Dataset Information

Unique Transcriptional Programs Identify Subtypes of AKI.

ABSTRACT: Two metrics, a rise in serum creatinine concentration and a decrease in urine output, are considered tantamount to the injury of the kidney tubule and the epithelial cells thereof (AKI). Yet neither criterion emphasizes the etiology or the pathogenetic heterogeneity of acute decreases in kidney excretory function. In fact, whether decreased excretory function due to contraction of the extracellular fluid volume (vAKI) or due to intrinsic kidney injury (iAKI) actually share pathogenesis and should be aggregated in the same diagnostic group remains an open question. To examine this possibility, we created mouse models of iAKI and vAKI that induced a similar increase in serum creatinine concentration. Using laser microdissection to isolate specific domains of the kidney, followed by RNA sequencing, we found that thousands of genes responded specifically to iAKI or to vAKI, but very few responded to both stimuli. In fact, the activated gene sets comprised different, functionally unrelated signal transduction pathways and were expressed in different regions of the kidney. Moreover, we identified distinctive gene expression patterns in human urine as potential biomarkers of either iAKI or vAKI, but not both. Hence, iAKI and vAKI are biologically unrelated, suggesting that molecular analysis should clarify our current definitions of acute changes in kidney excretory function.

SUBMITTER: Xu K

PROVIDER: S-EPMC5461802 | biostudies-other | 2017 Jun

REPOSITORIES: biostudies-other

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Unique Transcriptional Programs Identify Subtypes of AKI.

Xu Katherine K Rosenstiel Paul P Paragas Neal N Hinze Christian C Gao Xiaobo X Huai Shen Tian T Werth Max M Forster Catherine C Deng Rong R Bruck Efrat E Boles Roger W RW Tornato Alexandra A Gopal Tejashree T Jones Madison M Konig Justin J Stauber Jacob J D'Agati Vivette V Erdjument-Bromage Hediye H Saggi Subodh S Wagener Gebhard G Schmidt-Ott Kai M KM Tatonetti Nicholas N Tempst Paul P Oliver Juan A JA Guarnieri Paolo P Barasch Jonathan J

Journal of the American Society of Nephrology : JASN 20161227 6

Two metrics, a rise in serum creatinine concentration and a decrease in urine output, are considered tantamount to the injury of the kidney tubule and the epithelial cells thereof (AKI). Yet neither criterion emphasizes the etiology or the pathogenetic heterogeneity of acute decreases in kidney excretory function. In fact, whether decreased excretory function due to contraction of the extracellular fluid volume (vAKI) or due to intrinsic kidney injury (iAKI) actually share pathogenesis and shoul ...[more]

PMID: 28028135

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Project description:An important but largely unmet challenge in understanding the mechanisms that govern the formation of specific organs is to decipher the complex and dynamic genetic programs exhibited by the diversity of cell types within the tissue of interest. Here, we use an integrated genetic, genomic, and computational strategy to comprehensively determine the molecular identities of distinct myoblast subpopulations within the Drosophila embryonic mesoderm at the time that cell fates are initially specified. A compendium of gene expression profiles was generated for primary mesodermal cells purified by flow cytometry from appropriately staged wild-type embryos and from 12 genotypes in which myogenesis was selectively and predictably perturbed. A statistical meta-analysis of these pooled datasets--based on expected trends in gene expression and on the relative contribution of each genotype to the detection of known muscle genes--provisionally assigned hundreds of differentially expressed genes to particular myoblast subtypes. Whole embryo in situ hybridizations were then used to validate the majority of these predictions, thereby enabling true-positive detection rates to be estimated for the microarray data. This combined analysis reveals that myoblasts exhibit much greater gene expression heterogeneity and overall complexity than was previously appreciated. Moreover, it implicates the involvement of large numbers of uncharacterized, differentially expressed genes in myogenic specification and subsequent morphogenesis. These findings also underscore a requirement for considerable regulatory specificity for generating diverse myoblast identities. Finally, to illustrate how the developmental functions of newly identified myoblast genes can be efficiently surveyed, a rapid RNA interference assay that can be scored in living embryos was developed and applied to selected genes. This integrated strategy for examining embryonic gene expression and function provides a substantially expanded framework for further studies of this model developmental system.

Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

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Unique Transcriptional Programs Identify Subtypes of AKI.

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Unique Transcriptional Programs Identify Subtypes of AKI.

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