Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice.

Autism spectrum disorder (ASD) manifests as alterations in complex human behaviors including social communication and stereotypies. In addition to genetic risks, the gut microbiome differs between typically developing (TD) and ASD individuals, though it remains unclear whether the microbiome contributes to symptoms. We transplanted gut microbiota from human donors with ASD or TD controls into germ-free mice and reveal that colonization with ASD microbiota is sufficient to induce hallmark autistic behaviors. The brains of mice colonized with ASD microbiota display alternative splicing of ASD-relevant genes. Microbiome and metabolome profiles of mice harboring human microbiota predict that specific bacterial taxa and their metabolites modulate ASD behaviors. Indeed, treatment of an ASD mouse model with candidate microbial metabolites improves behavioral abnormalities and modulates neuronal excitability in the brain. We propose that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.

Independent Markov decomposition: Toward modeling kinetics of biomolecular complexes.

To advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) simulations and Markov state models (MSMs) has enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increases exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called independent Markov decomposition (IMD) that leverages weak coupling between subsystems to compute a global kinetic model without requiring the sampling of all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD models can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.

Benchmarking ensemble docking methods in D3R Grand Challenge 4.

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

Structural basis for ligand modulation of the CCR2 conformational landscape.

CC chemokine receptor 2 (CCR2) is a part of the chemokine receptor family, an important class of therapeutic targets. These class A G-protein coupled receptors (GPCRs) are involved in mammalian signaling pathways and control cell migration toward endogenous CC chemokine ligands, named for the adjacent cysteine motif on their N terminus. Chemokine receptors and their associated ligands are involved in a wide range of diseases and thus have become important drug targets. CCR2, in particular, promotes the metastasis of cancer cells and is also implicated in autoimmunity-driven type-1 diabetes, diabetic nephropathy, multiple sclerosis, asthma, atherosclerosis, neuropathic pain, and rheumatoid arthritis. Although promising, CCR2 antagonists have been largely unsuccessful to date. Here, we investigate the effect of an orthosteric and an allosteric antagonist on CCR2 dynamics by coupling long-timescale molecular dynamics simulations with Markov-state model theory. We find that the antagonists shift CCR2 into several stable inactive conformations that are distinct from the crystal structure conformation and disrupt a continuous internal water and sodium ion pathway, preventing transitions to an active-like state. Several metastable conformations present a cryptic drug-binding pocket near the allosteric site that may be amenable to targeting with small molecules. Without antagonists, the apo dynamics reveal intermediate conformations along the activation pathway that provide insight into the basal dynamics of CCR2 and may also be useful for future drug design.

Sequence-structure-function relationships in the microbial protein universe.

For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.

Signatures of HIV and Major Depressive Disorder in the Plasma Microbiome.

Inter-individual differences in the gut microbiome are linked to alterations in inflammation and blood-brain barrier permeability, which may increase the risk of depression in people with HIV (PWH). The microbiome profile of blood, which is considered by many to be typically sterile, remains largely unexplored. We aimed to characterize the blood plasma microbiome composition and assess its association with major depressive disorder (MDD) in PWH and people without HIV (PWoH). In this cross-sectional, observational cohort, we used shallow-shotgun metagenomic sequencing to characterize the plasma microbiome of 151 participants (84 PWH and 67 PWoH), all of whom underwent a comprehensive neuropsychiatric assessment. The microbial composition did not differ between PWH and PWoH or between participants with MDD and those without it. Using the songbird model, we computed the log ratio of the highest and lowest 30% of the ranked classes associated with HIV and MDD. We found that HIV infection and lifetime MDD were enriched in a set of differentially abundant inflammatory classes, such as Flavobacteria and Nitrospira. Our results suggest that the circulating plasma microbiome may increase the risk of MDD related to dysbiosis-induced inflammation in PWH. If confirmed, these findings may indicate new biological mechanisms that could be targeted to improve treatment of MDD in PWH.

Impact of Vaginal Estrogen on the Urobiome in Postmenopausal Women With Recurrent Urinary Tract Infection.

OBJECTIVE: The aim of this study was to describe effects of vaginal estrogen (VE) on the urogenital microbiome in postmenopausal women with recurrent urinary tract infections (rUTIs). METHODS: This is a secondary analysis of 17 participants enrolled in a randomized controlled trial of VE versus placebo on urinary tract infection recurrence in postmenopausal women with rUTIs. Paired clean-catch urine samples were collected at baseline and after 6 months of VE and sequenced using 16S rRNA gene sequencing. Sequence reads were analyzed using Quantitative Insights Into Microbial Ecology 2. Changes in α diversity, ß diversity, and differentially abundant genera were measured between paired baseline and 6-month samples and between those with a urinary tract infection at 6 months (failures) and those without (successes). RESULTS: Of the 17 women, 11 were successes and 6 were failures after 6 months of VE treatment. There was a significant change in α diversity from baseline to month 6 in samples overall (Kruskal-Wallis χ2 = 3.47, P = 0.037) and in the treatment success group (Yuen T = -2.53, P = 0.035). The increase in relative abundance of Lactobacillus crispatus, Lactobacillus gasseri, and Lactobacillus iners AB-1 was correlated with month 6. A relative bloom of L. crispatus compared with L. gasseri was associated with treatment success (Kruskal-Wallis χ2 = 4.9, P = 0.0014). CONCLUSIONS: Lactobacillus increases in the urogenital microbiome of postmenopausal women with rUTI after 6 months of VE. However, only the relative increase in L. crispatus specifically may be associated with treatment success.

Skin inflammation activates intestinal stromal fibroblasts and promotes colitis.

Inflammatory disorders of the skin are frequently associated with inflammatory bowel diseases (IBDs). To explore mechanisms by which these organs communicate, we performed single-cell RNA-Seq analysis on fibroblasts from humans and mice with IBD. This analysis revealed that intestinal inflammation promoted differentiation of a subset of intestinal stromal fibroblasts into preadipocytes with innate antimicrobial host defense activity. Furthermore, this process of reactive adipogenesis was exacerbated if mouse skin was inflamed as a result of skin wounding or infection. Since hyaluronan (HA) catabolism is activated during skin injury and fibroblast-to-adipocyte differentiation is dependent on HA, we tested the hypothesis that HA fragments could alter colon fibroblast function by targeted expression of human hyaluronidase-1 in basal keratinocytes from mouse skin. Hyaluronidase expression in the skin activated intestinal stromal fibroblasts, altered the fecal microbiome, and promoted excessive reactive adipogenesis and increased inflammation in the colon after challenge with dextran sodium sulfate. The response to digested HA was dependent on expression of TLR4 by preadipocytes. Collectively, these results suggest that the association between skin inflammation and IBD may be due to recognition by mesenchymal fibroblasts in the colon of HA released during inflammation of the skin.

Structure-based protein function prediction using graph convolutional networks.

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/ .

Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike Protein.

The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 28,000,000 infections and 900,000 deaths worldwide to date. Antibody development efforts mainly revolve around the extensively glycosylated SARS-CoV-2 spike (S) protein, which mediates host cell entry by binding to the angiotensin-converting enzyme 2 (ACE2). Similar to many other viral fusion proteins, the SARS-CoV-2 spike utilizes a glycan shield to thwart the host immune response. Here, we built a full-length model of the glycosylated SARS-CoV-2 S protein, both in the open and closed states, augmenting the available structural and biological data. Multiple microsecond-long, all-atom molecular dynamics simulations were used to provide an atomistic perspective on the roles of glycans and on the protein structure and dynamics. We reveal an essential structural role of N-glycans at sites N165 and N234 in modulating the conformational dynamics of the spike's receptor binding domain (RBD), which is responsible for ACE2 recognition. This finding is corroborated by biolayer interferometry experiments, which show that deletion of these glycans through N165A and N234A mutations significantly reduces binding to ACE2 as a result of the RBD conformational shift toward the "down" state. Additionally, end-to-end accessibility analyses outline a complete overview of the vulnerabilities of the glycan shield of the SARS-CoV-2 S protein, which may be exploited in the therapeutic efforts targeting this molecular machine. Overall, this work presents hitherto unseen functional and structural insights into the SARS-CoV-2 S protein and its glycan coat, providing a strategy to control the conformational plasticity of the RBD that could be harnessed for vaccine development.

Beyond Shielding: The Roles of Glycans in SARS-CoV-2 Spike Protein.

The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 15,000,000 infections and 600,000 deaths worldwide to date. Antibody development efforts mainly revolve around the extensively glycosylated SARS-CoV-2 spike (S) protein, which mediates the host cell entry by binding to the angiotensin-converting enzyme 2 (ACE2). Similar to many other viruses, the SARS-CoV-2 spike utilizes a glycan shield to thwart the host immune response. Here, we built a full-length model of glycosylated SARS-CoV-2 S protein, both in the open and closed states, augmenting the available structural and biological data. Multiple microsecond-long, all-atom molecular dynamics simulations were used to provide an atomistic perspective on the roles of glycans, and the protein structure and dynamics. We reveal an essential structural role of N-glycans at sites N165 and N234 in modulating the conformational dynamics of the spike's receptor binding domain (RBD), which is responsible for ACE2 recognition. This finding is corroborated by biolayer interferometry experiments, which show that deletion of these glycans through N165A and N234A mutations significantly reduces binding to ACE2 as a result of the RBD conformational shift towards the "down" state. Additionally, end-to-end accessibility analyses outline a complete overview of the vulnerabilities of the glycan shield of SARS-CoV-2 S protein, which may be exploited by therapeutic efforts targeting this molecular machine. Overall, this work presents hitherto unseen functional and structural insights into the SARS-CoV-2 S protein and its glycan coat, providing a strategy to control the conformational plasticity of the RBD that could be harnessed for vaccine development.

Reduced Independence in Daily Living Is Associated with the Gut Microbiome in People with HIV and HCV.

Alterations in the gut microbiome are associated with neurocognition and related disorders, including in the context of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infection. However, the connection between the gut microbiome and cognitive decline, gauged by increased dependence in instrumental activities of daily living (IADL), remains largely unexplored in the context of these diseases. Here we characterized the gut microbiome using 16S rRNA amplicon sequencing and untargeted metabolomics with liquid chromatography-mass spectrometry from 347 people with HIV, HIV and HCV, or neither, all of whom underwent a comprehensive neuropsychiatric assessment. We observed that IADL-dependent and -independent HIV-monoinfected (HIV-positive [HIV+]/HCV-negative [HCV-]) and coinfected (HIV+/HCV+) individuals have distinct gut microbiomes. Moreover, we found that dependent individuals with HIV or HIV and HCV were enriched in Bacteroides These results may have implications for the characterization of cognitive decline, as well as the development of potential prevention and treatment strategies for individuals infected with HIV and/or HCV. Of particular interest is the possibility that dietary interventions that are known to modify the microbiome could be used to shift the microbiome toward more favorable states for preserving independence.IMPORTANCE The microbes in the gut and the chemicals they produce by metabolism have been linked to brain function. In earlier work, we showed that infection with two viruses, HIV and HCV, changed the gut microbes and metabolism in ways that were associated with a lifetime history of major depressive disorder. Here, we extend this analysis looking at a measurement of independence in daily living. We find that in individuals with HIV, whether or not they also have HCV, those who reported reduced independence were enriched in a genus of bacteria called Bacteroides This result is interesting because Bacteroides is strongly associated with diets low in carbohydrates and high in animal protein, suggesting that diet changes may help preserve independent living in people living long-term with HIV (although clinical intervention trials would be needed in order to confirm this).

Consumption of Fermented Foods Is Associated with Systematic Differences in the Gut Microbiome and Metabolome.

Lifestyle factors, such as diet, strongly influence the structure, diversity, and composition of the microbiome. While we have witnessed over the last several years a resurgence of interest in fermented foods, no study has specifically explored the effects of their consumption on gut microbiota in large cohorts. To assess whether the consumption of fermented foods is associated with a systematic signal in the gut microbiome and metabolome, we used a multi-omic approach (16S rRNA amplicon sequencing, metagenomic sequencing, and untargeted mass spectrometry) to analyze stool samples from 6,811 individuals from the American Gut Project, including 115 individuals specifically recruited for their frequency of fermented food consumption for a targeted 4-week longitudinal study. We observed subtle but statistically significant differences between consumers and nonconsumers in beta diversity as well as differential taxa between the two groups. We found that the metabolome of fermented food consumers was enriched with conjugated linoleic acid (CLA), a putatively health-promoting molecule. Cross-omic analyses between metagenomic sequencing and mass spectrometry suggest that CLA may be driven by taxa associated with fermented food consumers. Collectively, we found modest yet persistent signatures associated with fermented food consumption that appear present in multiple -omic types which motivate further investigation of how different types of fermented food impact the gut microbiome and overall health.IMPORTANCE Public interest in the effects of fermented food on the human gut microbiome is high, but limited studies have explored the association between fermented food consumption and the gut microbiome in large cohorts. Here, we used a combination of omics-based analyses to study the relationship between the microbiome and fermented food consumption in thousands of people using both cross-sectional and longitudinal data. We found that fermented food consumers have subtle differences in their gut microbiota structure, which is enriched in conjugated linoleic acid, thought to be beneficial. The results suggest that further studies of specific kinds of fermented food and their impacts on the microbiome and health will be useful.

Depression in Individuals Coinfected with HIV and HCV Is Associated with Systematic Differences in the Gut Microbiome and Metabolome.

Depression is influenced by the structure, diversity, and composition of the gut microbiome. Although depression has been described previously in human immunodeficiency virus (HIV) and hepatitis C virus (HCV) monoinfections, and to a lesser extent in HIV-HCV coinfection, research on the interplay between depression and the gut microbiome in these disease states is limited. Here, we characterized the gut microbiome using 16S rRNA amplicon sequencing of fecal samples from 373 participants who underwent a comprehensive neuropsychiatric assessment and the gut metabolome on a subset of these participants using untargeted metabolomics with liquid chromatography-mass spectrometry. We observed that the gut microbiome and metabolome were distinct between HIV-positive and -negative individuals. HCV infection had a large association with the microbiome that was not confounded by drug use. Therefore, we classified the participants by HIV and HCV infection status (HIV-monoinfected, HIV-HCV coinfected, or uninfected). The three groups significantly differed in their gut microbiome (unweighted UniFrac distances) and metabolome (Bray-Curtis distances). Coinfected individuals also had lower alpha diversity. Within each of the three groups, we evaluated lifetime major depressive disorder (MDD) and current Beck Depression Inventory-II. We found that the gut microbiome differed between depression states only in coinfected individuals. Coinfected individuals with a lifetime history of MDD were enriched in primary and secondary bile acids, as well as taxa previously identified in people with MDD. Collectively, we observe persistent signatures associated with depression only in coinfected individuals, suggesting that HCV itself, or interactions between HCV and HIV, may drive HIV-related neuropsychiatric differences.IMPORTANCE The human gut microbiome influences depression. Differences between the microbiomes of HIV-infected and uninfected individuals have been described, but it is not known whether these are due to HIV itself, or to common HIV comorbidities such as HCV coinfection. Limited research has explored the influence of the microbiome on depression within these groups. Here, we characterized the microbial community and metabolome in the stools from 373 people, noting the presence of current or lifetime depression as well as their HIV and HCV infection status. Our findings provide additional evidence that individuals with HIV have different microbiomes which are further altered by HCV coinfection. In individuals coinfected with both HIV and HCV, we identified microbes and molecules that were associated with depression. These results suggest that the interplay of HIV and HCV and the gut microbiome may contribute to the HIV-associated neuropsychiatric problems.

Best practices for analysing microbiomes.

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.