to WT littermates and have normal glucose homeostasis at 8 and 15 weeks of age ( Figure 1F-K ). These data are similar to Alox15 -/- mice, which also appear normal and healthy with normal glucose homeostasis [ 43 ]. GPR31 mediates inflammatory and reactive oxygen species signaling in islets Because 12-LOX inhibition reduces pro-inflammatory islet responses [ 44 , 45 ], we next wanted to investigate how GPR31 affects the transcriptome of pancreatic islets under normal and pro-inflammatory conditions. Therefore, we isolated islets from WT and Gpr31b KO mice treated with vehicle or pro-inflammatory cytokines (PIC; TNF-α, IFN-γ, IL-1β), extracted RNA, and performed RNA sequencing ( Figure 2A ). Of note, Gpr31b expression in this RNA sequencing analysis was only identified in WT islets treated with PIC ( Figure 2B ). Principal components analysis (PCA) revealed two distinct clusters between vehicle-treated WT and Gpr31b KO islets ( Figure 2C ). When comparing vehicle-treated islets from WT and Gpr31b KO mice, only 35 out of 15,212 identified genes reached the differential expression threshold (absolute fold change ≥2 and adjusted P-value <0.05). Lowering the cutoff for absolute fold change to ≥1.5 with the same adjusted P-value cutoff resulted in 64 out of 15,212 identified genes reaching the differential expression threshold ( Figure 2D ). Using gene set enrichment analysis (GSEA) with hallmark gene sets, 16 pathways were significantly enriched (adjusted P-value <0.05). Significantly enriched pathways included those related to epithelial-mesenchymal transition, translation (E2F targets), proliferation (G2M checkpoint), oxidative phosphorylation, and inflammatory responses ( Figure S1A ). Download figure Open in new tab Figure S1: Additional gene set enrichment analysis of islet RNA sequencing. (A) Gene set enrichment analysis (GSEA) of WT and Gpr31b KO islets treated with vehicle (n=3 mice per condition) using hallmark gene sets. Both suppressed ( top panel ) and activated gene sets ( bottom panel ) are shown. Normalized enrichment score (bars) and log-transformed P-value (blue dots). All pathways meeting significance criteria of adjusted P-value <0.05 are shown. (B) Suppressed gene sets from GSEA of WT and Gpr31b KO islets treated with cytokines using gene ontology (GO) annotations. (C) Activated gene sets from GSEA of WT and Gpr31b KO islets treated with cytokines using GO annotations. Normalized enrichment score (bars) and log-transformed P-value (blue dots). Download figure Open in new tab Figure 2: GPR31 mediates inflammatory and reactive oxygen species signaling in islets. (A) Schematic of experimental approach for RNA sequencing of islets treated with vehicle or pro-inflammatory cytokines (PIC). (B) RNA sequencing data showing relative expression of Gpr31b in WT and KO islets treated with PIC (n=3 mice per condition). (C) Principal components analysis (PCA) of WT and Gpr31b KO islets treated with vehicle (n=3 mice per condition). (D) Volcano plot of differentially expressed genes in WT and Gpr31b KO islets treated with vehicle. Genes which meet the threshold of absolute fold change ≥1.5 and adjusted P-value <0.05 are in red; genes which only meet the adjusted P-value threshold are in blue. (E) Principal components analysis (PCA) of WT and Gpr31b KO islets treated with PIC (n=3 mice per condition). (F) Volcano plot of differentially expressed genes in WT and Gpr31b KO islets treated with PIC (n=3 mice per condition). Genes which meet the threshold of absolute fold change ≥1.5 and adjusted P-value <0.05 are in red; genes which only meet the adjusted P-value threshold are in blue. (G) Analysis of CD5L concentration by enzyme-linked immunosorbent assay (ELISA) in the supernatant of islets treated with vehicle or PIC. (H) Gene set enrichment analysis (GSEA) of WT and Gpr31b KO islets treated with PIC (n=3 mice per condition) using hallmark gene sets. Both suppressed ( top panel ) and activated gene sets ( bottom panel ) are shown. Normalized enrichment score (bars) and log-transformed P-value (blue dots). All pathways meeting significance criteria of adjusted P-value <0.05 are shown. (I) Heatmap from WT and Gpr31b KO islets treated with PIC showing Z-scores for differentially expressed genes meeting more stringent criteria of absolute fold change ≥2 with adjusted P-value <0.05. (J) Heatmap from WT and Gpr31b KO islets treated with PIC showing Z-scores for genes in the leading edge for the ROS hallmark gene set. Data presented as mean with SEM. Statistical significance determined by Student’s t-test or paired one-way analysis of variance (Friedman ANOVA). Upon PIC treatment, PCA analysis revealed two distinct clusters ( Figure 2E ). When comparing PIC-treated islets from WT and Gpr31b KO mice, only 65 out of 15,813 identified genes reached the differential expression threshold (absolute fold change ≥2 and adjusted P-value <0.05). Lowering the cutoff for absolute fold change to ≥1.5 with the same adjusted P-value cutoff, 150 genes out of 15,813 identified reached the differential expression threshold ( Figure 2F ) . The top gene upregulated in the Gpr31b KO islets was Cd5l , which encodes the scavenger receptor CD5L with known roles in anti-inflammatory responses and lipid metabolism [ 46 , 47 ]. Given that CD5L is a secreted protein, we confirmed by enzyme-linked immunosorbent assay (ELISA) that CD5L protein levels are greater in the supernatant of Gpr31b KO islets treated with PIC compared to WT islets ( Figure 2G ). Using GSEA with hallmark gene sets to analyze PIC-treated islets, 24 pathways were significantly enriched (adjusted P-value <0.05) ( Figure 2H ). These pathways included those related to translation (E2F targets), proliferation (G2M checkpoint), oxidative phosphorylation, inflammatory responses, and reactive oxygen species (ROS). We visualized a subset of the differentially expressed genes in this data set meeting more stringent criteria (absolute fold change ≥2 and adjusted P-value <0.05) ( Figure 2I ). For a more granular analysis of pathway enrichment, we performed GSEA with gene ontology (GO) annotations, which showed suppression of multiple pathways related to neurotransmitter or synapse function, as well as activation in pathways related to immunity ( Figure S1B-C ). Given the known importance of ROS in islet inflammation, we examined expression of the gene transcripts found in the leading edge of the hallmark ROS gene set, which showed upregulation of multiple antioxidant genes including catalase ( Cat ), glutathione peroxidase 4 ( Gpx4 ), two peroxide scavengers ( Prdx1 , Prdx4 ), and protein disulfide reduction ( Txn1 ) ( Figure 2J ). Given this enrichment of transcripts encoding antioxidant genes, we next directly asked whether GPR31 regulates cytokine-induced oxidative stress in islets. To answer this question with a functional readout, we used a fluorescent dye-based free radical sensor (CellROX). We treated WT islets with PIC for 18 h which resulted in an increase in ROS production ( Figure 3 ). By contrast, PIC-induced ROS production was reduced in Gpr31b KO islets, consistent with the results of our RNA sequencing and what we have previously observed with Alox15 -/- mice or inhibition of 12-LOX [ 19 , 44 ]. Download figure Open in new tab Figure 3: GPR31 promotes oxidative stress in islets. (A) Representative fluorescence images of WT and Gpr31b KO islets treated with vehicle or PIC. Imaging shows nuclei stained by 4’,6-diamidino-2-phenylindole (DAPI; blue) and CellROX (magenta). Scale bars are 100 µm. (B) Quantification of CellROX mean intensity for the experiment in (A) (n=5-7 mice). Data presented as mean with SEM. Single data points represent individual mice. Statistical significance determined by one-way ANOVA. GPR31 promotes macrophage inflammatory signaling and migration Given our data showing expression of Gpr31b in the myeloid lineage ( Figure 1A ), we next asked what function GPR31 might have in myeloid cells. First, to determine if GPR31 affects macrophage polarization, we isolated peritoneal macrophages from WT and Gpr31b KO mice. We polarized these macrophages into classically activated (M1) macrophages using LPS and IFN-γ or alternatively activated (M2) macrophages using IL-4 and initially analyzed their phenotypes by flow cytometry ( Figure 4A ). The macrophages from both WT and Gpr31b KO mice showed indistinguishable propensity to polarize to an M1-like state, as assessed by flow cytometry of the M1 marker inducible nitric oxide synthase (iNOS) ( Figure 4B ). Similarly, upon alternative polarization, macrophages from both WT and Gpr31b KO mice showed no differences by flow cytometry in the M2 marker CD206 ( Figure 4C ). Download figure Open in new tab Figure 4: GPR31 promotes macrophage inflammatory signaling and migration. (A) Schematic of experimental approach for polarization of macrophages. (B) Flow cytometry for the M1 marker iNOS as a percentage of F4/80+ cells in macrophages from WT and Gpr31b KO mice (n=5-6 mice per condition). (C) Flow cytometry for the M2 marker CD206 as a percentage of F4/80+ cells in macrophages from WT and Gpr31b KO mice (n=6 mice per condition). (D) Relative expression of Gpr31b from both M0 and M1-like macrophage by RNA sequencing (n=3 mice per condition). (E) Volcano plot of differentially expressed genes in M0 macrophages from WT and Gpr31b KO mice (n=3 mice per condition). Genes which meet the threshold of absolute fold change ≥1.5 and adjusted P-value <0.05 are in red; genes which only meet the adjusted P-value threshold are in blue. (F) Volcano plot of differentially expressed genes in M1-like macrophages from WT and Gpr31b KO mice (n=3 mice per condition). Genes which meet the threshold of absolute fold change ≥1.5 and adjusted P-value <0.05 are in red; genes which only meet the adjusted P-value threshold are in blue. (G) GSEA of M1-like macrophages from WT and Gpr31b KO mice (n=3 mice per condition) using hallmark gene sets. Both suppressed ( top panel ) and activated gene sets ( bottom panel ) are shown. Normalized enrichment score (bars) and log-transformed P-value (blue dots). All significant pathways are shown. (H) Leading edge genes in M1-like macrophages from WT and Gpr31b KO mice from the IFN-γ and the IFN-α response hallmark gene sets. Genes represented in both leading edges are in the far left heatmap. (I) Schematic of BMDM transwell migration assay. (J) Quantification of transwell migration assay of BMDMs from WT and Gpr31b KO mice (n=3 mice per condition). (K) Schematic of zebrafish tailfin cut assay. (L) Representative images of zebrafish tailfin cut assay showing DNA (TO-PRO; yellow) and macrophages (GFP; green) ( left panel ). Scale bars are 100 µm. Quantification of assay results as macrophage count ( right panel ) (n=26-32 zebrafish per condition). Data presented as mean with SEM. Statistical significance determined by Student’s t-test or one-way ANOVA. We next performed RNA sequencing on both M0 and M1-like BMDMs isolated as previously described [ 48 ] and polarized as above. Normalized counts from RNA sequencing confirmed Gpr31b KO in both M0 and M1-like macrophages ( Figure 4D ). PCA revealed two distinct clusters between M0 macrophages from WT and Gpr31b KO mice ( Figure S2A ). When comparing M0 macrophages from WT and Gpr31b KO mice, only 4 genes out of a total 13,882 identified genes reached the differential expression threshold (absolute fold change ≥2 and adjusted P-value <0.05). Lowering the cutoff for absolute fold change to ≥1.5 with the same adjusted P-value cutoff resulted in 71 genes out of a total 13,882 identified genes reaching the differential expression threshold ( Figure 4E ). PCA revealed two distinct clusters between M1-like macrophages from WT and Gpr31b KO mice ( Figure S2B ). When comparing M1-like macrophages from WT and Gpr31b KO mice, only 3 genes out of a total 12,914 identified genes reached the differential expression threshold (absolute fold change ≥2 and adjusted P-value <0.05). Lowering the cutoff for absolute fold change to ≥1.5 with the same adjusted P-value cutoff resulted in 17 genes out of a total 12,914 identified genes reaching the differential expression threshold ( Figure 4F ). Using GSEA with hallmark gene sets revealed decreased enrichment of multiple inflammatory signaling pathways in the Gpr31b KO macrophages, including IFN-γ and IFN-α responses, as well as increased enrichment of metabolic pathways including glycolysis and cholesterol homeostasis ( Figure 4G ). Using the leading edge genes from the hallmark IFN-γ and IFN-α gene sets, we visualized expression by heatmap, which showed consistent downregulation of genes in each leading edge, including overlapping genes shared between both pathways ( Figure 4H ). Complementary analysis using Reactome gene sets showed 77 significant pathways, including decreased enrichment of GPCR ligand binding, signaling by GPCR, G α1 signaling events, antigen processing and cross presentation, fatty acid metabolism, and carbohydrate metabolism ( Supplemental Table S1 ). Collectively, these data show that GPR31 drives pro-inflammatory signaling in M1-polarized macrophages. Download figure Open in new tab Figure S2: PCA analysis of RNA sequencing data from macrophages and tailfin transection assay of alox12 MO-injected zebrafish. (A) PCA of M0 macrophages (n=3 mice per condition). (B) PCA of M1-like macrophages (n=3 mice per condition). (C) Macrophage count from the tailfin transection assay in zebrafish injected with alox12 MO. Single data points represent individual zebrafish. Quantification of assay results as macrophage count (n=16-22 zebrafish per condition). Data presented as mean with SEM. Statistical significance determined by one-way ANOVA. To determine if GPR31 affects cell migration, another major macrophage function, we isolated BMDMs from WT and Gpr31b KO mice and performed an in vitro migration assay using transwell chambers ( Figure 4I ). The number of Gpr31b KO macrophages that migrated through the transwell membrane was significantly less than the number of WT macrophages ( Figure 4J ). To verify these results and quantify macrophage migration in vivo , we used the tailfin transection model in Tg(mpeg:eGFP) transgenic zebrafish in which macrophages are fluorescently labeled with green fluorescent protein (GFP) ( Figure 4K ). We performed knockdown of the zebrafish ortholog, gpr31 , using a morpholino (MO) as previously described [ 23 ]. Both gpr31 MO knockdown and control zebrafish underwent tailfin transection at 3 days post fertilization (dpf), and the number of macrophages migrating to the injury site after 6 h was quantified ( Figure 4L ). In control zebrafish, we observed the expected migration of macrophages (10.7±3.8 macrophages) to the injury site, whereas gpr31 MO zebrafish showed a significant reduction in macrophage recruitment (6.60±3.95 macrophages) ( Figure 4L ). Given that the pro-inflammatory eicosanoid 12-HETE is a ligand for GPR31 [ 20 ], we also tested whether exogenous 12-HETE could rescue the migration defect by including 12-HETE treatment (3 µM) or vehicle in the same experiment. Treatment with 12-HETE did not significantly alter migration in either control MO (13.0±4.7 macrophages) or gpr31 MO injected zebrafish (5.5±3.94 macrophages), failing to rescue the knockdown phenotype. In contrast, zebrafish injected with a MO against the 12-LOX zebrafish ortholog alox12 showed a similar reduction in migration, but this defect was rescued by the addition of 12-HETE ( Figure S2C ). These results suggest that GPR31 functions downstream of 12-HETE. Taken together, these data indicate that the 12-HETE–GPR31 axis is required for macrophage migration but not for cytokine-induced polarization of macrophages. Gpr31b KO mice are protected against multiple low-dose streptozotocin-induced diabetes To test whether Gpr31b KO mice are resistant to cytokine-induced oxidative stress and β-cell dysfunction in vivo , we subjected male WT and Gpr31b KO mice to multiple low-dose streptozotocin (mldSTZ) injections (55 mg/kg daily for 5 days) ( Figure 5A ). In this model, STZ selectively damages β cells, leading to the recruitment of macrophages and dendritic cells into the vicinity of the islets and the local release of pro-inflammatory cytokines. This leads to oxidative stress, islet dysfunction, and eventual β-cell death [ 29 , 49 – 51 ]. In response to mldSTZ injections, WT mice exhibited worsening blood glucose levels as early as day 5 of injections, whereas, by day 12, Gpr31b KO mice had improved glucose levels ( Figure 5B ). Likewise, Gpr31b KO mice had improved glucose tolerance compared to WT controls at day 10 ( Figure 5C ). Additionally, β-cell mass was significantly higher in Gpr31b KO mice than WT controls ( Figure 5D ). We next examined both oxidative stress levels and macrophage recruitment into the islet based on our previous results. WT mice exhibited increased levels of 4-HNE (a marker of oxidative stress) while islets of Gpr31b KO mice had reduced levels of 4-HNE ( Figure 5E ). Download figure Open in new tab Figure 5: Gpr31b KO mice are protected against multiple low-dose streptozotocin (mldSTZ)-induced diabetes. (A) Schematic of multiple low-dose STZ (mldSTZ)-induced diabetes experiment. (B) Blood glucose measurements for WT (black dots) and Gpr31b KO (blue dots) mice for 30 days after STZ administration (n=5-6 mice per condition). GTT at 10 days after STZ is also indicated (C) GTT at 10 days after STZ administration ( left panel ) with AUC ( right panel ). (D) Representative immunohistochemical labeling of insulin in pancreatic tissue from WT and Gpr31b KO mice ( left panel ). Scale bars are 250 µm. Quantification of β-cell mass ( right panel ) (n=5 mice per condition). (E) Representative immunofluorescence labeling for nuclei (DAPI; blue), insulin (teal), and the oxidative stress marker 4-HNE (magenta) ( left panel ). Scale bars are 50 µm. Quantification of 4-HNE mean intensity (n=17-51 islets per condition) ( right panel ). (F) Immunohistochemical labeling for the macrophage marker F4/80 in pancreatic tissue from WT and Gpr31b KO mice ( left panel ). Scale bars are 200 µm. Quantification of infiltrated (intra-islet) macrophages per area (n=14-28 islets per condition) ( right panel ). Islets are marked with a dotted line and infiltrated (intra-islet) macrophages are marked with arrowheads. Data presented as mean with SEM. Statistical significance determined by one-way ANOVA. Additionally, Gpr31b KO mice had less macrophages within their islets compared to WT mice, though this did not meet statistical significance ( Figure 5F ). These data are consistent with prior studies showing that global and β-cell specific Alox15 -/- mice are protected from mldSTZ-induced diabetes [ 19 , 52 ]. siRNA knockdown of Gpr31b reduces insulitis, macrophage migration, and oxidative stress in NOD mice We next sought to study the role of GPR31 in the context of a T1D mouse model. To do this, we utilized small interfering (siRNA) against Gpr31b (si Gpr31b ) and a non-targeting control pool siRNA (siNTC or NTC). We first confirmed that si Gpr31b produced the expected knockdown of Gpr31b in β cells. In MIN6 murine β cells, we stimulated Gpr31b expression using PIC and found that Gpr31b levels are reduced upon siRNA knockdown ( Figure 6A ) to the approximate predicted levels based on gene expression of the other Gpr31b isoforms which should not be altered ( Figure 1E ). We administered siRNA by intraperitoneal (IP) injection into pre-diabetic 6-week-old female NOD mice, a timeframe during immune invasion and when the islets exhibit ER and oxidative stress [ 3 ]. Mice received three injections of 1.7 mg/kg each of either si Gpr31b or siNTC spaced one day apart. The study ended 2 weeks after the initial dose of siRNA at which point we harvested pancreatic tissue and performed flow cytometry on pancreatic lymph nodes (pLN) ( Figure 6B ). Download figure Open in new tab Figure 6: Small interfering RNA (siRNA) knockdown of Gpr31b reduces insulitis, macrophage migration, and oxidative stress in NOD mice. (A) Relative Gpr31b mRNA expression in MIN6 murine β cells treated with vehicle or PIC as well as non-targeting control pool siRNA (siNTC or NTC) and siRNA against Gpr31b (si Gpr31b ). (B) Schematic of siRNA injection experiment in NOD mice. (C) Representative images of immunohistochemistry for insulin in pancreatic tissue from NOD mice injected with siNTC or si Gpr31b ( top left panel ). Scale bars are 250 µm. Representative immunofluorescence labeling from the same tissues for nuclei (DAPI; blue), insulin (green), and glucagon (magenta) ( bottom left panel ). Scale bars are 100 µm. Quantification of β-cell mass ( right panel ) (n=5 mice per condition). (D) Mean insulitis score for NOD mice injected with siNTC or si Gpr31b (n=5 mice per condition). (E) Representative immunofluorescence labeling in pancreatic tissue from siNTC or si Gpr31b injected mice showing nuclei (DAPI; blue), insulin (green), and the oxidative marker 4-HNE (magenta) ( left panel ). Scale bars are 100 µm; islets are marked with a dotted line. Quantification of 4-HNE mean intensity (n=5 mice per condition) ( right panel ). (F) Representative immunohistochemistry for the macrophage marker F4/80 in pancreatic tissue from NOD mice injected with siNTC or si Gpr31b (n=38-42 islets per condition) ( left panel ). Islets are marked with a dotted line, and representative infiltrated (intra-islet) macrophages are marked with arrowheads. Scale bars are 100 µm. Quantification of number of F4/80+ macrophages within the islet area ( right panel ). Data presented as mean with SEM. Statistical significance determined by Student’s t-test. We first examined the islets by immunohistochemistry and found that si Gpr31b did not alter the islet architecture or β-cell mass ( Figure 6C ). However, the amount of insulitis was reduced in the NOD mice treated with si Gpr31b compared to control-injected mice ( Figure 6D ). We next analyzed macrophage migration by immunohistochemical staining for F4/80 in the pancreas. We observed a reduced number of macrophages in the islet area of NOD mice treated with si Gpr31b compared to control-injected mice ( Figure 6E ), although this data was not statistically significant. Additionally, these islets exhibited less oxidative stress as shown by 4-HNE staining ( Figure 6F ). Lastly, to assess for an immune cell phenotype in lymphocytes with Gpr31b knockdown, we performed flow cytometry from both splenic tissue and pLN. We labeled for CD19 (B cell), CD4 and CD8 (T cells), FoxP3 (T regulatory cells), IFN-γ (Th1 cells), and IL17A (Th17 cells). Flow cytometry of splenic tissue showed no differences between siNTC and si Gpr31b injected mice ( Figure 7A ). In contrast, flow cytometry of pLN showed a trend toward decreased IFN-γ+ and IL-17A+ T cells; however, this did not meet statistical significance ( Figure 7B ). Collectively, these findings demonstrate that GPR31 promotes insulitis, macrophage infiltration, and oxidative stress in pre-diabetic NOD mice. Download figure Open in new tab Figure 7: Flow cytometry from NOD mice after siRNA knockdown of Gpr31b. (A) Flow cytometry from splenic tissue of NOD mice injected with siNTC or si Gpr31b . (B) Flow cytometry from pancreatic lymph nodes (pLN) of NOD mice injected with siNTC or si Gpr31b . DISCUSSION Using multiple animal models and complementary genetic approaches, we identify the orphan G protein-coupled receptor GPR31 as a key mediator of pancreatic islet inflammation and autoimmune diabetes development. Our findings place GPR31 at the intersection of β-cell stress and macrophage activation, paralleling but also extending the established role of 12/15-LOX and its lipid metabolite 12-HETE in T1D pathogenesis. Specifically, our data show that genetic deletion or knockdown of Gpr31b results in: (1) reduced islet oxidative stress, (2) reduced macrophage inflammatory signaling; (3) reduced macrophage migration, (4) protection from hyperglycemia after STZ-induced diabetes, and (5) decreased immune cell infiltration into the islet of NOD mice. We discuss the implications of these findings below, focusing on cell-type specific roles as well as the relation of GPR31 signaling to 12-LOX–12-HETE. 12-LOX and 12-HETE are established mediators of T1D pathogenesis in both β cells and macrophages. In human islets, 12-HETE treatment impairs β-cell function, reducing insulin secretion and cell viability in part through p38-MAPK signaling [ 45 ]. Conversely, treatment with the 12-LOX inhibitor ML-355 protects human islets from cytokine-induced loss of insulin secretion and increased oxidative stress [ 15 ]. In vivo , whole-body deletion of 12-LOX confers protection from both STZ-induced diabetes and autoimmune diabetes in NOD mice [ 52 – 54 ]. Conditional knockout of 12-LOX in either β cells or the myeloid lineage likewise protects against autoimmune diabetes [ 1 , 4 ], suggesting distinct but converging roles for 12-LOX expression in both cell types. These converging roles underscore the centrality of the 12-LOX pathway in β-cell dysfunction and macrophage activation. Given that GPR31 is a high-affinity receptor for 12-HETE [ 20 ], we asked whether GPR31 deficiency would recapitulate the protective effects of 12-LOX loss. Although 12-LOX is most abundant in macrophages [ 16 ], we found GPR31 expressed in pancreatic islets, as well as in peritoneal and bone marrow-derived macrophages. Whole-body Gpr31b KO mice—like whole-body 12-LOX KO mice—were protected from hyperglycemia and loss of β-cell mass after STZ-induced diabetes [ 52 ]. Although we did not generate cell type-specific KO mice, our data showed that Gpr31b KO directly protected islets from cytokine-induced injury, most notably by reducing cytokine-induced oxidative stress. Oxidative stress occurs when the accumulation of reactive oxygen species exceeds the antioxidant scavenging capacity of the cell. Due to their high protein secretory load, β cells are highly susceptible to oxidative damage [ 55 ], and this susceptibility is amplified by cytokines from infiltrating immune cells in T1D [ 56 , 57 ]. Because 12-LOX activity promotes oxidative stress in β cells and its inhibition increases antioxidant defenses [ 16 , 19 , 44 ], our findings support a model in which blockade of 12-HETE signaling via GPR31 deletion reduces oxidative stress and thereby sustains β-cell health. GPR31 also exerts distinct effects in macrophages. siRNA-mediated knockdown of Gpr31b resulted in reduced infiltrating macrophages into islets of the NOD mouse. Similarly, islets from STZ-treated Gpr31b KO mice demonstrated fewer macrophages. These findings mirror the phenotype of whole-body and myeloid 12-LOX knockout mice, which also show reduced islet immune-cell invasion and protection from diabetes [ 1 , 53 , 54 ]. Macrophages play a pivotal role in initiating and amplifying the immune response in early T1D, in part through presentation of antigens to T cells [ 58 ]. Although the precise mechanisms remain unclear, it is likely that resident islet antigen-presenting cells such as macrophages migrate through the pancreatic stroma into the draining lymph nodes to present antigens to T cells [ 59 ]. In support of this, we observed impaired macrophage migration in both BMDMs and the zebrafish tailfin transection assay. Notably, the loss of macrophage migration with gpr31 knockdown in the tailfin transection assay was unresponsive to 12-HETE. This was not true for a parallel experiment with alox12 knockdown which demonstrated migration responses dependent on 12-HETE even in the absence of 12-LOX. Together, these findings suggest that GPR31 directly regulates macrophage motility as a receptor for 12-HETE and contributes to early islet infiltration. Our transcriptomic analysis revealed additional relevant signaling pathways linked to GPR31. Cytokine-treated Gpr31b KO islets unexpectedly showed loss of synaptic and neurotransmitter-related genes (see Figure S1 ). It is known that both sympathetic and parasympathetic innervation is present within the pancreatic islet and functionally important for insulin secretion [ 60 – 63 ]. This effect may reflect loss of Gpr31b in neural elements near islets, or alternatively, loss of neuronal-like gene expression within β cells themselves. It is known that neurons upregulate GPR31 under inflammatory conditions in the context of spinal cord injury and that activation of GPR31 in these neurons is associated with increased JNK signaling mediating neuropathic pain [ 64 ]. Alternatively, β cells are known to express a small subset of neuronal genes and share molecular machinery essential for development [ 65 – 67 ]. Therefore, the results of our pathway analysis could reflect loss of these genes or features and loss of β-cell identity, which can be protective under the pro-inflammatory conditions of T1D [ 68 ]. Lastly, the highest differential expressed gene in the cytokine-treated Gpr31b KO mouse islets was Cd5l , which encodes an anti-inflammatory scavenger receptor. CD5L has known roles in restraining Th17 cell pathogenicity [ 46 ], and merits future investigation given our data suggesting decreased IL-17A and IFN-γ in T cells in the pLN, markers which define Th17 cell state. In macrophages, the function of GPR31 appears to drive inflammation, in particular through IFN-α and IFN-γ signaling. This suggests an early role for GPR31 in inflammatory signaling in T1D, especially as an IFN-α signal is evident in circulation before autoantibody conversion in genetically susceptible individuals [ 69 – 71 ]. In early phase T1D, the source of this type 1 IFN signal is thought to be secreted IFN-α from innate immune cells, such as macrophages. Underlining roles in disease progression, IFN-α signaling has been implicated in β-cell apoptosis and ER stress and is known to increase ROS production [ 72 – 74 ]. These data also raise the possibility that the islet-specific effects of Gpr31b KO are in part secondary to activated IFN-α signaling by GPR31 in macrophages. There are also clues as to mechanisms that promote macrophage inflammation independent of the presumed roles of the 12-LOX–12-HETE–GPR31 axis. Recent work has identified both pyruvate and lactic acid as ligands for GPR31 in the setting of gut immune responses in response to bacterial infection [ 75 , 76 ]. These ligands are produced in the intestinal lumen and were shown to stimulate GPR31, resulting in enhanced immune responses through changes in dendrite protrusion and antigen processing in myeloid cells. Our transcriptomic data also showed decreased pathway enrichment of antigen presentation in M1-polarized macrophages without GPR31 (see Supplemental Table S1 ), suggesting a possible mechanism of GPR31-mediated inflammation through enhanced antigen presentation in myeloid cells. This same pathway analysis also showed perturbations of fatty acid and carbohydrate metabolism, suggesting the possibility of other metabolic mechanisms downstream of GPR31. This study has several limitations. We employed whole-body Gpr31b knockout mice, which precluded resolution of tissue-specific effects; conditional models targeting β cells and myeloid lineages will be needed to define cell-specific function of GPR31. The STZ model we used primarily induces oxidative β-cell damage and innate responses without engaging adaptive immunity. Additional models, including genetically-manipulated NOD mice or others, will be required to test the broader relevance of GPR31. Finally, while genetic loss-of-function was informative, pharmacologic tools are essential for translation. Recently described GPR31 antagonists provide a direct opportunity for pre-clinical testing in T1D [ 64 , 77 ]. In summary, our data establish GPR31 as a critical inflammatory mediator in diabetes pathogenesis. By linking 12-LOX–12-HETE signaling to β-cell oxidative stress as well as macrophage migration and interferon responses, GPR31 integrates distinct cell-type-specific mechanisms that converge to drive islet inflammation. The availability of high-affinity antagonists positions GPR31 as a potential therapeutic target for T1D. ACKNOWLEDGEMENTS The authors wish to acknowledge Svetlana Navitskaya, Advaita Chakraborty, Kayla Figatner, Xiaxia Saavedra, and James Tersey at the University of Chicago for their technical assistance in these studies. This work was supported in part by National Institutes of Health grants R03 TR003381 (SAT and RGM), R01 DK124906 (SAT), R01 DK105588 (RGM), R01 DK060581 (RGM), P30 DK020595 (Diabetes Research and Training Center grant to the University of Chicago), and K12 DK133995 (KBK; multi-center program directors David Maahs and Linda DiMeglio). Additional support was provided by the Pollak family through the Kovler Diabetes Center of the University of Chicago (KBK). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. RGM and SAT conceived and designed the research and provided funding. KBK, RGM, and SAT wrote the manuscript. KBK, CC, JRE, TN, EE, AAP, JEW, MW, CM, JBN, AK, RMA, and SAT performed the research and acquired the data. All authors reviewed and edited the manuscript. Funder Information Declared National Institute of Diabetes and Digestive and Kidney Diseases , R01 DK124906 , R01 DK105588 , R01 DK060581 , P30 DK020595 , K12 DK133995 National Center for Advancing Translational Sciences, https://ror.org/04pw6fb54 , R03 TR003381 Kovler Diabetes Center REFERENCES 1. ↵ Kulkarni A , Pineros AR , Walsh MA , et al. ( 2021 ) 12-Lipoxygenase governs the innate immune pathogenesis of islet inflammation and autoimmune diabetes . 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