Gene-level metagenomic architectures across diseases yield high-resolution microbiome diagnostic indicators

Metagenomic analysis reveals ecological and functional signatures of oral phageome associated with severe early childhood caries

OBJECTIVES

Severe early childhood caries (S-ECC) is highly prevalent, affecting children's oral health. S-ECC development is closely associated with the complex oral microbial microbiome and its microorganism interactions, such as the imbalance of bacteriophages and bacteria. Till now, little is known about oral phageome on S-ECC. Therefore, this study aimed to investigate the potential role of the oral phageome in the pathogenesis of S-ECC.

METHODS

Unstimulated saliva (2 mL) was collected from 20 children with and without S-ECC for metagenomics analysis. Metagenomics sequencing and bioinformatic analysis were performed to determine the two groups' phageome diversity, taxonomic and functional annotations. Statistical analysis and visualization were performed with R and SPSS Statistics software.

RESULTS

85.7 % of the extracted viral sequences were predicted from phages, in which most phages were classified into Myoviridae, Siphoviridae, and Podoviridae. Alpha diversity decreased, and Beta diversity increased in the S-ECC phageome compared to the healthy group. The abundance of Podoviridae phages increased, and the abundance of Inoviridae, Herelleviridae, and Streptococcus phages decreased in the S-ECC group. Functional annotation revealed increased annotation on glycoside hydrolases and nucleotide metabolism, decreased glycosyl transferases, carbohydrate-binding modules, and biogenic metabolism in the S-ECC phageome.

CONCLUSIONS

Metagenomic analysis revealed reduced Streptococcus phages and significant changes in functional annotations within the S-ECC phageome. These findings suggest a potential weakening of the regulatory influence of oral bacteria, which may indicate the development of innovative prevention and treatment strategies for S-ECC. These implications deserve further investigation and hold promise for advancing our understanding and management of S-ECC.

CLINICAL SIGNIFICANCE

The findings of this study indicate that oral phageomes are associated with bacterial genomes and metabolic processes, affecting the development of S-ECC. The reduced modulatory effect of the oral phageome in counteracting S-ECC's cariogenic activity suggests a new avenue for the prevention and treatment of S-ECC.

Co-evolution of vaginal microbiome and cervical cancer

BACKGROUND

Exploration of adaptive evolutionary changes at the genetic level in vaginal microbial communities during different stages of cervical cancer remains limited. This study aimed to elucidate the mutational profile of the vaginal microbiota throughout the progression of cervical disease and subsequently establish diagnostic models.

METHODS

This study utilized a metagenomic dataset consisting of 151 subjects classified into four categories: invasive cervical cancer (CC) (n = 42), cervical intraepithelial neoplasia (CIN) (n = 43), HPV-infected (HPVi) patients without cervical lesions (n = 34), and healthy controls (n = 32). The analysis focused on changes in microbiome abundance and extracted information on genetic variation. Consequently, comprehensive multimodal microbial signatures associated with CC, encompassing taxonomic alterations, mutation signatures, and enriched metabolic functional pathways, were identified. Diagnostic models for predicting CC were established considering gene characteristics based on single nucleotide variants (SNVs).

RESULTS

In this study, we screened and analyzed the abundances of 18 key microbial strains during CC progression. Additionally, 71,6358 non-redundant mutations were identified, predominantly consisting of SNVs that were further annotated into 25,773 genes. Altered abundances of SNVs and mutation types were observed across the four groups. Specifically, there were 9847 SNVs in the HPV-infected group and 14,892 in the CC group. Furthermore, two distinct mutation signatures corresponding to the benign and malignant groups were identified. The enriched metabolic pathways showed limited similarity with only two overlapping pathways among the four groups. HPVi patients exhibited active nucleotide biosynthesis, whereas patients with CC demonstrated a significantly higher abundance of signaling and cellular-associated protein families. In contrast, healthy controls showed a distinct enrichment in sugar metabolism. Moreover, biomarkers based on microbial SNV abundance displayed stronger diagnostic capability (cc.AUC = 0.87) than the species-level biomarkers (cc.AUC = 0.78). Ultimately, the integration of multimodal biomarkers demonstrated optimal performance for accurately identifying different cervical statuses (cc.AUC = 0.86), with an acceptable performance (AUC = 0.79) in the external testing set.

CONCLUSIONS

The vaginal microbiome exhibits specific SNV evolution in conjunction with the progression of CC, and serves as a specific biomarker for distinguishing between different statuses of cervical disease.

Single-cell multi-ome and immune profiles of the Inspiration4 crew reveal conserved, cell-type, and sex-specific responses to spaceflight

Spaceflight induces an immune response in astronauts. To better characterize this effect, we generated single-cell, multi-ome, cell-free RNA (cfRNA), biochemical, and hematology data for the SpaceX Inspiration4 (I4) mission crew. We found that 18 cytokines/chemokines related to inflammation, aging, and muscle homeostasis changed after spaceflight. In I4 single-cell multi-omics data, we identified a "spaceflight signature" of gene expression characterized by enrichment in oxidative phosphorylation, UV response, immune function, and TCF21 pathways. We confirmed the presence of this signature in independent datasets, including the NASA Twins Study, the I4 skin spatial transcriptomics, and 817 NASA GeneLab mouse transcriptomes. Finally, we observed that (1) T cells showed an up-regulation of FOXP3, (2) MHC class I genes exhibited long-term suppression, and (3) infection-related immune pathways were associated with microbiome shifts. In summary, this study reveals conserved and distinct immune disruptions occurring and details a roadmap for potential countermeasures to preserve astronaut health.

Longitudinal multi-omics analysis of host microbiome architecture and immune responses during short-term spaceflight

Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes. However, documenting microbial shifts during spaceflight has been difficult due to mission constraints that lead to limited sampling and profiling. Here we executed a six-month longitudinal study to quantify the high-resolution human microbiome response to three days in orbit for four individuals. Using paired metagenomics and metatranscriptomics alongside single-nuclei immune cell profiling, we characterized time-dependent, multikingdom microbiome changes across 750 samples and 10 body sites before, during and after spaceflight at eight timepoints. We found that most alterations were transient across body sites; for example, viruses increased in skin sites mostly during flight. However, longer-term shifts were observed in the oral microbiome, including increased plaque-associated bacteria (for example, Fusobacteriota), which correlated with immune cell gene expression. Further, microbial genes associated with phage activity, toxin-antitoxin systems and stress response were enriched across multiple body sites. In total, this study reveals in-depth characterization of microbiome and immune response shifts experienced by astronauts during short-term spaceflight and the associated changes to the living environment, which can help guide future missions, spacecraft design and space habitat planning.

Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases

Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings detected by our pipeline provide valuable insights into these diseases.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer

Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

The Next Generation Fecal Microbiota Transplantation: To Transplant Bacteria or Virome

Fecal microbiota transplantation (FMT) has emerged as a promising therapeutic approach for dysbiosis-related diseases. However, the clinical practice of crude fecal transplants presents limitations in terms of acceptability and reproductivity. Consequently, two alternative solutions to FMT are developed: transplanting bacteria communities or virome. Advanced methods for transplanting bacteria mainly include washed microbiota transplantation and bacteria spores treatment. Transplanting the virome is also explored, with the development of fecal virome transplantation, which involves filtering the virome from feces. These approaches provide more palatable options for patients and healthcare providers while minimizing research heterogeneity. In general, the evolution of the next generation of FMT in global trends is fecal microbiota components transplantation which mainly focuses on transplanting bacteria or virome.

Viral activation and ecological restructuring characterize a microbiome axis of spaceflight-associated immune activation

Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes, which play a role in some space-derived health disorders. However, documenting the response of microbiota to spaceflight has been difficult thus far due to mission constraints that lead to limited sampling. Here, we executed a six-month longitudinal study centered on a three-day flight to quantify the high-resolution microbiome response to spaceflight. Via paired metagenomics and metatranscriptomics alongside single immune profiling, we resolved a microbiome "architecture" of spaceflight characterized by time-dependent and taxonomically divergent microbiome alterations across 750 samples and ten body sites. We observed pan-phyletic viral activation and signs of persistent changes that, in the oral microbiome, yielded plaque-associated pathobionts with strong associations to immune cell gene expression. Further, we found enrichments of microbial genes associated with antibiotic production, toxin-antitoxin systems, and stress response enriched universally across the body sites. We also used strain-level tracking to measure the potential propagation of microbial species from the crew members to each other and the environment, identifying microbes that were prone to seed the capsule surface and move between the crew. Finally, we identified associations between microbiome and host immune cell shifts, proposing both a microbiome axis of immune changes during flight as well as the sources of some of those changes. In summary, these datasets and methods reveal connections between crew immunology, the microbiome, and their likely drivers and lay the groundwork for future microbiome studies of spaceflight.

Statistical modeling of gut microbiota for personalized health status monitoring

BACKGROUND

The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host's health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated.

METHODS

We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets.

RESULTS

We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ([Formula: see text]) and species richness ([Formula: see text]) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ([Formula: see text]), species richness ([Formula: see text]), and functional redundancy ([Formula: see text]). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases.

CONCLUSIONS

Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstract.

Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

Microbiome Signature of Endophytes in Wheat Seed Response to Wheat Dwarf Bunt Caused by Tilletia controversa Kühn

Wheat dwarf bunt leads to the replacement of seeds with fungal galls containing millions of teliospores of the pathogen Tilletia controversa Kühn. As one of the most devastating internationally quarantined wheat diseases, wheat dwarf bunt spreads to cause distant outbreaks by seeds containing teliospores. In this study, based on a combination of amplicon sequencing and isolation approaches, we analyzed the seed microbiome signatures of endophytes between resistant and susceptible cultivars after infection with T. controversa. Among 310 bacterial species obtained only by amplicon sequencing and 51 species obtained only by isolation, we found 14 overlapping species by both methods; we detected 128 fungal species only by amplicon sequencing, 56 only by isolation, and 5 species by both methods. The results indicated that resistant uninfected cultivars hosted endophytic communities that were much more stable and beneficial to plant health than those in susceptible infected cultivars. The susceptible group showed higher diversity than the resistant group, the infected group showed more diversity than the uninfected group, and the microbial communities in seeds were related to infection or resistance to the pathogen. Some antagonistic microbes significantly suppressed the germination rate of the pathogen's teliospores, providing clues for future studies aimed at developing strategies against wheat dwarf bunt. Collectively, this research advances the understanding of the microbial assembly of wheat seeds upon exposure to fungal pathogen (T. controversa) infection. IMPORTANCE This is the first study on the microbiome signature of endophytes in wheat seed response to wheat dwarf bunt caused by Tilletia controversa Kühn. Some antagonistic microbes suppressed the germination of teliospores of the pathogen significantly, which will provide clues for future studies against wheat dwarf bunt. Collectively, this research first advances the understanding of the microbial assembly of wheat seed upon exposure to the fungal pathogen (T. controversa) infection.

The gut microbiota pathway mechanisms of diabetes

The contribution of dysbiotic gut microbiota configuration is essential when making reference to the metabolic disorders by increasing energy. It is important to understand that the gut microbiota induced metabolic disease mechanisms and inflammations. Thus it is imperative to have an insight into the state of all chronic subclinical inflammations influencing disease outcomes. However, from the emerging studies, there still exist inconsistencies in the findings of such studies. While making the best out of the reasons for inconsistencies of the findings, this review is designed to make a clear spell out as to the inconsistence of gut microbiota with respect to diabetes. It considered gut-virome alterations and diabetes and gut-bacteriome-gut-virome-alterations and diabetes as confounding factors. The review further explained some study design strategies that will spontaneously eliminate any potential confounding factors to lead to a more evidence based diabetic-gut microbiota medicine. Lipopolysaccharide (LPS) pro-inflammatory, metabolic endotoxemia and diet/gut microbiota insulin-resistance and low-grade systemic inflammation induced by gut microbiota can trigger pro-inflammatory cytokines in insulin-resistance, consequently, leading to the diabetic condition. While diet influences the gut microbiota, the consequences are mainly the constant high levels of pro-inflammatory cytokines in the circulatory system. Of recent, dietary natural products have been shown to be anti-diabetic. The effects of resveratrol on the gut showed an improved lipid profile, anti-inflammatory properties and ameliorated the endotoxemia, tight junction and glucose intolerance.

A Taxonomy-Agnostic Approach to Targeted Microbiome Therapeutics-Leveraging Principles of Systems Biology

The study of human microbiomes has yielded insights into basic science, and applied therapeutics are emerging. However, conflicting definitions of what microbiomes are and how they affect the health of the "host" are less understood. A major impediment towards systematic design, discovery, and implementation of targeted microbiome therapeutics is the continued reliance on taxonomic indicators to define microbiomes in health and disease. Such reliance often confounds analyses, potentially suggesting associations where there are none, and conversely failing to identify significant, causal relationships. This review article discusses recent discoveries pointing towards a molecular understanding of microbiome "dysbiosis" and away from a purely taxonomic approach. We highlight the growing role of systems biological principles in the complex interrelationships between the gut microbiome and host cells, and review current approaches commonly used in targeted microbiome therapeutics, including fecal microbial transplant, bacteriophage therapies, and the use of metabolic toxins to selectively eliminate specific taxa from dysbiotic microbiomes. These approaches, however, remain wholly or partially dependent on the bacterial taxa involved in dysbiosis, and therefore may not capitalize fully on many therapeutic opportunities presented at the bioactive molecular level. New technologies capable of addressing microbiome-associated diseases as molecular problems, if solved, will open possibilities of new classes and categories of targeted microbiome therapeutics aimed, in principle, at all dysbiosis-driven disorders.

Gut Microbiota Analysis and In Silico Biomarker Detection of Children with Autism Spectrum Disorder across Cohorts

The study of human gut microbiota has attracted increasing interest in the fields of life science and healthcare. However, the complicated and interconnected associations between gut microbiota and human diseases are still difficult to determine in a predictive fashion. Artificial intelligence such as machine learning (ML) and deep learning can assist in processing and interpreting biological datasets. In this study, we aggregated data from different studies based on the species composition and relative abundance of gut microbiota in children with autism spectrum disorder (ASD) and typically developed (TD) individuals and analyzed the commonalities and differences of ASD-associated microbiota across cohorts. We established a predictive model using an ML algorithm to explore the diagnostic value of the gut microbiome for the children with ASD and identify potential biomarkers for ASD diagnosis. The results indicated that the Shenzhen cohort achieved a higher area under the receiver operating characteristic curve (AUROC) value of 0.984 with 97% accuracy, while the Moscow cohort achieved an AUROC value of 0.81 with 67% accuracy. For the combination of the two cohorts, the average prediction results had an AUROC of 0.86 and 80% accuracy. The results of our cross-cohort analysis suggested that a variety of influencing factors, such as population characteristics, geographical region, and dietary habits, should be taken into consideration in microbial transplantation or dietary therapy. Collectively, our prediction strategy based on gut microbiota can serve as an enhanced strategy for the clinical diagnosis of ASD and assist in providing a more complete method to assess the risk of the disorder.

Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.

Assembly and analytical validation of a metagenomic reference catalog of human gut microbiota based on co-barcoding sequencing

Human gut microbiota is associated with human health and disease, and is known to have the second-largest genome in the human body. The microbiota genome is important for their functions and metabolites; however, accurate genomic access to the microbiota of the human gut is hindered due to the difficulty of cultivating and the shortcomings of sequencing technology. Therefore, we applied the stLFR library construction method to assemble the microbiota genomes and demonstrated that assembly property outperformed standard metagenome sequencing. Using the assembled genomes as references, SNP, INDEL, and HGT gene analyses were performed. The results demonstrated significant differences in the number of SNPs and INDELs among different individuals. The individual displayed a unique species variation spectrum, and the similarity of strains within individuals decreased over time. In addition, the coverage depth analysis of the stLFR method shows that a sequencing depth of 60X is sufficient for SNP calling. HGT analysis revealed that the genes involved in replication, recombination and repair, mobilome prophages, and transposons were the most transferred genes among different bacterial species in individuals. A preliminary framework for human gut microbiome studies was established using the stLFR library construction method.

Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

Microbial dysbiosis in the gut drives systemic autoimmune diseases

Trillions of microbes survive and thrive inside the human body. These tiny creatures are crucial to the development and maturation of our immune system and to maintain gut immune homeostasis. Microbial dysbiosis is the main driver of local inflammatory and autoimmune diseases such as colitis and inflammatory bowel diseases. Dysbiosis in the gut can also drive systemic autoimmune diseases such as type 1 diabetes, rheumatic arthritis, and multiple sclerosis. Gut microbes directly interact with the immune system by multiple mechanisms including modulation of the host microRNAs affecting gene expression at the post-transcriptional level or production of microbial metabolites that interact with cellular receptors such as TLRs and GPCRs. This interaction modulates crucial immune functions such as differentiation of lymphocytes, production of interleukins, or controlling the leakage of inflammatory molecules from the gut to the systemic circulation. In this review, we compile and analyze data to gain insights into the underpinning mechanisms mediating systemic autoimmune diseases. Understanding how gut microbes can trigger or protect from systemic autoimmune diseases is crucial to (1) tackle these diseases through diet or lifestyle modification, (2) develop new microbiome-based therapeutics such as prebiotics or probiotics, (3) identify diagnostic biomarkers to predict disease risk, and (4) observe and intervene with microbial population change with the flare-up of autoimmune responses. Considering the microbiome signature as a crucial player in systemic autoimmune diseases might hold a promise to turn these untreatable diseases into manageable or preventable ones.

Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases

The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.

Current Understanding on the Genetic Basis of Key Metabolic Disorders: A Review

Simple Summary

Metabolic disorders (MD) are a challenge to healthcare systems; the emergence of the modern socio-economic system has led to a profound change in lifestyles in terms of dietary habits, exercise regimens, and behavior, all of which complement the genetic factors associated with MD. Diabetes Mellitus and Familial hypercholesterolemia are two of the 14 most widely researched MD, as they pose the greatest challenge to the public healthcare system and have an impact on productivity and the economy. Research findings have led to the development of new therapeutic molecules for the mitigation of MD as well as the invention of experimental strategies, which target the genes themselves via gene editing and RNA interference. Although these approaches may herald the emergence of a new toolbox to treat MD, the current therapeutic approaches still heavily depend on substrate reduction, dietary restrictions based on genetic factors, exercise, and the maintenance of good mental health. The development of orphan drugs for the less common MD such as Krabbe, Farber, Fabry, and Gaucher diseases, remains in its infancy, owing to the lack of investment in research and development, and this has driven the development of personalized therapeutics based on gene silencing and related technologies.

Abstract

Advances in data acquisition via high resolution genomic, transcriptomic, proteomic and metabolomic platforms have driven the discovery of the underlying factors associated with metabolic disorders (MD) and led to interventions that target the underlying genetic causes as well as lifestyle changes and dietary regulation. The review focuses on fourteen of the most widely studied inherited MD, which are familial hypercholesterolemia, Gaucher disease, Hunter syndrome, Krabbe disease, Maple syrup urine disease, Metachromatic leukodystrophy, Mitochondrial encephalopathy lactic acidosis stroke-like episodes (MELAS), Niemann-Pick disease, Phenylketonuria (PKU), Porphyria, Tay-Sachs disease, Wilson’s disease, Familial hypertriglyceridemia (F-HTG) and Galactosemia based on genome wide association studies, epigenetic factors, transcript regulation, post-translational genetic modifications and biomarker discovery through metabolomic studies. We will delve into the current approaches being undertaken to analyze metadata using bioinformatic approaches and the emerging interventions using genome editing platforms as applied to animal models.

Identification of shared and disease-specific host gene-microbiome associations across human diseases using multi-omic integration

While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic (n = 208) and gut microbiome (n = 208) profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease and irritable bowel syndrome. We identified associations between gut microbes and host genes that depict shared as well as disease-specific patterns. We found that a common set of host genes and pathways implicated in gastrointestinal inflammation, gut barrier protection and energy metabolism are associated with disease-specific gut microbes. Additionally, we also found that mucosal gut microbes that have been implicated in all three diseases, such as Streptococcus, are associated with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes. Our framework can be applied to other diseases for the identification of host gene-microbiome associations that may influence disease outcomes.

Microbiomics: The Next Pillar of Precision Medicine and Its Role in African Healthcare

Limited access to technologies that support early monitoring of disease risk and a poor understanding of the geographically unique biological and environmental factors underlying disease, represent significant barriers to improved health outcomes and precision medicine efforts in low to middle income countries. These challenges are further compounded by the rich genetic diversity harboured within Southern Africa thus necessitating alternative strategies for the prediction of disease risk and clinical outcomes in regions where accessibility to personalized healthcare remains limited. The human microbiome refers to the community of microorganisms (bacteria, archaea, fungi and viruses) that co-inhabit the human body. Perturbation of the natural balance of the gut microbiome has been associated with a number of human pathologies, and the microbiome has recently emerged as a critical determinant of drug pharmacokinetics and immunomodulation. The human microbiome should therefore not be omitted from any comprehensive effort towards stratified healthcare and would provide an invaluable and orthogonal approach to existing precision medicine strategies. Recent studies have highlighted the overarching effect of geography on gut microbial diversity as it relates to human health. Health insights from international microbiome datasets are however not yet verified in context of the vast geographical diversity that exists throughout the African continent. In this commentary we discuss microbiome research in Africa and its role in future precision medicine initiatives across the African continent.

Colorectal Cancer Screening in Inflammatory Bowel Diseases-Can Characterization of GI Microbiome Signatures Enhance Neoplasia Detection?

Current noninvasive methods for colorectal cancer (CRC) screening are not optimized for persons with inflammatory bowel diseases (IBDs), requiring patients to undergo frequent interval screening via colonoscopy. Although colonoscopy-based screening reduces CRC incidence in IBD patients, rates of interval CRC remain relatively high, highlighting the need for more targeted approaches. In recent years, the discovery of disease-specific microbiome signatures for both IBD and CRC has begun to emerge, suggesting that stool-based biomarker detection using metagenomics and other culture-independent technologies may be useful for personalized, early, noninvasive CRC screening in IBD patients. Here we discuss the utility of the stool microbiome as a noninvasive CRC screening tool. Comparing the performance of multiple microbiome-based CRC classifiers, including several multi-cohort meta-analyses, we find that noninvasive detection of colorectal adenomas and carcinomas from microbial biomarkers is an active area of study with promising early results.

Systematically assessing microbiome–disease associations identifies drivers of inconsistency in metagenomic research

Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency—or robustness—of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon–disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe–disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders—sequencing depth, glucose levels, cholesterol, and body mass index, for example—influenced associations, analyzing how these variables affect the ostensible correlation between Faecalibacterium prausnitzii abundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.

The human microbiome has been associated with many aspects of our health, but how many of these associations are truly reproducible? This study attempts to address this question by systematically testing the robustness of 581 microbial features that have been reported as being disease-associated.

Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.

Washed microbiota transplantation improves patients with metabolic syndrome in South China

BACKGROUND

Metabolic syndrome (MS) is a growing public health problem worldwide. The clinical impact of fecal microbiota transplantation (FMT) from healthy donors in MS patients is unclear, especially in southern Chinese populations. This study aimed to investigate the effect of washed microbiota transplantation (WMT) in MS patients in southern China.

METHODS

The clinical data of patients with different indications receiving 1-3 courses of WMT were retrospectively collected. The changes of BMI, blood glucose, blood lipids, blood pressure and other indicators before and after WMT were compared, such as fasting blood glucose (FBG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c)), high-density lipoprotein cholesterol (HDL-c), non-high-density lipoprotein (non-HDL-c), systolic blood pressure (SBP), diastolic blood pressure (DBP), etc. At the same time, comprehensive efficacy evaluation and atherosclerotic cardiovascular disease (ASCVD) grade assessment were performed on MS patients. Finally, 16S rRNA gene amplicon sequencing was performed on fecal samples of MS patients before and after transplantation.

RESULTS

A total of 237 patients were included, including 42 in the MS group and 195 in the non-MS group. For MS patients, WMT significantly improved the comprehensive efficacy of MS in short term 40.48% (p<0.001), medium term 36.00% (p=0.003), and long term 46.15% (p=0.020). Short-term significantly reduced FBG (p=0.023), TG (p=0.030), SBP (p=0.026) and BMI (p=0.031), and increased HDL-c (p=0.036). The medium term had a significant reduction in FBG (p=0.048), TC (p=0.022), LDL-c (p=0.043), non-HDL-c (p=0.024) and BMI (p=0.048). WMT had a significant short term (p=0.029) and medium term (p=0.011) ASCVD downgrading effect in the high-risk group of MS patients. WMT improved gut microbiota in MS patients.

CONCLUSION

WMT had a significant improvement effect on MS patients and a significant downgrade effect on ASCVD risk in the high-risk group of patients with MS. WMT could restore gut microbiota homeostasis in MS patients. Therefore, the regulation of gut microbiota by WMT may provide a new clinical approach for the treatment of MS.

A Predictive Model Based on the Gut Microbiota Improves the Diagnostic Effect in Patients With Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a malignant hepatic tumor with a poor prognosis, which needs early diagnosis urgently. The gut microbiota has been shown to play a crucial role in the progression of liver cancer. Here, we explored a gut microbiota model covering genera Burkholderia-Caballeronia-Paraburkholderia, Faecalibacterium, and Ruminococcus_1 (B-F-R) for CCA early diagnosis. A case-control study was conducted to enroll 53 CCA patients, 47 cholelithiasis patients, and 40 healthy controls. The feces samples and clinical information of participants were collected in the same period. The gut microbiota and its diversity of individuals were accessed with 16S rDNA sequencing, and the gut microbiota profile was evaluated according to microbiota diversity. Finally, four enriched genera in the CCA group (genera Bacteroides, Muribaculaceae_unclassified, Muribaculum, and Alistipes) and eight enriched genera in the cholelithiasis group (genera Bifidobacterium, Streptococcus, Agathobacter, Ruminococcus_gnavus_group, Faecalibacterium, Subdoligranulum, Collinsella, Escherichia-Shigella) constitute an overall different microbial community composition (P = 0.001). The B-F-R genera model with better diagnostic value than carbohydrate antigen 19-9 (CA19-9) was identified by random forest and Statistical Analysis of Metagenomic Profiles (STAMP) to distinguish CCA patients from healthy controls [area under the curve (AUC) = 0.973, 95% CI = 0.932–1.0]. Moreover, the correlative analysis found that genera Burkholderia-Caballeronia-Paraburkholderia were positively correlated with body mass index (BMI). The significantly different microbiomes between cholelithiasis and CCA were found via principal coordinates analysis (PCoA) and linear discriminant analysis effect size (LEfSe), and Venn diagram and LEfSe were utilized to identify four genera by comparing microbial compositions among patients with malignant obstructive jaundice (MOJ-Y) or not (MOJ-N). In brief, our findings suggest that gut microbiota vary from benign and malignant hepatobiliary diseases to healthy people and provide evidence supporting gut microbiota to be a non-invasive biomarker for the early diagnosis of CCA.

Leveraging vibration of effects analysis for robust discovery in observational biomedical data science

Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called "vibration of effects" (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.

Essential Hypertension Is Associated With Changes in Gut Microbial Metabolic Pathways: A Multisite Analysis of Ambulatory Blood Pressure

[Figure: see text].

Washed Microbiota Transplantation Lowers Blood Pressure in Patients With Hypertension

BACKGROUND

Although transplantation of the fecal microbiota from normotensive donors has been shown to have an antihypertensive effect in hypertensive animal models, its effect on blood pressure in patients with hypertension is unclear. This study aimed to assess the effect of washed microbiota transplantation (WMT) from normotensive donors on blood pressure regulation in hypertensive patients.

METHODS

The clinical data of consecutive patients treated with washed microbiota transplantation (WMT) were collected retrospectively. The blood pressures of hypertensive patients before and after WMT were compared. The factors influencing the antihypertensive effect of WMT in hypertensive patients and fecal microbial composition of donors and hypertensive patients were also analyzed.

RESULTS

WMT exhibited an antihypertensive effect on blood pressure: the blood pressure at hospital discharge was significantly lower than that at hospital admission (change in systolic blood pressure: -5.09 ± 15.51, P = 0.009; change in diastolic blood pressure: -7.74 ± 10.42, P < 0.001). Hypertensive patients who underwent WMT via the lower gastrointestinal tract (β = -8.308, standard error = 3.856, P = 0.036) and those not taking antihypertensive drugs (β = -8.969, standard error = 4.256, P = 0.040) had a greater decrease in systolic blood pressure, and hypertensive patients not taking antihypertensive drugs also had a greater decrease in diastolic blood pressure (β = -8.637, standard error = 2.861, P = 0.004). After WMT, the Shannon Diversity Index was higher in six of eight hypertensive patients and the microbial composition of post-WMT samples tended to be closer to that of donor samples.

CONCLUSION

WMT had a blood pressure-lowering effect in hypertensive patients, especially in those who underwent WMT via the lower gastrointestinal tract and in those not taking antihypertensive drugs. Therefore, modulation of the gut microbiota by WMT may offer a novel approach for hypertension treatment.