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Abstract Background Microbial consortia are a major form of life; however their stability conditions are poorly understood and are often explained in terms of species-specific defence mechanisms (secretion of extracellular matrix, antimicrobial compounds, siderophores, etc.). Here we propose a hypothesis that the primarily local nature of intercellular signalling can be a general mechanism underlying the stability of many forms of microbial communities. Presentation of the hypothesis We propose that a large microbial community can be pictured as a theatre of spontaneously emerging, partially overlapping, locally recruited microcommunities whose members interact primarily among themselves, via secreted (signalling) molecules or cell-cell contacts. We hypothesize that stability in an open environment relies on a predominantly local steady state of intercellular communication which ensures that i) deleterious mutants or strains can be excluded by a localized collapse, while ii) microcommunities harbouring useful traits can persist and/or spread even in the absence of specific protection mechanisms. Testing the hypothesis Some elements of this model can be tested experimentally by analyzing the behaviour of synthetic consortia composed of strains having well-defined communication systems and devoid of specific defence mechanisms. Supporting evidence can be obtained by in silico simulations. Implications of the hypothesis The hypothesis provides a framework for a systematic comparison of bacterial community behavior in open and closed environments. The model predicts that local signalling may enable multispecies communities to colonize open, structured environments. On the other hand, a confined niche or a host may be more likely to be colonized by a bacterial mono-species community, and local communication here provides a control against spontaneously arising cheaters, provided that survival depends on cooperation. Reviewers This article was reviewed by G. Jékely, L. Aravind and E. Szathmáry (nominated by F. Eisenhaber)

Abstract Background Quorum sensing (QS) is a form of gene regulation based on cell-density that depends on inter-cellular communication. While there are a variety of models for bacterial colony morphology, there is little work linking QS genes to movement in an open system. Results The onset of swarming in environmental P. aeruginosa PUPa3 was described with a simplified computational model in which cells in random motion communicate via a diffusible signal (representing N-acyl homoserine lactones, AHL) as well as diffusible, secreted factors (enzymes, biosurfactans, i.e. "public goods") that regulate the intensity of movement and metabolism in a threshold-dependent manner. As a result, an "activation zone" emerges in which nutrients and other public goods are present in sufficient quantities, and swarming is the spontaneous displacement of this high cell-density zone towards nutrients and/or exogenous signals. The model correctly predicts the behaviour of genomic knockout mutants in which the QS genes responsible either for the synthesis (lasI, rhlI) or the sensing (lasR, rhlR) of AHL signals were inactivated. For wild type cells the model predicts sustained colony growth that can however be collapsed by the overconsumption of nutrients. Conclusion While in more complex models include self-orienting abilities that allow cells to follow concentration gradients of nutrients and chemotactic agents, in this model, displacement towards nutrients or environmental signals is an emergent property of the community that results from the action of a few, well-defined QS genes and their products. Still the model qualitatively describes the salient properties of QS bacteria, i.e. the density-dependent onset of swarming as well as the response to exogenous signals or cues. Reviewers This paper was reviewed by Gáspár Jékely, L. Aravind, Eugene V. Koonin and Artem Novozhilov (nominated by Eugene V. Koonin).

Abstract The cytoplasmic tail of Notch ligands drives endocytosis, mediates association with proteins implicated in the organization of cell-cell junctions and, through regulated intra-membrane proteolysis, is released from the membrane as a signaling fragment. We survey these findings and discuss the role of Notch ligands intracellular region in bidirectional signaling and possibly in signal modulation in mammals. Reviewers This article was reviewed by Frank Eisenhaber, L Aravind, and Eugene V. Koonin.

Abstract Background The process of oxidative folding combines the formation of native disulfide bond with conformational folding resulting in the native three-dimensional fold. Oxidative folding pathways can be described in terms of disulfide intermediate species (DIS) which can also be isolated and characterized. Each DIS corresponds to a family of folding states (conformations) that the given DIS can adopt in three dimensions. Results The oxidative folding space can be represented as a network of DIS states interconnected by disulfide interchange reactions that can either create/abolish or rearrange disulfide bridges. We propose a simple 3D representation wherein the states having the same number of disulfide bridges are placed on separate planes. In this representation, the shuffling transitions are within the planes, and the redox edges connect adjacent planes. In a number of experimentally studied cases (bovine pancreatic trypsin inhibitor, insulin-like growth factor and epidermal growth factor), the observed intermediates appear as part of contiguous oxidative folding pathways. Conclusions Such networks can be used to visualize folding pathways in terms of the experimentally observed intermediates. A simple visualization template written for the Tulip package http://www.tulip-software.org/ can be obtained from V.A.

Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions., Competing Interests: The authors have declared that no competing interests exist.

Regular exercise can upgrade the efficiency of the immune system and beneficially alter the composition of the gastro-intestinal microbiome. We tested the hypothesis that active athletes have a more diverse microbiome than sedentary subjects, which could provide better protection against COVID-19 during infection. Twenty active competing athletes (CA) (16 male and 4 females of the national first and second leagues), aged 24.15 ± 4.7 years, and 20 sedentary subjects (SED) (15 male and 5 females), aged 27.75 ± 7.5 years, who had been diagnosed as positive for COVID-19 by a PCR test, served as subjects for the study. Fecal samples collected five to eight days after diagnosis and three weeks after a negative COVID-19 PCR test were used for microbiome analysis. Except for two individuals, all subjects reported very mild and/or mild symptoms of COVID-19 and stayed at home under quarantine. Significant differences were not found in the bacterial flora of trained and untrained subjects. On the other hand, during COVID-19 infection, at the phylum level, the relative abundance of Bacteroidetes was elevated during COVID-19 compared to the level measured three weeks after a negative PCR test ( p < 0.05) when all subjects were included in the statistical analysis. Since it is known that Bacteroidetes can suppress toll-like receptor 4 and ACE2-dependent signaling, thus enhancing resistance against pro-inflammatory cytokines, it is suggested that Bacteroidetes provide protection against severe COVID-19 infection. There is no difference in the microbiome bacterial flora of trained and untrained subjects during and after a mild level of COVID-19 infection.

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient's age as the input and shows high classification efficiency with an area under the curve (AUC) of 0.94 [confidence interval (CI): [0.912, 0.962]] and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) that is capable to use a viral sequence and the patient's age as the input and provides a percentage estimation of disease severity. We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes., (© The Author(s) 2021. Published by Oxford University Press.)