Graph algorithms for predicting subcellular localization at the pathway level

Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.Comment: 35 pages, 14 figure

Bayes Optimal Informer Sets for Early-Stage Drug Discovery

An important experimental design problem in early-stage drug discovery is how to prioritize available compounds for testing when very little is known about the target protein. Informer based ranking (IBR) methods address the prioritization problem when the compounds have provided bioactivity data on other potentially relevant targets. An IBR method selects an informer set of compounds, and then prioritizes the remaining compounds on the basis of new bioactivity experiments performed with the informer set on the target. We formalize the problem as a two-stage decision problem and introduce the Bayes Optimal Informer SEt (BOISE) method for its solution. BOISE leverages a flexible model of the initial bioactivity data, a relevant loss function, and effective computational schemes to resolve the two-step design problem. We evaluate BOISE and compare it to other IBR strategies in two retrospective studies, one on protein-kinase inhibition and the other on anti-cancer drug sensitivity. In both empirical settings BOISE exhibits better predictive performance than available methods. It also behaves well with missing data, where methods that use matrix completion show worse predictive performance. We provide an R implementation of BOISE at https://github.com/wiscstatman/esdd/BOISEComment: 18 pages, 6 figure

Outbreak of encephalitic listeriosis in red-legged partridges (Alectoris rufa)

An outbreak of neurological disease was investigated in red-legged partridges between 8 and 28 days of age. Clinical signs included torticollis, head tilt and incoordination and over an initial eight day period approximately 30–40 fatalities occurred per day. No significant gross post mortem findings were detected. Histopathological examination of the brain and bacterial cultures followed by partial sequencing confirmed a diagnosis of encephalitis due to Listeria monocytogenes. Further isolates were obtained from follow-up carcasses, environmental samples and pooled tissue samples of newly imported day-old chicks prior to placement on farm. These isolates had the same antibiotic resistance pattern as the isolate of the initial post mortem submission and belonged to the same fluorescent amplified fragment length polymorphism (fAFLP) subtype. This suggested that the isolates were very closely related or identical and that the pathogen had entered the farm with the imported day-old chicks, resulting in disease manifestation in partridges between 8 and 28 days of age. Reports of outbreaks of encephalitic listeriosis in avian species are rare and this is to the best of our knowledge the first reported outbreak in red-legged partridges

A network-based approach for predicting missing pathway interactions

Embedded within large-scale protein interaction networks are signaling pathways that encode response cascades in the cell. Unfortunately, even for well-studied species like S. cerevisiae, only a fraction of all true protein interactions are known, which makes it difficult to reason about the exact flow of signals and the corresponding causal relations in the network. To help address this problem, we introduce a framework for predicting new interactions that aid connectivity between upstream proteins (sources) and downstream transcription factors (targets) of a particular pathway. Our algorithms attempt to globally minimize the distance between sources and targets by finding a small set of shortcut edges to add to the network. Unlike existing algorithms for predicting general protein interactions, by focusing on proteins involved in specific responses our approach homes-in on pathway-consistent interactions. We applied our method to extend pathways in osmotic stress response in yeast and identified several missing interactions, some of which are supported by published reports. We also performed experiments that support a novel interaction not previously reported. Our framework is general and may be applicable to edge prediction problems in other domains

An Open-Publishing Response to the COVID-19 Infodemic

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript\u27s figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis

Cancer and thrombosis: Managing the risks and approaches to thromboprophylaxis

Patients with cancer are at increased risk of venous thromboembolism (VTE) compared with patients without cancer. This results from both the prothrombotic effects of the cancer itself and iatrogenic factors, such as chemotherapy, radiotherapy, indwelling central venous devices and surgery, that further increase the risk of VTE. Although cancer-associated thrombosis remains an important cause of morbidity and mortality, it is often underdiagnosed and undertreated. However, evidence is accumulating to support the use of low-molecular-weight heparins (LMWHs) in the secondary prevention of VTE in patients with cancer. Not only have LMWHs been shown to be at least as effective as coumarin derivatives in this setting, but they have a lower incidence of complications, including bleeding, and are not associated with the practical problems of warfarin therapy. Furthermore, a growing number of studies indicate that LMWHs may improve survival among patients with cancer due to a possible antitumor effect. Current evidence suggests that LMWHs should increasingly be considered for the long-term management of VTE in patients with cancer

Efficient Photodynamic Therapy against Gram-Positive and Gram-Negative Bacteria Using THPTS, a Cationic Photosensitizer Excited by Infrared Wavelength

The worldwide rise in the rates of antibiotic resistance of bacteria underlines the need for alternative antibacterial agents. A promising approach to kill antibiotic-resistant bacteria uses light in combination with a photosensitizer to induce a phototoxic reaction. Concentrations of 1, 10 and 100µM of tetrahydroporphyrin-tetratosylat (THPTS) and different incubation times (30, 90 and 180min) were used to measure photodynamic efficiency against two Gram-positive strains of S.aureus (MSSA and MRSA), and two Gram-negative strains of E.coli and P.aeruginosa. We found that phototoxicity of the drug is independent of the antibiotic resistance pattern when incubated in PBS for the investigated strains. Also, an incubation with 100µM THPTS followed by illumination, yielded a 6lg (≥99.999%) decrease in the viable numbers of all bacteria strains tested, indicating that the THPTS drug has a high degree of photodynamic inactivation. We then modulated incubation time, photosensitizer concentration and monitored the effect of serum on the THPTS activity. In doing so, we established the conditions to obtain the strongest bactericidal effect. Our results suggest that this new and highly pure synthetic compound should improve the efficiency of photodynamic therapy against multiresistant bacteria and has a significant potential for clinical applications in the treatment of nosocomial infections

Replication and Explorations of High-Order Epistasis Using a Large Advanced Intercross Line Pedigree

Dissection of the genetic architecture of complex traits persists as a major challenge in biology; despite considerable efforts, much remains unclear including the role and importance of genetic interactions. This study provides empirical evidence for a strong and persistent contribution of both second- and third-order epistatic interactions to long-term selection response for body weight in two divergently selected chicken lines. We earlier reported a network of interacting loci with large effects on body weight in an F2 intercross between these high– and low–body weight lines. Here, most pair-wise interactions in the network are replicated in an independent eight-generation advanced intercross line (AIL). The original report showed an important contribution of capacitating epistasis to growth, meaning that the genotype at a hub in the network releases the effects of one or several peripheral loci. After fine-mapping of the loci in the AIL, we show that these interactions were persistent over time. The replication of five of six originally reported epistatic loci, as well as the capacitating epistasis, provides strong empirical evidence that the originally observed epistasis is of biological importance and is a contributor in the genetic architecture of this population. The stability of genetic interaction mechanisms over time indicates a non-transient role of epistasis on phenotypic change. Third-order epistasis was for the first time examined in this study and was shown to make an important contribution to growth, which suggests that the genetic architecture of growth is more complex than can be explained by two-locus interactions only. Our results illustrate the importance of designing studies that facilitate exploration of epistasis in populations for obtaining a comprehensive understanding of the genetics underlying a complex trait