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Brown, K. S.

Publications and source records attributed to Brown, K. S..

3 recordsLinked to original sources

Systems-level Consequences of Low RAF Abundance for EGFR-ERK Signaling

The RAF kinases are central links between RAS, once activated by receptor tyrosine kinases (RTKs), and the extracellular signal-regulated kinases (ERK). In many cancer cells, RAFs are the least abundantly expressed RTK-ERK pathway proteins and can be present at just hundreds of copies per cell at the plasma membrane, but the consequences of limited RAF expression are unclear. By developing continuum and stochastic computational models of the epidermal growth factor receptor (EGFR)-ERK pathway, we showed that low RAF abundance creates stoichiometric bottlenecks between RTKs and ERK with concomitant stochastic RAF dynamics that propagate to weakly expressed downstream pathway proteins. Advanced sensitivity and Sloppiness analyses identified RAS activation and RAS-RAF interactions as strong determinants of signaling in low-RAF settings and revealed an efficient model fitting approach. RAF bottlenecks were predicted to impede ERK activation by oncogenic RAS mutants and explained a tendency for RAF1 membrane localization to be noisy. This work provides quantitative insight into a common, yet unexplored, regime for EGFR-ERK signaling and a systematic approach to develop and characterize dynamic models of receptor-mediated signaling. STATEMENT OF SIGNIFICANCERAF kinases connect receptors to the mitogenic ERK signaling pathway by translocating to the plasma membrane, but in a substantial fraction of cancer cell contexts RAFs are greatly outnumbered by other pathway proteins, potentially creating an unrecognized and consequential signaling bottleneck. We trained a novel computational model of EGFR-ERK signaling and characterized it comprehensively using integrated multivariate sensitivity analyses and Sloppiness analysis. The results revealed that low RAF abundance suppresses EGFR-mediated ERK activation, limits the effects of upstream oncogenic RAS mutants, and creates stochastic RAF dynamics that can propagate downstream. Thus, the canonical EGFR-ERK pathway exhibits divergent behaviors in a parameter space representative of a substantial fraction of cancer cell settings.

systems biology↗

Transkingdom Network Analysis (TkNA): a systems approach inferring causal factors underlying host-microbiota interactions.

Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the networks local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

systems biology↗

Monomerization of Far-Red Fluorescent Proteins

Anthozoa class red fluorescent proteins (RFPs) are frequently used as biological markers, with far-red emitting variants ({lambda}em ~ 600 - 900 nm) sought for whole animal imaging because biological tissues are permeable to light in this range. A barrier to the use of naturally occurring RFP variants as molecular markers is that all are tetrameric, which is not ideal for cell biological applications. Efforts to engineer monomeric RFPs have usually produced dimmer and blue-shifted variants, as the chromophore is sensitive to small structural perturbations. In fact, despite much effort, only four native RFPs have been successfully monomerized, leaving the vast majority of RFP biodiversity untapped in biomarker development. Here we report the generation of monomeric variants of HcRed and mCardinal, both far-red dimers, and describe a comprehensive methodology for the rapid monomerization of novel red-shifted oligomeric RFPs. Among the resultant variants, is mKelly1 (emission maximum: {lambda}em = 656 nm), which along with the recently reported mGarnet2 (1), forms a new class of bright, monomeric, far-red FPs.

biochemistry↗