Jeremy M. Rock

Mycobacterium tuberculosis , the causative agent of tuberculosis, remains the world’s deadliest pathogen ¹ . Despite intensive study, many of its genes remain uncharacterized, which limits the insight that can be gained into tuberculosis biology and constrains drug development. Genetic interaction (GI) mapping is a powerful strategy to define functional relationships between genes by quantifying how the phenotypic effects of combined perturbations deviate from expectations based on single-gene perturbations ^2,3 . Here we develop a dual-sgRNA CRISPR-interference platform that enables high-throughput GI mapping in mycobacteria. We address key technical challenges in library construction and data interpretation by incorporating unique barcodes for each sgRNA pair and implementing a robust statistical pipeline for GI detection. Focusing on essential genes, we generate comprehensive GI maps in the model mycobacterium Mycobacterium smegmatis and the pathogen M. tuberculosis . These maps recapitulate known biology, uncover thousands of previously unrecognized interactions and identify functional connections between essential genes and drug targets. By generating AlphaFold3-based protein–protein interaction predictions ⁴ for all gene pairs in our GI maps, we show that a subset of GIs is likely to reflect underlying physical associations. Validation of selected GIs confirms both novel biological relationships and opportunities for synergistic targeting. Overall, this work establishes a generalizable framework for dual-gene perturbation and defines the architecture of essential-gene networks in mycobacteria, advancing both our understanding of tuberculosis biology and the rational design of combination therapies.

High-dimensional phenotypic screens of bacterial loss-of-function mutant libraries have determined gene-gene connections and specific phenotypes for thousands of bacterial genes in many species, but deciphering the underlying mechanisms remains decidedly low-throughput. Here, we demonstrate the utility of proteome-wide AI-based protein-protein interaction (PPI) predictions for overcoming this gap by using pooled-AlphaFold3 to assess all ∼1.3 million possible pairwise interactions in the proteome of Mycobacterium leprae. We identify ∼2,000 strong and intermediate PPIs that underlie a significant fraction of phenotypes and gene-gene connections observed in large-scale chemical genomics screens from Mycobacterium tuberculosis, Mycobacterium smegmatis, and Corynebacterium glutamicum . This combined approach predicts specific functions for dozens of previously uncharacterized core, conserved, and essential mycobacterial proteins. We highlight new information derived from the study, including insights into mycobacterial envelope assembly, peptidoglycan remodeling, and new modulators of the central dogma enzymes RNA polymerase and DNA gyrase. These data establish combined pooled-AlphaFold3 PPI prediction and high-throughput genomics approach as the gold standard for large-scale characterization of protein function.

Mycobacterium tuberculosis (Mtb) acquires resistance to rifampicin (Rif) through mutations in the β-subunit of RNA polymerase (RNAP) that prevent the drug from binding. The most common mutation is a single amino acid substitution, βS450L, that confers antibiotic resistance. This mutation also results in collateral effects that impact bacterial physiology and fitness, although the mechanisms underlying many of these effects remain unclear. Here we employed a CRISPRi comparative functional genomics approach to analyse gene vulnerability differences between βS450L Mtb and two alternative Rif-resistant (RifR) Mtb strains, βD435V and βH445Y. Among the strongest βS450L-specific vulnerabilities, we identified thiamine and branched-chain amino acid (BCAA) biosynthesis pathways. These vulnerabilities arise, at least in part, due to transcription attenuation, which impairs βS450L Mtb’s ability to upregulate expression of the critical BCAA biosynthetic enzyme ilvB1 in response to genetic or chemical inhibition. Together, our findings highlight the distinct physiological impacts of RifR in Mtb, identify transcription attenuation as a key driver of βS450L-specific vulnerabilities, and suggest potential avenues for targeted intervention.

Mycobacterium tuberculosis (Mtb) remains a leading cause of infectious disease mortality worldwide, largely due to its ability to survive within host macrophages. Despite advances in understanding the environmental pressures Mtb encounters in vivo , the genetic requirements for adaptation and survival within the intracellular niche remain incompletely defined. Here, we employed a genome-wide CRISPR interference (CRISPRi) screen in an ex vivo model exploiting single-cell suspensions from Mtb-infected mouse lung homogenates to identify genes critical for intracellular survival at different time points in the infection continuum. Using a library comprising ~20,000 sgRNAs covering >96% of Mtb open reading frames, we identified genes required for growth within the changing immune microenvironment. Mutant depletion patterns varied across immune environments sampled at 2, 4, and 6 weeks post-infection, which revealed a weighted dependency on cell wall biosynthesis genes early and the reliance on cholesterol catabolism and iron acquisition across all time points. Functional validation of three genes— embB , fadE29 , and mbtI —confirmed their temporal significance in vivo . This screen provides increased resolution of the differential metabolic vulnerabilities in Mtb in the evolving immune environments during infection, stressing the temporal nature of conditional essentiality in vivo . IMPORTANCE: Mycobacterium tuberculosis (Mtb) remains a leading cause of infectious disease mortality worldwide, largely due to its ability to survive within host macrophages. Despite advances in understanding the environmental pressures Mtb encounters in vivo , the genetic requirements for adaptation and survival within the intracellular niche remain incompletely defined. Here, we employed a genome-wide CRISPR interference (CRISPRi) screen in an ex vivo model exploiting single-cell suspensions from Mtb-infected mouse lung homogenates to identify genes critical for intracellular survival at different time points in the infection continuum. This novel approach enabled us to identify how different bacterial metabolic pathways were of greater importance to the bacterium at different time points post-infection. The results provide insights into how the evolving immune response to infection shapes the metabolic and replicative status of the bacterium. This information has significance in the design of therapeutic strategies toward cure.

Despite the approval of two first-in-class anti-tuberculars over the past two decades, the global burden of tuberculosis (TB) remains unacceptably high, in part due to the emergence and spread of drug-resistant strains of Mycobacterium tuberculosis ( Mtb ). This review summarizes advances and ongoing challenges in anti-TB drug discovery, focusing on identifying and validating novel targets. Highlighted is a framework developed by the TB Drug Accelerator (TBDA) consortium for target validation in Mtb . Two computational platforms, DAIKON and PARSNIP, allow the systematic evaluation of targets across multiple dimensions, including chemical validation, genetic essentiality, vulnerability, and the feasibility to identify drug-like molecules for a target of interest. Case studies of Pks13 and NadE illustrate how these parameters guide target prioritization and risk assessment. By integrating these metrics, the framework enables dynamic, transparent target ranking, supporting development of both pan-TB and treatment-shortening regimens. This paradigm is adaptable to other bacterial pathogens and is designed to improve evidence-based decision-making in antibacterial drug discovery.

Pyrazinamide (PZA) is a cornerstone of modern tuberculosis therapy. Yet its activity is highly context-dependent and poorly captured by standard in vitro assays, complicating both mechanistic studies and the detection of resistance. To address this gap, we develop a lipid-rich, acidic culture system that more closely recapitulates the host infection environment and enables robust, reproducible in vitro measurement of PZA activity. Using this platform, we demonstrate that acidic pH is essential for PZA-mediated killing, independent of bacterial replication. Integrating this system with genome-wide CRISPR interference profiling, comparative genomics, and metabolomics, we identify the previously uncharacterized ion channel Rv2571c as a clinically relevant determinant of PZA susceptibility. Rv2571c is structurally homologous to plant anion channels and facilitates the net efflux of α-ketoglutarate, a central TCA cycle metabolite. We show that α-ketoglutarate export sensitizes Mtb to PZA-induced cytoplasmic acidification, a critical step in bacterial killing. Loss of Rv2571c function confers PZA resistance in vitro , during macrophage infection, and in mice, whereas increased channel expression results in hypersusceptibility. Together, these findings reveal a metabolite-driven mechanism that links ion channel activity, central carbon metabolism, and cytoplasmic pH homeostasis to antibiotic susceptibility, providing a framework for improved detection of PZA resistance and the rational development of PZA-like therapeutics that shorten treatment.

Unlike commonly studied rod-shaped bacteria, mycobacteria grow from their poles, requiring precise coordination between division and initiation of new pole growth. The mechanisms that mediate this transition are largely unknown, but likely represent a rich source of drug targets for the treatment of mycobacterial infections, including tuberculosis. Here, we identify TapA (MSMEG_3748/Rv1697) as a key regulator of this transition. TapA interacts with the sensor kinase MtrB at the septum to initiate a signaling cascade that ultimately results in the expression of the essential peptidoglycan hydrolases RipAB, among others, at the end of division. Loss of TapA disrupts division, dysregulates pole formation, and sensitizes Mycobacterium tuberculosis and other mycobacteria to several first and second-line TB antibiotics, establishing TapA as a potential therapeutic target, and defining a link between cell cycle progression, envelope remodeling, and intrinsic antibiotic resistance in mycobacteria.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, remains the deadliest human pathogen. Treatment is hampered by drug resistance and the persistence of slow-growing or non-replicating populations. Rifampicin, a cornerstone of first-line therapy, inhibits transcription during promoter escape, but resistance mutations undermine efficacy and drive resistance spread. We revisited the transcription cycle as an antibacterial target by characterizing AAP-SO₂, an RNA polymerase inhibitor with whole-cell activity against Mtb. AAP-SO₂ slows the nucleotide addition cycle, disrupting elongation and termination. Rifampicin-resistant mutations impose fitness costs by perturbing the balance of these steps, creating exploitable weaknesses. Inhibition of transcription with AAP-SO₂ reduced the evolution of rifampicin resistance and was especially effective against the most common resistant mutant. Combination treatment with rifampicin and AAP-SO₂ synergistically killed non-replicating Mtb in an ex vivo rabbit granuloma model. These findings show that exploiting functional vulnerabilities of the transcription cycle can counter rifampicin resistance and improve clearance of recalcitrant Mtb populations.

Mycobacterium tuberculosis remains the most common infectious killer worldwide despite decades of antitubercular drug development. Effectively controlling the tuberculosis (TB) pandemic will require innovation in drug discovery. In this review, we provide a brief overview of the two main approaches to discovering new TB drugs—phenotypic screens and target‐based drug discovery—and outline some of the limitations of each method. We then explore recent advances in genetic tools that aim to overcome some of these limitations. In particular, we highlight a novel metric to prioritize essential targets, termed vulnerability. Stratifying targets based on their vulnerability presents new opportunities for future target‐based drug discovery campaigns.