Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm.

A major complication in COVID-19 infection consists in the onset of acute respiratory distress fueled by a dysregulation of the host immune network that leads to a run-away cytokine storm. Here, we present an in silico approach that captures the host immune system's complex regulatory dynamics, allowing us to identify and rank candidate drugs and drug pairs that engage with minimal subsets of immune mediators such that their downstream interactions effectively disrupt the signaling cascades driving cytokine storm. Drug-target regulatory interactions are extracted from peer-reviewed literature using automated text-mining for over 5000 compounds associated with COVID-induced cytokine storm and elements of the underlying biology. The targets and mode of action of each compound, as well as combinations of compounds, were scored against their functional alignment with sets of competing model-predicted optimal intervention strategies, as well as the availability of like-acting compounds and known off-target effects. Top-ranking individual compounds identified included a number of known immune suppressors such as calcineurin and mTOR inhibitors as well as compounds less frequently associated for their immune-modulatory effects, including antimicrobials, statins, and cholinergic agonists. Pairwise combinations of drugs targeting distinct biological pathways tended to perform significantly better than single drugs with dexamethasone emerging as a frequent high-ranking companion. While these predicted drug combinations aim to disrupt COVID-induced acute respiratory distress syndrome, the approach itself can be applied more broadly to other diseases and may provide a standard tool for drug discovery initiatives in evaluating alternative targets and repurposing approved drugs.

The Aphelenchus avenae genome highlights evolutionary adaptation to desiccation.

Some organisms can withstand complete body water loss (losing up to 99% of body water) and stay in ametabolic state for decades until rehydration, which is known as anhydrobiosis. Few multicellular eukaryotes on their adult stage can withstand life without water. We still have an incomplete understanding of the mechanism for metazoan survival of anhydrobiosis. Here we report the 255-Mb genome of Aphelenchus avenae, which can endure relative zero humidity for years. Gene duplications arose genome-wide and contributed to the expansion and diversification of 763 kinases, which represents the second largest metazoan kinome to date. Transcriptome analyses of ametabolic state of A. avenae indicate the elevation of ATP level for global recycling of macromolecules and enhancement of autophagy in the early stage of anhydrobiosis. We catalogue 74 species-specific intrinsically disordered proteins, which may facilitate A. avenae to survive through desiccation stress. Our findings refine a molecular basis evolving for survival in extreme water loss and open the way for discovering new anti-desiccation strategies.

Circadian Genes as Exploratory Biomarkers in DMD: Results From Both the mdx Mouse Model and Patients.

Duchenne muscular dystrophy (DMD) is a rare genetic disease due to dystrophin gene mutations which cause progressive weakness and muscle wasting. Circadian rhythm coordinates biological processes with the 24-h cycle and it plays a key role in maintaining muscle functions, both in animal models and in humans. We explored expression profiles of circadian circuit master genes both in Duchenne muscular dystrophy skeletal muscle and in its animal model, the mdx mouse. We designed a customized, mouse-specific Fluidic-Card-TaqMan-based assay (Fluid-CIRC) containing thirty-two genes related to circadian rhythm and muscle regeneration and analyzed gastrocnemius and tibialis anterior muscles from both unexercised and exercised mdx mice. Based on this first analysis, we prioritized the 7 most deregulated genes in mdx mice and tested their expression in skeletal muscle biopsies from 10 Duchenne patients. We found that CSNK1E, SIRT1, and MYOG are upregulated in DMD patient biopsies, consistent with the mdx data. We also demonstrated that their proteins are detectable and measurable in the DMD patients' plasma. We suggest that CSNK1E, SIRT1, and MYOG might represent exploratory circadian biomarkers in DMD.

Comparative genomics of two jute species and insight into fibre biogenesis.

Jute (Corchorus sp.) is one of the most important sources of natural fibre, covering ∼80% of global bast fibre production1. Only Corchorus olitorius and Corchorus capsularis are commercially cultivated, though there are more than 100 Corchorus species2 in the Malvaceae family. Here we describe high-quality draft genomes of these two species and their comparisons at the functional genomics level to support tailor-designed breeding. The assemblies cover 91.6% and 82.2% of the estimated genome sizes for C. olitorius and C. capsularis, respectively. In total, 37,031 C. olitorius and 30,096 C. capsularis genes are identified, and most of the genes are validated by cDNA and RNA-seq data. Analyses of clustered gene families and gene collinearity show that jute underwent shared whole-genome duplication ∼18.66 million years (Myr) ago prior to speciation. RNA expression analysis from isolated fibre cells reveals the key regulatory and structural genes involved in fibre formation. This work expands our understanding of the molecular basis of fibre formation laying the foundation for the genetic improvement of jute.

Deep RNA profiling identified CLOCK and molecular clock genes as pathophysiological signatures in collagen VI myopathy.

Collagen VI myopathies are genetic disorders caused by mutations in collagen 6 A1, A2 and A3 genes, ranging from the severe Ullrich congenital muscular dystrophy to the milder Bethlem myopathy, which is recapitulated by collagen-VI-null (Col6a1(-/-)) mice. Abnormalities in mitochondria and autophagic pathway have been proposed as pathogenic causes of collagen VI myopathies, but the link between collagen VI defects and these metabolic circuits remains unknown. To unravel the expression profiling perturbation in muscles with collagen VI myopathies, we performed a deep RNA profiling in both Col6a1(-/-)mice and patients with collagen VI pathology. The interactome map identified common pathways suggesting a previously undetected connection between circadian genes and collagen VI pathology. Intriguingly, Bmal1(-/-)(also known as Arntl) mice, a well-characterized model displaying arrhythmic circadian rhythms, showed profound deregulation of the collagen VI pathway and of autophagy-related genes. The involvement of circadian rhythms in collagen VI myopathies is new and links autophagy and mitochondrial abnormalities. It also opens new avenues for therapies of hereditary myopathies to modulate the molecular clock or potential gene-environment interactions that might modify muscle damage pathogenesis.

Gene expression profiling for targeted cancer treatment.

INTRODUCTION: There is certain degree of frustration and discontent in the area of microarray gene expression data analysis of cancer datasets. It arises from the mathematical problem called 'curse of dimensionality,' which is due to the small number of samples available in training sets, used for calculating transcriptional signatures from the large number of differentially expressed (DE) genes, measured by microarrays. The new generation of causal reasoning algorithms can provide solutions to the curse of dimensionality by transforming microarray data into activity of a small number of cancer hallmark pathways. This new approach can make feature space dimensionality optimal for mathematical signature calculations. AREAS COVERED: The author reviews the reasons behind the current frustration with transcriptional signatures derived from DE genes in cancer. He also provides an overview of the novel methods for signature calculations based on differentially variable genes and expression regulators. Furthermore, the authors provide perspectives on causal reasoning algorithms that use prior knowledge about regulatory events described in scientific literature to identify expression regulators responsible for the differential expression observed in cancer samples. EXPERT OPINION: The author advocates causal reasoning methods to calculate cancer pathway activity signatures. The current challenge for these algorithms is in ensuring quality of the knowledgebase. Indeed, the development of cancer hallmark pathway collections, together with statistical algorithms to transform activity of expression regulators into pathway activity, are necessary for causal reasoning to be used in cancer research.

Facial pain with localized and widespread manifestations: separate pathways of vulnerability.

Human association studies of common genetic polymorphisms have identified many loci that are associated with risk of complex diseases, although individual loci typically have small effects. However, by envisaging genetic associations in terms of cellular pathways, rather than any specific polymorphism, combined effects of many biologically relevant alleles can be detected. The effects are likely to be most apparent in investigations of phenotypically homogenous subtypes of complex diseases. We report findings from a case-control, genetic association study of relationships between 2925 single nucleotide polymorphisms (SNPs) and 2 subtypes of a commonly occurring chronic facial pain condition, temporomandibular disorder (TMD): 1) localized TMD and 2) TMD with widespread pain. When compared to healthy controls, cases with localized TMD differed in allelic frequency of SNPs that mapped to a serotonergic receptor pathway (P=0.0012), while cases of TMD with widespread pain differed in allelic frequency of SNPs that mapped to a T-cell receptor pathway (P=0.0014). A risk index representing combined effects of 6 SNPs from the serotonergic pathway was associated with greater odds of localized TMD (odds ratio 2.7, P=1.3 E-09), and the result was reproduced in a replication case-control cohort study of 639 people (odds ratio 1.6, P=0.014). A risk index representing combined effects of 8 SNPs from the T-cell receptor pathway was associated with greater odds of TMD with widespread pain (P=1.9 E-08), although the result was not significant in the replication cohort. These findings illustrate potential for clinical classification of chronic pain based on distinct molecular profiles and genetic background.

Draft genome sequence of the rubber tree Hevea brasiliensis.

BACKGROUND: Hevea brasiliensis, a member of the Euphorbiaceae family, is the major commercial source of natural rubber (NR). NR is a latex polymer with high elasticity, flexibility, and resilience that has played a critical role in the world economy since 1876. RESULTS: Here, we report the draft genome sequence of H. brasiliensis. The assembly spans ~1.1 Gb of the estimated 2.15 Gb haploid genome. Overall, ~78% of the genome was identified as repetitive DNA. Gene prediction shows 68,955 gene models, of which 12.7% are unique to Hevea. Most of the key genes associated with rubber biosynthesis, rubberwood formation, disease resistance, and allergenicity have been identified. CONCLUSIONS: The knowledge gained from this genome sequence will aid in the future development of high-yielding clones to keep up with the ever increasing need for natural rubber.

Complete genome sequencing and analysis of Saprospira grandis str. Lewin, a predatory marine bacterium.

Saprospira grandis is a coastal marine bacterium that can capture and prey upon other marine bacteria using a mechanism known as 'ixotrophy'. Here, we present the complete genome sequence of Saprospira grandis str. Lewin isolated from La Jolla beach in San Diego, California. The complete genome sequence comprises a chromosome of 4.35 Mbp and a plasmid of 54.9 Kbp. Genome analysis revealed incomplete pathways for the biosynthesis of nine essential amino acids but presence of a large number of peptidases. The genome encodes multiple copies of sensor globin-coupled rsbR genes thought to be essential for stress response and the presence of such sensor globins in Bacteroidetes is unprecedented. A total of 429 spacer sequences within the three CRISPR repeat regions were identified in the genome and this number is the largest among all the Bacteroidetes sequenced to date.

Contextual data integration in drug discovery.

INTRODUCTION: The interpretation of high-throughput profiling data depends on the pathway analysis database. Currently, pathway analysis often has to rely on a set of interactions and pathways measured in every possible human tissue, due to insufficient knowledge about interactions and pathways in the context of the profiling experiment. However, a recent global scale analysis of human tissue proteomes and interactomes reveals significant differences among tissues, suggesting that interaction and pathway data that are used out of biological context are the major source of inaccuracies and noise in the analysis of profiling data. AREAS COVERED: In this review, the major classes of biological context used for experimental detection of molecular interactions and pathways in molecular biology are described. Furthermore, the author reviews methods for predicting biological interactions in order to evaluate the applicability of various contextual interaction data in pathway analysis. Using the results from recent publications that study large-scale tissue composition, the article provides an estimation of the gain in pathway analysis accuracy if only the interactions predicted for the context of a molecular profiling experiment are used, relative to the analysis performed with a context-independent knowledge base. EXPERT OPINION: It is of the author's opinion that the major source of inaccuracy in pathway analysis is the lack of knowledge about tissue-specific transcriptional regulation. It is therefore suggested that the accuracy of the analysis can be substantially improved if only context-specific interactions and pathways are used for interpretation.

Integrating fragmented software applications into holistic solutions: focus on drug discovery.

INTRODUCTION: Current advances in software development and global molecular profiling technologies allow the development of holistic software solutions for drug discovery. Such solutions must streamline in silico drug and therapy development by integrating all types of data into one knowledge base and also by enabling continuous analysis workflows uninterrupted by manual restructuring of inputs and outputs from workflow components. They must provide a collaborative environment for data sharing between multiple users and allow importing of all types of experimental data for subsequent analysis. AREAS COVERED: The reader is provided with a review of disparate software applications currently used in drug development and a discussion of existing organizational challenges for development of holistic software solutions. The reader is also provided with a proposed conceptual framework for integration of software components and some details for its implementation are suggested. EXPERT OPINION: Holistic solutions can undoubtedly affect the speed, quality and cost of drug development and personalized therapy. However, it must be constantly evolved to rapidly adopt new experimental and statistical methods, incorporate advances in software technologies and allow perpetual optimization of its components. Perpetual improvements in data structure, data quality, statistical algorithms and other mathematical approaches for computer modeling can gradually shift financial and cultural emphasis in the pharmaceutical industry away from traditional experimental approaches and towards computational approaches.

Analysis and construction of pathogenicity island regulatory pathways in Salmonella enterica serovar Typhi.

Signal transduction through protein-protein interactions and protein modifications are the main mechanisms controlling many biological processes. Here we described the implementation of MedScan information extraction technology and Pathway Studio software (Ariadne Genomics Inc.) to create a Salmonella specific molecular interaction database. Using the database, we have constructed several signal transduction pathways in Salmonella enterica serovar Typhi which causes Typhoid Fever, a major health threat especially in developing countries. S. Typhi has several pathogenicity islands that control rapid switching between different phenotypes including adhesion and colonization, invasion, intracellular survival, proliferation, and biofilm formation in response to environmental changes. Understanding of the detailed mechanism for S. Typhi survival in host cells is necessary for development of efficient detection and treatment of this pathogen. The constructed pathways were validated using publically available gene expression microarray data for Salmonella.

Computational approaches for drug repositioning and combination therapy design.

Heterogeneous high-throughput biological data become readily available for various diseases. The amount of data points generated by such experiments does not allow manual integration of the information to design the most optimal therapy for a disease. We describe a novel computational workflow for designing therapy using Ariadne Genomics Pathway Studio software. We use publically available microarray experiments for glioblastoma and automatically constructed ResNet and ChemEffect databases to exemplify how to find potentially effective chemicals for glioblastoma--the disease yet without effective treatment. Our first approach involved construction of signaling pathway affected in glioblastoma using scientific literature and data available in ResNet database. Compounds known to affect multiple proteins in this pathway were found in ChemEffect database. Another approach involved analysis of differential expression in glioblastoma patients using Sub-Network Enrichment Analysis (SNEA). SNEA identified angiogenesis-related protein Cyr61 as the major positive regulator upstream of genes differentially expressed in glioblastoma. Using our findings, we then identified breast cancer drug Fulvestrant as a major inhibitor of glioblastoma pathway as well as Cyr61. This suggested Fulvestrant as a potential treatment against glioblastoma. We further show how to increase efficacy of glioblastoma treatment by finding optimal combinations of Fulvestrant with other drugs.

Custom CGH array profiling of copy number variations (CNVs) on chromosome 6p21.32 (HLA locus) in patients with venous malformations associated with multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a complex disorder thought to result from an interaction between environmental and genetic predisposing factors which have not yet been characterised, although it is known to be associated with the HLA region on 6p21.32. Recently, a picture of chronic cerebrospinal venous insufficiency (CCSVI), consequent to stenosing venous malformation of the main extra-cranial outflow routes (VM), has been described in patients affected with MS, introducing an additional phenotype with possible pathogenic significance. METHODS: In order to explore the presence of copy number variations (CNVs) within the HLA locus, a custom CGH array was designed to cover 7 Mb of the HLA locus region (6,899,999 bp; chr6:29,900,001-36,800,000). Genomic DNA of the 15 patients with CCSVI/VM and MS was hybridised in duplicate. RESULTS: In total, 322 CNVs, of which 225 were extragenic and 97 intragenic, were identified in 15 patients. 234 known polymorphic CNVs were detected, the majority of these being situated in non-coding or extragenic regions. The overall number of CNVs (both extra- and intragenic) showed a robust and significant correlation with the number of stenosing VMs (Spearman: r = 0.6590, p = 0.0104; linear regression analysis r = 0.6577, p = 0.0106). The region we analysed contains 211 known genes. By using pathway analysis focused on angiogenesis and venous development, MS, and immunity, we tentatively highlight several genes as possible susceptibility factor candidates involved in this peculiar phenotype. CONCLUSIONS: The CNVs contained in the HLA locus region in patients with the novel phenotype of CCSVI/VM and MS were mapped in detail, demonstrating a significant correlation between the number of known CNVs found in the HLA region and the number of CCSVI-VMs identified in patients. Pathway analysis revealed common routes of interaction of several of the genes involved in angiogenesis and immunity contained within this region. Despite the small sample size in this pilot study, it does suggest that the number of multiple polymorphic CNVs in the HLA locus deserves further study, owing to their possible involvement in susceptibility to this novel MS/VM plus phenotype, and perhaps even other types of the disease.

Atlas of signaling for interpretation of microarray experiments.

Microarray-based expression profiling of living systems is a quick and inexpensive method to obtain insights into the nature of various diseases and phenotypes. A typical microarray profile can yield hundreds or even thousands of differentially expressed genes and finding biologically plausible themes or regulatory mechanisms underlying these changes is a non-trivial and daunting task. We describe a novel approach for systems-level interpretation of microarray expression data using a manually constructed "overview" pathway depicting the main cellular signaling channels (Atlas of Signaling). Currently, the developed pathway focuses on signal transduction from surface receptors to transcription factors and further transcriptional regulation of cellular "workhorse" proteins. We show how the constructed Atlas of Signaling in combination with an enrichment analysis algorithm allows quick identification and visualization of the main signaling cascades and cellular processes affected in a gene expression profiling experiment. We validate our approach using several publicly available gene expression datasets.

Ariadne's ChemEffect and Pathway Studio knowledge base.

IMPORTANCE OF THE FIELD: Drug discovery and development is a very complex and costly process. Understanding the detailed molecular mechanisms of a disease and drug actions can make it more efficient not only for new target discovery but also for lead prioritization, drug repositioning and development of biomarkers for drug efficacy and safety. Access to formalized knowledge about functions of proteins and small molecules is crucial for rationalization of the drug development process, and scientific publications are the main source of this knowledge. Protein knowledge networks capturing protein functions, protein-protein relations and organization of proteins in complex cellular sub-systems are making their way into modern drug discovery. Chemical networks representing multiple aspects of chemical functional information integrated into a protein systems biology network is even more advanced and promising paradigm. AREAS COVERED IN THIS REVIEW: This review describes utilization of literature-derived protein and chemical functional knowledge bases in drug development. WHAT THE READER WILL GAIN: Readers will gain an understanding of how integrated protein and chemical knowledge networks can be used for understanding and building the models of cellular events, disease mechanisms, and drug actions, finding biomarkers of drug efficacy and safety, as well as interpretation of high-throughput gene expression, proteomic and metabolomic experiments. TAKE HOME MESSAGE: Integrated literature-derived protein and chemical knowledge bases can rationalize many aspects of drug development process including drug repositioning and biomarker design.

In silico pathway analysis: the final frontier towards completely rational drug design.

Pathway and network analyses are rapidly becoming the mainstream tools for functional interpretation of high-throughput data and for drug discovery. Current scientific literature has plenty of examples on how pathway analysis tools are used across all steps of drug development pipeline. Pathway and network analyses already enable rational selection of drug targets based on the knowledge about disease biology. Pathway analysis tools are also popular for the analysis of drug action and validation of drug efficacy and toxicity. This article overviews current achievements of pathway analysis and suggests future directions for its application in drug development such as rational design of combinatorial therapy and personalized medicine.

Methane oxidation by an extremely acidophilic bacterium of the phylum Verrucomicrobia.

Aerobic methanotrophic bacteria consume methane as it diffuses away from methanogenic zones of soil and sediment. They act as a biofilter to reduce methane emissions to the atmosphere, and they are therefore targets in strategies to combat global climate change. No cultured methanotroph grows optimally below pH 5, but some environments with active methane cycles are very acidic. Here we describe an extremely acidophilic methanotroph that grows optimally at pH 2.0-2.5. Unlike the known methanotrophs, it does not belong to the phylum Proteobacteria but rather to the Verrucomicrobia, a widespread and diverse bacterial phylum that primarily comprises uncultivated species with unknown genotypes. Analysis of its draft genome detected genes encoding particulate methane monooxygenase that were homologous to genes found in methanotrophic proteobacteria. However, known genetic modules for methanol and formaldehyde oxidation were incomplete or missing, suggesting that the bacterium uses some novel methylotrophic pathways. Phylogenetic analysis of its three pmoA genes (encoding a subunit of particulate methane monooxygenase) placed them into a distinct cluster from proteobacterial homologues. This indicates an ancient divergence of Verrucomicrobia and Proteobacteria methanotrophs rather than a recent horizontal gene transfer of methanotrophic ability. The findings show that methanotrophy in the Bacteria is more taxonomically, ecologically and genetically diverse than previously thought, and that previous studies have failed to assess the full diversity of methanotrophs in acidic environments.

PCR primer design using statistical modeling.

I describe the approaches for choosing primer parameters and calculating primer properties to build a statistical model for PCR primer design. Statistical modeling allows you to fine-tune the PCR primer design for your standard PCR conditions. It is most appropriate for the large organizations routinely performing PCR on the large scale or for the instruments that utilize PCR. This chapter shows how to use the statistical model to optimize the PCR primer design and to cluster primers for multiplex PCR. These methods have been developed to optimize single-nucleotide polymorphism-identification technology (SNP-IT) reaction for SNP genotyping and implemented in the Autoprimer program (http://www.autoprimer.com). The approaches for combining the individual primer scores into statistical model are described in the next chapter.