Biology subjects

Turkarslan, S.

Publications and source records attributed to Turkarslan, S..

5 recordsLinked to original sources

bioRxiv2021

MtrA regulation of essential peptidoglycan cleavage in Mycobacterium tuberculosis during infection

The success of Mycobacterium tuberculosis (Mtb) is largely due to its ability to withstand multiple stresses encountered in the host. Here, we present a data-driven model that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. The model captures the genome-wide distribution of cis-acting gene regulatory elements and the conditional influences of transcription factors at those elements to elicit environment-specific responses. Analysis of transcriptional responses that may be essential for Mtb to survive acidic stress within the maturing macrophage, identified regulatory control by the MtrAB two-component signal system. Using genome-wide transcriptomics as well as imaging studies, we have characterized the MtrAB circuit by tunable CRISPRi knockdown in both Mtb and the non-pathogenic organism, M. smegmatis (Msm). These experiments validated the essentiality of MtrA in Mtb, but not Msm. We identified that MtrA regulates multiple enzymes that cleave cell wall peptidoglycan and is required for efficient cell division. Moreover, our results suggest that peptidoglycan cleavage, regulated by MtrA, is necessary for Mtb to survive intracellular stress. Further, we present MtrA as an attractive drug target, as even weak repression of mtrA results in loss of Mtb viability and completely clears the bacteria with low-dose isoniazid or rifampicin treatment.

systems biology↗

bioRxiv2021

A single-cell based precision medicine approach using glioblastoma patient-specific models

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multimodal single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multimodal data with single cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that regulate subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches in a concurrent, neo-adjuvant, or recurrent setting. SummaryInference of mechanistic drivers of therapy-induced evolution of glioblastoma at single cell resolution using RNA-seq and ATAC-seq from patient samples and model systems undergoing standard-of-care treatment informs strategy for identification of tumor evolutionary trajectories and possible cell state-directed therapeutics.

systems biology↗

bioRxiv2020

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile

Though Clostridioides difficile is among the most studied anaerobes, the interplay of metabolism and regulation that underlies its ability to colonize the human gut is unknown. We have compiled public resources into three models and a portal to support comprehensive systems analysis of C. difficile. First, by leveraging 151 transcriptomes from 11 studies we generated a regulatory model (EGRIN) that organizes 90% of C. difficile genes into 297 high quality conditional co-regulation modules. EGRIN predictions, validated with independent datasets, recapitulated and extended regulons of key transcription factors, implicating new genes for sporulation, carbohydrate transport and metabolism. Second, by advancing a metabolic model, we discovered that 15 amino acids, diverse carbohydrates, and 10 metabolic genes are essential for C. difficile growth within an intestinal environment. Finally, by integrating EGRIN with the metabolic model, we developed a PRIME model that revealed unprecedented insights into combinatorial control of essential processes for in vivo colonization of C. difficile and its interactions with commensals. We have developed an interactive web portal (http://networks.systemsbiology.net/cdiff-portal/) to disseminate all data, algorithms, and models to support collaborative systems analyses of C. difficile.

systems biology↗

bioRxiv2020

Synergistic epistasis enhances cooperativity of mutualistic interspecies interactions

Frequent fluctuations in sulfate availability rendered syntrophic interactions between the sulfate reducing bacterium Desulfovibrio vulgaris (Dv) and the methanogenic archaeon Methanococcus maripaludis (Mm) unsustainable. By contrast, prolonged laboratory evolution in obligate syntrophy conditions improved the productivity of this community but at the expense of erosion of sulfate respiration (SR). Hence, we sought to understand the evolutionary trajectories that could both increase the productivity of syntrophic interactions and sustain SR. We combined a temporal and combinatorial survey of mutations accumulated over 1000 generations of 9 independently-evolved communities with analysis of the genotypic structure for one community down to the single-cell level. We discovered a high level of parallelism across communities despite considerable variance in their evolutionary trajectories and the perseverance of a rare SR+ Dv lineage within many evolution lines. An in-depth investigation revealed that synergistic epistasis across Dv and Mm genotypes had enhanced cooperativity within SR- and SR+ assemblages, allowing their co-existence as r- and K-strategists, respectively.

evolutionary biology↗

bioRxiv2020

Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to their previous therapies. Although many therapies exist with diverse mechanisms of action, it is not yet clear how the differences in MM biology across patients impacts the likelihood of success for existing therapies and those in the pipeline. Therefore, we not only need the ability to predict which patients are at high risk for disease progression, but also a means to understand the mechanisms underlying their risk. We hypothesized that knowledge of the biological networks that give rise to MM, specifically the transcriptional regulatory network (TRN) and the mechanisms by which mutations impact gene regulation, would enable improved predictions of disease progression and actionable insights for treatment. Here we present a method to infer TRNs from multi-omics data and apply it to the generation of a MM TRN that links chromosomal abnormalities and somatic mutations to downstream effects on gene expression via perturbation of transcriptional regulators. We find that 141 genetic programs underlie the disease and that the activity profile of these programs fall into one of 25 distinct transcriptional states. These transcriptional signatures prove to be more predictive of outcomes than do mutations and reveal plausible mechanisms for relapse, including the establishment of an immuno-suppressive microenvironment. Moreover, we observe subtype-specific vulnerabilities to interventions with existing drugs and motivate the development of new targeted therapies that appear especially promising for relapsed refractory MM.

systems biology↗