Linear Discriminant Analysis Effect Size Research Articles

Abnormal Development of Microbiota May Be a Risk Factor for Febrile Urinary Tract Infection in Infancy.

Febrile urinary tract infection (fUTI) is common in infants, but specific risk factors for developing it remain unclear. As most fUTIs are caused by ascending infections of intestinal bacteria, dysbiosis-an imbalance in gut microbial communities-may increase fUTI risk. This study was conducted to test the hypothesis that abnormal development of gut microbiota during infancy increases the risk of developing fUTI. Stool samples were collected from 28 infants aged 3-11 months with first-onset fUTI (fUTI group) and 51 healthy infants of the same age (HC group). After bacterial DNA extraction, 16S rRNA expression was measured and the diversity of gut microbiota and constituent bacteria were compared between the two groups. The alpha diversity of gut microbiota (median Shannon index and Chao index) was significantly lower in the fUTI group (3.0 and 42.5) than in the HC group (3.7 and 97.0; p < 0.001). The beta diversity also formed different clusters between the two groups (p < 0.001), suggesting differences in their microbial composition. The linear discriminant analysis effect size showed that the fUTI group proportionally featured significantly more Escherichia-Shigella in the gut microbiota (9.5%) than the HC group (3.1%; p < 0.001). In summary, abnormal gut microbiota development during infancy may increase the risk of fUTI.

Explore the changes of intestinal flora in patients with coronavirus disease 2019 based on bioinformatics.

Studies have revealed that there were significant changes in intestinal flora composition in patients with coronavirus disease 2019 (COVID-19) compared to non-COVID-19 patients, regardless of whether they were treated with medication. Therefore, a comprehensive study of the intestinal flora of COVID-19 patients is needed to further understand the mechanisms of COVID-19 development. In total, 20 healthy samples and 20 COVID-19 samples were collected in this study. Firstly, alpha diversity and beta diversity were analyzed to assess whether there were difference in species richness and diversity as well as species composition between COVID-19 and control groups. The observed features index, Evenness index, PD index, and Shannon index were utilized to measure alpha diversity. The principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) were performed to analyzed beta diversity. Linear discriminant analysis Effect Size (LEfSe) was utilized to analyze the variability in the abundance of bacterial taxa from different classification levels. The random forest (RF), Least absolute shrinkage and selection operator (LASSO), and univariate logistic regression were utilized to identify key Amplicon Sequence Variant (ASVs). Finally, the relevant networks of bacterial taxa were created in COVID-19 and control groups, separately. There were more species in the control group than in COVID-19 group. The observed features index, Shannon index, and Evenness index in the control groups were markedly higher than in the COVID-19 group. Therefore, there were marked variations in bacterial taxa composition between the COVID-19 and control groups. The nine bacterial taxa were significantly more abundant in the COVID-19 group, such as g-Streptococcus, f-Streptococcaceae, o-Lactobacillales, c-Bacilli and so on. In the control group, 26 bacterial taxa were significantly more abundant, such as c-Clostrjdia, o-Oscillospirales, f-Ruminococcaceae, etc. The 5 key ASVs were obtained through taking the intersection of the characteristic ASVs obtained by the three algorithms, namely ASV6, ASV53, ASV92, ASV96, and ASV105, which had diagnostic value for COVID-19. The relevance network in the control group was more complex compared to the COVID-19 group. Our findings provide five key ASVs for diagnosis of COVID-19, providing a scientific reference for further studies of COVID-19.

Characteristics of the pulmonary microbiota in patients with mild and severe pulmonary infection.

Lung infection is a global health problem associated with high morbidity and mortality and increasing rates of hospitalization. The correlation between pulmonary microecology and infection severity remains unclear. Therefore, the purpose of this study was to investigate the differences in lung microecology and potential biomarkers in patients with mild and severe pulmonary infection. Patients with pulmonary infection or suspected infection were divided into the mild group (140 cases) and the severe group (80 cases) according to pneomonia severity index (PSI) scores. Here, we used metagenomic next-generation sequencing (mNGS) to detect DNA mainly from bronchoalveolar lavage fluid (BALF) collected from patients to analyze changes in the lung microbiome of patients with different disease severity. We used the mNGS to analyze the pulmonary microecological composition in patients with pulmonary infection. The results of alpha diversity and beta diversity analysis showed that the microbial composition between mild and severe groups was similar on the whole. The dominant bacteria were Acinetobacter, Bacillus, Mycobacterium, Staphylococcus, and Prevotella, among others. Linear discriminant analysis effect size (LEfSe) results showed that there were significant differences in virus composition between the mild and severe patients, especially Simplexvirus and Cytomegalovirus, which were prominent in the severe group. The random forest model screened 14 kinds of pulmonary infection-related pathogens including Corynebacterium, Mycobacterium, Streptococcus, Klebsiella, and Acinetobacter. In addition, it was found that Rothia was negatively correlated with Acinetobacter, Mycobacterium, Bacillus, Enterococcus, and Klebsiella in the mild group through co-occurrence network, while no significant correlation was found in the severe group. Here, we describe the composition and diversity of the pulmonary microbiome in patients with pulmonary infection. A significant increase in viral replication was found in the severe group, as well as a significant difference in microbial interactions between patients with mild and severe lung infections, particularly the association between the common pathogenic bacteria and Rothia. This suggests that both pathogen co-viral infection and microbial interactions may influence the course of disease. Of course, more research is needed to further explore the specific mechanisms by which microbial interactions influence disease severity.

Associations of the gut microbiome with psychoneurological symptom cluster in women with gynecologic cancers: a longitudinal study.

The present study aimed to evaluate the associations between the gut microbiome and psychoneurological symptoms (PNS) cluster in women with gynecologic cancers over time. In this secondary data analysis, 19 women with cervical and endometrial cancers treated with radiotherapy were followed at pre-treatment, 6-8 weeks, and 6 months post-treatment. To measure symptoms, Functional Assessment of Cancer Therapy-General (FACT-G) and Patient Health Questionnaire-9 (PHQ-9) were used. An average Z score of at least three out of five symptoms was computed as the PNS cluster total score. Rectal swabs were also collected at the same time points and sequenced using 16S rRNA V4 regions. The Kruskal-Wallis and permutational multivariable analysis of variance tests were used to compare α- and β-diversity between patients with high and low PNS cluster. The linear discriminant analysis effect size (LEfSe) tested taxa differences between study groups. Also, the linear mixed-effect model was used to evaluate the association of the gut microbiome and the PNS cluster over cancer treatment. The patients' mean age was 58 years, 47% Black, 52% single/divorced, and 66% had college or above education. Among the participants, 63% had endometrial cancer with stage I disease. There was a different taxonomy profile between patients with high and low PNS. Patients with high PNS had a lower α-diversity than those with low PNS (Shannon, p = 0.03, evenness, p = 0.03). The mixed effects model results showed that low α-diversity and abundance of Fusicatenibacter and Ruminococcus were associated with high PNS cluster over cancer treatment. The association between the gut microbiome and PNS cluster suggest that the gut microbiota plays a role in developing the PNS cluster. Future larger studies are required to shed light on the gut microbiota role in symptom development in gynecologic cancer patients.

Temporal and Spatial Characterization of Sediment Bacterial Communities from Lake Wetlands in a Plain River Network Region

Sediment bacterial communities are a vital component of microbial communities in aquatic and terrestrial ecosystems and they play a critical role in lake wetlands. We aimed to investigate the effect of season, depth and regional environmental factors on the composition and diversity of bacterial communities in a plain river network area from Taihu Basin. The millions of Illumina reads (16S rRNA gene amplicons) at the surface 25 cm inside samples of the study area were examined using a technically consistent approach. Results from the diversity index, relative abundance, principal component analysis (PCA), redundancy analysis (RDA) and linear discriminant analysis effect size (LEfSe) analysis indicated that the diversity of the bacterial community in summer was generally higher than in other seasons. Proteobacteria were the most abundant phylum in the sediment samples in different seasons (43.15–57.41%) and different layers (39.66–77.97%); the autumn sediments were enriched with Firmicutes (5.67%) and Chloroflexi (12.5%); in all four seasons the sediments were enriched with Betaproteobacteria (14.98–23.45%), Gammaproteobacteria (11.98–14.36%) and Deltaproteobacteria (8.68–14.45%). In the bottom sediments (10–25 cm), Chloroflexi were abundant (average value 10.42%), while Bacteroidetes was the dominant phylum in the surface sediments; redundancy analysis found that total phosphorus (TP) (p = 0.036) was the main environmental factor influencing the sediment bacterial community in different layers. This study provides a reference for further understanding the effects of seasonal changes on sediment microorganisms in lake wetlands.

Gut Microbiome and Lipidome Signatures in Irritable Bowel Syndrome Patients from a Low-Income, Food-Desert Area: A Pilot Study.

Irritable bowel syndrome (IBS) is a common gastroenterological disorder with triggers such as fructose. We showed that our IBS patients suffering from socioeconomic challenges have a significantly high consumption of high-fructose corn syrup (HFCS). Here, we characterize gut microbial dysbiosis and fatty acid changes, with respect to IBS, HFCS consumption, and socioeconomic factors. Fecal samples from IBS patients and healthy controls were subjected to microbiome and lipidome analyses. We assessed phylogenetic diversity and community composition of the microbiomes, and used linear discriminant analysis effect size (LEfSe), analysis of compositions of microbiomes (ANCOM) on highly co-occurring subcommunities (modules), least absolute shrinkage and selection operator (LASSO) on phylogenetic isometric log-ratio transformed (PhILR) taxon abundances to identify differentially abundant taxa. Based on a Procrustes randomization test, the microbiome and lipidome datasets correlated significantly (p = 0.002). Alpha diversity correlated with economic factors (p < 0.001). Multiple subsets of the phylogenetic tree were associated with HFCS consumption (p < 0.001). In IBS patients, relative abundances of potentially beneficial bacteria such as Monoglobaceae, Lachnospiraceae, and Ruminococcaceae were lower (p = 0.007), and Eisenbergiella, associated with inflammatory disorders, was higher. In IBS patients, certain saturated fatty acids were higher and unsaturated fatty acids were lower (p < 0.05). Our study aims first to underscore the influence of HFCS consumption and socioeconomic factors on IBS pathophysiology, and provides new insights that inform patient care.

Analysis of the influence of host lifestyle (coffee consumption, drinking, and smoking) on Korean oral microbiome

If a DNA sample collected in the field is old or degraded, short tandem repeat analysis is difficult to perform, a representative analysis method currently used for individual identification. Given that microorganisms exist everywhere and within the human body, in similar amounts to human cells, microbial analysis could be used to identify individuals even in cases in which human DNA-based identification is difficult. Research has demonstrated that the types of microorganisms within the human body differ depending on various internal or external factors, such as body part or bodily fluid type, lifestyle, geographical area of residence, sex, and age. In this study, we aimed to examine the relationship between lifestyle factors and the composition and diversity of the oral microbiome in individuals living in Korea. We collected 43 saliva samples from Korean individuals and analyzed the oral microbiome and its variations due to external factors, such as coffee consumption, drinking, and smoking. Linear discriminant analysis effect size revealed that Oribacterium, Campylobacter, and Megasphaera were abundant in coffee consumers, whereas Saccharimonadales, Clostridia, and Catonella were abundant in alcohol non-drinkers. We found increased levels of Stomatobaculum in the saliva of smokers, compared with that of non-smokers. Thus, our analysis revealed characteristic microorganisms for each parameter that was evaluated (coffee consumption, smoking, drinking). Consequently, our study provides insight into the oral microbiome in the Korean population and lays the foundation for developing the Korean Forensic Microbiome Database.

Host-specific signatures of the respiratory microbiota in domestic animals

While the importance of respiratory microbiota in maintaining respiratory health is increasingly recognized, we still lack a comprehensive understanding of the unique characteristics of respiratory microbiota specific to individual hosts. This study aimed to address this gap by analyzing publicly available 16S rRNA gene datasets from various domestic animals (cats, dogs, pigs, donkeys, chickens, sheep, and cattle) to identify host-specific signatures of respiratory microbiota. The findings revealed that cattle and pigs exhibited the highest Shannon diversity index and observed features, indicating a greater microbial variety compared to other animals. Discriminant analysis demonstrated distinct composition of respiratory microbiota across different animals, with no overlapping abundant taxa. The linear discriminant analysis effect size highlighted prevalent host-specific microbiota signatures in different animal species. Moreover, the composition and diversity of respiratory microbiota were significantly influenced by various factors such as individual study, health status, and sampling sites within the respiratory tract. While associations between host and respiratory microbiota have been uncovered, the relative contributions of host and environment in the selection of respiratory microbiota and their impact on host fitness remain unclear. Further investigations involving diverse hosts are necessary to fully comprehend the significance of host-microbial coevolution in maintaining respiratory health.

Association Between the Diabetic Foot Ulcer and the Bacterial Colony of the Skin Based on 16S rRNA Gene Sequencing: An Observational Study.

Microorganisms have been the main cause of refractory and high recurrence of diabetic foot ulcer (DFU). This study attempted to observe the skin bacterial colony in healthy skin, diabetic skin and DFU skin. Forty-eight diabetes patients were recruited at Panyu Central Hospital from March 2021 to March 2022 and divided into DFU group (T group, n = 22), diabetes without foot ulcer group (TW group, n = 26). Besides, a healthy control group (H group, n = 10) was recruited at the same time. The swab samples of foot skin in the same position in the three groups were collected. The microorganisms obtained from the skin were analyzed by 16S rRNA gene sequencing. The composition of the skin microorganisms was determined, and the species diversity of the skin microbiota was analyzed by α and β diversity. The species differences in the skin microbiota and the relative abundance of different operational taxonomic units (OUTs) with the most significant abundance were analyzed by linear discriminant analysis effect size (LEfSe). Significant changes were found in the composition of the skin microbiota in the T and TW groups relative to the H group. However, the species diversity of the skin microbiota was significantly reduced in the T and TW groups, with the lowest one in the T group. The composition of microbial diversity in the T group was significantly different from that of the TW and H groups. Among the skin bacterial colonies, the abundance of Staphylococcus, Enhydrobacter, and Corynebacterium_1 was obviously reduced, while that of Escherichia coli and Pseudomonas was significantly increased. Changes in the abundance of Staphylococcus, Enhydrobacter, Corynebacterium_1, Escherichia coli and Pseudomonas in the skin bacterial colonies can be the main causative factors for DFU. This study indicates that altering the microbiota composition of wounds may help the treatment of DFU.

Changes in oral health during aging in a novel non-human primate model.

Oral health plays a significant role in the quality of life and overall well-being of the aging population. However, age-related changes in oral health are not well understood due to challenges with current animal models. In this study, we analyzed the oral health and microbiota of a short-lived non-human primate (i.e., marmoset), as a step towards establishing a surrogate for studying the changes that occur in oral health during human aging. We investigated the oral health of marmosets using cadaveric tissues in three different cohorts: young (aged ≤6 years), middle-aged, and older (>10 years) and assessed the gingival bacterial community using analyses of the V3-V4 variable region of 16S rRNA gene. The oldest cohort had a significantly higher number of dental caries, increased dental attrition/erosion, and deeper periodontal pocket depth scores. Oral microbiome analyses showed that older marmosets had a significantly greater abundance of Escherichia-Shigella and Propionibacterium, and a lower abundance of Agrobacterium/Rhizobium at the genus level. Alpha diversity of the microbiome between the three groups showed no significant differences; however, principal coordinate analysis and non-metric multidimensional scaling analysis revealed that samples from middle-aged and older marmosets were more closely clustered than the youngest cohort. In addition, linear discriminant analysis effect size (LEFSe) identified a higher abundance of Esherichia-Shigella as a potential pathogenic biomarker in older animals. Our findings confirm that changes in the oral microbiome are associated with a decline in oral health in aging marmosets. The current study suggests that the marmoset model recapitulates some of the changes in oral health associated with human aging and may provide opportunities for developing new preventive strategies or interventions which target these disease conditions.

Skin Microbiome Composition is Associated with Radiation Dermatitis in Breast Cancer Patients Undergoing Radiation after Reconstructive Surgery: A Prospective, Longitudinal Study

Approximately 95% of breast cancer (BC) patients receiving radiotherapy (RT) develop varying degrees of radiation dermatitis (RD), which can greatly affect the patient's quality of life and aesthetics. Severe acute RD can lead to interruption or delay of RT. Currently, there is no consensus on the prevention and management of RD. The skin microbiota (SM), which are mainly composed of bacteria and fungi, are essential for skin homeostasis and microbial dysbiosis is correlated with the onset and progression of many common skin diseases. However, to date, research on the role of the SM in RD remains scarce. This prospective, longitudinal study aims to analyze the association of SM with RD. We collected 200 SM samples both before and after RT from the region in the irradiated chest wall of 100 BC patients receiving RT after reconstructive surgery and samples from the corresponding region in the contralateral breast for bacterial 16S and fungal ITS (internal transcribed spacer) rRNA sequencing. Acute RD was graded according to the Toxicity Criteria of the Radiation Therapy Oncology Group (RTOG). Patients were divided into no or mild dermatitis (N/MD, RTOG grade 0 or 1) and severe dermatitis (SD, RTOG grade 2 and above). The compositional differences across groups were compared using STAMP and high-dimensional class comparisons by linear discriminant analysis of effect size (LEfSe). Differences in metabolic function between groups were predicted by the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) method. Significant differences were observed in the diversity and composition of the SM between N/MD and SD both before and after RT. Analysis of bacterial 16S sequencing (n = 89, 63 N/MD, 26 SD) showed significantly higher relative abundance of particular taxa such as Ralstonia, Truepera, Methyloversatilis genera and lower abundance of particular taxa including Staphylococcus, Corynebacterium genera in N/MD patients. Besides, analysis of fungal ITS sequencing (n = 71, 53 N/MD, 18 SD) showed significantly higher relative abundance of particular taxa such as Hypocreaceae family and lower abundance of particular taxa such as Sporidiobolus genus in N/MD patients. Pathways including fatty acid synthesis were predicted to be enriched in N/MD patients. The SM and pathway markers were identified in this study to be associated with the severity of acute RD in BC patients undergoing RT after reconstructive surgery. More patient data is needed to verify the current findings and the results of metagenomic, metatranscriptomic, and metabolomic analyses will further mine key biomarkers at the compositional and functional level.

Functional metagenomic and metabolomics analysis of gut dysbiosis induced by hyperoxia.

Inhaled oxygen is the first-line therapeutic approach for maintaining tissue oxygenation in critically ill patients, but usually exposes patients to damaging hyperoxia. Hyperoxia adversely increases the oxygen tension in the gut lumen which harbors the trillions of microorganisms playing an important role in host metabolism and immunity. Nevertheless, the effects of hyperoxia on gut microbiome and metabolome remain unclear, and metagenomic and metabolomics analysis were performed in this mouse study. C57BL/6 mice were randomly divided into a control (CON) group exposed to room air with fractional inspired oxygen (FiO2) of 21% and a hyperoxia (OXY) group exposed to FiO2 of 80% for 7 days, respectively. Fecal pellets were collected on day 7 and subjected to metagenomic sequencing. Another experiment with the same design was performed to explore the impact of hyperoxia on gut and serum metabolome. Fecal pellets and blood were collected and high-performance liquid chromatography with mass spectrometric analysis was carried out. At the phylum level, hyperoxia increased the ratio of Firmicutes/Bacteroidetes (p = 0.049). At the species level, hyperoxia reduced the abundance of Muribaculaceae bacterium Isolate-037 (p = 0.007), Isolate-114 (p = 0.010), and Isolate-043 (p = 0.011) etc. Linear discriminant analysis effect size (LEfSe) revealed that Muribaculaceae and Muribaculaceae bacterium Isolate-037, both belonging to Bacteroidetes, were the marker microbes of the CON group, while Firmicutes was the marker microbes of the OXY group. Metagenomic analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Carbohydrate-Active enZYmes (CAZy) revealed that hyperoxia provoked disturbances in carbohydrate and lipid metabolism. Fecal metabolomics analysis showed hyperoxia reduced 11-dehydro Thromboxane B2-d4 biosynthesis (p = 1.10 × 10-11). Hyperoxia blunted fecal linoleic acid metabolism (p = 0.008) and alpha-linolenic acid metabolism (p = 0.014). We showed that 1-docosanoyl-glycer-3-phosphate (p = 1.58 × 10-10) was the most significant differential serum metabolite inhibited by hyperoxia. In addition, hyperoxia suppressed serum hypoxia-inducible factor-1 (HIF-1, p = 0.007) and glucagon signaling pathways (p = 0.007). Hyperoxia leads to gut dysbiosis by eliminating beneficial and oxygen strictly intolerant Muribaculaceae with genomic dysfunction of carbohydrate and lipid metabolism. In addition, hyperoxia suppresses unsaturated fatty acid metabolism in the gut and inhibits the HIF-1 and glucagon signaling pathways in the serum.

Metagenomic sequencing reveals altered gut microbial compositions and gene functions in patients with non-segmental vitiligo

BackgroundVitiligo has been correlated with an abnormal gut microbiota. We aimed to systematically identify characteristics of the gut microbial compositions, genetic functions, and potential metabolic features in patients with non-segmental vitiligo.MethodsTwenty-five patients with non-segmental vitiligo and 25 matched healthy controls (HCs) were enrolled. Metagenomic sequencing and bioinformatic analysis were performed to determine the gut microbiota profiles. Differences in gut microbiota diversity and composition between patients with vitiligo and HCs were analyzed. Gene functions and gut metabolic modules were predicted with the Kyoto Encyclopedia of Gene and Genomes (KEGG) and MetaCyc databases.ResultsCompared with HCs, alpha diversity of intestinal microbiome in vitiligo patients was significantly reduced. At the species level, the relative abundance of Staphylococcus thermophiles was decreased, and that of Bacteroides fragilis was increased in patients with vitiligo compared with those of the HCs. Linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed representative microbial markers of Lachnospiraceae_bacterium_BX3, Massilioclostridium_coli, TM7_phylum_sp_oral_taxon_348 and Bacteroides_fragilis for patients with vitiligo. KEGG gene function analysis showed that the NOD-like receptor signaling pathway was significantly enriched in patients with vitiligo. Gut metabolic modules (GMMs) analysis showed that cysteine degradation was significantly down-regulated, and galactose degradation was up-regulated in patients with vitiligo. A panel of 28 microbial features was constructed to distinguish patients with vitiligo from HCs.ConclusionsThe gut microbial profiles and genetic functions of patients with vitiligo were distinct from those of the HCs. The identified gut microbial markers may potentially be used for earlier diagnosis and treatment targets.

Impact of feeding dried distillers' grains with solubles diet on microbiome and metabolome of ruminal and cecal contents in Guanling yellow cattle.

Dried distillers' grains with solubles (DDGS) are rich in nutrients, and partially alternative feeding of DDGS effectively reduces cost of feed and improves animals' growth. We used 16S rDNA gene sequencing and LC/MS-based metabolomics to explore the effect of feeding cattle with a basal diet (BD) and a Jiang-flavor DDGS diet (replaces 25% concentrate of the diet) on microbiome and metabolome of ruminal and cecal contents in Guanling yellow cattle. The results showed that the ruminal and cecal contents shared the same dominance of Bacteroidetes, Firmicutes and Proteobacteria in two groups. The ruminal dominant genera were Prevotella_1, Rikenellaceae_RC9_gut_group, and Ruminococcaceae_UCG-010; and the cecal dominant genera were Ruminococcaceae_UCG-005, Ruminococcaceae_UCG-010, and Rikenellaceae_RC9_gut_group. Linear discriminant analysis effect size analysis (LDA > 2, P < 0.05) revealed the significantly differential bacteria enriched in the DDGS group, including Ruminococcaceae_UCG_012, Prevotellaceae_UCG_004 and Anaerococcus in the ruminal contents, which was associated with degradation of plant polysaccharides. Besides, Anaerosporobacter, Anaerovibrio, and Caproiciproducens in the cecal contents were involved in fatty acid metabolism. Compared with the BD group, 20 significantly different metabolites obtained in the ruminal contents of DDGS group were down-regulated (P < 0.05), and based on them, 4 significantly different metabolic pathways (P < 0.05) were enriched including "Linoleic acid metabolism," "Biosynthesis of unsaturated fatty acids," "Taste transduction," and "Carbohydrate digestion and absorption." There were 65 significantly different metabolites (47 were upregulated, 18 were downregulated) in the cecal contents of DDGS group when compared with the BD group, and 4 significantly different metabolic pathways (P < 0.05) were enriched including "Longevity regulating pathway," "Bile secretion," "Choline metabolism in cancer," and "HIF-1 signaling pathway." Spearman analysis revealed close negative relationships between the top 20 significantly differential metabolites and Anaerococcus in the ruminal contents. Bacteria with high relevance to cecal differential metabolites were Erysipelotrichaceae_UCG-003, Dielma, and Solobacterium that affect specific metabolic pathways in cattle. Collectively, our results suggest that feeding cattle with a DDGS diet improves the microbial structure and the metabolic patterns of lipids and carbohydrates, thus contributing to the utilization efficiency of nutrients and physical health to some extent. Our findings will provide scientific reference for the utilization of DDGS as feed in cattle industry.

The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders.

Gut microbiota have been associated with many psychiatric disorders. However, the changes in the composition of gut microbiota in patients with post-stroke sleep disorders (PSSDs) remain unclear. Here, we determined the gut microbial signature of PSSD patients. Fecal samples of 205 patients with ischemic stroke were collected within 24 h of admission and were further analyzed using 16 s RNA gene sequencing followed by bioinformatic analysis. The diversity, community composition, and differential microbes of gut microbiota were assessed. The outcome of sleep disorders was determined by the Pittsburgh Sleep Quality Index (PSQI) at 3 months after admission. The diagnostic performance of microbial characteristics in predicting PSSDs was assessed by receiver operating characteristic (ROC) curves. Our results showed that the composition and structure of microbiota in patients with PSSDs were different from those without sleep disorders (PSNSDs). Moreover, the linear discriminant analysis effect size (LEfSe) showed significant differences in gut-associated bacteria, such as species of Streptococcus, Granulicatella, Dielma, Blautia, Paeniclostridium, and Sutterella. We further managed to identify the optimal microbiota signature and revealed that the predictive model with eight operational-taxonomic-unit-based biomarkers achieved a high accuracy in PSSD prediction (AUC = 0.768). Blautia and Streptococcus were considered to be the key microbiome signatures for patients with PSSD. These findings indicated that a specific gut microbial signature was an important predictor of PSSDs, which highlighted the potential of microbiota as a promising biomarker for detecting PSSD patients.

Altered gut microbiota and metabolites in untreated myasthenia gravis patients.

The homeostasis of the immune system is influenced by the gut microbiota. Previous studies have reported dysbiosis in the gut microbiota of myasthenia gravis (MG) patients. To investigate potential alterations in gut microbiota and metabolites in newly diagnosed and untreated MG patients, we conducted a case-control study. Fecal samples were collected from 11 newly diagnosed and untreated MG patients as well as 11 age-and sex-matched healthy controls. These samples underwent analysis for gut microbiota using 16S ribosomal RNA (rRNA) gene sequencing, while fecal metabolome was analyzed using liquid chromatography-electrospray tandem mass spectrometry system (LC-ESI-MS/MS). The microbial community richness (observed species) and diversity (Shannon and Simpson indices) were significantly lower in the MG group compared to the control group. Microbiota composition analysis revealed significant differences between the MG and control groups at phylum, family, and genus levels. Linear discriminant analysis effect size (LEfSe) analysis showed a substantial decrease in abundance of the genus Faecalibacterium within the MG group. Fecal metabolome analysis identified three up-regulated metabolites involved in amino acid metabolism (taurine, creatinine, L-carnitine), one up-regulated metabolite involved in lipid metabolism (oleic acid), with correlation analysis indicating a positive association between Faecalibacterium abundance and creatinine levels. Our findings suggest that dysbiosis already exists in newly diagnosed and untreated MG patients, implying that dysbiosis within the gut microbiota may be an initiating factor contributing to MG pathogenesis. Furthermore, F. prausnitzii may hold promise as a probiotic for treating MG.

Alteration of microbial composition in the skin and blood in vasculitis

Vasculitis is a systemic autoimmune disease characterized by leukocyte infiltration into blood vessels. Various microorganisms have been associated with the pathogenesis of vasculitis; however, the causal microbial agents and underlying mechanisms are not fully understood, possibly because of the technical limitations of pathogen detection. In the present study, we characterized the microbiome profile of patients with cutaneous vasculitis using comprehensive metagenome shotgun sequencing. We found that the abundance of the SEN virus was increased in the affected skin and serum of patients with vasculitis compared to healthy donors. In particular, the abundance of SEN virus reads was increased in the sera of patients with cutaneous arteritis. Among the bacteria identified, Corynebacteriales was the most differentially associated with vasculitis. Linear discriminant analysis effect size also indicated differences in the microbial taxa between patients with vasculitis and healthy donors. These findings demonstrate that vasculitis is associated with considerable alteration of the microbiome in the blood and skin and suggest a role for the infectious trigger in vasculitis.

The distribution variation of pathogens and virulence factors in different geographical populations of giant pandas.

Intestinal diseases caused by opportunistic pathogens seriously threaten the health and survival of giant pandas. However, our understanding of gut pathogens in different populations of giant pandas, especially in the wild populations, is still limited. Here, we conducted a study based on 52 giant panda metagenomes to investigate the composition and distribution of gut pathogens and virulence factors (VFs) in five geographic populations (captive: GPCD and GPYA; wild: GPQIN, GPQIO, and GPXXL). The results of the beta-diversity analyzes revealed a close relationship and high similarity in pathogen and VF compositions within the two captive groups. Among all groups, Proteobacteria, Firmicutes, and Bacteroidetes emerged as the top three abundant phyla. By using the linear discriminant analysis effect size method, we identified pathogenic bacteria unique to different populations, such as Klebsiella in GPCD, Salmonella in GPYA, Hafnia in GPQIO, Pedobacter in GPXXL, and Lactococcus in GPQIN. In addition, we identified 12 VFs that play a role in the intestinal diseases of giant pandas, including flagella, CsrA, enterobactin, type IV pili, alginate, AcrAB, capsule, T6SS, urease, type 1 fimbriae, polar flagella, allantoin utilization, and ClpP. These VFs influence pathogen motility, adhesion, iron uptake, acid resistance, and protein regulation, thereby contributing to pathogen infection and pathogenicity. Notably, we also found a difference in virulence of Pseudomonas aeruginosa between GPQIN and non-GPQIN wild populations, in which the relative abundance of VFs (0.42%) of P. aeruginosa was the lowest in GPQIN and the highest in non-GPQIN wild populations (GPXXL: 23.55% and GPQIO: 10.47%). In addition to enhancing our understanding of gut pathogens and VFs in different geographic populations of giant pandas, the results of this study provide a specific theoretical basis and data support for the development of effective conservation measures for giant pandas.

SARS-CoV-2 genetic variation and bacterial communities of naso-oropharyngeal samples in middle-aged and elderly COVID-19 patients in West Java, Indonesia

ObjectiveThe number of COVID-19 cases in Indonesia reflects the disease severity and rapid dissemination. In response to the mounting threat, SARS-CoV-2 genomic surveillance and the investigation of naso-oropharyngeal bacterial communities in West Java were conducted, as dysbiosis of the upper respiratory tract microbiota might adversely affect the clinical condition of patients. MethodsWe utilized the Oxford Nanopore sequencing platform to analyze genetic variation of 43 samples of SARS-CoV-2 and 11 selected samples for 16S rRNA gene sequencing, using samples collected from May to August 2021. ResultsThe prevalence of AY.23 (>82%) predominated among five virus lineages in the populations (AY.23, AY.24, AY.26, AY.42, B.1.1.7). The region in the SARS-CoV-2 genome found to have the highest number of mutations was the spike (S) protein (>20%). There was no association between SARS-CoV-2 lineages, mutation frequency, patient profile, and COVID-19 rapid spread-categorized cases. There was no association of bacterial relative abundance, alpha-beta diversity, and linear discriminant analysis effect size analysis with patient profile and rapid spread cases. MetagenomeSeq analysis showed eight differential abundance species in individual patient profiles, including Pseudomonas aeruginosa and Haemophilus parainfluenzae. ConclusionsThe data demonstrated relevant AY.23 dominance (the Delta variant) in West Java during that period supporting the importance of surveillance program in monitoring disease progression. The inconsistent results of the bacterial communities suggest that a complex multifactor process may contribute to the progression of bacterial-induced disease in each patient.

The effect of different levels of Hermetia illucens oil inclusion on caecal microbiota of Japanese quails (Coturnix japonica, Gould, 1837)

Abstract In this study, we investigated the effect of the dietary inclusion of Hermetia illucens larvae oil on the diversity and structure of the bacterial community of the caecal content of Japanese quails (Coturnix japonica). A total of 40 quails, equally selected for slaughter from 100 animals which were divided evenly into four treatment groups including control group (C) with a diet containing corn oil and 3 experimental groups with partial (25%, 50%) or total (100%) substitution of corn oil by H. illucens larvae oil, here referred to as Black soldier fly larvae oil (BSFO): BSFO 25, BSFO 50 and BSFO 100, respectively. After slaughtering (42 days of age), the microbiota of caecal samples was examined by high-throughput sequencing using the V4-V5 region of the 16S rRNA gene. In all the studied groups the dominant phylum was Firmicutes with prevailing families of Ruminococcaceae and Lachnospiraceae. Caecal microbiota was meaningly influenced on genus level. The linear discriminant analysis effect size (LefSe) analysis for the differential taxa abundance showed that Lactobacillus was significantly increased in BSFO 25 group, Fusicatenibacter was significantly enriched in all the experimental groups fed larvae oil (BSFO 25, 50 and 100) and Subdoligranulum was highly elevated in BSFO 100 group. The analysis revealed statistical dissimilarities between the control group (C) and the groups with 50% and 100% oil replacement (BSFO 50 and 100). The bacterial diversity was significantly suppressed in the samples of quails fed the diet with a total inclusion of H. illucens oil (BSFO 100). The results showed the considerable effect of Black soldier fly larvae oil on the caecal microbiota of Japanese quails.