Human Gene Variants Linked to Enhanced NLRP3 Activity Limit Intramacrophage Growth of Mycobacterium tuberculosis

Drug resistant tuberculosis (TB), especially multidrug (MDR) and extensively drug-resistant (XDR) TB, is still a serious problem in global TB control. Slovenia and North Macedonia are low-incidence countries with TB incidence rates of 5.4 and 10.4 in 2017, respectively. In both countries, the percentage of drug resistant TB is very low with sporadic cases of MDR-TB. However, global burden of drug-resistant TB continues to increase imposing huge impact on public health systems and strongly stimulating the detection of gene variants related with drug resistance in TB. Next-generation sequencing (NGS) can provide comprehensive analysis of gene variants linked to drug resistance in Mycobacterium tuberculosis. Therefore, the aim of our study was to examine the feasibility of a full-length gene analysis for the drug resistance related genes (inhA, katG, rpoB, embB) using Ion Torrent technology and to compare the NGS results with those obtained from conventional phenotypic drug susceptibility testing (DST) in TB isolates. Between 1996 and 2017, we retrospectively selected 56 TB strains from our National mycobacterial culture collection. Of those, 33 TB isolates from Slovenian patients were isolated from various clinical samples and subjected to phenotypic DST testing in Laboratory for Mycobacteria (University Clinic Golnik, Slovenia). The remaining 23 TB isolates were isolated from Macedonian patients and sent to our laboratory for assistance in phenotypic DST testing. TB strains included were either mono-, poly- or multidrug resistant. For control purposes, we also randomly selected five TB strains susceptible to first-line anti-TB drugs. High concordance between genetic (Ion Torrent technology) and standard phenotypic DST testing for isoniazid, rifampicin and ethambutol was observed, with percent of agreement of 77%, 93.4% and 93.3%, sensitivities of 68.2%, 100% and 100%, and specificities of 100%, 80% and 88.2%, respectively. In conclusion, the genotypic DST using Ion Torrent semiconductor NGS successfully predicted drug resistance with significant shortening of time needed to obtain the resistance profiles from several weeks to just a few days.

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KEGG: integrating viruses and cellular organisms

Nucleic Acids Research ◽

10.1093/nar/gkaa970 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D545-D551 ◽

Cited By ~ 6

Author(s):

Minoru Kanehisa ◽

Miho Furumichi ◽

Yoko Sato ◽

Mari Ishiguro-Watanabe ◽

Mao Tanabe

Keyword(s):

Kegg Pathway ◽

Human Gene ◽

Building Blocks ◽

Molecular Networks ◽

Gene Variants ◽

Genome Sequences ◽

Network Systems ◽

Network Database ◽

Molecular Building Blocks ◽

Functional Orthologs

Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.

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Annotation of Human Exome Gene Variants with Consensus Pathogenicity

10.20944/preprints202007.0735.v1 ◽

2020 ◽

Author(s):

Victor Zharavin ◽

James Balmford ◽

Patrick Metzger ◽

Melanie Boerries ◽

Harald Binder ◽

...

Keyword(s):

Prediction Models ◽

Human Gene ◽

Gene Variants ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Functional Conservation ◽

Mutation Impact ◽

Uncertain Significance ◽

Direct Use ◽

Variant Effect Prediction

Pathogenicity is unknown for the majority of human gene variants. For prioritization of sequenced somatic and germline mutation variants, in silico approaches can be utilized. In this study, 84 million non-synonymous Single Nucleotide Variants (SNVs) in the human coding genome were annotated using consensus Variant Effect Prediction (cVEP) method. An algorithm, implemented as a stacked ensemble of supervised learners, performed combination of the 39 functional, conservation mutation impact scores from dbNSFP4.0. Adding gene indispensability score, accounting for differences in the pathogenicities of the variants in the essential and the mutation-tolerant genes, improved the predictions. For each SNV the consensus combination gives either a continuous-value pathogenicity score, or a categorical score in five classes: pathogenic, likely pathogenic, uncertain significance, likely benign, benign. The provided class database is aimed for direct use in clinical practice. The trained prediction models were 5-fold cross-validated on the evidence-based categorical annotations from the ClinVar database. The rankings of the scores based on their ability to predict pathogenicity were obtained. A two-step strategy using the rankings, scores and class annotations is suggested for filtering and prioritization of the human exome mutations in clinical and biological applications of NGS technology.

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