Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

ABSTRACT Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time, and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses, hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10,320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20%, 50%, and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modelling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.

Grad-seq identifies KhpB as a global RNA-binding protein in Clostridioides difficile that regulates toxin production

Much of our current knowledge about cellular RNA-protein complexes in bacteria is derived from analyses in gram-negative model organisms, with the discovery of RNA-binding proteins (RBPs) generally lagging behind in gram-positive species. Here, we have applied Grad-seq analysis of native RNA-protein complexes to a major gram-positive human pathogen, Clostridioides difficile, whose RNA biology remains largely unexplored. Our analysis resolves in-gradient distributions for ∼88% of all annotated transcripts and ∼50% of all proteins, thereby providing a comprehensive resource for the discovery of RNA-protein and protein-protein complexes in C. difficile and related microbes. The sedimentation profiles together with pulldown approaches identify KhpB, previously identified in Streptococcus pneumoniae, as an uncharacterized, pervasive RBP in C. difficile. Global RIP-seq analysis establishes a large suite of mRNA and small RNA targets of KhpB, similar to the scope of the Hfq targetome in C. difficile. The KhpB-bound transcripts include several functionally related mRNAs encoding virulence-associated metabolic pathways and toxin A whose transcript levels are observed to be increased in a khpB deletion strain. Moreover, the production of toxin protein is also increased upon khpB deletion. In summary, this study expands our knowledge of cellular RNA protein interactions in C. difficile and supports the emerging view that KhpB homologues constitute a new class of globally acting RBPs in gram-positive bacteria.