Differences in gut microbiota between Dutch and South-Asian Surinamese: potential implications for type 2 diabetes mellitus.

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.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters.

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective.

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.

Lower respiratory tract microbiome composition and community interactions in smokers.

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.

Microbiome Dysbiosis Shows Strong Association of Gut-Derived Altered Metabolomic Profile in Gulf War Chronic Multisymptom Illness Symptom Persistence Following Western Diet Feeding and Development of Obesity.

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.

Prior exposure to microcystin alters host gut resistome and is associated with dysregulated immune homeostasis in translatable mouse models.

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.

Potential Autoimmunity Resulting from Molecular Mimicry between SARS-CoV-2 Spike and Human Proteins.

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.

Inferring directional relationships in microbial communities using signed Bayesian networks.

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.

The gut microbiome and neuropsychiatric disorders: implications for attention deficit hyperactivity disorder (ADHD).

Neuropsychiatric disorders (NPDs) such as depression, anxiety, bipolar disorder, autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) all relate to behavioural, cognitive and emotional disturbances that are ultimately rooted in disordered brain function. More specifically, these disorders are linked to various neuromodulators (i.e. serotonin and dopamine), as well as dysfunction in both cognitive and socio-affective brain networks. Increasing evidence suggests that the gut environment, and particularly the microbiome, plays a significant role in individual mental health. Although the presence of a gut-brain communication axis has long been established, recent studies argue that the development and regulation of this axis is dictated by the gut microbiome. Many studies involving both animals and humans have connected the gut microbiome with depression, anxiety and ASD. Microbiome-centred treatments for individuals with these same NPDs have yielded promising results. Despite its recent rise and underlying similarities to other NPDs, both biochemically and symptomatically, connections between the gut microbiome and ADHD currently lag behind those for other NPDs. We demonstrate that all evidence points to the importance of, and dire need for, a comprehensive and in-depth analysis of the role of the gut microbiome in ADHD, to deepen our understanding of a condition that affects millions of individuals worldwide.

MATria: a unified centrality algorithm.

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.

Constructing lightweight and flexible pipelines using Plugin-Based Microbiome Analysis (PluMA).

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.

ATria: a novel centrality algorithm applied to biological networks.

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.

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis.

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.

GPUDePiCt: A Parallel Implementation of a Clustering Algorithm for Computing Degenerate Primers on Graphics Processing Units.

In order to make multiple copies of a target sequence in the laboratory, the technique of Polymerase Chain Reaction (PCR) requires the design of "primers", which are short fragments of nucleotides complementary to the flanking regions of the target sequence. If the same primer is to amplify multiple closely related target sequences, then it is necessary to make the primers "degenerate", which would allow it to hybridize to target sequences with a limited amount of variability that may have been caused by mutations. However, the PCR technique can only allow a limited amount of degeneracy, and therefore the design of degenerate primers requires the identification of reasonably well-conserved regions in the input sequences. We take an existing algorithm for designing degenerate primers that is based on clustering and parallelize it in a web-accessible software package GPUDePiCt, using a shared memory model and the computing power of Graphics Processing Units (GPUs). We test our implementation on large sets of aligned sequences from the human genome and show a multi-fold speedup for clustering using our hybrid GPU/CPU implementation over a pure CPU approach for these sequences, which consist of more than 7,500 nucleotides. We also demonstrate that this speedup is consistent over larger numbers and longer lengths of aligned sequences.

Long Timestep Molecular Dynamics on the Graphical Processing Unit.

Molecular dynamics (MD) simulations now play a key role in many areas of theoretical chemistry, biology, physics, and materials science. In many cases, such calculations are significantly limited by the massive amount of computer time needed to perform calculations of interest. Herein, we present Long Timestep Molecular Dynamics (LTMD), a method to significantly speed MD simulations. In particular, we discuss new methods to calculate the needed terms in LTMD as well as issues germane to a GPU implementation. The resulting code, implemented in the OpenMM MD library, can achieve a significant 6-fold speed increase, leading to MD simulations on the order of 5 µs/day using implicit solvent models.

The mechanisms of microtubule catastrophe and rescue: implications from analysis of a dimer-scale computational model.

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.

MDLab: a molecular dynamics simulation prototyping environment.

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 Deltat. 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.

From Genes to Organisms Via the Cell A Problem-Solving Environment for Multicellular Development.

To gain performance, developers often build scientific applications in procedural languages, such as C or Fortran, which unfortunately reduces flexibility. To address this imbalance, the authors present CompuCell3D, a multitiered, flexible, and scalable problem-solving environment for morphogenesis simulations that's written in C++ using object-oriented design patterns.

A framework for three-dimensional simulation of morphogenesis.

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.