22 23 Classification: BIOLOGICAL SCIENCES: Microbiology; Biophysics and Computational Biology 24 25 ABSTRACT 34 35 The rapid spread of multi-drug resistant strains has created a pressing need for new drug 36 regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new 37 regimens has been challenging due to the slow growth of the pathogen M. tuberculosis (MTB), 38 coupled with large number of possible drug combinations. Here we present a computational 39 model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-40 TB drugs after screening in silico over 1 million potential drug combinations using MTB drug 41 transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key 42 transcriptional regulator of multiple drug interactions, and we confirmed experimentally that 43 Rv1353c up-regulation reduces the antagonism of the bedaquiline-streptomycin combination. 44 Retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that 45 synergistic combinations were significantly more efficacious than antagonistic combinations (p-46 value = 1 x 10 -4 ) based on the percentage of patients with negative sputum cultures after 8 47 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug 48 combinations and is also applicable to other bacterial pathogens. 49 50 IMPORTANCE 51 52 Multi-drug combination therapy is an important strategy for treating tuberculosis, the world's 53 deadliest bacterial infection. Long treatment durations and growing rates of drug resistance 54 have created an urgent need for new approaches to prioritize effective drug regimens. Hence, 55 we developed a computational model called INDIGO-MTB, which identifies synergistic drug 56 regimens from an immense set of possible drug combinations using pathogen response 57 transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB 58 was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB 59 correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also 60identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, 61which could be targeted for rationally enhancing drug synergy. 62 63 64 65 66 67 68 69 70 71 131 gene associations that are correlated with synergy and antagonism. In the example above, MTB 132 upregulation of both gene 1 and gene 3 in response to the drugs measured in monotherapy is predictive 133 of antagonism when the drugs are combined. By perturbing individual genes and known targets of 134 Transcription Factors (TFs) in the model, we can infer the impact of individual gene and TF activity 135 respectively on drug interactions and subsequently engineer interaction outcomes.136 137 138