Causal Inference in Microbiome Medicine: Principles and Applications

Identification of intratumor bacteria-associated prognostic risk score in adrenocortical carcinoma

A landmark study by Poore et al. showed intratumor bacteria (ITBs) playing a critical role in most cancers by reproduction of The Cancer Genome Atlas (TCGA) transcriptome data. A recent study by Salzberg et al. argued that ITBs, being overstated as a methodology by Poore et al., were problematic. We previously reported that ITBs were prognostic in adrenocortical carcinoma (ACC), a highly aggressive rare disease using data by Poore et al., and here, we aimed to answer whether ITBs truly existed and were prognostic in ACC. ACC samples from our institutes underwent 16S rRNA sequencing [adrenocortical carcinoma blocks from Huashan Hospital and China Medical University (HS) cohort]. The ITB profile was compared to TCGA data processed by Poore et al. (TCGA-P) and TCGA data processed by Salzberg et al. (TCGA-S), respectively. The primary outcome was overall survival (OS). A total of 26 ACC cases (HS cohort) and 10 paraffin controls were sequenced. The TCGA cohort encompassed 77 cases. Two and four amid the top 10 abundant genera in HS cohort were not detected in TCGA-P and TCGA-S, respectively. Neither was alpha or beta diversity associated with survival nor could ACC be subtyped by ITB signature in the HS cohort. Notably, a five-genera ITB risk score (Corynebacterium, Mycoplasma, Achromobacter, Anaerococcus, and Streptococcus) for OS trained in the HS cohort was validated in both TCGA-P and TCGA-S cohorts and was independently prognostic. Whereas ITB signature on the whole may not be associated with ACC subtypes, certain ITB features are associated with prognosis, and a risk score could be generated and validated externally.

IMPORTANCE

In this report, we looked at the role of ITBs in ACC in patients with different race and sequencing platforms. We found a five-genera ITB risk score consistently predicted overall survival in all cohorts. We conclude that certain ITB features are universally pathogenic to ACC.

Causal effects between gut microbiota and pulmonary arterial hypertension: A bidirectional Mendelian randomization study

BACKGROUND

Multiple studies have highlighted a potential link between gut microbes and the onset of Pulmonary Arterial Hypertension (PAH). Nonetheless, the precise cause-and-effect relationship remains uncertain.

OBJECTIVES

In this investigation, we utilized a two-sample Mendelian randomization (TSMR) approach to probe the presence of a causal connection between gut microbiota and PAH.

METHODS

Genome-wide association (GWAS) data for gut microbiota and PAH were sourced from MiBioGen and FinnGen research, respectively. Inverse variance weighting (IVW) was used as the primary method to explore the causal effect between gut flora and PAH, supplemented by MR-Egger, weighted median (WM). Sensitivity analyses examined the robustness of the MR results. Reverse MR analysis was used to rule out the effect of reverse causality on the results.

RESULTS

The results indicate that Genus Ruminococcaceae UCG004 (OR = 0.407, P = 0.031) and Family Alcaligenaceae (OR = 0.244, P = 0.014) were protective factors for PAH. Meanwhile Genus Lactobacillus (OR = 2.446, P = 0.013), Class Melainabacteria (OR = 2.061, P = 0.034), Phylum Actinobacteria (OR = 3.406, P = 0.010), Genus Victivallis (OR = 1.980, P = 0.010), Genus Dorea (OR = 3.834, P = 0.024) and Genus Slackia (OR = 2.622, P = 0.039) were associated with an increased Prevalence of PAH. Heterogeneity and pleiotropy were not detected by sensitivity analyses, while there was no reverse causality for these nine specific gut microorganisms.

CONCLUSIONS

This study explores the causal effects of eight gut microbial taxa on PAH and provides new ideas for early prevention of PAH.

Current Trends and Challenges of Microbiome Research in Bladder Cancer

PURPOSE OF THE REVIEW

Microbiome research has provided valuable insights into the associations between microbial communities and bladder cancer. However, this field faces significant challenges that hinder the interpretation, generalization, and translation of findings into clinical practice. This review aims to elucidate these challenges and highlight the importance of addressing them for the advancement of microbiome research in bladder cancer.

RECENT FINDINGS

Recent findings underscore the complexities involved in microbiome research, particularly in the context of bladder cancer. Challenges include low microbial biomass in urine samples, potential contamination issues during collection and processing, variability in sequencing methods and primer selection, and the difficulty of establishing causality between microbiota and bladder cancer. Studies have shown the impact of sample storage conditions and DNA isolation kits on microbiome analysis, emphasizing the need for standardization. Additionally, variations in urine collection methods can introduce contamination and affect results. The choice of 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing introduces technical challenges, including primer selection and sequencing read length. Establishing causality between the microbiota and bladder cancer requires experimental methods like fecal microbiota transplantation and human microbiota-associated murine models, which face their own set of challenges. Translating microbiome research into therapeutic applications is hindered by methodological variability, incomplete understanding of bioactive molecules, imperfect animal models, and the inherent heterogeneity of microbiome communities among individuals. Microbiome research in bladder cancer presents significant challenges stemming from technical and conceptual complexities. Addressing these challenges through standardization, improved experimental models, and advanced analytical approaches is essential for advancing our understanding of the microbiome's role in bladder cancer and its potential clinical applications. Achieving this goal can lead to improved patient outcomes and novel therapeutic strategies in the future.

Causal relationship between gut microbiota and risk of gastroesophageal reflux disease: a genetic correlation and bidirectional Mendelian randomization study

Background

Numerous observational studies have identified a linkage between the gut microbiota and gastroesophageal reflux disease (GERD). However, a clear causative association between the gut microbiota and GERD has yet to be definitively ascertained, given the presence of confounding variables.

Methods

The genome-wide association study (GWAS) pertaining to the microbiome, conducted by the MiBioGen consortium and comprising 18,340 samples from 24 population-based cohorts, served as the exposure dataset. Summary-level data for GERD were obtained from a recent publicly available genome-wide association involving 78 707 GERD cases and 288 734 controls of European descent. The inverse variance-weighted (IVW) method was performed as a primary analysis, the other four methods were used as supporting analyses. Furthermore, sensitivity analyses encompassing Cochran's Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out methodology were carried out to identify potential heterogeneity and horizontal pleiotropy. Ultimately, a reverse MR assessment was conducted to investigate the potential for reverse causation.

Results

The IVW method's findings suggested protective roles against GERD for the Family Clostridiales Vadin BB60 group (P = 0.027), Genus Lachnospiraceae UCG004 (P = 0.026), Genus Methanobrevibacter (P = 0.026), and Phylum Actinobacteria (P = 0.019). In contrast, Class Mollicutes (P = 0.037), Genus Anaerostipes (P = 0.049), and Phylum Tenericutes (P = 0.024) emerged as potential GERD risk factors. In assessing reverse causation with GERD as the exposure and gut microbiota as the outcome, the findings indicate that GERD leads to dysbiosis in 13 distinct gut microbiota classes. The MR results' reliability was confirmed by thorough assessments of heterogeneity and pleiotropy.

Conclusions

For the first time, the MR analysis indicates a genetic link between gut microbiota abundance changes and GERD risk. This not only substantiates the potential of intestinal microecological therapy for GERD, but also establishes a basis for advanced research into the role of intestinal microbiota in the etiology of GERD.

Gut microbiota-driven metabolic alterations reveal gut-brain communication in Alzheimer's disease model mice

The gut microbiota (GM) and its metabolites affect the host nervous system and are involved in the pathogeneses of various neurological diseases. However, the specific GM alterations under pathogenetic pressure and their contributions to the "microbiota - metabolite - brain axis" in Alzheimer's disease (AD) remain unclear. Here, we investigated the GM and the fecal, serum, cortical metabolomes in APP/PS1 and wild-type (WT) mice, revealing distinct hub bacteria in AD mice within scale-free GM networks shared by both groups. Moreover, we identified diverse peripheral - central metabolic landscapes between AD and WT mice that featured bile acids (e.g. deoxycholic and isodeoxycholic acid) and unsaturated fatty acids (e.g. 11Z-eicosenoic and palmitoleic acid). Machine-learning models revealed the relationships between the differential/hub bacteria and these metabolic signatures from the periphery to the brain. Notably, AD-enriched Dubosiella affected AD occurrence via cortical palmitoleic acid and vice versa. Considering the transgenic background of the AD mice, we propose that Dubosiella enrichment impedes AD progression via the synthesis of palmitoleic acid, which has protective properties against inflammation and metabolic disorders. We identified another association involving fecal deoxycholic acid-mediated interactions between the AD hub bacteria Erysipelatoclostridium and AD occurrence, which was corroborated by the correlation between deoxycholate levels and cognitive scores in humans. Overall, this study elucidated the GM network alterations, contributions of the GM to peripheral - central metabolic landscapes, and mediatory roles of metabolites between the GM and AD occurrence, thus revealing the critical roles of bacteria in AD pathogenesis and gut - brain communications under pathogenetic pressure.

A systematic review exploring the association between the human gut microbiota and brain connectivity in health and disease

A body of pre-clinical evidence shows how the gut microbiota influence brain functioning, including brain connectivity. Linking measures of brain connectivity to the gut microbiota can provide important mechanistic insights into the bi-directional gut-brain communication. In this systematic review, we therefore synthesized the available literature assessing this association, evaluating the degree of consistency in microbiota-connectivity associations. Following the PRISMA guidelines, a PubMed search was conducted, including studies published up to September 1, 2022. We identified 16 studies that met the inclusion criteria. Several bacterial genera, including Prevotella, Bacteroides, Ruminococcus, Blautia, and Collinsella were most frequently reported in association with brain connectivity. Additionally, connectivity of the salience (specifically the insula and anterior cingulate cortex), default mode, and frontoparietal networks were most frequently associated with the gut microbiota, both in terms of microbial diversity and composition. There was no discernible pattern in the association between microbiota and brain connectivity. Altogether, based on our synthesis, there is evidence for an association between the gut microbiota and brain connectivity. However, many findings were poorly replicated across studies, and the specificity of the association is yet unclear. The current studies show substantial inter-study heterogeneity in methodology and reporting, limiting the robustness and reproducibility of the findings and emphasizing the need to harmonize methodological approaches. To enhance comparability and replicability, future research should focus on further standardizing processing pipelines and employing data-driven multivariate analysis strategies.

Causal inference in drug discovery and development

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.

Genetically predicted the causal relationship between gut microbiota and infertility: bidirectional Mendelian randomization analysis in the framework of predictive, preventive, and personalized medicine

Objective

Several studies have reported the association between gut microbiota and infertility; however, the causal association between them remains unclear. This study aimed to explore the causal relationship between gut microbiota and infertility and evaluate how specific gut microbiota can support early monitoring and prevention of infertility in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

The gut microbiota GWAS data included 18,340 individuals. Female infertility (6481 cases and 68,969 controls) and male infertility data (680 cases and 72,799 controls) were obtained from the FinnGen consortium. The inverse variance weighting (IVW), MR-Egger, weighted median (WM), Cochran Q tests, MR-PRESSO, and leave-one-out were used as a supplement to Mendelian randomization (MR) results and sensitivity analysis.

Results

The results of MR analysis indicated a significant causal association between Eubacterium oxidoreducens (OR = 2.048, P = 0.008), Lactococcus (OR = 1.445, P = 0.042), Eubacterium ventriosum (OR = 0.436, P = 0.018), Eubacterium rectale (OR = 0.306, P = 0.002), and Ruminococcaceae NK4A214 (OR = 0.537, P = 0.045) and male infertility. Genetically predicted Eubacterium ventriosum (OR = 0.809, P = 0.018), Holdemania (OR = 0.836, P = 0.037), Lactococcus (OR = 0.867, P = 0.020), Ruminococcaceae NK4A214 (OR = 0.830, P < 0.050), Ruminococcus torques (OR = 0.739, P = 0.022), and Faecalibacterium (OR = 1.311, P = 0.007) were associated with female infertility. Sensitivity analysis did not detect heterogeneity and pleiotropy (P > 0.05).

Conclusions

Our results provided evidence for the causal relationship between some gut microbiota and male and female infertility. These findings might be valuable in providing personalized treatment options for preventing infertility and improving reproductive function by monitoring and regulating the gut microbiota of infertility patients in the context of PPPM. Moreover, detecting the abundance of microbiota in feces can support preventive and personalized strategies, which may benefit more infertility patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00332-6.

A microbial causal mediation analytic tool for health disparity and applications in body mass index

BACKGROUND

Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality.

METHODS

Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups and innovatively and successfully extends the existing microbial mediation methods, which are originally proposed under potential outcome or counterfactual outcome study design, to address health disparities.

RESULTS

Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating the microbiome's contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between ethnicities or regions. 20.63%, 33.09%, and 25.71% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 18, and 16 species are identified to play the mediating role respectively.

CONCLUSIONS

The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles. Video Abstract.

Microbial composition across body sites in polycystic ovary syndrome: a systematic review and meta-analysis

Polycystic ovary syndrome (PCOS) is an endocrine disorder affecting reproductive-aged women, but the cause remains unclear. Recent evidence has linked microbial composition with PCOS; however, the results are inconsistent. The aim of this systematic review was to gather current knowledge of the microbes across body sites (oral cavity, blood, vagina/cervix, gut) in women with PCOS, and meta-analyse the microbial diversity in PCOS. For this purpose, a systematic search using PubMed, Web of Science, Cochrane and Scopus was carried out. After selection, 34 studies met the inclusion criteria. Most of the studies associated changes in the microbiome with PCOS, whereas heterogeneity of the studies in terms of ethnicity, body mass index (BMI) and methodology, among other confounders, made it difficult to corroborate this relationship. In fact, 19 out of 34 of the studies were categorised as having high risk of bias when the quality assessment was conducted. Our meta-analysis on the gut microbiome of 14 studies demonstrated that women with PCOS possess significantly lower microbial alpha diversity compared with controls (SMD = -0.204; 95% CI -0.360 to -0.048; P = 0.010; I2 = 5.508, by Shannon Index), which may contribute to the development of PCOS. Nevertheless, future studies should specifically overcome the shortcomings of the current studies by through well planned and conducted studies with larger sample sizes, proper negative and positive controls and adequate case-control matching.

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.

The gut microbiota links disease to human genome evolution

A large number of studies have established a causal relationship between the gut microbiota and human disease. In addition, the composition of the microbiota is substantially influenced by the human genome. Modern medical research has confirmed that the pathogenesis of various diseases is closely related to evolutionary events in the human genome. Specific regions of the human genome known as human accelerated regions (HARs) have evolved rapidly over several million years since humans diverged from a common ancestor with chimpanzees, and HARs have been found to be involved in some human-specific diseases. Furthermore, the HAR-regulated gut microbiota has undergone rapid changes during human evolution. We propose that the gut microbiota may serve as an important mediator linking diseases to human genome evolution.

Assigning cause for emerging diseases of aquatic organisms

Resolving the cause of disease (= aetiology) in aquatic organisms is a challenging but essential goal, heightened by increasing disease prevalence in a changing climate and an interconnected world of anthropogenic pathogen spread. Emerging diseases play important roles in evolutionary ecology, wildlife conservation, the seafood industry, recreation, cultural practices, and human health. As we emerge from a global pandemic of zoonotic origin, we must focus on timely diagnosis to confirm aetiology and enable response to diseases in aquatic ecosystems. Those systems' resilience, and our own sustainable use of seafood, depend on it. Synchronising traditional and recent advances in microbiology that span ecological, veterinary, and medical fields will enable definitive assignment of risk factors and causal agents for better biosecurity management and healthier aquatic ecosystems.

A microbial causal mediation analytic tool for health disparity and applications in body mass index

Background: Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework is available to analyze microbiome as a mediator between health disparity and clinical outcome, due to the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. Methods: Considering the modifiable and quantitative features of microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g. race or region) to a continuous outcome through microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups. Moreover, two tests checking the impact of microbiome on health disparity are proposed. Results: Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating microbiome’s contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between the reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between races or regions. 11.99%, 12.90%, and 7.4% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 21, and 12 species are identified to play the mediating role respectively. Conclusions: The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles.

Targeting keystone species helps restore the dysbiosis of butyrate-producing bacteria in nonalcoholic fatty liver disease

The dysbiosis of the gut microbiome is one of the pathogenic factors of nonalcoholic fatty liver disease (NAFLD) and also affects the treatment and intervention of NAFLD. Among gut microbiomes, keystone species that regulate the integrity and stability of an ecological community have become the potential intervention targets for NAFLD. Here, we collected stool samples from 22 patients with nonalcoholic steatohepatitis (NASH), 25 obese patients, and 16 healthy individuals from New York for 16S rRNA gene sequencing. An algorithm was implemented to identify keystone species based on causal inference theories and dynamic intervention simulation. External validation was performed in an independent cohort from California. Eight keystone species in the gut of NAFLD, represented by Porphyromonas loveana, Alistipes indistinctus, and Dialister pneumosintes, were identified, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome with 92.3% recovery. These keystone species regulate intestinal amino acid metabolism and acid-base environment to promote the growth of the butyrate-producing Lachnospiraceae and Ruminococcaceae species that are significantly reduced in NAFLD patients. Our findings demonstrate the importance of keystone species in restoring the microbial composition toward a normal gut microbiome, suggesting a novel potential microbial treatment for NAFLD.

Association between Microbiome-Related Human Genetic Variants and Fasting Plasma Glucose in a High-Cardiovascular-Risk Mediterranean Population

Background and Objectives: The gut microbiota has been increasingly recognized as a relevant factor associated with metabolic diseases. However, directly measuring the microbiota composition is a limiting factor for several studies. Therefore, using genetic variables as proxies for the microbiota composition is an important issue. Landmark microbiome–host genome-wide association studies (mbGWAS) have identified many SNPs associated with gut microbiota. Our aim was to analyze the association between relevant microbiome-related genetic variants (Mi-RSNPs) and fasting glucose and type 2 diabetes in a Mediterranean population, exploring the interaction with Mediterranean diet adherence. Materials and Methods: We performed a cross-sectional study in a high-cardiovascular-risk Mediterranean population (n = 1020), analyzing the association of Mi-RSNPs (from four published mbGWAS) with fasting glucose and type 2 diabetes. A single-variant approach was used for fitting fasting glucose and type 2 diabetes to a multivariable regression model. In addition, a Mendelian randomization analysis with multiple variants was performed as a sub-study. Results: We obtained several associations between Mi-RSNPs and fasting plasma glucose involving gut Gammaproteobacteria_HB, the order Rhizobiales, the genus Rumminococcus torques group, and the genus Tyzzerella as the top ranked. For type 2 diabetes, we also detected significant associations with Mi-RSNPs related to the order Rhizobiales, the family Desulfovibrionaceae, and the genus Romboutsia. In addition, some Mi-RSNPs and adherence to Mediterranean diet interactions were detected. Lastly, the formal Mendelian randomization analysis suggested combined effects. Conclusions: Although the use of Mi-RSNPs as proxies of the microbiome is still in its infancy, and although this is the first study analyzing such associations with fasting plasma glucose and type 2 diabetes in a Mediterranean population, some interesting associations, as well as modulations, with adherence to the Mediterranean diet were detected in these high-cardiovascular-risk subjects, eliciting new hypotheses.

Extremely small and incredibly close: Gut microbes as modulators of inflammation and targets for therapeutic intervention

Chronic inflammation is a hallmark for a variety of disorders and is at least partially responsible for disease progression and poor patient health. In recent years, the microbiota inhabiting the human gut has been associated with not only intestinal inflammatory diseases but also those that affect the brain, liver, lungs, and joints. Despite a strong correlation between specific microbial signatures and inflammation, whether or not these microbes are disease markers or disease drivers is still a matter of debate. In this review, we discuss what is known about the molecular mechanisms by which the gut microbiota can modulate inflammation, both in the intestine and beyond. We identify the current gaps in our knowledge of biological mechanisms, discuss how these gaps have likely contributed to the uncertain outcome of fecal microbiota transplantation and probiotic clinical trials, and suggest how both mechanistic insight and -omics-based approaches can better inform study design and therapeutic intervention.

A systematic, complexity-reduction approach to dissect the kombucha tea microbiome

One defining goal of microbiome research is to uncover mechanistic causation that dictates the emergence of structural and functional traits of microbiomes. However, the extraordinary degree of ecosystem complexity has hampered the realization of the goal. Here, we developed a systematic, complexity-reducing strategy to mechanistically elucidate the compositional and metabolic characteristics of microbiome by using the kombucha tea microbiome as an example. The strategy centered around a two-species core that was abstracted from but recapitulated the native counterpart. The core was convergent in its composition, coordinated on temporal metabolic patterns, and capable for pellicle formation. Controlled fermentations uncovered the drivers of these characteristics, which were also demonstrated translatable to provide insights into the properties of communities with increased complexity and altered conditions. This work unravels the pattern and process underlying the kombucha tea microbiome, providing a potential conceptual framework for mechanistic investigation of microbiome behaviors.

IBD Subtype-Regulators IFNG and GBP5 Identified by Causal Inference Drive More Intense Innate Immunity and Inflammatory Responses in CD Than Those in UC

Background: The pathological differences between Crohn’s disease (CD) and ulcerative colitis (UC) are substantial and unexplained yet. Here, we aimed to identify potential regulators that drive different pathogenesis of CD and UC by causal inference analysis of transcriptome data.

Methods: Kruskal–Wallis and Dunnett’s tests were performed to identify differentially expressed genes (DEGs) among CD patients, UC patients, and controls. Subsequently, differentially expressed pathways (DEPs) between CD and UC were identified and used to construct the interaction network of DEPs. Causal inference was performed to identify IBD subtype-regulators. The expression of the subtype-regulators and their downstream genes was validated by qRT-PCR with an independent cohort.

Results: Compared with the control group, we identified 1,352 and 2,081 DEGs in CD and UC groups, respectively. Multiple DEPs between CD and UC were closely related to inflammation-related pathways, such as NOD-like receptor signaling, IL-17 signaling, and chemokine signaling pathways. Based on the priori interaction network of DEPs, causal inference analysis identified IFNG and GBP5 as IBD subtype-regulators. The results with the discovery cohort showed that the expression level of IFNG, GBP5, and NLRP3 was significantly higher in the CD group than that in the UC group. The regulation relationships among IFNG, GBP5, and NLRP3 were confirmed with transcriptome data from an independent cohort and validated by qRT-PCR.

Conclusion: Our study suggests that IFNG and GBP5 were IBD subtype-regulators that trigger more intense innate immunity and inflammatory responses in CD than those in UC. Our findings reveal pathomechanical differences between CD and UC that may contribute to personalized treatment for CD and UC.

Over-feeding the gut microbiome: A scoping review on health implications and therapeutic perspectives

The human gut microbiome has gained increasing attention over the past two decades. Several findings have shown that this complex and dynamic microbial ecosystem can contribute to the maintenance of host health or, when subject to imbalances, to the pathogenesis of various enteric and non-enteric diseases. This scoping review summarizes the current knowledge on how the gut microbiota and microbially-derived compounds affect host metabolism, especially in the context of obesity and related disorders. Examples of microbiome-based targeted intervention strategies that aim to restore and maintain an eubiotic layout are then discussed. Adjuvant therapeutic interventions to alleviate obesity and associated comorbidities are traditionally based on diet modulation and the supplementation of prebiotics, probiotics and synbiotics. However, these approaches have shown only moderate ability to induce sustained changes in the gut microbial ecosystem, making the development of innovative and tailored microbiome-based intervention strategies of utmost importance in clinical practice. In this regard, the administration of next-generation probiotics and engineered microbiomes has shown promising results, together with more radical intervention strategies based on the replacement of the dysbiotic ecosystem by means of fecal microbiota transplantation from healthy donors or with the introduction of synthetic communities specifically designed to achieve the desired therapeutic outcome. Finally, we provide a perspective for future translational investigations through the implementation of bioinformatics approaches, including machine and deep learning, to predict health risks and therapeutic outcomes.

Reporting guidelines for human microbiome research: the STORMS checklist

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Multi-Omics Interpretation of Anti-Aging Mechanisms for ω-3 Fatty Acids

Aging is one of the hottest topics in biomedicine. Previous research suggested that ω-3 fatty acids have preventive effects on aging. However, most of previous studies on the anti-aging effects of ω-3 fatty acids are focused on clinical observations, and the anti-aging mechanisms of ω-3 fatty acids have not been fully elucidated. This stimulated our interest to use multi-omics data related to ω-3 fatty acids in order to interpret the anti-aging mechanisms of ω-3 fatty acids. First, we found that ω-3 fatty acids can affect methylation levels and expression levels of genes associated with age-related diseases or pathways in humans. Then, a Mendelian randomization analysis was conducted to determine whether there is a causal relationship between the effect of ω-3 fatty acids on blood lipid levels and variation in the gut microbiome. Our results indicate that the impact of ω-3 fatty acids on aging is partially mediated by the gut microbiome (including Actinobacteria, Bifidobacteria and Streptococcus). In conclusion, this study provides deeper insights into the anti-aging mechanisms of ω-3 fatty acids and supports the dietary supplementation of ω-3 fatty acids in aging prevention.

A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels

The dysbiosis of microbiome may have negative effects on a host phenotype. The microbes related to the host phenotype are regarded as microbial association signals. Recently, statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals. However, the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels (i.e., sparse, low sparse, non-sparse). Actually, the real association patterns related to different host phenotypes are not unique. Here, we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels, designated as MiATDS. In particular, we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information. We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method. We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power. By applying to real data analysis, MiATDS displays reliable practicability too. The R package is available at https://github.com/XiaoyunHuang33/MiATDS.