HGV&TB: a comprehensive online resource on human genes and genetic variants associated with tuberculosis

An overview of next generation sequencing strategies and genomics tools used for tuberculosis research

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.

Association of the MARCO polymorphism rs6761637 with hepatocellular carcinoma susceptibility and clinical characteristics

Hepatocellular carcinoma (HCC) remains a significant health problem with a substantial genetic predisposition. The liver harbors the largest proportion of macrophages among all the solid organs. There is considerable controversy regarding the relationship between the macrophage receptor with collagenous structure (MARCO) and tumor development and progression. Accordingly, we performed this case-control study to determine whether associations exist between the MARCO single nucleotide polymorphism rs6761637 and HCC susceptibility and clinical characteristics. We successfully genotyped 586 HCC cases and 647 controls using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The overall genotype distribution of rs6761637 was similar in the HCC and control groups (P = 0.143). However, the CT + CC genotypes of rs6761637 were slightly more common in the HCC group among female (P = 0.021), overweight (body mass index ≥ 24 kg/m², P = 0.003), and nonsmoking (P = 0.022) individuals. The minor C allele carriers had a 1.47-fold increased risk of developing large tumor nodules (P = 0.041). rs6761637 did not affect the recurrence-free or overall survival rate of patients with HCC (P = 0.247 and 0.304, respectively). In conclusion, this is the first report of the association between MARCO genetic variations and HCC risk. These results suggest that the MARCO rs6761637 polymorphism may play a regulatory role in HCC carcinogenesis, but it does not seem to predict prognosis.

Mutalyzer 2: next generation HGVS nomenclature checker

MOTIVATION

Unambiguous variant descriptions are of utmost importance in clinical genetic diagnostics, scientific literature and genetic databases. The Human Genome Variation Society (HGVS) publishes a comprehensive set of guidelines on how variants should be correctly and unambiguously described. We present the implementation of the Mutalyzer 2 tool suite, designed to automatically apply the HGVS guidelines so users do not have to deal with the HGVS intricacies explicitly to check and correct their variant descriptions.

RESULTS

Mutalyzer is profusely used by the community, having processed over 133 million descriptions since its launch. Over a five year period, Mutalyzer reported a correct input in ∼50% of cases. In 41% of the cases either a syntactic or semantic error was identified and for ∼7% of cases, Mutalyzer was able to automatically correct the description.

AVAILABILITY AND IMPLEMENTATION

Mutalyzer is an Open Source project under the GNU Affero General Public License. The source code is available on GitHub (https://github.com/mutalyzer/mutalyzer) and a running instance is available at: https://mutalyzer.nl.