Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.

The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells.

Corona virus disease (COVID-19) has been emerged as pandemic infectious disease. The recent epidemiological data suggest that the smokers are more vulnerable to infection with COVID-19; however, the influence of smoking (SMK) on the COVID-19 infected patients and the mortality is not known yet. In this study, we aimed to discern the influence of SMK on COVID-19 infected patients utilizing the transcriptomics data of COVID-19 infected lung epithelial cells and transcriptomics data smoking matched with controls from lung epithelial cells. The bioinformatics based analysis revealed the molecular insights into the level of transcriptional changes and pathways which are important to identify the impact of smoking on COVID-19 infection and prevalence. We compared differentially expressed genes (DEGs) between COVID-19 and SMK and 59 DEGs were identified as consistently dysregulated at transcriptomics levels. The correlation network analyses were constructed for these common genes using WGCNA R package to see the relationship among these genes. Integration of DEGs with network analysis (protein-protein interaction) showed the presence of 9 hub proteins as key so called "candidate hub proteins" overlapped between COVID-19 patients and SMK. The Gene Ontology and pathways analysis demonstrated the enrichment of inflammatory pathway such as IL-17 signaling pathway, Interleukin-6 signaling, TNF signaling pathway and MAPK1/MAPK3 signaling pathways that might be the therapeutic targets in COVID-19 for smoking persons. The identified genes, pathways, hubs genes, and their regulators might be considered for establishment of key genes and drug targets for SMK and COVID-19.

Functional Analysis of Monkeypox and Interrelationship between Monkeypox and COVID-19 by Bioinformatic Analysis.

The outbreak of monkeypox may be considered a novel and urgent threat after the coronavirus disease (COVID-19). No wide-ranging studies have been conducted on this disease since it was first reported. We systematically assessed the functional role of gene expression in cells infected with the monkeypox virus using transcriptome profiling and compared the functional relation with that of COVID-19. Based on the Gene Expression Omnibus database, we obtained 212 differentially expressed genes (DEGs) of GSE36854 and GSE21001 of monkeypox datasets. Enrichment analyses, including KEGG and gene ontology (GO) analyses, were performed to identify the common function of 212 DEGs of GSE36854 and GSE21001. CytoHubba and Molecular Complex Detection were performed to determine the core genes after a protein-protein interaction (PPI). Metascape/COVID-19 was used to compare DEGs of monkeypox and COVID-19. GO analysis of 212 DEGs of GSE36854 and GSE21001 for monkeypox infection showed cellular response to cytokine stimulus, cell activation, and cell differentiation regulation. KEGG analysis of 212 DEGs of GSE36854 and GSE21001 for monkeypox infection showed involvement of monkeypox in COVID-19, cytokine-cytokine receptor interaction, inflammatory bowel disease, atherosclerosis, TNF signaling, and T cell receptor signaling. By comparing our data with published transcriptome of severe acute respiratory syndrome coronavirus 2 infections in other cell lines, the common function of monkeypox and COVID-19 includes cytokine signaling in the immune system, TNF signaling, and MAPK cascade regulation. Thus, our data suggest that the molecular connections identified between COVID-19 and monkeypox elucidate the causes of monkeypox.

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Unpacking COVID-19 Systems Biology in Lung and Whole Blood with Transcriptomics and miRNA Regulators.

COVID-19 is a systemic disease affecting tissues and organs, including and beyond the lung. Apart from the current pandemic context, we also have vastly inadequate knowledge of consequences of repeated exposures to SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, in multiple organ systems and the whole organism scales when the disease evolves from a pandemic to an endemic state. This calls for a systems biology and systems medicine approach and unpacking the effects of COVID-19 in lung as well as other tissues. We report here original findings from transcriptomics analyses and differentially expressed genes (DEGs) in lung samples from 60 patients and 27 healthy controls, and in whole blood samples from 255 patients and 103 healthy individuals. A total of 11 datasets with RNA-seq transcriptomic data were obtained from the Gene Expression Omnibus and the European Nucleotide Archive. The identified DEGs were used to construct protein interaction and functional networks and to identify related pathways and miRNAs. We found 35 DEGs common between lung and the whole blood, and importantly, 2 novel genes, namely CYP1B1 and TNFAIP6, which have not been previously implicated with COVID-19. We also identified four novel miRNA potential regulators, hsa-mir-192-5p, hsa-mir-221-3p, hsa-mir-4756-3p, and hsa-mir-10a-5p, implicated in lung or other diseases induced by coronaviruses. In summary, these findings offer new molecular leads and insights to unpack COVID-19 systems biology in a whole organism context and might inform future antiviral drug, diagnostics, and vaccine discovery efforts.

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19.

BACKGROUND: To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection. METHODS: We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them. RESULTS: The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities. CONCLUSIONS: Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

Understanding saffron biology using omics- and bioinformatics tools: stepping towards a better Crocus phenome.

Saffron is a unique plant in many aspects, and its cellular processes are regulated at multiple levels. The genetic makeup in the form of eight chromosome triplets (2n = 3x = 24) with a haploid genetic content (genome size) of 3.45 Gbp is decoded into different types of RNA by transcription. The RNA then translates into peptides and functional proteins, sometimes involving post-translational modifications too. The interactions of the genome, transcriptome, proteome and other regulatory molecules ultimately result in the complex set of primary and secondary metabolites of saffron metabolome. These complex interactions manifest in the form of a set of traits 'phenome' peculiar to saffron. The phenome responds to the environmental changes occurring in and around saffron and modify its response in respect of growth, development, disease response, stigma quality, apocarotenoid biosynthesis, and other processes. Understanding these complex relations between different yet interconnected biological activities is quite challenging in saffron where classical genetics has a very limited role owing to its sterility, and the absence of a whole-genome sequence. Omics-based technologies are immensely helpful in overcoming these limitations and developing a better understanding of saffron biology. In addition to creating a comprehensive picture of the molecular mechanisms involved in apocarotenoid synthesis, stigma biogenesis, corm activity, and flower development, omics-technologies will ultimately lead to the engineering of saffron plants with improved phenome.

Deregulation of ceRNA Networks in Frontal Cortex and Choroid Plexus of Brain during SARS-CoV-2 Infection Aggravates Neurological Manifestations: An Insight from Bulk and Single-Cell Transcriptomic Analyses.

Although transcriptomic studies of SARS-CoV-2-infected brains have depicted variability in gene expression, the landscape of deregulated cell-specific regulatory circuits has not been elucidated yet. Hence, bulk and single-cell RNA-seq data are analyzed to gain detailed insights. Initially, two ceRNA networks with 19 and 3 differentially expressed (DE) hub lncRNAs are reconstructed in SARS-CoV-2 infected Frontal Cortex (FC) and Choroid Plexus (CP), respectively. Functional and pathway enrichment analyses of downstream mRNAs of deregulated ceRNA axes demonstrate impairment of neurological processes. Mapping of hub lncRNA-mRNA pairs from bulk RNA-seq with snRNA-seq data has indicated that NORAD, NEAT1, and STXBP5-AS1 are downregulated across 4, 4, and 2 FC cell types, respectively. At the same time, MIRLET7BHG and MALAT1 are upregulated in excitatory neurons of FC and neurons of CP, respectively. Here, it is hypothesized that downregulation of NORAD, NEAT1, and STXBP5-AS1, and upregulation of MIRLET7BHG and MALAT1 might deregulate respectively 51, 6, and 37, and 31 and 19 mRNAs in cell types of FC and CP. Afterward, 13 therapeutic miRNAs are traced that might safeguard against deregulated lncRNA-mRNA pairs of NORAD, NEAT1, and MIRLET7BHG in FC. This study helps to explain the plausible mechanism of post-COVID neurological manifestation and also to devise therapeutics against it.

Detection techniques for epitranscriptomic marks.

Similar to epigenetic DNA modification, RNA can be methylated and altered for stability and processing. RNA modifications, namely, epitranscriptomes, involve the following three functions: writing, erasing, and reading of marks. Methods for measurement and position detection are useful for the assessment of cellular function and human disease biomarkers. After pyrimidine 5-methylcytosine was reported for the first time a hundred years ago, numerous techniques have been developed for studying nucleotide modifications, including RNAs. Recent studies have focused on high-throughput and direct measurements for investigating the precise function of epitranscriptomes, including the characterization of severe acute respiratory syndrome coronavirus 2. The current study presents an overview of the development of detection techniques for epitranscriptomic marks and briefs about the recent progress in this field.

Analyzing single cell transcriptome data from severe COVID-19 patients.

We describe the protocol for identifying COVID-19 severity specific cell types and their regulatory marker genes using single-cell transcriptomics data. We construct COVID-19 comorbid disease-associated gene list using multiple databases and literature resources. Next, we identify specific cell type where comorbid genes are upregulated. We further characterize the identified cell type using gene enrichment analysis. We detect upregulation of marker gene restricted to severe COVID-19 cell type and validate our findings using in silico, in vivo, and in vitro cellular models. For complete details on the use and execution of this protocol, please refer to Nassir et al. (2021b).

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.

First report of de novo assembly and annotation from brain and blood transcriptome of an anadromous shad, Alosa sapidissima.

OBJECTIVES: American shad (Alosa sapidissima) is an important migratory fish under Alosinae and has long been valued for its economic, nutritional and cultural attributes. Overfishing and barriers across the passage made it vulnerable to sustain. To protect this valuable species, aquaculture action plans have been taken though there are no published genetic resources prevailing yet. Here, we reported the first de novo assembled and annotated transcriptome of A. sapidissima using blood and brain tissues. DATA DESCRIPTION: We generated 160,481 and 129,040 non-redundant transcripts from brain and blood tissues. The entire work strategy involved RNA extraction, library preparation, sequencing, de novo assembly, filtering, annotation and validation. Both coding and non-coding transcripts were annotated against Swissprot and Pfam datasets. Nearly, 83% coding transcripts were functionally assigned. Protein clustering with clupeiform and non-clupeiform taxa revealed ~ 82% coding transcripts retained the orthologue relationship which improved confidence over annotation procedure. This study will serve as a useful resource in future for the research community to elucidate molecular mechanisms for several key traits like migration which is fascinating in clupeiform shads.

Comparative trachea transcriptome analysis in SPF broiler chickens infected with avian infectious bronchitis and avian influenza viruses.

Infectious bronchitis virus (IBV) and avian influenza virus (AIV) are two major respiratory infections in chickens. The coinfection of these viruses can cause significant financial losses and severe complications in the poultry industry across the world. To examine transcriptome profile changes during the early stages of infection, differential transcriptional profiles in tracheal tissue of three infected groups (i.e., IBV, AIV, and coinfected) were compared with the control group. Specific-pathogen-free chickens were challenged with Iranian variant-2-like IBV (IS/1494), UT-Barin isolates of H9N2 (A/chicken/Mashhad/UT-Barin/2017), and IBV-AIV coinfection; then, RNA was extracted from tracheal tissue. The Illumina RNA-sequencing (RNA-seq) technique was employed to investigate changes in the Transcriptome. Up- and downregulated differentially expressed genes (DEGs) were detected in the trachea transcriptome of all groups. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology databases were examined to identify possible relationships between DEGs. In the experimental groups, upregulated genes were higher compared to downregulated genes. A more severe immune response was observed in the coinfected group; further, cytokine-cytokine receptor interaction, RIG-I-like receptor signaling, Toll-like receptor signaling, NOD-like receptor signaling, Janus kinase/signal transducer, and activator of transcription, and apoptotic pathways were important upregulated genes in this group. The findings of this paper may give a better understanding of transcriptome changes in the trachea during the early stages of infection with these viruses.

Commentary on "Poor evidence for host-dependent regular RNA editing in the transcriptome of SARS-CoV-2".

Analysis of the SARS-CoV-2 transcriptome has revealed a background of low-frequency intra-host genetic changes with a strong bias towards transitions. A similar pattern is also observed when inter-host variability is considered. We and others have shown that the cellular RNA editing machinery based on ADAR and APOBEC host-deaminases could be involved in the onset of SARS-CoV-2 genetic variability. Our hypothesis is based both on similarities with other known forms of viral genome editing and on the excess of transition changes, which is difficult to explain with errors during viral replication. Zong et al. criticize our analysis on both conceptual and technical grounds. While ultimate proof of an involvement of host deaminases in viral RNA editing will depend on experimental validation, here, we address the criticism to suggest that viral RNA editing is the most reasonable explanation for the observed intra- and inter-host variability.

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.