The landscape of viral proteomics and its potential to impact human health

Proteomics reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients

SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Previous studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited. We addressed the gap of existing knowledge with regard to a differentiated understanding of disease dynamics in SARS-CoV-2 infection while specifically considering disease stage and severity. We biomedically characterized quantitative proteomics in a hospitalized cohort of COVID-19 patients with mild to severe symptoms suffering from different (co)-morbidities in comparison to both healthy individuals and patients with non-COVID related inflammation. Deep clinical phenotyping enabled the identification of individual disease trajectories in COVID-19 patients. By the use of the individualized disease phase assignment, proteome analysis revealed a severity dependent general type-2-centered host response side-by-side with a disease specific antiviral immune reaction in early disease. The identification of phenomena such as neutrophil extracellular trap (NET) formation and a pro-coagulatory response characterizing severe disease was successfully validated in a second cohort. Together with the regulation of proteins related to SARS-CoV-2-specific symptoms identified by proteome screening, we not only confirmed results from previous studies but provide novel information for biomarker and therapy development.

Proteome Analysis of Molecular Events in Oral Pathogenesis and Virus: A Review with a Particular Focus on Periodontitis

Some systemic diseases are unquestionably related to periodontal health, as periodontal disease can be an extension or manifestation of the primary disease process. One example is spontaneous gingival bleeding, resulting from anticoagulant treatment for cardiac diseases. One important aspect of periodontal therapy is the care of patients with poorly controlled disease who require surgery, such as patients with uncontrolled diabetes. We reviewed research on biomarkers and molecular events for various diseases, as well as candidate markers of periodontal disease. Content of this review: (1) Introduction, (2) Periodontal disease, (3) Bacterial and viral pathogens associated with periodontal disease, (4) Stem cells in periodontal tissue, (5) Clinical applications of mass spectrometry using MALDI-TOF-MS and LC-MS/MS-based proteomic analyses, (6) Proteome analysis of molecular events in oral pathogenesis of virus in GCF, saliva, and other oral Components in periodontal disease, (7) Outlook for the future and (8) Conclusions. This review discusses proteome analysis of molecular events in the pathogenesis of oral diseases and viruses, and has a particular focus on periodontitis.

A systems approach to infectious disease

Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

Nucleophosmin (NPM1)/B23 in the Proteome of Human Astrocytic Cells Restricts Chikungunya Virus Replication

Chikungunya virus (CHIKV), a positive-stranded RNA virus, can cause neurological complications by infecting the major parenchymal cells of the brain such as neurons and astrocytes. A proteomic analysis of CHIKV-infected human astrocytic cell line U-87 MG revealed tight functional associations among the modulated proteins. The predominant cellular pathways involved were of transcription-translation machinery, cytoskeletol reorganization, apoptosis, ubiquitination, and metabolism. In the proteome, we could also identify a few proteins that are reported to be involved in host-virus interactions. One such protein, Nucleophosmin (NPM1)/B23, a nucleolar protein, showed enhanced cytoplasmic aggregation in CHIKV-infected cells. NPM1 aggregation was predominantly localized in areas wherein CHIKV antigen could be detected. Furthermore, we observed that inhibition of this aggregation using a specific NPM1 oligomerization inhibitor, NSC348884, caused a significant dose-dependent enhancement in virus replication. There was a marked increase in the amount of intracellular viral RNA, and ∼10⁵-fold increase in progeny virions in infected cells. Our proteomic analysis provides a comprehensive spectrum of host proteins modulated in response to CHIKV infection in astrocytic cells. Our results also show that NPM1/B23, a multifunctional chaperone, plays a critical role in restricting CHIKV replication and is a possible target for antiviral strategies.

Influenza-Omics and the Host Response: Recent Advances and Future Prospects

Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. The various-omics infection systems that include but are not limited to ferrets, mice, pigs, and even the controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infection outcomes.

Cellular proteomic analysis of porcine circovirus type 2 and classical swine fever virus coinfection in porcine kidney-15 cells using isobaric tags for relative and absolute quantitation-coupled LC-MS/MS

Viral coinfection or superinfection in host has caused public health concern and huge economic losses of farming industry. The influence of viral coinfection on cellular protein abundance is essential for viral pathogenesis. Based on a coinfection model for porcine circovirus type 2 (PCV2) and classical swine fever virus (CSFV) developed previously by our laboratory, isobaric tags for relative and absolute quantitation (iTRAQ)-coupled LC-MS/MS proteomic profiling was performed to explore the host cell responses to PCV2-CSFV coinfection. Totally, 3932 proteins were identified in three independent mass spectrometry analyses. Compared with uninfected cells, 304 proteins increased (fold change >1.2) and 198 decreased (fold change <0.833) their abundance in PCV2-infected cells (p < 0.05), 60 and 61 were more and less abundant in CSFV-infected cells, and 196 and 158 were more and less abundant, respectively in cells coinfected with PCV2 and CSFV. Representative differentially abundant proteins were validated by quantitative real-time PCR, Western blotting and confocal laser scanning microscopy. Bioinformatic analyses confirmed the dominant role of PCV2, and indicated that mitochondrial dysfunction, nuclear factor erythroid 2-related factor 2 (Nrf2)-mediated oxidative stress response and apoptosis signaling pathways might be the specifical targets during PCV2-CSFV coinfection.