Genome-scale analyses of health-promoting bacteria: probiogenomics. Nat Rev Microbiol. 2009;7:61-71. [PMID: 19029955 DOI: 10.1038/nrmicro2047]

A graph neural network approach for predicting drug susceptibility in the human microbiome

Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastrointestinal microbiome, resulting in the conversion of these drugs into inactive metabolites. It is imperative to explore the field of pharmacomicrobiomics during the early stages of drug discovery, prior to clinical trials. To achieve this, the utilization of machine learning and deep learning models is highly desirable. In this study, we have proposed graph-based neural network models, namely GCN, GAT, and GINCOV models, utilizing the SMILES dataset of drug microbiome. Our primary objective was to classify the susceptibility of drugs to depletion by gut microbiota. Our results indicate that the GINCOV surpassed the other models, achieving impressive performance metrics, with an accuracy of 93% on the test dataset. This proposed Graph Neural Network (GNN) model offers a rapid and efficient method for screening drugs susceptible to gut microbiota depletion and also encourages the improvement of patient-specific dosage responses and formulations.

The communication mechanism of the gut-brain axis and its effect on central nervous system diseases: A systematic review

Gut microbiota is involved in intricate and active metabolic processes the host's brain function, especially its role in immune responses, secondary metabolism, and symbiotic connections with the host. Gut microbiota can promote the production of essential metabolites, neurotransmitters, and other neuroactive chemicals that affect the development and treatment of central nervous system diseases. This article introduces the relevant pathways and manners of the communication between the brain and gut, summarizes a comprehensive overview of the current research status of key gut microbiota metabolites that affect the functions of the nervous system, revealing those adverse factors that affect typical communication between the brain-gut axis, and outlining the efforts made by researchers to alleviate these neurological diseases through targeted microbial interventions. The relevant pathways and manners of communication between the brain and gut contribute to the experimental design of new treatment plans and drug development. The factors that may cause changes in gut microbiota and affect metabolites, as well as current intervention methods are summarized, which helps improve gut microbiota brain dialogue, prevent adverse triggering factors from interfering with the gut microbiota system, and minimize neuropathological changes.

Development of systemic and mucosal immune responses against gut microbiota in early life and implications for the onset of allergies

The early microbial colonization of human mucosal surfaces is essential for the development of the host immune system. Already during pregnancy, the unborn child is prepared for the postnatal influx of commensals and pathogens via maternal antibodies, and after birth this protection is continued with antibodies in breast milk. During this critical window of time, which extends from pregnancy to the first year of life, each encounter with a microorganism can influence children's immune response and can have a lifelong impact on their life. For example, there are numerous links between the development of allergies and an altered gut microbiome. However, the exact mechanisms behind microbial influences, also extending to how viruses influence host-microbe interactions, are incompletely understood. In this review, we address the impact of infants' first microbial encounters, how the immune system develops to interact with gut microbiota, and summarize how an altered immune response could be implied in allergies.

Neurodevelopmental Disorders Associated with Gut Microbiome Dysbiosis in Children

The formation of the human gut microbiome initiates in utero, and its maturation is established during the first 2-3 years of life. Numerous factors alter the composition of the gut microbiome and its functions, including mode of delivery, early onset of breastfeeding, exposure to antibiotics and chemicals, and maternal stress, among others. The gut microbiome-brain axis refers to the interconnection of biological networks that allow bidirectional communication between the gut microbiome and the brain, involving the nervous, endocrine, and immune systems. Evidence suggests that the gut microbiome and its metabolic byproducts are actively implicated in the regulation of the early brain development. Any disturbance during this stage may adversely affect brain functions, resulting in a variety of neurodevelopmental disorders (NDDs). In the present study, we reviewed recent evidence regarding the impact of the gut microbiome on early brain development, alongside its correlation with significant NDDs, such as autism spectrum disorder, attention-deficit/hyperactivity disorder, Tourette syndrome, cerebral palsy, fetal alcohol spectrum disorders, and genetic NDDs (Rett, Down, Angelman, and Turner syndromes). Understanding changes in the gut microbiome in NDDs may provide new chances for their treatment in the future.

Role of Gut Microecology in the Pathogenesis of Drug-Induced Liver Injury and Emerging Therapeutic Strategies

Drug-induced liver injury (DILI) is a common clinical pharmacogenic disease. In the United States and Europe, DILI is the most common cause of acute liver failure. Drugs can cause hepatic damage either directly through inherent hepatotoxic properties or indirectly by inducing oxidative stress, immune responses, and inflammatory processes. These pathways can culminate in hepatocyte necrosis. The role of the gut microecology in human health and diseases is well recognized. Recent studies have revealed that the imbalance in the gut microecology is closely related to the occurrence and development of DILI. The gut microecology plays an important role in liver injury caused by different drugs. Recent research has revealed significant changes in the composition, relative abundance, and distribution of gut microbiota in both patients and animal models with DILI. Imbalance in the gut microecology causes intestinal barrier destruction and microorganism translocation; the alteration in microbial metabolites may initiate or aggravate DILI, and regulation and control of intestinal microbiota can effectively mitigate drug-induced liver injury. In this paper, we provide an overview on the present knowledge of the mechanisms by which DILI occurs, the common drugs that cause DILI, the gut microbiota and gut barrier composition, and the effects of the gut microbiota and gut barrier on DILI, emphasizing the contribution of the gut microecology to DILI.

HKFGCN: A novel multiple kernel fusion framework on graph convolutional network to predict microbe-drug associations

Accumulating clinical studies have consistently demonstrated that the microbes in the human body closely interact with the human host, actively participating in the regulation of drug effectiveness. Identifying the associations between microbes and drugs can facilitate the development of drug discovery, and microbes have become a new target in antimicrobial drug development. However, the discovery of microbe-drug associations relies on clinical or biological experiments, which are not only time-consuming but also financially burdensome. Thus, the utilization of computational methods to predict microbe-drug associations holds promise for reducing costs and enhancing the efficiency of biological experiments. Here, we introduce a new computational method, called HKFGCN (Heterogeneous information Kernel Fusion Graph Convolution Network), to predict the microbe-drug associations. Instead of extracting feature from a single network in previous studies, HKFGCN separately extracts topological information features from different networks, and further refines them by generating Gaussian kernel features. HKFGCN consists of three main steps. Firstly, we constructed two similarity networks and a microbe-drug association network based on numerous biological data. Second, we employed two types of encoders to extract features from these networks. Next, Gaussian kernel features were obtained from the drug and microbe features at each layer. Finally, we reconstructed the bipartite microbe-drug graph based on the learned representations. Experimental results demonstrate the excellent performance of the HKFGCN model across different datasets using the cross-validation scheme. Additionally, we conduced case studies on human immunodeficiency virus, and the results were corroborated by existing literatures. The prediction model's code is available at https://github.com/roll-of-bubble/HKFGCN.

LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble

Introduction

Numerous studies show that microbes in the human body are very closely linked to the human host and can affect the human host by modulating the efficacy and toxicity of drugs. However, discovering potential microbe-drug associations through traditional wet labs is expensive and time-consuming, hence, it is important and necessary to develop effective computational models to detect possible microbe-drug associations.

Methods

In this manuscript, we proposed a new prediction model named LCASPMDA by combining the learnable graph convolutional attention network and the self-paced iterative sampling ensemble strategy to infer latent microbe-drug associations. In LCASPMDA, we first constructed a heterogeneous network based on newly downloaded known microbe-drug associations. Then, we adopted the learnable graph convolutional attention network to learn the hidden features of nodes in the heterogeneous network. After that, we utilized the self-paced iterative sampling ensemble strategy to select the most informative negative samples to train the Multi-Layer Perceptron classifier and put the newly-extracted hidden features into the trained MLP classifier to infer possible microbe-drug associations.

Results and discussion

Intensive experimental results on two different public databases including the MDAD and the aBiofilm showed that LCASPMDA could achieve better performance than state-of-the-art baseline methods in microbe-drug association prediction.

Cultivable microbial diversity, peptide profiles, and bio-functional properties in Parmigiano Reggiano cheese

Introduction

Lactic acid bacteria (LAB) communities shape the sensorial and functional properties of artisanal hard-cooked and long-ripened cheeses made with raw bovine milk like Parmigiano Reggiano (PR) cheese. While patterns of microbial evolution have been well studied in PR cheese, there is a lack of information about how this microbial diversity affects the metabolic and functional properties of PR cheese.

Methods

To fill this information gap, we characterized the cultivable fraction of natural whey starter (NWS) and PR cheeses at different ripening times, both at the species and strain level, and investigated the possible correlation between microbial composition and the evolution of peptide profiles over cheese ripening.

Results and discussion

The results showed that NWS was a complex community of several biotypes belonging to a few species, namely, Streptococcus thermophilus, Lactobacillus helveticus, and Lactobacillus delbrueckii subsp. lactis. A new species-specific PCR assay was successful in discriminating the cheese-associated species Lacticaseibacillus casei, Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, and Lacticaseibacillus zeae. Based on the resolved patterns of species and biotype distribution, Lcb. paracasei and Lcb. zeae were most frequently isolated after 24 and 30 months of ripening, while the number of biotypes was inversely related to the ripening time. Peptidomics analysis revealed more than 520 peptides in cheese samples. To the best of our knowledge, this is the most comprehensive survey of peptides in PR cheese. Most of them were from β-caseins, which represent the best substrate for LAB cell-envelope proteases. The abundance of peptides from β-casein 38-88 region continuously increased during ripening. Remarkably, this region contains precursors for the anti-hypertensive lactotripeptides VPP and IPP, as well as for β-casomorphins. We found that the ripening time strongly affects bioactive peptide profiles and that the occurrence of Lcb. zeae species is positively linked to the incidence of eight anti-hypertensive peptides. This result highlighted how the presence of specific LAB species is likely a pivotal factor in determining PR functional properties.

Ecology- and genome-based identification of the Bifidobacterium adolescentis prototype of the healthy human gut microbiota

Bifidobacteria are among the first microbial colonizers of the human gut, being frequently associated with human health-promoting activities. In the current study, an in silico methodology based on an ecological and phylogenomic-driven approach allowed the selection of a Bifidobacterium adolescentis prototype strain, i.e., B. adolescentis PRL2023, which best represents the overall genetic content and functional features of the B. adolescentis taxon. Such features were confirmed by in vitro experiments aimed at evaluating the ability of this strain to survive in the gastrointestinal tract of the host and its ability to interact with human intestinal cells and other microbial gut commensals. In this context, co-cultivation of B. adolescentis PRL2023 and several gut commensals revealed various microbe-microbe interactions and indicated co-metabolism of particular plant-derived glycans, such as xylan.IMPORTANCEThe use of appropriate bacterial strains in experimental research becomes imperative in order to investigate bacterial behavior while mimicking the natural environment. In the current study, through in silico and in vitro methodologies, we were able to identify the most representative strain of the Bifidobacterium adolescentis species. The ability of this strain, B. adolescentis PRL2023, to cope with the environmental challenges imposed by the gastrointestinal tract, together with its ability to switch its carbohydrate metabolism to compete with other gut microorganisms, makes it an ideal choice as a B. adolescentis prototype and a member of the healthy microbiota of adults. This strain possesses a genetic blueprint appropriate for its exploitation as a candidate for next-generation probiotics.

Milk and Its Derivatives as Sources of Components and Microorganisms with Health-Promoting Properties: Probiotics and Bioactive Peptides

Milk is a source of many valuable nutrients, including minerals, vitamins and proteins, with an important role in adult health. Milk and dairy products naturally containing or with added probiotics have healthy functional food properties. Indeed, probiotic microorganisms, which beneficially affect the host by improving the intestinal microbial balance, are recognized to affect the immune response and other important biological functions. In addition to macronutrients and micronutrients, biologically active peptides (BPAs) have been identified within the amino acid sequences of native milk proteins; hydrolytic reactions, such as those catalyzed by digestive enzymes, result in their release. BPAs directly influence numerous biological pathways evoking behavioral, gastrointestinal, hormonal, immunological, neurological, and nutritional responses. The addition of BPAs to food products or application in drug development could improve consumer health and provide therapeutic strategies for the treatment or prevention of diseases. Herein, we review the scientific literature on probiotics, BPAs in milk and dairy products, with special attention to milk from minor species (buffalo, sheep, camel, yak, donkey, etc.); safety assessment will be also taken into consideration. Finally, recent advances in foodomics to unveil the probiotic role in human health and discover novel active peptide sequences will also be provided.

Genomic and ecological approaches to identify the Bifidobacterium breve prototype of the healthy human gut microbiota

Members of the genus Bifidobacterium are among the first microorganisms colonizing the human gut. Among these species, strains of Bifidobacterium breve are known to be commonly transmitted from mother to her newborn, while this species has also been linked with activities supporting human wellbeing. In the current study, an in silico approach, guided by ecology- and phylogenome-based analyses, was employed to identify a representative strain of B. breve to be exploited as a novel health-promoting candidate. The selected strain, i.e., B. breve PRL2012, was found to well represent the genetic content and functional genomic features of the B. breve taxon. We evaluated the ability of PRL2012 to survive in the gastrointestinal tract and to interact with other human gut commensal microbes. When co-cultivated with various human gut commensals, B. breve PRL2012 revealed an enhancement of its metabolic activity coupled with the activation of cellular defense mechanisms to apparently improve its survivability in a simulated ecosystem resembling the human microbiome.

MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec

Introduction

Recent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes.

Methods

In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions.

Results and discussion

Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.

NMGMDA: a computational model for predicting potential microbe-drug associations based on minimize matrix nuclear norm and graph attention network

The prediction of potential microbe-drug associations is of great value for drug research and development, especially, methods, based on deep learning, have been achieved significant improvement in bio-medicine. In this manuscript, we proposed a novel computational model named NMGMDA based on the nuclear norm minimization and graph attention network to infer latent microbe-drug associations. Firstly, we created a heterogeneous microbe-drug network in NMGMDA by fusing the drug and microbe similarities with the established drug-microbe associations. After this, by using GAT and NNM to calculate the predict scores. Lastly, we created a fivefold cross validation framework to assess the new model NMGMDA's progressiveness. According to the simulation results, NMGMDA outperforms some of the most advanced methods, with a reliable AUC of 0.9946 on both MDAD and aBioflm databases. Furthermore, case studies on Ciprofloxacin, Moxifoxacin, HIV-1 and Mycobacterium tuberculosis were carried out in order to assess the effectiveness of NMGMDA even more. The experimental results demonstrated that, following the removal of known correlations from the database, 16 and 14 medications as well as 19 and 17 microbes in the top 20 predictions were validated by pertinent literature. This demonstrates the potential of our new model, NMGMDA, to reach acceptable prediction performance.

The gut-lung axis in critical illness: microbiome composition as a predictor of mortality at day 28 in mechanically ventilated patients

BACKGROUND

Microbial communities are of critical importance in the human host. The lung and gut microbial communities represent the most essential microbiota within the human body, collectively referred to as the gut-lung axis. However, the differentiation between these communities and their influence on clinical outcomes in critically ill patients remains uncertain.

METHODS

An observational cohort study was obtained in the intensive care unit (ICU) of an affiliated university hospital. Sequential samples were procured from two distinct anatomical sites, namely the respiratory and intestinal tracts, at two precisely defined time intervals: within 48 h and on day 7 following intubation. Subsequently, these samples underwent a comprehensive analysis to characterize microbial communities using 16S ribosomal RNA (rRNA) gene sequencing and to quantify concentrations of fecal short-chain fatty acids (SCFAs). The primary predictors in this investigation included lung and gut microbial diversity, along with indicator species. The primary outcome of interest was the survival status at 28 days following mechanical ventilation.

RESULTS

Sixty-two mechanically ventilated critically ill patients were included in this study. Compared to the survivors, the diversity of microorganisms was significantly lower in the deceased, with a significant contribution from the gut-originated fraction of lung microorganisms. Lower concentrations of fecal SCFAs were detected in the deceased. Multivariate Cox regression analysis revealed that not only lung microbial diversity but also the abundance of Enterococcaceae from the gut were correlated with day 28 mortality.

CONCLUSION

Critically ill patients exhibited lung and gut microbial dysbiosis after mechanical ventilation, as evidenced by a significant decrease in lung microbial diversity and the proliferation of Enterococcaceae in the gut. Levels of fecal SCFAs in the deceased served as a marker of imbalance between commensal and pathogenic flora in the gut. These findings emphasize the clinical significance of microbial profiling in predicting the prognosis of ICU patients.

Comprehensive probiogenomics analysis of the commensal Escherichia coli CEC15 as a potential probiotic strain

BACKGROUND

Probiotics have gained attention for their potential maintaining gut and immune homeostasis. They have been found to confer protection against pathogen colonization, possess immunomodulatory effects, enhance gut barrier functionality, and mitigate inflammation. However, a thorough understanding of the unique mechanisms of effects triggered by individual strains is necessary to optimize their therapeutic efficacy. Probiogenomics, involving high-throughput techniques, can help identify uncharacterized strains and aid in the rational selection of new probiotics. This study evaluates the potential of the Escherichia coli CEC15 strain as a probiotic through in silico, in vitro, and in vivo analyses, comparing it to the well-known probiotic reference E. coli Nissle 1917. Genomic analysis was conducted to identify traits with potential beneficial activity and to assess the safety of each strain (genomic islands, bacteriocin production, antibiotic resistance, production of proteins involved in host homeostasis, and proteins with adhesive properties). In vitro studies assessed survival in gastrointestinal simulated conditions and adhesion to cultured human intestinal cells. Safety was evaluated in BALB/c mice, monitoring the impact of E. coli consumption on clinical signs, intestinal architecture, intestinal permeability, and fecal microbiota. Additionally, the protective effects of both strains were assessed in a murine model of 5-FU-induced mucositis.

RESULTS

CEC15 mitigates inflammation, reinforces intestinal barrier, and modulates intestinal microbiota. In silico analysis revealed fewer pathogenicity-related traits in CEC15, when compared to Nissle 1917, with fewer toxin-associated genes and no gene suggesting the production of colibactin (a genotoxic agent). Most predicted antibiotic-resistance genes were neither associated with actual resistance, nor with transposable elements. The genome of CEC15 strain encodes proteins related to stress tolerance and to adhesion, in line with its better survival during digestion and higher adhesion to intestinal cells, when compared to Nissle 1917. Moreover, CEC15 exhibited beneficial effects on mice and their intestinal microbiota, both in healthy animals and against 5FU-induced intestinal mucositis.

CONCLUSIONS

These findings suggest that the CEC15 strain holds promise as a probiotic, as it could modulate the intestinal microbiota, providing immunomodulatory and anti-inflammatory effects, and reinforcing the intestinal barrier. These findings may have implications for the treatment of gastrointestinal disorders, particularly some forms of diarrhea.

Intestinal microbiota: A bridge between intermittent fasting and tumors

Intestinal microbiota and their metabolites are essential for maintaining intestinal health, regulating inflammatory responses, and enhancing the body's immune function. An increasing number of studies have shown that the intestinal microbiota is tightly tied to tumorigenesis and intervention effects. Intermittent fasting (IF) is a method of cyclic dietary restriction that can improve energy metabolism, prolong lifespan, and reduce the progression of various diseases, including tumors. IF can affect the energy metabolism of tumor cells, inhibit tumor cell growth, improve the function of immune cells, and promote an anti-tumor immune response. Interestingly, recent research has further revealed that the intestinal microbiota can be impacted by IF, in particular by changes in microbial composition and metabolism. These findings suggest the complexity of the IF as a promising tumor intervention strategy, which merits further study to better understand and encourage the development of clinical tumor intervention strategies. In this review, we aimed to outline the characteristics of the intestinal microbiota and its mechanisms in different tumors. Of note, we summarized the impact of IF on intestinal microbiota and discussed its potential association with tumor suppressive effects. Finally, we proposed some key scientific issues that need to be addressed and envision relevant research prospects, which might provide a theoretical basis and be helpful for the application of IF and intestinal microbiota as new strategies for clinical interventions in the future.

Probiogenomics of Leuconostoc Mesenteroides Strains F-21 and F-22 Isolated from Human Breast Milk Reveal Beneficial Properties

Bacteria of the Leuconostoc genus are Gram-positive bacteria that are commonly found in raw milk and persist in fermented dairy products and plant food. Studies have already explored the probiotic potential of L. mesenteroides, but not from a probiogenomic perspective, which aims to explore the molecular features responsible for their phenotypes. In the present work, probiogenomic approaches were applied in strains F-21 and F-22 of L. mesenteroides isolated from human milk to assess their biosafety at the molecular level and to correlate molecular features with their potential probiotic characteristics. The complete genome of strain F-22 is 1.99 Mb and presents one plasmid, while the draft genome of strain F-21 is 1.89 Mb and presents four plasmids. A high percentage of average nucleotide identity among other genomes of L. mesenteroides (≥ 96%) corroborated the previous taxonomic classification of these isolates. Genomic regions that influence the probiotic properties were identified and annotated. Both strains exhibited wide genome plasticity, cell adhesion ability, proteolytic activity, proinflammatory and immunomodulation capacity through interaction with TLR-NF-κB and TLR-MAPK pathway components, and no antimicrobial resistance, denoting their potential to be candidate probiotics. Further, the strains showed bacteriocin production potential and the presence of acid, thermal, osmotic, and bile salt resistance genes, indicating their ability to survive under gastrointestinal stress. Taken together, our results suggest that L. mesenteroides F-21 and F-22 are promising candidates for probiotics in the food and pharmaceutical industries.

Genome Analysis of Bifidobacterium Bifidum E3, Structural Characteristics, and Antioxidant Properties of Exopolysaccharides

In this study, the antioxidant properties of intact cells (IC), cell-free supernatant (CFS), and cell-free extracts (CFE) and whole genome sequencing of Bifidobacterium bifidum E3 (B. bifidum E3), as well as the structural characteristics and antioxidant properties of EPS-1, EPS-2, and EPS-3, were evaluated. The results revealed that intact cells (IC), cell-free supernatant (CFS), and cell-free extracts (CFE) had potent DPPH (1,1-Diphenyl-2-picrylhydrazyl radical), hydroxyl, and superoxide anion radical scavenging capacities, among which CFS was the best. At the genetic level, we identified a strong carbohydrate metabolism capacity, an EPS synthesis gene cluster, and five sugar nucleotides in B. bifidum E3. Therefore, we extracted cEPS from B. bifidum E3 and purified it to obtain EPS-1, EPS-2, and EPS-3. EPS-1, EPS-2, and EPS-3 were heteropolysaccharides with an average molecular weight of 4.15 × 104 Da, 3.67 × 104 Da, and 5.89 × 104 Da, respectively. The EPS-1 and EPS-2 are mainly comprised of mannose and glucose, and the EPS-3 is mainly comprised of rhamnose, mannose, and glucose. The typical characteristic absorption peaks of polysaccharides were shown in Fourier transform infrared spectroscopy (FT-IR spectroscopy). The microstructural study showed a rough surface structure for EPS-1, EPS-2, and EPS-3. Furthermore, EPS-1, EPS-2, and EPS-3 exhibited potent DPPH, hydroxyl, and superoxide anion radical scavenging capacities. Correlation analysis identified that antioxidant capacities may be influenced by various factors, especially molecular weight, chemical compositions, and monosaccharide compositions. In summary, the EPS that was produced by B. bifidum E3 may provide insights into health-promoting benefits in humans.

A review on extraction, composition, structure, and biological activities of polysaccharides from different parts of Nelumbo nucifera

Nelumbo nucifera (lotus plant) is an important member of the Nelumbonaceae family. This review summarizes the studies conducted on it since the past 15 years to provide an understanding on future areas of focus. Different parts of this plant, that is, leaves, roots, and seeds, have been used as food and for the treatment of various diseases. Polysaccharides have been extracted from different parts using different methods. The manuscript reviews the methods of extraction of polysaccharides used for leaves, roots, and seeds, along with their yield. Some methods can provide better yield while some provide better biological activity with low yield. The composition and structure of extracted polysaccharides have been determined in some studies. Although monosaccharide composition has been determined in various studies, too little information about the structure of polysaccharides from N. nucifera is available in the current literature. Different useful biological activities have been explored using in vivo and in vitro methods, which include antioxidant, antidiabetic, antitumor, anti-osteoporotic, immunomodulatory, and prebiotic activities. Antitumor activity from polysaccharides of lotus leaves is yet to be explored, besides lotus root has been underexplored as compared to other parts (leaves and seeds) according to our literature survey. Studies dedicated to the successful use of combination of extraction methods can be conducted in future. The plant provides a therapeutic as well as nutraceutical potential; however, antimicrobial activity and synergistic relationships of polysaccharides from different parts of the plant need further exploration.

A novel microbe-drug association prediction model based on stacked autoencoder with multi-head attention mechanism

Microbes are intimately tied to the occurrence of various diseases that cause serious hazards to human health, and play an essential role in drug discovery, clinical application, and drug quality control. In this manuscript, we put forward a novel prediction model named MDASAE based on a stacked autoencoder (SAE) with multi-head attention mechanism to infer potential microbe-drug associations. In MDASAE, we first constructed three kinds of microbe-related and drug-related similarity matrices based on known microbe-disease-drug associations respectively. And then, we fed two kinds of microbe-related and drug-related similarity matrices respectively into the SAE to learn node attribute features, and introduced a multi-head attention mechanism into the output layer of the SAE to enhance feature extraction. Thereafter, we further adopted the remaining microbe and drug similarity matrices to derive inter-node features by using the Restart Random Walk algorithm. After that, the node attribute features and inter-node features of microbes and drugs would be fused together to predict scores of possible associations between microbes and drugs. Finally, intensive comparison experiments and case studies based on different well-known public databases under 5-fold cross-validation and 10-fold cross-validation respectively, proved that MDASAE can effectively predict the potential microbe-drug associations.

Probiotics: insights and new opportunities for Clostridioides difficile intervention

Clostridioides difficile infection (CDI) is a life-threatening disease caused by the Gram-positive, opportunistic intestinal pathogen C. difficile. Despite the availability of antimicrobial drugs to treat CDI, such as vancomycin, metronidazole, and fidaxomicin, recurrence of infection remains a significant clinical challenge. The use of live commensal microorganisms, or probiotics, is one of the most investigated non-antibiotic therapeutic options to balance gastrointestinal (GI) microbiota and subsequently tackle dysbiosis. In this review, we will discuss major commensal probiotic strains that have the potential to prevent and/or treat CDI and its recurrence, reassess the efficacy of probiotics supplementation as a CDI intervention, delve into lessons learned from probiotic modulation of the immune system, explore avenues like genome-scale metabolic network reconstructions, genome sequencing, and multi-omics to identify novel strains and understand their functionality, and discuss the current regulatory framework, challenges, and future directions.

Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /- 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.

multiMiAT: an optimal microbiome-based association test for multicategory phenotypes

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy

MOTIVATION

Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions.

RESULTS

In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations.

AVAILABILITY

The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.

A Novel Synbiotic Alleviates Autoimmune Hepatitis by Modulating the Gut Microbiota-Liver Axis and Inhibiting the Hepatic TLR4/NF-κB/NLRP3 Signaling Pathway

Autoimmune hepatitis (AIH) is a liver disease characterized by chronic liver inflammation. The intestinal barrier and microbiome play critical roles in AIH progression. AIH treatment remains challenging because first-line drugs have limited efficacy and many side effects. Thus, there is growing interest in developing synbiotic therapies. This study investigated the effects of a novel synbiotic in an AIH mouse model. We found that this synbiotic (Syn) ameliorated liver injury and improved liver function by reducing hepatic inflammation and pyroptosis. The Syn reversed gut dysbiosis, as indicated by an increase in beneficial bacteria (e.g., Rikenella and Alistipes) and a decrease in potentially harmful bacteria (e.g., Escherichia-Shigella) and lipopolysaccharide (LPS)-bearing Gram-negative bacterial levels. The Syn maintained intestinal barrier integrity, reduced LPS, and inhibited the TLR4/NF-κB and NLRP3/Caspase-1 signaling pathway. In addition, microbiome phenotype prediction by BugBase and bacterial functional potential prediction using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) showed that Syn improved gut microbiota function involving inflammatory injury, metabolism, immune response, and pathopoiesia. Furthermore, the new Syn was as effective as prednisone against AIH. Therefore, this novel Syn could be a candidate drug for alleviating AIH through its anti-inflammatory and antipyroptosis properties that relieve endothelial dysfunction and gut dysbiosis. IMPORTANCE Synbiotics can ameliorate liver injury and improve liver function by reducing hepatic inflammation and pyroptosis. Our data indicate that our new Syn not only reverses gut dysbiosis by increasing beneficial bacteria and decreasing lipopolysaccharide (LPS)-bearing Gram-negative bacteria but also maintains intestinal barrier integrity. Thus, its mechanism might be associated with modulating gut microbiota composition and intestinal barrier function by inhibiting the TLR4/NF-κB/NLRP3/pyroptosis signaling pathway in the liver. This Syn is as effective as prednisone in treating AIH without side effects. Based on these findings, this novel Syn represents a potential therapeutic agent for AIH in clinical practice.

Identification of a prototype human gut Bifidobacterium longum subsp. longum strain based on comparative and functional genomic approaches

Bifidobacteria are extensively exploited for the formulation of probiotic food supplements due to their claimed ability to exert health-beneficial effects upon their host. However, most commercialized probiotics are tested and selected for their safety features rather than for their effective abilities to interact with the host and/or other intestinal microbial players. In this study, we applied an ecological and phylogenomic-driven selection to identify novel B. longum subsp. longum strains with a presumed high fitness in the human gut. Such analyses allowed the identification of a prototype microorganism to investigate the genetic traits encompassed by the autochthonous bifidobacterial human gut communities. B. longum subsp. longum PRL2022 was selected due to its close genomic relationship with the calculated model representative of the adult human-gut associated B. longum subsp. longum taxon. The interactomic features of PRL2022 with the human host as well as with key representative intestinal microbial members were assayed using in vitro models, revealing how this bifidobacterial gut strain is able to establish extensive cross-talk with both the host and other microbial residents of the human intestine.

GACNNMDA: a computational model for predicting potential human microbe-drug associations based on graph attention network and CNN-based classifier

As new drug targets, human microbes are proven to be closely related to human health. Effective computational methods for inferring potential microbe-drug associations can provide a useful complement to conventional experimental methods and will facilitate drug research and development. However, it is still a challenging work to predict potential interactions for new microbes or new drugs, since the number of known microbe-drug associations is very limited at present. In this manuscript, we first constructed two heterogeneous microbe-drug networks based on multiple measures of similarity of microbes and drugs, and known microbe-drug associations or known microbe-disease-drug associations, respectively. And then, we established two feature matrices for microbes and drugs through concatenating various attributes of microbes and drugs. Thereafter, after taking these two feature matrices and two heterogeneous microbe-drug networks as inputs of a two-layer graph attention network, we obtained low dimensional feature representations for microbes and drugs separately. Finally, through integrating low dimensional feature representations with two feature matrices to form the inputs of a convolutional neural network respectively, a novel computational model named GACNNMDA was designed to predict possible scores of microbe-drug pairs. Experimental results show that the predictive performance of GACNNMDA is superior to existing advanced methods. Furthermore, case studies on well-known microbes and drugs demonstrate the effectiveness of GACNNMDA as well. Source codes and supplementary materials are available at: https://github.com/tyqGitHub/TYQ/tree/master/GACNNMDA.

Gut fungal community composition analysis of myostatin mutant cattle prepared by CRISPR/Cas9

Myostatin (MSTN) regulates muscle development and body metabolism through a variety of pathways and is a core target gene for gene editing in livestock. Gut fungi constitute a small part of the gut microbiome and are important to host health and metabolism. The influence of MSTN mutations on bovine gut fungi remains unknown. In this study, Internal Transcribed Spacer (ITS) high-throughput sequencing was conducted to explore the composition of gut fungi in the MSTN mutant (MT) and wild-type (WT) cattle, and 5,861 operational taxonomic units (OTUs) were detected and classified into 16 phyla and 802 genera. The results of the alpha diversity analysis indicated that no notable divergence was displayed between the WT and MT cattle; however, significant differences were noticed in the composition of fungal communities. Eight phyla and 18 genera were detected. According to the prediction of fungal function, saprotroph fungi were significantly more abundant in the MT group. The correlation analysis between gut fungal and bacterial communities revealed that MSTN mutations directly changed the gut fungal composition and, at the same time, influenced some fungi and bacteria by indirectly regulating the interaction between microorganisms, which affected the host metabolism further. This study analyzed the role of MSTN mutations in regulating the host metabolism of intestinal fungi and provided a theoretical basis for the relationship between MSTN and gut fungi.

Intracellular glycogen accumulation by human gut commensals as a niche adaptation trait

The human gut microbiota is a key contributor to host metabolism and physiology, thereby impacting in various ways on host health. This complex microbial community has developed many metabolic strategies to colonize, persist and survive in the gastrointestinal environment. In this regard, intracellular glycogen accumulation has been associated with important physiological functions in several bacterial species, including gut commensals. However, the role of glycogen storage in shaping the composition and functionality of the gut microbiota offers a novel perspective in gut microbiome research. Here, we review what is known about the enzymatic machinery and regulation of glycogen metabolism in selected enteric bacteria, while we also discuss its potential impact on colonization and adaptation to the gastrointestinal tract. Furthermore, we survey the presence of such glycogen biosynthesis pathways in gut metagenomic data to highlight the relevance of this metabolic trait in enhancing survival in the highly competitive and dynamic gut ecosystem.

Comparative Genomics Analysis Provides New Insights into High Ethanol Tolerance of Lactiplantibacillus pentosus LTJ12, a Novel Strain Isolated from Chinese Baijiu

Lactic acid bacteria have received a significant amount of attention due to their probiotic characteristics. The species Lactiplantibacillus plantarum and Lactiplantibacillus pentosus are genotypically closely related, and their phenotypes are so similar that they are easily confused and mistaken. In the previous study, an ethanol-resistant strain, LTJ12, isolated from the fermented grains of soy sauce aroma type baijiu in North China, was originally identified as L. plantarum through a 16S rRNA sequence analysis. Here, the genome of strain LTJ12 was further sequenced using PacBio and Illumina sequencing technology to obtain a better understanding of the metabolic pathway underlying its resistance to ethanol stress. The results showed that the genome of strain LTJ12 was composed of one circular chromosome and three circular plasmids. The genome size is 3,512,307 bp with a GC content of 46.37%, and the number of predicted coding genes is 3248. Moreover, by comparing the coding genes with the GO (Gene Ontology), COG (Cluster of Orthologous Groups) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases, the functional annotation of the genome and an assessment of the metabolic pathways were performed, with the results showing that strain LTJ12 has multiple genes that may be related to alcohol metabolism and probiotic-related genes. Antibiotic resistance gene analysis showed that there were few potential safety hazards. Further, after conducting the comparative genomics analysis, it was found that strain LTJ12 is L. pentosus but not L. plantarum, but it has more functional genes than other L. pentosus strains that are mainly related to carbohydrate transport and metabolism, transcription, replication, recombination and repair, signal transduction mechanisms, defense mechanisms and cell wall/membrane/envelope biogenesis. These unique functional genes, such as gene 2754 (encodes alcohol dehydrogenase), gene 3093 (encodes gamma-D-glutamyl-meso-diaminopimelate peptidase) and some others may enhance the ethanol tolerance and alcohol metabolism of the strain. Taken together, L. pentosus LTJ12 might be a potentially safe probiotic with a high ethanol tolerance and alcohol metabolism. The findings of this study will also shed light on the accurate identification and rational application of the Lactiplantibacillus species.

The Human Milk Microbiota Produces Potential Therapeutic Biomolecules and Shapes the Intestinal Microbiota of Infants

Human milk not only provides a perfect balance of nutrients to meet all the needs of the infant in the first months of life but also contains a variety of bacteria that play a key role in tailoring the neonatal faecal microbiome. Microbiome analysis of human milk and infant faeces from mother-breastfed infant pairs was performed by sequencing the V1-V3 region of the 16S rRNA gene using the Illumina MiSeq platform. According to the results, there is a connection in the composition of the microbiome in each mother-breastfed infant pair, supporting the hypothesis that the infant's gut is colonised with bacteria from human milk. MiSeq sequencing also revealed high biodiversity of the human milk microbiome and the infant faecal microbiome, whose composition changes during lactation and infant development, respectively. A total of 28 genetically distinct strains were selected by hierarchical cluster analysis of RAPD-PCR (Random Amplified Polymorphic DNA-Polymerase Chain Reaction) electrophoresis profiles of 100 strains isolated from human milk and identified by 16S RNA sequencing. Since certain cellular molecules may support their use as probiotics, the next focus was to detect (S)-layer proteins, bacteriocins and exopolysaccharides (EPSs) that have potential as therapeutic biomolecules. SDS-PAGE (Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis) coupled with LC-MS (liquid chromatography-mass spectrometry) analysis revealed that four Levilactobacillus brevis strains expressed S-layer proteins, which were identified for the first time in strains isolated from human milk. The potential biosynthesis of plantaricin was detected in six Lactiplantibacillus plantarum strains by PCR analysis and in vitro antibacterial studies. ¹H NMR (Proton Nuclear Magnetic Resonance) analysis confirmed EPS production in only one strain, Limosilactobacillus fermentum MC1. The overall microbiome analysis suggests that human milk contributes to the establishment of the intestinal microbiota of infants. In addition, it is a promising source of novel Lactobacillus strains expressing specific functional biomolecules.

GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder

Background

Clinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods for predicting microbe–drug associations have been proposed.

Results

In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe–drug associations. In GSAMDA, we first built a heterogeneous network through integrating known microbe–drug associations, microbe similarities and drug similarities. And then, we adopted a GAT-based autoencoder and a sparse autoencoder module respectively to learn topological representations and attribute representations for nodes in the newly constructed heterogeneous network. Finally, based on these two kinds of node representations, we constructed two kinds of feature matrices for microbes and drugs separately, and then, utilized them to calculate possible association scores for microbe–drug pairs.

Conclusion

A novel computational model is proposed for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder. Compared with other five state-of-the-art competitive methods, the experimental results illustrated that our model can achieve better performance. Moreover, case studies on two categories of representative drugs and microbes further demonstrated the effectiveness of our model as well.

Pan-genome evolution and its association with divergence of metabolic functions in Bifidobacterium genus

Previous studies were mainly focused on genomic evolution and diversity of type species of Bifidobacterium genus due to their health-promoting effect on host. However, those studies were mainly based on species-level taxonomic resolution, adaptation, and characterization of carbohydrate metabolic features of the bifidobacterial species. Here, a comprehensive analysis of the type strain genome unveils the association of pan-genome evolution with the divergence of metabolic function of the Bifidobacterium genus. This study has also demonstrated that horizontal gene transfer, as well as genome expansion and reduction events, leads to the divergence of metabolic functions in Bifidobacterium genus. Furthermore, the genome-based search of probiotic traits among all the available bifidobacterial type strains gives hints on type species, that could confer health benefits to nutrient-deficient individuals. Altogether, the present study provides insight into the developments of genomic evolution, functional divergence, and potential probiotic type species of the Bifidobacterium genus.

Effects of Chronic Bifidobacteria Administration in Adult Male Rats on Plasma Metabolites: A Preliminary Metabolomic Study

Probiotics are live microorganisms distributed in the gastrointestinal tract that confer health benefits to the host when administered in adequate amounts. Bifidobacteria have been widely tested as a therapeutic strategy in the prevention and treatment of a broad spectrum of gastrointestinal disorders as well as in the regulation of the “microbiota-gut-brain axis”. Metabolomic techniques can provide details in the study of molecular metabolic mechanisms involved in Bifidobacteria function through the analysis of metabolites that positively contribute to human health. This study was focused on the effects of the chronic assumption of a mixture of Bifidobacteria in adult male rats using a metabolomic approach. Plasma samples were collected at the end of treatment and analyzed with a gas chromatography-mass spectrometry (GC-MS) platform. Partial least square discriminant analysis (PLS-DA) was performed to compare the metabolic pattern in control and probiotic-treated rats. Our results show, in probiotic-treated animals, an increase in metabolites involved in the energetic cycle, such as glucose, erythrose, creatinine, taurine and glycolic acid, as well as 3-hydroxybutyric acid. This is an important metabolite of short-chain fatty acids (SCFA) with multitasking roles in energy circuit balance, and it has also been proposed to have a key role in the prevention and treatment of neurodegenerative diseases.

Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction

Background

Efficient identification of microbe-drug associations is critical for drug development and solving problem of antimicrobial resistance. Traditional wet-lab method requires a lot of money and labor in identifying potential microbe-drug associations. With development of machine learning and publication of large amounts of biological data, computational methods become feasible.

Methods

In this article, we proposed a computational model of neighborhood-based inference (NI) and restricted Boltzmann machine (RBM) to predict potential microbe-drug association (NIRBMMDA) by using integrated microbe similarity, integrated drug similarity and known microbe-drug associations. First, NI was used to obtain a score matrix of potential microbe-drug associations by using different thresholds to find similar neighbors for drug or microbe. Second, RBM was employed to obtain another score matrix of potential microbe-drug associations based on contrastive divergence algorithm and sigmoid function. Because generalization ability of individual method is poor, we used an ensemble learning to integrate two score matrices for predicting potential microbe-drug associations more accurately. In particular, NI can fully utilize similar (neighbor) information of drug or microbe and RBM can learn potential probability distribution hid in known microbe-drug associations. Moreover, ensemble learning was used to integrate individual predictor for obtaining a stronger predictor.

Results

In global leave-one-out cross validation (LOOCV), NIRBMMDA gained the area under the receiver operating characteristics curve (AUC) of 0.8666, 0.9413 and 0.9557 for datasets of DrugVirus, MDAD and aBiofilm, respectively. In local LOOCV, AUCs of 0.8512, 0.9204 and 0.9414 were obtained for NIRBMMDA based on datasets of DrugVirus, MDAD and aBiofilm, respectively. For five-fold cross validation, NIRBMMDA acquired AUC and standard deviation of 0.8569 ± -0.0027, 0.9248 ± -0.0014 and 0.9369 ± -0.0020 on the basis of datasets of DrugVirus, MDAD and aBiofilm, respectively. Moreover, case study for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed that 13 out of the top 20 predicted drugs were verified by searching literature. The other two case studies indicated that 17 and 17 out of the top 20 predicted microbes for the drug of ciprofloxacin and minocycline were confirmed by identifying published literature, respectively.

A Class IIb Bacteriocin Plantaricin NC8 Modulates Gut Microbiota of Different Enterotypes in vitro

The gut microbiota is engaged in multiple interactions affecting host health. Bacteriocins showed the ability of impeding the growth of intestinal pathogenic bacteria and modulating gut microbiota in animals. Few studies have also discovered their regulation on human intestinal flora using an in vitro simulated system. However, little is known about their effect on gut microbiota of different enterotypes of human. This work evaluated the modification of the gut microbiota of two enterotypes (ET B and ET P) by the class IIb bacteriocin plantaricin NC8 (PLNC8) by using an in vitro fermentation model of the intestine. Gas chromatography results revealed that PLNC8 had no influence on the gut microbiota’s production of short-chain fatty acids in the subjects’ samples. PLNC8 lowered the Shannon index of ET B’ gut microbiota and the Simpson index of ET P’ gut microbiota, according to 16S rDNA sequencing. In ET B, PLNC8 enhanced the abundance of Bacteroides, Bifidobacterium, Megamonas, Escherichia-Shigella, Parabacteroides, and Lactobacillus while decreasing the abundance of Streptococcus. Prevotella_9, Bifidobacterium, Escherichia-Shigella, Mitsuokella, and Collinsella were found more abundant in ET P. The current study adds to our understanding of the impact of PLNC8 on the human gut microbiota and lays the groundwork for future research into PLNC8’s effects on human intestinal disease.

NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation

A growing number of clinical studies have provided substantial evidence of a close relationship between the microbe and the disease. Thus, it is necessary to infer potential microbe-disease associations. But traditional approaches use experiments to validate these associations that often spend a lot of materials and time. Hence, more reliable computational methods are expected to be applied to predict disease-associated microbes. In this article, an innovative mean for predicting microbe-disease associations is proposed, which is based on network consistency projection and label propagation (NCPLP). Given that most existing algorithms use the Gaussian interaction profile (GIP) kernel similarity as the similarity criterion between microbe pairs and disease pairs, in this model, Medical Subject Headings descriptors are considered to calculate disease semantic similarity. In addition, 16S rRNA gene sequences are borrowed for the calculation of microbe functional similarity. In view of the gene-based sequence information, we use two conventional methods (BLAST+ and MEGA7) to assess the similarity between each pair of microbes from different perspectives. Especially, network consistency projection is added to obtain network projection scores from the microbe space and the disease space. Ultimately, label propagation is utilized to reliably predict microbes related to diseases. NCPLP achieves better performance in various evaluation indicators and discovers a greater number of potential associations between microbes and diseases. Also, case studies further confirm the reliable prediction performance of NCPLP. To conclude, our algorithm NCPLP has the ability to discover these underlying microbe-disease associations and can provide help for biological study.

Tumor tissue-specific bacterial biomarker panel for colorectal cancer: Bacteroides massiliensis, Alistipes species, Alistipes onderdonkii, Bifidobacterium pseudocatenulatum, Corynebacterium appendicis

Human microbiome studies have shown diversity to exist among different ethnic populations. However, studies pertaining to the microbial composition of CRC among the Indian population have not been well explored. We aimed to decipher the microbial signature in tumor tissues from North Indian CRC patients. Next-generation sequencing of tumor and adjacent tissue-derived bacterial 16S rRNA V3-V4 hypervariable regions was performed to investigate the abundance of specific microbes. The expression profile analysis deciphered a decreased diversity among the tumor-associated microbial communities. At the phyla level, Proteobacteria was differentially expressed in CRC tissues than adjacent normal. Further, DeSeq2 normalization identified 4 out of 79 distinct species (p < 0.005) only in CRC, Bacteroides massiliensis, Alistipes onderdonkii, Bifidobacterium pseudocatenulatum, and Corynebacterium appendicis. Thus, the findings suggest that microbial signatures can be used as putative biomarkers in diagnosis, prognosis and treatment management of CRC.

Intestinal Flora Mediates Antiobesity Effect of Rutin in High-Fat-Diet Mice

SCOPE

Intestinal flora plays a critical role in the development of . Rutin is a natural flavonoid with potential prebiotic effects on regulating the intestinal flora composition that is beneficial for host health. Therefore, this study hypothesizes that rutin supplementation has beneficial effects on high-fat-diet (HFD)-induced obesity and metabolic disorder through the modulation of intestinal flora in mice.

METHODS AND RESULTS

The obesity-alleviating property of rutin using 6-week-old C57BL/6J male mice fed on HFD with or without rutin supplementation for 16 weeks is investigated. Rutin supplementation effectively reduces body-weight gain, insulin resistance, and acted favorably on the intestinal barrier, thereby reducing endotoxemia and systemic inflammation. Sequencing of 16S rRNA genes from fecal samples indicate that rutin exerted modulatory effects on HFD-induced intestinal flora disorders (e.g., rutin decreased Firmicutes abundance and increased Bacteroidetes and Verrucomicrobia abundance). Antibiotic treatment and fecal microbiota transplantation further demonstrate that the salutary effects of rutin on obesity control are strongly dependent on the intestinal flora.

CONCLUSION

Rutin can be considered as a prebiotic agent for improving intestinal flora disorders and obesity-associated metabolic perturbations in obese individuals.

MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network

Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insights into the mechanism of drug–virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on the graph convolutional networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an area under the curve (AUC) of 0.9726 and an area under the PR curve (AUPR) of 0.9112. In this case study, two drugs related to SARS-CoV-2 were successfully predicted and verified by the relevant literature. The data and code are open source and can be accessed from https://github.com/Pijiangsheng/MDGNN.

Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.

Genome-Wide Assessment of Stress-Associated Genes in Bifidobacteria

Over the last decade, the genomes of several Bifidobacterium strains have been sequenced, delivering valuable insights into their genetic makeup. However, bifidobacterial genomes have not yet been systematically mined for genes associated with stress response functions and their regulation. In this work, a list of 76 genes related to stress response in bifidobacteria was compiled from previous studies. The prevalence of the genes was evaluated among the genome sequences of 171 Bifidobacterium strains. Although genes of the protein quality control and DNA repair systems appeared to be highly conserved, genome-wide in silico screening for consensus sequences of putative regulators suggested that the regulation of these systems differs among phylogenetic groups. Homologs of multiple oxidative stress-associated genes are shared across species, albeit at low sequence similarity. Bee isolates were confirmed to harbor unique genetic features linked to oxygen tolerance. Moreover, most studied Bifidobacterium adolescentis and all Bifidobacterium angulatum strains lacked a set of reactive oxygen species-detoxifying enzymes, which might explain their high sensitivity to oxygen. Furthermore, the presence of some putative transcriptional regulators of stress responses was found to vary across species and strains, indicating that different regulation strategies of stress-associated gene transcription contribute to the diverse stress tolerance. The presented stress response gene profiles of Bifidobacterium strains provide a valuable knowledge base for guiding future studies by enabling hypothesis generation and the identification of key genes for further analyses.

IMPORTANCE Bifidobacteria are Gram-positive bacteria that naturally inhabit diverse ecological niches, including the gastrointestinal tract of humans and animals. Strains of the genus Bifidobacterium are widely used as probiotics, since they have been associated with health benefits. In the course of their production and administration, probiotic bifidobacteria are exposed to several stressors that can challenge their survival. The stress tolerance of probiotic bifidobacteria is, therefore, an important selection criterion for their commercial application, since strains must maintain their viability to exert their beneficial health effects. As the ability to cope with stressors varies among Bifidobacterium strains, comprehensive understanding of the underlying stress physiology is required for enabling knowledge-driven strain selection and optimization of industrial-scale production processes.

MDADP: A webserver integrating database and prediction tools for microbe-disease associations

More and more evidence has demonstrated that microbiota play important roles in the life processes of the human body. In recent years, various computational methods have been proposed for identifying potentially disease-associated microbes to save costs in traditional biological experiments. However, prediction performances of these methods are generally limited by outdated and incomplete datasets. And moreover, until now, there are limited studies that can provide visual predictive tools for inferring possible microbe-disease associations (MDAs) as well. Hence, in this manuscript, a novel webserver called MDADP will be proposed to identify latent MDAs, in which, a new MDA database together with interactive prediction tools for MDAs studies will be designed simultaneously. Especially, in the newly constructed MDA database, 2019 known MDAs between 58 diseases and 703 microbes have been manually collected first. And then, through adopting the average ranking method and the co-confidence method respectively, eight representative computational models have been integrated together to identify potential disease-related microbes. As a result, MDADP can provide not only interactive features for users to access and capture MDAs entities, but also effective tools for users to identify candidate microbes for different diseases. To our knowledge, MDADP is the first online platform that incorporates a new MDA database with comprehensive MDA prediction tools. Therefore, we believe that it will be a valuable source of information for researches in microbiology and disease-related fields. MDADP can be accessed at http://mdadp.leelab2997.cn.

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations

MOTIVATION

Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations.

RESULTS

In this article, we proposed a novel method, Graph2MDA, to predict microbe-drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe-drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaning of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75-95% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method.

AVAILABILITY AND IMPLEMENTATION

Source codes and preprocessed data are available at https://github.com/moen-hyb/Graph2MDA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Probiotic Identity Card: A Novel “Probiogenomics” Approach to Investigate Probiotic Supplements

Probiotic bacteria are widely administered as dietary supplements and incorporated as active ingredients in a variety of functional foods due to their purported health-promoting features. Currently available probiotic products may have issues with regards to their formulation, such as insufficient levels of viable probiotic bacteria, complete lack of probiotic strains that are stated to be present in the product, and the presence of microbial contaminants. To avoid the distribution of such unsuitable or misleading products, we propose here a novel approach named Probiotic Identity Card (PIC), involving a combination of shotgun metagenomic sequencing and bacterial cell enumeration by flow cytometry. PIC was tested on 12 commercial probiotic supplements revealing several inconsistencies in the formulation of five such products based on their stated microbial composition and viability.

Characteristic properties of spray-drying Bifidobacterium adolescentis microcapsules with biosurfactant

The surfactants used for emulsion is one of the best techniques for microencapsulation of lactic acid bacteria (LAB) since it is economical. The biosurfactants have many advantages such as lower toxicity, higher biodegradability. In this study, microcapsules were prepared via spray drying using Bifidobacterium adolescentis species cultured in soy milk extract with biosurfactant prepared using Alcaligenes piechaudii CC-ESB2 to improve their powder properties. The soy milk was used to increase the health benefits instead of the milk. The optimum bacterial strain viability, water activity, and moisture content of the microcapsules were achieved at a spray dryer inlet/outlet temperature of 120/60°C. The composition of the carrier affects the particle size of the microcapsules. Using 90% maltodextrin (MD), 5% isomalto-oligosaccharide syrup (IMOS) and 5% biosurfactant as a carrier increased the viability of the LAB. Scanning electron microscope observations showed that the LAB microcapsules were able to effectively retain their completeness. Furthermore, microcapsules added with a biosurfactant prepared using A. piechaudii CC-ESB2 displayed significantly better flow properties than those without the surfactant and biosurfactant, which indicates that the biosurfactant assists in enhancing the powder properties of the microcapsules. It also has sufficient biological activity as a LAB product because the probiotics exceed 10⁶ CFU/mL The spray-dried abandoned supernatant with biosurfactant exhibited superior bacteriostasis, which suggests that the supernatant of B. adolescentis during microencapsulation not only retains its bacteriostatic effect under high spray drying temperatures, but also provides additional antibacterial effects for the microcapsules.

Diversity of Human-Associated Bifidobacterial Prophage Sequences

Members of Bifidobacterium play an important role in the development of the immature gut and are associated with positive long-term health outcomes for their human host. It has previously been shown that intestinal bacteriophages are detected within hours of birth, and that induced prophages constitute a significant source of such gut phages. The gut phageome can be vertically transmitted from mother to newborn and is believed to exert considerable selective pressure on target prokaryotic hosts affecting abundance levels, microbiota composition, and host characteristics. The objective of the current study was to investigate prophage-like elements and predicted CRISPR-Cas viral immune systems present in publicly available, human-associated Bifidobacterium genomes. Analysis of 585 fully sequenced bifidobacterial genomes identified 480 prophage-like elements with an occurrence of 0.82 prophages per genome. Interestingly, we also detected the presence of very similar bifidobacterial prophages and corresponding CRISPR spacers across different strains and species, thus providing an initial exploration of the human-associated bifidobacterial phageome. Our analyses show that closely related and likely functional prophages are commonly present across four different species of human-associated Bifidobacterium. Further comparative analysis of the CRISPR-Cas spacer arrays against the predicted prophages provided evidence of historical interactions between prophages and different strains at an intra- and inter-species level. Clear evidence of CRISPR-Cas acquired immunity against infection by bifidobacterial prophages across several bifidobacterial strains and species was obtained. Notably, a spacer representing a putative major capsid head protein was found on different genomes representing multiple strains across B. adolescentis, B. breve, and B. bifidum, suggesting that this gene is a preferred target to provide bifidobacterial phage immunity.

Edible fungal polysaccharides, the gut microbiota, and host health

The plasticity of the gut microbiota (GM) creates an opportunity to reshape the biological output of gut microbes by manipulating external factors. It is well known that edible fungal polysaccharides (EFPs) can reach the distal intestine and be assimilated to reshape the GM. The GM has unique devices that utilize various EFPs and produce oligosaccharides, which can selectively promote the growth of beneficial bacteria and are fermented into short-chain fatty acids that interact closely with intestinal cells. Here we review EFPs-based interventions for the GM, particularly the key microorganisms, functions, and metabolites. In addition, we discuss the bi-directional causality between GM imbalance and diseases, and the beneficial effects of EFPs on host health via GM. This review can offer a valuable reference for the design of edible fungal polysaccharide- or oligosaccharide-based nutrition interventions or drug development for maintaining human health by targeted regulation of the GM.