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Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset (targeted 16S, v3-4 region, qPCR) from an observational, case-control study of 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential abundance, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field. [ABSTRACT FROM AUTHOR]

Background: Computing centrality is a foundational concept in social networking that involves finding the most "central" or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm. Results: We instead generalize the results of any k centrality algorithms through our iterative algorithm MATRIA, producing a single ranked and unified set of central nodes. Through tests on three biological networks, we demonstrate evident and balanced correlations with the results of these k algorithms. We also improve its speed through GPU parallelism. Conclusions: Our results show iteration to be a powerful technique that can eliminate spatial bias among central nodes, increasing the level of agreement between algorithms with various importance definitions. GPU parallelism improves speed and makes iteration a tractable problem for larger networks. [ABSTRACT FROM AUTHOR]

Motivation Software pipelines have become almost standardized tools for microbiome analysis. Currently many pipelines are available, often sharing some of the same algorithms as stages. This is largely because each pipeline has its own source language and file formats, making it typically more economical to reinvent the wheel than to learn and interface to an existing package. We present Plugin-Based Microbiome Analysis (PluMA), which addresses this problem by providing a lightweight back end that can be infinitely extended using dynamically loaded plugin extensions. These can be written in one of many compiled or scripting languages. With PluMA and its online plugin pool, algorithm designers can easily plug-and-play existing pipeline stages with no knowledge of their underlying implementation, allowing them to efficiently test a new algorithm alongside these stages or combine them in a new and creative way. Results We demonstrate the usefulness of PluMA through an example pipeline (P-M16S) that expands an obesity study involving gut microbiome samples from the mouse, by integrating multiple plugins using a variety of source languages and file formats, and producing new results. Availability and implementation Links to github repositories for the PluMA source code and P-M16S, in addition to the plugin pool are available from the Bioinformatics Research Group (BioRG) at: http://biorg.cis.fiu.edu/pluma. [ABSTRACT FROM AUTHOR]

Background: The notion of centrality is used to identify "important" nodes in social networks. Importance of nodes is not well-defined, and many different notions exist in the literature. The challenge of defining centrality in meaningful ways when network edges can be positively or negatively weighted has not been adequately addressed in the literature. Existing centrality algorithms also have a second shortcoming, i.e., the list of the most central nodes are often clustered in a specific region of the network and are not well represented across the network. Methods: We address both by proposing Ablatio Triadum (ATria), an iterative centrality algorithm that uses the concept of "payoffs" from economic theory. Results: We compare our algorithm with other known centrality algorithms and demonstrate how ATria overcomes several of their shortcomings. We demonstrate the applicability of our algorithm to synthetic networks as well as biological networks including bacterial co-occurrence networks, sometimes referred to as microbial social networks. Conclusions: We show evidence that ATria identifies three different kinds of "important" nodes in microbial social networks with different potential roles in the community. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]

This article presents information on PROTOMOL, an object-oriented framework for prototyping novel algorithms for molecular dynamics. PROTOMOL is a framework for rapid development of efficient algorithms and applications in molecular modeling, particularly of biomolecules such as proteins and DNA. It supports state-of-the-art molecular dynamics (MD) and N-body algorithms that improve the speed and efficiency of these simulations. PROTOMOL automatically detects appropriate parameters for many of the algorithms implemented. Thus, algorithms successfully incorporated into this framework can be more easily incorporated into the most popular MD programs. PROTOMOL provides components to handle input/output, graphical user interface and visualization, as well as optimized algorithmic frameworks for easy incorporation and efficient implementation of new algorithms. An important optimization encapsulated in PROTOMOL is parallelism at a modest scale (e.g., clusters or medium size symmetric multiprocessors with typical size of 32 to 64 nodes), which addresses the needs of many MD users.

Gut microbiota, or the collection of diverse microorganisms in a specific ecological niche, are known to significantly impact human health. Decreased gut microbiota production of short-chain fatty acids (SCFAs) has been implicated in type 2 diabetes mellitus (T2DM) disease progression. Most microbiome studies focus on ethnic majorities. This study aims to understand how the microbiome differs between an ethnic majority (the Dutch) and minority (the South-Asian Surinamese (SAS)) group with a lower and higher prevalence of T2DM, respectively. Microbiome data from the Healthy Life in an Urban Setting (HELIUS) cohort were used. Two age- and gender-matched groups were compared: the Dutch (n = 41) and SAS (n = 43). Microbial community compositions were generated via DADA2. Metrics of microbial diversity and similarity between groups were computed. Biomarker analyses were performed to determine discriminating taxa. Bacterial co-occurrence networks were constructed to examine ecological patterns. A tight microbiota cluster was observed in the Dutch women, which overlapped with some of the SAS microbiota. The Dutch gut contained a more interconnected microbial ecology, whereas the SAS network was dispersed, i.e., contained fewer inter-taxonomic correlational relationships. Bacteroides caccae, Butyricicoccus, Alistipes putredinis, Coprococcus comes, Odoribacter splanchnicus, and Lachnospira were enriched in the Dutch gut. Haemophilus, Bifidobacterium, and Anaerostipes hadrus discriminated the SAS gut. All but Lachnospira and certain strains of Haemophilus are known to produce SCFAs. The Dutch gut microbiome was distinguished from the SAS by diverse, differentially abundant SCFA-producing taxa with significant cooperation. The dynamic ecology observed in the Dutch was not detected in the SAS. Among several potential gut microbial biomarkers, Haemophilus parainfluenzae likely best characterizes the ethnic minority group, which is more predisposed to T2DM. The higher prevalence of T2DM in the SAS may be associated with the gut dysbiosis observed. [ABSTRACT FROM AUTHOR]

Gut microbiota, or the collection of diverse microorganisms in a specific ecological niche, are known to significantly impact human health. Decreased gut microbiota production of short-chain fatty acids (SCFAs) has been implicated in type 2 diabetes mellitus (T2DM) disease progression. Most microbiome studies focus on ethnic majorities. This study aims to understand how the microbiome differs between an ethnic majority (the Dutch) and minority (the South-Asian Surinamese (SAS)) group with a lower and higher prevalence of T2DM, respectively. Microbiome data from the Healthy Life in an Urban Setting (HELIUS) cohort were used. Two age- and gender-matched groups were compared: the Dutch (n = 41) and SAS (n = 43). Microbial community compositions were generated via DADA2. Metrics of microbial diversity and similarity between groups were computed. Biomarker analyses were performed to determine discriminating taxa. Bacterial co-occurrence networks were constructed to examine ecological patterns. A tight microbiota cluster was observed in the Dutch women, which overlapped with some of the SAS microbiota. The Dutch gut contained a more interconnected microbial ecology, whereas the SAS network was dispersed, i.e., contained fewer inter-taxonomic correlational relationships. Bacteroides caccae, Butyricicoccus, Alistipes putredinis, Coprococcus comes, Odoribacter splanchnicus, and Lachnospira were enriched in the Dutch gut. Haemophilus, Bifidobacterium, and Anaerostipes hadrus discriminated the SAS gut. All but Lachnospira and certain strains of Haemophilus are known to produce SCFAs. The Dutch gut microbiome was distinguished from the SAS by diverse, differentially abundant SCFA-producing taxa with significant cooperation. The dynamic ecology observed in the Dutch was not detected in the SAS. Among several potential gut microbial biomarkers, Haemophilus parainfluenzae likely best characterizes the ethnic minority group, which is more predisposed to T2DM. The higher prevalence of T2DM in the SAS may be associated with the gut dysbiosis observed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. The coronavirus SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. Thus, we computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative high autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins, such as PRKG1 involved in platelet activation and calcium regulation, and tropomyosin, which is linked to cardiac disease. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions. [ABSTRACT FROM AUTHOR]

Background: Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. Results: In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. Conclusions: BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order. [ABSTRACT FROM AUTHOR]

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes. [ABSTRACT FROM AUTHOR]