Human-virus Interaction Research Articles

Pandemic modeling with the game of life

The generalized semi-classical game of life (gSCGOL) is a cellular automaton in which qubits evolve through repeated applications of birth, death, and survival operators. gSCGOL is used to model human–virus interactions during the COVID-19 pandemic by using cell “liveness” to be equivalent to the human population that is vulnerable to the disease. A match to the United Kingdom (UK) daily 7-day average COVID-19 deaths was achieved by daily changes to a parameter V representing the vulnerability of the population to the virus. The model predicts that the first COVID-19 virus entered the UK on December 22, 2019 with an uncertainty of ±1 days. The changes in the vulnerability V elucidate the human response to the unfolding pandemic during 2020, quantifying the impact of the lockdown, the relaxation of restrictions, and the opening and closing of schools. The model predicts that starting the first UK national lockdown one week earlier would have led to 40% fewer COVID-19-related deaths, equivalent to 17 000 lives.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Dissecting Human and Influenza Virus Interaction with qFRET Technology

Influenza viruses cause seasonal epidemics and occasional pandemics around the world. During each flu season, IAV and IBV viruses are circulated widely in the community, with IAV being the dominant circulating virus and IBV accounting for 25% of all flu cases on average. Due to the significant threat posed by the flu virus, international organizations, including the World Health Organization (WHO), have risen to prominence in limiting its global effect. Despite the vaccinations and anti-flu medications that have been develop to combat influenza, drug resistance development highlights the necessity of further studies for influenzas virus pathogenesis and new therapeutic development. Förster resonance energy transfer (FRET) is a technique for detecting protein interactions in vitro and in vivo that is widely employed in biological and biomedical research. Here we report that the IBV M1 protein has a high affinity with human SUMOylation enzymes, the conjugating enzyme UBC9 and the ligase PIAS1, and conform M1 can be SUMOylated determined with a quantitative FRET (qFRET) assay developed in our lab. Understanding the viral infection process and developing new treatment methods requires identifying and deciphering the host route of viral infection. It is critical to comprehend the viral infection process and develop new therapeutics. Blocking the host human SUMOylation pathway is particularly effective for IBV reduction. Our research provides a direct interaction of human proteins with influenza B protein, providing new insights in human-virus interactions for future therapeutics development.

Retroviruses: a broad view of SARS-CoV-2 and its relatives, with a narrative essay on the current state of biomedical sciences.

The actual "pandemic" times, beside their burden of sorrow in terms of both victims, destruction of societal links and economic consequences, are an unprecedented occasion to give a closer look to the status of biomedical research. Beside the undoubted technological advances, the general impression is alarming: the fragmentation of science culture prevents any wise synthesis of the many aspects involved in a global phenomenon as SARS-CoV-2 epidemics. Here we try to acquire a "detached" view to some evolutionary and physiological aspects of the human-virus interaction highlighting the need to revitalize science by a strong departure from ultra-specialization toward a real integration of different fields of investigation.

Comprehensive characterization of human–virus protein-protein interactions reveals disease comorbidities and potential antiviral drugs

The protein-protein interactions (PPIs) between human and viruses play important roles in viral infection and host immune responses. Rapid accumulation of experimentally validated human–virus PPIs provides an unprecedented opportunity to investigate the regulatory pattern of viral infection. However, we are still lack of knowledge about the regulatory patterns of human–virus interactions. We collected 27,293 experimentally validated human–virus PPIs, covering 8 virus families, 140 viral proteins and 6059 human proteins. Functional enrichment analysis revealed that the viral interacting proteins were likely to be enriched in cell cycle and immune-related pathways. Moreover, we analysed the topological features of the viral interacting proteins and found that they were likely to locate in central regions of human PPI network. Based on network proximity analyses of diseases genes and human–virus interactions in the human interactome, we revealed the associations between complex diseases and viral infections. Network analysis also implicated potential antiviral drugs that were further validated by text mining. Finally, we presented the Human–Virus Protein-Protein Interaction database (HVPPI, http://bio-bigdata.hrbmu.edu.cn/HVPPI), that provides experimentally validated human–virus PPIs as well as seamlessly integrates online functional analysis tools. In summary, comprehensive understanding the regulatory pattern of human–virus interactome will provide novel insights into fundamental infectious mechanism discovery and new antiviral therapy development.

Metaepistemological Analysis of Formation of Knowledge in the Covid-19 Pandemic

Post-truth plays a significant role in forming public social knowledge in the pandemic era, where the flow of information is getting convoluted. This problem is raised in line with the high number of fake news cases, distrust of an expert, massive exchange of information both in real and digital social media, including high case of misinformation, and pseudoscience. This article proposes two general frameworks over the constitution of knowledge in the pandemic era: a naturalistic and social-constructive approach. Naturalism tends to focus its analysis of formation of social knowledge in pandemic on the scientific ground, such as discussion on the ontology of virus whether virus understood as an individual microscopic entity or as a life process. Conversely, the social constructivist approaches in analyzing constitution of knowledge of the pandemics as nothing, but a social construct such as non-natural disasters and public discourse consensus. In one position, naturalism offers an objective and cognitive ground based on scientific consensus, but in another case, social-constructivism also offers an explanatory role such as explaining the human-virus interaction. In order to confront this issue, this article will discuss metaepistemological analysis of the formation of social knowledge based on those two general frameworks and initially propose position under the Epistemological Plurality. Nevertheless, this article still left difficult, especially how to apply epistemic pluralism under practical domain without falling into relativism? Therefore, this article initially opens further philosophical works and discussions to offer a critical epistemological view in forming epistemology of pandemic and maybe as a post-pandemic policy consideration. 

Dynamic, but Not Necessarily Disordered, Human-Virus Interactions Mediated through SLiMs in Viral Proteins

Most viruses have small genomes that encode proteins needed to perform essential enzymatic functions. Across virus families, primary enzyme functions are under functional constraint; however, secondary functions mediated by exposed protein surfaces that promote interactions with the host proteins may be less constrained. Viruses often form transient interactions with host proteins through conformationally flexible interfaces. Exposed flexible amino acid residues are known to evolve rapidly suggesting that secondary functions may generate diverse interaction potentials between viruses within the same viral family. One mechanism of interaction is viral mimicry through short linear motifs (SLiMs) that act as functional signatures in host proteins. Viral SLiMs display specific patterns of adjacent amino acids that resemble their host SLiMs and may occur by chance numerous times in viral proteins due to mutational and selective processes. Through mimicry of SLiMs in the host cell proteome, viruses can interfere with the protein interaction network of the host and utilize the host-cell machinery to their benefit. The overlap between rapidly evolving protein regions and the location of functionally critical SLiMs suggest that these motifs and their functional potential may be rapidly rewired causing variation in pathogenicity, infectivity, and virulence of related viruses. The following review provides an overview of known viral SLiMs with select examples of their role in the life cycle of a virus, and a discussion of the structural properties of experimentally validated SLiMs highlighting that a large portion of known viral SLiMs are devoid of predicted intrinsic disorder based on the viral SLiMs from the ELM database.

HVIDB: a comprehensive database for human-virus protein-protein interactions.

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.

A holistic perspective on herpes simplex virus (HSV) ecology and evolution.

Herpes simplex viruses (HSV) cause chronic infection in humans that are characterized by periodic episodes of mucosal shedding and ulcerative disease. HSV causes millions of infections world-wide, with lifelong bouts of viral reactivation from latency in neuronal ganglia. Infected individuals experience different levels of disease severity and frequency of reactivation. There are two distantly related HSV species, with HSV-1 infections historically found most often in the oral niche and HSV-2 infections in the genital niche. Over the last two decades, HSV-1 has emerged as the leading cause of first-episode genital herpes in multiple countries. While HSV-1 has the highest level of genetic diversity among human alpha-herpesviruses, it is not yet known how quickly the HSV-1 viral population in a human host adapts over time, or if there are population bottlenecks associated with viral reactivation and/or transmission. It is also unknown how the ecological environments in which HSV infections occur influence their evolutionary trajectory, or that of co-occurring viruses and microbes. In this review, we explore how HSV accrues genetic diversity within each new infection, and yet maintains its ability to successfully infect most of the human population. A holistic examination of the ecological context of natural human infections can expand our awareness of how HSV adapts as it moves within and between human hosts, and reveal the complexity of these lifelong human-virus interactions. These insights may in turn suggest new areas of exploration for other chronic pathogens that successfully evolve and persist among their hosts.

The Application of Humanized Mouse Models for the Study of Human Exclusive Viruses.

The symbiosis between humans and viruses has allowed human tropic pathogens to evolve intricate means of modulating the human immune response to ensure its survival among the human population. In doing so, these viruses have developed profound mechanisms that mesh closely with our human biology. The establishment of this intimate relationship has created a species-specific barrier to infection, restricting the virus-associated pathologies to humans. This specificity diminishes the utility of traditional animal models. Humanized mice offer a model unique to all other means of study, providing an in vivo platform for the careful examination of human tropic viruses and their interaction with human cells and tissues. These types of animal models have provided a reliable medium for the study of human-virus interactions, a relationship that could otherwise not be investigated without questionable relevance to humans.

Dengue and chikungunya: modelling the expansion of mosquito-borne viruses into naïve populations.

With the recent global spread of a number of mosquito-borne viruses, there is an urgent need to understand the factors that contribute to the ability of viruses to expand into naïve populations. Using dengue and chikungunya viruses as case studies, we detail the necessary components of the expansion process: presence of the mosquito vector; introduction of the virus; and suitable conditions for local transmission. For each component we review the existing modelling approaches that have been used to understand recent emergence events or to assess the risk of future expansions. We identify gaps in our knowledge that are related to each of the distinct aspects of the human-mosquito transmission cycle: mosquito ecology; human-mosquito contact; mosquito-virus interactions; and human-virus interactions. Bridging these gaps poses challenges to both modellers and empiricists, but only through further integration of models and data will we improve our ability to better understand, and ultimately control, several infectious diseases that exert a significant burden on human health.

Functional Genomic Strategies for Elucidating Human-Virus Interactions: Will CRISPR Knockout RNAi and Haploid Cells?

Over the last several years a wealth of transformative human–virus interaction discoveries have been produced using loss-of-function functional genomics. These insights have greatly expanded our understanding of how human pathogenic viruses exploit our cells to replicate. Two technologies have been at the forefront of this genetic revolution, RNA interference (RNAi) and random retroviral insertional mutagenesis using haploid cell lines (haploid cell screening), with the former technology largely predominating. Now the cutting edge gene editing of the CRISPR/Cas9 system has also been harnessed for large-scale functional genomics and is poised to possibly displace these earlier methods. Here we compare and contrast these three screening approaches for elucidating host–virus interactions, outline their key strengths and weaknesses including a comparison of an arrayed multiple orthologous RNAi reagent screen to a pooled CRISPR/Cas9 human rhinovirus 14–human cell interaction screen, and recount some notable insights made possible by each. We conclude with a brief perspective on what might lie ahead for the fast evolving field of human–virus functional genomics.