The demographic determinants of human microbiome health

fastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data

MOTIVATION

The composition and structure of microbial communities on the body surface are closely related to human health. The interaction relationship among microbes can help us understand the formation of the microecological environment and the biological mechanism by which microorganisms influence host health. With the help of high-throughput sequencing technologies, microbial abundances in a natural environment can be directly measured without the isolation of microorganisms in culture. Sequencing experiments in microbiome studies can measure the relative abundance of microbes, which is called compositional data. Although there are already many methods for correlation analysis for compositional data, the computation time or accuracy still needs to be improved for current microbiome studies.

RESULTS

We develop a fast and efficient algorithm, called fastCCLasso, based on a penalized weighted least squares for inferring the correlation structure of microbes from compositional data in microbiome studies. We perform a large number of numerical experiments and the simulation results show that fastCCLasso outperforms its competitors in edge detection for inferring the correlation network. We also apply fastCCLasso for estimating microbial networks in microbiome studies and fastCCLasso provides a conservative network with comparable false discovery counts that are derived from shuffled data.

AVAILABILITY AND IMPLEMENTATION

FastCCLasso is open source and freely available from https://github.com/ShenZhang-Statistics/fastCCLasso under GNU LGPL v3.

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens (Clostridioides difficile, methicillin-resistant Staphylococcus aureus, multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.

Community interactions and spatial structure shape selection on antibiotic resistant lineages

Polymicrobial interactions play an important role in shaping the outcome of antibiotic treatment, yet how multispecies communities respond to antibiotic assault is still little understood. Here we use an individual-based simulation model of microbial biofilms to investigate how competitive and mutualistic interactions between an antibiotic-resistant and a susceptible strain (or species) influence the two-lineage community response to antibiotic exposure. Our model predicts that while increasing competition and antibiotics leads to increasing competitive release of the antibiotic-resistant strain, hitting a mutualistic community of cross-feeding species with antibiotics leads to a mutualistic suppression effect where both susceptible and resistant species are harmed. We next show that the impact of antibiotics is further governed by emergent spatial feedbacks within communities. Mutualistic cross-feeding communities can rescue susceptible members by subsidizing their growth inside the biofilm despite lack of access to the nutrient-rich and high-antibiotic growing front. Moreover, we show that antibiotic detoxification by resistant cells can protect nearby susceptible cells, but such cross-protection is more effective in mutualistic communities because mutualism drives mixing of resistant and susceptible cells. In contrast, competition leads to segregation, which ultimately prevents susceptible cells to profit from detoxification. Understanding how the interplay between microbial metabolic interactions and community spatial structuring shapes the outcome of antibiotic treatment can be key to effectively leverage the power of antibiotics and promote microbiome health.

Pathogens -microorganisms that make us sick- often live within dynamic and complex multispecies communities, where they may not only compete for limiting resources but also exchange beneficial resources or services with other resident species. While antibiotics are commonly used to get rid of such harmful microbes, the community-wide effects of antibiotic treatment and its consequences for antibiotic resistance are still not well understood. How do competitive or mutually beneficial interactions between antibiotic resistant and susceptible species influence community resistance to antibiotics? Here we investigate this question using a computational model. We find that antibiotic exposure favours the resistant lineage when resistant and susceptible strains are competitors but harms both types when they are mutualists. With antibiotic-detoxifying resistant cells, cross-protection of susceptible cells is more effective in mutualistic communities because mutualism drives mixing of susceptible and resistant cells. In contrast, competition leads to their segregation, precluding susceptible cells to profit from their competitor’s local detoxification. Our findings highlight that knowing not only what species are present but also how they interact with each other and arrange themselves in space is central to understanding antibiotic resistance and to informing the development of strategies that promote microbiome health.

Applying fecal microbiota transplantation (FMT) to treat recurrent Clostridium difficile infections (rCDI) in children

BACKGROUND

Fecal Microbiota Transplantation (FMT) is an innovative means of treating recurrent Clostridium difficile infection (rCDI), through restoration of gut floral balance. However, there is a lack of data concerning the efficacy of FMT and its impact on the gut microbiome among pediatric patients. This study analyzes clinical outcomes and microbial community composition among 15 pediatric patients treated for rCDI via FMT.

METHODS

This is a prospective, observational, pilot study of 15 children ≤18 years, who presented for rCDI and who met inclusion criteria for FMT at a pediatric hospital and pediatric gastroenterology clinic. Past medical history and demographics were recorded at enrollment and subsequent follow-up. Specimens of the donors' and the patients' pre-FMT and post-FMT fecal specimen were collected and used to assess microbiome composition via 16S rRNA gene sequencing.

RESULTS

FMT successfully prevented rCDI episodes for minimum of 3 months post-FMT in all patients, with no major adverse effects. Three patients reported continued GI bleeding; however, all three also had underlying Inflammatory Bowel Disease (IBD). Our analyses confirm a significant difference between pre-and post-FMT gut microbiome profiles (Shannon diversity index), whereas no significant difference was observed between post-FMT and donor microbiome profiles. At the phyla level, post-FMT profiles showed significantly increased levels of Bacteroidetes and significantly decreased levels of Proteobacteria. Subjects with underlying IBD showed no difference in their pre-and post-FMT profiles.

CONCLUSION

The low rate of recurrence or re-infection by C. difficile, coupled with minimal adverse effects post-FMT, suggests that FMT is a viable therapeutic means to treat pediatric rCDI. Post-FMT microbiomes are different from pre-FMT microbiomes, and similar to those of healthy donors, suggesting successful establishment of a healthier microbiome.

Bacterial Pathogens and the Microbiome

Bacterial pathogens and microbiome alterations can contribute to the initiation and propagation of mucosal inflammation in chronic rhinosinusitis (CRS). In this article, the authors review the clinical and research implications of key pathogens, discuss the role of the microbiome, and connect bacteria to mechanisms of mucosal immunity relevant in CRS.

Exometabolomics Assisted Design and Validation of Synthetic Obligate Mutualism

Synthetic microbial ecology has the potential to enhance the productivity and resiliency of biotechnology processes compared to approaches using single isolates. Engineering microbial consortia is challenging; however, one approach that has attracted significant attention is the creation of synthetic obligate mutualism using auxotrophic mutants that depend on each other for exchange or cross-feeding of metabolites. Here, we describe the integration of mutant library fitness profiling with mass spectrometry based exometabolomics as a method for constructing synthetic mutualism based on cross-feeding. Two industrially important species lacking known ecological interactions, Zymomonas mobilis and Escherichia coli, were selected as the test species. Amino acid exometabolites identified in the spent medium of Z. mobilis were used to select three corresponding E. coli auxotrophs (proA, pheA and IlvA), as potential E. coli counterparts for the coculture. A pooled mutant fitness assay with a Z. mobilis transposon mutant library was used to identify mutants with improved growth in the presence of E. coli. An auxotroph mutant in a gene (ZMO0748) with sequence similarity to cysteine synthase A (cysK), was selected as the Z. mobilis counterpart for the coculture. Exometabolomic analysis of spent E. coli medium identified glutathione related metabolites as potentially available for rescue of the Z. mobilis cysteine synthase mutant. Three sets of cocultures between the Z. mobilis auxotroph and each of the three E. coli auxotrophs were monitored by optical density for growth and analyzed by flow cytometry to confirm high cell counts for each species. Taken together, our methods provide a technological framework for creating synthetic mutualisms combining existing screening based methods and exometabolomics for both the selection of obligate mutualism partners and elucidation of metabolites involved in auxotroph rescue.

The evolution of cooperation within the gut microbiota

Cooperative phenotypes are considered central to the functioning of microbial communities in many contexts, including communication via quorum sensing, biofilm formation, antibiotic resistance, and pathogenesis^1-5. The human intestine houses a dense and diverse microbial community critical to health^1,2,4-9, yet we know little about cooperation within this important ecosystem. Here we experimentally test for evolved cooperation within the Bacteroidales, the dominant Gram-negative bacteria of the human intestine. We show that during growth on certain dietary polysaccharides, the model member Bacteroides thetaiotaomicron exhibits only limited cooperation. Although this organism digests these polysaccharides extracellularly, mutants lacking this ability are outcompeted. In contrast, we discovered a dedicated cross-feeding enzyme system in the prominent gut symbiont Bacteroides ovatus, which digests polysaccharide at a cost to itself but at a benefit to another species. Using in vitro systems and gnotobiotic mouse colonization models, we find that extracellular digestion of inulin increases the fitness of B.ovatus due to reciprocal benefits when it feeds other gut species such as Bacteroides vulgatus. This is a rare example of naturally-evolved cooperation between microbial species. Our study reveals both the complexity and importance of cooperative phenotypes within the mammalian intestinal microbiota.

Building the microbiome in health and disease: niche construction and social conflict in bacteria

Microbes collectively shape their environment in remarkable ways via the products of their metabolism. The diverse environmental impacts of macro-organisms have been collated and reviewed under the banner of ‘niche construction’. Here, we identify and review a series of broad and overlapping classes of bacterial niche construction, ranging from biofilm production to detoxification or release of toxins, enzymes, metabolites and viruses, and review their role in shaping microbiome composition, human health and disease. Some bacterial niche-constructing traits can be seen as extended phenotypes, where individuals actively tailor their environment to their benefit (and potentially to the benefit of others, generating social dilemmas). Other modifications can be viewed as non-adaptive by-products from a producer perspective, yet they may lead to remarkable within-host environmental changes. We illustrate how social evolution and niche construction perspectives offer complementary insights into the dynamics and consequences of these traits across distinct timescales. This review highlights that by understanding the coupled bacterial and biochemical dynamics in human health and disease we can better manage host health.

Challenges in microbial ecology: building predictive understanding of community function and dynamics

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Dual lifestyle of Porphyromonas gingivalis in biofilm and gingival cells

Porphyromonas gingivalis is deeply involved in the pathogenesis of marginal periodontitis, and recent findings have consolidated its role as an important and unique pathogen. This bacterium has a unique dual lifestyle in periodontal sites including subgingival dental plaque (biofilm) and gingival cells, as it has been clearly shown that P. gingivalis is able to exert virulence using completely different tactics in each environment. Inter-bacterial cross-feeding enhances the virulence of periodontal microflora, and such metabolic and adhesive interplay creates a supportive environment for P. gingivalis and other species. Human oral epithelial cells harbor a large intracellular bacterial load, resembling the polymicrobial nature of periodontal biofilm. P. gingivalis can enter gingival epithelial cells and pass through the epithelial barrier into deeper tissues. Subsequently, from its intracellular position, the pathogen exploits cellular recycling pathways to exit invaded cells, by which it is able to control its population in infected tissues, allowing for persistent infection in gingival tissues. Here, we outline the dual lifestyle of P. gingivalis in subgingival areas and its effects on the pathogenesis of periodontitis.

Does Whole Grain Consumption Alter Gut Microbiota and Satiety?

This review summarizes recent studies examining whole grain consumption and its effect on gut microbiota and satiety in healthy humans. Studies comparing whole grains to their refined grain counterparts were considered, as were studies comparing different grain types. Possible mechanisms linking microbial metabolism and satiety are described. Clinical trials show that whole grain wheat, maize, and barley alter the human gut microbiota, but these findings are based on a few studies that do not include satiety components, so no functional claims between microbiota and satiety can be made. Ten satiety trials were evaluated and provide evidence that whole oats, barley, and rye can increase satiety, whereas the evidence for whole wheat and maize is not compelling. There are many gaps in the literature; no one clinical trial has examined the effects of whole grains on satiety and gut microbiota together. Once understanding the impact of whole grains on satiety and microbiota is more developed, then particular grains might be used for better appetite control. With this information at hand, healthcare professionals could make individual dietary recommendations that promote satiety and contribute to weight control.