Antimicrobial resistance (AMR) and the evolution of multiple drug-resistant (MDR) bacteria is of grave public health concern. To combat the pandemic of AMR, it is necessary to focus on novel alternatives for drug development. Within the host, the interaction of the pathogen with the microbiome plays a pivotal role in determining the outcome of pathogenesis. Therefore, microbiome-pathogen interaction is one of the potential targets to be explored for novel antimicrobials.

This review focuses on how the gut microbiome has evolved as a significant component of the resistome as a source of antibiotic resistance genes (ARGs). Antibiotics alter the composition of the native microbiota of the host by favouring resistant bacteria that can manifest as opportunistic infections. Furthermore, gut dysbiosis has also been linked to low-dosage antibiotic ingestion or subtherapeutic antibiotic treatment (STAT) from food and the environment.

Colonization by MDR bacteria is potentially acquired and maintained in the gut microbiota. Therefore, it is pivotal to understand microbial diversity and its role in adapting pathogens to AMR. Implementing several strategies to prevent or treat dysbiosis is necessary, including faecal microbiota transplantation, probiotics and prebiotics, phage therapy, drug delivery models, and antimicrobial stewardship regulation.

Dietary fiber interventions to modulate the gut microbiota have largely relied on isolated fibers or specific fiber sources. We hypothesized that fibers systematically blended could promote more health-related bacterial groups. Initially, pooled in vitro fecal fermentations were used to design dietary fiber mixtures to support complementary microbial groups related to health. Then, microbial responses were compared for the designed mixtures versus their single fiber components in vitro using fecal samples from a separate cohort of 10 healthy adults. The designed fiber mixtures outperformed individual fibers in supporting bacterial taxa across donors resulting in superior alpha diversity and unexpected higher SCFA production. Moreover, unique shifts in community structure and specific taxa were observed for fiber mixtures that were not observed for single fibers, suggesting a synergistic effect when certain fibers are put together. Fiber mixture responses were remarkably more consistent than individual fibers across donors in promoting several taxa, especially butyrate producers from the Clostridium cluster XIVa. This is the first demonstration of synergistic fiber interactions for superior support of a diverse group of important beneficial microbes consistent across people, and unexpectedly high SCFA production. Overall, harnessing the synergistic potential of designed fiber mixtures represents a promising and more efficacious avenue for future prebiotic development.

In the diverse landscape of African hominids, the obligate relationship between the host and its microbiome narrates signals of adaptation and co-evolution. Sequencing 546 African hominid metagenomes, including those from indigenous Hadza and wild chimpanzees, identified similar bacterial richness and diversity surpassing those of westernized populations. While hominids share core bacterial communities, they also harbor distinct, population-specific bacterial taxa tailored to specific diets, ecology and lifestyles, differentiating non-indigenous and indigenous humans and chimpanzees. Even amongst shared bacterial communities, several core bacteria have co-diversified to fulfil unique dietary degradation functions within their host populations. These co-evolutionary trends extend to non-bacterial elements, such as mitochondrial DNA, antimicrobial resistance, and parasites. Our findings indicate that microbiome-host co-adaptations have led to both taxonomic and within taxa functional displacements to meet host physiological demands. The microbiome, in turn, transcends its taxonomic interchangeable role, reflecting the lifestyle, ecology and dietary history of its host.

The role of xenobiotic disruption of microbiota, corresponding dysbiosis, and potential links to host metabolic diseases are of critical importance. In this study, we used a widely prescribed antipsychotic drug, risperidone, known to influence weight gain in humans, to induce weight gain in C57BL/6J female mice. We hypothesized that microbes essential for maintaining gut homeostasis and energy balance would be depleted following treatment with risperidone, leading to enhanced weight gain relative to controls. Thus, we performed metagenomic analyses on stool samples to identify microbes that were excluded in risperidone-treated animals but remained present in controls. We identified multiple taxa including Limosilactobacillus reuteri as a candidate for further study. Oral supplementation with L. reuteri protected against risperidone-induced weight gain (RIWG) and was dependent on cellular production of a specialized metabolite, reutericyclin. Further, synthetic reutericyclin was sufficient to mitigate RIWG. Both synthetic reutericyclin and L. reuteri restored energy balance in the presence of risperidone to mitigate excess weight gain and induce shifts in the microbiome associated with leanness. In total, our results identify reutericyclin production by L. reuteri as a potential probiotic to restore energy balance induced by risperidone and to promote leanness.

IgA-coated fractions of the intestinal microbiota of Crohn's disease (CD) patients have been shown to contain taxa that hallmark the compositional dysbiosis in CD microbiomes. However, the correlation between other cellular properties of intestinal bacteria and disease has not been explored further, especially for features that are not directly driven by the host immune-system, e.g. the expression of surface sugars by bacteria. By sorting and sequencing IgA-coated and lectin-stained fractions from CD patients microbiota and healthy controls, we found that lectin-stained bacteria were distinct from IgA-coated bacteria, but still displayed specific differences between CD and healthy controls. To exploit the discriminatory potential of both, immunoglobulin coated bacteria and the altered surface sugar expression of bacteria in CD, we developed a multiplexed single cell-based analysis approach for intestinal microbiota. By multi-parameter microbiota flow cytometry (mMFC) we characterized the intestinal microbiota of 55 CD patients and 44 healthy controls for 11-parameters in total, comprising host-immunoglobulin coating and the presence of distinct surface sugar moieties. The data were analyzed by machine-learning to assess disease-specific marker patterns in the microbiota phenotype. mMFC captured detailed characteristics of CD microbiota and identified patterns to classify CD patients. In addition, we identified phenotypic signatures in the CD microbiota which not only reflected remission after 6 weeks of anti-TNF treatment, but were also able to predict remission before the start of an adalimumab treatment course in a pilot study. We here present the proof-of-concept demonstrating that multi-parameter single-cell bacterial phenotyping by mMFC could be a novel tool with high translational potential to expand current microbiome investigations by phenotyping of bacteria to identify disease- and therapy-associated cellular alterations and to reveal novel target properties of bacteria for functional assays and therapeutic approaches.

The gut microbiota undergoes continuous variations among individuals and across their lifespan, shaped by diverse factors encompassing diet, age, lifestyle choices, medication intake, and disease states. These microbial inhabitants play a pivotal role in orchestrating physiological metabolic pathways through the production of metabolites like bile acids, choline, short-chain fatty acids, and neurotransmitters, thereby establishing a dynamic "gut-organ axis" with the host. The intricate interplay between the gut microbiota and the host is indispensable for gut health, and RNA N6-methyladenosine modification, a pivotal epigenetic mark on RNA, emerges as a key player in this process. M6A modification, the most prevalent internal modification of eukaryotic RNA, has garnered significant attention in the realm of RNA epigenetics. Recent findings underscore its potential to influence gut microbiota diversity and intestinal barrier function by modulating host gene expression patterns. Conversely, the gut microbiota, through its impact on the epigenetic landscape of host cells, may indirectly regulate the recruitment and activity of RNA m6A-modifying enzymes. This review endeavors to delve into the biological functions of m6A modification and its consequences on intestinal injury and disease pathogenesis, elucidating the partial possible mechanisms by which the gut microbiota and its metabolites maintain host intestinal health and homeostasis. Furthermore, it also explores the intricate crosstalk between them in intestinal injury, offering a novel perspective that deepens our understanding of the mechanisms underlying intestinal diseases.

Excessive intake of dietary fats is strongly associated with an increased risk of various chronic diseases, such as obesity, diabetes, hepatic metabolic disorders, cardiovascular disease, chronic intestinal inflammation, and certain cancers. A significant portion of the adverse effects of high-fat diet on disease risk is mediated through modifications in the gut microbiota. Specifically, high-fat diets are linked to reduced microbial diversity, an overgrowth of gram-negative bacteria, an elevated Firmicutes-to-Bacteroidetes ratio, and alterations at various taxonomic levels. These microbial alterations influence the intestinal metabolism of small molecules, which subsequently increases intestinal permeability, exacerbates inflammatory responses, disrupts metabolic functions, and impairs signal transduction pathways in the host. Consequently, diet-induced changes in the gut microbiota play a crucial role in the initiation and progression of chronic diseases. This review explores the relationship between high-fat diets and gut microbiota, highlighting their roles and underlying mechanisms in the development of chronic metabolic diseases. Additionally, we propose probiotic interventions may serve as a promising adjunctive therapy to counteract the negative effects of high-fat diet-induced alterations in gut microbiota composition.

Gut microbiota, which act as a determinant of pharmacokinetics, have long been overlooked. In recent years, a growing body of evidence indicates that the gut microbiota influence drug metabolism and efficacy. Conversely, drugs also exert a substantial influence on the function and composition of the gut microbiota. Pharmacomicrobiomics, an emerging field focusing on the interplay of drugs and gut microbiota, provides a potential foundation for making certain advances in personalized medicine. Understanding the communication between gut microbiota and antiparkinsonian drugs is critical for precise treatment of Parkinson's disease. Here, we provide a historical overview of the interplay between gut microbiota and antiparkinsonian drugs. Moreover, we discuss potential mechanistic insights into the complex associations between gut microbiota and drug metabolism. In addition, we also draw attention to microbiota-based biomarkers for predicting antiparkinsonian drug efficacy and examine current state-of-the-art knowledge of microbiota-based strategies to optimize drug therapy in Parkinson's disease.

Cardiovascular diseases (CVDs), including hypertension, atherosclerosis, and cardiomyopathy among others, remain the leading cause of global morbidity and mortality. Despite advances in treatment, the complex pathophysiology of CVDs necessitates innovative approaches to improve patient outcomes. Recent research has uncovered a dynamic interplay between mitochondria and gut microbiota, fundamentally altering our understanding of cardiovascular health. However, while existing studies have primarily focused on individual components of this axis, this review examines the bidirectional communication between these biological systems and their collective impact on cardiovascular health. Mitochondria, serving as cellular powerhouses, are crucial for maintaining cardiovascular homeostasis through oxidative phosphorylation (OXPHOS), calcium regulation, and redox balance. Simultaneously, the gut microbiota influences cardiovascular function through metabolite production, barrier integrity maintenance, and immune system modulation. The mitochondria-gut microbiota axis operates through various molecular mechanisms, including microbial metabolites such as trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFA), and secondary bile acids, which directly influence mitochondrial function. Conversely, mitochondrial stress signals and damage-associated molecular patterns (DAMPs) affect gut microbial communities and barrier function. Key signalling pathways, including AMP-activated protein kinase (AMPK), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and the silent information regulator 1-peroxisome proliferator-activated receptor gamma coactivator 1-alpha (SIRT1-PGC-1α) axis, integrate these interactions, highlighting their role in CVD pathogenesis. Understanding these interactions has revealed promising therapeutic targets, suggesting new therapies aimed at both mitochondrial function and gut microbiota composition. Thus, this review provides a comprehensive framework for leveraging the mitochondria-gut microbiota axis in providing newer therapeutics for CVDs by targeting the AMPK/SIRT-1/PGC-1α/NF-κB signalling.

Identifying potential connections between microbe-drug pairs play an important role in drug discovery and clinical treatment. Techniques like graph neural networks effectively derive accurate node representations from sparse topologies,however, they struggle with over-smoothing and over-compression, and their interpretability is relatively poor. Conversely, mathematical methods with low-rank approximations are interpretable but often get trapped in local optima. To address these issues, we propose a new prediction model named NNSFMDA, in which, the bounded nuclear norm minimization and the simplified transformer were combined to infer possible drug-microbe associations. In NNSFMDA, we first constructed a heterogeneous microbe-drug network by integrating multiple microbe and drug similarity metrics, according to which, we subsequently transformed the prediction problem to a matrix filling problem, and then, iteratively approximated the matrix by minimizing the number of bounded nuclear norm. Finally, based on the newly-filled matrix, we introduced a simplified transformer to estimate possible scores of microbe-drug pairs. Results showed that NNSFMDA could achieve reliable AUC value of 0.98, which outperformed existing state-of-the-art competitive methods. In the experimental section, ablation experiments and modular analyses further demonstrate the superiority of the model, and case studies of microbe-drug associations confirm the validity of the model. These tests have all highlighted the potential of the NNSFMDA to predict latent microbe-drug associations in the future.

Diet has a profound impact in human health, which is partly driven by changes in the intestinal microbiota. Several associations between dietary intake and the intestinal microbiota composition and function have been described. Namely, the Mediterranean diet is associated with beneficial bacteria, while the intake of ultraprocessed foods is linked to dysbiosis. It is, therefore, very tempting to tailor dietary approaches to the individual needs of the microbiota; however, high-quality prospective data are lacking. Provisionally, a diet rich in fruits and vegetables and low in ultraprocessed foods is recommended to improve the intestinal microbiota composition and function.

In patients with alcohol-associated cirrhosis, the intestinal microbiome composition is disturbed with a loss of beneficial functions and an increase in pathobionts. These changes are associated with disease severity and decompensation, due in part to the exacerbation of liver inflammation by an altered microbiome. Microbes or their antigens may translocate to the liver to potentiate the activation of immune cells and thereby contribute to inflammatory injury. Moreover, microbes may aggravate liver disease through the production of toxins or metabolites, via the effects on bile acids or the intestinal immune system.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has become a major health risk and a serious worldwide issue. MAFLD typically arises from aberrant lipid metabolism, insulin resistance, oxidative stress, and inflammation. However, subjacent causes are multifactorial. The gut has been proposed as a major factor in health and disease, and over the last decade, bacterial strains with potentially beneficial effects on the host have been identified. In vitro cell models have been commonly used as an early step before in vivo drug assessment and can confer complementary advantages in gut and liver health research. In this study, several selected strains of the order Bacteroidales were used in a three-cell line in vitro analysis (HT-29, Caco-2, and HepG2 cell lines) to investigate their potential as new-generation probiotics and microbiota therapeutics. Antimicrobial activity, a potentially useful trait, was studied, and the results showed that Bacteroidales can be a source of either wide- or narrow-spectrum antimicrobials targeting other closely related strains. Moreover, Bacteroides sp. 4_1_36 induced a significant decrease in gut permeability, as evidenced by the high TEER values in the Caco-2 monolayer assay, as well as a reduction in free fatty acid accumulation and improved fatty acid clearance in a steatosis HepG2 model. These results suggest that Bacteroidales may spearhead the next generation of probiotics to prevent or diminish MAFLD.

The fruit fly Drosophila melanogaster is a vital model for studying the microbiome due to the availability of genetic resources and procedures. To understand better the importance of microbial composition in shaping immune modulation, we can investigate the role of the microbiota through parasitic infection. For this, we use entomopathogenic nematodes (EPN) of the genus Steinernema which exhibit remarkable ability to efficiently infect a diverse array of insect species, facilitated by the mutualistic bacteria Xenorhabdus found within their gut. To examine the microbiome changes in D. melanogaster larvae in response to Steinernema nematode infection, D. melanogaster late second to early third instar larvae were exposed separately to S. carpocapsae and S. hermaphroditum infective juveniles. We have found that S. carpocapsae infective juveniles are more pathogenic to D. melanogaster larvae compared to the closely related S. hermaphroditum. Our microbiome analysis also indicates substantial changes in the size and composition of the D. melanogaster larval microbiome during infection with either nematode species compared to the uninfected controls. Our results serve as a foundation for future studies to elucidate the entomopathogenic-specific effector molecules that alter the D. melanogaster microbiome and understand the role of the microbiome in regulating insect anti-nematode immune processes.

Adult human facial skin microbiomes are remarkably similar at the species level, dominated by Cutibacterium acnes and Staphylococcus epidermidis, yet each person harbors a unique community of strains. Understanding how person-specific communities assemble is critical for designing microbiome-based therapies. Here, using 4,055 isolate genomes and 356 metagenomes, we reconstruct on-person evolutionary history to reveal on- and between-person strain dynamics. We find that multiple cells are typically involved in transmission, indicating ample opportunity for migration. Despite this accessibility, family members share only some of their strains. S. epidermidis communities are dynamic, with each strain persisting for an average of only 2 years. C. acnes strains are more stable and have a higher colonization rate during the transition to an adult facial skin microbiome, suggesting this window could facilitate engraftment of therapeutic strains. These previously undetectable dynamics may influence the design of microbiome therapeutics and motivate the study of their effects on hosts.

Sickle cell disease (SCD) is a chronic blood disorder that disrupts multiple organ systems and can lead to severe morbidity. Persistent and acute symptoms caused by immune system dysregulation in individuals with SCD could contribute to disease either directly or indirectly via dysbiosis of commensal microbes and increased susceptibility to infection. Here, we explored the nasal and oral microbiomes of children with SCD (cwSCD) to uncover potential dysbiotic associations with the blood disorder. Microbiota collected from nasal and oral swabs of 40 cwSCD were compared to eight healthy siblings using shotgun metagenomic sequencing. Commensal taxa were present at similar levels in the nasal and oral microbiome of both groups. However, the nasal microbiomes of cwSCD contained a higher prevalence of Pseudomonadota species, including pathobionts such as Yersinia enterocolitica and Klebsiella pneumoniae. Furthermore, the oral microbiome of cwSCD displayed lower α-diversity and fewer commensal and pathobiont species compared to the healthy siblings. Thus, subtle but notable shifts seem to exist in the nasal and oral microbiomes of cwSCD, suggesting an interaction between SCD and the microbiome that may influence health outcomes.

The oral and nasal cavities are susceptible to environmental exposures including pathogenic microbes. In individuals with systemic disorders, antibiotic exposure, changes to the immune system, or changes to organ function could influence the composition of the microbes at these sites and the overall health of individuals. Children with sickle cell disease (SCD) commonly experience respiratory infections, such as pneumonia or sinusitis, and may have increased susceptibility to infection because of disrupted microbiota at these body sites. We found that children with SCD (cwSCD) had more pathobiont bacteria in the nasal cavity and reduced bacterial diversity in the oral cavity compared to their healthy siblings. Defining when, why, and how these changes occur in cwSCD could help identify specific microbial signatures associated with susceptibility to infection or adverse outcomes, providing insights into personalized treatment strategies and preventive measures.

Intratumoral microbiota plays a crucial role in cancer progression. However, the relationship between host genetics and intratumoral microbiota, as well as their interaction in colorectal cancer (CRC) progression, remains unclear. With 748 Chinese CRC patients enrolled from three cohorts, we find that the single nucleotide polymorphism (SNP) rs2355016, located in the intron of ATP-sensitive inward rectifier potassium channel 11 (KCNJ11), is significantly associated with the abundance of Fusobacterium. Compared with the rs2355016 GG genotype, patients carrying the A allele exhibit downregulation of KCNJ11 and enrichment of Fusobacterium, which corresponds to accelerated proliferation and progression. Low expression of KCNJ11 can increase the level of galactose-N-acetyl-d-galactosamine (Gal-GalNAc) on the surface of CRC cells, thereby facilitating the binding of the Fap2 protein from F. nucleatum to Gal-GalNAc. This further enhances the adhesion and invasion of F. nucleatum and promotes CRC growth. Our study explores the interaction between intratumoral microbiota and SNPs in CRC patients, which will enhance our understanding of CRC proliferation.

Although bacteria exist in complex microbial communities in the environment, their features and behavior are most often studied in monoculture. While environmental enrichments or complex co-cultures with tens or hundreds of members might more accurately represent the natural communities of bacteria, we sought to create simple pairs of organisms to learn what conditions create successful co-culture and how bacteria change transcriptionally when a partner species is present. We grew two pairs of organisms in co-culture, Pseudomonas aeruginosa and Escherichia coli and Lacticaseibacillus rhamnosus and Bacteroides thetaiotaomicron. At first, both co-cultures failed, with one organism outcompeting the other. However, through manipulating media and environmental conditions, we created co-cultures with stable member ratios over many generations for each community. We then show that changes in the expression of metabolic genes are present in all studied species, with key catabolic and anabolic pathways often upregulated in the presence of another organism. These changes in gene expression fail to occur in conditions that will not lead to successful co-culture, suggesting they are essential for adapting to and surviving in the presence of others.

In 1882, Robert Koch and Fanny Hesse developed the agar plate, which enabled microbiologists to separate individual microbial cells from each other and create monocultures of a single strain of bacteria. This powerful tool has been used in the almost 150 years since to develop a robust understanding of how bacterial cells are structured, how they manage and process their information, and how they respond to the environment to produce behaviors that match their circumstances. We were curious about how the behavior of bacteria, as measured by their gene expression, changes between well-studied monoculture conditions and co-culture. We found that only specific growth conditions permit co-culture and that bacteria change their metabolic strategies in the presence of a partner.

Cholesterol serves as a fundamental molecule in various structural and biochemical pathways; however, high cholesterol levels are linked to cardiovascular diseases. Some selected strains of Lactobacilli are known for modulating cholesterol levels. However, the molecular mechanism underlying cholesterol transformation by lactobacilli has remained elusive. This study describes the discovery and function of a microbial 3β-OH-Δ5-6-cholesterol-5β-reductase (5βChR) from Limosilactobacillus fermentum NKN51, which directly converts cholesterol to coprostanol, thereby unraveling this longstanding mystery. Protein engineering of the reductase enzyme identified the cholesterol and NADPH interacting amino acid residues, detailing the catalytic mechanism of 5βChR. Phylogenetic analyses highlight the prevalence of 5βChRs in gut commensal lactobacilli, which share a common evolutionary origin with plant 5β reductases. Meta-analysis of microbiomes from healthy individuals underscores the importance of 5βChR homologs, while a cohort study demonstrates an inverse association between 5βChR abundance and diabetes. The discovery of the 5βChR enzyme and its molecular mechanism in cholesterol metabolism paves the way for a better understanding of the gut-associated microbiome and the design of practical applications to ameliorate dyslipidemia.

Growing evidence suggests that microbes located within the gastrointestinal tract and other anatomical locations influence the development and progression of diseases such as cancer.

Clinical and preclinical evidence suggests that microbes in the gastrointestinal tract and other anatomical locations, such as the respiratory tract, may affect carcinogenesis, development of metastases, cancer treatment response, and cancer treatment-related adverse effects. Within tumors of patients with cancer, microbes may affect response to treatment, and therapies that reduce or eliminate these microbes may improve outcomes in patients with cancer. Modulating gastrointestinal tract (gut) microbes through fecal microbiota transplant and other strategies such as dietary intervention (eg, high-fiber diet intervention) has improved outcomes in small studies of patients treated with cancer immunotherapy. In contrast, disruption of the gut microbiota by receipt of broad-spectrum antibiotics prior to treatment with cancer immunotherapy has been associated with poorer overall survival and higher rates of adverse effects in patients treated with immune checkpoint blockade for solid tumors and also with chimeric antigen receptor T-cell therapy for hematologic malignancies.

Microbes in the gut and other locations in the body may influence the development and progression of cancer and may affect the response to adverse effects from cancer therapy. Future therapies targeting microbes in the gut and other locations in the body could potentially improve outcomes in patients with cancer.

Oral lichen planus (OLP) is a prevalent T-cell mediated inflammatory-immune disease with uncertain etiology. Recently, there is emerging evidence suggesting that oral bacteria may exert a prominent role in the onset and development of OLP. They might promote the initiation and progression of OLP by disrupting the oral epithelia, invading the lamina propria, stimulating pro-inflammatory cytokines production and inducing immune dysfunction. In this review, we will focus on the possible mechanisms of oral bacteria contributing to occurrence and development of OLP, and provide new insights into the bacteria-related diagnosis, prevention and treatment strategies for OLP.

Understanding mixed infections in the female lower genital tract is a critical challenge in modern infection research. The interplay of multiple pathogens complicates disease progression, often resulting in treatment failure, recurrent infections, and significant public health and economic burdens. These infections are further exacerbated by disrupted host immune responses, which hinder the recovery of the vaginal microecosystem. Additionally, microbial biofilms-a fundamental mode of pathogen coexistence-contribute to the persistence and drug resistance of these infections, complicating management strategies. This review examines the pathogenesis, diagnosis, and treatment of mixed infections in the female lower genital tract while exploring potential avenues for future research. These findings emphasize the need for greater focus on these infections and offer insights to enhance further research in this area.

Gihawi et al. (mBio 14:e01607-23, 2023, https://doi.org/10.1128/mbio.01607-23) argued that the analysis of tumor-associated microbiome data by Poore et al. (Nature 579:567-574, 2020, https://doi.org/10.1038/s41586-020-2095-1) is invalid because features that were originally very sparse (genera with mostly zero read counts) became associated with the phenotype following batch correction. Here, we examine whether such an observation should necessarily indicate issues with processing or machine learning pipelines. We show counterexamples using the centered log ratio (CLR) transformation, which is often used for analysis of compositional microbiome data. The CLR transformation has similarities to voom-SNM, the batch-correction method brought into question by Gihawi et al., and yet is a sample-wise operation that cannot, in itself, "leak" information or invalidate downstream analyses. We show that because the CLR transformation divides each value by the geometric mean of its sample, common imputation strategies for missing or zero values result in transformed features that are associated with the geometric mean. Through analyses of both synthetic and vaginal microbiome data sets, we demonstrate that when the geometric mean is associated with a phenotype, sparse and CLR-transformed features will also become associated with it. We re-analyze features highlighted by Gihawi et al. and demonstrate that the phenomenon of sparse features becoming phenotype-associated can also be observed after a CLR transformation, which serves as a counterexample to the claim that such an observation necessarily means information leakage. While we do not intend to address other concerns regarding tumor microbiome analyses, validate Poore et al.'s results, or evaluate batch-correction pipelines, we conclude that because phenotype-associated features that were initially sparse can be created by a sample-wise transformation that cannot artifactually inflate machine learning performance, their detection is not independently sufficient to demonstrate information leakage in machine learning pipelines. Microbiome data are multivariate, and as such, a value of 0 carries a different meaning for each sample. Many transformations, including CLR and other batch-correction methods, are likewise multivariate, and, as these issues demonstrate, each individual feature should be interpreted with caution.

Gihawi et al. claim that finding that a transformation turned highly sparse (mostly zero) features into features that are associated with a phenotype is sufficient to conclude that there is information leakage and to invalidate an analysis. This claim has critical implications for both the debate regarding The Cancer Genome Atlas (TCGA) cancer microbiome analysis and for interpretation and evaluation of analyses in the microbiome field at large. We show by counterexamples and by reanalysis that such transformations can be valid.

Dysbiosis refers to the disruption of the gut microbiota balance and is the pathological basis of various diseases. The main pathogenic mechanisms include impaired intestinal mucosal barrier function, inflammation activation, immune dysregulation, and metabolic abnormalities. These mechanisms involve dysfunctions in the gut-brain axis, gut-liver axis, and others to cause broader effects. Although the association between diseases caused by dysbiosis has been extensively studied, many questions remain regarding the specific pathogenic mechanisms and treatment strategies. This review begins by examining the causes of gut microbiota dysbiosis and summarizes the potential mechanisms of representative diseases caused by microbiota imbalance. It integrates clinical evidence to explore preventive and therapeutic strategies targeting gut microbiota dysregulation, emphasizing the importance of understanding gut microbiota dysbiosis. Finally, we summarized the development of artificial intelligence (AI) in the gut microbiota research and suggested that it will play a critical role in future studies on gut dysbiosis. The research combining multiomics technologies and AI will further uncover the complex mechanisms of gut microbiota dysbiosis. It will drive the development of personalized treatment strategies.

The oral microbiome likely plays key roles in human health. Yet, population-representative characterizations are lacking.

To characterize the composition, diversity, and correlates of the oral microbiome in US adults.

This cross-sectional study analyzed data from the population-representative National Health and Nutrition Examination Survey (NHANES) from 2009 to 2012. Microbiome data were made publicly available in 2024. NHANES participants were aged 18 to 69 years and provided oral rinse samples in 1 of 2 consecutive NHANES cycles (2009-2010 and 2011-2012).

Demographic, socioeconomic, behavioral, anthropometric, metabolic, and clinical characteristics.

Oral microbiome measures, characterized through 16S ribosomal RNA gene sequencing, included α diversity (observed amplicon sequence variants [ASVs], Faith phylogenetic diversity, Shannon-Weiner Index, and Simpson Index); β diversity (unweighted UniFrac, weighted UniFrac, and Bray-Curtis dissimilarity); and prevalence and relative abundance at phylum level through genus level. Analyses accounted for the NHANES complex sample design.

This study included 8237 US adults aged 18 to 69 years, representing 202 314 000 individuals (102 813 000 men [50.8%]; mean [SD] age, 42.3 [14.4] years; 9.3% self-reported as Mexican American, 12.1% as non-Hispanic Black, 64.7% as non-Hispanic White, 5.9% as other Hispanic, and 8.1% as other non-Hispanic individuals). The oral microbiome encompassed 37 bacterial phyla, 99 classes, 212 orders, 446 families, and 1219 genera. Five phyla (Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Fusobacteria) and 6 genera (Veillonella, Streptococcus, Prevotella 7, Rothia, Actinomyces, and Gemella) were present in nearly all US adults (weighted prevalence, >99%). These genera were the most abundant, accounting for 65.7% of total abundance. Observed ASVs showed a quadratic pattern with age (peak at 30 years), were similar by sex, significantly lower among non-Hispanic White individuals, and increased with greater body mass index (BMI), alcohol use, and periodontal disease severity. All covariates together accounted for a modest proportion of oral microbiome variability as measured by β diversity: R2 = 8.7% (95% CI, 8.4%-9.1%) for unweighted UniFrac, R2 = 7.2% (95% CI, 6.6%-7.7%) for weighted UniFrac, and R2 = 6.3% (95% CI, 3.1%-6.7%) for Bray-Curtis matrices. By contrast, relative abundance of a few genera explained a high percentage of variability in β diversity for weighted UniFrac: Aggregatibacter (R2 = 22.4%; 95% CI, 22.1%-22.8%), Lactococcus (R2 = 21.6%; 95% CI, 20.9%-22.3%), and Haemophilus (R2 = 18.4%; 95% CI, 18.1%-18.8%). Prevalence and relative abundance of numerous genera were associated with age, race and ethnicity, smoking, BMI categories, alcohol use, and periodontal disease severity.

This cross-sectional study of the oral microbiome in US adults showed that a few genera were universally present and a different set of genera explained a high percentage of oral microbiome diversity across the population. This comprehensive characterization provides a contemporary reference standard for future studies.

All biological systems are subject to perturbations arising from thermal fluctuations, external environments, or mutations. Yet, while biological systems consist of thousands of interacting components, recent high-throughput experiments have shown that their response to perturbations is surprisingly low dimensional: confined to only a few stereotyped changes out of the many possible. In this review, we explore a unifying dynamical systems framework-soft modes-to explain and analyze low dimensionality in biology, from molecules to ecosystems. We argue that this soft mode framework makes nontrivial predictions that generalize classic ideas from developmental biology to disparate systems, namely phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.

Everyday actions such as eating, tooth brushing or applying cosmetics inherently modulate our microbiome. Advances in sequencing technologies now facilitate detailed microbial profiling, driving intentional microbiome-targeted product development. Inspired by an academic-industry workshop held in January 2024, this review explores the oral, skin and gut microbiomes, focussing on the potential long-term implications of perturbations. Key challenges in microbiome safety assessment include confounding factors (ecological variability, host influences and external conditions like geography and diet) and biases from experimental measurements and bioinformatics analyses. The taxonomic composition of the microbiome has been associated with both health and disease, and perturbations like regular disruption of the dental biofilm are essential for preventing caries and inflammatory gum disease. However, further research is required to understand the potential long-term impacts of microbiome disturbances, particularly in vulnerable populations including infants. We propose that emerging technologies, such as omics technologies to characterize microbiome functions rather than taxa, leveraging artificial intelligence to interpret clinical study data and in vitro models to characterize and measure host-microbiome interaction endpoints, could all enhance the risk assessments. The workshop emphasized the importance of detailed documentation, transparency and openness in computational models to reduce uncertainties. Harmonisation of methods could help bridge regulatory gaps and streamline safety assessments but should remain flexible enough to allow innovation and technological advancements. Continued scientific collaboration and public engagement are critical for long-term microbiome monitoring, which is essential to advancing safety assessments of microbiome perturbations.

Seborrheic dermatitis (SD) is a common, multifaceted skin condition, but its undefined etiology hampers the development of effective therapeutic strategies. In this review, we describe the intricate relationship between the skin microbiome and the pathogenesis of SD, focusing on the complex interplay between three major groups of organisms that can either induce inflammation (Malassezia spp., Staphylococcus aureus) or else promote healthy skin (Propionibacterium spp.). We describe how the disequilibrium of these microorganisms in the skin microbiome can develop skin inflammation in SD patients. Understanding these complex interactions of the skin microbiome has led to development of novel probiotics (e.g., Vitreoscilla spp. and Lactobacillus spp.) to restore normal skin physiology in SD. There are also differences in the skin microbiomes of healthy and SD infant and adult patients that impact pathogenesis and prompt different management strategies. A deeper understanding of the skin microbiome and its dynamic interactions will provide valuable insights into the pathogenesis of SD and prompt further development of targeted probiotic treatments to restore the balance of the skin microbiome in SD patients.

The ability to detect and quantify microbiota over time from shotgun metagenomic data has a plethora of clinical, basic science and public health applications. Given these applications, and the observation that pathogens and other taxa of interest can reside at low relative abundance, there is a critical need for algorithms that accurately profile low-abundance microbial taxa with strain-level resolution. Here we present ChronoStrain: a sequence quality- and time-aware Bayesian model for profiling strains in longitudinal samples. ChronoStrain explicitly models the presence or absence of each strain and produces a probability distribution over abundance trajectories for each strain. Using synthetic and semi-synthetic data, we demonstrate how ChronoStrain outperforms existing methods in abundance estimation and presence/absence prediction. Applying ChronoStrain to two human microbiome datasets demonstrated its improved interpretability for profiling Escherichia coli strain blooms in longitudinal faecal samples from adult women with recurring urinary tract infections, and its improved accuracy for detecting Enterococcus faecalis strains in infant faecal samples. Compared with state-of-the-art methods, ChronoStrain's ability to detect low-abundance taxa is particularly stark.

The intestine, as a critical interface between the external environment and the internal body, plays a central role in nutrient absorption, immune regulation, and maintaining homeostasis. The intestinal epithelium, composed of specialized epithelial cells, harbors apical anion transporters that primarily mediate the transport of chloride and bicarbonate ions, essential for maintaining electrolyte balance, pH homeostasis, and fluid absorption/secretion. In addition, the intestine hosts a diverse population of gut microbiota that plays a pivotal role in various physiological processes including nutrient metabolism, immune regulation, and maintenance of intestinal barrier integrity, all of which are critical for host gut homeostasis and health. The anion transporters and gut microbiome are intricately interconnected, where alterations in one can trigger changes in the other, leading to compromised barrier integrity and increasing susceptibility to pathophysiological states including gut inflammation. This review focuses on the interplay of key apical anion transporters including Down-Regulated in Adenoma (DRA, SLC26A3), Putative Anion Transporter-1 (PAT1, SLC26A6), and Cystic Fibrosis Transmembrane Conductance Regulator [CFTR, ATP-binding cassette subfamily C member 7 (ABCC7)] with the gut microbiome, barrier integrity, and their relationship to gut inflammation.

This study aimed to examine the correlation between daily physical activity levels and gut microbiota during the perimenopausal period. To explore the physiopathological traits of perimenopause women, and provide a theoretical basis for the development of menopause exercise intervention program. In this study, daily physical activity was assessed using the ActiGraph wGT3X-BT wearable sensor, and 16S rRNA sequencing was employed to analyze gut microbiota diversity and composition. Comparative analysis was conducted to evaluate the association between physical activity and specific phyla and genera in the gut microbiota, comparing perimenopausal women to youth women. The results showed perimenopausal women exhibited significantly lower levels of daily physical activity, including Kcals, metabolic equivalents (METs), and moderate-to-vigorous physical activity (MVPA), compared to youth women. The composition of the gut microbiota was markedly different between the two groups. Specifically, the abundance of Acidobacteria, Chloroflexi, Nitrospirae, and Gemmatimonadetes were lower at the phylum level (p < 0.01). Collinsella, Ruminococcus gnavus, Rothia, Haemophilus, Sphingomonas, Lactobacillales, and Lactococcus were lower at the genus level, while Phascolarctobacterium, Paraprevotella, Acinetobacter, Flavonifractor, and Intestinimonas exhibited a significant increase (p < 0.05, p < 0.01). Furthermore, a positive correlation was observed between Chloroflexi or Sphingomonas and physical activity (kcals, METs, and MVPA), while a negative correlation was found between Intestinimonas and physical activity (METs: p < 0.01, and MVPA: p < 0.05). Doubtlessly, the perimenopausal period is associated with lower levels of physical activity and distinct changes in gut microbiota. Sphingomonas may serve as a sensitive bacterium closely linked to physical activity.

Determining the time since deposition (TsD) and sex of saliva stains is crucial for revealing the time of the crime's occurrence and clarifying the nature of the crime. This process not only shortens the time required to solve the case but also helps narrow down the scope of investigation, thereby enhancing the efficiency of case resolution. Currently, the forensic study of the microbial composition in long-term saliva stains remains a relatively underexplored field. The purpose of this study was to explore the succession pattern of long-placed human saliva stains microbial communities and identify relevant microbial markers for estimating TsD and identifying the sex of the donor, in order to be an effective alternative tool for solving practical forensic cases. Therefore, in this study, saliva stains exposed to indoor environmental conditions for up to 140 days were collected and 16S rRNA full-length sequencing was performed using single-molecule real-time sequencing technology based on the PacBio sequencing platform. The study reveals that after 140 days of placement, the relative abundance of Firmicutes significantly decreased (p = 0.00304). At the genus level, the relative abundances of Streptococcus (p = 0.0008), Rothia (p = 0.0448), Gemella (p = 0.016), and Veillonella (p = 0.0208) also significantly decreased. Additionally, significant differences were found in the microbial communities between saliva stains from males and females (p = 0.00013). Then, we constructed a TsD estimating model for microbial community markers based on random forest, and the results showed that the mean absolute error was 9.59 days, and the accuracy of sex classification model based on stepwise logistic regression model and 4 bacterial markers was 84.21%. This indicates that saliva stains that have been in place for a long time still retain significant forensic value, and microbial markers can be used to determine the time since deposition (TsD) of dried saliva stains as well as to identify the sex of the donor.

Evidence of changes in the composition and function of the gut microbiota (GM) in post-stroke depression (PSD) patients is gradually accumulating. This study aimed to systematically evaluate the relationship between PSD and GM.

We searched in PubMed, Web of Science, Embase, Cochrane databases, Wangfang, VIP, CBM, and CNKI from the establishment of the database to April 17, 2024, and systematic review and meta-analysis were performed to investigate the differences of GM between patients with PSD spectrum and healthy controls (HC) or stroke spectrum.

There were 14 studies consisting a total of 1,556 individuals included in the meta-analysis. The pooled results showed that PSD spectrum demonstrated significantly increased α diversity as indexed by Chao1 index, ACE indexes, Shannon index, and Simpson index as compared to HC. Additionally, stroke spectrum significantly increased α diversity as indexed by Simpson index compared to PSD. Furthermore, the pooled estimation of relative abundance showed that Bacteroidota, Fusobacteriota, and Pseudomonadota in PSD patients were significantly higher than those in the HC group, while the abundance of Bacillota was higher in the HC group. Moreover, significant differences in GM were observed between PSD patients and HC at the family and genus levels.

This study found that the α diversity of PSD patients was higher than that of HC. Moreover, there were also differences in the distribution of GM at the phylum, family, and genus levels, respectively. At the same time, the level of Lachnospira in PSD patients was lower than that in the stroke group.

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024582708.

Reference genomes of root microbes are essential for metagenomic analyses and mechanistic studies of crop root microbiomes. By combining high-throughput bacterial cultivation with metagenomic sequencing, we constructed comprehensive bacterial and viral genome collections from the roots of wheat, rice, maize, and Medicago. The crop root bacterial genome collection (CRBC) significantly expands the quantity and phylogenetic diversity of publicly available crop root bacterial genomes, with 6,699 bacterial genomes (68.9% from isolates) and 1,817 undefined species, expanding crop root bacterial diversity by 290.6%. The crop root viral genome collection (CRVC) contains 9,736 non-redundant viral genomes, with 1,572 previously unreported genus-level clusters in crop root microbiomes. From these, we identified conserved bacterial functions enriched in root microbiomes across soils and host species and uncovered previously unexplored bacteria-virus connections in crop root ecosystems. Together, the CRBC and CRVC serve as valuable resources for investigating microbial mechanisms and applications, supporting sustainable agriculture.