Applications of omics approaches to the development of microbiological risk assessment using RNA virus dose-response models as a case study

Systems Biology Approaches for Therapeutics Development Against COVID-19

Understanding the systems biology approaches for promoting the development of new therapeutic drugs is attaining importance nowadays. The threat of COVID-19 outbreak needs to be vanished for global welfare, and every section of research is focusing on it. There is an opportunity for finding new, quick, and accurate tools for developing treatment options, including the vaccine against COVID-19. The review at this moment covers various aspects of pathogenesis and host factors for exploring the virus target and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers the extensive details of multiomics tools i.e., transcriptomics, proteomics, genomics, lipidomics, immunomics, and in silico computational modeling aiming towards the study of host-virus interactions in search of therapeutic targets against the COVID-19.

Rotavirus infection beyond the gut

The landscape of rotavirus (RV) infection has changed substantially in recent years. Autoimmune triggering has been added to clinical spectrum of this pathology, which is now known to be much broader than diarrhea. The impact of RV vaccines in these other conditions is becoming a growing field of research. The importance of host genetic background in RV susceptibility has been revealed, therefore increasing our understanding of vaccine effectiveness and giving some clues about the limited efficacy of RV vaccines in low-income settings. Also, interaction of RV with intestinal microbiota seems to play a key role in the process of infection vaccine effect. This article reviews current findings on the extraintestinal impact of RV infection and their widening clinical picture, and the recently described mechanisms of host susceptibility to infection and vaccine effectiveness. RV infection is a systemic disease with clinical and pathophysiological implications beyond the gut. We propose an “iceberg” model for this pathology with almost hidden clinical implications away from the gastrointestinal tract and eventually triggering the development of autoimmune diseases. Impact of current vaccines is being influenced by host genetics and gut microbiota interactions and these factors must be taken into account in the development of public health programs.

Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus

Assessing the risk of infection from emerging viruses or of existing viruses jumping the species barrier into novel hosts is limited by the lack of dose response data. The initial stages of the infection of a host by a virus involve a series of specific contact interactions between molecules in the host and on the virus surface. The strength of the interaction is quantified in the literature by the dissociation constant (K_d) which is determined experimentally and is specific for a given virus molecule/host molecule combination. Here, two stages of the initial infection process of host intestinal cells are modelled, namely escape of the virus in the oral challenge dose from the innate host defenses (e.g. mucin proteins in mucus) and the subsequent binding of any surviving virus to receptor molecules on the surface of the host epithelial cells. The strength of virus binding to host cells and to mucins may be quantified by the association constants, K_a and K_mucin, respectively. Here, a mechanistic dose-response model for the probability of infection of a host by a given virus dose is constructed using K_a and K_mucin which may be derived from published K_d values taking into account the number of specific molecular interactions. It is shown that the effectiveness of the mucus barrier is determined not only by the amount of mucin but also by the magnitude of K_mucin. At very high K_mucin values, slight excesses of mucin over virus are sufficient to remove all the virus according to the model. At lower K_mucin values, high numbers of virus may escape even with large excesses of mucin. The output from the mechanistic model is the probability (p₁) of infection by a single virion which is the parameter used in conventional dose-response models to predict the risk of infection of the host from the ingested dose. It is shown here how differences in K_a (due to molecular differences in an emerging virus strain or new host) affect p₁, and how these differences in K_a may be quantified in terms of two thermodynamic parameters, namely enthalpy and entropy. This provides the theoretical link between sequencing data and risk of infection. Lack of data on entropy is a limitation at present and may also affect our interpretation of K_d in terms of infectivity. It is concluded that thermodynamic approaches have a major contribution to make in developing dose-response models for emerging viruses.

Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata

Foodborne infection is a result of exposure to complex, dynamic food systems. The efficiency of foodborne infection is driven by ongoing shifts in genetic machinery. Next-generation sequencing technologies can provide high-fidelity data about the genetics of a pathogen. However, food safety surveillance systems do not currently provide similar high-fidelity epidemiological metadata to associate with genetic data. As a consequence, it is rarely possible to transform genetic data into actionable knowledge that can be used to genuinely inform risk assessment or prevent outbreaks. Big data approaches are touted as a revolution in decision support, and pose a potentially attractive method for closing the gap between the fidelity of genetic and epidemiological metadata for food safety surveillance. We therefore developed a simple food chain model to investigate the potential benefits of combining 'big' data sources, including both genetic and high-fidelity epidemiological metadata. Our results suggest that, as for any surveillance system, the collected data must be relevant and characterize the important dynamics of a system if we are to properly understand risk: this suggests the need to carefully consider data curation, rather than the more ambitious claims of big data proponents that unstructured and unrelated data sources can be combined to generate consistent insight. Of interest is that the biggest influencers of foodborne infection risk were contamination load and processing temperature, not genotype. This suggests that understanding food chain dynamics would probably more effectively generate insight into foodborne risk than prescribing the hazard in ever more detail in terms of genotype.

Could Bat Cell Temperature and Filovirus Filament Length Explain the Emergence of Ebola Virus in Mammals? Predictions of a Thermodynamic Model

The host reservoir of Zaire ebolavirus (EBOV) remains elusive. One suggestion is that EBOV emerges in mammals when the precursor virus jumps from mayflies (or other riverine insects) to insectivorous bats. However, this does not fit with the current view that filoviruses cannot infect arthropods. Here, it is first argued that the evidence that arthropods are refractory is not definitive. Second, it is proposed that a combination of filovirus filament length and the high temperature (~42°C) experienced by an insect virus ingested by a flying bat, together with the large number of insects eaten by bats (e.g. during an ephemeral mass emergence of mayflies), facilitate jumping the species barrier. The length of a filovirus filament is related to the number of genome copies (GC). Predictions from a preliminary thermodynamic model developed here suggest that filament length could greatly affect EBOV infectivity to mammalian cells with infectivity peaking for filaments of a certain length. Importantly, the infectivity to mammals of even short filaments may be more than one million-fold higher than that for the single GC virion. Third, it is proposed that at the high temperature within the bat, the phospholipid phosphatidylserine in the virus envelope promotes filament formation through fusion of single GC particles within the ingested insect, thus hugely increasing their infectivity to bats. Forth, according to the thermodynamic model, increasing the temperature from 27°C (insect cell temperature at average air temperature in Guinea, West Africa) to 42°C (bat) could increase the affinity of the filaments for bat cells by 1-2 orders of magnitude, while having no effect on the binding affinity of the single GC virions. The thermodynamic model developed here is supported by the counterintuitive observation that high glycoprotein densities on the EBOV surface reduce its infectivity in contrast to other viruses such as HIV.

A proof-of-concept model for the identification of the key events in the infection process with specific reference to Pseudomonas aeruginosa in corneal infections

BACKGROUND

It is a common medical practice to characterise an infection based on the causative agent and to adopt therapeutic and prevention strategies targeting the agent itself. However, from an epidemiological perspective, exposure to a microbe can be harmless to a host as a result of low-level exposure or due to host immune response, with opportunistic infection only occurring as a result of changes in the host, pathogen, or surrounding environment.

METHODS

We have attempted to review systematically the key host, pathogen, and environmental factors that may significantly impact clinical outcomes of exposure to a pathogen, using Pseudomonas aeruginosa eye infection as a case study.

RESULTS AND DISCUSSION

Extended contact lens wearing and compromised hygiene may predispose users to microbial keratitis, which can be a severe and vision-threatening infection. P. aeruginosa has a wide array of virulence-associated genes and sensing systems to initiate and maintain cell populations at the corneal surface and beyond. We have adapted the well-known concept of the epidemiological triangle in combination with the classic risk assessment framework (hazard identification, characterisation, and exposure) to develop a conceptual pathway-based model that demonstrates the overlapping relationships between the host, the pathogen, and the environment; and to illustrate the key events in P. aeruginosa eye infection.

CONCLUSION

This strategy differs from traditional approaches that consider potential risk factors in isolation, and hopefully will aid the identification of data and models to inform preventive and therapeutic measures in addition to risk assessment. Furthermore, this may facilitate the identification of knowledge gaps to direct research in areas of greatest impact to avert or mitigate adverse outcomes of infection.