BioSimulators: a central registry of simulation engines and services for recommending specific tools.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

Concentration-Dependent Effect of the Steroid Drug Prednisolone on a Lung Surfactant Monolayer.

The lung surfactant monolayer (LSM) is the main barrier for particles entering the lung, including steroid drugs used to treat lung diseases. The present study combines Langmuir experiments and coarse-grained (CG) molecular dynamics simulations to investigate the concentration-dependent effect of steroid drug prednisolone on the structure and morphology of a model LSM. The surface pressure-area isotherms for the Langmuir monolayers reveal a concentration-dependent decrease in area per lipid (APL). Results from simulations at a fixed surface tension, representing inhalation and exhalation conditions, suggest that at high drug concentrations, prednisolone induces a collapse of the LSM, which is likely caused by the inability of the drug to diffuse into the bilayer. Overall, the monolayer is most susceptible to drug-induced collapse at surface tensions representing exhalation conditions. The presence of cholesterol also exacerbates the instability. The findings of this investigation might be helpful for better understanding the interaction between steroid drug prednisolone and lung surfactants in relation to off-target effects.

Microbiome dynamics and genomic determinants of bovine mastitis.

The milk of lactating cows presents a complex ecosystem of interconnected microbial communities which can influence the pathophysiology of mastitis. We hypothesized possible dynamic shifts of microbiome composition and genomic features with different pathological conditions of mastitis (Clinical Mastitis; CM, Recurrent CM; RCM, Subclinical Mastitis; SCM). To evaluate this hypothesis, we employed whole metagenome sequencing (WMS) in 20 milk samples (CM, 5; RCM, 6; SCM, 4; H, 5) to unravel the microbiome dynamics, interrelation, and relevant metabolic functions. The WMS data mapped to 442 bacterial, 58 archaeal and 48 viral genomes with distinct variation in microbiome composition (CM > H > RCM > SCM). Furthermore, we identified a number of microbial genomic features, including 333, 304, 183 and 50 virulence factors-associated genes (VFGs) and 48, 31, 11 and 6 antibiotic resistance genes (ARGs) in CM, RCM, SCM, and H-microbiomes, respectively. We also detected different metabolic pathway and functional genes associated with mastitis pathogenesis. Therefore, profiling microbiome dynamics in different conditions of mastitis and associated microbial genomic features contributes to developing microbiome-based diagnostics and therapeutics for bovine mastitis.

Emergence of European and North American mutant variants of SARS-CoV-2 in South-East Asia.

The SARS-CoV-2 coronavirus is responsible for the current COVID-19 pandemic, with an ongoing toll of over 5 million infections and 333 thousand deaths worldwide within the first 5 months. Insight into the phylodynamics and mutation variants of this virus is vital to understanding the nature of its spread in different climate conditions. The incidence rate of COVID-19 is increasing at an alarming pace within subtropical South-East Asian nations with high temperatures and humidity. To understand this spread, we analysed 444 genome sequences of SARS-CoV-2 available on the GISAID platform from six South-East Asian countries. Multiple sequence alignments and maximum-likelihood phylogenetic analyses were performed to analyse and characterize the non-synonymous (NS) mutant variants circulating in this region. Global mutation distribution analysis showed that the majority of the mutations found in this region are also prevalent in Europe and North America, and the concurrent presence of these mutations at a high frequency in other countries indicates possible transmission routes. Unique spike protein and non-structural protein mutations were observed circulating within confined area of a given country. We divided the circulating viral strains into four major groups and three subgroups on the basis of the most frequent NS mutations. Strains with a unique set of four co-evolving mutations were found to be circulating at a high frequency within India, specifically. Group 2 strains characterized by two co-evolving NS mutants which alter in RdRp (P323L) and spike (S) protein (D614G) were found to be common in Europe and North America. These European and North American variants have rapidly emerged as dominant strains within South-East Asia, increasing from a 0% prevalence in January to an 81% by May 2020. These variants may have an evolutionary advantage over their ancestral types and could present a large threat to South-East Asia for the coming winter.

Comprehensive annotations of the mutational spectra of SARS-CoV-2 spike protein: a fast and accurate pipeline.

Infecting millions of people, the SARS-CoV-2 is evolving at an unprecedented rate, demanding advanced and specified analytic pipeline to capture the mutational spectra. In order to explore mutations and deletions in the spike (S) protein - the most-discussed protein of SARS-CoV-2 - we comprehensively analyzed 35,750 complete S protein-coding sequences through a custom Python-based pipeline. This GISAID-collected dataset of until 24 June 2020 covered six continents and five major climate zones. We identified 27,801 (77.77% sequences) mutated strains compared to reference Wuhan-Hu-1 wherein 84.40% of these strains mutated by only a single amino acid (aa). An outlier strain (EPI_ISL_463893) from Bosnia and Herzegovina possessed six aa substitutions. We also identified 11 residues with high aa mutation frequency, and each contains four types of aa variations. The infamous D614G variant has spread worldwide with ever-rising dominance and across regions with different climatic conditions alongside L5F and D936Y mutants, which have been documented throughout all regions and climate zones, respectively. We also found 988 unique aa substitutions spanned across 660 residues, which differed significantly among different continents (p = .003) and climatic zones (p = .021) as inferred with the Kruskal-Wallis test. Besides, 17 in-frame deletions at four sites adjacent to receptor-binding-domain were determined that may have a possible impact on attenuation. This study provides a fast and accurate pipeline for identifying mutations and deletions from the large dataset for coding and also non-coding sequences as evidenced by the representative analysis on existing S protein data. By using separate multi-sequence alignment, removing ambiguous sequences and in-frame stop codons, and utilizing pairwise alignment, this method can derive both synonymous and non-synonymous mutations (strain_ID reference aa:mutation position:strain aa). We suggest that the pipeline will aid in the evolutionary surveillance of any SARS-CoV-2 encoded proteins and will prove to be crucial in tracking the ever-increasing variation of many other divergent RNA viruses in the future. The code is available at https://github.com/SShaminur/Mutation-Analysis.