Biology subjects

Dorji, T.

Publications and source records attributed to Dorji, T..

4 recordsLinked to original sources

bioRxiv2026

Nitrogen fertilization outweighs plant species loss in shaping bacterial belowground diversity in an alpine meadow on the central Tibetan Plateau

Plant species loss and nitrogen fertilization affect grassland biodiversity. However, their interactive effects on plant communities, soil properties, and the soil microbiome remain insufficiently understood. We analyzed how the removal of plant species, with and without urea addition, influenced plant diversity, soil properties, and soil bacterial communities in a Tibetan Plateau grassland. Continuous plant species removal and urea addition over seven years modified plant beta-diversity equally strong, while urea exerted a stronger negative effect on plant alpha-diversity. Both, plant species removal and urea addition caused soil acidification and an increase in NO2-/NO-, while dynamics in TOC, TON and TOC: TON were mainly driven by the growing season. Structural equation modeling identified soil acidification via urea addition as the most important indirect driver that negatively affected bacterial alpha-diversity and shifted bacterial beta-diversity. Urea addition also exerted direct negative effects on bacterial alpha- and beta-diversity, causing repression of oligotrophic (Acidobacteriota, Chloroflexota, Planctomycetota, Gemmatimonadota) and stimulation of copiotrophic (Bacillota, Bacteroidota, Pseudomonadota) bacterial taxa. Plant species removal caused slight increases in bacterial alpha-diversity, paralleled by less diverse but more even plant communities. We show that soil acidification by urea fertilization outweighs plant species loss in its negative effect on bacterial soil biodiversity in Tibetan grasslands.

ecology↗

bioRxiv2025

Extensive endemic transmission of multidrug resistant Mycobacterium tuberculosis in Bhutan: A retrospective genomic-epidemiological study

Despite decreasing overall tuberculosis notifications, the proportion of multidrug-resistant tuberculosis (MDR-TB) cases are increasing in Bhutan. While most MDR-TB cases are diagnosed among patients in the bordering districts and the capital, current diagnostic tests are limited in their ability to differentiate between the recurrent introductions and local transmission. For the first time, we conducted a retrospective genomic-epidemiological study to provide insights into the population structure, genotypic resistance patterns, and explore recent transmission of drug-resistant TB in Bhutan. Whole genome sequencing was performed on randomly selected drug-resistant and drug-sensitive TB isolates from Bhutan, collected between 2018-2022 at Microbiological Diagnostic Unit Public Health Laboratory in Melbourne, Australia. We investigated drug resistance mutations, and genomic clustering of cases using different single nucleotide polymorphisms (SNPs) thresholds. Of the 203 sequences that passed the quality control, 126 (62.1%) were MDR-TB and 15 (7.4%) were isoniazid-resistant TB. There were four different circulating lineages, with most sequences belonging to lineage 2 (86.2%). Using a SNP-threshold of [≤]12 SNPs, 71% of sequences formed 12 genomic clusters. Surprisingly, the largest cluster included of 88% of all MDR-TB sequences and spanned the entire study period. These cases were highly clonal (mean pairwise SNP-distance of 10, range 0-25). Phylogenetic analysis with publicly available international sequence data showed that this MDR-TB cluster formed a distinct clade. The major burden of MDR-TB in Bhutan appears to be due to recent local transmission of cases resulting in a large, single endemic cluster. This information will be critical for TB control program in Bhutan to tackle the major burden of MDR-TB through enhanced contact tracing of this MDR-TB clade. Additionally, this genomic data will be valuable to regional neighbours to monitor for dissemination of the strain. This study highlights the significant value of investing in TB genomics in resource limited settings globally to gain actionable insights into transmission dynamics.

genomics↗

bioRxiv2023

Bringing TB genomics to the clinic: A comprehensive pipeline to predict antimicrobial susceptibility from genomic data, validated and accredited to ISO standards.

BackgroundWhole genome sequencing (WGS) is increasingly contributing to the clinical management of tuberculosis. Whilst the availability of bioinformatic tools for analysis and clinical reporting of Mycobacterium tuberculosis sequence data is improving, However, there remains a need for accessible, flexible bioinformatic tools that can be easily tailored for clinical reporting needs in different settings and are suitable for accreditation to international standards. MethodsWe developed tbtAMR, a flexible yet comprehensive tool for analysis of Mycobacterium tuberculosis genomic data, including inference of phenotypic susceptibility and lineage calling. Validation was undertaken using local and publicly-available real-world data (phenotype and genotype) and synthetic genomic data to determine the appropriate quality control metrics and extensively validate the pipeline for clinical use. FindingstbtAMR accurately predicted lineages and phenotypic susceptibility for first- and second-line drugs, with equivalent computational and predictive performance compared to other bioinformatics tools currently available. tbtAMR is flexible with modifiable criteria to tailor results to users needs. InterpretationThe tbtAMR tool is suitable for use in clinical and public health microbiology laboratory settings, and can be tailored to specific local needs by non-programmers. We have accredited this tool to ISO standards in our laboratory, and it has been implemented for routine reporting of AMR from genomic sequence data in a clinically relevant timeframe. Reporting templates, validation methods and datasets are provided to offer a pathway for laboratories to adopt and seek their own accreditation for this critical test, to improve the management of tuberculosis globally. FundingVictorian Government Department of Health; Australian National Health and Medical Research Council and Medical Research Futures Fund. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies using the search terms: "Mycobacterium tuberculosis", "clinical", "bioinformatics", "genomics", "drug resistance (OR antimicrobial)", published prior to 31st July 2024 without language restrictions (n=258). We considered all studies from this search that used genomics to infer likely drug-resistance in M. tuberculosis. Many of these studies highlight the challenges in making meaningful interpretations from genomic data for the purpose of inferring AMR for clinical applications. Despite the development of bioinformatics tools and compilation of catalogues of resistance conferring mutations, few of these studies directly address the challenges specific to implementation of a whole genome sequencing (WGS) and bioinformatics pipelines to deliver validated and accredited results for use in clinical applications and none provide sustainable solutions. Added value of this studyTo the best of our knowledge, this study is the first to detail practical solutions to challenges to validating and accrediting the routine inference of AMR from WGS data for clinical applications for treatment of M. tuberculosis. We developed, validated and accredited a bioinformatics tool, tbtAMR, which is robust and flexible, using a data-driven approach to inferring resistance, allowing for simple customisation of behaviour on a large collection of in-house data supplemented with publicly available data. Furthermore, we have made this pipeline and the accompanying data available for use by the wider public health community to aid others in implementing similar programs. Consultation with infectious disease clinicians, microbiologists and epidemiologists has allowed us to develop a robust workflow, that encompasses sequencing, analysis, interpretation, reporting, discrepancy resolution and ongoing verification. Implications of all the available evidenceThis study addressed the specific needs of reporting genomic AMR for M. tuberculosis in a clinical and public health laboratory. Furthermore, this work makes available a pipeline and data for laboratories to utilise to implement genomic AMR for M. tuberculosis. Through the use and further development of publicly available and open-source bioinformatics software, we have demonstrated the feasibility of clinical reporting of genomic AMR results for M. tuberculosis in a low-incidence high-income setting. We believe that this work lays the foundation for making reporting of genomic AMR for M. tuberculosis more accessible to public health and reference laboratories in general.

genomics↗

bioRxiv2023

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications

The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variants sensitivity across a breadth of drugs in a panel, or a given drugs range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 {beta}-lactamase alleles and seven {beta}-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

systems biology↗