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Journal ArticleDOI: 10.1681/ASN.2020101406

A Comprehensive Map of mRNAs and Their Isoforms across All 14 Renal Tubule Segments of Mouse.

04 Mar 2021-Journal of The American Society of Nephrology (J Am Soc Nephrol)-Vol. 32, Iss: 4, pp 897-912
Abstract: Background The repertoire of protein expression along the renal tubule depends both on regulation of transcription and regulation of alternative splicing that can generate multiple proteins from a single gene. Methods A full-length, small-sample RNA-seq protocol profiled transcriptomes for all 14 renal tubule segments microdissected from mouse kidneys. Results This study identified >34,000 transcripts, including 3709 that were expressed in a segment-specific manner. All data are provided as an online resource (https://esbl.nhlbi.nih.gov/MRECA/Nephron/). Many of the genes expressed in unique patterns along the renal tubule were solute carriers, transcription factors, or G protein-coupled receptors that account for segment-specific function. Mapping the distribution of transcripts associated with Wnk-SPAK-PKA signaling, renin-angiotensin-aldosterone signaling, and cystic diseases of the kidney illustrated the applications of the online resource. The method allowed full-length mapping of RNA-seq reads, which facilitated comprehensive, unbiased characterization of alternative exon usage along the renal tubule, including known isoforms of Cldn10, Kcnj1 (ROMK), Slc12a1 (NKCC2), Wnk1, Stk39 (SPAK), and Slc14a2 (UT-A urea transporter). It also identified many novel isoforms with segment-specific distribution. These included variants associated with altered protein structure (Slc9a8, Khk, Tsc22d1, and Scoc), and variants that may affect untranslated, regulatory regions of transcripts (Pth1r, Pkar1a, and Dab2). Conclusions Full-length, unbiased sequencing of transcripts identified gene-expression patterns along the mouse renal tubule. The data, provided as an online resource, include both quantitative and qualitative differences in transcripts. Identification of alternative splicing along the renal tubule may prove critical to understanding renal physiology and pathophysiology.

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Topics: Alternative splicing (53%), Kidney (53%), Nephron (52%) ... show more
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11 results found


Journal ArticleDOI: 10.1681/ASN.2020101407
Lihe Chen1, Chun-Lin Chou1, Mark A. Knepper1Institutions (1)
Abstract: Background Proximal tubule cells dominate the kidney parenchyma numerically, although less abundant cell types of the distal nephron have disproportionate roles in water and electrolyte balance. Methods Coupling of a FACS-based enrichment protocol with single-cell RNA-seq profiled the transcriptomes of 9099 cells from the thick ascending limb (CTAL)/distal convoluted tubule (DCT) region of the mouse nephron. Results Unsupervised clustering revealed Slc12a3 +/Pvalb + and Slc12a3 +/Pvalb - cells, identified as DCT1 and DCT2 cells, respectively. DCT1 cells appear to be heterogeneous, with orthogonally variable expression of Slc8a1, Calb1, and Ckb. An additional DCT1 subcluster showed marked enrichment of cell cycle-/cell proliferation-associated mRNAs (e.g., Mki67, Stmn1, and Top2a), which fit with the known plasticity of DCT cells. No DCT2-specific transcripts were found. DCT2 cells contrast with DCT1 cells by expression of epithelial sodium channel β- and γ-subunits and much stronger expression of transcripts associated with calcium transport (Trpv5, Calb1, S100g, and Slc8a1). Additionally, scRNA-seq identified three distinct CTAL (Slc12a1 +) cell subtypes. One of these expressed Nos1 and Avpr1a, consistent with macula densa cells. The other two CTAL clusters were distinguished by Cldn10 and Ptger3 in one and Cldn16 and Foxq1 in the other. These two CTAL cell types were also distinguished by expression of alternative Iroquois homeobox transcription factors, with Irx1 and Irx2 in the Cldn10 + CTAL cells and Irx3 in the Cldn16 + CTAL cells. Conclusions Single-cell transcriptomics revealed unexpected diversity among the cells of the distal nephron in mouse. Web-based data resources are provided for the single-cell data.

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Topics: Distal convoluted tubule (57%), Macula densa (55%), Cell type (53%) ... show more

11 Citations


Open accessPosted ContentDOI: 10.1101/2021.07.28.454201
Blue B. Lake1, Rajasree Menon2, Seth Winfree3, Qiwen Hu4  +40 moreInstitutions (14)
29 Jul 2021-bioRxiv
Abstract: Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of ∼1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.

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5 Citations


Journal ArticleDOI: 10.1152/AJPRENAL.00204.2021
Abstract: Abetted by the advent of systems biology-based (“-omics”) techniques in the 21st century, there has been a massive expansion of published data relevant to virtually every physiological question. Th...

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1 Citations


Open accessJournal ArticleDOI: 10.1152/AJPRENAL.00077.2021
Brian G. Poll1, Lihe Chen1, Chung-Lin Chou1, Viswanathan Raghuram1  +1 moreInstitutions (1)
Abstract: Kidney transport and other renal functions are regulated by multiple G protein-coupled receptors (GPCRs) expressed along the renal tubule. The rapid, recent appearance of comprehensive unbiased gene expression data in the various renal tubule segments, chiefly RNA sequencing and protein mass spectrometry data, has provided a means of identifying patterns of GPCR expression along the renal tubule. To allow for comprehensive mapping, we first curated a comprehensive list of GPCRs in the genomes of mice, rats, and humans (https://hpcwebapps.cit.nih.gov/ESBL/Database/GPCRs/) using multiple online data sources. We used this list to mine segment-specific and cell type-specific expression data from RNA-sequencing studies in microdissected mouse tubule segments to identify GPCRs that are selectively expressed in discrete tubule segments. Comparisons of these mapped mouse GPCRs with other omics datasets as well as functional data from isolated perfused tubule and micropuncture studies confirmed patterns of expression for well-known receptors and identified poorly studied GPCRs that are likely to play roles in the regulation of renal tubule function. Thus, we provide data resources for GPCR expression across the renal tubule, highlighting both well-known GPCRs and understudied receptors to provide guidance for future studies.

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Topics: Tubule (58%)

1 Citations


Journal ArticleDOI: 10.1016/J.KINT.2021.10.014
Yash R. Mehta1, Spencer Lewis1, Kirby T. Leo1, Lihe Chen1  +8 moreInstitutions (1)
Abstract: The regulation of cyclic AMP (cAMP) levels in kidney epithelial cells is important in at least two groups of disorders, namely water balance disorders and autosomal dominant polycystic kidney disease (ADPKD). Focussing on the latter, we review genes that code for proteins that are determinants of cAMP levels in cells. We identify which of these determinants are expressed in the 14 kidney tubule segments using recently published RNA-seq and protein mass spectrometry data ("ADPKD-omics"). This includes G protein-coupled receptors, adenylyl cyclases, cyclic nucleotide phosphodieterases, cAMP transporters, cAMP binding proteins, regulator of G protein signaling (RGS) proteins, G protein-coupled receptor kinases, arrestins, calcium transporters, and calcium binding proteins. In addition, compartmentalized cAMP signaling in the primary cilium is discussed and a specialized database of the proteome of the primary cilium of cultured "IMCD3" cells is provided as an online resource (https://esbl.nhlbi.nih.gov/Databases/CiliumProteome/). Overall, this paper provides a general resource in the form of a curated list of proteins likely to play roles in determination of cAMP levels in kidney epithelial cells and, therefore likely to be determinants of progression of ADPKD.

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References
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48 results found


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTP616
01 Jan 2010-Bioinformatics
Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

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Topics: Bioconductor (64%)

21,575 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTS635
01 Jan 2013-Bioinformatics
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Topics: MRNA Sequencing (57%)

20,172 Citations


Open accessJournal ArticleDOI: 10.1186/1471-2105-12-323
Bo Li1, Colin N. Dewey1Institutions (1)
04 Aug 2011-BMC Bioinformatics
Abstract: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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10,559 Citations


Open accessJournal ArticleDOI: 10.1038/NATURE07509
Eric T. Wang1, Rickard Sandberg2, Rickard Sandberg1, Shujun Luo3  +6 moreInstitutions (4)
27 Nov 2008-Nature
Abstract: Through alternative processing of pre-messenger RNAs, individual mammalian genes often produce multiple mRNA and protein isoforms that may have related, distinct or even opposing functions. Here we report an in-depth analysis of 15 diverse human tissue and cell line transcriptomes on the basis of deep sequencing of complementary DNA fragments, yielding a digital inventory of gene and mRNA isoform expression. Analyses in which sequence reads are mapped to exon-exon junctions indicated that 92-94% of human genes undergo alternative splicing, 86% with a minor isoform frequency of 15% or more. Differences in isoform-specific read densities indicated that most alternative splicing and alternative cleavage and polyadenylation events vary between tissues, whereas variation between individuals was approximately twofold to threefold less common. Extreme or 'switch-like' regulation of splicing between tissues was associated with increased sequence conservation in regulatory regions and with generation of full-length open reading frames. Patterns of alternative splicing and alternative cleavage and polyadenylation were strongly correlated across tissues, suggesting coordinated regulation of these processes, and sequence conservation of a subset of known regulatory motifs in both alternative introns and 3' untranslated regions suggested common involvement of specific factors in tissue-level regulation of both splicing and polyadenylation.

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4,242 Citations


Journal ArticleDOI: 10.1038/NRM3525
Abstract: Alternative splicing was discovered simultaneously with splicing over three decades ago. Since then, an enormous body of evidence has demonstrated the prevalence of alternative splicing in multicellular eukaryotes, its key roles in determining tissue- and species-specific differentiation patterns, the multiple post- and co-transcriptional regulatory mechanisms that control it, and its causal role in hereditary disease and cancer. The emerging evidence places alternative splicing in a central position in the flow of eukaryotic genetic information, between transcription and translation, in that it can respond not only to various signalling pathways that target the splicing machinery but also to transcription factors and chromatin structure.

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Topics: Alternative splicing (64%), RNA splicing (63%), Eukaryotic transcription (59%) ... show more

558 Citations