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The pathophysiology of Gulf War Illness (GWI) remains elusive even after three decades. The persistence of multiple complex symptoms along with metabolic disorders such as obesity worsens the health of present Gulf War (GW) Veterans often by the interactions of the host gut microbiome and inflammatory mediators. In this study, we hypothesized that the administration of a Western diet might alter the host metabolomic profile, which is likely associated with the altered bacterial species. Using a five-month symptom persistence GWI model in mice and whole-genome sequencing, we characterized the species-level dysbiosis and global metabolomics, along with heterogenous co-occurrence network analysis, to study the bacteriome-metabolomic association. Microbial analysis at the species level showed a significant alteration of beneficial bacterial species. The beta diversity of the global metabolomic profile showed distinct clustering due to the Western diet, along with the alteration of metabolites associated with lipid, amino acid, nucleotide, vitamin, and xenobiotic metabolism pathways. Network analysis showed novel associations of gut bacterial species with metabolites and biochemical pathways that could be used as biomarkers or therapeutic targets to ameliorate symptom persistence in GW Veterans.

A strong association between exposure to the common harmful algal bloom toxin microcystin and the altered host gut microbiome has been shown. We tested the hypothesis that prior exposure to the cyanotoxin microcystin-LR may alter the host resistome. We show that the mice exposed to microcystin-LR had an altered microbiome signature that harbored antibiotic resistance genes. Host resistome genotypes such as mefA, msrD, mel, ant6, and tet40 increased in diversity and relative abundance following microcystin-LR exposure. Interestingly, the increased abundance of these genes was traced to resistance to common antibiotics such as tetracycline, macrolides, glycopeptide, and aminoglycosides, crucial for modern-day treatment of several diseases. Increased abundance of these genes was positively associated with increased expression of PD1, a T-cell homeostasis marker, and pleiotropic inflammatory cytokine IL-6 with a concomitant negative association with immunosurveillance markers IL-7 and TLR2. Microcystin-LR exposure also caused decreased TLR2, TLR4, and REG3G expressions, increased immunosenescence, and higher systemic levels of IL-6 in both wild-type and humanized mice. In conclusion, the results show a first-ever characterization of the host resistome following microcystin-LR exposure and its connection to host immune status and antimicrobial resistance that can be crucial to understand treatment options with antibiotics in microcystin-exposed subjects in clinical settings.

Microtubule (MT) dynamic instability is fundamental to many cell functions, but its mechanism remains poorly understood, in part because it is difficult to gain information about the dimer-scale events at the MT tip. To address this issue, we used a dimer-scale computational model of MT assembly that is consistent with tubulin structure and biochemistry, displays dynamic instability, and covers experimentally relevant spans of time. It allows us to correlate macroscopic behaviors (dynamic instability parameters) with microscopic structures (tip conformations) and examine protofilament structure as the tip spontaneously progresses through both catastrophe and rescue. The model's behavior suggests that several commonly held assumptions about MT dynamics should be reconsidered. Moreover, it predicts that short, interprotofilament "cracks" (laterally unbonded regions between protofilaments) exist even at the tips of growing MTs and that rapid fluctuations in the depths of these cracks influence both catastrophe and rescue. We conclude that experimentally observed microtubule behavior can best be explained by a "stochastic cap" model in which tubulin subunits hydrolyze GTP according to a first-order reaction after they are incorporated into the lattice; catastrophe and rescue result from stochastic fluctuations in the size, shape, and extent of lateral bonding of the cap.

PROTOMOL is a high-performance framework in C++ for rapid prototyping of novel algorithms for molecular dynamics and related applications. Its flexibility is achieved primarily through the use of inheritance and design patterns (object-oriented programming). Performance is obtained by using templates that enable generation of efficient code for sections critical to performance (generic programming). The framework encapsulates important optimizations that can be used by developers, such as parallelism in the force computation. Its design is based on domain analysis of numerical integrators for molecular dynamics (MD) and of fast solvers for the force computation, particularly due to electrostatic interactions. Several new and efficient algorithms are implemented in PROTOMOL. Finally, it is shown that PROTOMOL'S sequential performance is excellent when compared to a leading MD program, and that it scales well for moderate number of processors. Binaries and source codes for Windows, Linux, Solaris, IRIX, HP-UX, and AIX platforms are available under open source license at http://protomol, sourceforge.net. Categories and Subject Descriptors: D.1.5 [Programming Techniques]: Object-oriented programming; D.2.11 [Software Engineering]: Software Architectures--Domain specific architectures; D.2.13 [Software Engineering]: Reusable Software--Reusable libraries; G.1.7 [Numerical Analysis]: Ordinary Differential Equations--Multistep and multivalue methods; G.4 [Mathematical Software]:Algorithm design and analysis, efficiency, parallel and vector implementations, user interfaces; J.2 [Physical Sciences and Engineering]: Chemistry, Physics; J.3 [Life and Medical Sciences]: Biology and Genetics General Terms: Algorithms, Design, Performance Additional Key Words and Phrases: Fast electrostatic methods, incremental parallelism, molecular dynamics, multigrid, multiple time-stepping integration, object-oriented framework.

Molecular dynamics (MD) simulation involves solving Newton's equations of motion for a system of atoms, by calculating forces and updating atomic positions and velocities over a timestep Δt. Despite the large amount of computing power currently available, the timescale of MD simulations is limited by both the small timestep required for propagation, and the expensive algorithm for computing pairwise forces. These issues are currently addressed through the development of efficient simulation methods, some of which make acceptable approximations and as a result can afford larger timesteps. We present MDLab, a development environment for MD simulations built with Python which facilitates prototyping, testing, and debugging of these methods. MDLab provides constructs which allow the development of propagators, force calculators, and high level sampling protocols that run several instances of molecular dynamics. For computationally demanding sampling protocols which require testing on large biomolecules, MDL includes an interface to the OpenMM libraries of Friedrichs et al. which execute on graphical processing units (GPUs) and achieve considerable speedup over execution on the CPU. As an example of an interesting high level method developed in MDLab, we present a parallel implementation of the On-The-Fly string method of Maragliano and Vanden-Eijnden. MDLab is available at http://mdlab.sourceforge.net. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]

The lung microbiome impacts on lung function, making any smoking-induced changes in the lung microbiome potentially significant. The complex co-occurrence and co-avoidance patterns between the bacterial taxa in the lower respiratory tract (LRT) microbiome were explored for a cohort of active (AS), former (FS) and never (NS) smokers. Bronchoalveolar lavages (BALs) were collected from 55 volunteer subjects (9 NS, 24 FS and 22 AS). The LRT microbiome composition was assessed using 16S rRNA amplicon sequencing. Identification of differentially abundant taxa and co-occurrence patterns, discriminant analysis and biomarker inferences were performed. The data show that smoking results in a loss in the diversity of the LRT microbiome, change in the co-occurrence patterns and a weakening of the tight community structure present in healthy microbiomes. The increased abundance of the genus Ralstonia in the lung microbiomes of both former and active smokers is significant. Partial least square discriminant and DESeq2 analyses suggested a compositional difference between the cohorts in the LRT microbiome. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS. The linear discriminant analysis effect size (LEfSe) analyses identified several bacterial taxa as potential biomarkers of smoking status. Network-based clustering analysis highlighted different co-occurring and co-avoiding microbial taxa in the three groups. The analysis found a cluster of bacterial taxa that co-occur in smokers and non-smokers alike. The clusters exhibited tighter and more significant associations in NS compared to FS and AS. Higher degree of rivalry between clusters was observed in the AS. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS., Competing Interests: The authors declare that there are no conflicts of interest., (© 2023 The Authors.)

We present COMPUCELL3D, a software framework for three-dimensional simulation of morphogenesis in different organisms. COMPUCELL3D employs biologically relevant models for cell clustering, growth, and interaction with chemical fields. COMPUCELL3D uses design patterns for speed, efficient memory management, extensibility, and flexibility to allow an almost unlimited variety of simulations. We have verified COMPUCELL3D by building a model of growth and skeletal pattern formation in the avian (chicken) limb bud. Binaries and source code are available, along with documentation and input files for sample simulations, at http:// compucell.sourceforge.net.