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Record W2890858012 · doi:10.1186/s12974-018-1307-1

Exome sequencing study in patients with multiple sclerosis reveals variants associated with disease course

2018· article· en· W2890858012 on OpenAlexafffund
Elia Gil‐Varea, Elena Urcelay, Carles Vilariño‐Güell, Carme Costa, Luciana Midaglia, Fuencisla Matesanz, Alfredo Rodríguez Antigüedad, Jorge R. Oksenberg, Laura Espino‐Paisán, A. Dessa Sadovnick, Albert Saiz, Luisa María Villar, J.A. García-Merino, Lluís Ramió‐Torrentà, Juan Carlos Triviño, Ester Quintana, René Carlos Calderón Robles, Antonio Sánchez, Rafael Arroyo, José C. Álvarez‐Cermeño, Ángela Vidal‐Jordana, Sunny Malhotra, Nicolás Fissolo, Xavier Montalbán, Manuel Comabella

Bibliographic record

VenueJournal of Neuroinflammation · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of British Columbia
FundersEuropean Regional Development FundInstituto de Salud Carlos IIICanadian Institutes of Health ResearchMichael Smith Health Research BCRed Española de Esclerosis MúltipleAgència de Gestió d'Ajuts Universitaris i de RecercaJunta de AndalucíaMinisterio de Economía y CompetitividadGeneralitat de CatalunyaMultiple Sclerosis Society of Canada
KeywordsExome sequencingSingle-nucleotide polymorphismMedicineCohortGenotypingSNPNeurologyExomeDiseaseGeneticsInternal medicinePhenotypeBioinformaticsGenotypeBiologyGenePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: It remains unclear whether disease course in multiple sclerosis (MS) is influenced by genetic polymorphisms. Here, we aimed to identify genetic variants associated with benign and aggressive disease courses in MS patients. METHODS: MS patients were classified into benign and aggressive phenotypes according to clinical criteria. We performed exome sequencing in a discovery cohort, which included 20 MS patients, 10 with benign and 10 with aggressive disease course, and genotyping in 2 independent validation cohorts. The first validation cohort encompassed 194 MS patients, 107 with benign and 87 with aggressive phenotypes. The second validation cohort comprised 257 patients, of whom 224 patients had benign phenotypes and 33 aggressive disease courses. Brain immunohistochemistries were performed using disease course associated genes antibodies. RESULTS: By means of single-nucleotide polymorphism (SNP) detection and comparison of allele frequencies between patients with benign and aggressive phenotypes, a total of 16 SNPs were selected for validation from the exome sequencing data in the discovery cohort. Meta-analysis of genotyping results in two validation cohorts revealed two polymorphisms, rs28469012 and rs10894768, significantly associated with disease course. SNP rs28469012 is located in CPXM2 (carboxypeptidase X, M14 family, member 2) and was associated with aggressive disease course (uncorrected p value < 0.05). SNP rs10894768, which is positioned in IGSF9B (immunoglobulin superfamily member 9B) was associated with benign phenotype (uncorrected p value < 0.05). In addition, a trend for association with benign phenotype was observed for a third SNP, rs10423927, in NLRP9 (NLR family pyrin domain containing 9). Brain immunohistochemistries in chronic active lesions from MS patients revealed expression of IGSF9B in astrocytes and macrophages/microglial cells, and expression of CPXM2 and NLRP9 restricted to brain macrophages/microglia. CONCLUSIONS: Genetic variants located in CPXM2, IGSF9B, and NLRP9 have the potential to modulate disease course in MS patients and may be used as disease activity biomarkers to identify patients with divergent disease courses. Altogether, the reported results from this study support the influence of genetic factors in MS disease course and may help to better understand the complex molecular mechanisms underlying disease pathogenesis.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.063
GPT teacher head0.293
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations34
Published2018
Admission routes2
Has abstractyes

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