{"doi":"10.3389/fimmu.2021.769972","title":"FcER1: A Novel Molecule Implicated in the Progression of Human Diabetic Kidney Disease","abstract":"Diabetic kidney disease (DKD) is a key microvascular complication of diabetes, with few therapies for targeting renal disease pathogenesis and progression. We performed transcriptional and protein studies on 103 unique blood and kidney tissue samples from patients with and without diabetes to understand the pathophysiology of DKD injury and its progression. The study was based on the use of 3 unique patient cohorts: peripheral blood mononuclear cell (PBMC) transcriptional studies were conducted on 30 patients with DKD with advancing kidney injury; Gene Expression Omnibus (GEO) data was downloaded, containing transcriptional measures from 51 microdissected glomerulous from patients with DKD. Additionally, 12 independent kidney tissue sections from patients with or without DKD were used for validation of target genes in diabetic kidney injury by kidney tissue immunohistochemistry and immunofluorescence. PBMC DKD transcriptional analysis, identified 853 genes (p < 0.05) with increasing expression with progression of albuminuria and kidney injury in patients with diabetes. GEO data was downloaded, normalized, and analyzed for significantly changed genes. Of the 325 significantly up regulated genes in DKD glomerulous (p < 0.05), 28 overlapped in PBMC and diabetic kidney, with perturbed FcER1 signaling as a significantly enriched canonical pathway. FcER1 was validated to be significantly increased in advanced DKD, where it was also seen to be specifically co-expressed in the kidney biopsy with tissue mast cells. In conclusion, we demonstrate how leveraging public and private human transcriptional datasets can discover and validate innate immunity and inflammation as key mechanistic pathways in DKD progression, and uncover FcER1 as a putative new DKD target for rational drug design.","journal":"Frontiers in Immunology","year":2021,"id":159917,"datarank":1.032393024029553,"base_score":3.6888794541139363,"endowment":3.6888794541139363,"self_citation_contribution":0.5533319181170905,"citation_network_contribution":0.47906110591246254,"self_endowment_contribution":0.5533319181170905,"citer_contribution":0.47906110591246254,"corpus_percentile":80.40535313684536,"corpus_rank":2534,"citation_count":39,"citer_count":26,"citers_with_citation_signal":18,"citers_with_endowment":18,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7313,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":58.3333,"fair_percentile":72.8829104249465,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":340626,"name":"Mark Nguyen","orcid":null,"position":1,"is_corresponding":false},{"id":672987,"name":"Patrick Boada","orcid":null,"position":2,"is_corresponding":false},{"id":2702,"name":"Tara K. Sigdel","orcid":"0000-0001-8448-0328","position":3,"is_corresponding":false},{"id":671951,"name":"Hans W. Sollinger","orcid":"0000-0002-6322-4963","position":4,"is_corresponding":false},{"id":2709,"name":"Minnie Sarwal","orcid":"0000-0003-1212-3959","position":5,"is_corresponding":false},{"id":338893,"name":"Swastika Sur","orcid":"0000-0001-9536-981X","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-18T23:44:43.908634Z","pmid":"34925339","pmcid":"PMC8672419","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":83.3333,"fair_a":62.5,"fair_i":20.0,"fair_r":25.0,"fair_zscore":0.9454,"fair_rationale":{"fair_score":58.33,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":83.33,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"Microarray data was uploaded to Gene Expression Omnibus (GEO) (Accession number GSE142153 ; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142153 ).","grounded":true,"rationale":"The paper provides a GEO accession (GSE142153), which is a persistent identifier scheme.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"Microarray data was uploaded to Gene Expression Omnibus (GEO)","grounded":true,"rationale":"The paper names Gene Expression Omnibus (GEO) as the repository holding the data.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/ , GSE142153 .","grounded":true,"rationale":"The statement points to a repository record with an accession number (GSE142153).","anchors":["Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li","Springer Nature research data policy — Data Availability Statements: standard statement templat","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes"],"scored":false,"signal":null},{"key":"f_discovery_metadata","label":"Description of the dataset as an object","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"Cohort 1 consisted of transcriptional profiling of PBMC from 30 unique DKD patients with different stages of CKD and 10 normal healthy controls without diabetes or kidney damage","grounded":true,"rationale":"The dataset content is described in running prose (number of samples, tissue source) but not in an itemised inventory (section, table, or list). 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'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No license or reuse terms are stated for the data; the article's CC BY license applies to the article, not the data.","gain":16.67,"priority":"essential","scored":true},{"key":"i_open_nonproprietary_format","dimension":"I","label":"Open file format","action":"Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The paper does not name any file format for the deposited data.","gain":8.33,"priority":"important","scored":true},{"key":"x_code_availability","dimension":"R","label":"Analysis code available","action":"Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI).","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No code location (repository, DOI, or supplementary material) is given for the study's own code.","gain":8.33,"priority":"important","scored":true},{"key":"f_dataset_cited","dimension":"F","label":"Dataset formally cited","action":"Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Microarray data was uploaded to Gene Expression Omnibus (GEO) (Accession number GSE142153 ; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142153 ).","why":"The dataset identifier (GSE142153) appears only in the body text, not in the reference list.","gain":4.17,"priority":"important","scored":true},{"key":"r_versioning","dimension":"R","label":"Snapshot identified","action":"Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). 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[majority verdict 'no' (3/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable.","Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI).","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list.","Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-07-20T11:27:28.510879Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}