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Although knowledge of the role of the oral microbiome in ischemic stroke is steadily increasing, little is known about the multikingdom microbiota interactions and their consequences. We enrolled participants from a prospective multicentre case-control study and investigated multikingdom microbiome differences using saliva metagenomic datasets (n = 308) from young patients diagnosed with cryptogenic ischemic stroke (CIS) and age- and sex-matched stroke-free controls. Differentially abundant taxa were identified using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC2). Functional potential was inferred using HUMANn3. Our findings revealed significant differences in the composition and functional capacity of the oral microbiota associated with CIS. We identified 51 microbial species, including 47 bacterial, 3 viral, and one fungal species associated with CIS in the adjusted model. Co-abundance network analysis highlighted a more intricate microbial network in CIS patients, indicating potential interactions and co-occurrence patterns among microbial species across kingdoms. The results of our metagenomic analysis reflect the complexity of the oral microbiome, with high diversity and multikingdom interactions, which may play a role in health and disease.

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BACKGROUND: A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. RESULTS: We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients. CONCLUSION: SOHPIE-DNA is the first attempt of introducing the regression framework for the DN analysis in microbiome data. This enables the prediction of characteristics of a connectivity of a network with the presence of additional covariate information in the regression. The R package with a vignette of our methodology is available through the CRAN repository ( https://CRAN.R-project.org/package=SOHPIE ), named SOHPIE (pronounced as Sofie). The source code and user manual can be found at https://github.com/sjahnn/SOHPIE-DNA .

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Recently, photogranules composed of bacteria and microalgae for carbon-negative nitrogen removal receive extensive attention worldwide, yet which type of bacteria is helpful for rapid formation of photogranules and whether they depend on signaling communication remain elusive. Varied signaling communication was analyzed using metagenomic method among bacteria and microalgae in via of two types of experimentally verified signaling molecule from bacteria to microalgae, which include indole-3-acetic acid (IAA) and N-acyl homoserine lactones (AHLs) during the operation of photo-bioreactors. Signaling communication is helpful for the adaptability of bacteria to survive with algae. Compared with non-signaling bacteria, signaling bacteria more easily adapt to the varied conditions, evidenced by the increased abundance in the operated reactors. Signaling bacteria are easier to enter the phycosphere, and they dominate the interactions between bacteria and algae rather than non-signaling bacteria. The co-abundance groups (CAGs) with signaling bacteria possess higher abundance than that without signaling bacteria (22.27 % and 6.67 %). Importantly, signaling bacteria accessibly interact with microalgae, which possess higher degree centralities and 32.50 % of them are keystone nodes in the network, in contrast to only 18.66 % of non-signaling bacteria. Thauera carrying both IAA and AHLs synthase genes are highly enriched and positively correlated with nitrogen removal rate. Our work not only highlights the essential roles of signaling communication between microalgae and bacteria in the development of photogranules, but also enriches our understanding of microbial sociobiology.

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A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.

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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by unexplained debilitating fatigue, cognitive dysfunction, gastrointestinal disturbances, and orthostatic intolerance. Here, we report a multi-omic analysis of a geographically diverse cohort of 106 cases and 91 healthy controls that revealed differences in gut microbiome diversity, abundances, functional pathways, and interactions. Faecalibacterium prausnitzii and Eubacterium rectale, which are both recognized as abundant, health-promoting butyrate producers in the human gut, were reduced in ME/CFS. Functional metagenomics, qPCR, and metabolomics of fecal short-chain fatty acids confirmed a deficient microbial capacity for butyrate synthesis. Microbiome-based machine learning classifier models were robust to geographic variation and generalizable in a validation cohort. The abundance of Faecalibacterium prausnitzii was inversely associated with fatigue severity. These findings demonstrate the functional nature of gut dysbiosis and the underlying microbial network disturbance in ME/CFS, providing possible targets for disease classification and therapeutic trials.

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The property theory is a unique principle instructing traditional Chinese doctors to prescribe proper medicines against diseases. As an essential part of it, the five-flavor theory catalogs various Chinese materia medicas (CMMs) into five flavors (sweet, bitter, sour, salty, and pungent) based on their taste and medical functions. Although CMM has been successfully applied in China for thousands of years, it is still a big challenge to interpret CMM flavor via modern biomarkers, further deepening its elusiveness. Herein, to identify the correlation between gut microbiota and CMM flavor, we selected 14 CMMs with different flavors to prepare their aqueous extracts, quantified the contained major chemical components, and then performed full-length 16S rRNA sequencing to analyze the gut microbiota of C57BL/6 mice administrated with CMM extracts. We found that flavones, alkaloids, and saponins were the richest components for sweet-, bitter-, and pungent-flavored CMMs, respectively. Medicines with merged flavors (bitter-pungent and sweet-pungent) displayed mixed profiles of components. According to gut microbial analysis, modulation of CMMs belonging to the same flavor on the taxonomic classification was inconsistent to an extent, while the functional sets of gut microbiota, co-abundance gene groups (CAGs), strongly and differentially responded to distinct flavors. Moreover, these correlations were in line with their pharmacological actions. Therefore, the gut microbial functional sets (CAGs) could act as the possible indicator to reflect CMM flavor, rather than the composition of microbial community.

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Studies on marine bacterial communities have revealed endemism in local communities, yet the underlying mechanisms remained elusive. Environmental gradient settings can benefit the straightaway study of community composition changes and the mechanisms explaining them. Here, MiSeq-based 16S rRNA gene sequencing was performed on 12 surface sediment samples from the northern South China Sea (nSCS) revealing that shallow-sea samples had a higher alpha diversity than deep-sea samples, and were differentiated from them significantly based on beta diversity. Temperature, seawater depth, and salinity were the top three influential factors. Bacterial 16S rRNA gene abundance was positively correlated with temperature, and negatively correlated with salinity. Sulfate-reducing bacteria including Desulfobacteraceae, Desulfobulbaceae, and Syntrophobacteraceae were enriched in shallow-sea sediments, co-abundant with nitrite-oxidizing Nitrospira and potential sulfur-oxidizing shallow-sea specific Woeseiaceae/JTB255 clade. Meanwhile, the co-existing and co-abundant of marine anammox and n-damo bacteria were enriched in deep-sea sediments, which was firstly evidenced in this study. The global deep-sea cosmopolitans, OM1 clade, and deep-sea specific Woeseiaceae/JTB255 clade were also found enriched in deep-sea sediments of nSCS. The discovery of novel taxa which were differentially enriched in shallow-/deep-sea sediments not only shed light on enigmatic physiological properties and the natural selection mechanism, but also provided the potential ecological-functional links which invoked further genomics-based metabolic characteristics.

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BACKGROUND AND OBJECTIVES: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder, and has been associated with adverse metabolic outcomes. There is increasing evidence indicating the important role of gut microbiota in OSA and its comorbidities, while the perturbation of intestinal microbial community elicited by OSA has yet to be well-characterized. Here, we investigated the effect of chronic intermittent hypoxia (IH), a hallmark feature of OSA, on gut microbiota in mice. METHODS: Male C57BL/6 mice were exposed to a pattern of chronic IH or normoxic conditions for 6 weeks. Fecal samples were collected. The composition of microbiota was determined by 16S rRNA gene amplicon sequencing, and PICRUSt2 was performed to predict functional potential of gut microbiome. RESULTS: In IH mice, accompanied with elevated systemic inflammation, gut microbiota were significantly altered, characterized by enriched Bacteroides, Desulfovibrionaceae and decreased Bifidobacterium. Bacterial operational taxonomic units (OTUs) were clustered into co-abundance groups (CAGs) as potential functional unit in response to IH exposure. One CAG including bacteria of Bacteroides acidifaciens and Desulfovibrionaceae was positively correlated with systemic inflammation in mice, while another CAG composed of bacteria in Muribaculaceae was negatively correlated. Prediction of metabolic pathways showed that, changes in microbiota from IH treatment mainly impacted on bile acid and fatty acid metabolism. CONCLUSION: Our data demonstrated that dysbiosis of gut microbiome was associated with systemic inflammation and metabolism disorder, and emerges as a mediator for IH and its consequences. Targeting microbiota will be a promising approach to curtail metabolic risks of OSA clinically.

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As with many other trophic interactions, the interchange of microorganisms between plants and their herbivorous insects is unavoidable. To test the hypothesis that the composition and diversity of the insect bacteriome are driven by the bacteriome of the plant, the bacteriomes of both the plant Datura inoxia and its specialist insect Lema daturaphila were characterised using 16S sRNA gene amplicon sequencing. Specifically, the bacteriomes associated with seeds, leaves, eggs, guts, and frass were described and compared. Then, the functions of the most abundant bacterial lineages found in the samples were inferred. Finally, the patterns of co-abundance among both bacteriomes were determined following a multilayer network approach. In accordance with our hypothesis, most genera were shared between plants and insects, but their abundances differed significantly within the samples collected. In the insect tissues, the most abundant genera were Pseudomonas (24.64%) in the eggs, Serratia (88.46%) in the gut, and Pseudomonas (36.27%) in the frass. In contrast, the most abundant ones in the plant were Serratia (40%) in seeds, Serratia (67%) in foliar endophytes, and Hymenobacter (12.85%) in foliar epiphytes. Indeed, PERMANOVA analysis showed that the composition of the bacteriomes was clustered by sample type (F = 9.36, p < 0.001). Functional inferences relevant to the interaction showed that in the plant samples, the category of Biosynthesis of secondary metabolites was significantly abundant (1.4%). In turn, the category of Xenobiotics degradation and metabolism was significantly present (2.5%) in the insect samples. Finally, the phyla Proteobacteria and Actinobacteriota showed a pattern of co-abundance in the insect but not in the plant, suggesting that the co-abundance and not the presence−absence patterns might be more important when studying ecological interactions.

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The gut microbiota alternations are associated with gestational anemia (GA); however, limited predictive value for the subsequent incidence of anemia in normal gestational women has been obtained. We sought to rigorously characterise gut dysbiosis in subjects with GA and explored the potential predictive value of novel microbial signatures for the risk of developing GA. A prospective cohort of subjects with GA (n = 156) and healthy control (n = 402), all of whom were free of GA in the second trimester, by 16S rRNA gene sequencing was conducted. Microbial signatures altered dramatically in GA compared with healthy control in the second trimester. Megamonas, Veillonella, and Haemophilus were confirmed to show differential abundances in GA after adjusting for covariates. On the contrary, Lachnospiraceae and Blautia were enriched in control. Microbial co-abundance group (CAG) network was constructed. Prospectively, CAG network relatively accurately predicted upcoming GA in normal pregnant women with an AUC of 0.7738 (95%CI: 0.7171, 0.8306) and the performance was further validated in Validation set (0.8223, 95%CI: 0.7573, 0.8874). Overall, our study demonstrated that alterations in the gut microbial community were associated with anemia in pregnancy and microbial signatures could accurately predict the subsequent incidence of anemia in normal pregnant women. Our findings provided new insights into understanding the role of gut microbiota in GA, identifying high-risk individuals, and modulating gut microbiota as a therapeutic target, thus improving quality of life and well-being of women and children.

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BACKGROUND: Penicillin allergy is frequently reported in adults and children. Recent studies suggest that microbiota plays a key role in the development and progression of allergy. In this study, the relationship between vaginal microbiome and pregnant women with penicillin allergy was investigated. METHODS: Vaginal samples before labor from 12 pregnant women with penicillin allergy and 15 non-allergic pregnant women were collected. Bacterial community structure of all study subjects and the discrepancies between the two groups were analyzed using 16S rRNA sequencing based on Illumina Hiseq 2500 platform. RESULTS: The abundant phyla among all participants were Firmicutes, Actinobacteria and Bacteroidetes. The predominant genus was Lactobacillus. Compared to non-allergic pregnant women, Actinobacteria, Coriobacteriaceae, Lachnospiraceae, Paraprevotella and Anoxybacillus significantly decreased, whereas Deltaproteobacteria, Peptostreptococcaceae, Enterococcus and Megamonas were more abundant in penicillin allergic women. Additionally, obvious discrepancies were observed in the co-abundance network at the genus level between the two groups. CONCLUSIONS: There were differences in the microbial community structure and composition of reproduction tract between penicillin allergic and non-allergic pregnant women. These shifts may be related to maternal and neonatal health.

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[This corrects the article DOI: 10.3389/fmicb.2018.02682.].

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BACKGROUND: Gut microbiota, by influencing multiple metabolic processes in the host, is an important determinant of human health and disease. However, gut dysbiosis associated with metabolic complications shows inconsistent patterns. This is likely driven by factors shaping gut microbial composition that have largely been under-evaluated, at a population level, in school-age children, especially from developing countries. RESULTS: Through characterization, by 16S sequencing, of the largest gut microbial population-based school-aged children cohort in Latin America (ORSMEC, N = 926, aged 6-12 y), we identified associations of 14 clinical and environmental covariates (PFDR<0.1), collectively explaining 15.7% of the inter-individual gut microbial variation. Extrinsic factors such as markers of socioeconomic status showed a major influence in the most abundant taxa and in the enterotypes' distribution. Age was positively correlated with higher diversity, but only in normal-weight children (rho = 0.138, P =2 × 10-3). In contrast, this correlation although not significant, was negative in overweight and obese children (rho = -0.125, P = 0.104 and rho = -0.058, P = 0.409, respectively). Finally, co-abundance groups (CAGs) were associated with the presence of metabolic complications. CONCLUSIONS: Our study offers evidence that the presence of overweight and obesity could impair the microbial diversity maturation associated with age. Furthermore, it provides novel results toward a better understanding of gut microbiota in the pediatric population that will ultimately help to develop therapeutic approaches to improve metabolic status.

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Researchers have long viewed patterns of species association as key to understanding the processes that structure communities. Community-level tests of species association have received the most attention; however, pairwise species associations may offer greater opportunity for linking patterns to specific mechanisms. Although several tests of pairwise association have been developed, there remain gaps in our understanding of their performance. Consequently, it is unclear whether these methods reliably detect patterns of association, or if any one method is superior. We maximized association patterns for single species pairs in synthetic community matrices and examined how accurately five pairwise association tests found that pair, while not finding others (i.e., type I and II error rates). All tests are more likely to miss patterns of association than to falsely detect them. When we maximized association for a species pair that included one or more rare or common species, tests were frequently unable to identify that pair as significantly associated. Consequently, these tests are best suited for identifying significant associations between pairs of species that occur in an intermediate number of samples; for such pairs, three of the five tests considered here detected 100% of the pairs for which we maximized associations.

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Cholestasis is a major hepatic disease in infants, with increasing morbidity in recent years. Accumulating evidence has revealed that the gut microbiota (GM) is associated with liver diseases, such as non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. However, GM alterations in cholestatic infants and the correlation between the GM and hepatic functions remain uninvestigated. In this study, 43 cholestatic infants (IC group) and 37 healthy infants (H group) were enrolled to detect GM discrepancies using 16S rDNA analysis. The diversity in the bacterial community was significantly lower in the IC group than that in the H group (P = 0.013). After determining the top 10 abundant genera of microbes in the IC and H groups, we found that 13 of them were differentially enriched, including Bifidobacterium, Bacteroides, Streptococcus, Enterococcus, and Staphylococcus. As compared with the H group, the IC group had a more complex GM co-occurrence network featured by three core nodes: Phyllobacterium, Ruminococcus, and Anaerostipes. In addition, the positive correlation between Faecalibacterium and Erysipelatoclostridium (r = 0.689, P = 0.000, FDR = 0.009) was not observed in the IC patients. Using the GM composition, the cholestatic patients can be distinguished from healthy infants with high accuracy [areas under receiver operating curve (AUC) > 0.97], wherein Rothia, Eggerthella, Phyllobacterium, and Blautia are identified as valuable biomarkers. Using KEGG annotation, we identified 32 functional categories with significant difference in enrichment of the GM of IC patients, including IC-enriched functional categories that were related to lipid metabolism, biodegradation and metabolism of xenobiotics, and various diseases. In contrast, the number of functions associated with amino acid metabolism, nucleotide metabolism, and vitamins metabolism was reduced in the IC patients. We also identified significant correlation between GM composition and indicators of hepatic function. Megasphaera positively correlated with total bilirubin (r = 0.455, P = 0.002) and direct bilirubin (r = 0.441, P = 0.003), whereas γ-glutamyl transpeptidase was positively associated with Parasutterella (r = 0.466, P = 0.002) and negatively related to Streptococcus (r = -0.450, P = 0.003). This study describes the GM characteristics in the cholestatic infants, illustrates the association between the GM components and the hepatic function, and provides a solid theoretical basis for GM intervention for the treatment of infantile cholestasis.

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Adult giant pandas (Ailuropoda melanoleuca) express transitional characteristics in that they consume bamboos, despite their carnivore-like digestive tracts. Their genome contains no cellulolytic enzymes; therefore, understanding the development of the giant panda gut microbiome, especially in early life, is important for decoding the rules underlying gut microbial formation, inheritance and dietary transitions. With deep metagenomic sequencing, we investigated the gut microbiomes of two newborn giant panda brothers and their parents living in Macao, China, from 2016 to 2017. Both giant panda cubs exhibited progressive increases in gut microbial richness during growth, particularly from the 6th month after birth. Enterobacteriaceae dominated the gut microbial compositions in both adult giant pandas and cubs. A total of 583 co-abundance genes (CAGs) and about 79 metagenomic species (MGS) from bacteria or viruses displayed significant changes with age. Seven genera (Shewanella, Oblitimonas, Helicobacter, Haemophilus, Aeromonas, Listeria, and Fusobacterium) showed great importance with respect to gut microbial structural determination in the nursing stage of giant panda cubs. Furthermore, 10 orthologous gene functions and 44 pathways showed significant changes with age. Of the significant pathways, 16 from Escherichia, Klebsiella, Propionibacterium, Lactobacillus, and Lactococcus displayed marked differences between parents and their cubs at birth, while 29 pathways from Escherichia, Campylobacter and Lactobacillus exhibited significant increase in cubs from 6 to 9 months of age. In addition, oxidoreductases, transferases, and hydrolases dominated the significantly changed gut microbial enzymes during the growth of giant panda cubs, while few of them were involved in cellulose degradation. The findings indicated diet-stimulated gut microbiome transitions and the important role of Enterobacteriaceae in the guts of giant panda in early life.

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The human gut microbiota (GM) has been associated, to date, with various complex functions, essentials for the host health. Among these, it is certainly worth noting the degradation of the so-called microbiota-accessible carbohydrates (MACs), which the GM breaks down through specific enzymes, referred to as carbohydrate-active enzymes (CAZymes). This degradation constitutes the first step in the production of short-chain fatty acids (SCFAs), key microbial small molecules having multiple health-promoting effects for the host organism. The decline in MAC dietary intake in urban Western populations forced the shrinkage of CAZyme repertoire in the GM, as shown by the literature comparing the microbiome layout between Western urban citizens and traditional rural populations. Even if this reduction in GM functional complexity has been associated with the onset of the so-called "diseases of civilization," only few information regarding the CAZyme variation within Western populations has been provided to date, and its connections with diet and health are still unexplored. In this scenario, here we explore the GM-encoded CAZyme repertoire across two Italian adult cohorts, including healthy lean subjects consuming a Mediterranean diet and obese patients affected by type 2 diabetes, consuming a high-fat diet. In order to impute the CAZyme panel, a pipeline consisting of publicly available software - QIIME, FragGeneScan and HMMER - was specifically implemented. Our study highlighted the existence of robust clusters of bacterial species sharing a common MAC degradation profile in the Italian GM, allowing the stratification of the individual GM into different steady states according to the carbohydrate degradation profile, with possible connections with diet and health.

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BACKGROUND: Infantile eczema is an immunological disease that is characterized by itchy and dry skin. Recent studies have suggested that gut microbiota (GM) plays a role in the development and progression of eczema. To further evaluate this potential link, we collected feces from 19 infants with eczema and 14 infants without eczema and analyzed the molecular discrepancies between the two groups using 16S rDNA analysis. RESULTS: Bacteroidaceae and Deinococcaceae were significantly enriched in eczema infants, and Bacteroidaceae was potentially involved in autoimmune diseases by promoting the Th17 (T helper cell 17) secretion of IL-17 (interleukin-17). In the infants without eczema, the co-abundance network featured three core nodes: Clostridiaceae, Veillonellaceae, and Lactobacillaceae, all of which were lacking in the infants with eczema. Furthermore, our data suggested that Enterobacteriaceae was the core of the co-abundance network for the diseased subjects. CONCLUSIONS: GM is closely connected to the human immune system, and the dysbiotic GM network plays a role in eczema. This study furthered our understanding of the dynamic GM network and its correlation to the occurrence of eczema.

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Structural changes in the gut microbial community have been shown to accompany the progressive development of colorectal cancer. In this review we discuss recent hypotheses on the mechanisms involved in the bacteria-mediated carcinogenesis, as well as the triggering factors favoring the shift of the gut microbiota from a mutualistic to a pro-carcinogenic configuration. The possible role of inflammation, bacterial toxins and toxic microbiota metabolites in colorectal cancer onset is specifically discussed. On the other hand, the strategic role of inflammation as the keystone factor in driving microbiota to become carcinogenic is suggested. As a common outcome of different environmental and endogenous triggers, such as diet, aging, pathogen infection or genetic predisposition, inflammation can compromise the microbiota-host mutualism, forcing the increase of pathobionts at the expense of health-promoting groups, and allowing the microbiota to acquire an overall pro-inflammatory configuration. Consolidating inflammation in the gut, and favoring the bloom of toxigenic bacterial drivers, these changes in the gut microbial ecosystem have been suggested as pivotal in promoting carcinogenesis. In this context, it will become of primary importance to implement dietary or probiotics-based interventions aimed at preserving the microbiota-host mutualism along aging, counteracting deviations that favor a pro-carcinogenic microbiota asset.

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