The ins and outs of eukaryotic viruses: Knowledge base and ontology of a viral infection

Coordinating virus research: The Virus Infectious Disease Ontology

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

A metagenomic study of gut viral markers in amyloid-positive Alzheimer's disease patients

BACKGROUND

Mounting evidence suggests the involvement of viruses in the development and treatment of Alzheimer's disease (AD). However, there remains a significant research gap in metagenomic studies investigating the gut virome of AD patients, leaving gut viral dysbiosis in AD unexplored. This study aimed to fill this gap by conducting a metagenomics analysis of the gut virome in both amyloid-positive AD patients (Aβ + ADs) and healthy controls (HCs), with the objective of identifying viral signatures linked with AD.

METHOD

Whole-genome sequence (WGS) data from 65 human participants, including 30 Aβ + ADs and 35 HCs, was obtained from the database NCBI SRA (Bio Project: PRJEB47976). The Metaphlan3 pipeline and linear discriminant analysis effect size (LEfSe) analysis were utilized for the bioinformatics process and the detection of viral signatures, respectively. In addition, the Benjamini-Hochberg method was applied with a significance cutoff of 0.05 to evaluate the false discovery rate for all biomarkers identified by LEfSe. The CombiROC model was employed to determine the discriminatory power of the viral signatures identified by LEfSe.

RESULTS

Compared to HCs, the gut virome profiles of Aβ + ADs showed lower alpha diversity, indicating a lower bacteriophage richness. The Siphoviridae family was decreased in Aβ + ADs. Significant decreases of Lactococcus phages were found in Aβ + ADs, including bIL285, Lactococcus phage bIL286, Lactococcus phage bIL309, and Lactococcus phage BK5 T, Lactococcus phage BM13, Lactococcus phage P335 sensu lato, Lactococcus phage phiLC3, Lactococcus phage r1t, Lactococcus phage Tuc2009, Lactococcus phage ul36, and Lactococcus virus bIL67. The predictive combined model of these viral signatures obtained an area under the curve of 0.958 when discriminating Aβ + ADs from HCs.

CONCLUSION

This is the first study to identify distinct viral signatures in the intestine that can be used to effectively distinguish individuals with AD from HCs.

Bacteriophage-Mediated Cancer Gene Therapy

Bacteriophages have long been considered only as infectious agents that affect bacterial hosts. However, recent studies provide compelling evidence that these viruses are able to successfully interact with eukaryotic cells at the levels of the binding, entry and expression of their own genes. Currently, bacteriophages are widely used in various areas of biotechnology and medicine, but the most intriguing of them is cancer therapy. There are increasing studies confirming the efficacy and safety of using phage-based vectors as a systemic delivery vehicle of therapeutic genes and drugs in cancer therapy. Engineered bacteriophages, as well as eukaryotic viruses, demonstrate a much greater efficiency of transgene delivery and expression in cancer cells compared to non-viral gene transfer methods. At the same time, phage-based vectors, in contrast to eukaryotic viruses-based vectors, have no natural tropism to mammalian cells and, as a result, provide more selective delivery of therapeutic cargos to target cells. Moreover, numerous data indicate the presence of more complex molecular mechanisms of interaction between bacteriophages and eukaryotic cells, the further study of which is necessary both for the development of gene therapy methods and for understanding the cancer nature. In this review, we summarize the key results of research into aspects of phage-eukaryotic cell interaction and, in particular, the use of phage-based vectors for highly selective and effective systemic cancer gene therapy.

Exploiting an Ontological Model to Study COVID-19 Contagion Chains in Sustainable Smart Cities

The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy to find studies focused on COVID-19 contagion chains. A deep analysis of contagion chains may contribute new findings that can be used to reduce the effects of COVID-19. For example, some interesting chains with specific behaviors could be identified and more in-depth analyses could be performed to investigate the reasons for such behaviors. To represent, validate and analyze the information of contagion chains, we adopted an ontological approach. Ontologies are artificial intelligence techniques that have become widely accepted solutions for the representation of knowledge and corresponding analyses. The semantic representation of information by means of ontologies enables the consistency of the information to be checked, as well as automatic reasoning to infer new knowledge. The ontology was implemented in Ontology Web Language (OWL), which is a formal language based on description logics. This approach could have a special impact on smart cities, which are characterized as using information to enhance the quality of basic services for citizens. In particular, health services could take advantage of this approach to reduce the effects of COVID-19.

Cross-correlation of virome-bacteriome-host-metabolome to study respiratory health

A comprehensive understanding of the microbiome-host relationship in respiratory diseases can be elucidated by exploring the landscape of virome-bacteriome-host metabolome data through unsupervised 'multi-omics' approaches. Here, we describe how the composition and function of airway and gut virome and bacteriome may contribute to pathogen establishment and propagation in airway districts and how the virome-bacteriome communities may react to respiratory diseases. A new systems medicine approach, including the characterization of respiratory and gut microbiome, may be crucial to demonstrate the likelihood and odds of respiratory disease pathophysiology, opening new avenues to the discovery of a chain of causation for key bacteria and viruses in disease severity.

Resources to Discover and Use Short Linear Motifs in Viral Proteins

Viral proteins evade host immune function by molecular mimicry, often achieved by short linear motifs (SLiMs) of three to ten consecutive amino acids (AAs). Motif mimicry tolerates mutations, evolves quickly to modify interactions with the host, and enables modular interactions with protein complexes. Host cells cannot easily coordinate changes to conserved motif recognition and binding interfaces under selective pressure to maintain critical signaling pathways. SLiMs offer potential for use in synthetic biology, such as better immunogens and therapies, but may also present biosecurity challenges. We survey viral uses of SLiMs to mimic host proteins, and information resources available for motif discovery. As the number of examples continues to grow, knowledge management tools are essential to help organize and compare new findings.

In vitro methods for testing antiviral drugs

Despite successful vaccination programs and effective treatments for some viral infections, humans are still losing the battle with viruses. Persisting human pandemics, emerging and re-emerging viruses, and evolution of drug-resistant strains impose continuous search for new antiviral drugs. A combination of detailed information about the molecular organization of viruses and progress in molecular biology and computer technologies has enabled rational antivirals design. Initial step in establishing efficacy of new antivirals is based on simple methods assessing inhibition of the intended target. We provide here an overview of biochemical and cell-based assays evaluating the activity of inhibitors of clinically important viruses.