Combined network analysis and interpretable machine learning reveals the environmental adaptations of more than 10,000 ruminant microbial genomes.

Background: The ruminant gastrointestinal contains numerous microbiomes that serve a crucial role in sustaining the host's productivity and health. In recent times, numerous studies have revealed that variations in influencing factors, including the environment, diet, and host, contribute to the shaping of gastrointestinal microbial adaptation to specific states. Therefore, understanding how host and environmental factors affect gastrointestinal microbes will help to improve the sustainability of ruminant production systems. Results: Based on a graphical analysis perspective, this study elucidates the microbial topology and robustness of the gastrointestinal of different ruminant species, showing that the microbial network is more resistant to random attacks. The risk of transmission of high-risk metagenome-assembled genome (MAG) was also demonstrated based on a large-scale survey of the distribution of antibiotic resistance genes (ARG) in the microbiota of most types of ecosystems. In addition, an interpretable machine learning framework was developed to study the complex, high-dimensional data of the gastrointestinal microbial genome. The evolution of gastrointestinal microbial adaptations to the environment in ruminants were analyzed and the adaptability changes of microorganisms to different altitudes were identified, including microbial transcriptional repair. Conclusion: Our findings indicate that the environment has an impact on the functional features of microbiomes in ruminant. The findings provide a new insight for the future development of microbial resources for the sustainable development in agriculture.

ROSes-FINDER: a multi-task deep learning framework for accurate prediction of microorganism reactive oxygen species scavenging enzymes.

Reactive oxygen species (ROS) are highly reactive molecules that play important roles in microbial biological processes. However, excessive accumulation of ROS can lead to oxidative stress and cellular damage. Microorganism have evolved a diverse suite of enzymes to mitigate the harmful effects of ROS. Accurate prediction of ROS scavenging enzymes classes (ROSes) is crucial for understanding the mechanisms of oxidative stress and developing strategies to combat related diseases. Nevertheless, the existing approaches for categorizing ROS-related proteins exhibit certain drawbacks with regards to their precision and inclusiveness. To address this, we propose a new multi-task deep learning framework called ROSes-FINDER. This framework integrates three component methods using a voting-based approach to predict multiple ROSes properties simultaneously. It can identify whether a given protein sequence is a ROSes and determine its type. The three component methods used in the framework are ROSes-CNN, which extracts raw sequence encoding features, ROSes-NN, which predicts protein functions based on sequence information, and ROSes-XGBoost, which performs functional classification using ensemble machine learning. Comprehensive experiments demonstrate the superior performance and robustness of our method. ROSes-FINDER is freely available at https://github.com/alienn233/ROSes-Finder for predicting ROSes classes.

On-target inhibition of Cryptosporidium parvum by nitazoxanide (NTZ) and paclitaxel (PTX) validated using a novel MDR1-transgenic host cell model and algorithms to quantify the effect on the parasite target.

Cryptosporidium parvum is a globally distributed zoonotic protozoan parasite that causes moderate to severe, sometime deadly, watery diarrhea in humans and animals, for which fully effective treatments are yet unavailable. In studying the mechanism of action of drugs against intracellular pathogens, it is important to validate whether the observed anti-infective activity is attributed to the drug action on the pathogen or host target. For the epicellular parasite Cryptosporidium, we have previously developed a concept that the host cells with significantly increased drug tolerance by transient overexpression of the multidrug resistance protein-1 (MDR1) could be utilized to evaluate whether and how much the observed anti-cryptosporidial activity of an inhibitor was attributed to the inhibitor's action on the parasite target. However, the transient transfection model was only applicable to evaluating native MDR1 substrates. Here we report an advanced model using stable MDR1-transgenic HCT-8 cells that allows rapid development of novel resistance to non-MDR1 substrates by multiple rounds of drug selection. Using the new model, we successfully validated that nitazoxanide, a non-MDR1 substrate and the only FDA-approved drug to treat human cryptosporidiosis, killed C. parvum by fully (100%) acting on the parasite target. We also confirmed that paclitaxel acted fully on the parasite target, while several other inhibitors including mitoxantrone, doxorubicin, vincristine and ivermectin acted partially on the parasite targets. Additionally, we developed mathematical models to quantify the proportional contribution of the on-parasite-target effect to the observed anti-cryptosporidial activity and to evaluate the relationships between several in vitro parameters, including antiparasitic efficacy (ECi), cytotoxicity (TCi), selectivity index (SI) and Hill slope (h). Owning to the promiscuity of the MDR1 efflux pump, the MDR1-transgenic host cell model could be applied to assess the on-parasite-target effects of newly identified hits/leads, either substrates or non-substrates of MDR1, against Cryptosporidium or other epicellular pathogens.

Interpretable machine learning framework reveals microbiome features of oral disease.

BACKGROUND: Although the oral microbiome plays an important role in the progression of oral diseases, the microbes closely related to these diseases remain largely uncharacterized. RESULTS: We collected saliva samples from 140 individuals and performed 16 S amplicon sequencing. An interpretable machine learning framework for imbalanced high-dimensional big data of clinical microbial samples was developed to identify 14 oral microbiome features associated with oral diseases. Microbiome risk scores (MRSs) with the identified features were constructed with SHapley Additive exPlanations (SHAP). Correlations of the MRSs with individual physiological indicators and lifestyle habits were calculated. CONCLUSION: Our results reveal a set of oral microbiome features associated with oral diseases. Our study demonstrates the feasibility of preventing oral disease through lifestyle interventions and provides a reference method for the era of precision medicine aimed at individualized medicine.

Transmission of the gut microbiome in cohousing goats and pigs.

Social interaction facilitates the horizontal transmission of the microbiota between different individuals. However, little is known about the level of microbiota transmission in different livestock animals and different digestive tracts. The Hainan black goat and Wuzhishan pig are typical tropical local breeds on Hainan Island in China. Thus, we sampled and analyzed the gut microbiome in Hainan black goats (cecum and rumen) and Wuzhishan pigs (cecum) to study horizontal transmission by rearing them in the same pen (six goats and six pigs) or separate pens (nine goats and nine pigs). De novo assembly and binning recovered 3,262 strain-level and 2,488 species-level metagenome-assembled genomes (MAGs) using ∼1.3 Tb sequencing data. Of these MAGs, 1,856 MAGs were identified as novel strain. Compared with goats living in separate pens, social interaction in the same pen promotes community homogeneity in the rumen microbiome (P < 0.05) and the cecum microbiome (P < 0.05), respectively. Notably, approximately 7.08% (231/3262) of the gut microbial population could transmit during cohousing, 12 strains only in inter-species transmission, versus 190 strains only in intra-species transmission, and 10 strains only in foregut and hindgut transmission. In addition, the social contact group has high transmitted strain abundance, which is correlated with community composition. This study provided a new insight into the influence of social interaction on the animal gut microbiota.

Genomic insights into the phylogeny and biomass-degrading enzymes of rumen ciliates.

Understanding the biodiversity and genetics of gut microbiomes has important implications for host physiology and industrial enzymes, whereas most studies have been focused on bacteria and archaea, and to a lesser extent on fungi and viruses. One group, still underexplored and elusive, is ciliated protozoa, despite its importance in shaping microbiota populations. Integrating single-cell sequencing and an assembly-and-identification pipeline, we acquired 52 high-quality ciliate genomes of 22 rumen morphospecies from 11 abundant morphogenera. With these genomes, we resolved the taxonomic and phylogenetic framework that revised the 22 morphospecies into 19 species spanning 13 genera and reassigned the genus Dasytricha from Isotrichidae to a new family Dasytrichidae. Comparative genomic analyses revealed that extensive horizontal gene transfers and gene family expansion provided rumen ciliate species with a broad array of carbohydrate-active enzymes (CAZymes) to degrade all major kinds of plant and microbial carbohydrates. In particular, the genomes of Diplodiniinae and Ophryoscolecinae species encode as many CAZymes as gut fungi, and ~80% of their degradative CAZymes act on plant cell-wall. The activities of horizontally transferred cellulase and xylanase of ciliates were experimentally verified and were 2-9 folds higher than those of the inferred corresponding bacterial donors. Additionally, the new ciliate dataset greatly facilitated rumen metagenomic analyses by allowing ~12% of the metagenomic sequencing reads to be classified as ciliate sequences.

Metagenomic and network analysis revealed wide distribution of antibiotic resistance genes in monkey gut microbiota.

The emergence and spread of drug-resistant microorganisms that have acquired new resistance mechanisms, leading to antibiotic resistance, continue to threaten the health of humans and animals worldwide. Non-human primates (NHPs), as close living relatives of human beings in the world, have a high degree of genetic and physiological similarity to humans. However, despite its importance, we lack a comprehensive characterization or understanding of the similarities and differences of the antibiotic resistance genes of the gut microbiome carried by non-human primates and humans. In the present study, the diversity and abundance of antibiotic resistance genes carried by the gut microbiota of cynomolgus monkeys (Macaca fascicularis) were investigated by metagenomic analysis. In total, 60 resistance types conferring resistance to 11 categories of antibiotics were identified in the gut microbiome of cynomolgus monkeys. Interestingly, the composition and abundance of ARGs carried by the gut microbiota of cynomolgus monkeys can be significantly affected by dietary changes. Moreover, we found that all ARG types carried by humans are also present in cynomolgus monkeys. The tetracycline resistance gene tet(37) is evolutionarily conserved and highly homologous. Taken together, our study provides a comprehensive overview of the diversity and richness of ARGs in the gut microbiota of cynomolgus monkeys and underlines the potentially crucial role of diet in the gut health of monkeys and humans.

Modes of genetic adaptations underlying functional innovations in the rumen.

The rumen is the hallmark organ of ruminants and hosts a diverse ecosystem of microorganisms that facilitates efficient digestion of plant fibers. We analyzed 897 transcriptomes from three Cetartiodactyla lineages: ruminants, camels and cetaceans, as well as data from ruminant comparative genomics and functional assays to explore the genetic basis of rumen functional innovations. We identified genes with relatively high expression in the rumen, of which many appeared to be recruited from other tissues. These genes show functional enrichment in ketone body metabolism, regulation of microbial community, and epithelium absorption, which are the most prominent biological processes involved in rumen innovations. Several modes of genetic change underlying rumen functional innovations were uncovered, including coding mutations, genes newly evolved, and changes of regulatory elements. We validated that the key ketogenesis rate-limiting gene (HMGCS2) with five ruminant-specific mutations was under positive selection and exhibits higher synthesis activity than those of other mammals. Two newly evolved genes (LYZ1 and DEFB1) are resistant to Gram-positive bacteria and thereby may regulate microbial community equilibrium. Furthermore, we confirmed that the changes of regulatory elements accounted for the majority of rumen gene recruitment. These results greatly improve our understanding of rumen evolution and organ evo-devo in general.

Metagenomic analysis reveals antibiotic resistance genes in the bovine rumen.

Metagenomics and network analysis were used to profile antibiotic resistance genes (ARGs) and their cooccurrence patterns in bovine rumen microbes. A total of 4941 ruminal microbial genomes and 20 metagenome samples were used in this study. In general, 103 ARG subtypes belonging to 20 ARG types in 79 candidate genomes were identified, showing the broad-spectrum profiles of ARGs in the bovine rumen environment. A wide distribution of genes encoding bacitracin resistance was found among the candidate genomes, suggesting the possibility that bovines might be one of the sources of bacitracin resistance genes. Cooccurrence patterns were found within or between the ARG types, and a positive correlation was found between some ARGs and bacteria, which revealed potential dominant hosts of ARGs. The investigation showed that bovine rumen systems are important ARG reservoirs, and our research might provide a theoretical basis for the evaluation of the harmfulness of ARGs and antibiotic-resistant bacteria (ARB) to food safety and human health.