Autoimmune diseases exhibit shared alterations in the gut microbiota.

OBJECTIVE: Accumulating evidence from microbial studies have highlighted the modulatory roles of intestinal microbes in numerous human diseases, however, the shared microbial signatures across different diseases remain relatively unclear. METHODS: To consolidate existing knowledge across multiple studies, we performed meta-analyses of 17 disease types, covering 34 case-control datasets of 16S rRNA sequencing data, to identify shared alterations among different diseases. Furthermore, the impact of a microbial species, Lactobacillus salivarius, was established in a dextran sodium sulphate-induced colitis model and a collagen type II-induced arthritis mouse model. RESULTS: Microbial alterations among autoimmune diseases were substantially more consistent compared with that of other diseases (cancer, metabolic disease and nervous system disease), with microbial signatures exhibiting notable discriminative power for disease prediction. Autoimmune diseases were characterized by the enrichment of Enterococcus, Veillonella, Streptococcus and Lactobacillus and the depletion of Ruminococcus, Gemmiger, Oscillibacter, Faecalibacterium, Lachnospiracea incertae sedis, Anaerostipes, Coprococcus, Alistipes, Roseburia, Bilophila, Barnesiella, Dorea, Ruminococcus2, Butyricicoccus, Phascolarctobacterium, Parabacteroides and Odoribacter, among others. Functional investigation of L. salivarius, whose genus was commonly enriched in numerous autoimmune diseases, demonstrated protective roles in two separate inflammatory mouse models. CONCLUSION: Our study highlights a strong link between autoimmune diseases and the gut microbiota, with notably consistent microbial alterations compared with that of other diseases, indicating that therapeutic strategies that target the gut microbiome may be transferable across different autoimmune diseases. Functional validation of L. salivarius highlighted that bacterial genera associated with disease may not always be antagonistic, but may represent protective or adaptive responses to disease.

Correlating mechanical and gene expression data on the single cell level to investigate metastatic phenotypes.

Stiffness has been observed to decrease for many cancer cell types as their metastatic potential increases. Although cell mechanics and metastatic potential are related, the underlying molecular factors associated with these phenotypes remain unknown. Therefore, we have developed a workflow to measure the mechanical properties and gene expression of single cells that is used to generate large linked-datasets. The process combines atomic force microscopy to measure the mechanics of individual cells with multiplexed RT-qPCR gene expression analysis on the same single cells. Surprisingly, the genes that most strongly correlated with mechanical properties were not cytoskeletal, but rather were markers of extracellular matrix remodeling, epithelial-to-mesenchymal transition, cell adhesion, and cancer stemness. In addition, dimensionality reduction analysis showed that cell clustering was improved by combining mechanical and gene expression data types. The single cell genomechanics method demonstrates how single cell studies can identify molecular drivers that could affect the biophysical processes underpinning metastasis.

Assessing Cellular and Transcriptional Diversity of Ileal Mucosa Among Treatment-Naïve and Treated Crohn's Disease.

BACKGROUND: Crohn's disease is a lifelong disease characterized by chronic inflammation of the gastrointestinal tract. Defining the cellular and transcriptional composition of the mucosa at different stages of disease progression is needed for personalized therapy in Crohn's. METHODS: Ileal biopsies were obtained from (1) control subjects (n = 6), (2) treatment-naïve patients (n = 7), and (3) established (n = 14) Crohn's patients along with remission (n = 3) and refractory (n = 11) treatment groups. The biopsies processed using 10x Genomics single cell 5' yielded 139 906 cells. Gene expression count matrices of all samples were analyzed by reciprocal principal component integration, followed by clustering analysis. Manual annotations of the clusters were performed using canonical gene markers. Cell type proportions, differential expression analysis, and gene ontology enrichment were carried out for each cell type. RESULTS: We identified 3 cellular compartments with 9 epithelial, 1 stromal, and 5 immune cell subtypes. We observed differences in the cellular composition between control, treatment-naïve, and established groups, with the significant changes in the epithelial subtypes of the treatment-naïve patients, including microfold, tuft, goblet, enterocyte,s and BEST4+ cells. Surprisingly, fewer changes in the composition of the immune compartment were observed; however, gene expression in the epithelial and immune compartment was different between Crohn's phenotypes, indicating changes in cellular activity. CONCLUSIONS: Our study identified cellular and transcriptional signatures associated with treatment-naïve Crohn's disease that collectively point to dysfunction of the intestinal barrier with an increase in inflammatory cellular activity. Our analysis also highlights the heterogeneity among patients within the same disease phenotype, shining a new light on personalized treatment responses and strategies.

Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system.

Single-cell transcriptomics enables the definition of diverse human immune cell types across multiple tissues and disease contexts. Further deeper biological understanding requires comprehensive integration of multiple single-cell omics (transcriptomic, proteomic, and cell-receptor repertoire). To improve the identification of diverse cell types and the accuracy of cell-type classification in multi-omics single-cell datasets, we developed SuPERR, a novel analysis workflow to increase the resolution and accuracy of clustering and allow for the discovery of previously hidden cell subsets. In addition, SuPERR accurately removes cell doublets and prevents widespread cell-type misclassification by incorporating information from cell-surface proteins and immunoglobulin transcript counts. This approach uniquely improves the identification of heterogeneous cell types and states in the human immune system, including rare subsets of antibody-secreting cells in the bone marrow.

More Positive or More Negative? Metagenomic Analysis Reveals Roles of Virome in Human Disease-Related Gut Microbiome.

Viruses are increasingly viewed as vital components of the human gut microbiota, while their roles in health and diseases remain incompletely understood. Here, we first sequenced and analyzed the 37 metagenomic and 18 host metabolomic samples related to irritable bowel syndrome (IBS) and found that some shifted viruses between IBS and controls covaried with shifted bacteria and metabolites. Especially, phages that infect beneficial lactic acid bacteria depleted in IBS covaried with their hosts. We also retrieved public whole-genome metagenomic datasets of another four diseases (type 2 diabetes, Crohn's disease, colorectal cancer, and liver cirrhosis), totaling 438 samples including IBS, and performed uniform analysis of the gut viruses in diseases. By constructing disease-specific co-occurrence networks, we found viruses actively interacting with bacteria, negatively correlated with possible dysbiosis-related and inflammation-mediating bacteria, increasing the connectivity between bacteria modules, and contributing to the robustness of the networks. Functional enrichment analysis showed that phages interact with bacteria through predation or expressing genes involved in the transporter and secretion system, metabolic enzymes, etc. We further built a viral database to facilitate systematic functional classification and explored the functions of viral genes on interacting with bacteria. Our analyses provided a systematic view of the gut virome in the disease-related microbial community and suggested possible positive roles of viruses concerning gut health.

Urinary detection of early responses to checkpoint blockade and of resistance to it via protease-cleaved antibody-conjugated sensors.

Immune checkpoint blockade (ICB) therapy does not benefit the majority of treated patients, and those who respond to the therapy can become resistant to it. Here we report the design and performance of systemically administered protease activity sensors conjugated to anti-programmed cell death protein 1 (αPD1) antibodies for the monitoring of antitumour responses to ICB therapy. The sensors consist of a library of mass-barcoded protease substrates that, when cleaved by tumour proteases and immune proteases, are released into urine, where they can be detected by mass spectrometry. By using syngeneic mouse models of colorectal cancer, we show that random forest classifiers trained on mass spectrometry signatures from a library of αPD1-conjugated mass-barcoded activity sensors for differentially expressed tumour proteases and immune proteases can be used to detect early antitumour responses and discriminate resistance to ICB therapy driven by loss-of-function mutations in either the B2m or Jak1 genes. Biomarkers of protease activity may facilitate the assessment of early responses to ICB therapy and the classification of refractory tumours based on resistance mechanisms.

Effect of SRB and Applied Potential on Stress Corrosion Behavior of X80 Steel in High-pH Soil Simulated Solution.

The effect of SRB and applied potential on the stress corrosion sensitivity of X80 pipeline steel was analyzed in high-pH soil simulated solution under different conditions using a slow strain rate tensile test, electrochemical test, and electronic microanalysis. The experimental results showed that X80 pipeline steel has a certain degree of SCC sensitivity in high-pH simulated solution, and the crack growth mode was trans-granular stress corrosion cracking. In a sterile environment, the SCC mechanism of X80 steel was a mixture mechanism of anode dissolution and hydrogen embrittlement at -850 mV potential, while X80 steel had the lowest SCC sensitivity due to the weak effect of AD and HE; after Sulfate Reducing Bacteria (SRB) were inoculated, the SCC mechanism of X80 steel was an AD-membrane rupture mechanism at -850 mV potential. The synergistic effect of Cl- and SRB formed an oxygen concentration cell and an acidification microenvironment in the pitting corrosion pit, and this promoted the formation of pitting corrosion which induced crack nucleation, thus significantly improving the SCC sensitivity of X80 steel. The strong cathodic polarization promoted the local corrosion caused by SRB metabolism in the presence of bacteria, whereby the SCC sensitivity in the presence of bacteria was higher than that in sterile conditions under strong cathodic potential.

DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach.

BACKGROUND: Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for elucidating their different roles in interactions with bacterial hosts and regulation of microbial communities. However, there is no experimental or computational approach to effectively classify their sequences in culture-independent metavirome. We present a new computational method, DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment. FINDINGS: DeePhage uses a "one-hot" encoding form to represent DNA sequences in detail. Sequence signatures are detected via a convolutional neural network to obtain valuable local features. The accuracy of DeePhage on 5-fold cross-validation reaches as high as 89%, nearly 10% and 30% higher than that of 2 similar tools, PhagePred and PHACTS. On real metavirome, DeePhage correctly predicts the highest proportion of contigs when using BLAST as annotation, without apparent preferences. Besides, DeePhage reduces running time vs PhagePred and PHACTS by 245 and 810 times, respectively, under the same computational configuration. By direct detection of the temperate viral fragments from metagenome and metavirome, we furthermore propose a new strategy to explore phage transformations in the microbial community. The ability to detect such transformations provides us a new insight into the potential treatment for human disease. CONCLUSIONS: DeePhage is a novel tool developed to rapidly and efficiently identify 2 kinds of phage fragments especially for metagenomics analysis. DeePhage is freely available via http://cqb.pku.edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.

Comprehensive evaluation of protein-coding sORFs prediction based on a random sequence strategy.

Background: Small open reading frames (sORFs) with protein-coding ability present unprecedented challenge for genome annotation because of their short sequence and low expression level. In the past decade, only several prediction methods have been proposed for discovery of protein-coding sORFs and lack of objective and uniform negative datasets has become an important obstacle to sORFs prediction. The prediction efficiency of current sORFs prediction methods needs to be further evaluated to provide better research strategies for protein-coding sORFs discovery. Methods: In this work, nine mainstream existing methods for predicting protein-coding potential of ORFs are comprehensively evaluated based on a random sequence strategy. Results: The results show that the current methods perform poorly on different sORFs datasets. For comparison, a sequence based prediction algorithm trained on prokaryotic sORFs is proposed and its better prediction performance indicates that the random sequence strategy can provide feasible ideas for protein-coding sORFs predictions. Conclusions: As a kind of important functional genomic element, discovery of protein-coding sORFs has shed light on the dark proteomes. This evaluation work indicates that there is an urgent need for developing specialized prediction tools for protein-coding sORFs in both eukaryotes and prokaryotes. It is expected that the present work may provide novel ideas for future sORFs researches.

Proteomics reveal biomethane production process induced by carbon nanotube.

Biomethane produced by methanogenic archaea is a main greenhouse resource of terrestrial and marine ecosystems, which strongly affects the global environment change. Conductive materials, especially nano-scale, show considerable intervention on biomethane production potential, but the mechanism is still unclear. Herein, we precisely quantified the absolute abundance of Methanosarcina spp. proteins affected by carbon nanotubes (CNTs) using tandem mass tag (TMT) proteomics technology. Among the 927 detectable proteins, more than three hundred, 304, showed differential expression. Gene Set Enrichment Analysis on KEGG pathways and GO biological processes revealed a trend of decreased protein synthesis induced by CNTs, suggesting these conductive nanomaterials may replace part of the cell structure and function. Interestingly, increased acetoclastic methanogenesis actually came at the expense of reduced protein synthesis in related pathways. CNTs stimulated biomethane production from acetate by stimulating intracellular redox activity and the -COOH oxidation process. These findings enhanced the understanding of the biomethane production process affected by conductive materials.

LightCUD: a program for diagnosing IBD based on human gut microbiome data.

BACKGROUND: The diagnosis of inflammatory bowel disease (IBD) and discrimination between the types of IBD are clinically important. IBD is associated with marked changes in the intestinal microbiota. Advances in next-generation sequencing (NGS) technology and the improved hospital bioinformatics analysis ability motivated us to develop a diagnostic method based on the gut microbiome. RESULTS: Using a set of whole-genome sequencing (WGS) data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles of strains and genera. The genus and strain profiles were used for the 16S-based and WGS-based diagnostic modules construction respectively. We designed a novel feature selection procedure to select those case-specific features. With these features, we built discrimination models using different machine learning algorithms. The machine learning algorithm LightGBM outperformed other algorithms in this study and thus was chosen as the core algorithm. Specially, we identified two small sets of biomarkers (strains) separately for the WGS-based health vs IBD module and ulcerative colitis vs Crohn's disease module, which contributed to the optimization of model performance during pre-training. We released LightCUD as an IBD diagnostic program built with LightGBM. The high performance has been validated through five-fold cross-validation and using an independent test data set. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rRNA sequencing data of gut microbiome samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD was released in open source with instructions at the webpage http://cqb.pku.edu.cn/ZhuLab/LightCUD/ . The identified strain biomarkers could be used to study the critical factors for disease development and recommend treatments regarding changes in the gut microbial community. CONCLUSIONS: As the first released human gut microbiome-based IBD diagnostic tool, LightCUD demonstrates a high-performance for both WGS and 16S sequencing data. The strains that either identify healthy controls from IBD patients or distinguish the specific type of IBD are expected to be clinically important to serve as biomarkers.

Multiomics Study of Gut Bacteria and Host Metabolism in Irritable Bowel Syndrome and Depression Patients.

Background and Aims: Irritable bowel syndrome (IBS) and depression have high tendencies of comorbidity. In particular, diarrhea-predominant IBS (IBS-D) and depression exhibit similar fecal microbiota signatures, yet little is known about their pathogenic mechanism. Here, we propose that the differences in structure and composition of IBS-D and depression gut microbiota give rise to different downstream functions, which lead to distinct clinical phenotypes via host metabolism and further influence the interaction of brain-gut axis. Methods: We performed multiomics study, including fecal metagenome-wide sequencing and serum metabolomics profiling in 65 individuals with IBS-D (n=22), depression (n=15), comorbid patients (n=13), and healthy controls (n=15). We analyzed functional genes contributed by the primary genus and evaluated their correlations with clinical indices and host metabolites. Results: Metagenomic analysis revealed 26 clusters of orthologous groups of protein (COG) categories consisting of a total of 4,631 functional genes. Trehalose and maltose hydrolase (COG1554) and fucose permease (COG0738) were the most relevant and enriched functional genes in the IBS-D patients; urease accessory proteins UreE (COG2371) was that in the depression patients. Context based genome annotation suggest that an alteration of Escherichia coli and Enterobacter cloacae in IBS-D and depression respectively may be responsible for the enrichment described above. Correlation with host metabolites, such as maltotriose and isomaltose in carbohydrate metabolism and anandamide in neuroactive metabolism, drew further connections between these findings. Conclusions: These changes led us to propose a connection between genomic signatures and clinical differences observed in IBS-D and depression. Our findings provide further insights into the involvement of gut microbiota in diseases related to brain-gut disorder.

Aging progression of human gut microbiota.

BACKGROUND: Human gut microbiota are important for human health and have been regarded as a "forgotten organ", whose variation is closely linked with various factors, such as host genetics, diet, pathological conditions and external environment. The diversity of human gut microbiota has been correlated with aging, which was characterized by different abundance of bacteria in various age groups. In the literature, most of the previous studies of age-related gut microbiota changes focused on individual species in the gut community with supervised methods. Here, we aimed to examine the underlying aging progression of the human gut microbial community from an unsupervised perspective. RESULTS: We obtained raw 16S rRNA sequencing data of subjects ranging from newborns to centenarians from a previous study, and summarized the data into a relative abundance matrix of genera in all the samples. Without using the age information of samples, we applied an unsupervised algorithm to recapitulate the underlying aging progression of microbial community from hosts in different age groups and identify genera associated to this progression. Literature review of these identified genera indicated that for individuals with advanced ages, some beneficial genera are lost while some genera related with inflammation and cancer increase. CONCLUSIONS: The multivariate unsupervised analysis here revealed the existence of a continuous aging progression of human gut microbiota along with the host aging process. The identified genera associated to this aging process are meaningful for designing probiotics to maintain the gut microbiota to resemble a young age, which hopefully will lead to positive impact on human health, especially for individuals in advanced age groups.

PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning.

BACKGROUND: Phages and plasmids are the major components of mobile genetic elements, and fragments from such elements generally co-exist with chromosome-derived fragments in sequenced metagenomic data. However, there is a lack of efficient methods that can simultaneously identify phages and plasmids in metagenomic data, and the existing tools identifying either phages or plasmids have not yet presented satisfactory performance. FINDINGS: We present PPR-Meta, a 3-class classifier that allows simultaneous identification of both phage and plasmid fragments from metagenomic assemblies. PPR-Meta consists of several modules for predicting sequences of different lengths. Using deep learning, a novel network architecture, referred to as the Bi-path Convolutional Neural Network, is designed to improve the performance for short fragments. PPR-Meta demonstrates much better performance than currently available similar tools individually for phage or plasmid identification, while testing on both artificial contigs and real metagenomic data. PPR-Meta is freely available via http://cqb.pku.edu.cn/ZhuLab/PPR_Meta or https://github.com/zhenchengfang/PPR-Meta. CONCLUSIONS: To the best of our knowledge, PPR-Meta is the first tool that can simultaneously identify phage and plasmid fragments efficiently and reliably. The software is optimized and can be easily run on a local PC by non-computer professionals. We developed PPR-Meta to promote the research on mobile genetic elements and horizontal gene transfer.

Characteristic dysbiosis of gut microbiota of Chinese patients with diarrhea-predominant irritable bowel syndrome by an insight into the pan-microbiome.

BACKGROUND: Irritable bowel syndrome (IBS) is reported associated with the alteration of gut microbial composition termed as dysbiosis. However, the pathogenic mechanism of IBS remains unclear, while the studies of Chinese individuals are scarce. This study aimed to understand the concept of dysbiosis among patients with Chinese diarrhea-predominant IBS (IBS-D), as a degree of variance between the gut microbiomes of IBS-D population and that of a healthy population. METHODS: The patients with IBS-D were recruited (assessed according to the Rome III criteria, by IBS symptom severity score) from the Outpatient Department of Gastroenterology of Peking University Third Hospital, and volunteers as healthy controls (HCs) were enrolled, during 2013. The 16S rRNA sequences were extracted from fecal samples. Ribosomal database project resources, basic local alignment search tool, and SparCC software were used to obtain the phylotype composition of samples and the internal interactions of the microbial community. Herein, the non-parametric test, Wilcoxon rank-sum test was carried out to find the statistical significance between HC and IBS-D groups. All the P values were adjusted to q values to decrease the error rate. RESULTS: The study characterized the gut microbiomes of Chinese patients with IBS-D, and demonstrated that the dysbiosis could be characterized as directed alteration of the microbiome composition leading to greater disparity between relative abundance of two phyla, Bacteroidetes (Z = 4.77, q = 1.59 × 10) and Firmicutes (Z = -3.87, q = 5.83 × 10). Moreover, it indicated that the IBS symptom features were associated with the dysbiosis of whole gut microbiome, instead of one or several certain genera even they were dominating. Two genera, Bacteroides and Lachnospiracea incertae sedis, were identified as the core genera, meanwhile, the non-core genera contribute to a larger pan-microbiome of the gut microbiome. Furthermore, the dysbiosis in patients with IBS-D was associated with a reduction of network complexity of the interacted microbial community (HC vs. IBS-D: 639 vs. 154). The disordered metabolic functions of patients with IBS-D were identified as the potential influence of gut microbiome on the host (significant difference with q < 0.01 between HC and IBS-D). CONCLUSIONS: This study supported the view of the potential influence of gut microbiome on the symptom of Chinese patients with IBS-D, and further characterized dysbiosis in Chinese patients with IBS-D, thus provided more pathological evidences for IBS-D with the further understanding of dysbiosis.

Relationship between Gut Microbiota and Phosphorus Metabolism in Hemodialysis Patients: A Preliminary Exploration.

BACKGROUND: Hyperphosphatemia is a risk factor associated with mortality in patients on maintenance hemodialysis. Gut absorption of phosphate is the major source. Recent studies indicated that the intestinal flora of uremic patients changed a lot compared with the healthy population, and phosphorus is an essential element of bacterial survival and reproduction. The purpose of this study was to explore the role of intestinal microbiota in phosphorus metabolism. METHODS: A prospective self-control study was performed from October 2015 to January 2016. Microbial DNA was isolated from the stools of 20 healthy controls and 21 maintenance hemodialysis patients. Fourteen out of the 21 patients were treated with lanthanum carbonate for 12 weeks. Thus, stools were also collected before and after the treatment. The bacterial composition was analyzed based on 16S ribosomal RNA pyrosequencing. Bioinformatics tools, including sequence alignment, abundance profiling, and taxonomic diversity, were used in microbiome data analyses. Correlations between genera and the serum phosphorus were detected with Pearson's correlation. For visualization of the internal interactions and further measurement of the microbial community, SparCC was used to calculate the Spearman correlation coefficient with the corresponding P value between each two genera. RESULTS: Thirteen genera closely correlated with serum phosphorus and the correlation coefficient was above 0.4 (P < 0.05). We also found that 58 bacterial operational taxonomic units (OTUs) were significantly different and more decreased OTUs were identified and seven genera (P < 0.05) were obviously reduced after using the phosphate binder. Meanwhile, the microbial richness and diversity presented downward trend in hemodialysis patients compared with healthy controls and more downward trend after phosphorus reduction. The co-occurrence network of genera revealed that the network complexity of hemodialysis patients was significantly higher than that of controls, whereas treatment with lanthanum carbonate reduced the network complexity. CONCLUSIONS: Gut flora related to phosphorus metabolism in hemodialysis patients, and improving intestinal microbiota may regulate the absorption of phosphate in the intestine. The use of phosphate binder lanthanum carbonate leads to a tendency of decreasing microbial diversity and lower network complexity.

[Impact of ecological protection construction on schistosomiasis transmission of Qionghai Lake wetland in Xichang City].

OBJECTIVE: To understand the impact of Qionghai Lake wetland ecological protection construction on the prevalence of schistosomiasis, so as to provide the evidence for formulating the strategies for schistosomiasis control and prevention. METHODS: A retrospective survey of the construction of Qionghai Lake wetland was performed, and eleven villages around the wetland were surveyed for schistosomiasis endemic situation. The influence of the wetland project on the schistosomiasis prevalence and Oncomelania hupensis snail status were investigated. RESULTS: Before the construction of Qionghai Lake wetland, the snail elimination and extended chemotherapy for residents was performed. After the project was finished, the roads and ditches were hardened. From 2009 to 2014, the schistosome infection rate of residents declined from 0.37% to 0. No schistosome infected snails were found and in recent 2 years, no snails were found. No mice were infected in the sentinel tests. CONCLUSIONS: The construction of Qionghai Lake wetland effectively eliminates snails, and interrupts the transmission of schistosomiasis. However, the environment of the wetland is more suitable for snail breeding, and therefore, the surveillance still should be strengthened.