Foxn4 Is a Temporal Identity Factor Conferring Mid/Late-Early Retinal Competence and Involved in Retinal Synaptogenesis
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Reconstruction of the Global Neural Crest Gene Regulatory Network in Vivo
Reconstruction of the global neural crest gene regulatory network in vivo Ruth M Williams1, Ivan Candido-Ferreira1, Emmanouela Repapi2, Daria Gavriouchkina1,4, Upeka Senanayake1, Jelena Telenius2,3, Stephen Taylor2, Jim Hughes2,3, and Tatjana Sauka-Spengler1,∗ Supplemental Material ∗Lead and corresponding author: Tatjana Sauka-Spengler ([email protected]) 1University of Oxford, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, Oxford, OX3 9DS, UK 2University of Oxford, MRC Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK 3University of Oxford, MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK 4Present Address: Okinawa Institute of Science and Technology, Molecular Genetics Unit, Onna, 904-0495, Japan A 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 10 10 10 10 10 log2(R1_5-6ss) log2(R1_5-6ss) log2(R1_8-10ss) log2(R1_8-10ss) log2(R1_non-NC) 5 5 5 5 5 0 r=0.92 0 r=0.99 0 r=0.96 0 r=0.99 0 r=0.96 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 log2(R2_non-NC) log2(R2_5-6ss) log2(R3_5-6ss) log2(R2_8-10ss) log2(R3_8-10ss) 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 10 10 10 10 10 log2(R1_5-6ss) log2(R2_5-6ss) log2(R1_8-10ss) log2(R2_8-10ss) log2(R1_non-NC) 5 5 5 5 5 0 r=0.94 0 r=0.96 0 r=0.95 0 r=0.96 0 r=0.95 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 log2(R3_non-NC) log2(R4_5-6ss) log2(R3_5-6ss) log2(R4_8-10ss) log2(R3_8-10ss) -
Overlap of Vitamin a and Vitamin D Target Genes with CAKUT- Related Processes [Version 1; Peer Review: 1 Approved with Reservations]
F1000Research 2021, 10:395 Last updated: 21 JUL 2021 BRIEF REPORT Overlap of vitamin A and vitamin D target genes with CAKUT- related processes [version 1; peer review: 1 approved with reservations] Ozan Ozisik1, Friederike Ehrhart 2,3, Chris T Evelo 2, Alberto Mantovani4, Anaı̈s Baudot 1,5 1Aix Marseille University, Inserm, MMG, Marseille, 13385, France 2Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6200 MD, The Netherlands 3Department of Bioinformatics, NUTRIM/MHeNs, Maastricht University, Maastricht, 6200 MD, The Netherlands 4Istituto Superiore di Sanità, Rome, 00161, Italy 5Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain v1 First published: 18 May 2021, 10:395 Open Peer Review https://doi.org/10.12688/f1000research.51018.1 Latest published: 18 May 2021, 10:395 https://doi.org/10.12688/f1000research.51018.1 Reviewer Status Invited Reviewers Abstract Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) are a 1 group of abnormalities affecting the kidneys and their outflow tracts, which include the ureters, the bladder, and the urethra. CAKUT version 1 patients display a large clinical variability as well as a complex 18 May 2021 report aetiology, as only 5% to 20% of the cases have a monogenic origin. It is thereby suspected that interactions of both genetic and 1. Elena Menegola, Università degli Studi di environmental factors contribute to the disease. Vitamins are among the environmental factors that are considered for CAKUT aetiology. In Milano, Milan, Italy this study, we collected vitamin A and vitamin D target genes and Any reports and responses or comments on the computed their overlap with CAKUT-related gene sets. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Gene PMID WBS Locus ABR 26603386 AASDH 26603386
Supplementary material J Med Genet Gene PMID WBS Locus ABR 26603386 AASDH 26603386 ABCA1 21304579 ABCA13 26603386 ABCA3 25501393 ABCA7 25501393 ABCC1 25501393 ABCC3 25501393 ABCG1 25501393 ABHD10 21304579 ABHD11 25501393 yes ABHD2 25501393 ABHD5 21304579 ABLIM1 21304579;26603386 ACOT12 25501393 ACSF2,CHAD 26603386 ACSL4 21304579 ACSM3 26603386 ACTA2 25501393 ACTN1 26603386 ACTN3 26603386;25501393;25501393 ACTN4 21304579 ACTR1B 21304579 ACVR2A 21304579 ACY3 19897463 ACYP1 21304579 ADA 25501393 ADAM12 21304579 ADAM19 25501393 ADAM32 26603386 ADAMTS1 25501393 ADAMTS10 25501393 ADAMTS12 26603386 ADAMTS17 26603386 ADAMTS6 21304579 ADAMTS7 25501393 ADAMTSL1 21304579 ADAMTSL4 25501393 ADAMTSL5 25501393 ADCY3 25501393 ADK 21304579 ADRBK2 25501393 AEBP1 25501393 AES 25501393 AFAP1,LOC84740 26603386 AFAP1L2 26603386 AFG3L1 21304579 AGAP1 26603386 AGAP9 21304579 Codina-Sola M, et al. J Med Genet 2019; 56:801–808. doi: 10.1136/jmedgenet-2019-106080 Supplementary material J Med Genet AGBL5 21304579 AGPAT3 19897463;25501393 AGRN 25501393 AGXT2L2 25501393 AHCY 25501393 AHDC1 26603386 AHNAK 26603386 AHRR 26603386 AIDA 25501393 AIFM2 21304579 AIG1 21304579 AIP 21304579 AK5 21304579 AKAP1 25501393 AKAP6 21304579 AKNA 21304579 AKR1E2 26603386 AKR7A2 21304579 AKR7A3 26603386 AKR7L 26603386 AKT3 21304579 ALDH18A1 25501393;25501393 ALDH1A3 21304579 ALDH1B1 21304579 ALDH6A1 21304579 ALDOC 21304579 ALG10B 26603386 ALG13 21304579 ALKBH7 25501393 ALPK2 21304579 AMPH 21304579 ANG 21304579 ANGPTL2,RALGPS1 26603386 ANGPTL6 26603386 ANK2 21304579 ANKMY1 26603386 ANKMY2 -
Watsonjn2018.Pdf (1.780Mb)
UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Department of Biology Investigating Differential Gene Expression in vivo of Cardiac Birth Defects in an Avian Model of Maternal Phenylketonuria A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Jamie N. Watson Edmond, OK June 5, 2018 J. Watson/Dr. Nikki Seagraves ii J. Watson/Dr. Nikki Seagraves Acknowledgements It is difficult to articulate the amount of gratitude I have for the support and encouragement I have received throughout my master’s thesis. Many people have added value and support to my life during this time. I am thankful for the education, experience, and friendships I have gained at the University of Central Oklahoma. First, I would like to thank Dr. Nikki Seagraves for her mentorship and friendship. I lucked out when I met her. I have enjoyed working on this project and I am very thankful for her support. I would like thank Thomas Crane for his support and patience throughout my master’s degree. I would like to thank Dr. Shannon Conley for her continued mentorship and support. I would like to thank Liz Bullen and Dr. Eric Howard for their training and help on this project. I would like to thank Kristy Meyer for her friendship and help throughout graduate school. I would like to thank my committee members Dr. Robert Brennan and Dr. Lilian Chooback for their advisement on this project. Also, I would like to thank the biology faculty and staff. I would like to thank the Seagraves lab members: Jailene Canales, Kayley Pate, Mckayla Muse, Grace Thetford, Kody Harvey, Jordan Guffey, and Kayle Patatanian for their hard work and support. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Supplementary Table 5. the 917 Candidate Marker Genes for the Diagnostic Model for Early HCC in the Training Set
Supplementary Table 5. The 917 candidate marker genes for the diagnostic model for early HCC in the training set. Early HCC vs. Controls Early HCC vs. CHB/LC Gene Coefficient a AUC b Coefficient a AUC b SOX9 0.722 81.30% 0.211 63.50% EVC 0.703 76.20% 0.314 68.30% CHST9 0.398 75.50% 0.150 62.40% PDX1 0.730 76.50% 0.204 60.40% NPBWR1 0.651 73.10% 0.317 63.40% FAT1 0.335 74.10% 0.108 61.00% MEIS2 0.398 71.40% 0.188 62.40% A2M 0.761 72.40% 0.235 58.90% SERPINA10 0.479 72.00% 0.177 58.40% LBP 0.597 70.20% 0.237 61.30% PROX1 0.239 69.70% 0.133 61.40% APOB 0.286 70.50% 0.104 59.10% FMO3 0.296 69.60% 0.151 60.60% FREM2 0.288 68.50% 0.130 62.80% SDC2 0.300 69.10% 0.151 60.70% FAM20A 0.453 68.50% 0.168 60.30% GPAM 0.309 68.50% 0.172 60.30% CFH 0.277 68.00% 0.178 60.80% PAH 0.208 68.30% 0.116 60.30% NR1H4 0.233 68.40% 0.108 59.80% PTPRS -0.572 66.80% -0.473 63.00% SIAH3 -0.690 66.10% -0.573 64.10% GATA4 0.296 68.00% 0.128 60.10% SALL1 0.344 68.10% 0.184 59.80% SLC27A5 0.463 67.30% 0.204 61.40% SS18L1 0.588 67.30% 0.271 60.90% TOX3 0.190 68.40% 0.079 59.00% KCNK1 0.224 67.70% 0.120 60.10% TF 0.445 68.50% 0.202 57.90% FARP1 0.417 67.50% 0.252 59.80% GOT2 0.675 67.60% 0.278 59.50% PQLC1 0.651 67.10% 0.258 60.50% SERPINA5 0.302 67.00% 0.161 60.50% SOX13 0.508 67.80% 0.187 59.30% CDH2 0.205 66.10% 0.153 62.20% ITIH2 0.322 66.20% 0.252 62.20% ADIG 0.443 65.20% 0.399 63.80% HSD17B6 0.524 67.20% 0.237 59.60% IL21R -0.451 65.90% -0.321 61.70% A1CF 0.255 67.10% 0.139 58.70% KLB 0.507 65.90% 0.383 61.20% SLC10A1 0.574 67.10% 0.218 58.80% YAP1 0.282 67.70% 0.118 -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. -
Methylome and Transcriptome Maps of Human Visceral and Subcutaneous
www.nature.com/scientificreports OPEN Methylome and transcriptome maps of human visceral and subcutaneous adipocytes reveal Received: 9 April 2019 Accepted: 11 June 2019 key epigenetic diferences at Published: xx xx xxxx developmental genes Stephen T. Bradford1,2,3, Shalima S. Nair1,3, Aaron L. Statham1, Susan J. van Dijk2, Timothy J. Peters 1,3,4, Firoz Anwar 2, Hugh J. French 1, Julius Z. H. von Martels1, Brodie Sutclife2, Madhavi P. Maddugoda1, Michelle Peranec1, Hilal Varinli1,2,5, Rosanna Arnoldy1, Michael Buckley1,4, Jason P. Ross2, Elena Zotenko1,3, Jenny Z. Song1, Clare Stirzaker1,3, Denis C. Bauer2, Wenjia Qu1, Michael M. Swarbrick6, Helen L. Lutgers1,7, Reginald V. Lord8, Katherine Samaras9,10, Peter L. Molloy 2 & Susan J. Clark 1,3 Adipocytes support key metabolic and endocrine functions of adipose tissue. Lipid is stored in two major classes of depots, namely visceral adipose (VA) and subcutaneous adipose (SA) depots. Increased visceral adiposity is associated with adverse health outcomes, whereas the impact of SA tissue is relatively metabolically benign. The precise molecular features associated with the functional diferences between the adipose depots are still not well understood. Here, we characterised transcriptomes and methylomes of isolated adipocytes from matched SA and VA tissues of individuals with normal BMI to identify epigenetic diferences and their contribution to cell type and depot-specifc function. We found that DNA methylomes were notably distinct between diferent adipocyte depots and were associated with diferential gene expression within pathways fundamental to adipocyte function. Most striking diferential methylation was found at transcription factor and developmental genes. Our fndings highlight the importance of developmental origins in the function of diferent fat depots. -
Ann. Anim. Sci., Vol. 19, No. 2 (2019) 343–357 DOI: 10.2478/Aoas-2019-0004
Ann. Anim. Sci., Vol. 19, No. 2 (2019) 343–357 DOI: 10.2478/aoas-2019-0004 FTO AND IRX3 GENES ARE NOT PROMISING MARKERS FOR OBESITY IN LABRADOR RETRIEVER DOGS* * Adrian Grzemski1, Monika Stachowiak1, Krzysztof Flisikowski2, Monika Mankowska1, Paulina Krzeminska1, Maciej Gogulski3, Roman Aleksiewicz4, Maciej Szydlowski1, Marek Switonski1, Joanna Nowacka-Woszuk1♦ 1Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wołyńska 33, 60-637 Poznań, Poland 2Chair of Livestock Biotechnology, Technische Universität München, Liesel-Beckmannstr. 1, 85354 Freising, Germany 3University Centre for Veterinary Medicine, Poznan University of Life Sciences, Szydłowska 43, 60-656 Poznań, Poland 4Department of Internal Medicine and Veterinary Diagnostics, Poznan University of Life Sciences, Wołyńska 35, 60-637 Poznań, Poland ♦Corresponding author: [email protected] Abstract Obesity is a serious problem in numerous dog breeds, but knowledge of its hereditary background is scarce. On the contrary, numerous DNA polymorphisms associated with human obesity have been identified, with the strongest effect being demonstrated forFTO gene. We used targeted next- generation sequencing (tNGS) to search for polymorphisms in the region harboring FTO and IRX3 in 32 Labrador dogs. Moreover, we investigated the selected regions of FTO and IRX3, ortholo- gous to the human regions associated with obesity, in 165 Labradors. For all dogs, the following information was available: age, sex, gonadal status, body weight, and body conformation score (BCS). The use of tNGS revealed 12,217 polymorphisms, but none of these obtained significance when lean and obese dogs were compared. Study of two SNPs in the 5’-flanking region of FTO in 165 dogs – creating two upstream reading frames (uORFs) – also showed no association with body weight and BCS but suggested the need for improvement in FTO annotation. -
Genome-Wide Expression Analysis Ofptf1a- and Ascl1-Deficient Mice
The Journal of Neuroscience, April 24, 2013 • 33(17):7299–7307 • 7299 Development/Plasticity/Repair Genome-Wide Expression Analysis of Ptf1a- and Ascl1-Deficient Mice Reveals New Markers for Distinct Dorsal Horn Interneuron Populations Contributing to Nociceptive Reflex Plasticity Hendrik Wildner,1 Rebecca Das Gupta,2 Dominique Bro¨hl,3 Paul A. Heppenstall,4 Hanns Ulrich Zeilhofer,1,2* and Carmen Birchmeier3* 1Institute of Pharmacology and Toxicology, University of Zurich, CH-8057 Zurich, Switzerland, 2Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, CH-8093 Zurich, Switzerland, 3Department of Neuroscience, Max Delbru¨ck Center for Molecular Medicine, D-13125 Berlin, Germany, and 4Mouse Biology Unit, European Molecular Biology Laboratory, I-00015 Monterotondo, Italy Inhibitoryinterneuronsofthespinaldorsalhornplaycriticalrolesintheprocessingofnoxiousandinnocuoussensoryinformation.They form a family of morphologically and functionally diverse neurons that likely fall into distinct subtypes. Traditional classifications rely mainly on differences in dendritic tree morphology and firing patterns. Although useful, these markers are not comprehensive and cannot be used to drive specific genetic manipulations targeted at defined subsets of neurons. Here, we have used genome-wide expres- sion profiling of spinal dorsal horns of wild-type mice and of two strains of transcription factor-deficient mice (Ptf1aϪ/Ϫ and Ascl1/ Mash1Ϫ/Ϫ mice) to identify new genetic markers for specific subsets of dorsal horn inhibitory interneurons. Ptf1aϪ/Ϫ mice lack all inhibitory interneurons in the dorsal horn, whereas only the late-born inhibitory interneurons are missing in Ascl1Ϫ/Ϫ mice. We found 30 genes that were significantly downregulated in the dorsal horn of Ptf1aϪ/Ϫ mice. Twenty-one of those also showed reduced expression in Ascl1Ϫ/Ϫ mice. -
Single Cell Regulatory Landscape of the Mouse Kidney Highlights Cellular Differentiation Programs and Disease Targets
ARTICLE https://doi.org/10.1038/s41467-021-22266-1 OPEN Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets Zhen Miao 1,2,3,8, Michael S. Balzer 1,2,8, Ziyuan Ma 1,2,8, Hongbo Liu1,2, Junnan Wu 1,2, Rojesh Shrestha 1,2, Tamas Aranyi1,2, Amy Kwan4, Ayano Kondo 4, Marco Pontoglio 5, Junhyong Kim6, ✉ Mingyao Li 7, Klaus H. Kaestner2,4 & Katalin Susztak 1,2,4 1234567890():,; Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene- regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development. 1 Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 2 Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.