Biology subjects

Stern, A. D.

Publications and source records attributed to Stern, A. D..

6 recordsLinked to original sources

bioRxiv2018

Gene-Specific Predictability of Protein Levels from mRNA Data in Humans

Transcriptomic data are widely available, and the extent to which they are predictive of protein abundances remains debated. Using multiple public databases, we calculate mRNA and mRNA-to-protein ratio variability across human tissues to quantify and classify genes for protein abundance predictability confidence. We propose that such predictability is best understood as a spectrum. A gene-specific, tissue-independent mRNA-to-protein ratio plus mRNA levels explains [~]80% of protein abundance variance for more predictable genes, as compared to [~]55% for less predictable genes. Protein abundance predictability is consistent with independent mRNA and protein data from two disparate cell lines, and mRNA-to-protein ratios estimated from publicly-available databases have predictive power in these independent datasets. Genes with higher predictability are enriched for metabolic function, tissue development/cell differentiation roles, and transmembrane transporter activity. Genes with lower predictability are associated with cell adhesion, motility and organization, the immune system, and the cytoskeleton. Surprisingly, many genes that regulate mRNA-to-protein ratios are constitutively expressed but also exhibit ratio variability, suggesting a general autoregulation mechanism whereby protein expression profile changes can be implemented quickly, or homeostatic sensing stabilizes protein abundances under fluctuating conditions. Gene classifications and their mRNA-to-protein ratios are provided as a resource to facilitate protein abundance predictions by others.

systems biology↗

bioRxiv2018

Fluorescence Multiplexing with Spectral Imaging and Combinatorics

Ultraviolet-to-infrared fluorescence is a versatile and accessible assay modality, but is notoriously hard to multiplex due to overlap of wide emission spectra. We present an approach for fluorescence multiplexing using spectral imaging and combinatorics (MuSIC). MuSIC consists of creating new independent probes from covalently-linked combinations of individual fluorophores, leveraging the wide palette of currently available probes with the mathematical power of combinatorics. Probe levels in a mixture can be inferred from spectral emission scanning data. Theory and simulations suggest MuSIC can increase fluorescence multiplexing ~4-5 fold using currently available dyes and measurement tools. Experimental proof-of-principle demonstrates robust demultiplexing of nine solution-based probes using ~25% of the available excitation wavelength window (380-480 nm), consistent with theory. The increasing prevalence of white lasers, angle filter-based wavelength scanning, and large, sensitive multi-anode photo-multiplier tubes make acquisition of such MuSIC-compatible datasets increasingly attainable.

biochemistry↗

bioRxiv2018

Network Reconstruction from Perturbation Time Course Data

Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.

systems biology↗

bioRxiv2017

Validating Antibodies for Quantitative Western Blot Measurements with Microwestern Array

Western blotting is often considered a semi-quantitative or even qualitative assay for assessing changes in protein or protein post-translational modification levels. Fluorescence-based measurement enables acquisition of quantitative data in principal, but requires determining the linear range of detection for each antibody--a labor-intensive task. Here, we describe the use of a high-throughput western blotting technique called microwestern array to more rapidly evaluate suitable conditions for quantitative western blotting with particular antibodies. We can evaluate up to 192 antibody/dilution/replicate combinations on a single standard size gel with a seven-point, two-fold lysate dilution series (~100-fold range). Pilot experiments demonstrate a surprisingly high proportion of investigated antibodies (17/22) are suitable for quantitative use, and that lack of validity might often be a consequence of lysate composition rather than antibody quality. Linear range for all validated antibodies is at least 8-fold, and in some cases nearly two orders of magnitude. That range could be greater as the presented tests did not find a limit for many antibodies. We find that phospho-specific and total antibodies do not have discernable trend differences in linear range or limit of detection, but total antibodies generally required higher working concentrations, suggesting phospho-specific antibodies may be generally higher affinity. Importantly, we demonstrate that results from microwestern analyses scale to normal \"macro\" western for a subset of antibodies. These data indicate that with initial validation, many antibodies can be readily used quantitatively in a reproducible manner. Antibody validation data and standard operating procedures are available online (www.birtwistlelab.com/protocols and www.dtoxs.org).

biochemistry↗

bioRxiv2017

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

Evidence that some influential biomedical results cannot be repeated has increased interest in practices that generate data meeting findable, accessible, interoperable and reproducible (FAIR) standards. Multiple papers have identified examples of irreproducibility, but practical steps for increasing reproducibility have not been widely studied. Here, seven research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cell biology, and patient stratification. While many experimental and computational factors have an impact on intra- and inter-center reproducibility, the factors most difficult to identify and correct are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways of identifying such context-sensitive factors, thereby advancing the conceptual and practical basis for greater experimental reproducibility.

cancer biology↗

bioRxiv2017

An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this context, synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD. AKT dynamics explain S-phase entry synergy between EGF and insulin, but stochastic ERK dynamics seem to drive cell-to-cell proliferation variability, which in simulations are predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations predict MEK alteration negligibly influences transformation, consistent with clinical data. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, moving towards more rational cancer combination therapy.

systems biology↗