On a fundamental structure of gene networks in living cells

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Toward identifying a cancer-specific gene signature we applied surprisal analysis to the RNAs expression behavior for a large cohort of breast, lung, ovarian, and prostate carcinoma patients. We characterize the cancer phenotypic state as a shared response of a set of mRNA or microRNAs (miRNAs) in cancer patients versus noncancer controls. The resulting signature is robust with respect to individual patient variability and distinguishes with high fidelity between cancer and noncancer patients. The mRNAs and miRNAs that are implicated in the signature are correlated and are known to contribute to the regulation of cancer-signaling pathways. The miRNA and mRNA networks are common to the noncancer and cancer patients, but the disease modulates the strength of the connectivities. Furthermore, we experimentally assessed the cancer-specific signatures as possible therapeutic targets. Specifically we restructured a single dominant connectivity in the cancerspecific gene network in vitro. We find a deflection from the cancer phenotype, significantly reducing cancer cell proliferation and altering cancer cellular physiology. Our approach is grounded in thermodynamics augmented by information theory. The thermodynamic reasoning is demonstrated to ensure that the derived signature is bias-free and shows that the most significant redistribution of free energy occurs in programming a system between the noncancer and cancer states. This paper introduces a platform that can elucidate miRNA and mRNA behavior on a systems level and provides a comprehensive systematic view of both the energetics of the expression levels of RNAs and of their changes during tumorigenicity. microarray | deep sequencing | network connectivity | biomarker | maximal entropy

Section: Resultsmentioning

confidence: 69%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients

Zadran

Remacle

Levine

2013

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“…The analysis was carried out as described in some detail before (19,25) and in Supporting Information. The particular application to pairs of cells has also been presented (10) for the purpose of determining the most stable steady-state separation in U87EGFRvIII and U87PTEN cell types.…”

Section: Methodsmentioning

confidence: 99%

Intercellular signaling through secreted proteins induces free-energy gradient-directed cell movement

Kravchenko‐Balasha

Shin

Sutherland

et al. 2016

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Controlling cell migration is important in tissue engineering and medicine. Cell motility depends on factors such as nutrient concentration gradients and soluble factor signaling. In particular, cell-cell signaling can depend on cell-cell separation distance and can influence cellular arrangements in bulk cultures. Here, we seek a physicalbased approach, which identifies a potential governed by cell-cell signaling that induces a directed cell-cell motion. A single-cell barcode chip (SCBC) was used to experimentally interrogate secreted proteins in hundreds of isolated glioblastoma brain cancer cell pairs and to monitor their relative motions over time. We used these trajectories to identify a range of cell-cell separation distances where the signaling was most stable. We then used a thermodynamicsmotivated analysis of secreted protein levels to characterize freeenergy changes for different cell-cell distances. We show that glioblastoma cell-cell movement can be described as Brownian motion biased by cell-cell potential. To demonstrate that the free-energy potential as determined by the signaling is the driver of motion, we inhibited two proteins most involved in maintaining the free-energy gradient. Following inhibition, cell pairs showed an essentially random Brownian motion, similar to the case for untreated, isolated single cells. Other examples include chemotaxis (2-4) and active transport (1), both of which show that overcoming a concentration gradient requires work in the sense of expenditure of free energy. In this study, we aim to show that the thermodynamic analog of the free energy of an intercellular signaling system, mediated by secreted proteins, acts to determine the direction of change in cell-cell movement. Secreted proteins are a vehicle for cell-cell communication and signaling (5) and, once received by a cell, can initiate intracellular signaling cascades, resulting in changes in gene transcription, protein expression, and the activation of cellular functions. Such functions might include cell division, the secretion of a new group of proteins, or, as investigated here, cell motility (1).Our experiment is a system of two interacting but otherwise isolated cells for which we measure both cell motion trajectories over a period of several hours and, at the terminal time point, the expression levels of a panel of secreted proteins. The experimental platform is the single-cell barcode chip (SCBC), which permits measurements of statistically significant numbers of cells (6-9).Our information-theoretic analysis (10) of the experimental data regards the signaling proteins as species mediating the exchange of information between cells. This analysis is used to determine the changes with distance of the free energy of the cell-cell signaling and to show that the cell-cell relative motion can be described as a constrained Brownian motion. We show that the direction of change in cell movement is toward a more stable cellular arrangement where cell-cell signaling is balanced. Inhibiting that signaling resu...

“…To use Eq. 1 to identify the steady state, we use the method of singular value decomposition (SVD) (11,14). For that calculation, the input matrix includes mean values of functional protein distributions (Table S2, rows) at a certain distance range (Table S2, columns).…”

Section: Predicting Spatial Distributions Of Gbm Cells From the Measuredmentioning

confidence: 99%

Glioblastoma cellular architectures are predicted through the characterization of two-cell interactions

Kravchenko‐Balasha

Wang

Remacle

et al. 2014

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To understand how pairwise cellular interactions influence cellular architectures, we measured the levels of functional proteins associated with EGF receptor (EGFR) signaling in pairs of U87EGFR variant III oncogene receptor cells (U87EGFRvIII) at varying cell separations. Using a thermodynamics-derived approach we analyzed the cell-separation dependence of the signaling stability, and identified that the stable steady state of EGFR signaling exists when two U87EGFRvIII cells are separated by 80-100 μm. This distance range was verified as the characteristic intercellular separation within bulk cell cultures. EGFR protein network signaling coordination for the U87EGFRvIII system was lowest at the stable state and most similar to isolated cell signaling. Measurements of cultures of less tumorigenic U87PTEN cells were then used to correctly predict that stable EGFR signaling occurs for those cells at smaller cell-cell separations. The intimate relationship between functional protein levels and cellular architectures explains the scattered nature of U87EGFRvIII cells relative to U87PTEN cells in glioblastoma multiforme tumors.GBM | surprisal analysis | cancer cell-cell signaling | biological steady state | two-body cell-cell interaction P athological analysis of tumor tissues is typically led by the analyses of cellular architectures within those tumors. Relationships between those architectures and molecular biomarkers of disease are often poorly understood. We seek to establish such a relationship, starting from physical principles. We take as an example glioblastoma multiforme (GBM) cancer cells that express the EGF receptor (EGFR) variant III oncogene receptor (EGFRvIII). Although these cells enhance tumorigenicity, invasion, and other hallmarks of cancer (1, 2), they comprise only a subpopulation of the cancer cells within an EGFRvIII+ tumor, and their distribution is diffuse (1, 3, 4). To help understand this diffuse cellular architecture, we developed an experimentaltheoretical methodology based on analysis of EGFR signaling in two interacting cells. In many physical systems-from planets to atomic solids-the interactions of an element of that system with its surroundings can be understood within the context of two-body interactions. This broad observation inspired our experimental approach, which was to measure EGFR-associated signaling activity in statistically significant numbers of two EGFRvIII+ GBM cells, as a function of intercellular separation. Our theoretical approach was similarly inspired: it assumed that the resultant two-cell data sets could be interpreted using thermodynamic-like considerations.Our approach allows a determination of the stability of a phosphoprotein signaling network in two interacting cells, and demonstrates how that stability dictates the cell-cell distance distribution in a bulk culture. Using this concept we determined the most probable intercellular separation distance range within cell populations, and the deviations thereof. The available literature suggests our conclusion...