A systems analysis of drug tolerance in Mycobacterium tuberculosis

结核分枝杆菌耐药性的系统分析

• 批准号: 10654540
• 负责人: Nitin S Baliga
• 金额: $ 88.63万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654540
• 关键词: Acceleration; Adopted; Algorithms; Biochemical Pathway; Bioreactors; CRISPR interference; Cause of Death; Cells; Cessation of life; Characteristics; Chemicals; Clinical; Communicable Diseases; Complex; Cues; Development; Disease; Drug Combinations; Drug Targeting; Drug Tolerance; Drug resistance; Environment; Essential Genes; Evolution; Experimental Designs; Gene Expression Profile; Genetic; Genetic Transcription; Growth; Heterogeneity; Infection; Infectious Agent; Intervention; Machine Learning; Macrophage; Malignant Neoplasms; Metabolic; Modeling; Mycobacterium tuberculosis; Outcome; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physiologic tolerance; Physiological; Physiological Adaptation; Population; Population Heterogeneity; Predisposition; Progress Reports; Publications; Recurrent disease; Regimen; Reporting; Resistance; Resolution; Sorting; Structure; System; Systems Analysis; Systems Biology; Techniques; Technology; Testing; Time; Tuberculosis; biomarker panel; combinatorial; disease heterogeneity; emerging antimicrobial resistance; network models; new technology; new therapeutic target; novel; novel drug combination; pathogen; promoter; response; support network; transcription factor; transcriptome; treatment duration; tuberculosis drugs; tuberculosis treatment

PROPOSAL SUMMARY This project will address the critical need for accelerated development of multidrug regimen to achieve fast and complete clearance of Mycobacterium tuberculosis (Mtb), thereby lowering the likelihood for the emergence of antimicrobial resistance. Mtb dynamically adapts to extra- and intracellular host environments by adopting heterogeneous physiologic states, with varied susceptibility profiles to frontline antitubercular drugs. In the first four years of the R01, we have made progress towards dissecting this capability of Mtb by developing technologies to (i) uncover regulatory mechanisms that drive the pathogen into dormant states in host-simulated environments (controlled bioreactors) and directly within host cells (Path-seq), (ii) sort and characterize at single cell resolution translationally-dormant persister-like subpopulations within isogenic cultures (PerSort), (iii) uncover and characterize context-specific vulnerabilities within regulatory and metabolic networks (EGRIN2 and PRIME), and (iv) rationally formulate novel synergistic drug combinations (DRonA and MLSynergy). Using these capabilities and their applications reported across sixteen publications, we discovered that heterogeneous drug tolerant subpopulations co-exist within an isogenic culture of Mtb, even in the absence of drug treatment. Furthermore, we discovered that stressful environments and treatments activate additional drug tolerance networks, which may potentiate the emergence of resistance. Based on these findings, we hypothesize that we can achieve fast and complete clearance of Mtb infection with a combination of drugs that target vulnerabilities across heterogeneous drug tolerant subpopulations that co-exist in varied combinations and proportions depending on host- and treatment-contexts. To test this hypothesis, we will mechanistically characterize how the heterogeneous population structure of Mtb changes dynamically in response to host-relevant environmental cues and drug treatments. We will then uncover and characterize vulnerabilities within regulatory and metabolic networks that support and drive transitions to drug tolerant states. Using machine-learning techniques, we will predict and validate synergistic drug combinations targeting multiple vulnerabilities to cripple heterogeneous environment- and drug-induced states of Mtb. By performing time kill curves, we will investigate whether validated combinatorial interventions accomplish complete and faster clearance of heterogeneous Mtb subpopulations in diverse contexts. Altogether, the proposed activities will identify novel drug targets, and novel drug combinations for fast and complete clearance of a heterogeneous Mtb population. Given that phenotypic heterogeneity as a means for tolerating and resisting drugs is a universal phenomenon, the systems biology framework developed in this project will be generalizable to the discovery of effective multidrug regimen for diverse infectious diseases and even cancers.

提案摘要 该项目将满足加速多饮用方案发展以实现快速发展的关键需求 结核分枝杆菌（MTB）的完全清除，从而降低了出现的可能性 抗菌耐药性。 MTB通过采用而动态地适应细胞内和细胞内主机环境 异质生理状态，具有多样化的抗抗结核药物的敏感性。在第一个 在R01的四年中，我们通过开发来剖析MTB的这种能力取得了进步 （i）发现的技术机制，这些机制将病原体驱动到宿主模拟的休眠状态 环境（受控的生物反应器）和直接在宿主单元内（路径隔板），（ii）排序并在单一表征 细胞分辨率在等生培养物（persort）内翻译持续性的持久性亚群，（iii） 在监管和代谢网络中发现并表征了上下文特定的漏洞（Egrin2和 Prime）和（iv）合理提出新型协同药物组合（Drona和Mlsynergy）。使用这些 能力及其在16个出版物中报道的应用程序，我们发现异质性药物 耐受的亚群在MTB的等源性培养物中共存，即使在没有药物治疗的情况下。 此外，我们发现压力很大的环境和治疗可以激活额外的药物耐受性 网络，这可能会增强电阻的出现。根据这些发现，我们假设我们 可以通过针对脆弱性的药物组合快速而完全清除MTB感染 在各种组合和比例中共存的异构药物耐受亚群中 取决于宿主和治疗文本。为了检验这一假设，我们将从机械上表征 MTB的异质种群结构是针对与宿主相关的环境线索动态变化的 和药物治疗。然后，我们将发现并表征监管和代谢中的漏洞 支持并推动向药物耐受状态过渡的网络。使用机器学习技术，我们将 预测和验证针对多种脆弱性的协同药物组合削弱异质 环境和药物诱导的MTB状态。通过执行时间杀死曲线，我们将调查是否 经过验证的组合干预措施完成了异质MTB的完整和更快的清除 在不同情况下的亚群。拟议的活动总之将确定新的药物靶标和新颖 用于快速和完全清除异质MTB种群的药物组合。鉴于该表型 异质性作为耐受和抵抗药物的一种手段是一种普遍现象，即系统生物学 该项目中开发的框架将可以推广到发现有效的多药方案的 多种传染病甚至癌症。

项目成果

Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.

• DOI: 10.1128/msystems.00057-20
• 发表时间: 2020-06-02
• 期刊: mSystems
• 影响因子: 6.4
• 作者: Arrieta-Ortiz ML;Hafemeister C;Shuster B;Baliga NS;Bonneau R;Eichenberger P
• 通讯作者: Eichenberger P

Intricate Genetic Programs Controlling Dormancy in Mycobacterium tuberculosis.

• DOI: 10.1016/j.celrep.2020.107577
• 发表时间: 2020-04-28
• 期刊: Cell reports
• 影响因子: 8.8
• 作者: Peterson EJR;Abidi AA;Arrieta-Ortiz ML;Aguilar B;Yurkovich JT;Kaur A;Pan M;Srinivas V;Shmulevich I;Baliga NS
• 通讯作者: Baliga NS

Transcriptome signature of cell viability predicts drug response and drug interaction in Mycobacterium tuberculosis.

• DOI: 10.1016/j.crmeth.2021.100123
• 发表时间: 2021-12-20
• 期刊: Cell reports methods
• 作者: Srinivas V;Ruiz RA;Pan M;Immanuel SRC;Peterson EJR;Baliga NS
• 通讯作者: Baliga NS

A systems-level gene regulatory network model for Plasmodium falciparum.

• DOI: 10.1093/nar/gkaa1245
• 发表时间: 2021-05-21
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Neal ML;Wei L;Peterson E;Arrieta-Ortiz ML;Danziger SA;Baliga NS;Kaushansky A;Aitchison JD
• 通讯作者: Aitchison JD

Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma.

• DOI: 10.1038/s41698-021-00185-0
• 发表时间: 2021-06-28
• 期刊: NPJ precision oncology
• 影响因子: 7.9
• 作者: Wall MA;Turkarslan S;Wu WJ;Danziger SA;Reiss DJ;Mason MJ;Dervan AP;Trotter MWB;Bassett D;Hershberg RM;Lomana ALG;Ratushny AV;Baliga NS
• 通讯作者: Baliga NS