Biology subjects

Thuong, N. T. T.

Publications and source records attributed to Thuong, N. T. T..

12 recordsLinked to original sources

bioRxiv2026

Long-read sequencing of Mycobacterial tuberculosis is comparable to short-read sequencing for antimicrobial resistance prediction and epidemiological studies.

BackgroundShort-read genetic sequencing technologies (mainly Illumina) have been extensively used for around a decade for Mycobacterium tuberculosis complex (MTBC) outbreak analysis and genomic drug susceptibility testing (gDST) with the result that Illumina has become the de facto gold standard. Long-read sequencing, as exemplified by Oxford Nanopore Technologies (ONT), offer the prospect of faster, simpler, and portable sequencing. In this work, we carry out the largest to date comparison of how well Illumina and ONT technologies sequence MTBC samples, making use of R10.4 ONT flowcells, updated basecalling models and deep-learning variant calling. MethodsA total of 508 samples were sequenced using both short and long-read platforms. All samples originated from South Africa or Vietnam and were over-selected for drug resistance and also included several local outbreaks and a range of lineages. The South African and Vietnamese samples had already been Illumina sequenced. Samples with [≥]50 read depth by Illumina were selected for sequencing by ONT using one of the GridION or PromethION platforms. Bioinformatics processing was done using a modified online cloud platform which included reference-based variant calling, catalogue-based gDST and identified related samples via SNP counting to inform outbreak detection. The lineages and gDST predictions obtained by short-and long-sequencing were compared for all samples as were all putative clusters identified via SNP counting. For convenience Illumina was used as the reference method. FindingsOf the 508 samples, 425 (83.7%) had sufficient read depths to permit comparison between the two sequencing technologies. The assigned lineages were identical for 407/425 (95.8%) samples and all discordances were due to mixed lineages being identified by one technology. Evidence of non-tuberculous mycobacterium (NTM) subpopulations were found in nine samples. Using Illumina as the reference method, the very major error (VME) rate of ONT for predicting resistance to all 15 drugs is 1.0% (0.6-1.5%) whilst the major error (ME) rate is 1.7% (1.3-2.2%) with an unclassified rate of 6.9% (6.3-7.5%). This is below the thresholds specified by the CLSI. Considering each of the 15 drugs individually they had VME and ME point estimates below [≤]3% in 29/30 cases; and most 25/30 below [≤]1.5%. Filtering out all samples containing mixtures left 382 isolates. By appropriate masking of the reference genome we were able to obtain a mean SNP distance between the two platforms of 0.13 (median of zero) for the same sample and for 376/382 samples (98.4%, CI:96.6-99.4%) the difference was [≤]1 SNPs. The high concordance in SNP identification ensured that few differences in the 43 putative clusters among 172 isolates were observed. InterpretationThe differences between the two sequencing platforms for the key clinical outputs is so small that it is now within the tolerances set by regulatory agencies. Provided the sequencing is of sufficient quality, we have therefore reached a threshold whereby sequencing data from long-and short-read platforms can be aggregated. This will enable large scale analyses by national and international public health agencies whilst allowing the MTBC community to take advantage of the portability and speed of long-read sequencing. FundingThe NIHR Health Protection Research Unit: Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford (NIHR200915), a partnership between the UK Health Security Agency (UKHSA) and the University of Oxford, the National Institute for Health and Care Research Biomedical Research Centre: Oxford (BRC) and the Ellison Institute of Technology, Oxford Ltd. The CRyPTIC project was funded by Wellcome [214560/Z/18/Z], a Wellcome Trust/Newton Fund-MRC Collaborative Award (200205/Z/15/Z); and the Bill & Melinda Gates Foundation Trust (OPP1133541). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe conducted a PubMed Central full text search for "tuberculosis" AND ("drug resistance prediction" OR "drug susceptibility prediction") AND ("genome" OR "genomic" OR "geno-typic") AND ("ont" OR "oxford nanopore") between 2022 and 2026 (conducted 1 April 2026). This returned 62 papers; of which, six used both Illumina and ONT sequencing. One of these, published in 2023, directly compared the performance of the two platforms on 151 M. tuberculosis isolates oversampled for resistance. The investigation yielded comparative results for the earlier generation ONT flow cell (R9{middle dot}4{middle dot}1) and base-caller (guppy version 5{middle dot}0{middle dot}16). Another, published in 2026, investigated a targeted next-generation sequencing panel of 20 amplicons using ONT sequencing on R10.4.1 flow cells with guppy 6{middle dot}4{middle dot}6. They compared the results on 71 isolates against phenotypic data and Illumina whole genome sequencing (for 53 isolates) but had low rates of resistance, with all drugs but isoniazid being limited to under five resistant isolates. Two other small studies (10 and 13 samples, respectively) conducted feasibility studies comparing ONT with Illumina, also using earlier generation flow cells and base-calling technology from ONT. Two further studies compared Illumina with ONT for direct sputum sequencing and did not investigate the comparative performance of the two platforms for variant call accuracy, resistance prediction, and outbreak detection. Illumina sequencing technology is widely used for genomic sequence analysis in research, and clinical and public health contexts. Consequently, it has become the de facto reference standard for generating whole genome sequence data. Whilst previous studies established the promise and limitations of long-read (ONT) sequencing as an alternative to short-read sequencing (mainly Illumina), the enhanced performance arising from newer flowcells (e.g. R10.4.1), V14 chemistry, and the latest basecallers (dorado v4.3.0/5.0.0) has not been analysed. Neither has any ONT analysis incorporating the new deep-learning variant callers been evaluated in a large-scale comparative study. Thus, it is currently unclear whether data generated by either platform can be used safely in aggregated analyses for research and clinical or public health service. Added value of this studyWe compared how well short-(Illumina) and long-read (ONT) sequencing platforms identify the genetic variants in M. tuberculosis, predict antituberculous drug resistance and recog-nise outbreaks. The long-reads were generated using the latest generation ONT R10.4.1 flows cells, V14 chemistry, super high accuracy basecalling (dorado v4.3.0/5.0.0) and a bioinformatics analysis pipeline built using the Clair3 deep-learning based variant caller. A total of 508 clinical samples were sequenced using both technologies, substantially more than previous studies. The sampling frame was much larger than previously investigations and included a large proportion of isolates with resistance to first-line and second-line antibiotics as well as bedaquiline. Thus, providing greater statistical power for resistance prediction than before. In particular, the inclusion of bedaquiline resistance provided evidence useful for predicting resistance to this newly deployed drug for treating multi-drug resistant (MDR) TB. We find that the differences between technologies are small meaning that either technology can be used alone safely, and services using both technologies can confidently aggregate the data for analysis. Implications of all the available evidenceThis will be a benefit to local, regional and international organisations, particularly public health agencies, which often have a mix of the two main sequencing technologies for characterising TB whole genome sequences. It also opens up the sequence based diagnostic market to greater competition, particularly if the observed performance can be replicated for other pathogen species.

microbiology↗

bioRxiv2026

Whole blood transcriptional responses associated with bacterial burden in pulmonary tuberculosis

BackgroundPulmonary TB (PTB) patients present with a wide range of pre-treatment Mycobacterium tuberculosis (Mtb) burdens, which predict poor treatment outcomes. We sought to identify immune pathways and biomarkers associated with pre-treatment Mtb burden. MethodsWe conducted whole-blood RNA sequencing in 295 Vietnamese adults with PTB, quantifying bacterial load using GeneXpert Ct values. Weighted gene co-expression network analysis (WGCNA) identified gene modules, pathways, and hub genes associated with Mtb burden. Deconvolution analysis assessed contributions of immune cell types. Key findings were validated in independent PTB (n=171) and TB meningitis (TBM, n=281) cohorts, and publicly available animal datasets. We used non-linear regression for variable selection to identify gene predictors of Mtb burden and hurdle regression to model Mtb loads below the detection limit. FindingsHigher Mtb burden correlated with prolonged symptom duration, elevated neutrophil and monocyte counts, and severe lung pathology. WGCNA identified a 1,093-gene module associated with Mtb burden, characterized by coordinated innate-adaptive pathway interactions. Within this module, IFN-{gamma} signaling participates in modulating the increase of innate signaling (Toll-like, Nod-like receptors, TNF) and the decrease of adaptive signaling (T- and B-cell receptor) pathways in high-burden patients. These responses were primarily driven by neutrophils and classical monocytes. CNIH4 emerged as the strongest hub-gene and a top predictor of bacterial burden, with consistent validation across independent PTB and TBM cohorts. InterpretationOur study reveals systemic innate-adaptive immune dynamics underlying bacterial burden in PTB and identifies CNIH4 as a potential biomarker for treatment monitoring as well as a therapeutic target. FundingNational Institute of Health; Wellcome Trust, UK. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed from Jan 1, 2000, to Dec 10, 2025, without language restrictions, for human studies examining the relationship between Mycobacterium tuberculosis (Mtb) burden and host blood transcriptional responses in pulmonary tuberculosis. Search terms were used in combination as follows: ("Tuberculosis, Pulmonary" OR "pulmonary tuberculosis" OR PTB) AND ("Mycobacterium tuberculosis" OR mycobacter*) AND ("bacterial load" OR "bacterial burden" OR "sputum smear" OR "smear grade" OR xpert OR genexpert OR "cycle threshold" OR ct OR "time to positivity" OR TTP OR CFU OR "molecular bacterial load" OR MBLA)) AND (blood OR "whole blood" OR "peripheral blood") AND (RNA-seq OR "RNA sequencing" OR transcriptom* OR "gene expression" OR microarray). We included studies of active pulmonary tuberculosis that measured quantitative or semi-quantitative bacterial burden and profiled host blood transcriptome-wide responses at baseline, reporting either differential expression by burden strata or associations between bacterial burden and host gene expression. We excluded studies limited to latent tuberculosis, animal or in vitro models, diagnostic or prognostic signature studies without bacterial burden measurement, studies focused on treatment response, and studies using targeted assays without transcriptome-wide profiling. This search identified 12 articles describing blood transcriptional signatures for tuberculosis diagnosis, prognosis, and treatment response. However, only one study directly examined the relationship between pre-treatment bacterial burden and whole-blood transcriptome-wide profiles. That study demonstrated differences in systemic gene expression between patients with higher and lower sputum mycobacterial load and proposed a 20-gene blood signature associated with bacterial burden. However, the analysis was limited by small sample size, lack of pathway-level and cellular interpretation or assessment of correlation between signature with bacterial load. Added value of this studyOur study advances existing evidence by leveraging the wide spectrum of pre-treatment bacterial burden observed in routine clinical populations, quantified using GeneXpert Ct values, and integrating this with whole-blood RNA sequencing in large, well-characterized clinical cohorts. Through network-based transcriptomic analysis, immune cell deconvolution, and non-linear modelling, we identify a bacterial burden-associated gene network characterized by enhanced innate inflammatory signaling and relative suppression of adaptive immune pathways, predominantly driven by neutrophils and classical monocytes and modulated by IFN-{gamma} signaling. Within this network, CNIH4 emerges as a central hub gene and a robust predictor of bacterial burden, with consistent validation across independent pulmonary tuberculosis and tuberculous meningitis cohorts. Implications of all the available evidenceTaken together, the available evidence indicates that host blood transcriptional responses correlate with bacterial burden in pulmonary tuberculosis, but previous studies have provided limited insight into the underlying immune processes. Our findings strengthen the biological link between pre-treatment mycobacterial burden and systemic immune dysregulation, showing that higher bacterial burden is associated with transcriptional state marked by coordinated upregulation of innate immune responses and downregulation of adaptive immune pathways. These results support the use of host transcriptomic profiling as a biologically informative complement to sputum-based measures of bacterial burden and highlight burden-associated immune pathways, particularly CNIH4, as a potential target for treatment monitoring and host-directed therapeutic development.

immunology↗

bioRxiv2025

Strain diversity drives heterogeneous responses to tuberculosis combination therapy

BackgroundStrain diversity in Mycobacterium tuberculosis (Mtb) underlies distinct clinical presentations and outcomes, but the range of drug susceptibility phenotypes among clinical isolates is poorly understood. We aimed to identify drug response patterns in phylogenetically diverse clinical isolates to combination treatment. MethodsOut of 641 drug-sensitive clinical isolates, we selected 13 strains that capture local and global phylogenetic diversity and included Erdman ATCC-35801 as a reference. We selected ten antibiotics with diverse mechanisms of action to study phenotypic responses to combination therapy. We treated each strain with 10 single drugs, 45 drug pairs, and 20 three-way combinations in standard and cholesterol-rich media. To compare combination treatment responses across strains and conditions that have varying doubling times, we computed normalized growth rate inhibition metrics (GRmax). FindingsMtb clinical strains displayed a broad range of drug response phenotypes across the 65 drug combinations and two metabolic conditions tested. The most effective drug pairs (based on potency and synergy) varied both by strain and metabolic condition. Within our 14-strain panel, strains that were less sensitive to single drugs were also less sensitive to combination treatment, with very few exceptions. For all drug combinations tested, the magnitude of GRmax variation across all strains was driven primarily by variation among genetically related strains, rather than between genetically distant strain groups. InterpretationPreclinical studies should reflect the diversity of Mtb clinical strains; our data suggest that selecting strains based on the range of drug response phenotypes displayed, rather than by genetic diversity alone, may better account for the effects of strain variation. Our findings also support the understanding that constituent drug pairs of high-order combinations target metabolically heterogeneous Mtb. Selection of these pairs should likely involve multiple factors including the infecting strain, metabolic niche, and drug response metrics. FundingGates Foundation INV-027276; NIH P01AI143575&1F32AI174653; Wellcome 206724/Z/17/Z

microbiology↗

bioRxiv2025

Evolution of Mycobacterium tuberculosis transcription regulation is associated with increased transmission and drug resistance

Mycobacterium tuberculosis (Mtb) has co-evolved with humans for thousands of years causing variation in virulence, transmissibility, and disease phenotypes. To identify bacterial contributors to phenotypic diversity, we developed new RNA-seq and phylogenomic tools to capture hundreds of Mtb isolate transcriptomes, link transcriptional and genetic variation, and find associations between variants and epidemiologic traits. Across 274 Mtb clinical isolates, we uncovered unexpected diversity in expression of virulence genes which we linked to known and previously unrecognized regulators. Surprisingly, we found that many isolates harbor variants associated with decreased expression of EsxA (Esat6) and EsxB (Cfp10), which are virulence effectors, dominant T cell antigens, and immunodiagnostic targets. Across >55,000 isolates, these variants associate with increased transmissibility, especially in drug resistant Mtb strains. Our data suggest expression of key Mtb virulence genes is evolving across isolates in part to optimize fitness under drug pressure, with sobering implications for immunodiagnostics and next-generation vaccines.

microbiology↗

bioRxiv2025

Single-cell profiling of blood and cerebrospinal fluid in tuberculous meningitis

Tuberculous meningitis (TBM) is the most severe form of tuberculosis, with a fatality rate of 20-50% in treated individuals. Although corticosteroid therapy can increase survival in HIV-negative people with TBM, better antimicrobial and host-directed therapies are required to improve outcome. There is, therefore, a need to better understand local immunopathologic pathways. Despite its power in identifying disease-specific cellular profiles, single-cell RNA-sequencing (scRNA-seq) has been underutilized in cerebral samples in brain infection. We employed scRNA-seq to analyze fresh pretreatment cerebrospinal fluid (CSF) from four TBM patients, along with paired peripheral blood mononuclear cells (PBMCs). While 29 cell subtypes were present in both tissues, their relative abundance varied significantly. In particular, CSF was enriched with highly inflammatory microglia-like macrophages, GZMK-expressing CD8+ T cells, and CD56bright NK cells. The latter two subsets exhibited features associated with dysfunctional cytotoxicity. Across multiple cell types, inflammatory signaling pathways were increased and oxidative phosphorylation was decreased in CSF compared to PBMCs. This study highlights the value of scRNA-seq for exploring CSF immunopathogenesis in TBM patients and offers a resource for future studies investigating the pathophysiology of TBM and other brain infections, including potentially targetable cell populations linked with immune-mediated pathology.

immunology↗

bioRxiv2023

Disruption of Aldehyde Dehydrogenase 2 protects against 1 bacterial infection

The ALDH2*2 (rs671) variant present in >500 million individuals reduces ALDH2 function, impairing aldehyde detoxification. While aldehyde accumulation in these individuals is associated with numerous negative health consequences, a previous study showed a cohort of ALDH2*2 carriers are less likely to develop active pulmonary tuberculosis. Here, we present additional human data that support this finding and show ALDH2-deficiency in mice provides a fitness advantage during bacterial infections. We found aldehydes normally detoxified by ALDH2 killed the bacterial pathogens Mycobacterium tuberculosis and Francisella tularensis. Infected macrophages from Aldh2-/-mice had higher levels of formaldehyde and 4-hydroxynonenal, which enhanced their microbicidal capacity. Aldh2-/- mice were more resistant to infection with Mycobacterium tuberculosis and Francisella tularensis than parental mice and displayed elevated inflammatory cytokine and chemokine levels, accompanied by an increased accumulation of inflammatory monocytes and macrophages. These findings support a model in which host-derived aldehydes are robust innate immune effectors, limiting bacterial infection through both direct microbicidal activity and immune modulation. Collectively, this work may explain why the ALDH2*2 allele was selected for in humans.

microbiology↗

bioRxiv2023

Mycobacterial formation of intracellular lipid inclusions is a dynamic process associated with rapid replication

Intracellular lipid inclusions (ILI) are triacylglyceride rich organelles produced by mycobacteria thought to serve as energy reservoirs. It is believed that ILI are formed as a result of a dosR mediated transition from replicative growth to non-replicating persistence (NRP). ILI rich Mycobacterium tuberculosis (Mtb) bacilli have been reported during infection and in sputum, establishing their importance in Mtb pathogenesis. Studies conducted in mycobacteria such as Mycobacterium smegmatis, Mycobacterium abscessus, or lab Mtb strains have demonstrated ILI formation in the presence of hypoxic, nitric oxide, nutrient limitation, or low nitrogen stress, conditions believed to emulate the host environment within which Mtb resides. Here, we show that M. marinum and clinical Mtb isolates make ILI during active replication in axenic culture independent of environmental stressors. By tracking ILI formation dynamics we demonstrate that ILI are quickly formed in the presence of fresh media or exogenous fatty acids but are rapidly depleted while bacteria are still actively replicating. We also show that the cell envelope is an alternate site for neutral lipid accumulation observed during stationary phase. In addition, we screen a panel of 60 clinical isolates and observe variation in ILI production during early log phase growth between and among Mtb lineages. Finally, we show that dosR expression level does not strictly correlate with ILI accumulation in fresh clinical isolates. Taken together, our data provide evidence of an active ILI formation pathway in replicating mycobacteria cultured in the absence of stressors, suggesting a decoupling of ILI formation from NRP.

microbiology↗

bioRxiv2023

High-throughput phenogenotyping of Mycobacteria tuberculosis clinical strains reveals bacterial determinants of treatment outcomes

BackgroundCombatting the tuberculosis (TB) epidemic caused by Mycobacterium tuberculosis (Mtb) necessitates a better understanding of the factors contributing to patient clinical outcomes and transmission. While host and environmental factors have been evaluated, the impact of Mtb genetic background and phenotypic diversity is underexplored. Previous work has made associations between Mtb genetic lineages and some clinical and epidemiological features, but the bacterial traits underlying these connections are largely unknown. MethodsWe developed a high-throughput functional genomics platform for defining genotype-phenotype relationships across a panel of Mtb clinical isolates. These phenotypic fitness profiles function as intermediate traits which can be linked to Mtb genetic variants and associated with clinical and epidemiological outcomes. We applied this approach to a collection of 158 Mtb strains from a study of Mtb transmission in Ho Chi Minh City, Vietnam. Mtb strains were genetically tagged in multiplicate, which allowed us to pool the strains and assess in vitro competitive fitness using deep sequencing across a set of 14 host-relevant antibiotic and metabolic conditions. Phylogenetic and monogenic associations with these intermediate traits were identified and then associated with clinical outcomes. FindingsMtb clinical strains have a broad range of growth and drug response dynamics that can be clustered by their phylogenetic relationships. We identified novel monogenic associations with Mtb fitness in various metabolic and antibiotic conditions. Among these, we find that mutations in Rv1339, a phosphodiesterase, which were identified through their association with slow growth in glycerol, are further associated with treatment failure. We also identify a previously uncharacterized subclade of Lineage 1 strains (L1.1.1.1) that is phenotypically distinguished by slow growth under most antibiotic and metabolic stress conditions in vitro. This clade is associated with cavitary disease, treatment failure, and demonstrates increased transmission potential. InterpretationHigh-throughput phenogenotyping of Mtb clinical strains enabled bacterial intermediate trait identification that can provide a mechanistic link between Mtb genetic variation and patient clinical outcomes. Mtb strains associated with cavitary disease, treatment failure, and transmission potential display intermediate phenotypes distinguished by slow growth under various antibiotic and metabolic conditions. These data suggest that Mtb growth regulation is an adaptive advantage for host bacterial success in human populations, in at least some circumstances. These data further suggest markers for the underlying bacterial processes that govern these clinical outcomes. FundingNational Institutes of Allergy and Infectious Diseases: P01 AI132130 (SS, SMF); P01 AI143575 (XW, SMF); U19 AI142793 (QL, SMF); 5T32AI132120-03 (SS); 5T32AI132120-04 (SS); 5T32AI049928-17 (SS) Wellcome Trust Fellowship in Public Health and Tropical Medicine: 097124/Z/11/Z (NTTT) National Health and Medical Research Council (NHMRC)/A*STAR joint call: APP1056689 (SJD) The funding sources had no involvement in study methodology, data collection, analysis, and interpretation nor in the writing or submission of the manuscript. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe used different combinations of the words mycobacterium tuberculosis, tuberculosis, clinical strains, intermediate phenotypes, genetic barcoding, phenogenomics, cavitary disease, treatment failure, and transmission to search the PubMed database for all studies published up until January 20th, 2022. We only considered English language publications, which biases our search. Previous work linking Mtb determinants to clinical or epidemiological data has made associations between bacterial lineage, or less frequently, genetic polymorphisms to in vitro or in vivo models of pathogenesis, transmission, and clinical outcomes such as cavitary disease, treatment failure, delayed culture conversion, and severity. Many of these studies focus on the global pandemic Lineage 2 and Lineage 4 Mtb strains due in part to a deletion in a polyketide synthase implicated in host-pathogen interactions. There are a number of Mtb GWAS studies that have led to novel genetic determinants of in vitro drug resistance and tolerance. Previous Mtb GWAS analyses with clinical outcomes did not experimentally test any predicted phenotypes of the clinical strains. Published laboratory-based studies of Mtb clinical strains involve relatively small numbers of strains, do not identify the genetic basis of relevant phenotypes, or link findings to the corresponding clinical outcomes. There are two recent studies of other pathogens that describe phenogenomic analyses. One study of 331 M. abscessus clinical strains performed one-by-one phenotyping to identify bacterial features associated with clearance of infection and another details a competition experiment utilizing three barcoded Plasmodium falciparum clinical isolates to assay antimalarial fitness and resistance. Added value of this studyWe developed a functional genomics platform to perform high-throughput phenotyping of Mtb clinical strains. We then used these phenotypes as intermediate traits to identify novel bacterial genetic features associated with clinical outcomes. We leveraged this platform with a sample of 158 Mtb clinical strains from a cross sectional study of Mtb transmission in Ho Chi Minh City, Vietnam. To enable high-throughput phenotyping of large numbers of Mtb clinical isolates, we applied a DNA barcoding approach that has not been previously utilized for the high-throughput analysis of Mtb clinical strains. This approach allowed us to perform pooled competitive fitness assays, tracking strain fitness using deep sequencing. We measured the replicative fitness of the clinical strains in multiplicate under 14 metabolic and antibiotic stress condition. To our knowledge, this is the largest phenotypic screen of Mtb clinical isolates to date. We performed bacterial GWAS to delineate the Mtb genetic variants associated with each fitness phenotype, identifying monogenic associations with several conditions. We then defined Mtb phenotypic and genetic features associated with clinical outcomes. We find that a subclade of Mtb strains, defined by variants largely involved in fatty acid metabolic pathways, share a universal slow growth phenotype that is associated with cavitary disease, treatment failure and increased transmission potential in Vietnam. We also find that mutations in Rv1339, a poorly characterized phosphodiesterase, also associate with slow growth in vitro and with treatment failure in patients. Implications of all the available evidencePhenogenomic profiling demonstrates that Mtb strains exhibit distinct growth characteristics under metabolic and antibiotic stress conditions. These fitness profiles can serve as intermediate traits for GWAS and association with clinical outcomes. Intermediate phenotyping allows us to examine potential processes by which bacterial strain differences contribute to clinical outcomes. Our study identifies clinical strains with slow growth phenotypes under in vitro models of antibiotic and host-like metabolic conditions that are associated with adverse clinical outcomes. It is possible that the bacterial intermediate phenotypes we identified are directly related to the mechanisms of these outcomes, or they may serve as markers for the causal yet unidentified bacterial determinants. Via the intermediate phenotyping, we also discovered a surprising diversity in Mtb responses to the new anti-mycobacterial drugs that target central metabolic processes, which will be important in considering roll-out of these new agents. Our study and others that have identified Mtb determinants of TB clinical and epidemiological phenotypes should inform efforts to improve diagnostics and drug regimen design.

microbiology↗

bioRxiv2022

Distribution and origins of Mycobacterium tuberculosis L4 in Southeast Asia

Molecular and genomic studies have revealed that Mycobacterium tuberculosis Lineage 4 (L4, Euro-American lineage) emerged in Europe before becoming distributed around the globe by trade routes, colonial migration, and other historical connections. Although L4 accounts for tens or hundreds of thousands of TB cases in multiple Southeast Asian countries, phylogeographic studies have either focussed on a single country or just included Southeast Asia as part of a global analysis. Therefore, we interrogated public genomic data to investigate the historical patterns underlying the distribution of L4 in Southeast Asia and surrounding countries. We downloaded 6037 genomes associated with 23 published studies, focusing on global analyses of L4 and Asian studies of M. tuberculosis. We identified 2256 L4 genomes including 968 from Asia. We show that 81% of L4 in Thailand, 51% of L4 in Vietnam and 9% of L4 in Indonesia belong to sub-clades of L4 that are rarely seen outside East and Southeast Asia. These sub-clades have been transmitted between East and Southeast Asian countries, with no recent European ancestor. Although there is considerable uncertainty about the exact direction and order of intra-Asian transmissions, due to differing sampling frames between countries, our analysis suggests that China may be the intermediate between Europe and Southeast Asia for two of the three predominantly East and Southeast Asian L4 sub-lineages. This new perspective on L4 in Southeast Asia raises the possibility of investigating host population specific evolution and highlights the need for more structured sampling from Southeast Asian countries to provide more certainty of the historical and current routes of transmission.

microbiology↗

bioRxiv2020

A Bayesian analysis of the association between Leukotriene A4 Hydrolase genotype and survival probability of tuberculous meningitis patients treated with adjunctive dexamethasone

Tuberculous meningitis (TBM) remains the most devastating form of tuberculosis (TB) with high mortality despite effective antimicrobial treatment. As mortality has been linked to excessive inflammation, anti-inflammatory glucocorticoids are now routinely used as adjunctive treatment with antimicrobial therapy. However, they reduce mortality by only ~ 30%, raising the possibility that only a subset of TBM deaths are caused by inflammatory pathophysiology. Studies in Vietnam found that the survival benefit of adjunctive glucocorticoids was limited to individuals with a common promoter variant in the leukotriene A4 hydrolase (LTA4H) gene encoding an enzyme that regulates inflammatory eicosanoid expression. The variant constitutes a C/T transition with TT homozygotes having increased expression over CT heterozygotes and CC homozygotes. In Vietnam, the LTA4H TT genotype predicted survival, consistent with dexamethasone benefiting only those individuals with a dysregulated hyper-inflammatory response. However, a study of TBM patients in Indonesia did not find the LTA4H TT genotype to confer a significant survival benefit. Given the potential of personalized life-saving anti-inflammatory therapies guided by LTA4H genotype, we have used Bayesian methods to analyze the data from both studies. Bayesian analysis reveals that the LTA4H TT genotype confers survival benefit in both the Vietnam and Indonesia cohorts that begins within days and continues long-term. However, its benefit is nullified in the most severe cases where other factors cause early mortality. LTA4H TT genotype is associated with increased survival in HIV-positive patients also. Thus, our analysis extends the association of LTA4H genotype with TBM survival to populations outside of Vietnam and to HIV-positive patients. Patient LTA4H genotyping used in conjunction with disease severity assessment may help to target glucocorticoids to patients most likely to benefit from this broadly-acting immunosuppressive regimen despite its significant adverse effects.

molecular biology↗

bioRxiv2020

Most-probable number based minimum duration of killing assay for determining the spectrum of rifampicin susceptibility in clinical M. tuberculosis isolates

Accurate antibiotic susceptibility testing is essential for successful tuberculosis treatment. Recent studies have highlighted the limitations of minimum inhibitory concentrations (MIC) based phenotypic susceptibility methods in detecting other aspects of antibiotic susceptibilities in bacteria. Duration and peak of antibiotic exposure, at or above the MIC required for killing the bacterial population, has emerged as another important factor for determining the antibiotic susceptibility. This is broadly defined as antibiotic tolerance. Antibiotic tolerance can further facilitate the emergence of antibiotic resistance. Currently there are limited methods to quantify antibiotic tolerance among clinical M. tuberculosis isolates. In this study, we develop a most-probable number (MPN) based minimum duration of killing (MDK) assay to quantify the spectrum of M. tuberculosis rifampicin susceptibility within subpopulations, based on time duration of rifampicin exposure required for killing the bacterial population. MDK90-99 and MDK99.99 defined as the minimum time duration of antibiotic exposure at or above MIC required for killing 90-99% and 99.99% of the initial (pre-treatment) bacterial population respectively. Results from the rifampicin MDK assay applied to 28 laboratory and clinical M. tuberculosis isolates showed that there is variation in rifampicin susceptibility among isolates. Rifampicin MDK99/99.99 time for isolates varied from less than 2 to 10 days. MDK duration was correlated with larger sub-populations of M. tuberculosis from clinical isolates that were rifampicin tolerant. Our study demonstrates the utility of MDK assays to measure the variation in antibiotic tolerance among clinical M. tuberculosis isolates and further expands clinically important aspects of antibiotic susceptibility testing.

microbiology↗

bioRxiv2020

Diagnostic Cerebrospinal Fluid Biomarker Discovery and Validation in Patients with Central Nervous System Infections

BackgroundCentral nervous system (CNS) infections are common causes of morbidity and mortality worldwide. Rapid, accurate identification of the likely cause is essential for clinical management and the early initiation of antimicrobial therapy, which potentially improves clinical outcome. MethodsWe applied liquid chromatography tandem mass-spectrometry on 45 cerebrospinal fluid (CSF) samples from a cohort of adults with/without CNS infections to discover potential diagnostic protein biomarkers. We then validated the diagnostic performance of a selected biomarker candidate in an independent cohort of 364 consecutively treated adults with CNS infections admitted to a referral hospital in southern Vietnam. ResultsIn the discovery cohort, we identified lipocalin 2 (LCN2) as a potential biomarker of bacterial meningitis. The analysis of the validation cohort showed that LCN2 could discriminate bacterial meningitis from other CNS infections, including tuberculous meningitis, cryptococcal meningitis and viral/antibody-mediated encephalitis (sensitivity: 0.88 (95% confident interval (CI): 0.77-0.94), specificity: 0.91 (95%CI: 0.88-0.94) and diagnostic odd ratio: 73.8 (95%CI: 31.8-171.4)). LCN2 outperformed other CSF markers (leukocytes, glucose, protein and lactate) commonly used in routine care worldwide. The combination of LCN2 and these four routine CSF markers resulted in the highest diagnostic performance for bacterial meningitis (area under receiver-operating-characteristic-curve 0.96; 95%CI: 0.93-0.99). ConclusionsOur results suggest that LCN2 is a sensitive and specific biomarker for discriminating bacterial meningitis from a broad spectrum of CNS infections. A prospective study is needed to further assess the diagnostic utility of LCN2 in the diagnosis and management of CNS infections.

microbiology↗