mad-lab

Publications

Newest first. My name is in bold; * marks co-first and † co-senior authors. Also available as BibTeX.

2026

  1. Utilization of a CRISPRi-based ex vivo challenge model to reveal temporally dependent gene essentiality in intracellular Mycobacterium tuberculosis

    Theriault, M.E., Wong, A.I., DeJesus, M.A., Pisu, D., Lee, B.N.R., Kirukubar, G., Li, S., Wallach, J.B., Schnappinger, D., Lê-Bury, G., Russell, D.G., Rock, J.M.

    mBio 17(5):e0061026

  2. Transcription attenuation amplifies collateral vulnerabilities in rifampicin-resistant Mycobacterium tuberculosis

    Eckartt, K.A., Munsamy-Govender, V., Quiñones-Garcia, S., DeJesus, M.A., Ju, X., Liu, S., Rock, J.M.

    Nature Microbiology 11:1696–1710

  3. Predicting the protein interaction landscape of a mycobacterial pathogen

    Todor, H., Kim, L.M., Billings, E., Grzegorzewicz, A.E., Burkhart, H.N., DeJesus, M.A., Na, A., Nitz, S., Shell, S.S., Campbell, E.A., Jackson, M., Mancia, F., Rock, J.M., Gross, C.A., Chen, J.

    bioRxiv Preprint

  4. Mycobacterium tuberculosis genes needed for intra-alveolar resuscitation after airborne transmission

    Singh, P.R., Gengenbacher, M., Mishra, S., Jinich, A., Jiang, X., Tsang, F., Cristaldo, M., DeJesus, M.A., Rhee, K., Kaner, R., Leopold, P., Crystal, R.G., Kathayat, D., VanderVen, B.C., Nathan, C.F.

    bioRxiv Preprint

  5. Metabolic control of drug resistance by a mycobacterial ion channel

    Gouzy, A., Li, S., Chen, J., Na, A., Saleh, A., Azadian, Z.A., Tam, K., Munsamy-Govender, V., Poulton, N.C., DeJesus, M.A., Schnappinger, D., Rhee, K.Y., Ehrt, S., Rock, J.M.

    bioRxiv Preprint

2025

  1. The Mycobacterium tuberculosis Transposon Sequencing Database (MtbTnDB): A large-scale guide to genetic conditional essentiality

    Jinich, A., Zaveri, A., DeJesus, M.A., Spencer, A., Almada-Monter, R., Flores-Bautista, E., Smith, C.M., Sassetti, C.M., Rock, J.M., Ehrt, S., Schnappinger, D., Ioerger, T.R., Rhee, K.Y.

    Molecular Microbiology 124(1):91–101

  2. Candidate transmission survival genome of Mycobacterium tuberculosis

    Mishra, S., Singh, P.R., Hu, X., Lopez-Quezada, L., Jinich, A., Jahn, R., Geurts, L., Shen, N., DeJesus, M.A., Hartman, T., Rhee, K., Zimmerman, M., Dartois, V., Jones, R.M., Jiang, X., Almada-Monter, R., Bourouiba, L., Nathan, C.

    Proceedings of the National Academy of Sciences 122(10):e2425981122

  3. Chlorotonils exhibit potent activity against Mycobacterium tuberculosis, while resistance is mediated by MmpR5-MmpL5

    Deschner, F., Chengalroyen, M.D., Ames, L., Quach, D., Aguilera Olvera, R., Bosch, B., Castro, A., Kim, H., Raman, K., Thornton, N., Wallach, J., da Costa, F.R., Allen, R., Lupien, A., Zuma, M., Lynch, S., Pogliano, J., Sugie, J., Rock, J.M., Schnappinger, D., Parish, T., Mizrahi, V., DeJesus, M.A., Müller, R., Herrmann, J.

    bioRxiv Preprint

2024

  1. Compensatory evolution in NusG improves fitness of drug-resistant M. tuberculosis

    Eckartt, K.A., Delbeau, M., Munsamy-Govender, V., DeJesus, M.A., Azadian, Z.A., Reddy, A.K., Chandanani, J., Poulton, N.C., Quiñones-Garcia, S., Bosch, B., Landick, R., Campbell, E.A., Rock, J.M.

    Nature 628(8006):186–194

  2. Beyond antibiotic resistance: The whiB7 transcription factor coordinates an adaptive response to alanine starvation in mycobacteria

    Poulton, N.C., DeJesus, M.A., Munsamy-Govender, V., Kanai, M., Roberts, C.G., Azadian, Z.A., Bosch, B., Lin, K.M., Li, S., Rock, J.M.

    Cell Chemical Biology 31(4):669–682.e7

  3. A dose-response model for statistical analysis of chemical genetic interactions in CRISPRi screens

    Choudhery, S., DeJesus, M.A., Srinivasan, A., Rock, J.M., Schnappinger, D., Ioerger, T.R.

    PLOS Computational Biology 20(5):e1011408

  4. Weak links: Advancing target-based drug discovery by identifying the most vulnerable targets

    Bosch, B., DeJesus, M.A., Schnappinger, D., Rock, J.M.

    Annals of the New York Academy of Sciences 1535(1):10–19

2022

  1. CRISPR interference reveals that all-trans-retinoic acid promotes macrophage control of Mycobacterium tuberculosis by limiting bacterial access to cholesterol and propionyl coenzyme A

    Babunovic, G.H., DeJesus, M.A., Bosch, B., Chase, M.R., Barbier, T., Dickey, A.K., Bryson, B.D., Rock, J.M., Fortune, S.M.

    mBio 13:e03683-21

  2. CRISPRi chemical genetics and comparative genomics identify genes mediating drug potency in Mycobacterium tuberculosis

    Li, S., Poulton, N.C., Chang, J.S., Azadian, Z.A., DeJesus, M.A., Ruecker, N., Zimmerman, M.D., Eckartt, K.A., Bosch, B., Engelhart, C.A., Sullivan, D.F., Gengenbacher, M., Dartois, V.A., Schnappinger, D., Rock, J.M.

    Nature Microbiology 7(6):766–779

  3. Mutations in rv0678 confer low-level resistance to benzothiazinone DprE1 inhibitors in Mycobacterium tuberculosis

    Poulton, N.C., Azadian, Z.A., DeJesus, M.A., Rock, J.M.

    Antimicrobial Agents and Chemotherapy 66:e00904-22

2021

  1. An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence

    Dutta, E., DeJesus, M.A., Ruecker, N., Zaveri, A., Koh, E.I., Sassetti, C.M., Schnappinger, D., Ioerger, T.R.

    PLOS ONE 16:e0257911

  2. Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

    Bosch, B.*, DeJesus, M.A.*, Poulton, N.C., Zhang, W., Engelhart, C.A., Zaveri, A., Lavalette, S., Ruecker, N., Trujillo, C., Wallach, J.B., Li, S., Ehrt, S., Chait, B.T., Schnappinger, D., Rock, J.M.

    Cell 184(17):4579–4592.e24

    *Co-first authors.

2019

  1. Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression

    Subramaniyam, S., DeJesus, M.A., Zaveri, A., Smith, C.M., Baker, R.E., Ehrt, S., Schnappinger, D., Sassetti, C.M., Ioerger, T.R.

    BMC Bioinformatics 20:603

2017

  1. Statistical analysis of genetic interactions in Tn-Seq data

    DeJesus, M.A., Nambi, S., Smith, C.M., Baker, R.E., Sassetti, C.M., Ioerger, T.R.

    Nucleic Acids Research 45(11):e93

  2. Chemical genetic interaction profiling reveals determinants of intrinsic antibiotic resistance in Mycobacterium tuberculosis

    Xu, W., DeJesus, M.A., Ruecker, N., Engelhart, C.A., Wright, M.G., Healy, C., Lin, K., Wang, R., Park, S.W., Ioerger, T.R., Schnappinger, D., Ehrt, S.

    Antimicrobial Agents and Chemotherapy 61:e01334-17

  3. Comprehensive essentiality analysis of the Mycobacterium tuberculosis genome via saturating transposon mutagenesis

    DeJesus, M.A., Gerrick, E.R., Xu, W., Park, S.W., Long, J.E., Boutte, C.C., Rubin, E.J., Schnappinger, D., Ehrt, S., Fortune, S.M., Sassetti, C.M., Ioerger, T.R.

    mBio 8(1):e02133-16

  4. Development of novel, non-toxic rifamycins that reverse drug resistance in diffuse large B-cell lymphoma (DLBCL)

    Maxwell, S.A., Wallis, D., Zhou, N., Baker, D., Mousavi-Fard, S., Loesch, K., Galaviz, S., Sun, Q., Threadgill, D.M., Rojas, C.M., O'Brien, M., Clubb, F.J., Ioerger, T.R., DeJesus, M.A., Dong, W., Seemann, G., Fossum, T., Sacchettini, J.C.

    Hematological Oncology 35(S2):253–254 Conference poster

2016

  1. Trehalose-6-phosphate-mediated toxicity determines essentiality of OtsB2 in Mycobacterium tuberculosis in vitro and in mice

    Korte, J., Alber, M., Trujillo, C.M., Syson, K., Koliwer-Brandl, H., Deenen, R., Köhrer, K., DeJesus, M.A., Hartman, T., Jacobs Jr., W.R., Bornemann, S., Ioerger, T.R., Ehrt, S., Kalscheuer, R.

    PLOS Pathogens 12(12):e1006043

  2. Behavioral and transcriptomic profiling of mice null for Lphn3, a gene implicated in ADHD and addiction

    Orsini, C., Setlow, B., DeJesus, M.A., Galaviz, S., Loesch, K., Ioerger, T.R., Wallis, D.

    Molecular Genetics & Genomic Medicine 4(3):322–343

  3. Normalization of transposon-mutant library sequencing datasets to improve identification of conditionally essential genes

    DeJesus, M.A., Ioerger, T.R.

    Journal of Bioinformatics and Computational Biology 14(3):1642004

2015

  1. TRANSIT - A software tool for Himar1 TnSeq analysis

    DeJesus, M.A., Ambadipudi, C., Baker, R., Sassetti, C., Ioerger, T.R.

    PLOS Computational Biology 11(10):e1004401

  2. High-throughput differentiation and screening of a library of mutant stem cell clones defines new host-based genes involved in rabies virus infection

    Wallis, D., Loesch, K., Galaviz, S., Sun, Q., DeJesus, M.A., Ioerger, T.R., Sacchettini, J.C.

    Stem Cells 33(8):2509–2522

  3. Functional genomics screening utilizing mutant mouse embryonic stem cells identifies novel radiation-response genes

    Loesch, K., Galaviz, S., Hamoui, Z., Clanton, R., Akabani, G., Deveau, M., DeJesus, M.A., Ioerger, T.R., Sacchettini, J.C., Wallis, D.

    PLOS ONE 10(4):e0120534

  4. Capturing uncertainty by modeling local transposon insertion frequencies improves discrimination of essential genes

    DeJesus, M.A., Ioerger, T.R.

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 12(1):92–102

  5. Reducing type I errors in Tn-Seq experiments by correcting the skew in read count distributions

    DeJesus, M.A., Ioerger, T.R.

    Proceedings of the 7th International Conference on Bioinformatics and Computational Biology (BICoB)

    Best Paper Award.

  6. Identifying essential genes in Mycobacterium tuberculosis by global phenotypic profiling

    Long, J.E., DeJesus, M.A., Ward, D., Baker, R.E., Ioerger, T.R., Sassetti, C.M.

    In Gene Essentiality: Methods and Protocols, Methods in Molecular Biology vol. 1279, pp. 79–95

2013

  1. A hidden Markov model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data

    DeJesus, M.A., Ioerger, T.R.

    BMC Bioinformatics 14:303

  2. Bayesian analysis of gene essentiality based on sequencing of transposon insertion libraries

    DeJesus, M.A., Zhang, Y.J., Sassetti, C.M., Rubin, E.J., Sacchettini, J.C., Ioerger, T.R.

    Bioinformatics 29(6):695–703

  3. Reannotation of translational start sites in the genome of Mycobacterium tuberculosis

    DeJesus, M.A., Sacchettini, J.C., Ioerger, T.R.

    Tuberculosis 93:18–25

  4. Improving discrimination of essential genes by modeling local insertion frequencies in transposon mutagenesis data

    DeJesus, M.A., Ioerger, T.R.

    Proceedings of the ACM Conference on Bioinformatics, Computational Biology, and Biomedical Informatics (ACM-BCB), pp. 144–151

    Best Paper Award.

2011

  1. High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism

    Griffin, J.E., Gawronski, J.D., DeJesus, M.A., Ioerger, T.R., Akerley, B.J., Sassetti, C.M.

    PLOS Pathogens 7:e1002251