Human-associated microbial signatures: examining their predictive value

Gut Microbiome Wellness Index 2 enhances health status prediction from gut microbiome taxonomic profiles

Recent advancements in translational gut microbiome research have revealed its crucial role in shaping predictive healthcare applications. Herein, we introduce the Gut Microbiome Wellness Index 2 (GMWI2), an enhanced version of our original GMWI prototype, designed as a standardized disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involves pooling existing 8069 stool shotgun metagenomes from 54 published studies across a global demographic landscape (spanning 26 countries and six continents) to identify gut taxonomic signals linked to disease presence or absence. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). This performance exceeds that of the original GMWI model and traditional species-level α-diversity indices, indicating a more robust gut microbiome signature for differentiating between healthy and non-healthy phenotypes across multiple diseases. When assessed through inter-study validation and external validation cohorts, GMWI2 maintains an average accuracy of nearly 75%. Furthermore, by reevaluating previously published datasets, GMWI2 offers new insights into the effects of diet, antibiotic exposure, and fecal microbiota transplantation on gut health. Available as an open-source command-line tool, GMWI2 represents a timely, pivotal resource for evaluating health using an individual's unique gut microbial composition.

The infants' gut microbiome: setting the stage for the early onset of obesity

In the past three decades, dietary and lifestyle changes worldwide have resulted in a global increase in the prevalence of obesity in both adults and children. Known to be highly influenced by genetic, environmental and lifestyle factors, obesity is characterized by a low-grade chronic inflammation that contributes to the development of other metabolic diseases such as diabetes and cardiovascular disease. Recently, the gut microbiome has been added as a cause/contributor to the development of obesity. As differences in the microbiome between obese and normoweight individuals have been observed, we set out to determine whether infants harbor an obesogenic microbiome early on and whether the pre-pregnancy status of the mother (obese or normoweight) is correlated to their infant's microbiome composition. Using shotgun sequencing, we analyzed stool samples throughout the first year of life from infants born to obese (n = 23 participants, m = 104 samples) and normoweight (n = 23 participants, m = 99 samples) mothers. We found that the infants' microbiome diversity at taxonomic and functional levels was significantly influenced by time (ANOVA p < 0.001) but not by the mother's pre-pregnancy status. Overall, no deterministic succession of taxa or functions was observed. However, infants born to obese mothers were found to have a significantly higher Bacillota/Bacteroidota ratio (p = 0.02) at six months, were significantly depleted from six months old of the well-established obesity biomarkers Akkermansia municiphila and Faecalibacterium prausnitzii (p < 0.01), and were at one week old, significantly enriched in pathways such as the UDP-N-acetyl-D-glucosamine biosynthesis II (p = 0.02) involved in leptin production, suggesting perhaps that there may exist some underlying mechanisms that dictate the development of an obesogenic microbiota early on.

Microbiota of Saliva: A Non-invasive Diagnostic Tool

Potential of salivary microbiota as a non-invasive diagnostic tool for various diseases are explained in the present review. Traditional diagnostic methods rely on blood, which has limitations in terms of collection and biomarker specificity. We discuss the concept of normal flora and how disruptions in oral microbiota can be indicative of diseases. Saliva, harboring a diverse microbial community, offers promise as a diagnostic biomarker source for oral and non-oral conditions. We delve into the role of microbial dysbiosis in disease pathogenesis and the prospects of using biological indicators like dysbiosis for diagnosis, prediction, and monitoring. This review also emphasizes the significance of saliva microbiota in advancing early disease detection and timely intervention. We addressed the following research question and objectives: Can the microbiota of saliva serve as a non-invasive diagnostic tool for the early detection and monitoring of both oral and non-oral diseases? To achieve this, we will explore the normal flora of microorganisms in the oral cavity, the impact of microbial dysbiosis, and the potential of using specific pathogenic microorganisms as biomarkers. Additionally, we will investigate the correlation between oral and non-oral diseases by analyzing total saliva or site-specific dental biofilms for signs of symbiosis or dysbiosis. This research seeks to contribute valuable insights into the development of a non-invasive diagnostic approach with broad applications in healthcare.

Forensic Microbiology: When, Where and How

Forensic microbiology is a relatively new discipline, born in part thanks to the development of advanced methodologies for the detection, identification and characterization of microorganisms, and also in relation to the growing impact of infectious diseases of iatrogenic origin. Indeed, the increased application of medical practices, such as transplants, which require immunosuppressive treatments, and the growing demand for prosthetic installations, associated with an increasing threat of antimicrobial resistance, have led to a rise in the number of infections of iatrogenic origin, which entails important medico-legal issues. On the other hand, the possibility of detecting minimal amounts of microorganisms, even in the form of residual traces (e.g., their nucleic acids), and of obtaining gene and genomic sequences at contained costs, has made it possible to ask new questions of whether cases of death or illness might have a microbiological origin, with the possibility of also tracing the origin of the microorganisms involved and reconstructing the chain of contagion. In addition to the more obvious applications, such as those mentioned above related to the origin of iatrogenic infections, or to possible cases of infections not properly diagnosed and treated, a less obvious application of forensic microbiology concerns its use in cases of violence or violent death, where the characterization of the microorganisms can contribute to the reconstruction of the case. Finally, paleomicrobiology, e.g., the reconstruction and characterization of microorganisms in historical or even archaeological remnants, can be considered as a sister discipline of forensic microbiology. In this article, we will review these different aspects and applications of forensic microbiology.

Interaction between Dietary Lactoferrin and Gut Microbiota in Host Health

The gut microbiota are known to play an important role in host health and disease. Alterations in the gut microbiota composition can disrupt the stability of the gut ecosystem, which may result in noncommunicable chronic diseases (NCCDs). Remodeling the gut microbiota through personalized nutrition is a novel therapeutic avenue for both disease control and prevention. However, whether there are commonly used gut microbiota-targeted diets and how gut microbiota-diet interactions combat NCCDs and improve health remain questions to be addressed. Lactoferrin (LF), which is broadly used in dietary supplements, acts not only as an antimicrobial in the defense against enteropathogenic bacteria but also as a prebiotic to propagate certain probiotics. Thus, LF-induced gut microbiota alterations can be harnessed to induce changes in host physiology, and the underpinnings of their relationships and mechanisms are beginning to unravel in studies involving humans and animal models.

Multi-omic integration of microbiome data for identifying disease-associated modules

The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.

Microbiota Implications in Endocrine-Related Diseases: From Development to Novel Therapeutic Approaches

This comprehensive review article delves into the critical role of the human microbiota in the development and management of endocrine-related diseases. We explore the complex interactions between the microbiota and the endocrine system, emphasizing the implications of microbiota dysbiosis for the onset and progression of various endocrine disorders. The review aims to synthesize current knowledge, highlighting recent advancements and the potential of novel therapeutic approaches targeting microbiota-endocrine interactions. Key topics include the impact of microbiota on hormone regulation, its role in endocrine pathologies, and the promising avenues of microbiota modulation through diet, probiotics, prebiotics, and fecal microbiota transplantation. We underscore the importance of this research in advancing personalized medicine, offering insights for more tailored and effective treatments for endocrine-related diseases.

Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles

Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level α-diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individual's unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.

It's all relative: analyzing microbiome compositions, its significance, pathogenesis and microbiota derived biofilms: Challenges and opportunities for disease intervention

Concept of microorganisms has largely been perceived from their pathogenic view point. Nevertheless, it is being gradually revisited in terms of its significance to human health and now appears to be the most dominant force that shapes the immune system of the human body and also determines an individual's predisposition to diseases. Human inhabits bacterial diversity (which is predominant among all microbial communities in human body) occupying 0.3% of body mass, known as microbiota. On birth, a part of microbiota that child obtains is essentially a mother's legacy. So, the review was initiated with this critical topic of microbiotal inheritance. Since, each body site has distinct physiological specifications; therefore, they contain discrete microbiome composition that has been separately discussed along with dysbiosis-induced pathologies originating in different body organs. Factors affecting microbiome composition and may cause dysbiosis like antibiotics, delivery, feeding method etc. as well as the strategies that immune system adopts to prevent dysbiosis have been highlighted. We also tried to bring into attention the topic of dysbiosis induced biofilms, that enables cohort to survive stresses, evolve, disseminate and infection resurgence that is still in dormancy. Eventually, we put spotlight on microbiome significance in medical therapeutics. We didn't merely confine article to gut microbiota, that is being studied more extensively. Numerous community forms at diverse body sites are inter-related, and being exposed to awfully variable perturbations appear to be challenging to evaluate perturbation risks holistically. All aspects have been elaborately discussed to achieve a global depiction of human microbiota in order to meet urgent necessity for protocol standardisation. Demonstrates that environmental challenges (antibiotic use, alterations in diet, stress, smoking etc.) might cause dysbiosis i.e. transition of healthy microbiome composition to the one in which pathogenic microorganisms become more abundant, and eventually results in an infected state.

Microbiome-Based Stain Analyses in Crime Scenes

Recent advances in next-generation sequencing technologies (NGS) coupled with machine learning have demonstrated the potential of microbiome-based analyses in applied areas such as clinical diagnostics and forensic sciences. Particularly in forensics, microbial markers in biological stains left at a crime scene can provide valuable information for the reconstruction of crime scene cases, as they contain information on bodily origin, the time since deposition, and donor(s) of the stain. Importantly, microbiome-based analyses provide a complementary or an alternative approach to current methods when these are limited or not feasible. Despite the promising results from recent research, microbiome-based stain analyses are not yet employed in routine casework. In this review, we highlight the two main gaps that need to be addressed before we can successfully integrate microbiome-based analyses in applied areas with a special focus on forensic casework: one is a comprehensive assessment of the method's strengths and limitations, and the other is the establishment of a standard operating procedure. For the latter, we provide a roadmap highlighting key decision steps and offering laboratory and bioinformatic workflow recommendations, while also delineating those aspects that require further testing. Our goal is to ultimately facilitate the streamlining of microbiome-based analyses within the existing forensic framework to provide alternate lines of evidence, thereby improving the quality of investigations.

Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations

Metagenomic analysis has been explored for disease diagnosis and biomarker discovery. Low sample sizes, high dimensionality, and sparsity of metagenomic data challenge metagenomic investigations. Here, an unsupervised microbial embedding, grouping, and mapping algorithm (MEGMA) was developed to transform metagenomic data into individualized multichannel microbiome 2D representation by manifold learning and clustering of microbial profiles (e.g., composition, abundance, hierarchy, and taxonomy). These 2D representations enable enhanced disease prediction by established ConvNet-based AggMapNet models, outperforming the commonly used machine learning and deep learning models in metagenomic benchmark datasets. These 2D representations combined with AggMapNet explainable module robustly identified more reliable and replicable disease-prediction microbes (biomarkers). Employing the MEGMA-AggMapNet pipeline for biomarker identification from 5 disease datasets, 84% of the identified biomarkers have been described in over 74 distinct works as important for these diseases. Moreover, the method also discovered highly consistent sets of biomarkers in cross-cohort colorectal cancer (CRC) patients and microbial shifts in different CRC stages.

Analysis of Postmortem Intestinal Microbiota Successional Patterns with Application in Postmortem Interval Estimation

Microorganisms play a vital role in the decomposition of vertebrate remains in natural nutrient cycling, and the postmortem microbial succession patterns during decomposition remain unclear. The present study used hierarchical clustering based on Manhattan distances to analyze the similarities and differences among postmortem intestinal microbial succession patterns based on microbial 16S rDNA sequences in a mouse decomposition model. Based on the similarity, seven different classes of succession patterns were obtained. Generally, the normal intestinal flora in the cecum was gradually decreased with changes in the living conditions after death, while some facultative anaerobes and obligate anaerobes grew and multiplied upon oxygen consumption. Furthermore, a random forest regression model was developed to predict the postmortem interval based on the microbial succession trend dataset. The model demonstrated a mean absolute error of 20.01 h and a squared correlation coefficient of 0.95 during 15-day decomposition. Lactobacillus, Dubosiella, Enterococcus, and the Lachnospiraceae NK4A136 group were considered significant biomarkers for this model according to the ranked list. The present study explored microbial succession patterns in terms of relative abundances and variety, aiding in the prediction of postmortem intervals and offering some information on microbial behaviors in decomposition ecology.

Age-Related NAFLD: The Use of Probiotics as a Supportive Therapeutic Intervention

Human aging, a natural process characterized by structural and physiological changes, leads to alterations of homeostatic mechanisms, decline of biological functions, and subsequently, the organism becomes vulnerable to external stress or damage. In fact, the elderly population is prone to develop diseases due to deterioration of physiological and biological systems. With aging, the production of reactive oxygen species (ROS) increases, and this causes lipid, protein, and DNA damage, leading to cellular dysfunction and altered cellular processes. Indeed, oxidative stress plays a key role in the pathogenesis of several chronic disorders, including hepatic diseases, such as non-alcoholic fatty liver disease (NAFLD). NAFLD, the most common liver disorder in the Western world, is characterized by intrahepatic lipid accumulation; is highly prevalent in the aging population; and is closely associated with obesity, insulin resistance, hypertension, and dyslipidemia. Among the risk factors involved in the pathogenesis of NAFLD, the dysbiotic gut microbiota plays an essential role, leading to low-grade chronic inflammation, oxidative stress, and production of various toxic metabolites. The intestinal microbiota is a dynamic ecosystem of microbes involved in the maintenance of physiological homeostasis; the alteration of its composition and function, during aging, is implicated in different liver diseases. Therefore, gut microbiota restoration might be a complementary approach for treating NAFLD. The administration of probiotics, which can relieve oxidative stress and elicit several anti-aging properties, could be a strategy to modify the composition and restore a healthy gut microbiota. Indeed, probiotics could represent a valid supplement to prevent and/or help treating some diseases, such as NAFLD, thus improving the already available pharmacological intervention. Moreover, in aging, intervention of prebiotics and fecal microbiota transplantation, as well as probiotics, will provide novel therapeutic approaches. However, the relevant research is limited, and several scientific research works need to be done in the near future to confirm their efficacy.

Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.

Super-taxon in human microbiome are identified to be associated with colorectal cancer

BACKGROUND

Microbial communities in the human body, also known as human microbiota, impact human health, such as colorectal cancer (CRC). However, the different roles that microbial communities play in healthy and disease hosts remain largely unknown. The microbial communities are typically recorded through the taxa counts of operational taxonomic units (OTUs). The sparsity and high correlations among OTUs pose major challenges for understanding the microbiota-disease relation. Furthermore, the taxa data are structured in the sense that OTUs are related evolutionarily by a hierarchical structure.

RESULTS

In this study, we borrow the idea of super-variant from statistical genetics, and propose a new concept called super-taxon to exploit hierarchical structure of taxa for microbiome studies, which is essentially a combination of taxonomic units. Specifically, we model a genus which consists of a set of OTUs at low hierarchy and is designed to reflect both marginal and joint effects of OTUs associated with the risk of CRC to address these issues. We first demonstrate the power of super-taxon in detecting highly correlated OTUs. Then, we identify CRC-associated OTUs in two publicly available datasets via a discovery-validation procedure. Specifically, four species of two genera are found to be associated with CRC: Parvimonas micra, Parvimonas sp., Peptostreptococcus stomatis, and Peptostreptococcus anaerobius. More importantly, for the first time, we report the joint effect of Parvimonas micra and Parvimonas sp. (p = 0.0084) as well as that of Peptostrepto-coccus stomatis and Peptostreptococcus anaerobius (p = 8.21e-06) on CRC. The proposed approach provides a novel and useful tool for identifying disease-related microbes by taking the hierarchical structure of taxa into account and further sheds new lights on their potential joint effects as a community in disease development.

CONCLUSIONS

Our work shows that proposed approaches are effective to study the microbiota-disease relation taking into account for the sparsity, hierarchical and correlated structure among microbes.

Bovine respiratory microbiota of feedlot cattle and its association with disease

Bovine respiratory disease (BRD), as one of the most common and costly diseases in the beef cattle industry, has significant adverse impacts on global food security and the economic stability of the industry. The bovine respiratory microbiome is strongly associated with health and disease and may provide insights for alternative therapy when treating BRD. The niche-specific microbiome communities that colonize the inter-surface of the upper and the lower respiratory tract consist of a dynamic and complex ecological system. The correlation between the disequilibrium in the respiratory ecosystem and BRD has become a hot research topic. Hence, we summarize the pathogenesis and clinical signs of BRD and the alteration of the respiratory microbiota. Current research techniques and the biogeography of the microbiome in the healthy respiratory tract are also reviewed. We discuss the process of resident microbiota and pathogen colonization as well as the host immune response. Although associations between the microbiota and BRD have been revealed to some extent, interpreting the development of BRD in relation to respiratory microbial dysbiosis will likely be the direction for upcoming studies, which will allow us to better understand the importance of the airway microbiome and its contributions to animal health and performance.

Application of Deep Learning in Plant-Microbiota Association Analysis

Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.

Neuropsychiatric Ramifications of COVID-19: Short-Chain Fatty Acid Deficiency and Disturbance of Microbiota-Gut-Brain Axis Signaling

COVID-19-associated neuropsychiatric complications are soaring. There is an urgent need to understand the link between COVID-19 and neuropsychiatric disorders. To that end, this article addresses the premise that SARS-CoV-2 infection results in gut dysbiosis and an altered microbiota-gut-brain (MGB) axis that in turn contributes to the neuropsychiatric ramifications of COVID-19. Altered MGB axis activity has been implicated independently as a risk of neuropsychiatric disorders. A review of the changes in gut microbiota composition in individual psychiatric and neurological disorders and gut microbiota in COVID-19 patients revealed a shared "microbial signature" characterized by a lower microbial diversity and richness and a decrease in health-promoting anti-inflammatory commensal bacteria accompanied by an increase in opportunistic proinflammatory pathogens. Notably, there was a decrease in short-chain fatty acid (SCFA) producing bacteria. SCFAs are key bioactive microbial metabolites with anti-inflammatory functions and have been recognized as a critical signaling pathway in the MGB axis. SCFA deficiency is associated with brain inflammation, considered a cardinal feature of neuropsychiatric disorders. The link between SARS-CoV-2 infection, gut dysbiosis, and altered MGB axis is further supported by COVID-19-associated gastrointestinal symptoms, a high number of SARS-CoV-2 receptors, angiotensin-cleaving enzyme-2 (ACE-2) in the gut, and viral presence in the fecal matter. The binding of SARS-CoV-2 to the receptor results in ACE-2 deficiency that leads to decreased transport of vital dietary components, gut dysbiosis, proinflammatory gut status, increased permeability of the gut-blood barrier (GBB), and systemic inflammation. More clinical research is needed to substantiate further the linkages described above and evaluate the potential significance of gut microbiota as a diagnostic tool. Meanwhile, it is prudent to propose changes in dietary recommendations in favor of a high fiber diet or supplementation with SCFAs or probiotics to prevent or alleviate the neuropsychiatric ramifications of COVID-19.

Pharyngeal Microbial Signatures Are Predictive of the Risk of Fungal Pneumonia in Hematologic Patients

The ability to predict invasive fungal infections (IFI) in patients with hematological malignancies is fundamental for successful therapy. Although gut dysbiosis is known to occur in hematological patients, whether airway dysbiosis also contributes to the risk of IFI has not been investigated. Nasal and oropharyngeal swabs were collected for functional microbiota characterization in 173 patients with hematological malignancies recruited in a multicenter, prospective, observational study and stratified according to the risk of developing IFI. A lower microbial richness and evenness were found in the pharyngeal microbiota of high-risk patients that were associated with a distinct taxonomic and metabolic profile. A murine model of IFI provided biologic plausibility for the finding that loss of protective anaerobes, such as Clostridiales and Bacteroidetes, along with an apparent restricted availability of tryptophan, is causally linked to the risk of IFI in hematologic patients and indicates avenues for antimicrobial stewardship and metabolic reequilibrium in IFI.

Effect of a Multispecies Probiotic Mixture on the Growth and Incidence of Diarrhea, Immune Function, and Fecal Microbiota of Pre-weaning Dairy Calves

The effects of different doses of a multispecies probiotic (MSP) mixture on growth performance, the incidence of diarrhea rate and immune function, and fecal microbial diversity and structure were evaluated in pre-weaning Holstein dairy calves at WK2, WK4, WK6, and WK8. Forty Chinese Holstein female newborn calves were randomly assigned to four treatments with 10 calves in each group, C (control group), T1 (0.5 g MSP/calf/day, T2 (1 g MSP/calf/day), and T3 (2 g MSP/calf/day) groups. The experimental period was 56 days. Feed intake and health scoring were recorded every day until the end of the experiment. Fecal contents and blood samples were sampled at WK2, WK4, WK6, and WK8. Growth performance, incidence of diarrhea, and total serum concentrations (IgA, IgG, and IgM) were analyzed. Bacterial 16S rRNA and fungal ITS genes were high-throughput sequenced for fecal microbiota. The relationships among the populations of the principal fecal microbiota at WK2 and the growth performance or serum immunoglobulin concentrations were analyzed using Pearson's rank correlation coefficients. The MSP supplementation reduced the incidence of diarrhea in the first 4 weeks of life, and serum IgA, IgG, and IgM concentrations increased between WK2 and WK8 in the T3 group. There was an increase in growth performance and reduction in the incidence of diarrhea until WK4 after birth in T3 group, compared with the control, T1, and T2 groups. The results of fecal microbiota analysis showed that Firmicutes and Bacteroides were the predominant phyla, with Blautia, Ruminococcaceae_UCG-005, norank_f__Muribaculaceae, Bacteroides, Subdoligranulum, and Bifidobacterium being the dominant genera in calf feces. Aspergillus, Thermomyces, and Saccharomyces were the predominant fungal phyla. Compared with the control, in T1 and T2 groups, the MSP supplementation reduced the relative abundance of Bacteroidetes and increased the relative abundance of Bifidobacterium, Lactobacillus, Collinsella, and Saccharomyces at WK2 in group T3. Thus, the fecal microbial composition and diversity was significantly affected by the MSP mixture during the first 2 weeks of the calves' life. MSP mixtures reduced the incidence of diarrhea in pre-weaning calves (during the first 4 weeks of life). There was a significant improvement in growth performance, reduction in calf diarrhea, balance in the fecal microbiota, and an overall improvement in serum immunity, compared with the control group. We, therefore, recommend adding 2 g/day of multispecies probiotic mixture supplementation in diets of dairy calves during their first 4 weeks of life before weaning.

Perinatal Nutritional and Metabolic Pathways: Early Origins of Chronic Lung Diseases

Lung development is not completed at birth, but expands beyond infancy, rendering the lung highly susceptible to injury. Exposure to various influences during a critical window of organ growth can interfere with the finely-tuned process of development and induce pathological processes with aberrant alveolarization and long-term structural and functional sequelae. This concept of developmental origins of chronic disease has been coined as perinatal programming. Some adverse perinatal factors, including prematurity along with respiratory support, are well-recognized to induce bronchopulmonary dysplasia (BPD), a neonatal chronic lung disease that is characterized by arrest of alveolar and microvascular formation as well as lung matrix remodeling. While the pathogenesis of various experimental models focus on oxygen toxicity, mechanical ventilation and inflammation, the role of nutrition before and after birth remain poorly investigated. There is accumulating clinical and experimental evidence that intrauterine growth restriction (IUGR) as a consequence of limited nutritive supply due to placental insufficiency or maternal malnutrition is a major risk factor for BPD and impaired lung function later in life. In contrast, a surplus of nutrition with perinatal maternal obesity, accelerated postnatal weight gain and early childhood obesity is associated with wheezing and adverse clinical course of chronic lung diseases, such as asthma. While the link between perinatal nutrition and lung health has been described, the underlying mechanisms remain poorly understood. There are initial data showing that inflammatory and nutrient sensing processes are involved in programming of alveolarization, pulmonary angiogenesis, and composition of extracellular matrix. Here, we provide a comprehensive overview of the current knowledge regarding the impact of perinatal metabolism and nutrition on the lung and beyond the cardiopulmonary system as well as possible mechanisms determining the individual susceptibility to CLD early in life. We aim to emphasize the importance of unraveling the mechanisms of perinatal metabolic programming to develop novel preventive and therapeutic avenues.

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability.IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

Variable selection in microbiome compositional data analysis

Though variable selection is one of the most relevant tasks in microbiome analysis, e.g. for the identification of microbial signatures, many studies still rely on methods that ignore the compositional nature of microbiome data. The applicability of compositional data analysis methods has been hampered by the availability of software and the difficulty in interpreting their results. This work is focused on three methods for variable selection that acknowledge the compositional structure of microbiome data: selbal, a forward selection approach for the identification of compositional balances, and clr-lasso and coda-lasso, two penalized regression models for compositional data analysis. This study highlights the link between these methods and brings out some limitations of the centered log-ratio transformation for variable selection. In particular, the fact that it is not subcompositionally consistent makes the microbial signatures obtained from clr-lasso not readily transferable. Coda-lasso is computationally efficient and suitable when the focus is the identification of the most associated microbial taxa. Selbal stands out when the goal is to obtain a parsimonious model with optimal prediction performance, but it is computationally greedy. We provide a reproducible vignette for the application of these methods that will enable researchers to fully leverage their potential in microbiome studies.

Intervention strategies for microbial therapeutics in cancer immunotherapy

Immunotherapies have drastically improved clinical outcomes in a wide range of malignancies. Nevertheless, patient responses remain highly variable, and reliable biomarkers that predict responses accurately are not yet fully understood. Compelling evidence from preclinical studies and observational data from clinical cohorts have shown that commensal microorganisms that reside in the human gastrointestinal tract, collectively termed the ‘microbiome’, can actively modify responses to chemotherapeutic agents and immunotherapies by influencing host immunosurveillance. Notably, microbial correlates are largely context specific, and response signatures may vary by patient population, geographic location and type of anticancer treatment. Therefore, the incongruence of beneficial microbiome signatures across studies, along with an emerging understanding of the mechanisms underlying the interactions between the microbiome, metabolome and host immune system, highlight a critical need for additional comprehensive and standardized multi-omics studies. Future research should consider key host factors, such as diet and use of medication, in both preclinical animal models and large-scale, multicenter clinical trials. In addition, there is a strong rationale to evaluate the microbiome as a tumor-extrinsic biomarker of clinical outcomes and to test the therapeutic potential of derived microbial products (e.g. defined microbial consortia), with the eventual goal of improving the efficacy of existing anticancer treatments. This review discusses the importance of the microbiome from the perspective of cancer immunotherapies, and outlines future steps that may contribute to wide-ranging clinical and translational benefits that may improve the health and quality of life of patients with cancer.

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The gut microbiome impacts the outcomes of cancer treatment by influencing host immunosurveillance.

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Modulation of microbiota represents a novel therapeutic strategy to improve responses.

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Incongruent beneficial bacterial signatures complicate the design of modulators.

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Reverse translation processes can be used to characterize candidate bacteria.

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Rationally designed microbial consortia catalyze transition to a healthy ecology.

Linking perturbations to temporal changes in diversity, stability, and compositions of neonatal calf gut microbiota: prediction of diarrhea

Perturbations in early life gut microbiota can have long-term impacts on host health. In this study, we investigated antimicrobial-induced temporal changes in diversity, stability, and compositions of gut microbiota in neonatal veal calves, with the objective of identifying microbial markers that predict diarrhea. A total of 220 samples from 63 calves in first 8 weeks of life were used in this study. The results suggest that increase in diversity and stability of gut microbiota over time was a feature of "healthy" (non-diarrheic) calves during early life. Therapeutic antimicrobials delayed the temporal development of diversity and taxa-function robustness (a measure of microbial stability). In addition, predicted genes associated with beta lactam and cationic antimicrobial peptide resistance were more abundant in gut microbiota of calves treated with therapeutic antimicrobials. Random forest machine learning algorithm revealed that Trueperella, Streptococcus, Dorea, uncultured Lachnospiraceae, Ruminococcus 2, and Erysipelatoclostridium may be key microbial markers that can differentiate "healthy" and "unhealthy" (diarrheic) gut microbiota, as they predicted early life diarrhea with an accuracy of 84.3%. Our findings suggest that diarrhea in veal calves may be predicted by the shift in early life gut microbiota, which may provide an opportunity for early intervention (e.g., prebiotics or probiotics) to improve calf health with reduced usage of antimicrobials.

PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data

Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introduce PopPhy-CNN, a novel convolutional neural network (CNN) learning framework that effectively exploits phylogenetic structure in microbial taxa for host phenotype prediction. Our approach takes an input format of a 2D matrix representing the phylogenetic tree populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. We show the competitiveness of our model compared to other available methods using nine metagenomic datasets of moderate size for binary classification. With synthetic and biological datasets, we show the superior and robust performance of our model for multi-class classification. Furthermore, we design a novel scheme for feature extraction from the learned CNN models and demonstrate improved performance when the extracted features. PopPhy-CNN is a practical deep learning framework for the prediction of host phenotype with the ability of facilitating the retrieval of predictive microbial taxa.

Targeting the microbiota in pharmacology of psychiatric disorders

There is increasing interest in the role of the gut microbiota in health and disease. In particular, gut microbiota influences the Central Nervous System (CNS) development and homeostasis through neural pathways or routes involving the immune and circulatory systems. The CNS, in turn, shapes the intestinal flora through endocrine or stress-mediated responses. These overall bidirectional interactions, known as gut microbiota-brain axis, profoundly affect some brain functions, such as neurogenesis and the production of neurotransmitters, up to influence behavioral aspects of healthy subjects. Consequently, a dysfunction within this axis, as observed in case of dysbiosis, can have an impact on the behavior of a given individual (e.g. anxiety and depression) or on the development of pathologies affecting the CNS, such as autism spectrum disorders and neurodegenerative diseases (e.g. Alzheimer's disease and Parkinson's disease). It should be considered that the whole microbiota has a significant role not only on aspects concerning human physiology, such as harvesting of nutrients and energy from the ingested food or production of a wide range of bioactive compounds, but also has positive effects on the gastrointestinal barrier function and actively contributes to the pharmacokinetics of several compounds including neuropsychiatric drugs. Indeed, the microbiota is able to affect drug absorption and metabolism up to have an impact on drug activity and/or toxicity. On the other hand, drugs are able to shape the human gut microbiota itself, where these changes may contribute to their pharmacologic profile. Therefore, the emerging picture on the complex drug-microbiota bidirectional interplay will have considerable implications in the future not only in terms of clinical practice but also, upstream, on drug development.

Simultaneous profiling and cultivation of the skin microbiome of healthy young adult skin for the development of therapeutic agents

Background

Studies on the impact of the skin microbiota on human health have been gaining more attention. Bacteria are associated with various diseases, although certain strains of bacteria, which are known as probiotics, are considered beneficial. Mixtures of several bacteria (bacterial cocktail) isolated from targeted organs have shown promising modulatory activities for use in skin therapeutics. The objectives of this study were to determine and identify the microbial communities on the skin that can potentially be used as probiotics, as determined by bacterial isolation and cultivation, followed by next-generation sequencing (NGS).

Results

Samples were collected by swabbing on forehead and cheek skin. Genomic DNA from bacterial swab samples were directly extracted to be further processed into NGS. Cultivation of skin bacteria was carried out in subsequent medium. Thus, around twenty bacterial isolates with different characteristics were selected and identified by both culture-based method and 16sRNA sequencing. We found that Actinobacteria and Firmicutes are the most abundant phylum present on the skin as presented by NGS data, which constitute to 67% and 28.59% of the whole bacterial population, consecutively. However, Staphylococcus hominis, Staphylococcus warneri, and Micrococcus luteus (AN MK968325.1; AN MK968315.1; and MK968318.1 respectively) were able to be obtained in the samples of cultivable, and could be potentially developed as probiotics in skin microbiome therapeutic as well as for postbiotic formulation.

Conclusion

Skin microbiome is considered to provide several probiotics for skin therapeutic. However, some opportunistic pathogens were discovered in this study population. Thus, the promising formula of bacterial cocktail for skin microbiome therapeutic must be thoroughly elucidated to avoid unwanted species. Our study is the first human skin microbiome profile of Indonesia resulted from a Next Generation Sequencing as an effort to show a representative of tropical country profile.

Microbial and metabolomic analysis of gingival crevicular fluid in general chronic periodontitis patients: lessons for a predictive, preventive, and personalized medical approach

Objectives

General chronic periodontitis (GCP) is a bacterial inflammatory disease with complex pathology. Despite extensive studies published on the variation in the oral microbiota and metabolic profiles of GCP patients, information is lacking regarding the correlation between host-bacterial interactions and biochemical metabolism. This study aimed to analyze the oral microbiome, the oral metabolome, and the link between them and to identify potential molecules as useful biomarkers for predictive, preventive, and personalized medicine (PPPM) in GCP.

Methods

In this study, gingival crevicular fluid (GCF) samples were collected from patients with GCP (n = 30) and healthy controls (n = 28). The abundance of oral microbiota constituents was obtained by Illumina sequencing, and the relative level of metabolites was measured by gas chromatography-mass spectrometry. Full-mouth probing depth, clinical attachment loss, and bleeding on probing were recorded as indices of periodontal disease.

Results

The relative abundances of 7 phyla and 82 genera differed significantly between the GCP and healthy groups. Seventeen differential metabolites involved in different metabolism pathways were selected based on variable influence on projection values (VIP > 1) and P values (P < 0.05). Through Spearman's correlation analysis, microorganisms, metabolites in GCF, and clinical data together showed a clear trend, and clinical data regarding periodontitis can be reflected in the shift of the oral microbial community and the change in metabolites in GCF. A combination of citramalic acid and N-carbamylglutamate yielded satisfactory accuracy (AUC = 0.876) for the predictive diagnosis of GCP.

Conclusions

Dysbiosis in the polymicrobial community structure and changes in metabolism could be mechanisms underlying periodontitis. The differential microorganisms and metabolites in GCF between periodontitis patients and healthy individuals are possibly biomarkers, pointing to a potential strategy for the prediction, diagnosis, prognosis, and management of personalized periodontal therapy.

Microbiome changes: an indicator of Parkinson's disease?

Parkinson’s disease is characterized by dopaminergic neuron loss and intracellular inclusions composed mainly of alpha synuclein (α-syn), but the mechanism of pathogenesis is still obscure. In recent years, more attention has been given to the gut as a key player in the initiation and progression of PD pathology. Several studies characterizing changes in the microbiome, particularly the gut microbiome, have been conducted. Although many studies found a decrease in the bacterial family Prevotellaceae and in butyrate-producing bacterial genera such as Roseburia and Faecalibacteria, and an increase in the genera Akkermansia many of the studies reported contradictory findings. In this review, we highlight the findings from the different studies and reflect on the future of microbiome studies in PD research.

Taxonomic and functional assessment using metatranscriptomics reveals the effect of Angus cattle on rumen microbial signatures

A greater understanding of the rumen microbiota and its function may help find new strategies to improve feed efficiency in cattle. This study aimed to investigate whether the cattle breed affects specific ruminal taxonomic microbial groups and functions associated with feed conversion ratio (FCR), using two genetically related Angus breeds as a model. Total RNA was extracted from 24 rumen content samples collected from purebred Black and Red Angus bulls fed the same forage diet and then subjected to metatranscriptomic analysis. Multivariate discriminant analysis (sparse partial least square discriminant analysis (sPLS-DA)) and analysis of composition of microbiomes were conducted to identify microbial signatures characterizing Black and Red Angus cattle. Our analyses revealed relationships among bacterial signatures, host breeds and FCR. Although Black and Red Angus are genetically similar, sPLS-DA detected 25 bacterial species and 10 functions that differentiated the rumen microbial signatures between those two breeds. In Black Angus, we identified bacterial taxa Chitinophaga pinensis, Clostridium stercorarium and microbial functions with large and small subunits ribosomal proteins L16 and S7 exhibiting a higher abundance in the rumen microbiome. In Red Angus, nonetheless, we identified the poorly characterized bacterial taxon Oscillibacter valericigenes with a higher abundance and pathways related to carbohydrate metabolism. Analysis of composition of microbiomes revealed that C. pinensis and C. stercorarium exhibited a higher abundance in Black Angus compared to Red Angus associated with FCR, suggesting that these bacterial species may play a key role in the feed conversion efficiency of forage-fed bulls. This study highlights how the discovery of signatures of bacterial taxa and their functions can be used to harness the full potential of the rumen microbiome in Angus cattle.

A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children

A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification.

MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations

We develop a metagenomic data analysis pipeline, MicroPro, that takes into account all reads from known and unknown microbial organisms and associates viruses with complex diseases. We utilize MicroPro to analyze four metagenomic datasets relating to colorectal cancer, type 2 diabetes, and liver cirrhosis and show that including reads from unknown organisms significantly increases the prediction accuracy of the disease status for three of the four datasets. We identify new microbial organisms associated with these diseases and show viruses play important prediction roles in colorectal cancer and liver cirrhosis, but not in type 2 diabetes. MicroPro is freely available at https://github.com/zifanzhu/MicroPro .

TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification

MOTIVATION

Learning associations of traits with the microbial composition of a set of samples is a fundamental goal in microbiome studies. Recently, machine learning methods have been explored for this goal, with some promise. However, in comparison to other fields, microbiome data are high-dimensional and not abundant; leading to a high-dimensional low-sample-size under-determined system. Moreover, microbiome data are often unbalanced and biased. Given such training data, machine learning methods often fail to perform a classification task with sufficient accuracy. Lack of signal is especially problematic when classes are represented in an unbalanced way in the training data; with some classes under-represented. The presence of inter-correlations among subsets of observations further compounds these issues. As a result, machine learning methods have had only limited success in predicting many traits from microbiome. Data augmentation consists of building synthetic samples and adding them to the training data and is a technique that has proved helpful for many machine learning tasks.

RESULTS

In this paper, we propose a new data augmentation technique for classifying phenotypes based on the microbiome. Our algorithm, called TADA, uses available data and a statistical generative model to create new samples augmenting existing ones, addressing issues of low-sample-size. In generating new samples, TADA takes into account phylogenetic relationships between microbial species. On two real datasets, we show that adding these synthetic samples to the training set improves the accuracy of downstream classification, especially when the training data have an unbalanced representation of classes.

AVAILABILITY AND IMPLEMENTATION

TADA is available at https://github.com/tada-alg/TADA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction

With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided.

Robust biomarker discovery for microbiome-wide association studies

According to the advances of high-throughput sequencing technology, massive microbiome data accumulated from environmental investigations to human studies. The microbiome-wide association studies are to study the relationship between the microbiome and human health or environment. Recently, Deep Neural Networks (DNNs) are encouraging due to their layer-wise learning ability for representation learning. However, DNNs are considered as black boxes and they require a large amount of training data which makes them impractical to conduct microbiome-wide association studies directly. Meanwhile, the microbiome data is high dimension with many features and noise. A single feature selection method for dealing with the kind of dataset is often unstable. In this work, we introduced a deep learning model named Deep Forest to conduct the microbiome-wide association studies and an ensemble feature selection method is proposed to guide microbial biomarkers' identification. The experiments showed that our ensemble feature method based on Deep Forest had good stability and robustness. The results of feature selection could guide the discovery of microbial biomarkers and help to diagnose microbial-related diseases. The code is available at https://github.com/MicroAVA/MWAS-Biomarkers.git.

Longitudinal changes of microbiome composition and microbial metabolomics after surgical weight loss in individuals with obesity

BACKGROUND

Some of the metabolic effects of bariatric surgery may be mediated by the gut microbiome.

OBJECTIVES

To study the effect of bariatric surgery on changes to gut microbiota composition and bacterial pathways, and their relation to metabolic parameters after bariatric surgery.

SETTINGS

University hospitals in the United States and Spain.

METHODS

Microbial diversity and composition by 16 S rRNA sequencing, putative bacterial pathways, and targeted circulating metabolites were studied in 26 individuals with severe obesity, with and without type 2 diabetes, before and at 3, 6, and 12 months after either gastric bypass or sleeve gastrectomy.

RESULTS

Bariatric surgery tended to increase alpha diversity, and significantly altered beta diversity, microbiota composition, and function up to 6 months after surgery, but these changes tend to regress to presurgery levels by 12 months. Twelve of 15 bacterial pathways enriched after surgery also regressed to presurgery levels at 12 months. Network analysis identified groups of bacteria significantly correlated with levels of circulating metabolites over time. There were no differences between study sites, surgery type, or diabetes status in terms of microbial diversity and composition at baseline and after surgery.

CONCLUSIONS

The association among changes in microbiome with decreased circulating biomarkers of inflammation, increased bile acids, and products of choline metabolism and other bacterial pathways suggest that the microbiome partially mediates improvement of metabolism during the first year after bariatric surgery.

Current understanding of the human microbiome

Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this Review, we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledge to move rapidly from correlation to causation, and ultimately to translation.

Longitudinal homogenization of the microbiome between both occupants and the built environment in a cohort of United States Air Force Cadets

BACKGROUND

The microbiome of the built environment has important implications for human health and wellbeing; however, bidirectional exchange of microbes between occupants and surfaces can be confounded by lifestyle, architecture, and external environmental exposures. Here, we present a longitudinal study of United States Air Force Academy cadets (n = 34), which have substantial homogeneity in lifestyle, diet, and age, all factors that influence the human microbiome. We characterized bacterial communities associated with (1) skin and gut samples from roommate pairs, (2) four built environment sample locations inside the pairs' dormitory rooms, (3) four built environment sample locations within shared spaces in the dormitory, and (4) room-matched outdoor samples from the window ledge of their rooms.

RESULTS

We analyzed 2,170 samples, which generated 21,866 unique amplicon sequence variants. Linear convergence of microbial composition and structure was observed between an occupants' skin and the dormitory surfaces that were only used by that occupant (i.e., desk). Conversely, bacterial community beta diversity (weighted Unifrac) convergence between the skin of both roommates and the shared dormitory floor between the two cadet's beds was not seen across the entire study population. The sampling period included two semester breaks in which the occupants vacated their rooms; upon their return, the beta diversity similarity between their skin and the surfaces had significantly decreased compared to before the break (p < 0.05). There was no apparent convergence between the gut and building microbiota, with the exception of communal bathroom door-handles, which suggests that neither co-occupancy, diet, or lifestyle homogenization had a significant impact on gut microbiome similarity between these cadets over the observed time frame. As a result, predictive classifier models were able to identify an individual more accurately based on the gut microbiota (74%) compared to skin (51%).

CONCLUSIONS

To the best of our knowledge, this is the first study to show an increase in skin microbial similarity of two individuals who start living together for the first time and who are not genetically related or romantically involved. Cohabitation was significantly associated with increased skin microbiota similarity but did not significantly influence the gut microbiota. Following a departure from the occupied space of several weeks, the skin microbiota, but not the gut microbiota, showed a significant reduction in similarity relative to the building. Overall, longitudinal observation of these dynamics enables us to dissect the influence of occupation, diet, and lifestyle factors on occupant and built environment microbial ecology.

The genital tract and rectal microbiomes: their role in HIV susceptibility and prevention in women

INTRODUCTION

Young women in sub-Saharan Africa are disproportionately affected by HIV, accounting for 25% of all new infections in 2017. Several behavioural and biological factors are known to impact a young woman's vulnerability for acquiring HIV. One key, but lesser understood, biological factor impacting vulnerability is the vaginal microbiome. This review describes the vaginal microbiome and examines its alterations, its influence on HIV acquisition as well as the efficacy of HIV prevention technologies, the role of the rectal microbiome in HIV acquisition, advances in technologies to study the microbiome and some future research directions.

DISCUSSION

Although the composition of each woman's vaginal microbiome is unique, a microbiome dominated by Lactobacillus species is generally associated with a "healthy" vagina. Disturbances in the vaginal microbiota, characterized by a shift from a low-diversity, Lactobacillus-dominant state to a high-diversity non-Lactobacillus-dominant state, have been shown to be associated with a range of adverse reproductive health outcomes, including increasing the risk of genital inflammation and HIV acquisition. Gardnerella vaginalis and Prevotella bivia have been shown to contribute to both HIV risk and genital inflammation. In addition to impacting HIV risk, the composition of the vaginal microbiome affects the vaginal concentrations of some antiretroviral drugs, particularly those administered intravaginally, and thereby their efficacy as pre-exposure prophylaxis (PrEP) for HIV prevention. Although the role of rectal microbiota in HIV acquisition in women is less well understood, the composition of this compartment's microbiome, particularly the presence of species of bacteria from the Prevotellaceae family likely contribute to HIV acquisition. Advances in technologies have facilitated the study of the genital microbiome's structure and function. While next-generation sequencing advanced knowledge of the diversity and complexity of the vaginal microbiome, the emerging field of metaproteomics, which provides important information on vaginal bacterial community structure, diversity and function, is further shedding light on functionality of the vaginal microbiome and its relationship with bacterial vaginosis (BV), as well as antiretroviral PrEP efficacy.

CONCLUSIONS

A better understanding of the composition, structure and function of the microbiome is needed to identify opportunities to alter the vaginal microbiome and prevent BV and reduce the risk of HIV acquisition.

Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension-reduction structure in the model. An efficient Monte Carlo expectation-maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.

Clinical Indications and Compassionate Use of Phage Therapy: Personal Experience and Literature Review with a Focus on Osteoarticular Infections

The history of phage therapy started with its first clinical application in 1919 and continues its development to this day. Phages continue to lack any market approval in Western medicine as a recognized drug, but are increasingly used as an experimental therapy for the compassionate treatment of patients experiencing antibiotic failure. The few formal experimental phage clinical trials that have been completed to date have produced inconclusive results on the efficacy of phage therapy, which contradicts the many successful treatment outcomes observed in historical accounts and recent individual case reports. It would therefore be wise to identify why such a discordance exists between trials and compassionate use in order to better develop future phage treatment and clinical applications. The multitude of observations reported over the years in the literature constitutes an invaluable experience, and we add to this by presenting a number of cases of patients treated compassionately with phages throughout the past decade with a focus on osteoarticular infections. Additionally, an abundance of scientific literature into phage-related areas is transforming our knowledge base, creating a greater understanding that should be applied for future clinical applications. Due to the increasing number of treatment failures anticipatedfrom the perspective of a possible post-antibiotic era, we believe that the introduction of bacteriophages into the therapeutic arsenal seems a scientifically sound and eminently practicable consideration today as a substitute or adjuvant to antibiotic therapy.

The Predictive Potentiality of Salivary Microbiome for the Recurrence of Early Childhood Caries

The aim of this study was to investigate the variation of the salivary microbiota in the recurrence of early childhood caries (ECC), and to explore and verify the potential microbial indicators of ECC recurrence. Saliva samples from kindergarten children were tracked every 6 months for 1 year. Finally, in total 28 children and 84 samples were placed on the analysis phase: 7 children with ECC recurrence made up the ECC-recurrence (ER) group, 6 children without ECC recurrence constituted the non-ECC-recurrence (NER) group, and 15 children who kept ECC-free were set as the ECC-free (EF) group. DNA amplicons of the V3-V4 hypervariable region of the bacterial 16S rDNA were generated and sequencing was performed using Illumina MiSeq PE250 platform. No statistically significant differences of the Shannon indices were found in both cross-sectional and longitudinal comparisons. Furthermore, both principal coordinates analysis (PCoA) and heatmap plots demonstrated that the salivary microbial community structure might have potentiality to predict ECC recurrence at an early phase. The relative abundance of Fusobacterium, Prevotella, Leptotrichia, and Capnocytophaga differed significantly between the ER and NER groups at baseline. The values of area under the curve (AUC) of the four genera and their combined synthesis in the prediction for ECC recurrence were 0.857, 0.833, 0.786, 0.833, and 0.952, respectively. The relative abundance of Fusobacterium, Prevotella, Leptotrichia, and Capnocytophaga and their combination showed satisfactory accuracy in the prediction for ECC recurrence, indicating that salivary microbiome had predictive potentiality for recurrence of this disease. These findings might facilitate more effective strategy to be taken in the management of the recurrence of ECC.