PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.226647v1?rss=1

Authors: Cui, Y., Li, M., Biswal, B., Jing, W., Zhou, C., Liu, H., Guo, D., Xia, Y., Yan, D.

Abstract:

The activity in the default mode network (DMN) rapidly fluctuates in different conscious stages during wakefulness and sleep, indicating high complexity for the role of DMN in consciousness. Tracking the dynamics of these fluctuations is critical for deeply understanding the physiological mechanism of consciousness. Here, we propose a coactive micropattern (CAMP) method to extract the dynamic configuration of local field potentials (LFPs) in the rat DMN. Three spatially stable CAMPs were detected from DMN gamma activity (40-80 Hz) across wakefulness and sleep, consisting of a common low-activity level micropattern, an anterior high-activity level micropattern and a posterior high-activity level micropattern. Temporal structures of these CAMPs were specific to different conscious stages. A dynamic balance across CAMPs emerged during wakefulness and was disrupted in sleep stages, demonstrating that the balanced dynamic configuration of CAMPs played a vital role in supporting higher cognitive functions and primary consciousness. Furthermore, all these CAMPs displayed strong phasic relationships to the up-down states of the slow DMN activity during deep sleep. Our study reveals that the consciousness levels of different conscious stages are determined by the dynamic configurations of DMN activity, and provides a potential three-state model for the consciousness during wakefulness and sleep.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.228726v1?rss=1

Authors: Cloete, R., Shahbaaz, M., Grobbelaar, M., Sampson, S. L., Christoffels, A.

Abstract:

Nicotinamide-nucleotide adenylyl transferase (Rv2421c) was selected as a potential drug target, because it has been shown, in vitro , to be essential for Mycobacterium tuberculosis growth. It is conserved between mycobacterium species, is up-regulated during dormancy, has a known 3D crystal structure and has no known human homologs. A model of Rv2421c in complex with nicotinic acid adenine dinucleotide and magnesium ion was constructed and subject to virtual ligand screening against the Prestwick Chemical Library and the ZINC database, which yielded 155 potential hit molecules. 3D-QSAR studies of the 155 drug molecules indicated five compounds with similar inhibitory efficiencies compared to known inhibitors of Rv2421c. Molecular docking validation and molecular dynamics simulation analysis of the top five compounds indicated that the identified inhibitor molecules bind to Rv2421c with comparable efficiency as the substrate DND. Subsequent in vitro testing of the five compounds identified Novobiocin sodium salt with activity against Mycobacterium tuberculosis at 50 M, 25M and weakly at 10M concentrations. Although, Novobiocin salt targets Mycobacterium tuberculosis DNA gyrase B our studies suggest that it has the potential to be repurposed to inhibit Rv2421c. Subsequent in silico structural analysis of known Novobiocin sodium salt derivatives against Rv2421c suggest promising alternatives for the treatment of Mycobacterium tuberculosis .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.228890v1?rss=1

Authors: Woronik, A., Shaffer, H. W., Kiontke, K., Laurent, J. M., Zambrano, R., Boeke, J. D., Fitch, D. H. A.

Abstract:

Due to the sheer number of COVID-19 (coronavirus disease 2019) cases, the prevalence of asymptomatic cases and the fact that undocumented cases appear to be significant for transmission of the causal virus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), there is an urgent need for increased SARS-CoV-2 testing capability that is both efficient and effective. In response to the growing threat of the COVID-19 pandemic in February, 2020, the FDA (US Food and Drug Administration) began issuing Emergency Use Authorizations (EUAs) to laboratories and commercial manufacturers for the development and implementation of diagnostic tests. So far, the gold standard assay for SARS-CoV-2 detection is the RT-qPCR (real-time quantitative polymerase chain reaction) test. However, the authorized RT-qPCR test protocols vary widely, not only in the reagents, controls, and instruments they use, but also in the SARS-CoV-2 genes they target, what results constitute a positive SARS-CoV-2 infection, and their limit of detection (LoD). The FDA has provided a web site that lists most of the tests that have been issued EUAs, along with links to the authorization letters and summary documents describing these tests. However, it is very challenging to use this site to compare or replicate these tests for a variety of reasons. First, at least 12 of 18 tests that were issued EUAs prior to March 31, 2020, are not listed there. Second, the data are not standardized and are only provided as longhand prose in the summary documents. Third, some details (e.g. primer sequences) are absent from several of the test descriptions. Fourth, for tests provided by commercial manufacturers, summary documents are completely missing. To address at least the first three issues, we have developed a database, EUAdb (EUAdb.org), that holds standardized information about EUA-issued tests and is focused on RT-qPCR diagnostic tests, or "high complexity molecular-based laboratory developed tests". By providing a standardized ontology and curated data in a relational architecture, we seek to facilitate comparability and reproducibility, with the ultimate goal of consistent, universal and high-quality testing nationwide. Here, we document the basics of the EUAdb data architecture and simple data queries. The source files can be provided to anyone who wants to modify the database for his/her own research purposes. We ask that the original source of the files be made clear and that the database not be used in its original or modified forms for commercial purposes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.224485v1?rss=1

Authors: Krützfeldt, L.-M., Schubach, M., Kircher, M.

Abstract:

Regulatory regions, like promoters and enhancers, cover an estimated 5-15% of the human genome. Changes to these sequences are thought to underlie much of human phenotypic variation and a substantial proportion of genetic causes of disease. However, our understanding of their functional encoding in DNA is still very limited. Applying machine or deep learning methods can shed light on this encoding and gapped k-mer support vector machines (gkm-SVMs) or convolutional neural networks (CNNs) are commonly trained on putative regulatory sequences. Here, we investigate the impact of negative sequence selection on model performance. By training gkm-SVM and CNN models on open chromatin data and corresponding negative training dataset, both learners and two approaches for negative training data are compared. Negative sets use either genomic background sequences or sequence shuffles of the positive sequences. Model performance was evaluated on three different tasks: predicting elements active in a cell-type, predicting cell-type specific elements, and predicting elements' relative activity as measured from independent experimental data. Our results indicate strong effects of the negative training data, with genomic backgrounds showing overall best results. Specifically, models trained on highly shuffled sequences perform worse on the complex tasks of tissue-specific activity and quantitative activity prediction, and seem to learn features of artificial sequences rather than regulatory activity. Further, we observe that insufficient matching of genomic background sequences results in model biases. While CNNs achieved and exceeded the performance of gkm-SVMs for larger training datasets, gkm-SVMs gave robust and best results for typical training dataset sizes without the need of hyperparameter optimization.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.226498v1?rss=1

Authors: Rana, P., Sowmya, A., Meijering, E., Song, Y.

Abstract:

Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature. The significance of including third plane information for low-resolution volumetric images is investigated by comparing the performance of 3D texture descriptor with its respective pseudo 3D form that ignores the interslice intensity correlations. Following classification, changes in chromatin pattern are estimated by computing the ratio of heterochromatin and euchromatin corresponding to their respective intensities and image gradient obtained by 3D SRP. The proposed method is evaluated on two publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines in two phenotypic states obtained from the public Statistics Online Computational Resource. Experimental results show that 3D SRP and 3D Local Binary Pattern provide better results than other utilised handcrafted descriptors and deep learning features extracted using a pre-trained model. The results also show the advantage of utilising 3D feature descriptor for classification over its corresponding pseudo version. In addition, the proposed method validates that as the cell passes to another phenotypic state, there is a change in intensity and aggregation of heterochromatin.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.226217v1?rss=1

Authors: Das, J., Chakrobarty, S., Roy, S.

Abstract:

Understanding the molecular mechanism of COVID19 disease pathogenesis helps in the rapid development of therapeutic targets. Usually, viral protein targets host proteins in an organized fashion. The pathogen may target cell signaling pathways to disrupt the pathway genes' regular activities, resulting in disease. Understanding the interaction mechanism of viral and host proteins involved in different signaling pathways may help decipher the attacking mechanism on the signal transmission during diseases, followed by discovering appropriate therapeutic solutions. The expression of any viral gene depends mostly on the host translational machinery. Recent studies report the great significance of codon usage biases in establishing host-viral protein-protein interactions (PPI). Exploiting the codon usage patterns between a pair of co-evolved host and viral proteins may present novel insight into the host-viral protein interactomes during disease pathogenesis. Leveraging the codon usage pattern similarity (and dissimilarity), we propose a computational scheme to recreate the host-viral protein interaction network (HVPPI). We use seventeen (17) essential signaling pathways for our current work and study the possible targeting mechanism of SARS-CoV2 viral proteins on such pathway proteins. We infer both negatively and positively interacting edges in the network. We can find a relationship where one host protein may target by more than one viral protein. Extensive analysis performed to understand the network topologically and the attacking behavior of the viral proteins. Our study reveals that viral proteins, mostly utilize codons, rare in the targeted host proteins (negatively correlated interaction). Among non-structural proteins, NSP3 and structural protein, Spike (S) protein, are the most influential proteins in interacting multiple host proteins. In ranking the most affected pathways, MAPK pathways observe to be worst affected during the COVID-19 disease. A good number of targeted proteins are highly central in host protein interaction networks. Proteins participating in multiple pathways are also highly connected in their own PPI and mostly targeted by multiple viral proteins.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225581v1?rss=1

Authors: Ferrarini, M. G., Lal, A., Rebollo, R., Gruber, A., Guarracino, A., Martinez Gonzalez, I., Floyd, T., Siqueira de Oliveira, D., Shanklin, J., Beausoleil, E., Pusa, T., Pickett, B., Aguiar-Pulido, V.

Abstract:

The novel betacoronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after initially emerging in Wuhan, China. Here we applied a novel, comprehensive bioinformatic strategy to public RNA sequencing and viral genome sequencing data, to better understand how SARS-CoV-2 interacts with human cells. To our knowledge, this is the first meta-analysis to predict host factors that play a specific role in SARS-CoV-2 pathogenesis, distinct from other respiratory viruses. We identified differentially expressed genes, isoforms and transposable element families specifically altered in SARS-CoV-2 infected cells. Well-known immunoregulators including CSF2, IL-32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were overexpressed. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as hnRNPA1, PABPC1 and eIF4b, which may play important roles in the viral life cycle. We also detected four viral sequence variants in the spike, polymerase, and nonstructural proteins that correlate with severity of COVID-19. The host factors we identified likely represent important mechanisms in the disease profile of this pathogen, and could be targeted by prophylactics and/or therapeutics against SARS-CoV-2.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225292v1?rss=1

Authors: Farris, J., Alam, M. S., MysoreRajashekara, A., Haldar, K.

Abstract:

Glycine decarboxylase (GLDC) is a mitochondrial protein, hundreds of mutations in which cause a neurometabolic disorder Non-ketotic Hyperglycinemia (NKH), associated with elevation of plasma glycine. But why a mutation induces severe or attenuated neurological disease is poorly understood. We combined a human multiparametric mutation scale that separates severe from attenuated clinical, neurological disease, with new in silico tools to assess 238 of 255 NKH mutations in murine GLDC. We unified novel murine and human genome level-analyses across a linear scale of neurological severity, with in vivo evidence from mice engineered with a top-ranking attenuated mutation and another mutation >10 times more pathogenic and integrated the data in a model of pre- and post-natal disease outcomes, relevant for over a hundred major and minor neurogenic mutations. Our findings suggest that highly severe neurogenic mutations predict fatal, prenatal disease that can be remedied by metabolic supplementation of dams, in absence of amelioration of persistent and age-dependent elevation of plasma glycine.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.223818v1?rss=1

Authors: Arroyuelo, A., Vila, J., Martin, O. A.

Abstract:

On the present work, we explore how ideas and practices common in Bayesian modeling can be applied in the context of protein structure assessment. We feature two different but related types of models in this work. The first is statistical while the second is related to the 3D structure of proteins. We fit a Bayesian hierarchical linear model to experimental and theoretical 13C Chemical Shifts. Based on the premise that evaluation of the Bayesian statistical models fit may reveal aspects of the quality of a 3D protein structural model, we propose two complementary approaches for its evaluation: 1) in terms of the expected differences between experimental and posterior predicted values; 2) in terms of the leave-one-out cross validation point-wise predictive accuracy computed from the hierarchical Bayesian models. As the expected differences are unknown from theory alone, empirical reference densities computed from a data set of high quality protein structures are used to provide context for those differences. Finally, we present visualizations that can help interpret all these comparisons. The analyses presented in this article are aimed to aid in detecting problematic residues or regions in protein structures. The code used in this paper is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation. We encourage others to make use of this code to reproduce our analysis, perform a similar analysis on proteins of interest and improve over our proposal, either by expanding the hierarchical linear models or including other observables to complement or replace 13C Chemical Shifts.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.221010v1?rss=1

Authors: Yao, G., Zhang, W., Yang, M., Yang, H., Wang, J., Zhang, H., Wei, L., Xie, Z., Li, W.

Abstract:

Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.

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