MétaCan
Menu
Back to cohort
Record W4407355947 · doi:10.1186/s40168-025-02028-7

The intestinal microbiome and metabolome discern disease severity in cytotoxic T-lymphocyte-associated protein 4 deficiency

2025· article· en· W4407355947 on OpenAlexafffund
Prabha Chandrasekaran, Máté Krausz, Yu Han, Noriko Mitsuiki, Annemarie Gabrysch, Christina Nöltner, Michele Proietti, Theo Heller, Caroline Grou, Virginie Calderon, Poorani Subramanian, Drew R. Jones, Yik Siu, Clayton Deming, Sean Conlan, Steven M. Holland, Julia A. Segre, Gülbu Uzel, Bodo Grimbacher, Emilia Liana Falcone

Bibliographic record

VenueMicrobiome · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsInstitute of Infection and ImmunityUniversité de MontréalMontreal Clinical Research Institute
FundersNHLBI Division of Intramural ResearchNational Institute of Allergy and Infectious DiseasesNational Human Genome Research InstituteCanadian Institutes of Health ResearchBundesministerium für Bildung und ForschungInstitut de Recherche Clinique De MontréalTokyo Medical and Dental UniversityUniversità degli Studi di BresciaMedizinische Fakultät der Albert-Ludwigs-Universität FreiburgUniversitätsklinikum RegensburgSungkyunkwan UniversityAlbert-Ludwigs-Universität FreiburgNational Institutes of HealthDeutsche ForschungsgemeinschaftUniversité de LiègeWilhelm Sander-Stiftung
KeywordsMetabolomeMicrobiomeImmunologyBiologyImmune dysregulationDysbiosisImmune systemCommon variable immunodeficiencyDiseaseImmunodeficiencyMedical microbiologyCytotoxic T cellMetabolomicsGut floraMedicineInternal medicineGeneticsBioinformaticsAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Cytotoxic T-lymphocyte-associated protein 4 deficiency (CTLA4-D) is an inborn error of immunity (IEI) caused by heterozygous mutations, and characterized by immune cell infiltration into the gut and other organs, leading to intestinal disease, immune dysregulation and autoimmunity. While regulatory T-cell dysfunction remains central to CTLA4-D immunopathogenesis, mechanisms driving disease severity and intestinal pathology are unknown but likely involve intestinal dysbiosis. We determined whether the intestinal microbiome and metabolome could distinguish individuals with severe CTLA4-D and identify biomarkers of disease severity. RESULTS: The genera Veillonella and Streptococcus emerged as biomarkers that distinguished CTLA4-D from healthy cohorts from both the National Institutes of Health (NIH) Clinical Center, USA (NIH; CTLA-D, n = 32; healthy controls, n = 16), and a geographically distinct cohort from the Center for Chronic Immunodeficiency (CCI) of the Medical Center - University of Freiburg, Germany (CCI; CTLA4-D, n = 25; healthy controls, n = 24). Since IEIs in general may be associated with perturbations of the microbiota, a disease control cohort of individuals with common variable immunodeficiency (CVID, n = 20) was included to evaluate for a CTLA4-D-specific microbial signature. Despite common IEI-associated microbiome changes, the two bacterial genera retained their specificity as biomarkers for CTLA4-D. We further identified intestinal microbiome and metabolomic signatures that distinguished patients with CTLA4-D having severe vs. mild disease. Microbiome changes were associated with distinct stool metabolomic profiles and predicted changes in metabolic pathways. These differences were impacted by the presence of gastrointestinal manifestations and were partially reversed by treatment with abatacept and/or sirolimus. CONCLUSIONS: Loss of intestinal microbial diversity and dysbiosis causing metabolomic changes was observed in CTLA4-D. Albeit some of these features were shared with CVID, the distinct changes associated with CTLA4-D highlight the fact that IEI-associated microbiome changes likely reflect the underlying immune dysregulation. Identified candidate intestinal microbial and metabolic biomarkers distinguishing individuals with CTLA4-D based on severity should be studied prospectively to determine their predictive value, and investigated as potential therapeutic ta. Video Abstract.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.214
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueMicrobiomeSame topicImmunodeficiency and Autoimmune DisordersFrench-language works237,207