Evidence map›Paper›PMID 32102984›Full record

ArticleJCI insight2020

Urinary cell transcriptomics and acute rejection in human kidney allografts.

Akanksha Verma, Thangamani Muthukumar, Hua Yang, Michelle Lubetzky, Michael F Cassidy, John R Lee, Darshana M Dadhania, Catherine Snopkowski, Divya Shankaranarayanan, Steven P Salvatore and 8 more

Open access · goldAbstract read
In one paragraph

Article in JCI insight, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
4.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 3 institutions in 4 countries.

Akanksha VermaDepartment of Physiology and Biophysics.
Thangamani MuthukumarDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Hua YangDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Michelle LubetzkyDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Michael F CassidyDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
John R LeeDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Darshana M DadhaniaDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Catherine SnopkowskiDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Divya ShankaranarayananDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Steven P SalvatoreDivision of Renal Pathology, Department of Pathology and Laboratory Medicine, and.
Vijay K SharmaDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Jenny Z XiangGenomics Resources Core Facility, Department of Microbiology and Immunology, Weill Cornell Medical College, New York, New York, USA.
Iwijn De VlaminckMeinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Surya V SeshanDivision of Renal Pathology, Department of Pathology and Laboratory Medicine, and.
Franco B MuellerDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Karsten SuhreBioinformatics Core, Weill Cornell Medical College, Doha, Qatar.
Olivier ElementoDepartment of Physiology and Biophysics.
Manikkam SuthanthiranDivision of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
Cornell University · USMetabolism and Renal Physiology · FRWeill Cornell Medical College in Qatar · QA

Funding

Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Clinical and Translational Science CenterUL1TR000457 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI IMPERATO-MCGINLEY, JULIANNE L · 2012 to 2016
$46.0M
Biomolecular Markers for Safe Minimization of ImmunosuppressionR37AI051652 · NIAID · WEILL MEDICAL COLL OF CORNELL UNIV · PI SUTHANTHIRAN, MANIKKAM · 2009 to 2021
$4.7M
The identification and validation of mechanisms and biomarkers for relapse in diffuse large B-cell lymphoma (Administrative Supplement)R01CA194547 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI ELEMENTO, OLIVIER, TAM, WAYNE · 2015 to 2019
$2.6M
Biomolecular Markers of Renal Allograft StatusK08DK087824 · NIDDK · WEILL MEDICAL COLL OF CORNELL UNIV · PI MUTHUKUMAR, THANGAMANI · 2011 to 2015
$658k
NCATS NIH HHS UL1 TR000457NCATS NIH HHS UL1 TR002384NCI NIH HHS R01 CA194547NIAID NIH HHS R37 AI051652NIDDK NIH HHS K08 DK087824
6 · The paper itself

Abstract

BACKGROUNDRNA sequencing (RNA-Seq) is a molecular tool to analyze global transcriptional changes, deduce pathogenic mechanisms, and discover biomarkers. We performed RNA-Seq to investigate gene expression and biological pathways in urinary cells and kidney allograft biopsies during an acute rejection episode and to determine whether urinary cell gene expression patterns are enriched for biopsy transcriptional profiles.METHODSWe performed RNA-Seq of 57 urine samples collected from 53 kidney allograft recipients (patients) with biopsies classified as acute T cell-mediated rejection (TCMR; n = 22), antibody-mediated rejection (AMR; n = 8), or normal/nonspecific changes (No Rejection; n = 27). We also performed RNA-Seq of 49 kidney allograft biopsies from 49 recipients with biopsies classified as TCMR (n = 12), AMR (n = 17), or No Rejection (n = 20). We analyzed RNA-Seq data for differential gene expression, biological pathways, and gene set enrichment across diagnoses and across biospecimens.RESULTSWe identified unique and shared gene signatures associated with biological pathways during an episode of TCMR or AMR compared with No Rejection. Gene Set Enrichment Analysis demonstrated enrichment for TCMR biopsy signature and AMR biopsy signature in TCMR urine and AMR urine, irrespective of whether the biopsy and urine were from the same or different patients. Cell type enrichment analysis revealed a diverse cellular landscape with an enrichment of immune cell types in urinary cells compared with biopsies.CONCLUSIONSRNA-Seq of urinary cells and biopsies, in addition to identifying enriched gene signatures and pathways associated with TCMR or AMR, revealed genomic changes between TCMR and AMR, as well as between allograft biopsies and urinary cells.

Indexed as

Kidney TransplantationTranscriptomeAcute DiseaseAllograftsBiopsyGraft RejectionHumansKidneyRNA, MessengerSequence Analysis, RNARNA, MessengerMolecular diagnosisTranscriptionTransplantation

Identifiers

PMID32102984
PMCPMC7101135
OpenAlexW3008451257

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.