Rethinking "enterotypes"

The intestine microbiota of shrimp and its impact on cultivation

Intestinal microbiome contains several times of functional genes compared to the host and mediates the generation of multiple metabolic products, and therefore it is called "second genome" for host. Crustaceans rank second among the largest subphylum of aquaculture animals that are considered potentially satisfy global substantial food and nutrition security, among which the Pacific white shrimp (Litopenaeus vannamei) ranks the first in the production. Currently, increasing evidences show that outbreaks of some most devastating diseases in shrimp, including white feces syndrome (WFS) and acute hepatopancreatic necrosis disease (AHPND), are related to intestinal microbiota dysbiosis. Importantly, the intestine microbial composition can be altered by environmental stress, diet, and age. In this review, we overview the progress of intestinal microbiota dysbiosis and WFS or ANPHD in shrimp, and how the microbial composition is altered by external factors. Hence, developing suitable microbial micro-ecological prevention and control strategy to maintain intestinal balance may be a feasible solution to reduce the risk of disease outbreaks. Moreover, we highlight that defining the "healthy intestine microbiota" and evaluating the causality of intestinal microbiota dysbiosis and diseases following the logic of "Microecological Koch's postulates" should be the key goal in future shrimp intestinal field, which help to guide disease diagnosis and prevent disease outbreaks in shrimp farming. KEY POINTS: • Intestinal microbiota dysbiosis is relevant to multiple shrimp diseases. • Microecological Koch's postulates help to evaluate the causality of shrimp diseases.

Advances in the mechanism of metformin with wide-ranging effects on regulation of the intestinal microbiota

Metformin is of great focus because of its high safety, low side effects, and various effects other than lowering blood sugar, such as anti-inflammation, anti-tumor, and anti-aging. Studies have shown that metformin has a modulating effect on the composition and function of the intestinal microbiota other than acting on the liver. However, the composition of microbiota is complex and varies to some extent between species and individuals, and the experimental design of each study is also different. Multiple factors present a major obstacle to better comprehending the effects of metformin on the gut microbiota. This paper reviews the regulatory effects of metformin on the gut microbiota, such as increasing the abundance of genus Akkermansia, enriching short-chain fatty acids (SCFAs)-producing bacterial genus, and regulating gene expression of certain genera. The intestinal microbiota is a large and vital ecosystem in the human body and is considered to be the equivalent of an "organ" of the human body, which is highly relevant to human health and disease status. There are a lot of evidences that the gut microbiota is responsible for metformin's widespread effects. However, there are only a few systematic studies on this mechanism, and the specific mechanism is still unclear. This paper aims to summarize the possible mechanism of metformin in relation to gut microbiota.

Assessing Engraftment Following Fecal Microbiota Transplant

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn't respond because the engrafted microbiome didn't produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn't engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient's microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment; investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria, providing illustrative examples of their application. We also introduce key terminology and recommendations on how FMT studies can be analyzed for rigorous engraftment extent assessment.

Dimension reduction of microbiome data linked Bifidobacterium and Prevotella to allergic rhinitis

Dimension reduction has been used to visualise the distribution of multidimensional microbiome data, but the composite variables calculated by the dimension reduction methods have not been widely used to investigate the relationship of the human gut microbiome with lifestyle and disease. In the present study, we applied several dimension reduction methods, including principal component analysis, principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and non-negative matrix factorization, to a microbiome dataset from 186 subjects with symptoms of  allergic rhinitis (AR) and 106 controls. All the dimension reduction methods supported that the distribution of microbial data points appeared to be continuous rather than discrete. Comparison of the composite variables calculated from the different dimension reduction methods showed that the characteristics of the composite variables differed depending on the distance matrices and the dimension reduction methods. The first composite variables calculated from PCoA and NMDS with the UniFrac distance were strongly associated with AR (FDR adjusted P = 2.4 × 10-4 for PCoA and P = 2.8 × 10-4 for NMDS), and also with the relative abundance of Bifidobacterium and Prevotella. The abundance of Bifidobacterium was also linked to intake of several nutrients, including carbohydrate, saturated fat, and alcohol via composite variables. Notably, the association between the composite variables and AR was much stronger than the association between the relative abundance of individual genera and AR. Our results highlight the usefulness of the dimension reduction methods for investigating the association of microbial composition with lifestyle and disease in clinical research.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Cluster-specific associations between the gut microbiota and behavioral outcomes in preschool-aged children

BACKGROUND

The gut microbiota is recognized as a regulator of brain development and behavioral outcomes during childhood. Nonetheless, associations between the gut microbiota and behavior are often inconsistent among studies in humans, perhaps because many host-microbe relationships vary widely between individuals. This study aims to stratify children based on their gut microbiota composition (i.e., clusters) and to identify novel gut microbiome cluster-specific associations between the stool metabolomic pathways and child behavioral outcomes.

METHODS

Stool samples were collected from a community sample of 248 typically developing children (3-5 years). The gut microbiota was analyzed using 16S sequencing while LC-MS/MS was used for untargeted metabolomics. Parent-reported behavioral outcomes (i.e., Adaptive Skills, Internalizing, Externalizing, Behavioral Symptoms, Developmental Social Disorders) were assessed using the Behavior Assessment System for Children (BASC-2). Children were grouped based on their gut microbiota composition using the Dirichlet multinomial method, after which differences in the metabolome and behavioral outcomes were investigated.

RESULTS

Four different gut microbiota clusters were identified, where the cluster enriched in both Bacteroides and Bifidobacterium (Ba2) had the most distinct stool metabolome. The cluster characterized by high Bifidobacterium abundance (Bif), as well as cluster Ba2, were associated with lower Adaptive Skill scores and its subcomponent Social Skills. Cluster Ba2 also had significantly lower stool histidine to urocanate turnover, which in turn was associated with lower Social Skill scores in a cluster-dependent manner. Finally, cluster Ba2 had increased levels of compounds involved in Galactose metabolism (i.e., stachyose, raffinose, alpha-D-glucose), where alpha-D-glucose was associated with the Adaptive Skill subcomponent Daily Living scores (i.e., ability to perform basic everyday tasks) in a cluster-dependent manner.

CONCLUSIONS

These data show novel associations between the gut microbiota, its metabolites, and behavioral outcomes in typically developing preschool-aged children. Our results support the concept that cluster-based groupings could be used to develop more personalized interventions to support child behavioral outcomes. Video Abstract.

Temporal dynamics of gut microbiomes in non-industrialized urban Amazonia

Increasing levels of industrialization have been associated with changes in gut microbiome structure and loss of features thought to be crucial for maintaining gut ecological balance. The stability of gut microbial communities over time within individuals seems to be largely affected by these changes but has been overlooked among transitioning populations from low- to middle-income countries. Here, we used metagenomic sequencing to characterize the temporal dynamics in gut microbiomes of 24 individuals living an urban non-industrialized lifestyle in the Brazilian Amazon. We further contextualized our data with 165 matching longitudinal samples from an urban industrialized and a rural non-industrialized population. We show that gut microbiome composition and diversity have greater variability over time among non-industrialized individuals when compared to industrialized counterparts and that taxa may present diverse temporal dynamics across human populations. Enterotype classifications show that community types are generally stable over time despite shifts in microbiome structure. Furthermore, by tracking genomes over time, we show that levels of bacterial population replacements are more frequent among Amazonian individuals and that non-synonymous variants accumulate in genes associated with degradation of host dietary polysaccharides. Taken together, our results suggest that the stability of gut microbiomes is influenced by levels of industrialization and that tracking microbial population dynamics is important to understand how the microbiome will adapt to these transitions.IMPORTANCEThe transition from a rural or non-industrialized lifestyle to urbanization and industrialization has been linked to changes in the structure and function of the human gut microbiome. Understanding how the gut microbiomes changes over time is crucial to define healthy states and to grasp how the gut microbiome interacts with the host environment. Here, we investigate the temporal dynamics of gut microbiomes from an urban and non-industrialized population in the Amazon, as well as metagenomic data sets from urban United States and rural Tanzania. We showed that healthy non-industrialized microbiomes experience greater compositional shifts over time compared to industrialized individuals. Furthermore, bacterial strain populations are more frequently replaced in non-industrialized microbiomes, and most non-synonymous mutations accumulate in genes associated with the degradation of host dietary components. This indicates that microbiome stability is affected by transitions to industrialization, and that strain tracking can elucidate the ecological dynamics behind such transitions.

Dynamic nature of viral and bacterial communities in human faeces

Bacteriophages are a major component of the gut microbiome and are believed to play a role in establishment and stabilization of microbial communities by influencing taxonomic and functional diversity. We show that the activity of lytic and temperate phages can also significantly affect bacterial community structure in a model of extended colonic retention. Intact fresh human feces were incubated anaerobically at 37°C without homogenization and subjected to metagenomic sequencing. We observed subject-specific blooms and collapses of selected bacteriophage and bacterial populations within some individuals. Most notable were striking collapses of Prevotella populations accompanied by increases in specific bacteriophages. In a number of cases, we even observed a shift from one bacterial "enterotype" to another within 48 h. These results confirm that intact feces represents a highly dynamic ecological system and suggests that colonic retention time could have a profound effect on microbiome composition, including a significant impact by bacteriophages.

Waste not, want not: revisiting the analysis that called into question the practice of rarefaction

In 2014, McMurdie and Holmes published the provocatively titled "Waste not, want not: why rarefying microbiome data is inadmissible." The claims of their study have significantly altered how microbiome researchers control for the unavoidable uneven sequencing depths that are inherent in modern 16S rRNA gene sequencing. Confusion over the distinction between the definitions of rarefying and rarefaction continues to cloud the interpretation of their results. More importantly, the authors made a variety of problematic choices when designing and analyzing their simulations. I identified 11 factors that could have compromised the results of the original study. I reproduced the original simulation results and assessed the impact of those factors on the underlying conclusion that rarefying data is inadmissible. Throughout, the design of the original study made choices that caused rarefying and rarefaction to appear to perform worse than they truly did. Most important were the approaches used to assess ecological distances, the removal of samples with low sequencing depth, and not accounting for conditions where sequencing effort is confounded with treatment group. Although the original study criticized rarefying for the arbitrary removal of valid data, repeatedly rarefying data many times (i.e., rarefaction) incorporates all the data. In contrast, it is the removal of rare taxa that would appear to remove valid data. Overall, I show that rarefaction is the most robust approach to control for uneven sequencing effort when considered across a variety of alpha and beta diversity metrics.IMPORTANCEOver the past 10 years, the best method for normalizing the sequencing depth of samples characterized by 16S rRNA gene sequencing has been contentious. An often cited article by McMurdie and Holmes forcefully argued that rarefying the number of sequence counts was "inadmissible" and should not be employed. However, I identified a number of problems with the design of their simulations and analysis that compromised their results. In fact, when I reproduced and expanded upon their analysis, it was clear that rarefaction was actually the most robust approach for controlling for uneven sequencing effort across samples. Rarefaction limits the rate of falsely detecting and rejecting differences between treatment groups. Far from being "inadmissible", rarefaction is a valuable tool for analyzing microbiome sequence data.

Kynurenines, Neuronal Excitotoxicity, and Mitochondrial Oxidative Stress: Role of the Intestinal Flora

The intestinal flora has been the focus of numerous investigations recently, with inquiries not just into the gastrointestinal aspects but also the pathomechanism of other diseases such as nervous system disorders and mitochondrial diseases. Mitochondrial disorders are the most common type of inheritable metabolic illness caused by mutations of mitochondrial and nuclear DNA. Despite the intensive research, its diagnosis is usually difficult, and unfortunately, treating it challenges physicians. Metabolites of the kynurenine pathway are linked to many disorders, such as depression, schizophrenia, migraine, and also diseases associated with impaired mitochondrial function. The kynurenine pathway includes many substances, for instance kynurenic acid and quinolinic acid. In this review, we would like to show a possible link between the metabolites of the kynurenine pathway and mitochondrial stress in the context of intestinal flora. Furthermore, we summarize the possible markers of and future therapeutic options for the kynurenine pathway in excitotoxicity and mitochondrial oxidative stress.

Coffee and Microbiota: A Narrative Review

Coffee is one of the most widely consumed beverages in the world, which has important repercussions on the health of the individual, mainly because of certain compounds it contains. Coffee consumption exerts significant influences on the entire body, including the gastrointestinal tract, where a central role is played by the gut microbiota. Dysbiosis in the gut microbiota is implicated in the occurrence of numerous diseases, and knowledge of the microbiota has proven to be of fundamental importance for the development of new therapeutic strategies. In this narrative review, we thoroughly investigated the link between coffee consumption and its effects on the gut microbiota and the ensuing consequences on human health. We have selected the most significant articles published on this very interesting link, with the aim of elucidating the latest evidence about the relationship between coffee consumption, its repercussions on the composition of the gut microbiota, and human health. Based on the various studies carried out in both humans and animal models, it has emerged that coffee consumption is associated with changes in the gut microbiota, although further research is needed to understand more about this link and the repercussions for the whole organism.

Deterministic and stochastic processes generating alternative states of microbiomes

The structure of microbiomes is often classified into discrete or semi-discrete types potentially differing in community-scale functional profiles. Elucidating the mechanisms that generate such "alternative states" of microbiome compositions has been one of the major challenges in ecology and microbiology. In a time-series analysis of experimental microbiomes, we here show that both deterministic and stochastic ecological processes drive divergence of alternative microbiome states. We introduced species-rich soil-derived microbiomes into eight types of culture media with 48 replicates, monitoring shifts in community compositions at six time points (8 media × 48 replicates × 6 time points = 2304 community samples). We then confirmed that microbial community structure diverged into a few state types in each of the eight medium conditions as predicted in the presence of both deterministic and stochastic community processes. In other words, microbiome structure was differentiated into a small number of reproducible compositions under the same environment. This fact indicates not only the presence of selective forces leading to specific equilibria of community-scale resource use but also the influence of demographic drift (fluctuations) on the microbiome assembly. A reference-genome-based analysis further suggested that the observed alternative states differed in ecosystem-level functions. These findings will help us examine how microbiome structure and functions can be controlled by changing the "stability landscapes" of ecological community compositions.

Meta-analysis reveals the predictable dynamic development of the gut microbiota in commercial pigs

IMPORTANCE

The swine gut microbiome undergoes an age-dependent assembly pattern with a developmental phase at early ages and a stabilization phase at later ages. Shorter time intervals and a wider range of data sources provided a clearer understanding of the gut microbiota colonization and succession and their associations with pig growth and development. The rapidly changing microbiota of suckling and weaning pigs implies potential time targets for growth and health regulation through gut microbiota manipulation. Since swine gut microbiota development is predictable, swine microbiota age can be calculated and compared between animal treatment groups rather than relying only on static time-matched comparisons.

Post-stress changes in the gut microbiome composition in rats with different levels of nervous system excitability

The gut-brain axis is a critical communication system influencing the interactions between the gastrointestinal tract (GI) and the central nervous system (CNS). The gut microbiota plays a significant role in this axis, affecting the development and function of the nervous system. Stress-induced psychopathologies, such as depression and anxiety, have been linked to the gut microbiota, but underlying mechanisms and genetic susceptibility remain unclear. In this study, we examined stress-induced changes in the gut microbiome composition in two rat strains with different levels of nervous system excitability: high threshold (HT strain) and low threshold (LT strain). Rats were exposed to long-term emotional and painful stress using the Hecht protocol, and fecal samples were collected at multiple time points before and after stress exposure. Using 16S rRNA amplicon sequencing, we assessed the qualitative and quantitative changes in the gut microbiota. Our results revealed distinct microbial diversity between the two rat strains, with the HT strain displaying higher diversity compared to the LT strain. Notably, under prolonged stress, the HT strain showed an increase in relative abundance of microorganisms from the genera Faecalibacterium and Prevotella in fecal samples. Additionally, both strains exhibited a decrease in Lactobacillus abundance following stress exposure. Our findings provide valuable insights into the impact of hereditary nervous system excitability on the gut microbiome composition under stress conditions. Understanding the gut-brain interactions in response to stress may open new avenues for comprehending stress-related psychopathologies and developing potential therapeutic interventions targeted at the gut microbiota. However, further research is needed to elucidate the exact mechanisms underlying these changes and their implications for stress-induced disorders. Overall, this study contributes to the growing body of knowledge on the gut-brain axis and its significance in stress-related neurobiology.

Bridging the Gap from Enterotypes to Personalized Dietary Recommendations: A Metabolomics Perspective on Microbiome Research

Advances in high-throughput DNA sequencing have propelled research into the human microbiome and its link to metabolic health. We explore microbiome analysis methods, specifically emphasizing metabolomics, how dietary choices impact the production of microbial metabolites, providing an overview of studies examining the connection between enterotypes and diet, and thus, improvement of personalized dietary recommendations. Acetate, propionate, and butyrate constitute more than 95% of the collective pool of short-chain fatty acids. Conflicting data on acetate's effects may result from its dynamic signaling, which can vary depending on physiological conditions and metabolic phenotypes. Human studies suggest that propionate has overall anti-obesity effects due to its well-documented chemistry, cellular signaling mechanisms, and various clinical benefits. Butyrate, similar to propionate, has the ability to reduce obesity by stimulating the release of appetite-suppressing hormones and promoting the synthesis of leptin. Tryptophan affects systemic hormone secretion, with indole stimulating the release of GLP-1, which impacts insulin secretion, appetite suppression, and gastric emptying. Bile acids, synthesized from cholesterol in the liver and subsequently modified by gut bacteria, play an essential role in the digestion and absorption of dietary fats and fat-soluble vitamins, but they also interact directly with intestinal microbiota and their metabolites. One study using statistical methods identified primarily two groupings of enterotypes Bacteroides and Ruminococcus. The Prevotella-dominated enterotype, P-type, in humans correlates with vegetarians, high-fiber and carbohydrate-rich diets, and traditional diets. Conversely, individuals who consume diets rich in animal fats and proteins, typical in Western-style diets, often exhibit the Bacteroides-dominated, B-type, enterotype. The P-type showcases efficient hydrolytic enzymes for plant fiber degradation but has limited lipid and protein fermentation capacity. Conversely, the B-type features specialized enzymes tailored for the degradation of animal-derived carbohydrates and proteins, showcasing an enhanced saccharolytic and proteolytic potential. Generally, models excel at predictions but often struggle to fully elucidate why certain substances yield varied responses. These studies provide valuable insights into the potential for personalized dietary recommendations based on enterotypes.

Gut microbiome diversity, variability, and latent community types compared with shifts in body weight during the freshman year of college in dormitory-housed adolescents

Significant human gut microbiome changes during adolescence suggest that microbial community evolution occurs throughout important developmental periods including the transition to college, a typical life phase of weight gain. In this observational longitudinal study of 139 college freshmen living in on-campus dormitories, we tracked changes in the gut microbiome via 16S amplicon sequencing and body weight across a single academic year. Participants were grouped by weight change categories of gain (WG), loss (WL), and maintenance (WM). Upon assessment of the community structure, unweighted and weighted UniFrac metrics revealed significant shifts with substantial variation explained by individual effects within weight change categories. Genera that positively contributed to these associations with weight change included Bacteroides, Blautia, and Bifidobacterium in WG participants and Prevotella and Faecalibacterium in WL and WM participants. Moreover, the Prevotella/Bacteroides ratio was significantly different by weight change category, with WL participants displaying an increased ratio. Importantly, these genera did not display co-dominance nor ease of transition between Prevotella- and Bacteroides-dominated states. We further assessed the overall taxonomic variation, noting the increased stability of the WL compared to the WG microbiome. Finally, we found 30 latent community structures within the microbiome with significant associations with waist circumference, sleep, and dietary factors, with alcohol consumption chief among them. Our findings highlight the high level of individual variation and the importance of initial gut microbiome community structure in college students during a period of major lifestyle changes. Further work is needed to confirm these findings and explore mechanistic relationships between gut microbes and weight change in free-living individuals.

Fecal short-chain fatty acids in non-constipated irritable bowel syndrome: a potential clinically relevant stratification factor based on catabotyping analysis

The gut microbiota is believed to be a critical factor in the pathogenesis of IBS, and its metabolic byproducts, such as short-chain fatty acids (SCFAs), are known to influence gut function and host health. Despite this, the precise role of SCFAs in IBS remains a topic of debate. In this study, we examined the bacterial community structure by 16S rRNA gene profiling and SCFA levels by UPLC-MS/MS in fecal samples from healthy controls (HC; n = 100) and non-constipated patients (IBS-D and IBS-M; NC-IBS; n = 240) enrolled in 19 hospitals in Italy. Our findings suggest a significant difference between the fecal microbiomes of NC-IBS patients and HC subjects, with HC exhibiting higher intra-sample biodiversity. Furthermore, we were able to classify non-constipated patients into two distinct subgroups based on their fecal SCFA levels (fecal catabotype "high" and "low"), each characterized by unique taxonomic bacterial signatures. Our results suggest that the fecal catabotype with higher SCFA levels may represent a distinct clinical phenotype of IBS that could have implications for its diagnosis and treatment. This study provides a new perspective on the intricate relationship between the gut microbiome and bowel symptoms in IBS, underscoring the importance of personalized strategies for its management.

Composition of gut microbiota involved in alleviation of dexamethasone-induced muscle atrophy by whey protein

Skeletal muscle atrophy is a condition associated with increased morbidity and mortality. While the concept of the gut-muscle axis has been proposed, the role of gut microbiota in dexamethasone (DEX)-induced skeletal muscle atrophy remains largely unknown, limiting its clinical applications. In this study, we found that administration of DEX caused a shift in the gut microbiota of mice, characterized by an increased ratio of Firmicutes/Bacteroidota and a reduction in alpha diversity. We also identified 480 new operational taxonomic units (OTUs), while 1168 specific OTUs were lost. Our Spearman correlation analysis revealed 28 key taxonomic genera of bacteria that were positively or negatively associated with skeletal muscle strength and weight (r: -0.881 to 0.845, p < 0.05). Moreover, supplementation with whey protein reshaped the gut microbiota structure in DEX-treated mice, making it more similar to that of the control group. Importantly, we further utilized a stepwise regression model to identify two enterotypes capable of predicting skeletal muscle function and weight. Notably, Ileibacterium and Lachnospiraceae_UCG-001 played significant roles in predicting both skeletal muscle function and weight. Our findings suggest that DEX causes shifts in the gut microbiota, which can be reversed by whey protein intervention. The enterotypes identified by our stepwise regression models predict muscle function and weight, underscoring the potential role of gut microbiota in modulating muscle atrophy and emphasizing the therapeutic opportunities of microbiota-altering interventions.

Modulation of gut microbiota: a novel approach to enhancing the effects of immune checkpoint inhibitors

Although immune checkpoint inhibitors (ICIs) have greatly improved the prognosis of some cancer patients, the majority still fail to respond adequately, and the available biomarkers cannot reliably predict drug efficacy. The gut microbiota has received widespread attention among the various intrinsic and extrinsic factors contributing to drug resistance. As an essential regulator of physiological function, the impact of gut microbiota on host immunity and response to cancer therapy is increasingly recognized. Several studies have demonstrated significant differences in gut microbiota between responders and nonresponders. The gut microbiota associated with better clinical outcomes is called 'favorable gut microbiota'. Significantly, interventions can alter the gut microbiota. By shifting the gut microbiota to the 'favorable' one through various modifications, preclinical and clinical studies have yielded more pronounced responses and better clinical outcomes when combined with ICIs treatment, providing novel approaches to improve the efficacy of cancer immunotherapy. These findings may be attributed to the effects of gut microbiota and its metabolites on the immune microenvironment and the systemic immune system, but the underlying mechanisms remain to be discovered. In this review, we summarize the clinical evidence that the gut microbiota is strongly associated with the outcomes of ICI treatment and describe the gut microbiota characteristics associated with better clinical outcomes. We then expand on the current prevalent modalities of gut microbiota regulation, provide a comprehensive overview of preclinical and clinical research advances in improving the therapeutic efficacy and prognosis of ICIs by modulating gut microbiota, and suggest fundamental questions we need to address and potential directions for future research expansion.

Exploring the gut microbiota: lifestyle choices, disease associations, and personal genomics

The gut microbiota is a rich and dynamic ecosystem that actively interacts with the human body, playing a significant role in the state of health and disease of the host. Diet, exercise, mental health, and other factors have exhibited the ability to influence the gut bacterial composition, leading to changes that can prevent and improve, or favor and worsen, both intestinal and extra-intestinal conditions. Altered gut microbial states, or 'dysbiosis', associated with conditions and diseases are often characterized by shifts in bacterial abundance and diversity, including an impaired Firmicutes to Bacteroidetes ratio. By understanding the effect of lifestyle on the gut microbiota, personalized advice can be generated to suit each individual profile and foster the adoption of lifestyle changes that can both prevent and ameliorate dysbiosis. The delivery of effective and reliable advice, however, depends not only on the available research and current understanding of the topic, but also on the methods used to assess individuals and to discover the associations, which can introduce bias at multiple stages. The aim of this review is to summarize how human gut microbial variability is defined and what lifestyle choices and diseases have shown association with gut bacterial composition. Furthermore, popular methods to investigate the human gut microbiota are outlined, with a focus on the possible bias caused by the lack of use of standardized methods. Finally, an overview of the current state of personalized advice based on gut microbiota testing is presented, underlining its power and limitations.

Small intestine vs. colon ecology and physiology: Why it matters in probiotic administration

Research on gut microbiota has generally focused on fecal samples, representing luminal content of the large intestine. However, nutrient uptake is restricted to the small intestine. Abundant immune cell populations at this anatomical site combined with diminished mucus secretion and looser junctions (partly to allow for more efficient fluid and nutrient absorption) also results in intimate host-microbe interactions despite more rapid transit. It is thus crucial to dissect key differences in both ecology and physiology between small and large intestine to better leverage the immense potential of human gut microbiota imprinting, including probiotic engraftment at biological sensible niches. Here, we provide a detailed review unfolding how the physiological and anatomical differences between the small and large intestine affect gut microbiota composition, function, and plasticity. This information is key to understanding how gut microbiota manipulation, including probiotic administration, may strain-dependently transform host-microbe interactions at defined locations.

Absence of enterotypes in the human gut microbiomes reanalyzed with non-linear dimensionality reduction methods

Enterotypes of the human gut microbiome have been proposed to be a powerful prognostic tool to evaluate the correlation between lifestyle, nutrition, and disease. However, the number of enterotypes suggested in the literature ranged from two to four. The growth of available metagenome data and the use of exact, non-linear methods of data analysis challenges the very concept of clusters in the multidimensional space of bacterial microbiomes. Using several published human gut microbiome datasets of variable 16S rRNA regions, we demonstrate the presence of a lower-dimensional structure in the microbiome space, with high-dimensional data concentrated near a low-dimensional non-linear submanifold, but the absence of distinct and stable clusters that could represent enterotypes. This observation is robust with regard to diverse combinations of dimensionality reduction techniques and clustering algorithms.

Age-Dependent and Body Composition-Dependent Association of Child Gut Microbial Enterotype With Puberty Timing: A Chinese Cohort

CONTEXT

Puberty timing, which is vital for adult well-being, has recently been suggested to be linked to specific gut taxa. However, the impact of comprehensive gut microbiome structure assessed by enterotype on puberty timing remains unknown.

OBJECTIVE

Investigate the prospective association of gut microbial enterotype with puberty timing and the potential interaction of age and body composition.

METHODS

This study included 1826 children from the Chinese Adolescent Cohort Study, a cohort that has collected information on sociodemographics, dietary intake, physical activity, anthropometry, and pubertal development of children aged 6-8 years since 2013 and follows them up annually until the age of 15 years. Fecal samples have been collected annually since 2019 and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Cox proportional hazard regression models were used to investigate the prospective association of enterotype with puberty timing and the impact of age and body mass index (BMI) sex- and age-independent standard deviation score (SDS).

RESULTS

592 (32.4%) and 1234 (67.6%) children belonged to the Prevotella-rich enterotype and the Bacteroides-rich enterotype, respectively. Children with the Bacteroides-rich enterotype experienced their menarche/voice break later than those with the Prevotella enterotype (hazard ratio 0.53, 95% CI 0.28-0.98), P = .02). Moreover, this association was more pronounced among younger children with higher BMI SDS (P for interaction = .006).

CONCLUSION

Our findings supported a role for gut microbial communities in pubertal development, in which younger children with higher body mass seems more sensitive.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

Coarse graining the human gut microbiome

The composition of the human gut microbiome is heterogeneous across people. However, if you squint, co-abundant microbial genera emerge, accounting for much of this ecological variability. In this issue of Cell Host & Microbe, Frioux et al. provide a workflow for identifying these bacterial guilds, or "enterosignatures."

Metabolic pathway prediction of core microbiome based on enterotype and orotype

Introduction

Identification of key microbiome components has been suggested to help address the maintenance of oral and intestinal health in humans. The core microbiome is similar in all individuals, whereas the diverse microbiome varies across individuals, based on their unique lifestyles and phenotypic and genotypic determinants. In this study, we aimed to predict the metabolism of core microorganisms in the gut and oral environment based on enterotyping and orotyping.

Materials and methods

Gut and oral samples were collected from 83 Korean women aged 50 years or older. The extracted DNA was subjected to next-generation sequencing analysis of 16S rRNA hypervariable regions V3-V4.

Results

Gut bacteria were clustered into three enterotypes, while oral bacteria were clustered into three orotypes. Sixty-three of the core microbiome between the gut and oral population were correlated, and different metabolic pathways were predicted for each type. Eubacterium_g11, Actinomyces, Atopobium, and Enterococcus were significantly positively correlated between the gut and oral abundance. The four bacteria were classified as type 3 in orotype and type 2 in enterotype.

Conclusion

Overall, the study suggested that collapsing the human body's multidimensional microbiome into a few categories may help characterize the microbiomes better and address health issues more deeply.

Neurodegenerative and Neurodevelopmental Diseases and the Gut-Brain Axis: The Potential of Therapeutic Targeting of the Microbiome

The human gut microbiome contains the largest number of bacteria in the body and has the potential to greatly influence metabolism, not only locally but also systemically. There is an established link between a healthy, balanced, and diverse microbiome and overall health. When the gut microbiome becomes unbalanced (dysbiosis) through dietary changes, medication use, lifestyle choices, environmental factors, and ageing, this has a profound effect on our health and is linked to many diseases, including lifestyle diseases, metabolic diseases, inflammatory diseases, and neurological diseases. While this link in humans is largely an association of dysbiosis with disease, in animal models, a causative link can be demonstrated. The link between the gut and the brain is particularly important in maintaining brain health, with a strong association between dysbiosis in the gut and neurodegenerative and neurodevelopmental diseases. This link suggests not only that the gut microbiota composition can be used to make an early diagnosis of neurodegenerative and neurodevelopmental diseases but also that modifying the gut microbiome to influence the microbiome-gut-brain axis might present a therapeutic target for diseases that have proved intractable, with the aim of altering the trajectory of neurodegenerative and neurodevelopmental diseases such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, autism spectrum disorder, and attention-deficit hyperactivity disorder, among others. There is also a microbiome-gut-brain link to other potentially reversible neurological diseases, such as migraine, post-operative cognitive dysfunction, and long COVID, which might be considered models of therapy for neurodegenerative disease. The role of traditional methods in altering the microbiome, as well as newer, more novel treatments such as faecal microbiome transplants and photobiomodulation, are discussed.

Multi-omics reveals that the host-microbiome metabolism crosstalk of differential rumen bacterial enterotypes can regulate the milk protein synthesis of dairy cows

BACKGROUND

Dairy cows' lactation performance is the outcome of the crosstalk between ruminal microbial metabolism and host metabolism. However, it is still unclear to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to regulating the milk protein yield (MPY).

METHODS

The rumen fluid, serum and milk of 12 Holstein cows with the same diet (45% coarseness ratio), parity (2-3 fetuses) and lactation days (120-150 d) were used for the microbiome and metabolome analysis. Rumen metabolism (rumen metabolome) and host metabolism (blood and milk metabolome) were connected using a weighted gene co-expression network (WGCNA) and the structural equation model (SEM) analyses.

RESULTS

Two different ruminal enterotypes, with abundant Prevotella and Ruminococcus, were identified as type1 and type2. Of these, a higher MPY was found in cows with ruminal type2. Interestingly, [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae (the differential bacteria) were the hub genera of the network. In addition, differential ruminal, serum and milk metabolome between enterotypes were identified, where the cows with type2 had higher L-tyrosine of rumen, ornithine and L-tryptophan of serum, and tetrahydroneopterin, palmitoyl-L-carnitine, S-lactoylglutathione of milk, which could provide more energy and substrate for MPY. Further, based on the identified modules of ruminal microbiome, as well as ruminal serum and milk metabolome using WGCNA, the SEM analysis indicated that the key ruminal microbial module1, which contains the hub genera of the network ([Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae) and high abundance of bacteria (Prevotella and Ruminococcus), could regulate the MPY by module7 of rumen, module2 of blood, and module7 of milk, which contained L-tyrosine and L-tryptophan. Therefore, in order to more clearly reveal the process of rumen bacterial regulation of MPY, we established the path of SEM based on the L-tyrosine, L-tryptophan and related components. The SEM based on the metabolites suggested that [Ruminococcus] gauvreauii group could inhibit the energy supply of serum tryptophan to MPY by milk S-lactoylglutathione, which could enhance pyruvate metabolism. Norank_f_Ruminococcaceae could increase the ruminal L-tyrosine, which could provide the substrate for MPY.

CONCLUSION

Our results indicated that the represented enterotype genera of Prevotella and Ruminococcus, and the hub genera of [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae could regulate milk protein synthesis by affecting the ruminal L-tyrosine and L-tryptophan. Moreover, the combined analysis of enterotype, WGCNA and SEM could be used to connect rumen microbial metabolism with host metabolism, which provides a fundamental understanding of the crosstalk between host and microorganisms in regulating the synthesis of milk composition.

Obesity, Gut Microbiota, and Metabolome: From Pathophysiology to Nutritional Interventions

Obesity is a disorder identified by an inappropriate increase in weight in relation to height and is considered by many international health institutions to be a major pandemic of the 21st century. The gut microbial ecosystem impacts obesity in multiple ways that yield downstream metabolic consequences, such as affecting systemic inflammation, immune response, and energy harvest, but also the gut-host interface. Metabolomics, a systematized study of low-molecular-weight molecules that take part in metabolic pathways, represents a serviceable method for elucidation of the crosstalk between hosts' metabolism and gut microbiota. In the present review, we confer about clinical and preclinical studies exploring the association of obesity and related metabolic disorders with various gut microbiome profiles, and the effects of several dietary interventions on gut microbiome composition and the metabolome. It is well established that various nutritional interventions may serve as an efficient therapeutic approach to support weight loss in obese individuals, yet no agreement exists in regard to the most effective dietary protocol, both in the short and long term. However, metabolite profiling and the gut microbiota composition might represent an opportunity to methodically establish predictors for obesity control that are relatively simple to measure in comparison to traditional approaches, and it may also present a tool to determine the optimal nutritional intervention to ameliorate obesity in an individual. Nevertheless, a lack of adequately powered randomized trials impedes the application of observations to clinical practice.

Human gut microbiome: A primer for the clinician

The human host gets tremendously influenced by a genetically and phenotypically distinct and heterogeneous constellation of microbial species-the human microbiome-the gut being one of the most densely populated and characterized site for these organisms. Microbiome science has advanced rapidly, technically with respect to the analytical methods and biologically with respect to its mechanistic influence in health and disease states. A clinician conducting a microbiome study should be aware of the nuances related to microbiome research, especially with respect to the technical and biological factors that can influence the interpretation of research outcomes. Hence, this review is an attempt to detail these aspects of the human gut microbiome, with emphasis on its determinants in a healthy state.

Analysis of the relationship between the gut microbiota enterotypes and colorectal adenoma

Introduction

The essence of enterotypes is to stratify the entire human gut microbiota, and dysregulation of gut microbiota is closely related to the development of colorectal adenoma. Enterotypes may therefore be a useful target for the prevention of colorectal adenoma. However, the relationship between gut microbiota and colorectal adenoma has not been fully elucidated. In this study, we aimed to analyze the differences in gut microbiome composition between adenoma and control populations.

Methods

We recruited 31 patients with colorectal adenoma and 71 non-adenoma controls. Patient demographics, risk factors, fecal samples from each subject were collected and metagenomic sequencing was performed. LEfSe analysis was used to reveal differences in intestinal microbiome composition. Multiple logistic regression analysis was used to determine the association between enterotypes and colorectal adenoma.

Results

The results showed that Prevotella enterotype (enterotype 4) is only present in adenoma group. Logistic regression analysis showed that Prevotella enterotype was an independent risk factor for colorectal adenoma.

Discussion

The Prevotella enterotype may increase the occurrence of colorectal adenoma through inflammatory association and interference with glucose and lipid metabolism in human body. In conclusion, the differences we observed between different enterotypes add a new potential factor to the development of colorectal adenoma.

Microbiome Contributions to Health

The human microbiome is vast and is present in spaces previously thought to be sterile such as the lungs. A healthy microbiome is diverse and functions in an adaptive way to support local as well as organism health and function. Furthermore, a normal microbiome is essential for normal immune system development rendering the array of microbes that live in and on the human body key components of homeostasis. A wide array of clinical conditions and interventions including anesthesia, analgesia, and surgical intervention may derange the human microbiome in a maladaptive fashion with bacterial responses spanning decreased diversity to transformation to a pathogenic phenotype. Herein, we explore the normal microbiome of the skin, gastrointestinal tract, and the lungs as prototype sites to describe the influence of the microbiomes in each of those locations on health, and how care may derange those relations.

Altered Faecal Microbiota Composition and Structure of Ghanaian Children with Acute Gastroenteritis

Acute gastroenteritis (AGE) is a disease of global public health importance. Recent studies show that children with AGE have an altered gut microbiota relative to non-AGE controls. Yet, how the gut microbiota differs in Ghanaian children with and without AGE remains unclear. Here, we explore the 16S rRNA gene-based faecal microbiota profiles of Ghanaian children five years of age and younger, comprising 57 AGE cases and 50 healthy controls. We found that AGE cases were associated with lower microbial diversity and altered microbial sequence profiles relative to the controls. The faecal microbiota of AGE cases was enriched for disease-associated bacterial genera, including Enterococcus, Streptococcus, and Staphylococcus. In contrast, the faecal microbiota of controls was enriched for potentially beneficial genera, including Faecalibacterium, Prevotella, Ruminococcus, and Bacteroides. Lastly, distinct microbial correlation network characteristics were observed between AGE cases and controls, thereby supporting broad differences in faecal microbiota structure. Altogether, we show that the faecal microbiota of Ghanaian children with AGE differ from controls and are enriched for bacterial genera increasingly associated with diseases.

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all four research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

Personalized Diets based on the Gut Microbiome as a Target for Health Maintenance: from Current Evidence to Future Possibilities

Recently, the concept of personalized nutrition has been developed, which states that food components do not always lead to the same metabolic responses, but vary from person to person. Although this concept has been studied based on individual genetic backgrounds, researchers have recently explored its potential role in the gut microbiome. The gut microbiota physiologically communicates with humans by forming a bidirectional relationship with the micronutrients, macronutrients, and phytochemicals consumed by the host. Furthermore, the gut microbiota can vary from person to person and can be easily shifted by diet. Therefore, several recent studies have reported the application of personalized nutrition to intestinal microflora. This review provides an overview of the interaction of diet with the gut microbiome and the latest evidence in understanding the inter-individual differences in dietary responsiveness according to individual baseline gut microbiota and microbiome-associated dietary intervention in diseases. The diversity of the gut microbiota and the presence of specific microorganisms can be attributed to physiological differences following dietary intervention. The difference in individual responsiveness based on the gut microbiota has the potential to become an important research approach for personalized nutrition and health management, although further well-designed large-scale studies are warranted.

Antibiotics and mental health: The good, the bad and the ugly

Antibiotics are recognised as, on occasion, producing psychiatric side effects, most notably depression and anxiety. Apart from antimicrobial activity, antibiotics have multiple off-target effects. The brain-gut-microbiota axis has multiple sites for off-target activity, which may produce either positive or negative antibiotic effects. Here we review how antibiotics impact mental health by acting through the brain-gut-microbiota axis. Microbes in the gut influence brain function by acting through the vagus nerve or by altering the production of short-chain fatty acids or the amino acid tryptophan, the building block of serotonin. Not all antimicrobial actions of antibiotics have a negative impact. The first antidepressant discovered was actually an antibiotic: isoniazid is an antibacterial drug developed for treating tuberculosis. Minocycline, which enters the brain and mediates its effects through microglia, shows antidepressant activity. Some antibiotics bring about a significant decrease in gut microbial diversity, and this is viewed as a risk factor for depression. Other risk factors induced by antibiotics include altered gut barrier function, activation of the hypothalamic-pituitary-adrenal axis, reducing levels of brain-derived neurotrophic factor or oxytocin and alteration of vagal tone. Although most patients taking antibiotics do not suffer from an iatrogenic psychiatric disorder, some do. As clinicians, we need to keep this in mind. The development of new antibiotics is primarily focused on antibiotic resistance, but efforts should be made to reduce off-target brain-gut-microbiota effects resulting in mental health problems.

Do we need to change our perspective about gut biomarkers? A public data mining approach to identify differentially abundant bacteria in intestinal inflammatory diseases

Introduction

The gut microbiome is involved in multiple processes that influence host physiology, and therefore, disruptions in microbiome homeostasis have been linked to diseases or secondary infections. Given the importance of the microbiome and the communities of microorganisms that compose it (microbiota), the term biomarkers were coined, which are bacteria correlated with disease states, diets, and the lifestyle of the host. However, a large field in the study of intestinal biomarkers remains unexplored because the bacterial communities associated with a given disease state have not been exactly defined yet.

Methods

Here, we analyzed public data of studies focused on describing the intestinal microbiota of patients with some intestinal inflammatory diseases together with their respective controls. With these analyses, we aimed to identify differentially abundant bacteria between the subjects with the disease and their controls.

Results

We found that frequently reported bacteria such as Fusobacterium, Streptococcus, and Escherichia/Shigella were differentially abundant between the groups, with a higher abundance mostly in patients with the disease in contrast with their controls. On the other hand, we also identified potentially beneficial bacteria such as Faecalibacterium and Phascolarctobacterium, with a higher abundance in control patients.

Discussion

Our results of the differentially abundant bacteria contrast with what was already reported in previous studies on certain inflammatory diseases, but we highlight the importance of considering more comprehensive approaches to redefine or expand the definition of biomarkers. For instance, the intra-taxa diversity within a bacterial community must be considered, as well as environmental and genetic factors of the host, and even consider a functional validation of these biomarkers through in vivo and in vitro approaches. With the above, these key bacterial communities in the intestinal microbiota may have potential as next-generation probiotics or may be functional for the design of specific therapies in certain intestinal diseases.

Facts and Hopes for Gut Microbiota Interventions in Cancer Immunotherapy

Immune checkpoint inhibitors (ICI) targeting cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed death 1 (PD-1) proteins transformed the management of advanced cancers. Many tumor-intrinsic factors modulate immunological and clinical responses to such therapies, but ample evidence also implicates the gut microbiome in responses. The gut microbiome, comprising the bacteria, archaea, fungi, and viruses that live in the human digestive tract, is an established determinant of host immunity, but its impact on response to ICI therapy in mice and humans with cancer has only recently been appreciated. Therapeutic interventions to optimize microbiota composition to improve immunotherapy outcomes show promise in mice and humans with cancer. In this review, we discuss the rationale for gut microbiome-based cancer therapies, the results from early-phase clinical trials, and possible future developments.

The gut microbiome molecular mimicry piece in the multiple sclerosis puzzle

The etiological complexity of multiple sclerosis, an immune-mediated, neurodegenerative disease with multifactorial etiology is still elusive because of an incomplete understanding of the complex synergy between contributing factors such as genetic susceptibility and aberrant immune response. Recently, the disease phenotypes have also been shown to be associated with dysbiosis of the gut microbiome, a dynamic reservoir of billions of microbes, their proteins and metabolites capable of mimicring the autoantigens. Microbial factors could potentially trigger the neuroinflammation and symptoms of MS. In this perspective article, we discussed how microbial molecules resulting from a leaky gut might mimic a host’s autoantigen, potentially contributing to the disease disequilibrium. It further highlights the importance of targeting the gut microbiome for alternate therapeutic options for the treatment of MS.

Metagenomic Shotgun Sequencing of Endocervical, Vaginal, and Rectal Samples among Fijian Women with and without Chlamydia trachomatis Reveals Disparate Microbial Populations and Function across Anatomic Sites: a Pilot Study

Chlamydia trachomatis is a sexually transmitted pathogen and a global public health concern. Little is known about the microbial composition and function across endocervical, vaginal, and rectal microbiomes in the context of C. trachomatis infection. We evaluated the microbiomes of 10 age-matched high-risk Fijian women with and without C. trachomatis using metagenomic shotgun sequencing (MSS). Lactobacillus iners and Lactobacillus crispatus dominated the vagina and endocervix of uninfected women. Species often found in higher relative abundance in bacterial vaginosis (BV)-Mageeibacillus indolicus, Prevotella spp., Sneathia spp., Gardnerella vaginalis, and Veillonellaceae spp.-were dominant in C. trachomatis-infected women. This combination of BV pathogens was unique to Pacific Islanders compared to previously studied groups. The C. trachomatis-infected endocervix had a higher diversity of microbiota and microbial profiles that were somewhat different from those of the vagina. However, community state type III (CST-III) and CST-IV predominated, reflecting pathogenic microbiota regardless of C. trachomatis infection status. Rectal microbiomes were dominated by Prevotella and Bacteroides, although four women had unique microbiomes with Gardnerella, Akkermansia, Bifidobacterium, and Brachyspira. A high level of microbial similarity across microbiomes in two C. trachomatis-infected women suggested intragenitorectal transmission. A number of metabolic pathways in the endocervix, driven by BV pathogens and C. trachomatis to meet nutritional requirements for survival/growth, 5-fold higher than that in the vagina indicated that endocervical microbial functions are likely more diverse and complex than those in the vagina. Our novel findings provide the impetus for larger prospective studies to interrogate microbial/microbiome interactions that promote C. trachomatis infection and better define the unique genitorectal microbiomes of Pacific Islanders. IMPORTANCE Chlamydia trachomatis is the primary cause of bacterial sexually transmitted infections worldwide, with a disturbing increase in annual rates. While there is a plethora of data on healthy and pathogenic vaginal microbiomes-defining microbial profiles and associations with sexually transmitted infections (STIs)-far fewer studies have similarly examined the endocervix or rectum. Further, vulnerable populations, such as Pacific Islanders, remain underrepresented in research. We investigated the microbial composition, structure, and function of these anatomic microbiomes using metagenomic shotgun sequencing among a Fijian cohort. We found, primarily among C. trachomatis-infected women, unique microbial profiles in endocervical, vaginal, and rectal microbiomes with an increased diversity and more complex microbial pathways in endocervical than vaginal microbiomes. Similarities in microbiome composition across sites for some women suggested intragenitorectal transmission. These novel insights into genitorectal microbiomes and their purported function require prospective studies to better define Pacific Islander microbiomes and microbial/microbiome interactions that promote C. trachomatis infection.

Quantifying the impact of ecological memory on the dynamics of interacting communities

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications.

Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity.

Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

An ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and further examine our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling long-term memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

Microbiome-based interventions to modulate gut ecology and the immune system

The gut microbiome lies at the intersection between the environment and the host, with the ability to modify host responses to disease-relevant exposures and stimuli. This is evident in how enteric microbes interact with the immune system, e.g., supporting immune maturation in early life, affecting drug efficacy via modulation of immune responses, or influencing development of immune cell populations and their mediators. Many factors modulate gut ecosystem dynamics during daily life and we are just beginning to realise the therapeutic and prophylactic potential of microbiome-based interventions. These approaches vary in application, goal, and mechanisms of action. Some modify the entire community, such as nutritional approaches or faecal microbiota transplantation, while others, such as phage therapy, probiotics, and prebiotics, target specific taxa or strains. In this review, we assessed the experimental evidence for microbiome-based interventions, with a particular focus on their clinical relevance, ecological effects, and modulation of the immune system.

Dynamic Distribution of Gut Microbiota in Pigs at Different Growth Stages: Composition and Contribution

Fully understanding the dynamic distribution of the gut microbiota in pigs is essential, as gut microorganisms play a fundamental role in physiological processes, immunity, and the metabolism of nutrients by the host. Here, we first summarize the characteristics and the dynamic shifts in the gut microbial community of pigs at different ages based on the results of 63 peer-review publications. Then a meta-analysis based on the sequences from 16 studies with accession numbers in the GenBank database is conducted to verify the characteristics of the gut microbiota in healthy pigs. A dynamic shift is confirmed in the gut microbiota of pigs at different ages and growth phases. In general, Bacteroides, Escherichia, Clostridium, Lactobacillus, Fusobacterium, and Prevotella are dominant in piglets before weaning, then Prevotella and Aneriacter shift to be the predominant genera with Fusobacterium, Lactobacillus, and Miscellaneous as comparative minors in postweaned pigs. A number of 19 bacterial genera, including Bacteroides, Prevotella, and Lactobacillus can be found in more than 90% of pigs and three enterotypes can be identified in all pigs at different ages, suggesting there is a “core” microbiota in the gut of healthy pigs, which can be a potential target for nutrition or health regulation. The “core” members benefit the growth and gut health of the host. These findings help to define an “optimal” gut microbial profile for assessing, or improving, the performance and health status of pigs at different growth stages.

IMPORTANCE The ban on feed antibiotics by more and more countries, and the expected ban on ZnO in feed supplementation from 2022 in the EU, urge researchers and pig producers to search for new alternatives. One possible alternative is to use the so-called “next-generation probiotics (NGPs)” derived from gastrointestinal tract. In this paper, we reveal that a total of 19 “core” bacterial genera including Bacteroides, Prevotella, and Lactobacillus etc., can be found in more than 90% of healthy pigs across different ages. These identified genera may probably be the potential candidates of NGPs or the potential target of microflora regulation. Adding substrates preferred by these target microbes will help to increase the abundance of specific symbiotic species and benefit the gut health of pigs. Further research targeting these “core” microbes and the dynamic distribution of microbiota, as well as the related function is of great importance in swine production.

Dynamics of the normal gut microbiota: A longitudinal one-year population study in Sweden

Temporal dynamics of the gut microbiota potentially limit the identification of microbial features associated with health status. Here, we used whole-genome metagenomic and 16S rRNA gene sequencing to characterize the intra- and inter-individual variations of gut microbiota composition and functional potential of a disease-free Swedish population (n = 75) over one year. We found that 23% of the total compositional variance was explained by intra-individual variation. The degree of intra-individual compositional variability was negatively associated with the abundance of Faecalibacterium prausnitzii (a butyrate producer) and two Bifidobacterium species. By contrast, the abundance of facultative anaerobes and aerotolerant bacteria such as Escherichia coli and Lactobacillus acidophilus varied extensively, independent of compositional stability. The contribution of intra-individual variance to the total variance was greater for functional pathways than for microbial species. Thus, reliable quantification of microbial features requires repeated samples to address the issue of intra-individual variations of the gut microbiota.

MiDAS 4: A global catalogue of full-length 16S rRNA gene sequences and taxonomy for studies of bacterial communities in wastewater treatment plants

Microbial communities are responsible for biological wastewater treatment, but our knowledge of their diversity and function is still poor. Here, we sequence more than 5 million high-quality, full-length 16S rRNA gene sequences from 740 wastewater treatment plants (WWTPs) across the world and use the sequences to construct the ‘MiDAS 4’ database. MiDAS 4 is an amplicon sequence variant resolved, full-length 16S rRNA gene reference database with a comprehensive taxonomy from domain to species level for all sequences. We use an independent dataset (269 WWTPs) to show that MiDAS 4, compared to commonly used universal reference databases, provides a better coverage for WWTP bacteria and an improved rate of genus and species level classification. Taking advantage of MiDAS 4, we carry out an amplicon-based, global-scale microbial community profiling of activated sludge plants using two common sets of primers targeting regions of the 16S rRNA gene, revealing how environmental conditions and biogeography shape the activated sludge microbiota. We also identify core and conditionally rare or abundant taxa, encompassing 966 genera and 1530 species that represent approximately 80% and 50% of the accumulated read abundance, respectively. Finally, we show that for well-studied functional guilds, such as nitrifiers or polyphosphate-accumulating organisms, the same genera are prevalent worldwide, with only a few abundant species in each genus.

Microbial communities are responsible for biological wastewater treatment. Here, Dueholm et al. generate more than 5 million high-quality, full-length 16S rRNA gene sequences from wastewater treatment plants across the world to construct a database with a comprehensive taxonomy, providing insights into diversity and function of these microbial communities.

Distinctive Microbiome Type Distribution in a Young Adult Balinese Cohort May Reflect Environmental Changes Associated with Modernization

An important public health question is understanding how changes in human environments can drive changes in the gut microbiota that influence risks associated with human health and wellbeing. It is well-documented that the modernization of societies is strongly correlated with intergenerational change in the frequency of nutrition-related chronic diseases in which microbial dysbiosis is implicated. The population of Bali, Indonesia, is well-positioned to study the interconnection between a changing food environment and microbiome patterns in its early stages, because of a recent history of modernization. Here, we characterize the fecal microbiota and diet history of the young adult women in Bali, Indonesia (n = 41) in order to compare microbial patterns in this generation with those of other populations with different histories of a modern food environment (industrialized supply chain). We found strong support for two distinct fecal microbiota community types in our study cohort at similar frequency: a Prevotella-rich (Type-P) and a Bacteroides-rich (Type-B) community (p < 0.001, analysis of similarity, Wilcoxon test). Although Type-P individuals had lower alpha diversity (p < 0.001, Shannon) and higher incidence of obesity, multivariate analyses with diet data showed that community types significantly influenced associations with BMI. In a multi-country dataset (n = 257), we confirmed that microbial beta diversity across subsistent and industrial populations was significantly associated with Prevotella and Bacteroides abundance (p < 0.001, generalized additive model) and that the prevalence of community types differs between societies. The young adult Balinese microbiota was distinctive in having an equal prevalence of two community types. Collectively, our study showed that the incorporation of community types as an explanatory factor into study design or modeling improved the ability to identify microbiome associations with diet and health metrics.

LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data

Background

Identification of biomarkers, which are measurable characteristics of biological datasets, can be challenging. Although amplicon sequence variants (ASVs) can be considered potential biomarkers, identifying important ASVs in high-throughput sequencing datasets is challenging. Noise, algorithmic failures to account for specific distributional properties, and feature interactions can complicate the discovery of ASV biomarkers. In addition, these issues can impact the replicability of various models and elevate false-discovery rates. Contemporary machine learning approaches can be leveraged to address these issues. Ensembles of decision trees are particularly effective at classifying the types of data commonly generated in high-throughput sequencing (HTS) studies due to their robustness when the number of features in the training data is orders of magnitude larger than the number of samples. In addition, when combined with appropriate model introspection algorithms, machine learning algorithms can also be used to discover and select potential biomarkers. However, the construction of these models could introduce various biases which potentially obfuscate feature discovery.

Results

We developed a decision tree ensemble, LANDMark, which uses oblique and non-linear cuts at each node. In synthetic and toy tests LANDMark consistently ranked as the best classifier and often outperformed the Random Forest classifier. When trained on the full metabarcoding dataset obtained from Canada’s Wood Buffalo National Park, LANDMark was able to create highly predictive models and achieved an overall balanced accuracy score of 0.96 ± 0.06. The use of recursive feature elimination did not impact LANDMark’s generalization performance and, when trained on data from the BE amplicon, it was able to outperform the Linear Support Vector Machine, Logistic Regression models, and Stochastic Gradient Descent models (p ≤ 0.05). Finally, LANDMark distinguishes itself due to its ability to learn smoother non-linear decision boundaries.

Conclusions

Our work introduces LANDMark, a meta-classifier which blends the characteristics of several machine learning models into a decision tree and ensemble learning framework. To our knowledge, this is the first study to apply this type of ensemble approach to amplicon sequencing data and we have shown that analyzing these datasets using LANDMark can produce highly predictive and consistent models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04631-z.

Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1

Ample evidence indicates that the gut microbiome is a tumor-extrinsic factor associated with antitumor response to anti-programmed cell death protein-1 (PD-1) therapy, but inconsistencies exist between published microbial signatures associated with clinical outcomes. To resolve this, we evaluated a new melanoma cohort, along with four published datasets. Time-to-event analysis showed that baseline microbiota composition was optimally associated with clinical outcome at approximately 1 year after initiation of treatment. Meta-analysis and other bioinformatic analyses of the combined data show that bacteria associated with favorable response are confined within the Actinobacteria phylum and the Lachnospiraceae/Ruminococcaceae families of Firmicutes. Conversely, Gram-negative bacteria were associated with an inflammatory host intestinal gene signature, increased blood neutrophil-to-lymphocyte ratio, and unfavorable outcome. Two microbial signatures, enriched for Lachnospiraceae spp. and Streptococcaceae spp., were associated with favorable and unfavorable clinical response, respectively, and with distinct immune-related adverse effects. Despite between-cohort heterogeneity, optimized all-minus-one supervised learning algorithms trained on batch-corrected microbiome data consistently predicted outcomes to programmed cell death protein-1 therapy in all cohorts. Gut microbial communities (microbiotypes) with nonuniform geographical distribution were associated with favorable and unfavorable outcomes, contributing to discrepancies between cohorts. Our findings shed new light on the complex interaction between the gut microbiome and response to cancer immunotherapy, providing a roadmap for future studies.

Colorectal Cancer-Associated Microbiome Patterns and Signatures

The gut microbiome is dynamic and shaped by diet, age, geography, and environment. The disruption of normal gut microbiota (dysbiosis) is closely related to colorectal cancer (CRC) risk and progression. To better identify and characterize CRC-associated dysbiosis, we collected six independent cohorts with matched normal pairs (when available) for comparison and exploration of the microbiota and their interactions with the host. Comparing the microbial community compositions between cancerous and adjacent noncancerous tissues, we found that more microbes were depleted than enriched in tumors. Despite taxonomic variations among cohorts, consistent depletion of normal microbiota (members of Clostridia and Bacteroidia) and significant enrichment of oral-originated pathogens (such as Fusobacterium nucleatum and Parvimonas micra) were observed in CRC compared to normal tissues. Sets of hub and hub-connecting microbes were subsequently identified to infer microbe-microbe interaction networks in CRC. Furthermore, biclustering was used for identifying coherent patterns between patients and microbes. Two patient-microbe interaction patterns, named P0 and P1, can be consistently identified among the investigated six CRC cohorts. Characterization of the microbial community composition of the two patterns revealed that patients in P0 and P1 differed significantly in microbial alpha and beta diversity, and CRC‐associated microbiota changes consist of continuous populations of widespread taxa rather than discrete enterotypes. In contrast to the P0, the patients in P1 have reduced microbial alpha diversity compared to the adjacent normal tissues, and P1 possesses more oral-related pathogens than P0 and controls. Collectively, our study investigated the CRC-associated microbiome changes, and identified reproducible microbial signatures across multiple independent cohorts. More importantly, we revealed that the CRC heterogeneity can be partially attributed to the variety and compositional differences of microbes and their interactions to humans.