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Enregistrement W2520021261 · doi:10.1016/j.ebiom.2016.09.014

Genetic Association Studies Identify Unanticipated Gene Pathways Influencing Sepsis Outcome

2016· letter· en· W2520021261 sur OpenAlexafffundabout
Keith R. Walley

Notice bibliographique

RevueEBioMedicine · 2016
Typeletter
Langueen
DomaineMedicine
ThématiqueLipoproteins and Cardiovascular Health
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésGenome-wide association studySepsisPCSK9Genetic associationDiseaseMedicineBioinformaticsClinical trialScopusMEDLINEGeneGeneticsBiologyGenotypeInternal medicineSingle-nucleotide polymorphismLDL receptorCholesterol

Résumé

récupéré en direct d'OpenAlex

Sepsis triggers multiple parallel inflammatory signaling pathways. Of these pathways, which ones contribute most substantially to adverse outcomes and, therefore, are relevant targets for new therapies? >100 clinical trials of mediator modulators in sepsis patients have failed (Marshall, 2014Marshall J.C. Why have clinical trials in sepsis failed?.Trends Mol. Med. 2014; 20: 195-203Summary Full Text Full Text PDF PubMed Scopus (423) Google Scholar) suggesting that we need new information to direct our search. In other disease states a genome-wide association study design is an unbiased approach that has identified genes in key pathways (Altshuler et al., 2008Altshuler D. Daly M.J. Lander E.S. Genetic mapping in human disease.Science. 2008; 322: 881-888Crossref PubMed Scopus (1066) Google Scholar). For example, PCSK9 was discovered using genetic association analysis of patients who had LDL levels measured (Abifadel et al., 2003Abifadel M. Varret M. Rabes J.P. Allard D. Ouguerram K. Devillers M. Cruaud C. Benjannet S. Wickham L. Erlich D. et al.Mutations in PCSK9 cause autosomal dominant hypercholesterolemia.Nat. Genet. 2003; 34: 154-156Crossref PubMed Scopus (2182) Google Scholar). This has led to the introduction of PCSK9 inhibitors as a treatment for hypercholesterolemia where other treatments have failed. Can a similar genetic association strategy work in the complex milieu of sepsis? In the current issue of EBioMedicine, (Scherag et al., 2016Scherag A. Schöneweck F. Kesselmeier M. et al.Genetic factors of the disease course after sepsis: a genome-wide study for 28 day mortality.EBioMedicine. 2016; 12 (in this issue): 239-246Summary Full Text Full Text PDF PubMed Scopus (39) Google Scholar) conducted a genome-wide association study for 28-day mortality, initially in 740 septic patients. The authors followed standard quality control practices to limit any potential methodological errors in their discovery GWAS. From 644,699 SNPs they imputed 7,993,459 SNPs for their GWAS analysis. These investigators found that a missense genetic variant located within the VPS13A gene was associated with 28-day mortality in sepsis. This was the strongest association observed within the primary GWAS analysis (p = 8.2 × 10−8). The minor allele, associated with adverse outcome, occurred only 1% of the time so this association is very susceptible to a false positive result since only a small number of patients would carry this adverse genetic variant – the conclusions are based on a few “affected” patients. Therefore, replication of this finding was essential. These investigators took the relatively rare (within the critical care community), but crucial, step of reaching out to previous investigators with large genotyped sepsis cohorts. This VPS13A finding replicated to a reasonable extent (associated with SOFA severity of illness score) in a second large cohort of patients from the PROGRESS study (ClinicalTrials.gov: NCT02782013). Finally, taking all genetic variants found by sequencing across the VPS13A gene, the VPS13A gene was also found to be associated with 28-day mortality. A bioinformatics in silico analysis suggests that this protein-altering SNP (rs117983287) is predicted to be highly deleterious to VPS13A function. The original finding plus replication and further support from the sequencing study and in silico analysis leads to the potentially important and exciting finding that a signaling pathway involving VPS13A is associated with sepsis outcome. Not much is known about VPS13A function as it relates to sepsis (Munoz-Braceras et al., 2015Munoz-Braceras S. Calvo R. Escalante R. TipC and the chorea-acanthocytosis protein VPS13A regulate autophagy in Dictyostelium and human HeLa cells.Autophagy. 2015; 11: 918-927Crossref PubMed Scopus (67) Google Scholar) so much work remains. Indeed, there are many other genes in this region so it is not yet certain that VPS13A causally impacts sepsis outcome. The authors identified 13 other genetic variants that are promising candidates. None of these additional genetic variants reached the pre-specified level of statistical significance and therefore do not meet the discovery threshold but remain as promising candidates requiring further work and validation. When tested for replication in the collaborators' genotyped sepsis cohorts, none replicated to the same extent as VPS13A. Among this set, the best candidates included CRISPLD2 (p = 5.99 × 10−6) and a region on chromosome 13q21.33 (p = 3.34 × 10−7). Reversing the replication strategy, these investigators tested for replication of top association findings previously reported by Rautanen et al. (Rautanen et al., 2015Rautanen A. Mills T.C. Gordon A.C. Hutton P. Steffens M. Nuamah R. Chiche J.D. Parks T. Chapman S.J. Davenport E.E. et al.Genome-wide association study of survival from sepsis due to pneumonia: an observational cohort study.Lancet Respir. Med. 2015; 3: 53-60Summary Full Text Full Text PDF PubMed Scopus (129) Google Scholar). They did not observe directionally similar significant findings for any of the reported SNPs. Again, it must be appreciated that for genetic association studies, the currently reported cohort is quite small and therefore does not have much statistical power to find true associations. The use of previous data to “look-up” potential new discoveries is a very encouraging event. First, replication of the key result is impressive validation and greatly increases the probability that the primary discovery is biologically real and not a statistical fluke. Second, sharing of data is an exciting trend that will certainly improve the veracity of reported results. Another encouraging step was the use of gene-based analysis for replication. Single SNP associations may not identify causal SNPs and may simply be markers in linkage disequilibrium with the underlying causal genetic variants. Sequencing all SNPs within the identified gene is a more powerful approach (Lee et al., 2012Lee S. Emond M.J. Bamshad M.J. Barnes K.C. Rieder M.J. Nickerson D.A. Team NGESP-ELP Christiani D.C. Wurfel M.M. Lin X. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies.Am. J. Hum. Genet. 2012; 91: 224-237Summary Full Text Full Text PDF PubMed Scopus (635) Google Scholar). The increased statistical power of this approach (Taudien et al., 2016Taudien S. Lausser L. Giamarellos-Bourboulis E.J. et al.Genetic factors of the disease course after sepsis: rare deleterious variants are predictive.EBioMedicine. 2016; 12: 227-238Summary Full Text Full Text PDF PubMed Scopus (27) Google Scholar) is tempered by the smaller number of patients within this substudy in the current report. Nevertheless, replication of gene association greatly reduces that chance that a SNP association is a false positive result. The current report highlights bad and good features of genetic association studies in sepsis, ARDS, and critical illness. A key bad feature is the relatively low power we currently have to make discoveries because we have not put together sufficiently large genotyped sepsis cohorts. Cohorts in the tens of thousands have successfully identified key genes in, for example, atherosclerosis and asthma. This has led to the development of highly successful new drugs. The very good feature of the current report is that these investigators, and indeed the critical care community, are now starting to coalesce in order to address the important observations arising from genetic association studies. Let's put together the first >10,000 patient genetic association study in sepsis and start to make the really exciting discoveries that will transform patient care and outcomes. Canadian Institutes of Health Research (136986). KW is an inventor on a patent application filed by the University of British Columbia (UBC) regarding the use of PCSK9 inhibitors in sepsis. KW is a founder and shareholder of Cyon Therapeutics which has licensed this IP from UBC. Genetic Factors of the Disease Course after Sepsis: A Genome-Wide Study for 28 Day MortalitySepsis is the dysregulated host response to an infection which leads to life-threatening organ dysfunction that varies by host genomic factors. We conducted a genome-wide association study (GWAS) in 740 adult septic patients and focused on 28 day mortality as outcome. Variants with suggestive evidence for an association (p ≤ 10−5) were validated in two additional GWA studies (n = 3470) and gene coding regions related to the variants were assessed in an independent exome sequencing study (n = 74). In the discovery GWAS, we identified 243 autosomal variants which clustered in 14 loci (p ≤ 10−5). Full-Text PDF Open AccessGenetic Factors of the Disease Course After Sepsis: Rare Deleterious Variants Are PredictiveSepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. For its clinical course, host genetic factors are important and rare genomic variants are suspected to contribute. We sequenced the exomes of 59 Greek and 15 German patients with bacterial sepsis divided into two groups with extremely different disease courses. Variant analysis was focusing on rare deleterious single nucleotide variants (SNVs). We identified significant differences in the number of rare deleterious SNVs per patient between the ethnic groups. Full-Text PDF Open Access

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,091
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,063
Tête enseignante GPT0,343
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2016
Routes d'admission3
Résumé présentoui

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