Time resolved multi-omics reveals diverse metabolic strategies of Salmonella during diet-induced inflammation.

With a rise in antibiotic resistance and chronic infection, the metabolic response of Salmonella enterica serovar Typhimurium to various dietary conditions over time remains an understudied avenue for novel, targeted therapeutics. Elucidating how enteric pathogens respond to dietary variation not only helps us decipher the metabolic strategies leveraged for expansion but also assists in proposing targets for therapeutic interventions. Here, we use a multi-omics approach to identify the metabolic response of Salmonella enterica serovar Typhimurium in mice on both a fibrous diet and high-fat diet over time. When comparing Salmonella gene expression between diets, we found a preferential use of respiratory electron acceptors consistent with increased inflammation of the high-fat diet mice. Looking at the high-fat diet over the course of infection, we noticed heterogeneity of samples based on Salmonella ribosomal activity, which separated into three infection phases: early, peak, and late. We identified key respiratory, carbon, and pathogenesis gene expression descriptive of each phase. Surprisingly, we identified genes associated with host-cell entry expressed throughout infection, suggesting sub-populations of Salmonella or stress-induced dysregulation. Collectively, these results highlight not only the sensitivity of Salmonella to its environment but also identify phase-specific genes that may be used as therapeutic targets to reduce infection. Importance: Identifying novel therapeutic strategies for Salmonella infection that occur in relevant diets and over time is needed with the rise of antibiotic resistance and global shifts towards Western diets that are high in fat and low in fiber. Mice on a high-fat diet are more inflamed compared to those on a fibrous diet, creating an environment that results in more favorable energy generation for Salmonella . Over time on a high-fat diet, we observed differential gene expression across infection phases. Together, these findings reveal the metabolic tuning of Salmonella to dietary and temporal perturbations. Research like this, exploring the dimensions of pathogen metabolic plasticity, can pave the way for rationally designed strategies to control disease.

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Gut microbiome carbon and sulfur metabolisms support Salmonella during pathogen infection.

Salmonella enterica serovar Typhimurium is a pervasive enteric pathogen and an ongoing global threat to public health. Ecological studies in the Salmonella impacted gut remain underrepresented in the literature, discounting the microbiome mediated interactions that may inform Salmonella physiology during colonization and infection. To understand the microbial ecology of Salmonella remodeling of the gut microbiome, here we performed multi-omics approaches on fecal microbial communities from untreated and Salmonella -infected mice. Reconstructed genomes recruited metatranscriptomic and metabolomic data providing a strain-resolved view of the expressed metabolisms of the microbiome during Salmonella infection. This data informed possible Salmonella interactions with members of the gut microbiome that were previously uncharacterized. Salmonella- induced inflammation significantly reduced the diversity of transcriptionally active members in the gut microbiome, yet increased gene expression was detected for 7 members, with Luxibacter and Ligilactobacillus being the most active. Metatranscriptomic insights from Salmonella and other persistent taxa in the inflamed microbiome further expounded the necessity for oxidative tolerance mechanisms to endure the host inflammatory responses to infection. In the inflamed gut lactate was a key metabolite, with microbiota production and consumption reported amongst transcriptionally active members. We also showed that organic sulfur sources could be converted by gut microbiota to yield inorganic sulfur pools that become oxidized in the inflamed gut, resulting in thiosulfate and tetrathionate that supports Salmonella respiration. Advancement of pathobiome understanding beyond inferences from prior amplicon-based approaches can hold promise for infection mitigation, with the active community outlined here offering intriguing organismal and metabolic therapeutic targets.

Targeted curation of the gut microbial gene content modulating human cardiovascular disease.

IMPORTANCE: One of the most-cited examples of the gut microbiome modulating human disease is the microbial metabolism of quaternary amines from protein-rich foods. By-products of this microbial processing promote atherosclerotic heart disease, a leading cause of human mortality globally. Our research addresses current knowledge gaps in our understanding of this microbial metabolism by holistically inventorying the microorganisms and expressed genes catalyzing critical atherosclerosis-promoting and -ameliorating reactions in the human gut. This led to the creation of an open-access resource, the Methylated Amine Gene Inventory of Catabolism database, the first systematic inventory of gut methylated amine metabolism. More importantly, using this resource we deliver here, we show for the first time that these gut microbial genes can predict human disease, paving the way for microbiota-inspired diagnostics and interventions.

Very early life microbiome and metabolome correlates with primary vaccination variability in children.

IMPORTANCE: We show that simultaneous study of stool and nasopharyngeal microbiome reveals divergent timing and patterns of maturation, suggesting that local mucosal factors may influence microbiome composition in the gut and respiratory system. Antibiotic exposure in early life as occurs commonly, may have an adverse effect on vaccine responsiveness. Abundance of gut and/or nasopharyngeal bacteria with the machinery to produce lipopolysaccharide-a toll-like receptor 4 agonist-may positively affect future vaccine protection, potentially by acting as a natural adjuvant. The increased levels of serum phenylpyruvic acid in infants with lower vaccine-induced antibody levels suggest an increased abundance of hydrogen peroxide, leading to more oxidative stress in low vaccine-responding infants.

Exposing new taxonomic variation with inflammation - a murine model-specific genome database for gut microbiome researchers.

BACKGROUND: The murine CBA/J mouse model widely supports immunology and enteric pathogen research. This model has illuminated Salmonella interactions with the gut microbiome since pathogen proliferation does not require disruptive pretreatment of the native microbiota, nor does it become systemic, thereby representing an analog to gastroenteritis disease progression in humans. Despite the value to broad research communities, microbiota in CBA/J mice are not represented in current murine microbiome genome catalogs. RESULTS: Here we present the first microbial and viral genomic catalog of the CBA/J murine gut microbiome. Using fecal microbial communities from untreated and Salmonella-infected, highly inflamed mice, we performed genomic reconstruction to determine the impacts on gut microbiome membership and functional potential. From high depth whole community sequencing (~ 42.4 Gbps/sample), we reconstructed 2281 bacterial and 4516 viral draft genomes. Salmonella challenge significantly altered gut membership in CBA/J mice, revealing 30 genera and 98 species that were conditionally rare and unsampled in non-inflamed mice. Additionally, inflamed communities were depleted in microbial genes that modulate host anti-inflammatory pathways and enriched in genes for respiratory energy generation. Our findings suggest decreases in butyrate concentrations during Salmonella infection corresponded to reductions in the relative abundance in members of the Alistipes. Strain-level comparison of CBA/J microbial genomes to prominent murine gut microbiome databases identified newly sampled lineages in this resource, while comparisons to human gut microbiomes extended the host relevance of dominant CBA/J inflammation-resistant strains. CONCLUSIONS: This CBA/J microbiome database provides the first genomic sampling of relevant, uncultivated microorganisms within the gut from this widely used laboratory model. Using this resource, we curated a functional, strain-resolved view on how Salmonella remodels intact murine gut communities, advancing pathobiome understanding beyond inferences from prior amplicon-based approaches. Salmonella-induced inflammation suppressed Alistipes and other dominant members, while rarer commensals like Lactobacillus and Enterococcus endure. The rare and novel species sampled across this inflammation gradient advance the utility of this microbiome resource to benefit the broad research needs of the CBA/J scientific community, and those using murine models for understanding the impact of inflammation on the gut microbiome more generally. Video Abstract.

Spatial and temporal metagenomics of river compartments reveals viral community dynamics in an urban impacted stream.

Although river ecosystems comprise less than 1% of Earth's total non-glaciated area, they are critical modulators of microbially and virally orchestrated global biogeochemical cycles. However, most studies either use data that is not spatially resolved or is collected at timepoints that do not reflect the short life cycles of microorganisms. As a result, the relevance of microbiome interactions and the impacts they have over time on biogeochemical cycles are poorly understood. To assess how viral and microbial communities change over time, we sampled surface water and pore water compartments of the wastewater-impacted River Erpe in Germany every 3 hours over a 48-hour period resulting in 32 metagenomes paired to geochemical and metabolite measurements. We reconstructed 6,500 viral and 1,033 microbial genomes and found distinct communities associated with each river compartment. We show that 17% of our vMAGs clustered to viruses from other ecosystems like wastewater treatment plants and rivers. Our results also indicated that 70% of the viral community was persistent in surface waters, whereas only 13% were persistent in the pore waters taken from the hyporheic zone. Finally, we predicted linkages between 73 viral genomes and 38 microbial genomes. These putatively linked hosts included members of the Competibacteraceae, which we suggest are potential contributors to carbon and nitrogen cycling. Together, these findings demonstrate that microbial and viral communities in surface waters of this urban river can exist as stable communities along a flowing river; and raise important considerations for ecosystem models attempting to constrain dynamics of river biogeochemical cycles.

Human-Gut Phages Harbor Sporulation Genes.

Spore-forming bacteria are prevalent in mammalian guts and have implications for host health and nutrition. The production of dormant spores is thought to play an important role in the colonization, persistence, and transmission of these bacteria. Spore formation also modifies interactions among microorganisms such as infection by phages. Recent studies suggest that phages may counter dormancy-mediated defense through the expression of phage-carried sporulation genes during infection, which can alter the transitions between active and inactive states. By mining genomes and gut-derived metagenomes, we identified sporulation genes that are preferentially carried by phages that infect spore-forming bacteria. These included genes involved in chromosome partitioning, DNA damage repair, and cell wall-associated functions. In addition, phages contained homologs of sporulation-specific transcription factors, notably spo0A, the master regulator of sporulation, which could allow phages to control the complex genetic network responsible for spore development. Our findings suggest that phages could influence the formation of bacterial spores with implications for the health of the human gut microbiome, as well as bacterial communities in other environments. IMPORTANCE Phages acquire bacterial genes and use them to alter host metabolism in ways that enhance phage fitness. To date, most auxiliary genes replace or modulate enzymes that are used by the host for nutrition or energy production. However, phage fitness is affected by all aspects of host physiology, including decisions that reduce the metabolic activity of the cell. Here, we focus on endosporulation, a complex and ancient form of dormancy found among the Bacillota that involves hundreds of genes. By coupling homology searches with host classification, we identified 31 phage-carried homologs of sporulation genes that are mostly limited to phages infecting spore-forming bacteria. Nearly one-third of the homologs recovered were regulatory genes, suggesting that phages may manipulate host genetic networks by tapping into their control elements. Our findings also suggest a mechanism by which phages can overcome the defensive strategy of dormancy, which may be involved in coevolutionary dynamics of spore-forming bacteria.

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase.

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations. AVAILABILITY AND IMPLEMENTATION: For kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM.

Draft Metagenome-Assembled Genome Sequence of a Novel Citricoccus Species from Agricultural Soil in Western Colorado.

Members of the genus Citricoccus are recognized as salt-tolerant soil microorganisms. Here, we report the metagenome-assembled genome sequence of a novel Citricoccus species recovered from untilled, surface agricultural soils in western Colorado.

SCNIC: Sparse correlation network investigation for compositional data.

Microbiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations, which is one dimensionality reduction method. Additionally, modules provide biological insight as correlated groups of microbes can have relationships among themselves. To address these challenges, we developed SCNIC: Sparse Cooccurrence Network Investigation for compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. Modules can be formed using either the Louvain Modularity Maximization (LMM) algorithm or a Shared Minimum Distance algorithm (SMD) that we newly describe here and relate to LMM using simulated data. We applied SCNIC to two published datasets and we achieved increased statistical power and identified microbes that not only differed across groups, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. SCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality and sparsity, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power through feature reduction.

Draft Metagenome-Assembled Genome Sequences of Three Novel Ammonia-Oxidizing Nitrososphaera Strains Recovered from Agricultural Soils in Western Colorado.

Microbial nitrification is critical to nitrogen loss from agricultural soils. Here, we report three thaumarchaeotal metagenome-assembled genomes (MAGs) representing a new species of Nitrososphaera. These genomes expand the representation of archaeal nitrifiers recovered from arid, agricultural soils.

Genome-Resolved Metaproteomics Decodes the Microbial and Viral Contributions to Coupled Carbon and Nitrogen Cycling in River Sediments.

Rivers have a significant role in global carbon and nitrogen cycles, serving as a nexus for nutrient transport between terrestrial and marine ecosystems. Although rivers have a small global surface area, they contribute substantially to worldwide greenhouse gas emissions through microbially mediated processes within the river hyporheic zone. Despite this importance, research linking microbial and viral communities to specific biogeochemical reactions is still nascent in these sediment environments. To survey the metabolic potential and gene expression underpinning carbon and nitrogen biogeochemical cycling in river sediments, we collected an integrated data set of 33 metagenomes, metaproteomes, and paired metabolomes. We reconstructed over 500 microbial metagenome-assembled genomes (MAGs), which we dereplicated into 55 unique, nearly complete medium- and high-quality MAGs spanning 12 bacterial and archaeal phyla. We also reconstructed 2,482 viral genomic contigs, which were dereplicated into 111 viral MAGs (vMAGs) of >10 kb in size. As a result of integrating gene expression data with geochemical and metabolite data, we created a conceptual model that uncovered new roles for microorganisms in organic matter decomposition, carbon sequestration, nitrogen mineralization, nitrification, and denitrification. We show how these metabolic pathways, integrated through shared resource pools of ammonium, carbon dioxide, and inorganic nitrogen, could ultimately contribute to carbon dioxide and nitrous oxide fluxes from hyporheic sediments. Further, by linking viral MAGs to these active microbial hosts, we provide some of the first insights into viral modulation of river sediment carbon and nitrogen cycling. IMPORTANCE Here we created HUM-V (hyporheic uncultured microbial and viral), an annotated microbial and viral MAG catalog that captures strain and functional diversity encoded in these Columbia River sediment samples. Demonstrating its utility, this genomic inventory encompasses multiple representatives of dominant microbial and archaeal phyla reported in other river sediments and provides novel viral MAGs that can putatively infect these. Furthermore, we used HUM-V to recruit gene expression data to decipher the functional activities of these MAGs and reconstruct their active roles in Columbia River sediment biogeochemical cycling. Ultimately, we show the power of MAG-resolved multi-omics to uncover interactions and chemical handoffs in river sediments that shape an intertwined carbon and nitrogen metabolic network. The accessible microbial and viral MAGs in HUM-V will serve as a community resource to further advance more untargeted, activity-based measurements in these, and related, freshwater terrestrial-aquatic ecosystems.

Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces.

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris-HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

ORT: a workflow linking genome-scale metabolic models with reactive transport codes.

MOTIVATION: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As 'omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. RESULTS: We propose a workflow ('Omics to Reactive Transport-ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN. AVAILABILITY AND IMPLEMENTATION: Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Team Approach: Treatment of Shoulder Instability in Athletes.

¼: Shoulder instability is a complex problem with a high rate of recurrence in athletes. Treatment of a first-time subluxation or dislocation event is controversial and depends on patient-specific factors as well as the identified pathology. ¼: Athletic trainers and physical therapists are an integral part of the treatment team of an in-season athlete who has experienced a shoulder instability event. Through comprehensive physiological assessments, these providers can effectively suggest modifications to the patient's training regimen as well as an appropriate rehabilitation program. ¼: Surgical intervention for shoulder stabilization should use an individualized approach for technique and timing. ¼: A team-based approach is necessary to optimize the care of this high-demand, high-risk population.

DRAM for distilling microbial metabolism to automate the curation of microbiome function.

Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

Modified Outpatient Physical Therapy Improvement in Movement Assessment Log (mOPTIMAL): A Responsive and Reliable Tool for Patients with Non-Operative Shoulder Pain.

Background: Medicare regulations require that physical therapists report functional limitations and severity modifiers utilizing a claims-based data collection tool. The Modified Outpatient Physical Therapy Improvement in Movement Assessment Log (mOPTIMAL) captures key constructs about patient confidence and difficulty but has not been evaluated for responsiveness/ reliability during a routine clinical encounter with patients who have shoulder pathology. The purposes of this retrospective study are to 1) explore if mOPTIMAL changes after a single session with a physical therapist, and 2) determine if the tool is reliable among people with non-operative shoulder pain. Methods: We included 106 individuals (58% female; mean age 45.8; range: 18-94 yrs.) with "non-operative" shoulder pathology who were seen in outpatient physical therapy from 2011 to 2012. Subjects completed a mOPTIMAL survey and a pain scale before and immediately after the initial physical therapy visit. The mOPTIMAL is a patient-centered instrument that assesses how much "Difficulty" and "Confidence" a client has in performing a battery of functional tasks. T-tests, Cronbach's Alpha, and Intra-class Correlations were used to assess responsiveness, internal consistency, and reliability, respectively. Results: After a single visit, participants reported improved Confidence with sleeping, dressing/ bathing, throwing, carrying, and lifting (adjusted for ceiling effects; p<0.002) but no change in pain. Cronbach's Alpha and Intra-class Correlations were excellent (0.821-0.923; 0.967, respectively). Conclusions: mOPTIMAL is a reliable and responsive tool with excellent internal consistency. This observational study revealed that patient Confidence may change independent of Pain after a single physical therapy visit. Taken together, the mOPTIMAL appears to be an excellent tool to report severity modifiers in compliance with Medicare regulations.Level of Evidence: IV.

A natural supplement formula reduces anti-oxidative stress and enhances osteo-chondrogenic differentiation potential in mesenchymal stem cells.

There is great interest in using natural supplements to treat various medical conditions. In this study, we evaluated the anti-oxidative and stem cell differentiation effects of a mixture of vitamin D, Lactobacillus rhamnosus, ginger, curcumin, and Boswellia extract. The calcein acetoxymethyl assay after H2O2 treatment showed that combined natural supplement had an anti-oxidative effect. NS-J also increased calcium deposition, as shown by Alizarin Red S staining, indicating bone formation activity. The contents of type II collagen and glycosaminoglycans, which are biomarkers of cartilage, were higher in mesenchymal stem cells treated with combined natural supplement than in cells treated with individual ingredients of the formula. In mesenchymal stem cells treated with human osteoarthritis synovial fluids, combined natural supplement enhanced the expression of type II collagen and PPAR-δ, overcoming the anti-chondrogenic effect of inflammatory conditions. Combined natural supplement also inhibited Oil Red O staining in cells, which indicates inhibited adipogenesis. Thus, combined natural supplement, a formula comprising vitamin D, Lactobacillus rhamnosus, ginger, curcumin and Boswellia extract, reduced oxidative stress, enhanced osteogenesis and chondrogenesis, and inhibited adipogenesis in mesenchymal stem cells to a greater extent than the individual ingredients, indicating synergistic interaction. In addition, combined natural supplement increased the expression PPAR-δ, suggesting that these effects correlate with the PPAR-δ pathway.

Correction to: An exploration of Prevotella-rich microbiomes in HIV and men who have sex with men.

Following publication of the original article [1], the authors reported an error in Fig. 2. The original Fig. 2 has been incorrectly replaced with the Supplementary Fig. 2. The correct Fig. 2 is presented here.