PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.27.270173v1?rss=1

Authors: do Valle, I. F., Roweth, H. G., Malloy, M. W., Moco, S., Barron, D., Battinelli, E., Loscalzo, J., Barabasi, A.-L.

Abstract:

Polyphenols, natural products present in most plant-based foods, play a protective role against several complex diseases through their antioxidant activity and by diverse molecular mechanisms. Here we developed a network medicine framework to uncover the mechanistic roles of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases. We find that the protein targets of polyphenols cluster in specific neighborhoods of the human interactome, whose network proximity to disease proteins is predictive of the known therapeutic effects of polyphenols. This finding allows us to predict that rosmarinic acid (RA) has a direct impact on platelet function, representing a novel mechanism through which it could affect cardiovascular health, and experimentally confirm that RA inhibits platelet aggregation and alpha granule secretion through inhibition of protein tyrosine phosphorylation. Our framework represents a starting point for mechanistic interpretation of the health effects underlying food-related compounds, allowing us to integrate into a predictive framework knowledge on food metabolism, bioavailability, and drug interaction.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.27.267153v1?rss=1

Authors: Gombolay, A., Storici, F.

Abstract:

Ribose-Map is a user-friendly, standardized bioinformatics toolkit for the comprehensive analysis of ribonucleotide sequencing experiments. It allows researchers to map the locations of ribonucleotides in DNA to single-nucleotide resolution and identify biological signatures of ribonucleotide incorporation. In addition, it can be applied to data generated using any currently available high-throughput ribonucleotide sequencing technique, thus standardizing the analysis of ribonucleotide sequencing experiments and allowing direct comparisons of results. This protocol describes in detail how to use Ribose-Map to analyze raw ribonucleotide sequencing data, including preparing the reads for analysis, locating the genomic coordinates of ribonucleotides, exploring the genome-wide distribution of ribonucleotides, determining the nucleotide sequence context of ribonucleotides, and identifying hotspots of ribonucleotide incorporation. Ribose-Map does not require background knowledge of ribonucleotide sequencing analysis and assumes only basic command-line skills. The protocol requires less than 3 hr of computing time for most datasets and about 30 min of hands-on time.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.26.268581v1?rss=1

Authors: DePasquale, E. A., Alganem, K., Bentea, E., Nawreen, N., McGuire, J., Naji, F., Hilhorst, R., Meller, J., McCullumsmith, R.

Abstract:

Motivation Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based approaches for measuring multiple kinases allow for the testing of differential phosphorylation between conditions for distinct sub-kinomes. While bioinformatics tools exist for processing and analyzing such kinome array data, current open-source tools lack the automated approach of upstream kinase prediction and network modeling. The presented tool, alongside other tools and methods designed for gene expression and protein-protein interaction network analyses, help the user better understand the complex regulation of gene and protein activities that forms biological systems and cellular signaling networks. Results We present the Kinome Random Sampling Analyzer (KRSA), a web-application for kinome array analysis. While the underlying algorithm has been experimentally validated in previous publications, we tested the full KRSA application on dorsolateral prefrontal cortex (DLPFC) in male (n=3) and female (n=3) subjects to identify differential phosphorylation and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar patterns to the global phosphorylation signal. Additionally, kinase hits were compared to gene expression databases for in silico validation at the transcript level and showed differential gene expression of kinases. Availability and implementation: KRSA as a web-based application can be found at http://bpg-n.utoledo.edu:3838/CDRL/KRSA/. The code and data are available at https://github.com/kalganem/KRSA. Supplementary information Supplementary data are available online.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.26.268193v1?rss=1

Authors: Imrie, F., Bradley, A. R., Deane, C. M.

Abstract:

An essential step in the development of virtual screening methods is the use of established sets of actives and decoys for benchmarking and training. However, the decoy molecules in commonly used sets are biased meaning that methods often exploit these biases to separate actives and decoys, rather than learning how to perform molecular recognition. This fundamental issue prevents generalisation and hinders virtual screening method development. We have developed a deep learning method (DeepCoy) that generates decoys to a user's preferred specification in order to remove such biases or construct sets with a defined bias. We validated DeepCoy using two established benchmarks, DUD-E and DEKOIS 2.0. For all DUD-E targets and 80 of the 81 DEKOIS 2.0 targets, our generated decoy molecules more closely matched the active molecules' physicochemical properties while introducing no discernible additional risk of false negatives. The DeepCoy decoys improved the Deviation from Optimal Embedding (DOE) score by an average of 81% and 66%, respectively, decreasing from 0.163 to 0.032 for DUD-E and from 0.109 to 0.038 for DEKOIS 2.0. Further, the generated decoys are harder to distinguish than the original decoy molecules via docking with Autodock Vina, with virtual screening performance falling from an AUC ROC of 0.71 to 0.63. The code is available at https://github.com/oxpig/DeepCoy. Generated molecules can be downloaded from http://opig.stats.ox.ac.uk/resources.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.26.263830v1?rss=1

Authors: Chen, Y., Sakurai, K., Maeda, S., Masui, T., Okano, H., Dewender, J., Seltmann, S., Kurtz, A., Masuya, H., Nakamura, Y., Sheldon, M., Schneider, J., Stacey, G., Panina, Y., Fujibuchi, W.

Abstract:

The last decade has witnessed an extremely rapid increase in the number of newly established stem cell lines worldwide. However, due to the lack of a standardized format, data exchange among stem cell line information resources has been challenging, and no system can search all stem cell lines across resources worldwide. To solve this problem, we have developed the Integrated Collection of Stem Cell Bank data (ICSCB) (http://icscb.stemcellinformatics.org/), a new and largest database search portal for stem cell line data from various resources, based on the standardized data items and terms of the MIACARM framework. Currently, ICSCB can retrieve 15,796 cell lines from four major data resources in Europe, Japan, and the U.S. ICSCB is automatically updated to provide the latest cell line information, and its integrative search engine helps users collect cell line information from donors with rare diseases worldwide, which has been a formidable task, thereby distinguishing itself from other database search portals.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.27.269456v1?rss=1

Authors: Agarwal, V., Tiwari, A., Varadwaj, P. K.

Abstract:

The emergence of COVID-19 as a pandemic with a high morbidity rate is posing serious global concern. There is an urgent need to design a suitable therapy or vaccine that could fight against SARS-CoV-2 infection. As spike glycoprotein of SARS-CoV-2 plays a crucial role in receptor binding and membrane fusion inside the host, it could be a suitable target for designing of an epitope-based vaccine. SARS-CoV-2 is an RNA virus and thus has a property to mutate. So, a conserved peptide region of spike glycoprotein was used for predicting suitable B cell and T cell epitopes. 4 T cell epitopes were selected based on stability, antigenicity, allergenicity and toxicity. Further, MHC-I were found from the immune database that could best interact with the selected epitopes. Population coverage analysis was also done to check the presence of identified MHC-I, in the human population of the affected countries. The T cell epitope that binds with the respective MHC-I with highest affinity was chosen. Molecular dynamic simulation results show that the epitope is well selected. This is an in-silico based study that predicts a novel T cell epitope from the conserved spike glycoprotein that could act as a target for designing of the epitope-based vaccine. Further, B cell epitopes have also been found but the main work focuses on T cell epitope as the immunity generated by it is long lasting as compared to B cell epitope.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.26.267617v1?rss=1

Authors: Karkar, S. I., Dandine-Roulland, C. I., Mangin, J.-F., Le Guen, Y., Philippe, C., Deleuze, J.-F., Pierre-Jean, M., Le Floch, E., Frouin, V.

Abstract:

Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a single block, while the last approach considers one model by haplotype. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single- SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.26.267815v1?rss=1

Authors: Fan, J., Wang, X., Xiao, R., Li, M.

Abstract:

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell-type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.25.267351v1?rss=1

Authors: Souza, A. S. d., Rivera, J. D., Medeiros, V., Ge, P., Souza, R. F. d., Farah, C. S., Ulrich, H., Marana, S. R., Kopke Salinas, R., Guzzo, C. R.

Abstract:

The SARS-CoV-2 pandemic has already killed more than 800,000 people worldwide. To gain entry, the virus uses its spike protein to bind to host hACE-2 receptors on the host cell surface and mediate fusion between viral and cell membranes. As initial steps leading to virus entry involves significant changes in protein conformation as well as in the electrostatic environment in the vicinity of the spike-hACE-2 complex, we explored the sensitivity of the interaction to changes in ionic strength through computational simulations and surface plasmon resonance. We identified two regions in the receptor-binding domain (RBD), E1 and E2, which interact differently with hACE-2. At high salt concentration, E2-mediated interactions are weakened but are compensated by strengthening E1-mediated hydrophobic interactions. These results provide a detailed molecular understanding of spike RBD/hACE-2 complex formation and stability under a wide range of ionic strengths.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.26.267997v1?rss=1

Authors: Mazzocco, G., Niemiec, I., Myronov, A., Skoczylas, P., Kaczmarczyk, J., Sanecka-Duin, A., Gruba, K., Krol, P., Drwal, M., Szczepanik, M., Pyrc, K., Stepniak, P.

Abstract:

The heavy burden imposed by the COVID-19 pandemic on our society triggered the race towards the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. 2020 and Iwasaki et al. 2020. All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.

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