Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation

Host M-CSF induced gene expression drives changes in susceptible and resistant mice-derived BMdMs upon Leishmania major infection

Leishmaniases are a group of diseases with different clinical manifestations. Macrophage-Leishmania interactions are central to the course of the infection. The outcome of the disease depends not only on the pathogenicity and virulence of the parasite, but also on the activation state, the genetic background, and the underlying complex interaction networks operative in the host macrophages. Mouse models, with mice strains having contrasting behavior in response to parasite infection, have been very helpful in exploring the mechanisms underlying differences in disease progression. We here analyzed previously generated dynamic transcriptome data obtained from Leishmania major (L. major) infected bone marrow derived macrophages (BMdMs) from resistant and susceptible mouse. We first identified differentially expressed genes (DEGs) between the M-CSF differentiated macrophages derived from the two hosts, and found a differential basal transcriptome profile independent of Leishmania infection. These host signatures, in which 75% of the genes are directly or indirectly related to the immune system, may account for the differences in the immune response to infection between the two strains. To gain further insights into the underlying biological processes induced by L. major infection driven by the M-CSF DEGs, we mapped the time-resolved expression profiles onto a large protein-protein interaction (PPI) network and performed network propagation to identify modules of interacting proteins that agglomerate infection response signals for each strain. This analysis revealed profound differences in the resulting responses networks related to immune signaling and metabolism that were validated by qRT-PCR time series experiments leading to plausible and provable hypotheses for the differences in disease pathophysiology. In summary, we demonstrate that the host's gene expression background determines to a large degree its response to L. major infection, and that the gene expression analysis combined with network propagation is an effective approach to help identifying dynamically altered mouse strain-specific networks that hold mechanistic information about these contrasting responses to infection.

Scalable probabilistic truss decomposition using central limit theorem and H-index

Truss decomposition is a popular notion of hierarchical dense substructures in graphs. In a nutshell, k-truss is the largest subgraph in which every edge is contained in at least k triangles. Truss decomposition aims to compute k-trusses for each possible value of k. There are many works that study truss decomposition in deterministic graphs. However, in probabilistic graphs, truss decomposition is significantly more challenging and has received much less attention; state-of-the-art approaches do not scale well to large probabilistic graphs. Finding the tail probabilities of the number of triangles that contain each edge is a critical challenge of those approaches. This is achieved using dynamic programming which has quadratic run-time and thus not scalable to real large networks which, quite commonly, can have edges contained in many triangles (in the millions). To address this challenge, we employ a special version of the Central Limit Theorem (CLT) to obtain the tail probabilities efficiently. Based on our CLT approach we propose a peeling algorithm for truss decomposition that scales to large probabilistic graphs and offers significant improvement over state-of-the-art. We also design a second method which progressively tightens the estimate of the truss value of each edge and is based on h-index computation. In contrast to our CLT-based approach, our h-index algorithm (1) is progressive by allowing the user to see near-results along the way, (2) does not sacrifice the exactness of final result, and (3) achieves all these while processing only one edge and its immediate neighbors at a time, thus resulting in smaller memory footprint. We perform extensive experiments to show the scalability of both of our proposed algorithms.

Integrative COVID-19 biological network inference with probabilistic core decomposition

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

A random effect regression based on the odd log-logistic generalized inverse Gaussian distribution

In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature.

Structure-Activity Relationship in the Leucettine Family of Kinase Inhibitors

The protein kinase DYRK1A is involved in Alzheimer's disease, Down syndrome, diabetes, viral infections, and leukemia. Leucettines, a family of 2-aminoimidazolin-4-ones derived from the marine sponge alkaloid Leucettamine B, have been developed as pharmacological inhibitors of DYRKs (dual specificity, tyrosine phosphorylation regulated kinases) and CLKs (cdc2-like kinases). We report here on the synthesis and structure-activity relationship (SAR) of 68 Leucettines. Leucettines were tested on 11 purified kinases and in 5 cellular assays: (1) CLK1 pre-mRNA splicing, (2) Threonine-212-Tau phosphorylation, (3) glutamate-induced cell death, (4) autophagy and (5) antagonism of ligand-activated cannabinoid receptor CB1. The Leucettine SAR observed for DYRK1A is essentially identical for CLK1, CLK4, DYRK1B, and DYRK2. DYRK3 and CLK3 are less sensitive to Leucettines. In contrast, the cellular SAR highlights correlations between inhibition of specific kinase targets and some but not all cellular effects. Leucettines deserve further development as potential therapeutics against various diseases on the basis of their molecular targets and cellular effects.

Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection

Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins. We experimentally tested treatments for a number of the predicted targets. We show that blocking one of the predicted indirect targets significantly reduces viral loads in stem cell-derived alveolar epithelial type II cells (iAT2s).

Dual-Specificity, Tyrosine Phosphorylation-Regulated Kinases (DYRKs) and cdc2-Like Kinases (CLKs) in Human Disease, an Overview

Dual-specificity tyrosine phosphorylation-regulated kinases (DYRK1A, 1B, 2-4) and cdc2-like kinases (CLK1-4) belong to the CMGC group of serine/threonine kinases. These protein kinases are involved in multiple cellular functions, including intracellular signaling, mRNA splicing, chromatin transcription, DNA damage repair, cell survival, cell cycle control, differentiation, homocysteine/methionine/folate regulation, body temperature regulation, endocytosis, neuronal development, synaptic plasticity, etc. Abnormal expression and/or activity of some of these kinases, DYRK1A in particular, is seen in many human nervous system diseases, such as cognitive deficits associated with Down syndrome, Alzheimer's disease and related diseases, tauopathies, dementia, Pick's disease, Parkinson's disease and other neurodegenerative diseases, Phelan-McDermid syndrome, autism, and CDKL5 deficiency disorder. DYRKs and CLKs are also involved in diabetes, abnormal folate/methionine metabolism, osteoarthritis, several solid cancers (glioblastoma, breast, and pancreatic cancers) and leukemias (acute lymphoblastic leukemia, acute megakaryoblastic leukemia), viral infections (influenza, HIV-1, HCMV, HCV, CMV, HPV), as well as infections caused by unicellular parasites (Leishmania, Trypanosoma, Plasmodium). This variety of pathological implications calls for (1) a better understanding of the regulations and substrates of DYRKs and CLKs and (2) the development of potent and selective inhibitors of these kinases and their evaluation as therapeutic drugs. This article briefly reviews the current knowledge about DYRK/CLK kinases and their implications in human disease.

FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2

Motivation

The novel coronavirus (SARS-CoV-2) currently spreads worldwide, causing the disease COVID-19. The number of infections increases daily, without any approved antiviral therapy. The recently released viral nucleotide sequence enables the identification of therapeutic targets, e.g. by analyzing integrated human-virus metabolic models. Investigations of changed metabolic processes after virus infections and the effect of knock-outs on the host and the virus can reveal new potential targets.

Results

We generated an integrated host–virus genome-scale metabolic model of human alveolar macrophages and SARS-CoV-2. Analyses of stoichiometric and metabolic changes between uninfected and infected host cells using flux balance analysis (FBA) highlighted the different requirements of host and virus. Consequently, alterations in the metabolism can have different effects on host and virus, leading to potential antiviral targets. One of these potential targets is guanylate kinase (GK1). In FBA analyses, the knock-out of the GK1 decreased the growth of the virus to zero, while not affecting the host. As GK1 inhibitors are described in the literature, its potential therapeutic effect for SARS-CoV-2 infections needs to be verified in in-vitro experiments.

Availability and implementation

The computational model is accessible at https://identifiers.org/biomodels.db/MODEL2003020001.

Prospects for RNAi Therapy of COVID-19

COVID-19 caused by the SARS-CoV-2 virus is a fast emerging disease with deadly consequences. The pulmonary system and lungs in particular are most prone to damage caused by the SARS-CoV-2 infection, which leaves a destructive footprint in the lung tissue, making it incapable of conducting its respiratory functions and resulting in severe acute respiratory disease and loss of life. There were no drug treatments or vaccines approved for SARS-CoV-2 at the onset of pandemic, necessitating an urgent need to develop effective therapeutics. To this end, the innate RNA interference (RNAi) mechanism can be employed to develop front line therapies against the virus. This approach allows specific binding and silencing of therapeutic targets by using short interfering RNA (siRNA) and short hairpin RNA (shRNA) molecules. In this review, we lay out the prospect of the RNAi technology for combatting the COVID-19. We first summarize current understanding of SARS-CoV-2 virology and the host response to viral entry and duplication, with the purpose of revealing effective RNAi targets. We then summarize the past experience with nucleic acid silencers for SARS-CoV, the predecessor for current SARS-CoV-2. Efforts targeting specific protein-coding regions within the viral genome and intragenomic targets are summarized. Emphasizing non-viral delivery approaches, molecular underpinnings of design of RNAi agents are summarized with comparative analysis of various systems used in the past. Promising viral targets as well as host factors are summarized, and the possibility of modulating the immune system are presented for more effective therapies. We place special emphasis on the limitations of past studies to propel the field faster by focusing on most relevant models to translate the promising agents to a clinical setting. Given the urgency to address lung failure in COVID-19, we summarize the feasibility of delivering promising therapies by the inhalational route, with the expectation that this route will provide the most effective intervention to halt viral spread. We conclude with the authors' perspectives on the future of RNAi therapeutics for combatting SARS-CoV-2. Since time is of the essence, a strong perspective for the path to most effective therapeutic approaches are clearly articulated by the authors.