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The occurrence and development of diabetic nephropathy (DN) are closely related to gut microbiota. Paecilomyces cicadae is a medicinal and edible fungus. Radix astragali is a therapeutic material for unifying Chinese Qi. They can delay the occurrence and development of kidney disease. In recent years, solid-state fermentation of edible fungi and traditional Chinese medicine has become a hot issue. Hypothesis/Gap Statement. We assumed that solid-state fermentation products of R. astragali and Paecilomyces cicadidae (RPF) could ameliorate diabetic nephropathy and modulate gut microbiota composition. We aimed to study the function and mechanism of the RPF for ameliorating DN in mice. We investigated the effect of the potential roles of RPF in DN mice and interaction between DN and gut microbiota using animal experiments and gut microbiota measurements. We found that RPF dramatically reduced urine protein, serum creatinine and blood urea nitrogen in DN mice. Furthermore, RPF ameliorated the physiological condition of DN mice by regulating the abundance of intestinal microbiota such as Ruminococcaceae_UCG-014, Allobaculum , Unclassified_f__Lachnospiraceae Alloprevotella and Bacteroides . RPF can ameliorate diabetic nephropathy and modulate gut microbiota composition.

Information derived from metagenome sequences through deep-learning techniques has significantly improved the accuracy of template free protein structure modeling. However, most of the deep learning-based modeling studies are based on blind sequence database searches and suffer from low efficiency in computational resource utilization and model construction, especially when the sequence library becomes prohibitively large. We proposed a MetaSource model built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil, and Fermentor) to decode the inherent linkage of microbial niches with protein homologous families. Large-scale protein family folding experiments on 8,700 unknown Pfam families showed that a microbiome targeted approach with multiple sequence alignment constructed from individual MetaSource biomes requires more than threefold less computer memory and CPU (central processing unit) time but generates contact-map and three-dimensional structure models with a significantly higher accuracy, compared with that using combined metagenome datasets. These results demonstrate an avenue to bridge the gap between the rapidly increasing metagenome databases and the limited computing resources for efficient genome-wide database mining, which provides a useful bluebook to guide future microbiome sequence database and modeling development for high-accuracy protein structure and function prediction.

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.

Background The explosive growth of microbiome data provides ample opportunities to gain a better understanding of the microbes and their interactions in microbial communities. Given these massive data, optimized data mining methods become important and necessary to perform deep and comprehensive analysis. Among the various priorities for microbiome data mining, the examination of species-species co-occurrence patterns becomes one of the key themes in urgent need. Results Hence, in this work, we propose the Meta-Network framework to lucubrate the microbial communities. Rooted in loose definitions of network (two species co-exist in a certain samples rather than all samples) as well as association rule mining (mining more complex forms of correlations like indirect correlation and mutual information), this framework outperforms other methods in restoring the microbial communities, based on two cohorts of microbial communities: (a) the loose definition strategy is capable to generate more reasonable relationships among species in the species-species co-occurrence network; (b) important species-species co-occurrence patterns could not be identified by other existing approaches, but could successfully generated by association rule mining. Conclusions Results have shown that the species-species co-occurrence network we generated are much more informative than those based on traditional methods. Meta-Network has consistently constructed more meaningful networks with biologically important clusters, hubs, and provides a general approach towards deciphering the species-species co-occurrence networks. Electronic supplementary material The online version of this article (10.1186/s12864-019-5471-1) contains supplementary material, which is available to authorized users.

Information extracted from microbiome sequences through deep-learning techniques can significantly improve protein structure and function modeling. However, the model training and metagenome search were largely blind with low efficiency. Built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil and Fermentor), we proposed a MetaSource model to decode the inherent link of microbial niches with protein homologous families. Large-scale protein family folding experiments showed that a targeted approach using predicted biomes significantly outperform combined metagenome datasets in both speed of MSA collection and accuracy of deep-learning structure assembly. These results revealed the important link of biomes with protein families and provided a useful bluebook to guide future microbiome sequence database and modeling development for protein structure and function prediction.

Antibiotic resistance gene (ARG) content is a well-established driver of microbial abundance and diversity in an environment. By reanalyzing 132 metagenomic datasets from the Tara Oceans project, we aim to unveil the associations between environmental factors, the ocean microbial community structure and ARG contents. We first investigated the structural patterns of microbial communities including both prokaryotes such as bacteria and eukaryotes such as protists. Additionally, several ARG-dominant horizontal gene transfer events between Protist and Prokaryote have been identified, indicating the potential roles of ARG in shaping the ocean microbial communities. For a deeper insight into the role of ARGs in ocean microbial communities on a global scale, we identified 1926 unique types of ARGs and discovered that the ARGs are more abundant and diverse in the mesopelagic zone than other water layers, potentially caused by limited resources. Finally, we found that ARG-enriched genera were often more abundant compared to their ARG-less neighbors in the same environment (e.g. coastal oceans). A deeper understanding of the ARG-microbiome relationships could help in the conservation of the oceanic ecosystem.

Gut microbial communities of athletes differ from that of sedentary persons in both diversity and the presence of certain taxa. However, it is unclear to what degree elite athletes and non-elite athletes harbor different gut microbial community patterns and if we can effectively monitor the potential of athletes based on microbiota. A team of professional female rowing athletes in China was recruited and 306 fecal samples were collected from 19 individuals, which were separated into three cohorts: adult elite athlete’s (AE), youth elite athlete’s (YE), and youth non-elite athlete’s (YN). The differences in gut microbiome among different cohorts were compared, and their associations with dietary factors, physical characteristics, and athletic performance were investigated. The microbial diversities of elite athletes were higher than those of youth non-elite athletes. The taxonomical, functional, and phenotypic compositions of AE, YE and YN were significantly different. Additionally, three enterotypes with clear separation were identified in athlete’s fecal samples, with majority of elite athletes stratified into enterotype 3. And this enterotype-dependent gut microbiome is strongly associated with athlete performances. These differences in athlete gut microbiota lead to establishment of a random forest classifier based on taxonomical and functional biomarkers, capable of differentiating elite athletes and non-elite athletes with high accuracy. Finally, these versatilities of athlete microbial communities of athletes were found to be associated with dietary factors and physical characteristics, which can in concert explain 41% of the variability in gut microbiome.

Ethnopharmacological relevance Ginseng (Panax ginseng Meyer) is a well-known herb in traditional Chinese medicine and has been used to treat many diseases for thousands of years. Recent studies have shown that ginseng is a promising agent for improving the gut microbiota and treating ulcerative colitis. Fermentation is a common process in traditional Chinese medicine making that can be used to enhance efficacy and reduce toxicity. Aim of the study The purpose of the present study was to research the efficacy of ginseng fermented with probiotics (Lactobacillus fermentum) on the gut microbiota and immunity of rats with antibiotic-associated diarrhea (AAD). Materials and methods SPF Sprague-Dawley rats were randomly divided into eight groups: control group, antibiotic group, natural recovery group, and five groups treated with different doses of fermented ginseng (FG1 to FG5). A model of AAD was established by treating the rats with triple antibiotics, and obvious symptoms of AAD were observed. A histopathological analysis of the colon was performed. The total bacteria in the intestinal microbiota and five types of gut microbes in the feces were detected by quantitative PCR. The expression levels of related immune factors TLR4 and NF-κB in the colon were assayed. Results An appropriate dose of fermented ginseng (0.5 g/kg/d) relieved some of the symptoms of AAD and colon inflammation and reduced the expression of the immune factors TLR4 and NF-κB in the colon. The alteration of the gut microbiota observed in the rats treated with antibiotics also returned to normal after treatment with fermented ginseng. Moreover, different doses of fermented ginseng exerted different influences on the gut microbiota, and excessively high or low doses of fermented ginseng were disadvantageous for resolving the symptoms of AAD and promoting recovery. Conclusions These results demonstrate that fermented ginseng can treat AAD symptoms and colon inflammation and restore the gut microbiota to its original state.

Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species–species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.

Qu-feng-sheng-shi Granules (QFSSG), a common prescription for the treatment of chronic inflammation and allergic rhinitis, is widely used in the clinic as a traditional Chinese medicine. Chemical analysis and quality control studies of this formulation are relatively limited compared with pharmacological studies. In this study, a high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (HPLC-ESI-Q/TOF-MSn ) was used to identify the components in this prescription. Next, to quantify six major compounds, an HPLC-UV method was developed and validated. The results showed that 53 compounds were identified based on the MSn data, retention time and previous reports, including 17 coumarins, 14 lignans, 10 chromones, nine phenylethanoid glycosides and three other compounds, were identified or tentatively assigned. Contents of six major bioactive compounds (4'-O-β-glucopyranosyl-5-O-methylvisamminol, Prim-O-glucosylcimifugin, forsythin, magnolin, imperatorin, isoimperatorin) could be determined by HPLC simultaneously. In addition, the potential anti-inflammatory activity of six major compounds was determined too, and we found that four compounds (4'-O-β-glucopyranosyl-5-O-methylvisamminol, Prim-O-glucosylcimifugin, forsythin, imperatorin) have a potent nitric oxide inhibitory effect. In conclusion, this work provided comprehensive information on the quality control of QFSSG and evaluated the potential biological activity of the main components in QFSSG, which can contribute to understanding and using it more scientifically.

Background: Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established, while this mortality prediction model could help for early triaging, optimizing the medical resource and adjust the treatments in time. Methods: To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Wurzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from Wuhan cohort, generating a mortality prediction model based on their clinical features. Then, the 81 patients from Wurzburg cohort were used as independent test data for this model. Findings: We identified five clinical features, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Wurzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. Interpretation: This study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients. Funding: National Science Foundation of China, National Key Research and Development Program of China and Urgent projects of scientific and technological research on COVID-19 funded by Hubei province. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: This study was approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Due to the retrospective nature of this study, the local institutional review board of the University of Wurzburg waived the requirement for additional approval.

Ethnopharmacological relevance Ginseng (Panax ginseng Meyer) is rich in a variety of biologically active ingredients, which shows good effect in the treatment of metabolic diseases. Monascus has lipid-lowering activity and one of its metabolites, lovastatin, is widely used in clinical practice. Aim of the study The main purpose of this study was to clarify the effects of fermented Panax ginseng by Monascus ruber (PM) on lipid metabolism and gut microbiota in rats fed a high-fat diet. Materials and methods SPF Sprague-Dawley rats were randomly divided into 5 groups, the therapeutic effect of PM on HFD-induced obesity, hyperlipidemia, hepatic steatosis, and disordered gut microbiota were determined in rats. Results PM could attenuate features of obesity in rats, decrease serum TC, LDL-C and IgA levels, increase excretion of bile acids in feces. Hepatic histopathologic analysis revealed that PM decrease lipid accumulation in hepatocytes. Consistently, mRNA expression levels of cholesterol metabolism-related genes were regulated in the livers of HFD-fed rats administered with PM. In addition, PM could enhance the diversity and relative abundance of gut microbiota, reduce the Firmicutes/Bacteroidetes (F/B) ratio, increase significantly the relative abundance of Prevotella_9, and decrease these of Muribaculaceae. Conclusions PM could regulate lipid metabolism and the structure of the gut microbiota in the HFD rats. Our findings provide valuable experience for the development of ginseng. PM could be a potentially effective strategy to prevent and treat metabolic diseases and alleviate the gut microbiota disturbance caused by it.

Qu-feng-sheng-shi Granules (QFSSG), a common prescription for the treatment of chronic inflammation and allergic rhinitis, is widely used in the clinic as a traditional Chinese medicine. Chemical analysis and quality control studies of this formulation are relatively limited compared with pharmacological studies. In this study, a high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (HPLC-ESI-Q/TOF-MS

Large-scale campus resembles a small “semi-open community,” harboring disturbances from the exchanges of people and vehicles, wherein stressors such as temperature and population density differ among the ground surfaces of functional partitions. Therefore, it represents a special ecological niche for the study on microbial ecology in the process of urbanization. In this study, we investigated outdoor microbial communities in four campuses in Wuhan, China. We obtained 284 samples from 55 sampling sites over six seasons, as well as their matching climatic and environmental records. The structure of campus outdoor microbial communities which influenced by multiple climatic factors featured seasonality. The dispersal influence of human activities on microbial communities also contributed to this seasonal pattern non-negligibly. However, despite the microbial composition alteration in response to multiple stressors, the overall predicted function of campus outdoor microbial communities remained stable across campuses. The spatial–temporal dynamic patterns on campus outdoor microbial communities and its predicted functions have bridged the gap between microbial and macro-level ecosystems, and provided hints toward a better understanding of the effects of climatic factors and human activities on campus micro-environments.

Quality control for next generation sequencing (NGS) has become increasingly important with the ever increasing importance of sequencing data for omics studies. Tools have been developed for filtering possible contaminants from species with known reference genome. Unfortunately, reference genomes for all the species involved, including the contaminants, are required for these tools to work. This precludes many real-life samples that have no information about the complete genome of the target species, and are contaminated with unknown microbial species. In this work we proposed QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. The pipeline merely requires the information about a few marker genes of the target species. The entire pipeline consists of unsupervised read assembly, contig binning, read clustering, and marker gene assignment. When evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind could serve well in situations where limited information is available for both target and contamination species.

Introduction The ocean microbiome represents one of the largest microbiomes and produces nearly half of the primary energy on the planet through photosynthesis or chemosynthesis. Using recent advances in marine genomics, we explore new applications of oceanic metagenomes for protein structure and function prediction. Results By processing 1.3 TB of high-quality reads from the Tara Oceans data, we obtain 97 million non-redundant genes. Of the 5721 Pfam families that lack experimental structures, 2801 have at least one member associated with the oceanic metagenomics dataset. We apply C-QUARK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, where 20 are predicted to have a reliable fold with estimated template modeling score (TM-score) at least 0.5. Detailed analyses reveal that the abundance of microbial genera in the ocean is highly correlated to the frequency of occurrence in the modeled Pfam families, suggesting the significant role of the Tara Oceans genomes in the contact-map prediction and subsequent ab initio folding simulations. Of interesting note, PF15461, which has a majority of members coming from ocean-related bacteria, is identified as an important photosynthetic protein by structure-based function annotations. The pipeline is extended to a set of 417 Pfam families, built on the combination of Tara with other metagenomics datasets, which results in 235 families with an estimated TM-score over 0.5. Conclusions These results demonstrate a new avenue to improve the capacity of protein structure and function modeling through marine metagenomics, especially for difficult proteins with few homologous sequences.

Figure S1. Threshold selection for FS-Weight and PCA-PMI method. (A) Global network properties for networks constructed by FS-Weight method. (DOCX 354 kb)

Additional file 3. Review history.

Radix astragali, a medicinal material for tonifying Chinese Qi, has widely been used for the treatment of Kidney disease in China and East Asia, especially in reducing the apoptosis of glomerular podocytes. Paecilomyces Cicadidae is a medicinal and edible fungus. In recent years, the application of traditional Chinese medicine (TCM) in solid-state fermentation of edible and medicinal fungi has become a hot issue. Fermentation is a special method to change the properties of TCM. Therefore, the potential roles and molecular mechanisms on podocytes of solid-state fermentation products of Radix astragali and Paecilomyces cicadidae (RPF) in diabetic nephropathy (DN) were studied. In vivo, the effect of RPF and Radix astragali on DN in mice was evaluated by detecting the biochemical indexes of blood and urine, renal function and podocyte integrity. In vitro, the expression of podocyte marker protein, autophagy marker protein and PI3K/AKT/mTOR signaling pathway protein were detected by Western blotting using a high glucose-induced podocyte injury model. The results showed that RPF had a significant alleviative effect on DN mice. RPF can significantly reduce urine protein, serum creatinine, and blood nitrogen urea in DN mice. Morphological analysis showed that RPF could improve kidney structure of DN and reduce the apoptosis of podocytes, and the effect was better than Radix astragali. In vitro results indicated that RPF could enhance autophagy and protect podocytes by inhibiting the PI3K/AKT/mTOR signaling pathway. In summary, RPF has better effect on delaying the development of DN than Radix astragali. RPF enhances autophagy in podocytes and delays DN probably by inhibiting the PI3K/AKT/mTOR signaling pathway.

D-borneolum is commonly used as a permeation enhancer in Traditional Chinese Medicine (TCM) formulas for transdermal application. Additionally, two other sources of borneolums were recorded in the 2015 edition of the Chinese Pharmacopoeia (ChP), including L-borneolum and borneolum syntheticum. To guide the selection and application of borneolum, the safety and enhancing effect of three sources of borneolums were investigated on transdermal permeation of compounds with different octanol-water partition coefficient (log P) values and molecular weights (MWs). Both the results of cellular cytotoxicity and in vitro transdermal permeation experiments showed that all three sources of borneolums could be applied in TDDS as permeation enhancers. Moreover, all three sources of borneolums achieved optimal permeation-enhancing performances on transdermal drugs with lower log P values as well as higher MWs. Further study was carried out to elucidate the potential molecular mechanism of borneolum enhancing transdermal drug delivery via transmission electron microscope (TEM) and coarse-grained molecular dynamic (CG-MD) simulation. Borneolum significantly promoted transdermal delivery of drugs via changing the dense morphology of the stratum corneum (SC), disturbing the ordered arrangement of ceramide (CER) and free fatty acid (FFA) molecules in lipid layers, and further increasing the diffusion rate of drugs in the lipid layers.

Objectives: Previous studies have reported that the gut microbiome has an important link with the development of hypertension. Though previous researches have focused on the links of gut bacteria with hypertension, little has been known about the linkage of gut viruses to hypertension and the development of hypertension, largely due to the lack of data mining tools for such investigation. In this work, we have analyzed 196 fecal metagenomic data related to hypertension aiming to profile the gut virome and link the gut virome to pre-hypertension and hypertension. Design: Here, we have applied a statistically sound method for mining of gut virome data and linking gut virome to hypertension. We characterized the viral composition and bacterial composition of 196 samples, identified the viral-type of each sample and linked gut virome to hypertension. Results: We stratified these 196 fecal samples into two viral-types and selected 32 viruses as the biomarkers for these groups. We found that viruses could have a superior resolution and discrimination power than bacteria for differentiation of healthy samples and pre-hypertension samples, as well as hypertension samples. Moreover, as to the co-occurrence networks linking viruses and bacteria, we found increasingly pervasive virus-bacteria linkages from healthy people to pre-hypertension people to hypertension patients. Conclusion: Overall, our results have shown ample indications of the link between human gut virome and hypertension, and could help provide microbial solutions toward early diagnoses of hypertension.

BackgroundAntibiotics have been spread widely in environments, asserting profound effects on environmental microbes as well as antibiotic resistance genes (ARGs) within these microbes. Therefore, investigating the associations between ARGs and bacterial communities become an important issue for environment protection. Ocean microbiomes are potentially large ARG reservoirs, but the marine ARG distribution and its associations with bacterial communities remain unclear.Methodswe have utilized the big-data mining techniques on ocean microbiome data to analysis the marine ARGs and bacterial distribution on a global scale, and applied comprehensive statistical analysis to unveil the associations between ARG contents, ocean microbial community structures, and environmental factors by reanalyzing 132 metagenomic samples from the Tara Oceans project.ResultsWe identified in total 1,926 unique ARGs and found that: firstly, ARGs are more abundant and diverse in the mesopelagic zone than other water layers. Additionally, ARG-enriched genera are closely connected in co-occurrence network. We also found that ARG-enriched genera are often more abundant than their ARG-less neighbors. Furthermore, we found that samples from the Mediterranean that is surrounded by human activities often contain more ARGs.ConclusionOur research for investigating the marine ARG distribution and revealing the association between ARG and bacterial communities provide a deeper insight into the marine bacterial communities. We found that ARG-enriched genera were often more abundant than their ARG-less neighbors in the same environment, indicating that genera enriched with ARGs might possess an advantage over others in the competition for survival in the oceanic microbial communities.

Additional file 7: Figure S5. Phylogenetic relationship of mtrâ omc clusters within different bacteria. The phylogenic trees built by protein sequences were showed for (A) mtrB, (B) mtrC, (C) mtrE, (D) mtrF, (E) omcA, (F) mtrA and (G) mtrD, respectively. The branches of Shewanella and clade of S. oneidensis MR-1 are marked in red.

The effects of dietary interventions on gut bacteria are ambiguous. Following a previous intervention study, we aimed to determine how differing diets impact gut bacteria and if bacterial profiles predict intervention response.Sixty-seven patients with IBS were randomised to traditional IBS (n=34) or low fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAPs) (n=33) diets for 4 weeks. Food intake was recorded for 4 days during screening and intervention. Faecal samples and IBS Symptom Severity Score (IBS-SSS) reports were collected before (baseline) and after intervention. A faecal microbiota dysbiosis test (GA-map Dysbiosis Test) evaluated bacterial composition. Per protocol analysis was performed on 61 patients from whom microbiome data were available.Responders (reduced IBS-SSS by ≥50) to low FODMAP, but not traditional, dietary intervention were discriminated from non-responders before and after intervention based on faecal bacterial profiles. Bacterial abundance tended to be higher in non-responders to a low FODMAP diet compared with responders before and after intervention. A low FODMAP intervention was associated with an increase in Dysbiosis Index (DI) scores in 42% of patients; while decreased DI scores were recorded in 33% of patients following a traditional IBS diet. Non-responders to a low FODMAP diet, but not a traditional IBS diet had higher DI scores than responders at baseline. Finally, while a traditional IBS diet was not associated with significant reduction of investigated bacteria, a low FODMAP diet was associated with reducedA low FODMAP, but not a traditional IBS diet may have significant impact on faecal bacteria. Responsiveness to a low FODMAP diet intervention may be predicted by faecal bacterial profiles.NCT02107625.

Microbes are ubiquitous on our planet, and it is well known that the total number of microbial cells on earth is huge. These organisms usually live in communities, and each of these communities has a different taxonomical structure. As such, microbial communities would serve as the largest reservoir of genes and genetic functions for a vast number of applications in "bio"-related disciplines, especially in biomedicine. Human microbiome is the area in which the relationships between ourselves as hosts and our microbiomes have been examined.In this chapter, we have first reviewed the researches in microbes on community, population and single-cell levels in general. Then we have focused on the effects of recent metagenomics and single-cell advances on human microbiome research, as well as their effects on translational biomedical research. We have also foreseen that with the advancement of big-data analysis techniques, deeper understanding of human microbiome, as well as its broader applications, could be realized.

Microbes are ubiquitous on our planet, and it is well known that the total number of microbial cells on earth is huge. These organisms usually live in communities, and each of these communities has a different taxonomical structure. As such, microbial communities would serve as the largest reservoir of genes and genetic functions for a vast number of applications in “bio”-related disciplines, especially in biomedicine. Human microbiome is the area in which the relationships between ourselves as hosts and our microbiomes have been examined.