Evidence map›Paper›PMID 33863921›Full record

ArticleScientific reports2021

Multi-omic analyses in Abyssinian cats with primary renal amyloid deposits.

Francesca Genova, Simona Nonnis, Elisa Maffioli, Gabriella Tedeschi, Maria Giuseppina Strillacci, Michela Carisetti, Giuseppe Sironi, Francesca Anna Cupaioli, Noemi Di Nanni, Alessandra Mezzelani and 6 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

6 citing papers in PubMed, 8 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

16 authors at 20 institutions in 14 countries.

Francesca Genova *Department of Veterinary Medicine, University of Milan, 26900, Lodi, Italy.
Simona Nonnis *Department of Veterinary Medicine, University of Milan, 26900, Lodi, Italy.
Elisa MaffioliDepartment of Veterinary Medicine, University of Milan, 26900, Lodi, Italy.
Gabriella TedeschiDepartment of Veterinary Medicine, University of Milan, 26900, Lodi, Italy.
Maria Giuseppina StrillacciDepartment of Veterinary Medicine, University of Milan, 26900, Lodi, Italy.
Michela CarisettiDepartment of Veterinary Medicine, University of Milan, 26900, Lodi, Italy.
Giuseppe SironiDepartment of Veterinary Medicine, University of Milan, 26900, Lodi, Italy.
Francesca Anna CupaioliInstitute of Biomedical Technologies, National Research Council of Italy (CNR-ITB), 20090, Segrate, Italy.
Noemi Di NanniInstitute of Biomedical Technologies, National Research Council of Italy (CNR-ITB), 20090, Segrate, Italy.
Alessandra MezzelaniInstitute of Biomedical Technologies, National Research Council of Italy (CNR-ITB), 20090, Segrate, Italy.
Ettore MoscaInstitute of Biomedical Technologies, National Research Council of Italy (CNR-ITB), 20090, Segrate, Italy.
Christopher R HelpsLangford Vets, University of Bristol, Langford, BS40 5DU, UK.
Peter A J LeegwaterDepartment of Clinical Sciences of Companion Animals, Utrecht University, 3508 TD, Utrecht, The Netherlands.
Laetitia DorsoPathology Service for Large Animals, Ecole Nationale Vétérinaire Oniris, 44300, Nantes, France.
99 Lives Consortium
Maria LongeriDepartment of Veterinary Medicine, University of Milan, 26900, Lodi, Italy. maria.longeri@unimi.it.
University of Milan · ITCornell University · USUniversity of California, Davis · USMassey University · NZSwedish University of Agricultural Sciences · SEFolkhälsans Forskningscentrum · FIInstitute of Biomedical Technologies · ITMissouri College · USNational Research Council · ITNippon Veterinary and Life Science University · JPUniversity of Sydney · AUBowling Green State University · USChicago Zoological Society · USCincinnati Zoo and Botanical Garden · USElanco (United States) · USHill's Pet Nutrition (United States)HudsonAlpha Institute for Biotechnology · USIowa State University · USMichigan State University · USNational MPS Society · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The amyloidoses constitute a group of diseases occurring in humans and animals that are characterized by abnormal deposits of aggregated proteins in organs, affecting their structure and function. In the Abyssinian cat breed, a familial form of renal amyloidosis has been described. In this study, multi-omics analyses were applied and integrated to explore some aspects of the unknown pathogenetic processes in cats. Whole-genome sequences of two affected Abyssinians and 195 controls of other breeds (part of the 99 Lives initiative) were screened to prioritize potential disease-associated variants. Proteome and miRNAome from formalin-fixed paraffin-embedded kidney specimens of fully necropsied Abyssinian cats, three affected and three non-amyloidosis-affected were characterized. While the trigger of the disorder remains unclear, overall, (i) 35,960 genomic variants were detected; (ii) 215 and 56 proteins were identified as exclusive or overexpressed in the affected and control kidneys, respectively; (iii) 60 miRNAs were differentially expressed, 20 of which are newly described. With omics data integration, the general conclusions are: (i) the familial amyloid renal form in Abyssinians is not a simple monogenic trait; (ii) amyloid deposition is not triggered by mutated amyloidogenic proteins but is a mix of proteins codified by wild-type genes; (iii) the form is biochemically classifiable as AA amyloidosis.

Indexed as

Amyloidogenic ProteinsAmyloidosis, FamilialAnimalsCat DiseasesCatsGenetic VariationKidneyKidney DiseasesMicroRNAsProteomicsWhole Genome SequencingAmyloidogenic ProteinsMicroRNAs

Identifiers

PMID33863921
PMCPMC8052419
OpenAlexW3156268329

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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.