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Over the past decade, a promising line of cancer research has utilized machine learning to mine statistical patterns of mutations in cancer genomes for information. Recent work shows that these statistical patterns, commonly referred to as "mutational signatures," have diverse therapeutic potential as biomarkers for cancer therapies. However, translating this potential into reality is hindered by limited access to sequencing in the clinic. Almost all methods for mutational signature analysis (MSA) rely on whole genome or whole exome sequencing data, while sequencing in the clinic is typically limited to small gene panels. To improve clinical access to MSA, we considered the question of whether targeted panels could be designed for the purpose of mutational signature detection. Here we present ScalpelSig, to our knowledge the first algorithm that automatically designs genomic panels optimized for detection of a given mutational signature. The algorithm learns from data to identify genome regions that are particularly indicative of signature activity. Using a cohort of breast cancer genomes as training data, we show that ScalpelSig panels substantially improve accuracy of signature detection compared to baselines. We find that some ScalpelSig panels even approach the performance of whole exome sequencing, which observes over 10 × as much genomic material. We test our algorithm under a variety of conditions, showing that its performance generalizes to another dataset of breast cancers, to smaller panel sizes, and to lesser amounts of training data.

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Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules "connected" in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway's downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways with statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.

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Motivation: Mathematical models of cellular processes can systematically predict the phenotypes of novel combinations of multi-gene mutations. Searching for informative predictions and prioritizing them for experimental validation is challenging since the number of possible combinations grows exponentially in the number of mutations. Moreover, keeping track of the crosses needed to make new mutants and planning sequences of experiments is unmanageable when the experimenter is deluged by hundreds of potentially informative predictions to test. Results: We present CrossPlan, a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. We base our approach on a generic experimental workflow used in performing genetic crosses in budding yeast. We prove that the CrossPlan problem is NP-complete. We develop an integer-linear-program (ILP) to maximize the number of target mutants that we can make under certain experimental constraints. We apply our method to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. We also extend our solution to incorporate other experimental conditions such as a delay factor that decides the availability of a mutant and genetic markers to confirm gene deletions. The experimental flow that underlies our work is quite generic and our ILP-based algorithm is easy to modify. Hence, our framework should be relevant in plant and animal systems as well. Availability and implementation: CrossPlan code is freely available under GNU General Public Licence v3.0 at https://github.com/Murali-group/crossplan. Supplementary information: Supplementary data are available at Bioinformatics online.

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The pathophysiological changes that occur during ischemic stroke can have a profound effect on the surrounding nerve tissue. To this end, we advance the hypothesis that retinal damage can occur as a consequence of ischemic stroke in animal models. We discuss the preclinical evidence over the last 3 decades supporting this hypothesis of retinal damage following ischemic stroke. In our evaluation of the hypothesis, we highlight the animal models providing evidence of pathological and mechanistic link between ischemic stroke and retinal damage. That retinal damage is closely associated with ischemic stroke, yet remains neglected in stroke treatment regimen, provides the impetus for recognizing the treatment of retinal damage as a critical component of stroke therapy.

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Our understanding of biological mechanisms and treatment options for traumatic brain injury (TBI) is limited. Here, we employed quantitative real-time PCR (QRT-PCR) and immunohistochemical analyses to determine the dynamic expression of cell proliferation and apoptosis in an effort to provide insights into the therapeutic window for developing regenerative strategies for TBI. For this purpose, young adult Sprague-Dawley rats were subjected to experimental TBI using a controlled cortical impactor, then euthanized 1-48 hours after TBI for QRT-PCR and immunohistochemistry. QRT-PCR revealed that brains from TBI exposed rats initially displayed nestin mRNA expression that modestly increased as early as 1-hour post-TBI, then significantly peaked at 8 hours, but thereafter reverted to pre-TBI levels. On the other hand, caspase-3 mRNA expression was slightly elevated at 8 hours post-TBI, which did not become significantly upregulated until 48 hours. Immunofluorescent microscopy revealed a significant surge in nestin immunoreactive cells in the cortex, corpus callosum, and subventricular zone at 24 hours post-TBI, whereas a significant increase in the number of active caspase-3 immunoreactive cells was only found in the cortex and not until 48 hours. These results suggest that the injured brain attempts to repair itself via cell proliferation immediately after TBI, but that this endogenous regenerative mechanism is not sufficient to abrogate the secondary apoptotic cell death. Treatment strategies designed to amplify cell proliferation and to prevent apoptosis are likely to exert maximal benefits when initiated at the acute phase of TBI.