Development of new drug regimens that allow rapid, sterilizing treatment of tuberculosis has been limited by the complexity and time required for genetic manipulations in Mycobacterium tuberculosis. CRISPR interference (CRISPRi) promises to be a robust, easily engineered, and scalable platform for regulated gene silencing. However, in M. tuberculosis, the existing Streptococcus pyogenes Cas9-based CRISPRi system is of limited utility because of relatively poor knockdown efficiency and proteotoxicity. To address these limitations, we screened eleven diverse Cas9 orthologues and identified four that are broadly functional for targeted gene knockdown in mycobacteria. The most efficacious of these proteins, the CRISPR1 Cas9 from Streptococcus thermophilus (dCas9Sth1), typically achieves 20–100 fold knockdown of endogenous gene expression with minimal proteotoxicity. In contrast to other CRISPRi systems, dCas9Sth1-mediated gene knockdown is robust when targeted far from the transcriptional start site, thereby allowing high-resolution dissection of gene function in the context of bacterial operons. We demonstrate the utility of this system by addressing persistent controversies regarding drug synergies in the mycobacterial folate biosynthesis pathway. We anticipate that the dCas9Sth1 CRISPRi system will have broad utility for functional genomics, genetic interaction mapping, and drug-target profiling in M. tuberculosis.

Cisplatin and its platinum analogues, carboplatin and oxaliplatin, are some of the most widely used cancer chemotherapeutics. However, although cisplatin and carboplatin are primarily used in germ cell, breast and lung malignancies, oxaliplatin is instead used almost exclusively in colorectal and other gastrointestinal cancers. Here, we utilize a unique multi-platform genetic approach to study the mechanism of action of these clinically established platinum anti-cancer agents as well as more recently developed cisplatin analogues. We show that oxaliplatin, unlike cisplatin and carboplatin, does not kill cells via the DNA damage response. Rather, oxaliplatin kills cells by inducing ribosome biogenesis stress. This difference in drug mechanism explains the distinct clinical implementation of oxaliplatin relative to cisplatin and may enable mechanistically informed selection of distinct platinum drugs for distinct malignancies. These data highlight the functional diversity of core components of front line cancer therapy and the potential benefits of applying a mechanism-based rationale to the use of our current arsenal of anti-cancer drugs.

Transposon-insertion sequencing (TIS) is a powerful approach for deciphering genetic requirements for bacterial growth in different conditions, as it enables simultaneous genome-wide analysis of the fitness of thousands of mutants. However, current methods for comparative analysis of TIS data do not adjust for stochastic experimental variation between datasets and are limited to interrogation of annotated genomic elements. Here, we present ARTIST, an accessible TIS analysis pipeline for identifying essential regions that are required for growth under optimal conditions as well as conditionally essential loci that participate in survival only under specific conditions. ARTIST uses simulation-based normalization to model and compensate for experimental noise, and thereby enhances the statistical power in conditional TIS analyses. ARTIST also employs a novel adaptation of the hidden Markov model to generate statistically robust, high-resolution, annotation-independent maps of fitness-linked loci across the entire genome. Using ARTIST, we sensitively and comprehensively define Mycobacterium tuberculosis and Vibrio cholerae loci required for host infection while limiting inclusion of false positive loci. ARTIST is applicable to a broad range of organisms and will facilitate TIS-based dissection of pathways required for microbial growth and survival under a multitude of conditions.

The coupling of high-density transposon mutagenesis to high-throughput DNA sequencing (transposon-insertion sequencing) enables simultaneous and genome-wide assessment of the contributions of individual loci to bacterial growth and survival. We have refined analysis of transposon-insertion sequencing data by normalizing for the effect of DNA replication on sequencing output and using a hidden Markov model (HMM)-based filter to exploit heretofore unappreciated information inherent in all transposon-insertion sequencing data sets. The HMM can smooth variations in read abundance and thereby reduce the effects of read noise, as well as permit fine scale mapping that is independent of genomic annotation and enable classification of loci into several functional categories (e.g. essential, domain essential or ‘sick’). We generated a high-resolution map of genomic loci (encompassing both intra- and intergenic sequences) that are required or beneficial for in vitro growth of the cholera pathogen, Vibrio cholerae. This work uncovered new metabolic and physiologic requirements for V. cholerae survival, and by combining transposon-insertion sequencing and transcriptomic data sets, we also identified several novel noncoding RNA species that contribute to V. cholerae growth. Our findings suggest that HMM-based approaches will enhance extraction of biological meaning from transposon-insertion sequencing genomic data.

, a rapidly growing mycobacterium (RGM) and an opportunistic human pathogen, is responsible for a wide spectrum of clinical manifestations ranging from pulmonary to skin and soft tissue infections. This intracellular organism can resist the bactericidal defense mechanisms of amoebae and macrophages, an ability that has not been observed in other RGM. can up-regulate several virulence factors during transient infection of amoebae, thereby becoming more virulent in subsequent respiratory infections in mice. Here, we sought to identify the genes required for replication within amoebae. To this end, we constructed and screened a transposon () insertion library of an subspcies clinical isolate for attenuated clones. This approach identified five genes within the ESX-4 locus, which in encodes an ESX-4 type VII secretion system that exceptionally also includes the ESX conserved EccE component. To confirm the screening results and to get further insight into the contribution of ESX-4 to growth and survival in amoebae and macrophages, we generated a deletion mutant of that encodes a core structural element of ESX-4. This mutant was less efficient at blocking phagosomal acidification than its parental strain. Importantly, and in contrast to the wild-type strain, it also failed to damage phagosomes and showed reduced signs of phagosome-to-cytosol contact, as demonstrated by a combination of cellular and immunological assays. This study attributes an unexpected and genuine biological role to the underexplored mycobacterial ESX-4 system and its substrates.

Identifying mechanisms of drug action remains a fundamental impediment to the development and effective use of chemotherapeutics. Here we describe an RNA interference (RNAi)-based strategy to characterize small-molecule function in mammalian cells. By examining the response of cells expressing short hairpin RNAs (shRNAs) to a diverse selection of chemotherapeutics, we could generate a functional shRNA signature that was able to accurately group drugs into established biochemical modes of action. This, in turn, provided a diversely sampled reference set for highresolution prediction of mechanisms of action for poorly characterized small molecules. We could further reduce the predictive shRNA target set to as few as eight genes and, by using a newly derived probability-based nearest-neighbors approach, could extend the predictive power of this shRNA set to characterize additional drug categories. Thus, a focused shRNA phenotypic signature can provide a highly sensitive and tractable approach for characterizing new anticancer drugs.Chemotherapy remains the frontline therapy for systemic malignancies. However, drug development has been severely hampered by an inability to efficiently elucidate mechanisms of drug action. This limits both the development of modified compounds with improved efficacy and the capability to predict mechanisms of drug resistance and select optimal patient populations for a given agent. Although drug-target interactions have traditionally been examined using biochemical approaches 1 , a number of genetic strategies have been developed to identify pathways targeted by uncharacterized small molecules. A wellestablished genetic approach to drug classification is chemogenomic profiling in yeast [2][3][4][5][6] . In this approach, bar-coded yeast deletion strains are exposed to select agents, and genotypedependent drug sensitivity is used to identify genes and pathways affected by a given drug, * hemann@mit.edu. Author contributionsH.J., J.R.P. and M.T.H. designed experiments. H.J. and J.R.P. performed RNAi knockdown and treatment studies. J.R.P. developed the computational approaches and performed all of the computational analyses. R.T.W. developed and characterized the B-ALL cell line. H.J., J.R.P., D.A.L. and M.T.H. analyzed the data and wrote the manuscript. Competing financial interestsThe authors declare no competing financial interests. Additional informationSupplementary information is available online at http://www.nature.com/naturechemicalbiology/. Reprints and permissions information is available online at http://npg.nature.com/reprintsandpermissions/. NIH Public Access Author ManuscriptNat Chem Biol. Author manuscript; available in PMC 2011 August 1. 5,7,8 . This approach has proven quite powerful and has been broadly disseminated; however, its efficacy in interrogating cancer chemotherapeutics is limited by the lack of conservation of certain drug targets from yeast to mammals. This is a particular problem in the context of targeted therapeutics, which are frequently direct...

Combination chemotherapies have been a mainstay in the treatment of disseminated malignancies for almost 60 y, yet even successful regimens fail to cure many patients. Although their singledrug components are well studied, the mechanisms by which drugs work together in clinical combination regimens are poorly understood. Here, we combine RNAi-based functional signatures with complementary informatics tools to examine drug combinations. This approach seeks to bring to combination therapy what the knowledge of biochemical targets has brought to single-drug therapy and creates a statistical and experimental definition of "combination drug mechanisms of action." We show that certain synergistic drug combinations may act as a more potent version of a single drug. Conversely, unlike these highly synergistic combinations, most drugs average extant single-drug variations in therapeutic response. When combined to form multidrug regimens, averaging combinations form averaging regimens that homogenize genetic variation in mouse models of cancer and in clinical genomics datasets. We suggest surprisingly simple and predictable combination mechanisms of action that are independent of biochemical mechanism and have implications for biomarker discovery as well as for the development of regimens with defined genetic dependencies.chemotherapy | lymphoma | RNAi signature | systems biology C urrent rationales for the design of combination chemotherapy regimens were developed in the 1940s and 1950s (1-5) and, remarkably, were concurrent with the identification of DNA as the genetic material. The development of these regimens in the absence of any knowledge of cancer genomics was a remarkable achievement. However, although our knowledge of the genetic drivers of cancer and the mechanisms of drug action has increased dramatically over the past 30 y, this information has been difficult to adapt to clinical practice. As such, even very successful combination regimens often fail to cure many patients (6, 7). We hypothesize that part of this failure is due to the absence of mechanistic information about how drugs in regimens interact to promote combination effects (we term these effects "combination mechanisms of action"). We sought to address this gap by investigating specific hypotheses concerning the "mechanisms of action" of combination therapy.The classic term "drug mechanism of action" refers to the description of a specific biochemical event, which is often the activation or inhibition of an enzymatic effect. However, in recent years, "signature"-based prediction has provided a powerful new strategy for examining drug mechanism. In signature-based approaches, a series of drug-induced molecular/phenotypic measurements are made in an experimental system. Collections of measurements from many small molecules form multivariate signatures that aim to fingerprint drugs based on their relative signature similarity (8-13). In several landmark studies using Saccharomyces cerevisiae; gene expression compendia (8); and, later, barcoded loss o...

SUMMARY The prevailing approach to addressing secondary drug resistance in cancer focuses on treating the resistance mechanisms at relapse. However, the dynamic nature of clonal evolution, along with potential fitness costs and cost compensations, may present exploitable vulnerabilities; a notion that we term ‘temporal collateral sensitivity’. Using a combined pharmacological screen and drug resistance selection approach in a murine model of Ph+ acute lymphoblastic leukemia, we indeed find that temporal and/or persistent collateral sensitivity to non-classical BCR-ABL1 drugs arises in emergent tumor subpopulations during the evolution of resistance toward initial treatment with BCR-ABL1 targeted inhibitors. We determined the sensitization mechanism via genotypic, phenotypic, signaling, and binding measurements in combination with computational models, and demonstrated significant overall survival extension in mice. Additional stochastic mathematical models and small molecule screens extended our insights, indicating the value of focusing on evolutionary trajectories and pharmacological profiles to identify new strategies to treat dynamic tumor vulnerabilities.