Integrative network modeling reveals mechanisms underlying T cell exhaustion

Bolouri, Hamid; Young, Mary; Beilke, Joshua; Johnson, Rebecca; Fox, Brian; Huang, Lu; Santini, Cristina Costa; Hill, Charles H.; Vries, Anne-Renée Van der Vuurst de; Shannon, Paul; Dervan, Andrew; Sivakumar, Pallavur V.; Trotter, Matthew; Bassett, Douglas E.; Ratushny, Alexander V.

doi:10.1101/582312

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…The time scale of the delay element, about 40 hours from entering 0% DO to Late hypoxia transition, is consistent with delayed translation observed in slow-growing MTB in response to nitric oxide (Cortes et al, 2017). It was recently demonstrated that delayed regulatory interactions within I-FFLs (with mutual inhibition present) produced state transitions related to T cell exhaustion, after a fixed time post-stimulation (Bolouri, 2019). As such, the delay element may function in MTB to incorporate robustness into the hypoxic response, ensuring that Late hypoxia (with large-scale expression changes) is not activated prematurely.…”

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confidence: 76%

Intricate genetic programs controlling dormancy inMycobacterium tuberculosis

Abidi

Peterson

Arrieta-Ortiz

et al. 2019

Preprint

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Mycobacterium tuberculosis (MTB), responsible for the deadliest infectious disease worldwide, displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogen's capacity to evade the immune system and opportunistically exploit immunocompromised individuals. Uncovering the gene regulatory programs that underlie the dramatic phenotypic shifts in MTB during disease latency and reactivation has posed an extraordinary challenge. We developed a novel experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the chain of transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genomewide transcription factor binding location map and insights from network topology analysis, we identified regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions. One Sentence Summary: High-resolution transcriptional time-course reveals six-state genetic program that enables MTB to enter and exit hypoxia-induced dormancy.Main Text: Mycobacterium tuberculosis (MTB) kills more people than any other infectious agent, causing ~10 million new cases of active tuberculosis (TB) disease and 1.7 million deaths each year (Murray et al., 2014). TB remains a major human public health burden, in large part due to the sizeable reservoir of latently infected individuals, who may relapse into active disease decades after acquiring the infection. MTB can persist in a stable, non-replicative (often termed dormant) state within the host for months or years without symptoms, and then revive to initiate the production of lesions and active TB disease. Moreover, dormant cells may be responsible for the slow treatment response of patients with active TB. Elucidation of the factors that affect treatment outcome, latency and activation requires a better characterization of functional states adopted by the pathogen during progression of the disease, as well as a mechanistic understanding of the genetic programs that orchestrate transitions between these states.Hypoxia, an environmental stress encountered by MTB within granulomas (Tsai et al., 2006), is sufficient to shift the pathogen into a defined non-growing survival form, which can be reversed upon aeration of the culture (Chao and Rubin, 2010). Therefore, hypoxia has been leveraged as an in vitro approximation to study MTB dormancy and the underlying genetic programs. However, previous transcriptional analyses under in vitro hypoxic conditions (via the Wayne model in which MTB cultures are sealed and gradually depleted of oxygen (Wayne and Hayes, 1996;Wayne and Sohaskey, 2001) or the defined hypoxia model in which nitrogen gas is flowed into...

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supporting

confidence: 76%

Intricate genetic programs controlling dormancy inMycobacterium tuberculosis

Abidi

Peterson

Arrieta-Ortiz

et al. 2019

Preprint

View full text Add to dashboard Cite

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Modelling of Immune Checkpoint Network Explains Synergistic Effects of Combined Immune Checkpoint Inhibitor Therapy and the Impact of Cytokines in Patient Response

Kondratova

Barillot

Zinovyev

et al. 2020

Cancers

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After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest efficient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling.

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Integrative network modeling reveals mechanisms underlying T cell exhaustion

Cited by 2 publications

References 54 publications

Intricate genetic programs controlling dormancy inMycobacterium tuberculosis

Intricate genetic programs controlling dormancy inMycobacterium tuberculosis

Modelling of Immune Checkpoint Network Explains Synergistic Effects of Combined Immune Checkpoint Inhibitor Therapy and the Impact of Cytokines in Patient Response

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