Homeobox Transcription Factor Prox1 in Sympathetic Ganglia of Vertebrate Embryos: Correlation with Human Stage 4S Neuroblastoma
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The P63 Target HBP-1 Is Required for Keratinocyte Differentiation and Stratification
The p63 target HBP-1 is required for keratinocyte differentiation and stratification. Roberto Mantovani, Serena Borrelli, Eleonora Candi, Diletta Dolfini, Olì Maria Victoria Grober, Alessandro Weisz, Gennaro Melino, Alessandra Viganò, Bing Hu, Gian Paolo Dotto To cite this version: Roberto Mantovani, Serena Borrelli, Eleonora Candi, Diletta Dolfini, Olì Maria Victoria Grober, et al.. The p63 target HBP-1 is required for keratinocyte differentiation and stratification.. Cell Death and Differentiation, Nature Publishing Group, 2010, 10.1038/cdd.2010.59. hal-00542927 HAL Id: hal-00542927 https://hal.archives-ouvertes.fr/hal-00542927 Submitted on 4 Dec 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Running title: HBP1 in skin differentiation. The p63 target HBP-1 is required for skin differentiation and stratification. Serena Borrelli1, Eleonora Candi2, Bing Hu3, Diletta Dolfini1, Maria Ravo4, Olì Maria Victoria Grober4, Alessandro Weisz4,5, GianPaolo Dotto3, Gerry Melino2,6, Maria Alessandra Viganò1 and Roberto Mantovani1*. 1) Dipartimento di Scienze Biomolecolari e Biotecnologie. Università degli Studi di Milano. Via Celoria 26, 20133 Milano, Italy. 2) Biochemistry IDI-IRCCS laboratory, c/o University of Rome "Tor Vergata", Via Montpellier 1, 00133 Roma, Italy. -
Identification of SNP-Containing Regulatory Motifs in the Myelodysplastic Syndromes Model Using SNP Arrays Ad Gene Expression Arrays" (2013)
University of Central Florida STARS Faculty Bibliography 2010s Faculty Bibliography 1-1-2013 Identification of SNP-containing egulatr ory motifs in the myelodysplastic syndromes model using SNP arrays ad gene expression arrays Jing Fan Jennifer G. Dy Chung-Che Chang University of Central Florida Xiaoboo Zhou Find similar works at: https://stars.library.ucf.edu/facultybib2010 University of Central Florida Libraries http://library.ucf.edu This Article is brought to you for free and open access by the Faculty Bibliography at STARS. It has been accepted for inclusion in Faculty Bibliography 2010s by an authorized administrator of STARS. For more information, please contact [email protected]. Recommended Citation Fan, Jing; Dy, Jennifer G.; Chang, Chung-Che; and Zhou, Xiaoboo, "Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays ad gene expression arrays" (2013). Faculty Bibliography 2010s. 3960. https://stars.library.ucf.edu/facultybib2010/3960 Chinese Journal of Cancer Original Article Jing Fan 1, Jennifer G. Dy 1, Chung鄄Che Chang 2 and Xiaobo Zhou 3 Abstract Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state鄄of 鄄th e鄄ar t array technologies single nucleotide polymor鄄 要 phism (SNP) array and gene expression array with gene motifs considered transcription factor -binding 要 sites (TFBS). We are particularly interested in SNP鄄co ntaining motifs introduced by genetic variation and mutation as TFBS. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
The Ire1a-XBP1 Pathway Promotes T Helper Cell Differentiation by Resolving Secretory Stress and Accelerating Proliferation
bioRxiv preprint doi: https://doi.org/10.1101/235010; this version posted December 15, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. The IRE1a-XBP1 pathway promotes T helper cell differentiation by resolving secretory stress and accelerating proliferation Jhuma Pramanik1, Xi Chen1, Gozde Kar1,2, Tomás Gomes1, Johan Henriksson1, Zhichao Miao1,2, Kedar Natarajan1, Andrew N. J. McKenzie3, Bidesh Mahata1,2*, Sarah A. Teichmann1,2,4* 1. Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom 2. EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom 3. MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 OQH, United Kingdom 4. Theory of Condensed Matter, Cavendish Laboratory, 19 JJ Thomson Ave, Cambridge CB3 0HE, United Kingdom. *To whom correspondence should be addressed: [email protected] and [email protected] Keywords: Th2 lymphocyte, XBP1, Genome wide XBP1 occupancy, Th2 lymphocyte proliferation, ChIP-seq, RNA-seq, Th2 transcriptome Summary The IRE1a-XBP1 pathway, a conserved adaptive mediator of the unfolded protein response, is indispensable for the development of secretory cells. It maintains endoplasmic reticulum homeostasis by facilitating protein folding and enhancing secretory capacity of the cells. Its role in immune cells is emerging. It is involved in dendritic cell, plasma cell and eosinophil development and differentiation. Using genome-wide approaches, integrating ChIPmentation and mRNA-sequencing data, we have elucidated the regulatory circuitry governed by the IRE1a-XBP1 pathway in type-2 T helper cells (Th2). -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Mice Lacking Hbp1 Function Are Viable and Fertile
RESEARCH ARTICLE Mice Lacking Hbp1 Function Are Viable and Fertile Cassy M. Spiller1¤a*, Dagmar Wilhelm1¤b, David A. Jans2, Josephine Bowles3, Peter Koopman1 1 Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia, 2 School of Biomedical Sciences, Monash University, Clayton, Victoria, Australia, 3 School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia ¤a Current address: School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia ¤b Current address: Department of Anatomy & Neuroscience, The University of Melbourne, Melbourne, Victoria, Australia a1111111111 * [email protected] a1111111111 a1111111111 a1111111111 a1111111111 Abstract Fetal germ cell development is tightly regulated by the somatic cell environment, and is char- acterised by cell cycle states that differ between XY and XX gonads. In the testis, gonocytes enter G1/G0 arrest from 12.5 days post coitum (dpc) in mice and maintain cell cycle arrest OPEN ACCESS until after birth. Failure to correctly maintain G1/G0 arrest can result in loss of germ cells or, Citation: Spiller CM, Wilhelm D, Jans DA, Bowles conversely, germ cell tumours. High mobility group box containing transcription factor 1 J, Koopman P (2017) Mice Lacking Hbp1 Function (HBP1) is a transcription factor that was previously identified in fetal male germ cells at the Are Viable and Fertile. PLoS ONE 12(1): e0170576. doi:10.1371/journal.pone.0170576 time of embryonic cell cycle arrest. In somatic cells, HBP1 is classified as a tumour suppres- sor protein, known to regulate proliferation and senescence. We therefore investigated the Editor: Stefan Schlatt, University Hospital of MuÈnster, GERMANY possible role of HBP1 in the initiation and maintenance of fetal germ cell G1/G0 arrest using the mouse model. -
Diversification Pattern of the HMG and SOX Family Members During Evolution
J Mol Evol (1999) 48:517–527 © Springer-Verlag New York Inc. 1999 Diversification Pattern of the HMG and SOX Family Members During Evolution Ste´phan Soullier,1 Philippe Jay,1 Francis Poulat,1 Jean-Marc Vanacker,2 Philippe Berta,1 Vincent Laudet2 1 ERS155 du CNRS, Centre de Recherche en Biochimie Macromole´culaire, CNRS, BP5051, Route de Mende, 34293 Montpellier Cedex 5, France 2 UMR 319 du CNRS, Oncologie Mole´culaire, Institut de Biologie de Lille, Institut Pasteur de Lille, 1 rue Calmette, 59019 Lille Cedex, France Received: 20 July 1998 / Accepted: 19 October 1998 Abstract. From a database containing the published brate HMG box 2, insect SSRP, and plant HMG. The HMG protein sequences, we constructed an alignment of various UBF boxes cannot be clustered together and their the HMG box functional domain based on sequence diversification appears to be extremely ancient, probably identity. Due to the large number of sequences (more before the appearance of metazoans. than 250) and the short size of this domain, several data sets were used. This analysis reveals that the HMG box Key words: HMG box — SOX proteins — Sry — superfamily can be separated into two clearly defined Molecular phylogeny subfamilies: (i) the SOX/MATA/TCF family, which clusters proteins able to bind to specific DNA sequences; and (ii) the HMG/UBF family, which clusters members Introduction which bind non specifically to DNA. The appearance and diversification of these subfamilies largely predate the split between the yeast and the metazoan lineages. Par- The high-mobility group (HMG) proteins were first de- ticular emphasis was placed on the analysis of the SOX fined by their electrophoretic behavior on SDS-PAGE, as subfamily. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
Phox2a Defines a Developmental Origin of the Anterolateral System in Mice And
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.10.144659; this version posted June 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Title: Phox2a defines a developmental origin of the anterolateral system in mice and 2 humans 3 4 Authors: R. Brian Roome1,2, Farin B. Bourojeni1,2, Bishakha Mona3, Shima Rastegar- 5 Pouyani1,2, Raphael Blain4, Annie Dumouchel1, Charleen Salesse1, W. Scott Thompson1, 6 Megan Brookbank1, Yorick Gitton4, Lino Tessarollo5, Martyn Goulding6, Jane E. 7 Johnson3,7, Marie Kmita1,9, Alain Chédotal4 and Artur Kania1,2,8,9,#,* 8 9 Affiliations : 10 1Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, H2W 1R7, 11 Canada 12 2Integrated Program in Neuroscience, McGill University, Montréal, QC, H3A 2B4, 13 Canada 14 3Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, 15 United States 16 4Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 Rue Moreau, Paris, 17 75012, France 18 5Neural Development Section, Mouse Cancer Genetics Program, National Cancer 19 Institute, Frederick, MD, 21702, United States 20 6Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, 21 CA, 92037, United States 22 7Department of Pharmacology, UT Southwestern Medical Center, Dallas, TX, 75390, 23 United States 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.10.144659; this version posted June 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. -
Spatial Distribution of Leading Pacemaker Sites in the Normal, Intact Rat Sinoa
Supplementary Material Supplementary Figure 1: Spatial distribution of leading pacemaker sites in the normal, intact rat sinoatrial 5 nodes (SAN) plotted along a normalized y-axis between the superior vena cava (SVC) and inferior vena 6 cava (IVC) and a scaled x-axis in millimeters (n = 8). Colors correspond to treatment condition (black: 7 baseline, blue: 100 µM Acetylcholine (ACh), red: 500 nM Isoproterenol (ISO)). 1 Supplementary Figure 2: Spatial distribution of leading pacemaker sites before and after surgical 3 separation of the rat SAN (n = 5). Top: Intact SAN preparations with leading pacemaker sites plotted during 4 baseline conditions. Bottom: Surgically cut SAN preparations with leading pacemaker sites plotted during 5 baseline conditions (black) and exposure to pharmacological stimulation (blue: 100 µM ACh, red: 500 nM 6 ISO). 2 a &DUGLDFIoQChDQQHOV .FQM FOXVWHU &DFQDG &DFQDK *MD &DFQJ .FQLS .FQG .FQK .FQM &DFQDF &DFQE .FQM í $WSD .FQD .FQM í .FQN &DVT 5\U .FQM &DFQJ &DFQDG ,WSU 6FQD &DFQDG .FQQ &DFQDJ &DFQDG .FQD .FQT 6FQD 3OQ 6FQD +FQ *MD ,WSU 6FQE +FQ *MG .FQN .FQQ .FQN .FQD .FQE .FQQ +FQ &DFQDD &DFQE &DOP .FQM .FQD .FQN .FQG .FQN &DOP 6FQD .FQD 6FQE 6FQD 6FQD ,WSU +FQ 6FQD 5\U 6FQD 6FQE 6FQD .FQQ .FQH 6FQD &DFQE 6FQE .FQM FOXVWHU V6$1 L6$1 5$ /$ 3 b &DUGLDFReFHSWRUV $GUDF FOXVWHU $GUDD &DY &KUQE &KUP &KJD 0\O 3GHG &KUQD $GUE $GUDG &KUQE 5JV í 9LS $GUDE 7SP í 5JV 7QQF 3GHE 0\K $GUE *QDL $QN $GUDD $QN $QN &KUP $GUDE $NDS $WSE 5DPS &KUP 0\O &KUQD 6UF &KUQH $GUE &KUQD FOXVWHU V6$1 L6$1 5$ /$ 4 c 1HXURQDOPURWHLQV -
The NANOG Transcription Factor Induces Type 2 Deiodinase Expression and Regulates the Intracellular Activation of Thyroid Hormone in Keratinocyte Carcinomas
Cancers 2020, 12 S1 of S18 Supplementary Materials: The NANOG Transcription Factor Induces Type 2 Deiodinase Expression and Regulates the Intracellular Activation of Thyroid Hormone in Keratinocyte Carcinomas Annarita Nappi, Emery Di Cicco, Caterina Miro, Annunziata Gaetana Cicatiello, Serena Sagliocchi, Giuseppina Mancino, Raffaele Ambrosio, Cristina Luongo, Daniela Di Girolamo, Maria Angela De Stefano, Tommaso Porcelli, Mariano Stornaiuolo and Monica Dentice Figure S1. Strategy for the mutagenesis of Dio2 promoter. (A) Schematic representation of NANOG Binding Site within the Dio2 promoter region. (B) Schematic diagram for site‐directed mutagenesis of NANOG Binding Site on Dio2 promoter region by Recombinant PCR. (C) Representation of the mutated NANOG Binding Site on Dio2 promoter region. (D) Electropherogram of the NANOG Binding Site mutation within the Dio2 promoter. Cancers 2020, 12 S2 of S18 Figure S2. Strategy for the silencing of NANOG expression. (A) Cloning strategies for the generation of NANOG shRNA expression vectors. (B) Electropherograms of the NANOG shRNA sequences cloned into pcDNA3.1 vector. (C) Validation of effective NANOG down-modulation by two different NANOG shRNA vectors was assessed by Western Blot analysis of NANOG expression in BCC cells. (D) Quantification of NANOG protein levels versus Tubulin levels in the same experiment as in C is represented by histograms. Cancers 2020, 12 S3 of S18 Figure S3. The CD34+ cells are characterized by the expression of typical epithelial stemness genes. The mRNA levels of a panel of indicated stemness markers of epidermis were measured by Real Time PCR in the same experiment indicated in figure 3F and G. Cancers 2020, 12 S4 of S18 Figure S4. -
Beta Cell Adaptation to Pregnancy Requires Prolactin Action on Both
www.nature.com/scientificreports OPEN Beta cell adaptation to pregnancy requires prolactin action on both beta and non‑beta cells Vipul Shrivastava1, Megan Lee1, Daniel Lee1, Marle Pretorius1, Bethany Radford1, Guneet Makkar1 & Carol Huang1,2,3* Pancreatic islets adapt to insulin resistance of pregnancy by up regulating β‑cell mass and increasing insulin secretion. Previously, using a transgenic mouse with global, heterozygous deletion of prolactin receptor (Prlr+/−), we found Prlr signaling is important for this adaptation. However, since Prlr is expressed in tissues outside of islets as well as within islets and prolactin signaling afects β‑cell development, to understand β‑cell‑specifc efect of prolactin signaling in pregnancy, we generated a transgenic mouse with an inducible conditional deletion of Prlr from β‑cells. Here, we found that β‑cell‑specifc Prlr reduction in adult mice led to elevated blood glucose, lowed β‑cell mass and blunted in vivo glucose‑stimulated insulin secretion during pregnancy. When we compared gene expression profle of islets from transgenic mice with global (Prlr+/−) versus β‑cell‑specifc Prlr reduction (βPrlR+/−), we found 95 diferentially expressed gene, most of them down regulated in the Prlr+/− mice in comparison to the βPrlR+/− mice, and many of these genes regulate apoptosis, synaptic vesicle function and neuronal development. Importantly, we found that islets from pregnant Prlr+/− mice are more vulnerable to glucolipotoxicity‑induced apoptosis than islets from pregnant βPrlR+/− mice. These observations suggest that down regulation of prolactin action during pregnancy in non‑β‑cells secondarily and negatively afect β‑cell gene expression, and increased β‑cell susceptibility to external insults.