High-throughput phenogenotyping of Mycobacteria tuberculosis clinical strains reveals bacterial determinants of treatment outcomes (2023)

<b>Evidence before this study:</b> We 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 20 <sup>th</sup> , 2022. We only considered English language publications, which biases our search. Previous work linking <i>Mtb</i> determinants to clinical or epidemiological data has made associations between bacterial lineage, or less frequently, genetic polymorphisms to <i>in vitro</i> or <i>in vivo</i> 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 <i>Mtb</i> strains due in part to a deletion in a polyketide synthase implicated in host-pathogen interactions. There are a number of <i>Mtb</i> GWAS studies that have led to novel genetic determinants of <i>in vitro</i> drug resistance and tolerance. Previous <i>Mtb</i> GWAS analyses with clinical outcomes did not experimentally test any predicted phenotypes of the clinical strains. Published laboratory-based studies of <i>Mtb</i> 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 <i>M. abscessus</i> 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 <i>Plasmodium falciparum</i> clinical isolates to assay antimalarial fitness and resistance. <b>Added value of this study:</b> We developed a functional genomics platform to perform high-throughput phenotyping of <i>Mtb</i> 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 <i>Mtb</i> clinical strains from a cross sectional study of <i>Mtb</i> transmission in Ho Chi Minh City, Vietnam. To enable high-throughput phenotyping of large numbers of <i>Mtb</i> clinical isolates, we applied a DNA barcoding approach that has not been previously utilized for the high-throughput analysis of <i>Mtb</i> 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 <i>Mtb</i> clinical isolates to date. We performed bacterial GWAS to delineate the <i>Mtb</i> genetic variants associated with each fitness phenotype, identifying monogenic associations with several conditions. We then defined <i>Mtb</i> phenotypic and genetic features associated with clinical outcomes. We find that a subclade of <i>Mtb</i> 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 <i>Rv1339</i> , a poorly characterized phosphodiesterase, also associate with slow growth <i>in vitro</i> and with treatment failure in patients. <b>Implications of all the available evidence:</b> Phenogenomic profiling demonstrates that <i>Mtb</i> 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 <i>in vitro</i> 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 <i>Mtb</i> 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 <i>Mtb</i> determinants of TB clinical and epidemiological phenotypes should inform efforts to improve diagnostics and drug regimen design.