Human Microbiome Research Articles

Evaluation of azo dyes degradation potential of Staphylococcus strains: A strategy for dye-waste management

The current investigation aimed to identify the bacterial isolates that could prove helpful in the degeneration of harmful azo dyes from wastewater. The bacterial strains 1b, 1 m, and 4v could decolorize azo dyes up to 81 %, 79 %, and 87 % within 5 days. The degraded products by thin layer chromatography (TLC) showed Rf values of 0.89, 0.95, 0.90, 0.92, and 0.98 while the control showed an Rf value of 0.94. The comparison of the retention time of control and treated samples by the high-performance liquid chromatography (HPLC) system convinced that remarkable decolorization had occurred by the bacterial strains. The Fourier transform infrared spectroscopy (FTIR) analysis of the control and degraded samples was proof that the bond stretching occurred in the treated samples due to the action of bacterial strains. The release of compounds by bacteria i.e., 3-Aminobutanoic acid, pyrrolo pyrazine-1, 4-dione, and palmitic acid was inspected by Gas Chromatography-Mass Spectroscopy (GC-MS) analysis. No clear zones showed that the bacterial dye-degraded wastewater had no harm to the normal flora. At last, phytotoxicity was studied on Vigna radiata which had negative results. Given their pollutant degrading capabilities, these bacterial isolates are a good bioresource for green chemistry to exterminate azo dyes from the environment.

Valid and accepted novel bacterial taxa isolated from non-domestic animals and taxonomic revisions published in 2023.

Continued investigation into the bacteria associated with non-domestic animals provides important information for recognizing normal flora, assessing the health status of these unique species of animals, and identifying new or emerging pathogens of concern. In this summary of novel taxa and taxonomic revisions, considerable additions have been made toward understanding fecal and mucosal flora in multiple wild animal species. In addition, novel pathogenic bacteria are discussed, including multiple Chlamydia spp. causing disease in a hawk and crocodile, two Corynebacterium spp. causing oral lesions in penguins and a lesser-known genus, Mergibacter within Family Pasteurellaceae, causing disease in multiple wild bird species. Finally, a few revisions to bacteria isolated from normal non-domestic animal body sites are mentioned.

Gut-lung microbiota dynamics in mice exposed to Nanoplastics

Concern has grown over potential health effects of micro- and nanoplastics (M/NPs) exposure. There is significant interest in understanding their impact on animal and human microbiota due to its crucial role in preserving health, as research in this area is rapidly advancing. We conducted a sub-chronic exposure study involving 12 male mice, divided into two groups: a control group (n = 6) and a PET-NPs exposure group (n = 6). PET-NPs, administered by oral gavage at a dose of 0.5 mg/day in 0.1 ml/mice, were given daily for 28 days. Microbiota analyses were performed on lung, colon, oral cavity, and stool samples using 16S rRNA sequencing. Additionally, fecal short and medium-chain fatty acids were analyzed by GC/MS. No significant changes were observed in the fecal and oral microbiome of the treated mice, nor in the fecal fatty acid levels. However, there were prominent alterations in the colon, characterized by increased abundance of Gram-negative bacteria belonging to Veillonella and Prevotella genera, and of amino acid metabolism pathways, coupled with a decrease in Lactobacillus. PET-NPs ingestion caused unexpected alterations in the lung microbiome with an increase in the Pseudomonas and changes in microbial energy metabolism and nitrogen utilization. This study provides insights into the differential impact of PET-NPs exposure on various microbiome niches.

Candida albicans Virulence Traits in Commensalism and Disease

Abstract Purpose of Review Candida albicans is a common constituent of the normal human microbiota of the oro-gastrointestinal and vaginal tract. The immune system and the microbiota work together to maintain stable colonization and prevent excessive fungal growth and disease. Disruption of the delicate balance between C. albicans and the host can result in superficial and disseminated infections, as observed in individuals with a compromised immune system or dysbiosis. Invasive candidiasis accounts for a million deaths every year. C. albicans-intrinsic factors promoting stable commensalism in the human host are poorly established. Understanding the processes that regulate microbial homeostasis is important for evaluating novel intervention strategies that aim at targeting fungal virulence but at the same time prevent eradication of commensal organisms, which ultimately benefit the host. Recent Findings C. albicans commensalism is generally associated with a lack of filamentation, epithelial invasion and host cell damage. Fungal isolates recovered from healthy individuals are however not completely avirulent. In contrast, they exhibit varying degrees of virulence attributes and retain the capacity to cause disease, thereby challenging the notion that avirulence is a hallmark of superior commensal fitness. Recent advances in the field provide insights in how virulence traits, which are primarily known for inducing disease in the host, such as hyphae formation and candidalysin production, play an unexpected active part in establishing C. albicans gut commensalism. Overt filamentation and tissue damage is in turn prevented by adaptive antifungal immunity, which is induced in response to fungal virulence. Summary This review discusses the emerging paradigm shift in our understanding of how fungal virulence determinants are not per se inconsistent with commensalism but are actually a prerequisite for colonization and for triggering a host-protective homeostatic immunity through mutual adaptation with the host.

Stratification of human gut microbiomes by succinotype is associated with inflammatory bowel disease status

BackgroundThe human gut microbiome produces and consumes a variety of compounds that interact with the host and impact health. Succinate is of particular interest as it intersects with both host and microbiome metabolism. However, which gut bacteria are most responsible for the consumption of intestinal succinate is poorly understood.ResultsWe build upon an enrichment-based whole fecal sample culturing approach and identify two main bacterial taxa that are responsible for succinate consumption in the human intestinal microbiome, Phascolarctobacterium and Dialister. These two taxa have the hallmark of a functional guild and are strongly mutual exclusive across 21,459 fecal samples in 94 cohorts and can thus be used to assign a robust “succinotype” to an individual. We show that they differ with respect to their rate of succinate consumption in vitro and that this is associated with higher concentrations of fecal succinate. Finally, individuals suffering from inflammatory bowel disease (IBD) are more likely to have the Dialister succinotype compared to healthy subjects.ConclusionsWe identified that only two bacterial genera are the key succinate consumers in human gut microbiome, despite the fact that many more intestinal bacteria encode for the succinate pathway. This highlights the importance of phenotypic assays in functionally profiling intestinal microbiota. A stratification based on “succinotype” is to our knowledge the first function-based classification of human intestinal microbiota. The association of succinotype with IBD thus builds a bridge between microbiome function and IBD pathophysiology related to succinate homeostasis.1f3imNYWH_SuaBpDzz9CS6Video

Multi-Omics Analysis Unravels the Impact of Stool Sample Logistics on Metabolites and Microbial Composition.

Human health and the human microbiome are inevitably intertwined, increasing their relevance in clinical research. However, the collection, transportation and storage of faecal samples may introduce bias due to methodological differences, especially since postal shipping is a common practise in large-scale clinical cohort studies. Using four different Omics layer, we determined the structural (16S rRNA sequencing, cytometric microbiota profiling) and functional integrity (SCFAs, global metabolome) of the microbiota in relation to different easy-to-handle conditions. These conditions were storage at -20 °C, -20 °C as glycerol stock, 4 °C and room temperature with and without oxygen exposure for a maximum of one week. Storage time affected the microbiota on all Omics levels. However, the magnitude was donor-dependent, highlighting the need for purpose-optimized sample collection in clinical multi-donor studies. The effects of oxygen exposure were negligible for all analyses. At ambient temperature, SCFA and compositional profiles were stable for 24 h and 48 h, respectively, while at 4 °C, SCFA profiles were maintained for 48 h. The global metabolome was highly susceptible, already changing at 24 h in non-frozen conditions. Thus, faecal microbiota was best preserved on all levels when transported as a native sample frozen within 24 h, leading to the least biased outcomes in the analysis. We conclude that the immediate freezing of native stool samples for transportation to the lab is best suited for planned multi-Omics analyses that include metabolomics to extend standard sequencing approaches.

Оценка степени высвобождения и токсичности соединений цинка, используемых в фармацевтических и косметических средствах

The degree of release from ointments and the toxicity of three zinc compounds were assessed to establish their minimum sufficient concentration in antibacterial pharmaceutical and cosmetic products. The properties of zinc oxide (ZnO), zinc pyrrolidone carboxylate (ZnРСА) and zinc salicylate (Zn(Sal)2·2H2О) were compared. The release rate of active substances from petrolatum-based ointments (the mass fraction of zinc compounds is 1%) was determined by the Kruvchinsky method. A cellophane film for dialysis served as a semipermeable membrane, which was fixed on a glass tube (length 15 cm, cross section 20 mm2). A sample of ointment (2.00 g) was applied to the inner surface of the membrane. The dialysis tube was inserted into a beaker with dialysis medium and immersed to a depth of no more than 3 mm. The toxicity of aqueous solutions (distilled water) of zinc compounds was determined in a bioluminescent biotest using “Ecolume” bacterial preparation based on a non-pathogenic strain of Escherichia coli. It was found that a high level of toxicity of is achieved at 10-5 mol/dm3 zinc salts in a solution, zinc oxide – 10-4 mol/dm3. A similar amount of Zn2+ diffuses from ointments through a semipermeable cellophane membrane used for the study of dialysis into a 50% aqueous alcohol solution. Zinc ion release from ointment with ZnO was only 0.31%, as well as from ointments with ZnPCA and Zn(Sal)2·2H2О – 1.59% and 1.87% respectively. Thus, the tested approach is economically and environmentally effective, as it allows you to determine the lowest effective concentration of active substances, reducing the burden on the human microbiome and the environment.

Prediction of Crohn's disease based on deep feature recognition

BackgroundCrohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic information can be used to predict Crohn's disease. Recent advances in deep learning have demonstrated its effectiveness in feature extraction and the use of deep learning to decode genetic information for disease prediction. MethodsIn this paper, we present a deep learning-based model that utilizes a sequential convolutional attention network (SCAN) for feature extraction, incorporates adaptive additive interval losses to enhance these features, and employs support vector machines (SVM) for classification. To address the challenge of unbalanced Crohn's disease samples, we propose a random noise one-hot encoding data augmentation method. ResultsData augmentation with random noise accelerates training convergence, while SCAN-SVM effectively extracts features with adaptive additive interval loss enhancing differentiation. Our approach outperforms benchmark methods, achieving an average accuracy of 0.80 and a kappa value of 0.76, and we validate the effectiveness of feature enhancement. ConclusionsIn summary, we use deep feature recognition to effectively analyze the potential information in genes, which has a good application potential for gene analysis and prediction of Crohn's disease.

Effect of Growth Conditions on Hemolysin Production from Clinical Isolates of <i>Escherichia Coli</i>

Background: Escherichia coli belongs to the Enterobacteriaceae family and is found in the digestive systems of humans and various animals as normal flora. Objective: This study aims to identify the optimal conditions necessary for the synthesis of hemolysin by E. coli isolated from a variety of clinical contexts. Methods: A total of 120 specimens were collected from different clinical sources in Baghdad Hospitals. Screening for hemolysin production was done by plate and Spectrophotometric methods. Furthermore, many conditions for producing hemolysin from clinical E. coli isolates were investigated including temperature, pH, incubation time, inoculum size, culture medium, and aeration. Results: Six isolates of E. coli exhibited hemolysin production. Optimal conditions for hemolysin production were determined: the temperature was 37 °C with an optical density of 0.931, the pH was 7 with an optical density of 0.976, the incubation period was 24 hours with an optical density of 0.885, 1% inoculum resulting in an optical density of 0.558, brain heart infusion as the best medium with an optical density of 1.697, and a shaker method showing superior results (optical density of 1.235) compared to static aeration (optical density of 0.988). Conclusions: This study identified the optimal conditions necessary for the synthesis of hemolysin by E. coli. By understanding these optimal conditions, researchers may be better equipped to develop strategies for managing and controlling the spread of these pathogenic bacteria.

The human gut microbiome in health and disease: time for a new chapter?

The gut microbiome, composed of the colonic microbiota and their host environment, is important for many aspects of human health. A gut microbiome imbalance (gut dysbiosis) is associated with major causes of human morbidity and mortality. Despite the central part our gut microbiome plays in health and disease, mechanisms that maintain homeostasis and properties that demarcate dysbiosis remain largely undefined. Here we discuss that sorting taxa into meaningful ecological units reveals that the availability of respiratory electron acceptors, such as oxygen, in the host environment has a dominant influence on gut microbiome health. During homeostasis, host functions that limit the diffusion of oxygen into the colonic lumen shelter a microbial community dominated by primary fermenters from atmospheric oxygen. In turn, primary fermenters break down unabsorbed nutrients into fermentation products that support host nutrition. This symbiotic relationship is disrupted when host functions that limit the luminal availability of host-derived electron acceptors become weakened. The resulting changes in the host environment drive alterations in the microbiota composition, which feature an elevated abundance of facultatively anaerobic microbes. Thus, the part of the gut microbiome that becomes imbalanced during dysbiosis is the host environment, whereas changes in the microbiota composition are secondary to this underlying cause. This shift in our understanding of dysbiosis provides a novel starting point for therapeutic strategies to restore microbiome health. Such strategies can either target the microbes through metabolism-based editing or strengthen the host functions that control their environment.

The Art of Asking and Analyzing Sensitive Questions in Breast Milk Microbiome Research

Human milk includes, in addition to various bioactive molecules and microorganisms, that make up the “human milk microbiota (HMM)”. The breast milk microbiome is essential in gastrointestinal coloni¬zation of microbiome in children during the first 100 days of life. Microbial imbalance can be a starting point for necrotizing enterocolitis in premature infants, and can also contribute to the development of chronic diseases such as asthma and obesity later in life. In medical and biomedical research, particularly breast milk microbiome research, the use of surveys is often resorted to assess changes in the status of certain groups over a certain period. These questionnaires can focus on a specific sample and serve as an objective tool for professionals to acquire additional information. Creating a new questionnaire can be quite a challenge. The main difficulty lies in the development of an effective questionnaire for research and clinical use, meeting all legal frameworks, as well as the subsequent handling of sensitive data. The purpose of this study was to present the great need and difficulties in designing an effective survey for our research objectives. Research aimed at establishing the microbiome of breast milk and the associated pre¬diction of the gastrointestinal microbiome of the newborn has been of great importance recently. We have presented sample custom surveys that we have used to prepare and conduct various microbiome studies, and we believe that good preparation of an appropriate questionnaire contributes immensely to the overall completion of any biomedical study.

Identification of bio-markers for insulin resistance and sensitivity through multi-omics analysis

Aims: This study aims to identify multi-omics bio-markers for insulin resistance and sensitivity using machine learning approaches on a dataset integrated from several omics. Methods: The study included 362 patients with Insulin Resistance and Insulin Sensitivity from the Integrative Personal Omics Profiling (iPOP) database. Combining the multi-omics data from the Integrative Human Microbiome Project, this study used machine learning to reveal the relationship between insulin resistance and insulin sensitivity. Results: Of 362 patients 186 were insulin resistance and 176 were insulin sensitivity. 11,585 features were used, including clinical features, RNA transcripts, gut microbiota, cytokines, proteins, and metabolomics. We found 21 features capable of distinguishing insulin resistance from insulin sensitivity using a well-known artificial neural network (ANN) method. The model had an area under the receiver operating characteristic (AUC) of 0.97 in the validation dataset and 0.89 in the test dataset. The ANN model’s performance was compared with Random Forest model. Of the 21 new findings, two metabolites (methyl-uric acid and methylxanthine) are xenobiotics, and three RNA transcripts (SERPINF1, SLC2A2, and CHL1). Conclusion: A small number of multi-omics features identified from 11,585 potential candidates for a machine learning model can accurately predict insulin resistance and sensitivity.

Early-Life Gut Microbiota: A Possible Link Between Maternal Exposure to Non-Nutritive Sweeteners and Metabolic Syndrome in Offspring.

Metabolic syndrome (MetS) is recognized as a group of metabolic abnormalities, characterized by clustered interconnected traits that elevate the risks of obesity, cardiovascular and atherosclerotic diseases, hyperlipidemia, and type 2 diabetes mellitus. Non-nutritive sweeteners (NNS) are commonly consumed by those with imbalanced calorie intake, especially in the perinatal period. In the past, accumulating evidence showed the transgenerational and mediated roles of human microbiota in the development of early-life MetS. Maternal exposure to NNS has been recognized as a risk factor for filial metabolic disturbance through various mechanisms, among which gut microbiota and derived metabolites function as nodes linking NNS and MetS in early life. Despite the widespread consumption of NNS, there remain growing concerns about their transgenerational impact on metabolic health. There is growing evidence of NNS being implicated in the development of metabolic abnormalities. Intricate complexities exist and a comprehensive understanding of how the gut microbiota interacts with mechanisms related to maternal NNS intake and disrupts metabolic homeostasis of offspring is critical to realize its full potential in preventing early-life MetS. This review aims to elucidate the effects of early-life gut microbiota and links to maternal NNS exposure and imbalanced offspring metabolic homeostasis and discusses potential perspectives and challenges, which may provide enlightenment and understanding into optimal perinatal nutritional management.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

The RNA landscape of the human commensal Segatella copri reveals a small RNA essential for gut colonization.

The bacterium Segatella copri is a prevalent member of the human gut microbiota associated with health and disease states. However, the intrinsic factors that determine its ability to colonize the gut effectively remain largely unknown. By extensive transcriptome mapping of S.copri and examining human-derived samples, we discover a small RNA, which we name Segatella RNA colonization factor (SrcF), and show that SrcF is essential for S.copri gut colonization in gnotobiotic mice. SrcF regulates genes involved in nutrient acquisition, and complex carbohydrates, particularly fructans, control its expression. Furthermore, SrcF expression is strongly influenced by human microbiome composition and by the breakdown of fructans by cohabitating commensals, suggesting that the breakdown of complex carbohydrates mediates interspecies signaling among commensals beyond its established function in generating energy. Together, this study highlights the contribution of a small RNA as a critical regulator in gut colonization.

The Prognosis in Children With Pneumonia of Respiratory Syncytial Virus Co-detection With Airway Dominant Flora.

Airway bacterial microbiota influences the prognosis in children with respiratory syncytial virus infection. The study aimed to investigate the effect of the airway-dominant bacterial microbiota on disease severity in children with pneumonia of respiratory syncytial virus infection. A retrospective study was conducted in the Children's Hospital of Chongqing Medical University, which involved a cohort of patients with respiratory syncytial virus (RSV)-infected pneumonia from January 2012 to December 2021. Patients were assigned to a normal flora group or to a dominant flora group (with the top 5 individual bacteria) based on the nasopharyngeal aspirates culture and matched using propensity-score matching. Univariate analysis and multivariate analysis were performed to estimate the risk factors of poor prognosis in dominant flora. Five thousand five hundred and twelve patients in the normal flora and 4556 in the dominant flora were included ( Escherichia coli 514, Streptococcus pneumoniae 1516, Staphylococcus aureus 506, Moraxella catarrhalis 509 and Haemophilus influenzae 1516, respectively). The dominant flora had more patients developing severe pneumonia, needing mechanical ventilation/tracheal intubation (up to 15.8% in the S. aureus ) and admission to the intensive care unit (up to 4.5% in the E. coli ) than in the normal flora (28.5% vs. 25.9%; P = 0.001; 9.8% vs. 5.4%; P < 0.001; 2.0% vs. 1.2%; P <0.001). And the hospitalization was longer in the dominant flora than in the normal flora [8 (6-9) vs. 8 (7-9) days; P < 0.001], the E. coli and S. aureus had the longest hospitalization [8 (7-10) days]. Several factors were associated with critical illness in Dominant flora according to multivariate analysis ( P < 0.001), including age (OR: 0.965; CI: 0.954-0.976; P < 0.001), anhelation (OR: 0.530; CI: 0.446-0.631; P < 0.001), disorders of consciousness (OR: 0.055; CI: 0.016-0.185; P < 0.001) as well as assisted respiration (OR: 0.115; CI: 0.097-0.138; P < 0.001), C-reactive protein >10 mg/L (OR: 0.686; CI: 0.560-0.839; P < 0.001), SpO 2 <90% (OR: 0.366; CI: 0.214-0.628; P < 0.001), pulmonary consolidation (OR: 0.511; CI: 0.364-0.717; P < 0.001) and pulmonary atelectasis (OR: 0.362; CI: 0.236-0.555; P < 0.001). The airway-dominant bacterial microbiota influenced disease severity and comorbidities in children with RSV-infected pneumonia. Clinicians should pay attention to the nasopharyngeal aspirate culture, especially after detecting S. aureus and E. coli in RSV-infected children with pneumonia, closely observe the disease progression and take timely measures to avoid adverse outcomes.

Abundance measurements reveal the balance between lysis and lysogeny in the human gut microbiome.

The human gut contains diverse communities of bacteriophage, whose interactions with the broader microbiome and potential roles in human health are only beginning to be uncovered. Here, we combine multiple types of data to quantitatively estimate gut phage population dynamics and lifestyle characteristics in human subjects. Unifying results from previous studies, we show that an average human gut contains a low ratio of phage particles to bacterial cells (~1:100), but a much larger ratio of phage genomes to bacterial genomes (~4:1), implying that most gut phage are effectively temperate (e.g., integrated prophage, phage-plasmids, etc.). By integrating imaging and sequencing data with a generalized model of temperate phage dynamics, we estimate that phage induction and lysis occurs at a low average rate (~0.001-0.01 per bacterium per day), imposing only a modest fitness burden on their bacterial hosts. Consistent with these estimates, we find that the phage composition of a diverse synthetic community in gnotobiotic mice can be quantitatively predicted from bacterial abundances alone, while still exhibiting phage diversity comparable to native human microbiomes. These results provide a foundation for interpreting existing and future studies on links between the gut virome and human health.

Probio-Ichnos: A Database of Microorganisms with In Vitro Probiotic Properties.

Probiotics are live microorganisms that, when consumed in adequate amounts, exert health benefits on the host by regulating intestinal and extraintestinal homeostasis. Common probiotic microorganisms include lactic acid bacteria (LAB), yeasts, and Bacillus species. Here, we present Probio-ichnos, the first manually curated, literature-based database that collects and comprehensively presents information on the microbial strains exhibiting in vitro probiotic characteristics (i.e., resistance to acid and bile, attachment to host epithelia, as well as antimicrobial, immunomodulatory, antiproliferative, and antioxidant activity), derived from human, animal or plant microbiota, fermented dairy or non-dairy food products, and environmental sources. Employing a rigorous methodology, we conducted a systematic search of the PubMed database utilizing the keyword 'probiotic' within the abstracts or titles, resulting in a total of 27,715 studies. Upon further manual filtering, 2207 studies presenting in vitro experiments and elucidating strain-specific probiotic attributes were collected and used for data extraction. The Probio-ichnos database consists of 12,993 entries on the in vitro probiotic characteristics of 11,202 distinct strains belonging to 470 species and 143 genera. Data are presented using a binary categorization approach for the presence of probiotic attributes according to the authors' conclusions. Additionally, information about the availability of the whole-genome sequence (WGS) of strains is included in the database. Overall, the Probio-ichnos database aims to streamline the navigation of the available literature to facilitate targeted validation and comparative investigation of the probiotic properties of the microbial strains.

Microbe-drug association prediction model based on graph convolution and attention networks

The human microbiome plays a key role in drug development and precision medicine, but understanding its complex interactions with drugs remains a challenge. Identifying microbe-drug associations not only enhances our understanding of their mechanisms but also aids in drug discovery and repurposing. Traditional experiments are expensive and time-consuming, making computational methods for predicting microbe-drug associations a new trend. Currently, computational methods specifically designed for this task are still scarce. Therefore, to address the shortcomings of traditional experimental methods in predicting potential microbe-drug associations, this paper proposes a new prediction model named GCNATMDA. The model combines two deep learning models, Graph Convolutional Network and Graph Attention Network, and aims to reveal potential relationships between microbes and drugs by learning related features. Thus improve the efficiency and accuracy of prediction. We first integrated the microbe-drug association matrix from the existing dataset, and then combined the calculated microbe-drug characteristic matrix as the model input. The GCN module is used to dig deeper into the potential characterization of microbes and drugs, while the GAT module further learns the more complex interactions between them and generates the corresponding score matrix. The experimental results show that the GCNATMDA model achieves 96.59% and 93.01% in AUC and AUPR evaluation indexes, respectively, which is significantly better than the existing prediction models. In addition, the reliability of the prediction results is verified by a series of experiments.

Emergent ecological patterns and modelling of gut microbiomes in health and in disease.

Recent advancements in next-generation sequencing have revolutionized our understanding of the human microbiome. Despite this progress, challenges persist in comprehending the microbiome's influence on disease, hindered by technical complexities in species classification, abundance estimation, and data compositionality. At the same time, the existence of macroecological laws describing the variation and diversity in microbial communities irrespective of their environment has been recently proposed using 16s data and explained by a simple phenomenological model of population dynamics. We here investigate the relationship between dysbiosis, i.e. in unhealthy individuals there are deviations from the "regular" composition of the gut microbial community, and the existence of macro-ecological emergent law in microbial communities. We first quantitatively reconstruct these patterns at the species level using shotgun data, and addressing the consequences of sampling effects and statistical errors on ecological patterns. We then ask if such patterns can discriminate between healthy and unhealthy cohorts. Concomitantly, we evaluate the efficacy of different statistical generative models, which incorporate sampling and population dynamics, to describe such patterns and distinguish which are expected by chance, versus those that are potentially informative about disease states or other biological drivers. A critical aspect of our analysis is understanding the relationship between model parameters, which have clear ecological interpretations, and the state of the gut microbiome, thereby enabling the generation of synthetic compositional data that distinctively represent healthy and unhealthy individuals. Our approach, grounded in theoretical ecology and statistical physics, allows for a robust comparison of these models with empirical data, enhancing our understanding of the strengths and limitations of simple microbial models of population dynamics.