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Microorganisms Nov 2023During weaning, piglets experience various stressor events that disrupt their gut microbiota and immune balance, decrease growth parameters, and increase mortality...
During weaning, piglets experience various stressor events that disrupt their gut microbiota and immune balance, decrease growth parameters, and increase mortality rates. In this study, we assessed the efficacy of CACC616 as a probiotic supplement. We characterized this strain and evaluated its effect on improving growth performance, modulating gut microbiota composition, and reducing noxious odor components in weaned piglets compared to a non-supplementary diet (control). During the 26-day period, 40 crossbred weaned piglets were randomly assigned to pens with 20 animals each in two groups: control and treatment groups with CACC616. On day 26, the treatment group exhibited a lower feed conversion ratio (FCR) and a significant alteration in gut microbial composition, correlating with improved growth parameters and gut health ( < 0.05). The treatment group also exhibited significantly reduced digestibility- and intestinal-environment-related noxious odor components ( < 0.05). The CACC616 strain effectively reduced pathogenic genera numbers, including , , and spp., with the treatment group exhibiting lower fecal calprotectin levels than the control group ( < 0.05). Overall, this study revealed that the functional probiotic CACC616 contributes to enhanced FCR and effectively modulates weaned piglets' inflammation and intestinal microbiota.
PubMed: 38138034
DOI: 10.3390/microorganisms11122890 -
Journal of Dental Research Mar 2022An intuitive, clinically relevant index of microbial dysbiosis as a summary statistic of subgingival microbiome profiles is needed. Here, we describe a subgingival...
An intuitive, clinically relevant index of microbial dysbiosis as a summary statistic of subgingival microbiome profiles is needed. Here, we describe a subgingival microbial dysbiosis index (SMDI) based on machine learning analysis of published periodontitis/health 16S microbiome data. The raw sequencing data, split into training and test sets, were quality filtered, taxonomically assigned to the species level, and centered log-ratio transformed. The training data set was subject to random forest analysis to identify discriminating species (DS) between periodontitis and health. DS lists, compiled by various "Gini" importance score cutoffs, were used to compute the SMDI for samples in the training and test data sets as the mean centered log-ratio abundance of periodontitis-associated species subtracted by that of health-associated ones. Diagnostic accuracy was assessed with receiver operating characteristic analysis. An SMDI based on 49 DS provided the highest accuracy with areas under the curve of 0.96 and 0.92 in the training and test data sets, respectively, and ranged from -6 (most normobiotic) to 5 (most dysbiotic) with a value around zero discriminating most of the periodontitis and healthy samples. The top periodontitis-associated DS were spp., and , while and were the top health-associated DS. The index was highly reproducible by hypervariable region. Applying the index to additional test data sets in which nitrate had been used to modulate the microbiome demonstrated that nitrate has dysbiosis-lowering properties in vitro and in vivo. Finally, 3 genera (, and ) were identified that could be used for calculation of a simplified SMDI with comparable accuracy. In conclusion, we have developed a nonbiased, reproducible, and easy-to-interpret index that can be used to identify patients/sites at risk of periodontitis, to assess the microbial response to treatment, and, importantly, as a quantitative tool in microbiome modulation studies.
Topics: Dysbiosis; Humans; Microbiota; Periodontitis; RNA, Ribosomal, 16S; Treponema denticola
PubMed: 34428955
DOI: 10.1177/00220345211035775 -
Genes Aug 2022Spontaneous type 2 diabetes mellitus (T2DM) macaques are valuable resources for our understanding the pathological mechanism of T2DM. Based on one month's fasting blood...
Spontaneous type 2 diabetes mellitus (T2DM) macaques are valuable resources for our understanding the pathological mechanism of T2DM. Based on one month's fasting blood glucose survey, we identified seven spontaneous T2DM macaques and five impaired glucose regulation (IGR) macaques from 1408 captive individuals. FPG, HbA1c, FPI and IR values were significant higher in T2DM and IGR than in controls. 16S rRNA sequencing of fecal microbes showed the significantly greater abundance of , bacteria inhibiting the production of secondary bile acids, and , bacteria producing short-chain fatty acids was significantly lower in T2DM macaques. In addition, several opportunistic pathogens, such as and were significantly more abundant in both T2DM and IGR macaques. Fecal metabolites analysis based on UHPLC-MS identified 50 differential metabolites (DMs) between T2DM and controls, and 26 DMs between IGR and controls. The DMs were significantly enriched in the bile acids metabolism, fatty acids metabolism and amino acids metabolism pathways. Combining results from physiochemical parameters, microbiota and metabolomics, we demonstrate that the imbalance of gut microbial community leading to the dysfunction of glucose, bile acids, fatty acids and amino acids metabolism may contribute to the hyperglycaemia in macaques, and suggest several microbes and metabolites are potential biomarkers for T2DM and IGR macaques.
Topics: Amino Acids; Animals; Bile Acids and Salts; Blood Glucose; Diabetes Mellitus, Type 2; Fatty Acids, Volatile; Glucose; Glycated Hemoglobin; Macaca mulatta; Microbiota; Prediabetic State; RNA, Ribosomal, 16S
PubMed: 36140683
DOI: 10.3390/genes13091513 -
Microbiology Spectrum Dec 2022Numerous studies have reported dysbiosis in the naso- and/or oro-pharyngeal microbiota of COVID-19 patients compared with healthy individuals; however, only a few...
Numerous studies have reported dysbiosis in the naso- and/or oro-pharyngeal microbiota of COVID-19 patients compared with healthy individuals; however, only a few small-scale studies have also included a disease control group. In this study, we characterized and compared the bacterial communities of pooled nasopharyngeal and throat swabs from hospitalized COVID-19 patients ( = 76), hospitalized non-COVID-19 patients with respiratory symptoms or related illnesses ( = 69), and local community controls ( = 76) using 16S rRNA gene V3-V4 amplicon sequencing. None of the subjects received antimicrobial therapy within 2 weeks prior to sample collection. Both COVID-19 and non-COVID-19 hospitalized patients differed in the composition, alpha and beta diversity, and metabolic potential of the naso-oropharyngeal microbiota compared with local controls. However, the microbial communities in the two hospitalized patient groups did not differ significantly from each other. Differential abundance analysis revealed the enrichment of nine bacterial genera in the COVID-19 patients compared with local controls; however, six of them were also enriched in the non-COVID-19 patients. Bacterial genera uniquely enriched in the COVID-19 patients included and . In contrast, and were dramatically decreased in COVID-19 patients only. Association analysis revealed that in COVID-19 patients was positively correlated with the level of the inflammation biomarker C-reactive protein. Our findings reveal a limited impact of SARS-CoV-2 on the naso-oropharyngeal microbiota in hospitalized patients and suggest that and are more specific biomarkers for COVID-19 detection. Our results showed that while both COVID-19 and non-COVID-19 hospitalized patients differed in the composition, alpha and beta diversity, and metabolic potential of the naso-oropharyngeal microbiota compared with local controls, the microbial communities in the two hospitalized patient groups did not differ significantly from each other, indicating a limited impact of SARS-CoV-2 on the naso-oropharyngeal microbiota in hospitalized patients. Besides, we identified and as bacterial genera uniquely enriched in COVID-19 patients, which may serve as more specific biomarkers for COVID-19 detection.
Topics: Humans; SARS-CoV-2; COVID-19; RNA, Ribosomal, 16S; Oropharynx; Microbiota; Bacteria
PubMed: 36350127
DOI: 10.1128/spectrum.02196-22 -
Frontiers in Microbiology 2022With the advent of high throughput technology, it is now feasible to study the complex relationship of the rumen microbiota with methanogenesis in large populations of...
With the advent of high throughput technology, it is now feasible to study the complex relationship of the rumen microbiota with methanogenesis in large populations of ruminant livestock divergently ranked for enteric emissions. Recently, the residual methane emissions (RME) concept has been identified as the optimal phenotype for assessing the methanogenic potential of ruminant livestock due to the trait's independence from animal productivity but strong correlation with daily methane emissions. However, there is currently a dearth of data available on the bacterial and archaeal microbial communities residing in the rumens of animals divergently ranked for RME. Therefore, the objective of this study was to investigate the relationship between the rumen microbiota and RME in a population of finishing beef cattle. Methane emissions were estimated from individual animals using the GreenFeed Emissions Monitoring system for 21 days over a mean feed intake measurement period of 91 days. Residual methane emissions were calculated for 282 crossbred finishing beef cattle, following which a ∼30% difference in all expressions of methane emissions was observed between high and low RME ranked animals. Rumen fluid samples were successfully obtained from 268 animals during the final week of the methane measurement period using a trans-oesophageal sampling device. Rumen microbial DNA was extracted and subjected to 16S rRNA amplicon sequencing. Animals ranked as low RME had the highest relative abundances ( < 0.05) of lactic-acid-producing bacteria (, , and ) and , and the lowest ( < 0.05) proportions of , , and . Within the rumen methanogen community, an increased abundance ( < 0.05) of the genus and RO clade was observed in low RME animals. The relative abundances of both and were negatively correlated ( < 0.05) with RME and positively correlated with ruminal propionate. A similar relationship was observed for the abundance of and the RO clade. Findings from this study highlight the ruminal abundance of bacterial genera associated with the synthesis of propionate the acrylate pathway, as well as the methanogens and members of the RO clade as potential microbial biomarkers of the methanogenic potential of beef cattle.
PubMed: 35572638
DOI: 10.3389/fmicb.2022.855565 -
Progress in Neuro-psychopharmacology &... Dec 2023Previous studies have reported a variety of gut microbiota alterations in patients with schizophrenia. However, none of these studies has investigated gut microbiota in...
BACKGROUND
Previous studies have reported a variety of gut microbiota alterations in patients with schizophrenia. However, none of these studies has investigated gut microbiota in patients with the deficit subtype of schizophrenia (D-SCZ) that can be characterized by primary and enduring negative symptoms. Therefore, in this study we aimed to profile gut microbiota of individuals with D-SCZ, compared to those with non-deficit schizophrenia (ND-SCZ) and healthy controls (HCs).
METHODS
A total of 115 outpatients (44 individuals with D-SCZ and 71 individuals with ND-SCZ) during remission of positive and disorganization symptoms as well as 120 HCs were enrolled. Gut microbiota was analyzed using the 16 rRNA amplicon sequencing. Additionally, the levels of C-reactive protein (CRP), glucose and lipid metabolism markers were determined in the peripheral blood samples.
RESULTS
Altogether 14 genera showed differential abundance in patients with D-SCZ compared to ND-SCZ and HCs, including Candidatus Soleaferrea, Eubacterium, Fusobacterium, Lachnospiraceae UCG-002, Lachnospiraceae UCG-004, Lachnospiraceae UCG-010, Libanicoccus, Limosilactobacillus, Mogibacterium, Peptococcus, Prevotella, Prevotellaceae NK3B31 group, Rikenellaceae RC9 gut group, and Slackia after adjustment for potential confounding factors. Observed alterations were significantly associated with cognitive performance in both groups of patients. Moreover, several significant correlations of differentially abundant genera with the levels of CRP, lipid profile parameters, glucose and insulin were found across all subgroups of participants.
CONCLUSION
Findings from the present study indicate that individuals with D-SCZ show a distinct profile of gut microbiota alterations that is associated with cognitive performance, metabolic parameters and subclinical inflammation.
Topics: Humans; Gastrointestinal Microbiome; Schizophrenia; Case-Control Studies; Glucose; Clostridiales
PubMed: 37473955
DOI: 10.1016/j.pnpbp.2023.110834 -
PeerJ 2020The gut microbiome and microbiome-gut-brain (MGB) axis have been receiving increasing attention for their role in the regulation of mental behavior and possible...
BACKGROUND
The gut microbiome and microbiome-gut-brain (MGB) axis have been receiving increasing attention for their role in the regulation of mental behavior and possible biological basis of psychiatric disorders. With the advance of next-generation sequencing technology, characterization of the gut microbiota in schizophrenia (SZ) patients can provide rich clues for the diagnosis and prevention of SZ.
METHODS
In this study, we compared the differences in the fecal microbiota between 82 SZ patients and 80 demographically matched normal controls (NCs) by 16S rRNA sequencing and analyzed the correlations between altered gut microbiota and symptom severity.
RESULTS
The alpha diversity showed no significant differences between the NC and SZ groups, but the beta diversity revealed significant community-level separation in microbiome composition between the two groups (pseudo- =3.337, < 0.001, uncorrected). At the phylum level, relatively more and less ( < 0.05, FDR corrected) were found in the SZ group. At the genus level, the relative abundances of , , , , , undefined and undefined were significantly increased, whereas the abundances of , , and were decreased in the SZ group compared to the NC group ( < 0.05, FDR corrected). We performed PICRUSt analysis and found that several metabolic pathways differed significantly between the two groups, including the Polyketide sugar unit biosynthesis, Valine, Leucine and Isoleucine biosynthesis, Pantothenate and CoA biosynthesis, C5-Branched dibasic acid metabolism, Phenylpropanoid biosynthesis, Ascorbate and aldarate metabolism, Nucleotide metabolism and Propanoate metabolism pathways ( < 0.05, FDR corrected). Among the SZ group, the abundance of was positively correlated with the total Positive and Negative Syndrome Scale (PANSS) scores ( = 0.24, < 0.05, uncorrected) as well as the general PANSS scores ( = 0.22, < 0.05, uncorrected); was negatively related to the negative scores of PANSS ( = 0.22, < 0.05, uncorrected).
CONCLUSIONS
Our findings provided evidence of altered gut microbial composition in SZ group. In addition, we found that and were associated with the severity of symptoms for the first time, which may provide some new biomarkers for the diagnosis of SZ.
PubMed: 32821537
DOI: 10.7717/peerj.9574 -
Frontiers in Microbiology 2021Current microbiome profiling of type 1 diabetes mellitus (T1D) patients is mostly limited to gut microbiome. We characterized the oral microbiome associated with T1D in...
Current microbiome profiling of type 1 diabetes mellitus (T1D) patients is mostly limited to gut microbiome. We characterized the oral microbiome associated with T1D in children after the onset of the disease and explored its relationship with oral physiological factors and dental status. This cohort study comprised 37 children aged 5-15 years with T1D and 29 healthy children matched in age and gender. Unstimulated whole saliva was collected from diabetic and non-diabetic children, in the morning after brushing their teeth and a fasting period of at least 1 h before sampling. 16S rRNA gene-based analysis was performed by Powersoil Pro kit by Qiagen and Phusion High-Fidelity PCR Master Mix. Oral physiological and dental parameters studied included decayed, missing, and filled teeth index, salivary flow rate, and salivary pH, glucose, calcium, phosphate, and urea levels. Of the identified 105 different genera and 211 different species, the most abundant genera were , , , , and . was more abundant in T1D children. The diabetes group had 22 taxa at the genus level and 33 taxa at the species level that were not present in the control group and the control group exhibited 6 taxa at the genus level and 9 taxa at the species level that did not exist in the diabetes group. In addition, , , and differed between healthy and T1D subjects. Eight species and eight subspecies were significantly more abundant among healthy children than in T1D children. and genera were significantly correlated with salivary parameters. We found similarities between taxa revealed in the present study and those found in gut microbiome in type 1 diabetes mellitus according to gutMDisorder database. Salivary microbiome analysis revealed unique microbial taxa that differed between T1D children and healthy subjects. Several genera found in the saliva of T1D children were associated with gut microbiome in T1D individuals.
PubMed: 34777313
DOI: 10.3389/fmicb.2021.756808 -
Frontiers in Microbiology 2022Modeling subgingival microbiome in health and disease is key to identifying the drivers of dysbiosis and to studying microbiome modulation. Here, we optimize growth...
Modeling subgingival microbiome in health and disease is key to identifying the drivers of dysbiosis and to studying microbiome modulation. Here, we optimize growth conditions of our previously described subgingival microbiome model. Subgingival plaque samples from healthy and periodontitis subjects were used as inocula to grow normobiotic and dysbiotic microbiomes in MBEC assay plates. Saliva supplemented with 1%, 2%, 3.5%, or 5% (v/v) heat-inactivated human serum was used as a growth medium under shaking or non-shaking conditions. The microbiomes were harvested at 4, 7, 10 or 13 days of growth (384 microbiomes in total) and analyzed by 16S rRNA gene sequencing. Biomass significantly increased as a function of serum concentration and incubation period. Independent of growth conditions, the health- and periodontitis-derived microbiomes clustered separately with their respective inocula. Species richness/diversity slightly increased with time but was adversely affected by higher serum concentrations especially in the periodontitis-derived microbiomes. Microbial dysbiosis increased with time and serum concentration. and were substantially enriched in higher serum concentrations at the expense of , and . An increase in , and accompanied by a decrease in , and were the most prominent changes over time. Shaking had only minor effects. Overall, the health-derived microbiomes grown for 4 days in 1% serum, and periodontitis-derived microbiomes grown for 7 days in 3.5%-5% serum were the most similar to the respective inocula. In conclusion, normobiotic and dysbiostic subgingival microbiomes can be grown reproducibly in saliva supplemented with serum, but time and serum concentration need to be adjusted differently for the health and periodontitis-derived microbiomes to maximize similarity to inocula. The optimized model could be used to identify drivers of dysbiosis, and to evaluate interventions such as microbiome modulators.
PubMed: 36406462
DOI: 10.3389/fmicb.2022.1031029 -
Scientific Reports Feb 2023While the breed of cattle can impact on the composition and structure of microbial communities in the rumen, breed-specific effects on rumen microbial communities have...
While the breed of cattle can impact on the composition and structure of microbial communities in the rumen, breed-specific effects on rumen microbial communities have rarely been examined in sheep. In addition, rumen microbial composition can differ between ruminal fractions, and be associated with ruminant feed efficiency and methane emissions. In this study, 16S rRNA amplicon sequencing was used to investigate the effects of breed and ruminal fraction on bacterial and archaeal communities in sheep. Solid, liquid and epithelial rumen samples were obtained from a total of 36 lambs, across 4 different sheep breeds (Cheviot (n = 10), Connemara (n = 6), Lanark (n = 10) and Perth (n = 10)), undergoing detailed measurements of feed efficiency, who were offered a nut based cereal diet ad-libitum supplemented with grass silage. Our results demonstrate that the feed conversion ratio (FCR) was lowest for the Cheviot (most efficient), and highest for the Connemara breed (least efficient). In the solid fraction, bacterial community richness was lowest in the Cheviot breed, while Sharpea azabuensis was most abundant in the Perth breed. Lanark, Cheviot and Perth breeds exhibited a significantly higher abundance of epithelial associated Succiniclasticum compared to the Connemara breed. When comparing ruminal fractions, Campylobacter, Family XIII, Mogibacterium, and Lachnospiraceae UCG-008 were most abundant in the epithelial fraction. Our findings indicate that breed can impact the abundance of specific bacterial taxa in sheep while having little effect on the overall composition of the microbial community. This finding has implications for genetic selection breeding programs aimed at improving feed conversion efficiency of sheep. Furthermore, the variations in the distribution of bacterial species identified between ruminal fractions, notably between solid and epithelial fractions, reveals a rumen fraction bias, which has implications for sheep rumen sampling techniques.
Topics: Sheep; Animals; Cattle; Archaea; RNA, Ribosomal, 16S; Plant Breeding; Veillonellaceae; Campylobacter; Clostridiales
PubMed: 36849493
DOI: 10.1038/s41598-023-28909-1