Identification of potential host proteins for influenza A virus based on topological and biological characteristics by proteome-wide network approach

Unravelling the interaction between Influenza virus and the nuclear pore complex: insights into viral replication and host immune response

Influenza viruses are known to cause severe respiratory infections in humans, often associated with significant morbidity and mortality rates. Virus replication relies on various host factors and pathways, which also determine the virus's infectious potential. Nonetheless, achieving a comprehensive understanding of how the virus interacts with host cellular components is essential for developing effective therapeutic strategies. One of the key components among host factors, the nuclear pore complex (NPC), profoundly affects both the Influenza virus life cycle and the host's antiviral defenses. Serving as the sole gateway connecting the cytoplasm and nucleoplasm, the NPC plays a vital role as a mediator in nucleocytoplasmic trafficking. Upon infection, the virus hijacks and alters the nuclear pore complex and the nuclear receptors. This enables the virus to infiltrate the nucleus and promotes the movement of viral components between the nucleus and cytoplasm. While the nucleus and cytoplasm play pivotal roles in cellular functions, the nuclear pore complex serves as a crucial component in the host's innate immune system, acting as a defense mechanism against virus infection. This review provides a comprehensive overview of the intricate relationship between the Influenza virus and the nuclear pore complex. Furthermore, we emphasize their mutual influence on viral replication and the host's immune responses.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

TGF-β1 signaling inhibit the in vitro apoptotic, infection and stimulatory cell response induced by influenza H1N1 virus infection on A549 cells

Influenza A virus (IAV) infection induces host cell responses that could derive in inflammatory and apoptotic response. In this respect, in multiple pathological situations, TGF-β1 has shown anti-inflammatory effect, but its role during IAV infection is poorly understood. Interestingly, recent profiling expression studies have suggested that the TGF-β1 pathway could be functionally related to the IAV infection's host response. To gain an understanding of the involvement of TGF-β1's signaling pathway during IAV infection, we compared different apoptotic proteins such as TNFR1, Fas ligand, XIAP, cIAP, among others proteins, and pro-inflammatory elements like IL-1β in the A549 cells during IAV infection (H1N1/NC/99), with and without 1 h of pre-treatment with TGF-β1. Pre-incubation with TGF-β1 significantly inhibited apoptosis and the presence of pro-apoptotic factors. Moreover, the relative abundance of immunodetected IAV M1 protein along 24 -h post-infection period was abridged, which correlated with a disminished infectious viral progeny Additionally, caspase 1 activation and increase of IL-1β induced by IAV infection was also reduced by TGF-β1 signaling activation. Whereas IAV infection increase of Smad-7 and, as consequence, partially inhibiting Smad2/3 phosphorylation, pre-treatment with TGF-β1 blocked IAV-dependent Smad7 induction and prevented Smad2/3 signaling shutdown. All these data suggest the role of TGF-β1 signaling pathway in the control of host cell response induced by the IAV infection and identify a potential clinical target to modulate acute cell death.

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.

Bioinformatics Analysis of Proteome Changes in Calu-3 Cell Infected by Influenza A Virus (H5N1)

<b><i>Aim:</i></b> This paper aimed to identify the differentially expressed proteins (DEPs) in Calu-3 cells infected by influenza A virus (IAV) subtype H5N1. <b><i>Methods:</i></b> We downloaded proteome data (BTO: 0000762) from the Proteomics Identifications database and identified the DEPs in the IAV-infected Calu-3 cells. Then we constructed a protein-protein interaction network and a transcriptional regulatory network of the proteins. Finally, we performed gene ontology (GO) analysis to study the IAV infection at a functional level. <b><i>Results:</i></b> A total of 4 protein groups between the normal cells and the Calu-3 cells infected by IAV, severe acute respiratory syndrome or swine influenza were identified. In the networks, we found 5 significant proteins including FAN, CPSF2, AGO1, AGO2 and PAX5. In addition, we demonstrated those proteins were associated with GO terms such as phosphate metabolic process, calcium ion transport, cell division and regulation of cell motion. STAT1, NS2, CD5, NCKX6 and PDGFB were significant DEPs in these GO terms. <b><i>Conclusions:</i></b> By referring to the previous studies, we suggest that proteins including FAN, CPSF2, AGO1, AGO2, PAX5, STAT1 and PDGFB can be used as therapeutic targets of IAV infection.

Proteomic alteration of PK-15 cells after infection by porcine circovirus type 2

Porcine circovirus type 2 (PCV2) has been identified as the essential causal agent of post-weaning multisystemic wasting syndrome, which has spread worldwide. To discover cellular protein responses of PK-15 cells to PCV2 infection, two-dimensional liquid chromatography-tandem mass spectrometry (MS) coupled with isobaric tags for relative and absolute quantification (iTRAQ) labeling was employed to quantitatively identify the proteins that were differentially expressed in PK-15 from the PCV2-infected group compared to the uninfected control group. A total of 196 cellular proteins in PK-15 that were significantly altered at different time periods post-infection were identified. These differentially expressed proteins were related to the biological processes of binding, cell structure, signal transduction, cell adhesion, etc. and their interactions. Moreover, some of these proteins were further confirmed by Western blot. The high number of differentially expressed proteins identified should be very useful in elucidating the mechanism of replication and pathogenesis of PCV2 in the future.

Characterization of regulatory features of housekeeping and tissue-specific regulators within tissue regulatory networks

Background

Transcription factors (TFs) and miRNAs are essential for the regulation of gene expression; however, the global view of human gene regulatory networks remains poorly understood. For example, how is the expression of so many genes regulated by limited cohorts of regulators and how are genes differentially expressed in different tissues despite the genetic code being the same in all tissues?

Results

We analyzed the network properties of housekeeping and tissue-specific genes in gene regulatory networks from seven human tissues. Our results show that different classes of genes behave quite differently in these networks. Tissue-specific miRNAs show a higher average target number compared with non-tissue specific miRNAs, which indicates that tissue-specific miRNAs tend to regulate different sets of targets. Tissue-specific TFs exhibit higher in-degree, out-degree, cluster coefficient and betweenness values, indicating that they occupy central positions in the regulatory network and that they transfer genetic information from upstream genes to downstream genes more quickly than other TFs. Housekeeping TFs tend to have higher cluster coefficients compared with other genes that are neither housekeeping nor tissue specific, indicating that housekeeping TFs tend to regulate their targets synergistically. Several topological properties of disease-associated miRNAs and genes were found to be significantly different from those of non-disease-associated miRNAs and genes.

Conclusions

Tissue-specific miRNAs, TFs and disease genes have particular topological properties within the transcriptional regulatory networks of the seven human tissues examined. The tendency of tissue-specific miRNAs to regulate different sets of genes shows that a particular tissue-specific miRNA and its target gene set may form a regulatory module to execute particular functions in the process of tissue differentiation. The regulatory patterns of tissue-specific TFs reflect their vital role in regulatory networks and their importance to biological functions in their respective tissues. The topological differences between disease and non-disease genes may aid the discovery of new disease genes or drug targets. Determining the network properties of these regulatory factors will help define the basic principles of human gene regulation and the molecular mechanisms of disease.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Shotgun proteomic analysis of plasma from dairy cattle suffering from footrot: characterization of potential disease-associated factors

The plasma proteome of healthy dairy cattle and those with footrot was investigated using a shotgun LC-MS/MS approach. In total, 648 proteins were identified in healthy plasma samples, of which 234 were non-redundant proteins and 123 were high-confidence proteins; 712 proteins were identified from footrot plasma samples, of which 272 were non-redundant proteins and 138 were high-confidence proteins. The high-confidence proteins showed significant differences between healthy and footrot plasma samples in molecular weight, isoelectric points and the Gene Ontology categories. 22 proteins were found that may differentiate between the two sets of plasma proteins, of which 16 potential differential expression (PDE) proteins from footrot plasma involved in immunoglobulins, innate immune recognition molecules, acute phase proteins, regulatory proteins, and cell adhesion and cytoskeletal proteins; 6 PDE proteins from healthy plasma involved in regulatory proteins, cytoskeletal proteins and coagulation factors. Of these PDE proteins, haptoglobin, SERPINA10 protein, afamin precursor, haptoglobin precursor, apolipoprotein D, predicted peptidoglycan recognition protein L (PGRP-L) and keratan sulfate proteoglycan (KS-PG) were suggested to be potential footrot-associated factors. The PDE proteins PGRP-L and KS-PG were highlighted as potential biomarkers of footrot in cattle. The resulting protein lists and potential differentially expressed proteins may provide valuable information to increase understanding of plasma protein profiles in cattle and to assist studies of footrot-associated factors.