Role of network-mediated stochasticity in mammalian drug resistance

Farquhar, Kevin S.; Charlebois, Daniel A.; Szenk, Mariola; Cohen, Joseph; Nevozhay, Dmitry; Balázsi, Gábor

doi:10.1038/s41467-019-10330-w

Cited by 82 publications

(125 citation statements)

References 69 publications

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“…Therefore, our observation of a power-law decay in rejuvenation probability further highlights the need to develop new interventions to rejuvenate persister cells [as was previously demonstrated (64)] or new antibiotics that can directly target persister cells (65). Importantly, persistence is a general phenomenon as it has been observed not only for bacteria but also for fungi and mammalian cells (66)(67)(68). It would be interesting to investigate the generality of our findings.…”

Section: Mathematical Framework Bridging Lag Time Distribution and Time-supporting

confidence: 77%

Power-law tail in lag time distribution underlies bacterial persistence

Şimşek

Kim

2019

Proc. Natl. Acad. Sci. U.S.A.

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Genetically identical microbial cells respond to stress heterogeneously, and this phenotypic heterogeneity contributes to population survival. Quantitative analysis of phenotypic heterogeneity can reveal dynamic features of stochastic mechanisms that generate heterogeneity. Additionally, it can enable a priori prediction of population dynamics, elucidating microbial survival strategies. Here, we quantitatively analyzed the persistence of an Escherichia coli population. When a population is confronted with antibiotics, a majority of cells is killed but a subpopulation called persisters survives the treatment. Previous studies have found that persisters survive antibiotic treatment by maintaining a long period of lag phase. When we quantified the lag time distribution of E. coli cells in a large dynamic range, we found that normal cells rejuvenated with a lag time distribution that is well captured by an exponential decay [exp(−kt)], agreeing with previous studies. This exponential decay indicates that their rejuvenation is governed by a single rate constant kinetics (i.e., k is constant). Interestingly, the lag time distribution of persisters exhibited a long tail captured by a power-law decay. Using a simple quantitative argument, we demonstrated that this power-law decay can be explained by a wide variation of the rate constant k. Additionally, by developing a mathematical model based on this biphasic lag time distribution, we quantitatively explained the complex population dynamics of persistence without any ad hoc parameters. The quantitative features of persistence demonstrated in our work shed insights into molecular mechanisms of persistence and advance our knowledge of how a microbial population evades antibiotic treatment.

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Section: Mathematical Framework Bridging Lag Time Distribution and Time-supporting

confidence: 77%

Power-law tail in lag time distribution underlies bacterial persistence

Şimşek

Kim

2019

Proc. Natl. Acad. Sci. U.S.A.

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“…Thus, a population of isogenic cells in the same environment can exhibit single-cell-level stochastic fluctuations in gene expression. Such fluctuations, known as gene expression noise or transcriptional noise, can result in isogenic cells 'making' entirely different decisions with regard to their phenotype and hence, their ability to adapt themselves to the same environmental perturbation (Balázsi et al, 2011;Farquhar et al, 2019;Engl, 2019). Transcriptional noise can arise from the intrinsic randomness of underlying biochemical reactions or processes extrinsic to the gene (Swain et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

A Non-genetic Mechanism for Chemoresistance in Lung Cancer: The Role of Integrin β4/Paxillin Axis

Mohanty¹,

Nam²,

Pozhitkov³

et al. 2019

Preprint

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Tumor heterogeneity and cisplatin resistance are a major cause of tumor relapse and poor survival. Here we show that in lung adenocarcinoma (LUAD), paxillin (PXN) and integrin beta 4 (ITGB4) are associated with tumor progression, and cisplatin resistance. Silencing PXN and ITGB4 render cisplatin tolerant cells sensitive, and immunologically neutralizing ITGB4 improves sensitivity. The N-terminal half of PXN is intrinsically disordered and interacts with ITGB4 to regulate expression of USP1 and VDAC1 which are required for maintaining genomic stability and mitochondrial function in LUAD. By virtual screening an FDA-approved compound library, we identified compounds that interact with PXN in silico and attenuate cisplatin resistance in LUAD cells. RNAseq analysis identified a double negative feedback loop between ITGB4 and microRNA miR-1-3p, suggesting that bistability could lead to stochastic switching between cisplatinsensitive and resistant states in these cells. The data highlight an alternate, nongenetic, mechanism underlying chemoresistance in lung cancer.

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“…phenotypic) heterogeneity has been possible only recently through investigating cell-tocell variability in isogenic populations [40][41][42][43]. Higher phenotypic heterogeneity may encourage cancer invasion [44] as well as the evolution of therapy resistance [45]. It may arise from network topology features such as mutually inhibitory feedback loops [46], for instance, the loop between RKIP and BACH1 [47] or that between AMPK and AKT [48].…”

Section: Discussionmentioning

confidence: 99%

Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum

Chakraborty

George

Woodward

et al. 2020

Preprint

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Inflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.

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Role of network-mediated stochasticity in mammalian drug resistance

Cited by 82 publications

References 69 publications

Power-law tail in lag time distribution underlies bacterial persistence

Power-law tail in lag time distribution underlies bacterial persistence

A Non-genetic Mechanism for Chemoresistance in Lung Cancer: The Role of Integrin β4/Paxillin Axis

Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum

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