A Bioinformatics View of Glycan⁻Virus Interactions

Role of Extracellular Vesicles in Liver Diseases

Extracellular vesicles (EVs) are cell-derived membrane structures that are formed by budding from the plasma membrane or originate from the endosomal system. These microparticles (100 nm-100 µm) or nanoparticles (>100 nm) can transport complex cargos to other cells and, thus, provide communication and intercellular regulation. Various cells, such as hepatocytes, liver sinusoidal endothelial cells (LSECs) or hepatic stellate cells (HSCs), secrete and take up EVs in the healthy liver, and the amount, size and content of these vesicles are markedly altered under pathophysiological conditions. A comprehensive knowledge of the modified EV-related processes is very important, as they are of great value as biomarkers or therapeutic targets. In this review, we summarize the latest knowledge on hepatic EVs and the role they play in the homeostatic processes in the healthy liver. In addition, we discuss the characteristic changes of EVs and their potential exacerbating or ameliorating effects in certain liver diseases, such as non-alcoholic fatty liver disease (NAFLD), alcoholic fatty liver disease (AFLD), drug induced liver injury (DILI), autoimmune hepatitis (AIH), hepatocarcinoma (HCC) and viral hepatitis.

The Role of Extracellular Vesicles as Allies of HIV, HCV and SARS Viruses

Extracellular vesicles (EVs) are lipid bilayer-enclosed entities containing proteins and nucleic acids that mediate intercellular communication, in both physiological and pathological conditions. EVs resemble enveloped viruses in both structural and functional aspects. In full analogy with viral biogenesis, some of these vesicles are generated inside cells and, once released into the extracellular milieu, are called “exosomes”. Others bud from the plasma membrane and are generally referred to as “microvesicles”. In this review, we will discuss the state of the art of the current studies on the relationship between EVs and viruses and their involvement in three important viral infections caused by HIV, HCV and Severe Acute Respiratory Syndrome (SARS) viruses. HIV and HCV are two well-known pathogens that hijack EVs content and release to create a suitable environment for viral infection. SARS viruses are a new entry in the world of EVs studies, but are equally important in this historical framework. A thorough knowledge of the involvement of the EVs in viral infections could be helpful for the development of new therapeutic strategies to counteract different pathogens.

SARS-CoV-2 variants: Relevance for symptom granularity, epidemiology, immunity (herd, vaccines), virus origin and containment?

The origin of the SARS‐CoV‐2 virus remains enigmatic. It is likely to be a continuum resulting from inevitable mutations and recombination events. These genetic changes keep developing in the present epidemic. Mutations tending to deplete the genome in its cytosine content will progressively lead to attenuation as a consequence of Muller's ratchet, but this is counteracted by recombination when different mutants co‐infect the same host, in particular, in clusters of infection. Monitoring as a function of time the genome sequences in closely related cases is critical to anticipate the future of SARS‐CoV‐2 and hence of COVID‐19.

Glycosylator: a Python framework for the rapid modeling of glycans

BACKGROUND

Carbohydrates are a class of large and diverse biomolecules, ranging from a simple monosaccharide to large multi-branching glycan structures. The covalent linkage of a carbohydrate to the nitrogen atom of an asparagine, a process referred to as N-linked glycosylation, plays an important role in the physiology of many living organisms. Most software for glycan modeling on a personal desktop computer requires knowledge of molecular dynamics to interface with specialized programs such as CHARMM or AMBER. There are a number of popular web-based tools that are available for modeling glycans (e.g., GLYCAM-WEB (http:// https://dev.glycam.org/gp/ ) or Glycosciences.db ( http://www.glycosciences.de/ )). However, these web-based tools are generally limited to a few canonical glycan conformations and do not allow the user to incorporate glycan modeling into their protein structure modeling workflow.

RESULTS

Here, we present Glycosylator, a Python framework for the identification, modeling and modification of glycans in protein structure that can be used directly in a Python script through its application programming interface (API) or through its graphical user interface (GUI). The GUI provides a straightforward two-dimensional (2D) rendering of a glycoprotein that allows for a quick visual inspection of the glycosylation state of all the sequons on a protein structure. Modeled glycans can be further refined by a genetic algorithm for removing clashes and sampling alternative conformations. Glycosylator can also identify specific three-dimensional (3D) glycans on a protein structure using a library of predefined templates.

CONCLUSIONS

Glycosylator was used to generate models of glycosylated protein without steric clashes. Since the molecular topology is based on the CHARMM force field, new complex sugar moieties can be generated without modifying the internals of the code. Glycosylator provides more functionality for analyzing and modeling glycans than any other available software or webserver at present. Glycosylator will be a valuable tool for the glycoinformatics and biomolecular modeling communities.