Systems-wide view of host-pathogen interactions across COVID-19 severities using integrated omics analysis.

The mechanisms explaining the variability in COVID-19 clinical manifestations (mild, moderate, and severe) are not fully understood. To identify key gene expression markers linked to disease severity, we employed an integrated approach, combining host-pathogen protein-protein interaction data and viral-induced host gene expression data. We analyzed an RNA-seq dataset from peripheral blood mononuclear cells across 12 projects representing the spectrum of disease severity. We identified genes showing differential expression across mild, moderate, and severe conditions. Enrichment analysis of the pathways in host proteins targeted by each of the SARS-CoV-2 proteins revealed a strong association with processes related to ribosomal biogenesis, translation, and translocation. Interestingly, most of these pathways and associated cellular machinery, including ribosomal biogenesis, ribosomal proteins, and translation, were upregulated in mild conditions but downregulated in severe cases. This suggests that COVID-19 exhibits a paradoxical host response, boosting host/viral translation in mild cases but slowing it in severe cases.

Role of diosgenin extracted from Helicteres isora L in suppression of HIV-1 replication: An in vitro preclinical study.

Background: Diosgenin, an essential sapogenin steroid with significant biological implications, is composed of a hydrophilic sugar moiety intricately linked to a hydrophobic steroid aglycone. While the antiviral properties of diosgenin against numerous RNA viruses have been extensively documented, its potential in combating Human Immunodeficiency Virus infections remains unexplored. Experimental procedure: This current investigation presents a comprehensive and systematic analysis of extracts derived from the leaves of Helicteres isora, which are notably enriched with diosgenin. Rigorous methodologies, including established chromatographic techniques and Fourier-transform infrared spectroscopy were employed for the characterization of the active diosgenin compound followed by molecular interaction analyses with the key HIV enzymes and mechanistic validation of HIV inhibition. Key results: The inhibitory effects of extracted diosgenin on the replication of HIV-1 were demonstrated using a permissive cellular system, encompassing two distinct subtypes of HIV-1 strains. Computational analyses involving molecular interactions highlighted the substantial occupancy of critical active site pocket residues within the key HIV-1 proteins by diosgenin. Additionally, the mechanistic underpinnings of diosgenin activity in conjunction with standard controls were elucidated through specialized colorimetric assays, evaluating its impact on HIV-1 Reverse Transcriptase and Integrase enzymes. Conclusions: To our current state of knowledge, this study represents the inaugural demonstration of the anti-HIV efficacy inherent to diosgenin found in the leaves of Helicteres isora, and can be taken further for drug design and development for the management of HIV infection.

Microbial colonization dynamics of the postnatal digestive tract of Bos indicus calves.

The rumen and the jejunum of calves have distinct functional roles; the former is in the storage and fermentation of feed, and the latter is in transporting digesta to the ileum. It is unknown how nutrition changes the evolution of the microbiome of these organs after birth. We sequenced and characterized the entire microbiome of the rumen and the jejunum from Bos indicus calves of the Mexican Tropics to study their dynamics at Days 0, 7, 28, and 42 after birth. Operational taxonomic units (OTUs) belonging to 185 and 222 genera from 15 phylum were observed in the organs, respectively. The most abundant OTUs were Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. We observed that proteobacterial species were outcompeted after the first week of life by Bacteroidetes and Firmicutes in the rumen and the jejunum, respectively. Moreover, Prevotella species were found to predominate in the rumen (36% of total OTUs), while the jejunum microbiome is composed of small proportions of several genera. Presumably, their high relative abundance assists in specialized functions and is more likely in fermentation since they are anaerobes. In summary, the rumen and the jejunum microbiomes were outcompeted by new microbiomes in a dynamic process that begins at birth.

Search, Retrieve, Visualize, and Analyze Protein-Protein Interactions from Multiple Databases: A Guide for Experimental Biologists.

Functional annotation is lacking for over half of the proteins encoded in genomes and model or representative organisms are not an exception to this trend. One of the popular ways of assigning putative functions to uncharacterized proteins is based on the functions of well-characterized proteins that physically interact with them, i.e., guilt-by-association or functional context approach. In the last two decades, several powerful experimental and computational techniques have been used to determine protein-protein interactions (PPIs) at genome level and are made available through many public databases. The PPI data are often complex and heterogeneously represented across databases posing unique challenges in retrieving, integrating, and analyzing the data even for trained computational biologists, the end users-experimental biologists often struggle to work around the data for the protein of their interests. This chapter provides stepwise protocols to import interaction network of the protein of interest in Cytoscape using PSICQUIC, stringApp, and IntAct App. These are next-generation applications that import PPI from multiple databases/resources and provide seamless functions to study the protein of interest and its functional context directly in Cytoscape.

Centrality Analysis of Protein-Protein Interaction Networks Using R.

Proteins are structural and functional components of cells. They interact with each other to drive specific cellular functions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologically important proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.

Human transcriptional gene regulatory network compiled from 14 data resources.

The transcriptional regulatory network (TRN) in a cell orchestrates spatio-temporal expression of genes to generate cellular responses for maintenance, reproduction, development and survival of the cell and its hosting organism. Transcription factors (TF) regulate the expression of their target genes (TG) and are the fundamental units of TRN. Several databases have been developed to catalogue human TRN based on low- and high-throughput experimental and computational studies considering their importance in understanding cellular physiology. However, literature lacks their comparative assessment on the strengths and weaknesses. We compared over 2.2 million regulatory pairs between 1379 TF and 22,518 TG from 14 resources. Our study reveals that the TF and TG were common across data resources but not their regulatory pairs. TF and TG of the regulatory pairs showed weak expression correlation, significant gene ontology overlap, co-citations in PubMed and low numbers of TF-TG pairs representing transcriptional repression relationships. We assigned each TF-TG regulatory pair a combined confidence score reflecting its reliability based on its presence in multiple databases. The assembled TRN contains 2,246,598 TF-TG pairs, of which, 44,284 with information on TF's activating or repressing effects on their TG and is available upon request. This study brings the information about transcriptional regulation scattered over the literature and databases at one place in the form of one of the most comprehensive and complete human TRN assembled to date. It will be a valuable resource for benchmarking TRN prediction tools, and to the scientific community working in functional genomics, gene expression and regulation analysis.

Mathematical Programming for Modeling Expression of a Gene Using Gurobi Optimizer to Identify Its Transcriptional Regulators.

The cell expresses various genes in specific contexts with respect to internal and external perturbations to invoke appropriate responses. Transcription factors (TFs) orchestrate and define the expression level of genes by binding to their regulatory regions. Dysregulated expression of TFs often leads to aberrant expression changes of their target genes and is responsible for several diseases including cancers. In the last two decades, several studies experimentally identified target genes of several TFs. However, these studies are limited to a small fraction of the total TFs encoded by an organism, and only for those amenable to experimental settings. Experimental limitations lead to many computational techniques having been proposed to predict target genes of TFs. Linear modeling of gene expression is one of the most promising computational approaches, readily applicable to the thousands of expression datasets available in the public domain across diverse phenotypes. Linear models assume that the expression of a gene is the sum of expression of TFs regulating it. In this chapter, I introduce mathematical programming for the linear modeling of gene expression, which has certain advantages over the conventional statistical modeling approaches. It is fast, scalable to genome level and most importantly, allows mixed integer programming to tune the model outcome with prior knowledge on gene regulation.

Mathematical Linear Programming to Model MicroRNAs-Mediated Gene Regulation Using Gurobi Optimizer.

Genes are transcribed into various RNA molecules, and a portion of them called messenger RNA (mRNA) is then translated into proteins in the process known as gene expression. Gene expression is a high-energy demanding process, and aberrant expression changes often manifest into pathophysiology. Therefore, gene expression is tightly regulated by several factors at different levels. MicroRNAs (miRNAs) are one of the powerful post-transcriptional regulators involved in key biological processes and diseases. They inhibit the translation of their mRNA targets or degrade them in a sequence-specific manner, and hence control the rate of protein synthesis. In recent years, in response to experimental limitations, several computational methods have been proposed to predict miRNA target genes based on sequence complementarity and structural features. However, these predictions yield a large number of false positives. Integration of gene and miRNA expression data drastically alleviates this problem. Here, I describe a mathematical linear modeling approach to identify miRNA targets at the genome scale using gene and miRNA expression data. Mathematical modeling is faster and more scalable to genome-level compared to conventional statistical modeling approaches.

Evolutionary Perspective and Expression Analysis of Intronless Genes Highlight the Conservation of Their Regulatory Role.

The structure of eukaryotic genes is generally a combination of exons interrupted by intragenic non-coding DNA regions (introns) removed by RNA splicing to generate the mature mRNA. A fraction of genes, however, comprise a single coding exon with introns in their untranslated regions or are intronless genes (IGs), lacking introns entirely. The latter code for essential proteins involved in development, growth, and cell proliferation and their expression has been proposed to be highly specialized for neuro-specific functions and linked to cancer, neuropathies, and developmental disorders. The abundant presence of introns in eukaryotic genomes is pivotal for the precise control of gene expression. Notwithstanding, IGs exempting splicing events entail a higher transcriptional fidelity, making them even more valuable for regulatory roles. This work aimed to infer the functional role and evolutionary history of IGs centered on the mouse genome. IGs consist of a subgroup of genes with one exon including coding genes, non-coding genes, and pseudogenes, which conform approximately 6% of a total of 21,527 genes. To understand their prevalence, biological relevance, and evolution, we identified and studied 1,116 IG functional proteins validating their differential expression in transcriptomic data of embryonic mouse telencephalon. Our results showed that overall expression levels of IGs are lower than those of MEGs. However, strongly up-regulated IGs include transcription factors (TFs) such as the class 3 of POU (HMG Box), Neurog1, Olig1, and BHLHe22, BHLHe23, among other essential genes including the ß-cluster of protocadherins. Most striking was the finding that IG-encoded BHLH TFs fit the criteria to be classified as microproteins. Finally, predicted protein orthologs in other six genomes confirmed high conservation of IGs associated with regulating neural processes and with chromatin organization and epigenetic regulation in Vertebrata. Moreover, this study highlights that IGs are essential modulators of regulatory processes, such as the Wnt signaling pathway and biological processes as pivotal as sensory organ developing at a transcriptional and post-translational level. Overall, our results suggest that IG proteins have specialized, prevalent, and unique biological roles and that functional divergence between IGs and MEGs is likely to be the result of specific evolutionary constraints.

Conserved and divergent expression dynamics during early patterning of the telencephalon in mouse and chick embryos.

The mammalian and the avian telencephalon are nearly indistinguishable at early embryonic vesicle stages but differ substantially in form and function at their adult stage. We sequenced and analyzed RNA populations present in mouse and chick during the early stages of embryonic telencephalon to understand conserved and lineage-specific developmental differences. We found approximately 3000 genes that orchestrate telencephalon development. Many chromatin-associated epigenetic and transcription regulators show high expression in both species and some show species-specific expression dynamics. Interestingly, previous studies associated them to autism, intellectual disabilities, and mental retardation supporting a causal link between their impaired functions during telencephalon development and brain dysfunction. Strikingly, the conserved up-regulated genes were differentially enriched in ontologies related to development or functions of the adult brain. Moreover, a differential enrichment of distinct repertoires of transcription factor binding motifs in their upstream promoter regions suggest a species-specific regulation of the various gene groups identified. Overall, our results reveal that the ontogenetic divergences between the mouse and chick telencephalon result from subtle differences in the regulation of common patterning signaling cascades and regulatory networks unique to each species at their very early stages of development.

PDZ Domains Across the Microbial World: Molecular Link to the Proteases, Stress Response, and Protein Synthesis.

The PSD-95/Dlg-A/ZO-1 (PDZ) domain is highly expanded, diversified, and well distributed across metazoa where it assembles diverse signaling components by virtue of interactions with other proteins in a sequence-specific manner. In contrast, in the microbial world they are reported to be involved in protein quality control during stress response. The distribution, functions, and origins of PDZ domain-containing proteins in the prokaryotic organisms remain largely unexplored. We analyzed 7,852 PDZ domain-containing proteins in 1,474 microbial genomes in this context. PDZ domain-containing proteins from planctomycetes, myxobacteria, and other eubacteria occupying terrestrial and aquatic niches are found to be in multiple copies within their genomes. Over 93% of the 7,852 PDZ domain-containing proteins were classified into 12 families including six novel families based on additional structural and functional domains present in these proteins. The higher PDZ domain encoding capacity of the investigated organisms was observed to be associated with adaptation to the ecological niche where multicellular life might have originated and flourished. Predicted subcellular localization of PDZ domain-containing proteins and their genomic context argue in favor of crucial roles in translation and membrane remodeling during stress response. Based on rigorous sequence, structure, and phylogenetic analyses, we propose that the highly diverse PDZ domain of the uncharacterized Fe-S oxidoreductase superfamily, exclusively found in gladobacteria and several anaerobes and acetogens, might represent the most ancient form among all the existing PDZ domains.

Bidirectional promoters exhibit characteristic chromatin modification signature associated with transcription elongation in both sense and antisense directions.

BACKGROUND: In contrast to unidirectional promoters wherein antisense transcription results in short transcripts which are rapidly degraded, bidirectional promoters produce mature transcripts in both sense and antisense orientation. To understand the molecular mechanism of how productive bidirectional transcription is regulated, we focused on delineating the chromatin signature of bidirectional promoters. RESULTS: We report generation and utility of a reporter system that enables simultaneous scoring of transcriptional activity in opposite directions. Testing of putative bidirectional promoters in this system demonstrates no measurable bias towards any one direction of transcription. We analyzed the NUP26L-PIH1D3 bidirectional gene pair during Retinoic acid mediated differentiation of embryonic carcinoma cells. In their native context, we observed that the chromatin landscape at and around the transcription regulatory region between the pair of bidirectional genes is modulated in concordance with transcriptional activity of each gene in the pair. We then extended this analysis to 974 bidirectional gene pairs in two different cell lines, H1 human embryonic stem cells and CD4 positive T cells using publicly available ChIP-Seq and RNA-Seq data. Bidirectional gene pairs were classified based on the intergenic distance separating the two TSS of the transcripts analyzed as well as the relative expression of each transcript in a bidirectional gene pair. We report that for the entire range of intergenic distance separating bidirectional genes, the expression profile of such genes (symmetric or asymmetric) matches the histone modification profile of marks associated with active transcription initiation and elongation. CONCLUSIONS: We demonstrate unique distribution of histone modification marks that correlate robustly with the transcription status of genes regulated by bidirectional promoters. These findings strongly imply that occurrence of these marks might signal the transcription machinery to drive maturation of antisense transcription from the bidirectional promoters.

Cloning, characterization and transmission blocking potential of midgut carboxypeptidase A in Anopheles stephensi.

Transmission-blocking vaccines (TBV) interrupt malaria parasite transmission and hence form an important component for malaria eradication. Mosquito midgut exopeptidases such as aminopeptidase N & carboxypeptidase B have demonstrated TBV potential. In the present study, we cloned and characterized carboxypeptidase A (CPA) from the midgut of an important malarial vector, Anopheles stephensi. ClustalW amino acid alignment and in silico 3-dimensional structure analysis of CPA predicted the presence of active sites involved in zinc and substrate binding that are conserved among all the known mosquito species. Real-time PCR analysis demonstrated that CPA is predominantly expressed in the midgut throughout the mosquito life cycle and that this gene is significantly elevated in P. berghei-infected mosquitoes compared to uninfected blood-fed controls. The high midgut CPA activity correlated with the prominent mRNA levels observed. Peptide-based anti-CPA antibodies were raised that cross-reacted specifically to ∼48kDa and ∼37kDa bands, which correspond to zymogen and active forms of CPA. Further, the addition of CPA-directed antibodies to P. berghei-containing blood meal significantly reduced the mosquito infection rate in the test group compared to control and blocked the parasite development in the midgut. These results support further development of A. stephensi CPA as a candidate TBV.

Evaluation of physical and functional protein-protein interaction prediction methods for detecting biological pathways.

BACKGROUND: Cellular activities are governed by the physical and the functional interactions among several proteins involved in various biological pathways. With the availability of sequenced genomes and high-throughput experimental data one can identify genome-wide protein-protein interactions using various computational techniques. Comparative assessments of these techniques in predicting protein interactions have been frequently reported in the literature but not their ability to elucidate a particular biological pathway. METHODS: Towards the goal of understanding the prediction capabilities of interactions among the specific biological pathway proteins, we report the analyses of 14 biological pathways of Escherichia coli catalogued in KEGG database using five protein-protein functional linkage prediction methods. These methods are phylogenetic profiling, gene neighborhood, co-presence of orthologous genes in the same gene clusters, a mirrortree variant, and expression similarity. CONCLUSIONS: Our results reveal that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. These methods have predicted functional associations of proteins involved in amino acids, nucleotide, glycans and vitamins & co-factors pathways slightly better than the random performance on carbohydrate, lipid and energy metabolism. We also make similar observations for interactions involved among the environmental information processing proteins. On the contrary, genetic information processing or specialized processes such as motility related protein-protein linkages that occur in the subset of organisms are predicted with comparable accuracy. Metabolic pathways are best predicted by using neighborhood of orthologous genes whereas phyletic pattern is good enough to reconstruct central dogma pathway protein interactions. We have also shown that the effective use of a particular prediction method depends on the pathway under investigation. In case one is not focused on specific pathway, gene expression similarity method is the best option.

Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

BACKGROUND: Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. METHODS: We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. CONCLUSIONS: Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50-100 genomes for comparable accuracy of predictions when computational resources are limited.