UniProt genomic mapping for deciphering functional effects of missense variants

McGarvey, Peter B.; Nightingale, Andrew; Luo, Jie; Huang, Hongzhan; Martin, Maria Jesus; Wu, Cathy H.

doi:10.1002/humu.23738

Cited by 41 publications

(21 citation statements)

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“…Second, we investigated whether the distributions of UNEECON scores varied across different types of protein regions annotated by UniProt [47,49] and MobiDB [48]. We observed that the distributions of UNEECON scores were similar among α-helices, β-strands, and hydrogen-bonded turns (Fig 2b).…”

Section: Uneecon Scores Capture Variation Of Negative Selection Withimentioning

confidence: 96%

“…(b) Distributions of UNEECON scores estimated for potential missense mutations in various protein regions. The functional sites and protein secondary structures are based on UniProt annotations [47]. The predicted disordered protein regions are from MobiDB [48].…”

Section: Uneecon Scores Accurately Predict Missense Mutations Associamentioning

confidence: 99%

“…We obtained lists of haploinsufficient genes [37] (n = 294), autosomal dominant disease genes [35,36] (n = 709), autosomal recessive disease genes [35,36] (n = 1, 183), and olfactory receptor genes [45] (n = 371) from the GitHub repository for the MacArthur Lab at the Broad Institute (https://github.com/macarthur-lab/gene_lists). We obtained annotations of α-helices, β-strands, hydrogen-bonded turns, enzyme active sites, and binding sites from UniProt [47]. We obtained annotated disordered protein regions from MobiDB 3.0 [48].…”

Section: Distributions Of Uneecon Scores Across Gene Categories and Pmentioning

confidence: 99%

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Unified inference of missense variant effects and gene constraints in the human genome

Huang

2020

PLoS Genet

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A challenge in medical genomics is to identify variants and genes associated with severe genetic disorders. Based on the premise that severe, early-onset disorders often result in a reduction of evolutionary fitness, several statistical methods have been developed to predict pathogenic variants or constrained genes based on the signatures of negative selection in human populations. However, we currently lack a statistical framework to jointly predict deleterious variants and constrained genes from both variant-level features and gene-level selective constraints. Here we present such a unified approach, UNEECON, based on deep learning and population genetics. UNEECON treats the contributions of variant-level features and gene-level constraints as a variant-level fixed effect and a gene-level random effect, respectively. The sum of the fixed and random effects is then combined with an evolutionary model to infer the strength of negative selection at both variant and gene levels. Compared with previously published methods, UNEECON shows improved performance in predicting missense variants and protein-coding genes associated with autosomal dominant disorders, and feature importance analysis suggests that both gene-level selective constraints and variant-level predictors are important for accurate variant prioritization. Furthermore, based on UNEECON, we observe a low correlation between gene-level intolerance to missense mutations and that to loss-of-function mutations, which can be partially explained by the prevalence of disordered protein regions that are highly tolerant to missense mutations. Finally, we show that genes intolerant to both missense and loss-of-function mutations play key roles in the central nervous system and the autism spectrum disorders. Overall, UNEECON is a promising framework for both variant and gene prioritization.

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Section: Uneecon Scores Capture Variation Of Negative Selection Withimentioning

confidence: 96%

Section: Uneecon Scores Accurately Predict Missense Mutations Associamentioning

confidence: 99%

Section: Distributions Of Uneecon Scores Across Gene Categories and Pmentioning

confidence: 99%

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Unified inference of missense variant effects and gene constraints in the human genome

Huang

2020

PLoS Genet

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“…Clusters containing Ն4 members were analyzed further. Cluster representatives were annotated using protein profile searches against three databases: the Pfam-A database using HMMER3 (using family-specific gathering thresholds) (49), the NCBI Conserved Domain Database using RPS-BLAST (E value Ͻ 0.01) (51)(52)(53), and the Uniprot30 database (accessed February 2019, available from http://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/) using HHblits (54,55). Multiple sequence alignments were automatically generated from three iterations of the HHblits search and used for profile-profile comparisons against the PDB70 database (HHpred probability Ͼ 90, accessed February 2019, available from http://wwwuser.gwdg.de/~compbiol/data/hhsuite/ databases/hhsuite_dbs/).…”

Section: Methodsmentioning

confidence: 99%

A Distinct Contractile Injection System Gene Cluster Found in a Majority of Healthy Adult Human Microbiomes

et al. 2020

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Many commensal bacteria antagonize each other or their host by producing syringe-like secretion systems called contractile injection systems (CIS). Members of the Bacteroidales family have been shown to produce only one type of CIS—a contact-dependent type 6 secretion system that mediates bacterium-bacterium interactions. Here, we show that a second distinct cluster of genes from Bacteroidales bacteria from the human microbiome may encode yet-uncharacterized injection systems that we term Bacteroidales injection systems (BIS). We found that BIS genes are present in the gut microbiomes of 99% of individuals from the United States and Europe and that BIS genes are more prevalent in the gut microbiomes of healthy individuals than in those individuals suffering from inflammatory bowel disease. Gene clusters similar to that of the BIS mediate interactions between bacteria and diverse eukaryotes, like amoeba, insects, and tubeworms. Our findings highlight the ubiquity of the BIS gene cluster in the human gut and emphasize the relevance of the gut microbiome to the human host. These results warrant investigations into the structure and function of the BIS and how they might mediate interactions between Bacteroidales bacteria and the human host or microbiome. IMPORTANCE To engage with host cells, diverse pathogenic bacteria produce syringe-like structures called contractile injection systems (CIS). CIS are evolutionarily related to the contractile tails of bacteriophages and are specialized to puncture membranes, often delivering effectors to target cells. Although CIS are key for pathogens to cause disease, paradoxically, similar injection systems have been identified within healthy human microbiome bacteria. Here, we show that gene clusters encoding a predicted CIS, which we term Bacteroidales injection systems (BIS), are present in the microbiomes of nearly all adult humans tested from Western countries. BIS genes are enriched within human gut microbiomes and are expressed both in vitro and in vivo. Further, a greater abundance of BIS genes is present within healthy gut microbiomes than in those humans with with inflammatory bowel disease (IBD). Our discovery provides a potentially distinct means by which our microbiome interacts with the human host or its microbiome.

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“…Clusters containing ≥ 4 members were analyzed further. Cluster representatives were annotated using protein-profile searches against three databases: the Pfam-A database using HMMER3 (El-Gebali et al, 2019), the NCBI Conserved Domain Database using RPS-BLAST (Marchler-Bauer et al, 2017, 2011; Marchler-Bauer and Bryant, 2004), and the Uniprot30 database (accessed February 2019, available from http://www.user.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/) using HHblits (McGarvey et al, 2019; Remmert et al, 2012). Multiple sequence alignments were automatically generated from three iterations of the HHblits search and used for profile-profile comparisons against the PDB70 database (accessed February 2019, available from http://www.user.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/).…”

Section: Methodsmentioning

confidence: 99%

A Distinct Contractile Injection System Found in a Majority of Adult Human Microbiomes

Rojas

Cavalcanti

McNair

et al. 2019

Preprint

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UniProt genomic mapping for deciphering functional effects of missense variants

Cited by 41 publications

References 58 publications

Unified inference of missense variant effects and gene constraints in the human genome

Unified inference of missense variant effects and gene constraints in the human genome

A Distinct Contractile Injection System Gene Cluster Found in a Majority of Healthy Adult Human Microbiomes

A Distinct Contractile Injection System Found in a Majority of Adult Human Microbiomes

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