• Elderly
• Fermented milk
• Intestinal microbiota
• Lacticaseibacillus paracasei Shirota
• Probiotics

• Humans
• Gastrointestinal Microbiome
• Indonesia
• Probiotics/administration & dosage
• Double-Blind Method
• Feces/microbiology
• Feces/chemistry
• Male
• Female
• Aged
• RNA, Ribosomal, 16S/genetics
• Cultured Milk Products/microbiology
• Housing
• Animals
• Middle Aged
• Aged, 80 and over
• Lacticaseibacillus

• Butyric acid
• Dysbiosis
• Gastrointestinal microbiome
• Nephrotic syndrome
• Probiotics

• Humans
• Dysbiosis
• Nephrotic Syndrome/etiology
• Gastrointestinal Microbiome
• Child

• Anti-tumor
• Colorectal cancer
• Gut microbiome
• Natural polysaccharides

• 16S rRNA gene sequencing
• Bacterial culture
• Bovine
• Cattle
• IBK
• Infectious bovine keratoconjunctivitis
• Microbiome
• Moraxella bovis
• Pink eye
• Real-time PCR

• 12897461 USDA AFRI

• bifidobacteria
• gut microbiota
• human milk oligosaccharide
• infant
• organic acid

• 16S rRNA gene amplicon sequencing
• esophageal cancer
• gut microbiota
• neoadjuvant chemotherapy
• real time RT-PCR
• synbiotics

• allergy
• asthma
• atopic dermatitis
• children
• food allergy
• microbiota

• Enterobacteriaceae
• Enterobactin
• Monoclonal antibody
• ic-ELISA

Antimicrobial resistance belongs to the most demanding medical challenges, and antimicrobial photodynamic inactivation (aPDI) is considered a promising alternative to classical antibiotics. However, the pharmacologic characterization of novel compounds suitable for aPDI is a tedious and time-consuming task that usually requires preparation of bacterial cultures and counting of bacterial colonies. In this study, we established and utilized a luminescence-based microbial cell viability assay to analyze the aPDI effects of two porphyrin-based photosensitizers (TMPyP and THPTS) on several bacterial strains with antimicrobial resistance. We demonstrate that after adaptation of the protocol and initial calibration to every specific bacterial strain and photosensitizer, the luminometric method can be used to reliably quantify aPDI effects in most of the analyzed bacterial strains. The interference of photosensitizers with the luminometric readout and the bioluminescence of some bacterial strains were identified as possible confounders. Using this method, we could confirm the susceptibility of several bacterial strains to photodynamic treatment, including extensively drug-resistant pathogens (XDR). In contrast to the conventional culture-based determination of bacterial density, the luminometric assay allowed for a much more time-effective analysis of various treatment conditions. We recommend this luminometric method for high-throughput tasks requiring measurements of bacterial viability in the context of photodynamic treatment approaches.

• 16S rRNA gene sequencing
• Dysbiosis
• Food allergy
• Gut microbiota
• Regulatory T cells

• Bacteria
• Child
• Child, Preschool
• Dysbiosis
• Female
• Gastrointestinal Microbiome
• Gastrointestinal Tract
• Humans
• Hypersensitivity/epidemiology
• Pregnancy

• Akkermansia muciniphila
• biomarker
• health benefit
• host physiology
• probiotic

• Humans
• Gastrointestinal Microbiome
• Verrucomicrobia
• Microbiota
• Akkermansia

Diabetes mellitus is considered to be a global epidemic. The combination of genetic susceptibility and an unhealthy lifestyle is considered to be the main trigger of this metabolic disorder. Recently, there has been increased interest in the roles of gut microbiota as a new potential contributor to this epidemic. Research, in recent years, has contributed to an in-depth characterization of the human microbiome and its associations with various diseases, including metabolic diseases and diabetes mellitus. It is known that diet can change the composition of gut microbiota, but it is unclear how this, in turn, may influence metabolism. The main objective of this review is to evaluate the pathogenetic association between microbiota and diabetes and to explore any new therapeutic agents, including nutraceuticals that may modulate the microbiota. We also look at several mechanisms involved in this process. There is a clear, bidirectional relationship between microbiota and diabetes. Current treatments for diabetes influence microbiota in various ways, some beneficial, but others with still unclear effects. Microbiota-aimed treatments have seen no real-world significant effects on the progression of diabetes and its complications, with more studies needed in order to find a really beneficial agent.

• 16S rRNA gene sequencing
• Bifidobacteriales
• bilirubin
• microbiota
• neonatal jaundice

The etiology of periodontitis remains unclear, as is the place of gingivitis in its pathophysiology. A few studies linked the colonization by oral parasites (Entamoeba gingivalis and Trichomonas tenax) to periodontal disease and its severity. The aim of the current study was to estimate the prevalence of these oral parasites among healthy individuals, and in patients with gingivitis and periodontitis in Jordan.

The study was conducted during July 2019–December 2019. Samples were composed of saliva and periodontal material including dental plaque sampled with probes. The detection of oral parasites was done using conventional polymerase chain reaction (PCR).

The total number of study participants was 237: healthy (n=94), gingivitis (n=53) and periodontitis (n=90). The prevalence of E. gingivalis was 88.9% among the periodontitis patients, 84.9% among the gingivitis patients and 47.9% in the healthy group. For T. tenax, the prevalence was 25.6% among the periodontitis patients, 5.7% among the gingivitis patients and 3.2% in the heathy group. Positivity for E. gingivalis was significantly correlated with the presence of periodontal disease compared to the healthy group with odds ratio (OR) of 6.6. Periodontal disease was also correlated with lower monthly income (OR=8.2), lack of dental care (OR=4.8), and history of diabetes mellitus (OR=4.5). Colonization by E. gingivalis was correlated with gingivitis (OR=6.1) compared to the healthy group. Colonization by E. gingivalis and T. tenax were significantly correlated with periodontitis (OR=6.4 for E. gingivalis, and OR=4.7, for T. tenax) compared to the healthy group. T. tenax was only detected among individuals with generalized periodontal disease compared to its total absence among those with localized disease (19.6% vs. 0.0%; p=0.039). The co-infection rate by the two oral parasites was 11.0%.

The higher prevalence of human oral parasites in periodontal disease compared to healthy individuals appears to be more than a mere marker for the disease and might also be associated with disease severity and potential for progression. Thus, the dogmatic view of E. gingivalis and T. tenax as commensals needs to be re-evaluated and their contribution to pathophysiology of periodontal diseases cannot be neglected.

• Entamoeba
• Gingivitis/epidemiology
• Humans
• Periodontitis/epidemiology
• Polymerase Chain Reaction
• Porphyromonas gingivalis/genetics
• Trichomonas/genetics
• Trichomonas Infections/epidemiology

• Deanship of Academic Research, University of Jordan

• Bacterial translocation
• Esophageal cancer
• Neoadjuvant chemotherapy
• Synbiotics

• 16S rRNA
• Bacillus anthracis
• RT-PCR
• anthrax
• detection
• identification
• real-time PCR

• Bacillus anthracis/genetics
• DNA, Bacterial/genetics
• DNA, Ribosomal/genetics
• Polymorphism, Genetic
• RNA, Bacterial/genetics
• RNA, Ribosomal, 16S/genetics
• Real-Time Polymerase Chain Reaction

• Medical Biological Defense Research Program of the Bundeswehr Joint Medical Service Bundeswehr Joint Medical Service

• Lupinus albus
• butyryl-CoA transferase gene
• dysbiosis
• goblet cells
• intestinal microbiota
• short chain fatty acids
• ulcerative colitis

• Animals
• Gastrointestinal Microbiome/drug effects
• Mice
• Mice, Inbred C57BL
• Butyrates/metabolism
• Lupinus/chemistry
• Oligosaccharides/pharmacology
• Colitis, Ulcerative/drug therapy
• Colitis, Ulcerative/microbiology
• Colitis, Ulcerative/metabolism
• Colitis, Ulcerative/chemically induced
• Colitis, Ulcerative/pathology
• Prebiotics
• Male
• Fatty Acids, Volatile/metabolism
• Dysbiosis/microbiology
• Dysbiosis/drug therapy
• Dysbiosis/metabolism
• Galactose/pharmacology
• Galactose/metabolism
• Disease Models, Animal

We investigated the impact of human immunodeficiency virus (HIV) infection and anti-retroviral therapy (ART) on the gut microbiota of children.

This cross-sectional study investigated the gut microbiota of children with and without HIV.

We collected fecal samples from 59 children with HIV (29 treated with ART [ART(+)] and 30 without ART [HIV(+)]) and 20 children without HIV [HIV(–)] in Vietnam. We performed quantitative RT-PCR to detect 14 representative intestinal bacteria targeting 16S/23S rRNA molecules. We also collected the blood samples for immunological analyses.

In spearman’s correlation analyses, no significant correlation between the number of dominant bacteria and age was found among children in the HIV(−) group. However, the number of sub-dominant bacteria, including Streptococcus, Enterococcus, and Enterobacteriaceae, positively correlated with age in the HIV(−) group, but not in the HIV(+) group. In the HIV(+) group, Clostridium coccoides group positively associated with the CD4⁺ cell count and its subsets. In the ART(+) group, Staphylococcus and C. perfringens positively correlated with CD4⁺ cells and their subsets and negatively with activated CD8⁺ cells. C. coccoides group and Bacteroides fragilis group were associated with regulatory T-cell counts. In multiple linear regression analyses, ART duration was independently associated with the number of C. perfringens, and Th17 cell count with the number of Staphylococcus in the ART(+) group.

HIV infection and ART may influence sub-dominant gut bacteria, directly or indirectly, in association with immune status in children with HIV.

• Age Factors
• Antiretroviral Therapy, Highly Active
• Bacteria/genetics
• Child
• Child, Preschool
• Female
• Gastrointestinal Microbiome
• HIV Infections/drug therapy
• HIV Infections/immunology
• HIV Infections/microbiology
• Humans
• Linear Models
• Male
• Principal Component Analysis
• Staphylococcus/drug effects
• Trimethoprim, Sulfamethoxazole Drug Combination/pharmacology

• Ministry of Education, Culture, Sports, Science and Technology (JP)
• Kanazawa University (JP)

• Anaerobic preparation
• Fecal microbiota transplantation
• Obligate anaerobes

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.

Characterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties.

• Biomass
• Cross-Sectional Studies
• Datasets as Topic
• Ecosystem
• Gastrointestinal Microbiome
• Humans

• agency for science, technology and research (a*star)
• Biomedical Research Council

• Breastfeeding
• Complementary feeding
• Diet
• Gut microbiota

We previously reported lower counts of lactobacilli and Bifidobacterium in the gut microbiota of patients with major depressive disorder (MDD), compared with healthy controls. This prompted us to investigate the possible efficacy of a probiotic strain, Lacticaseibacillus paracasei strain Shirota (LcS; basonym, Lactobacillus casei strain Shirota; daily intake of 8.0 × 10¹⁰ colony-forming units), in alleviating depressive symptoms. A single-arm trial was conducted on 18 eligible patients with MDD or bipolar disorder (BD) (14 females and 4 males; 15 MDD and 3 BD), assessing changes in psychiatric symptoms, the gut microbiota, and biological markers for intestinal permeability and inflammation, over a 12-week intervention period. Depression severity, evaluated by the Hamilton Depression Rating Scale, was significantly alleviated after LcS treatment. The intervention-associated reduction of depressive symptoms was associated with the gut microbiota, and more pronounced when Bifidobacterium and the Atopobium clusters of the Actinobacteria phylum were maintained at higher counts. No significant changes were observed in the intestinal permeability or inflammation markers. Although it was difficult to estimate the extent of the effect of LcS treatment alone, the results indicated that it was beneficial to alleviate depressive symptoms, partly through its association with abundance of Actinobacteria in the gut microbiota.

• Actinobacteria
• bipolar disorder
• gut microbiota
• major depressive disorder
• probiotic

• RT-PCR
• directed evolution
• in vitro
• ribosome construction
• synthetic biology

• Army Research Office Grants
• the David and Lucile Packard Foundation and the Camille Dreyfus Teacher-Scholar Program

• Colorectal cancer
• Diagnostic biomarkers
• Epigenetic modifications
• Gut microbiota
• Non-coding RNAs

Delirium after cardiac surgery affects mortality, but the mechanism remains unclear. Previous studies have reported gut microbiota are associated with brain activity. Systemic inflammation and antibiotics can damage the gut microbiota after cardiac surgery. We aimed to investigate changes in the gut microbiota and the association between the gut microbiota and delirium after cardiac surgery.

Twenty-one patients who underwent cardiac surgery were enrolled. Microbiota counts and fecal organic acid concentrations were measured in fecal samples harvested before surgery, just after surgery, and before discharge. To quantify the microbiota, we extracted total RNA fractions and examined gut microbiota composition using 16S and 23S rRNA-targeted quantitative-reverse Transcription-PCR. Postoperative delirium, insomnia, and pseudopsia were assessed for 1 week. Postoperative total bacterial counts changed significantly from 10.2 ± 0.2 log₁₀ cells/g of feces to 9.8 ± 0.5 in the first postoperative samples (p = 0.003) and 10.0 ± 0.4 in the samples before discharge (p = 0.039). Fecal pH was 6.9 ± 0.6 before surgery and 7.4 ± 0.7 in the first postoperative samples (p = 0.001). Postoperative Staphylococcus and Pseudomonas counts were significantly higher in patients with postoperative pseudopsia than in patients without pseudopsia (3.2 ± 1.3 vs. 5.4 ± 0.9; p = 0.012 and 1.7 ± 0.8 vs. 4.6 ± 2.7; p = 0.001).

Total bacterial counts were significantly lower after surgery and until discharge. Fecal pH was significantly higher than preoperative levels. Staphylococcus and Pseudomonas counts were significantly higher in patients with postoperative pseudopsia.

• Cardiac surgery
• Delirium
• Gut–brain axis
• Microbiota

• Bacteremia
• Bacterial translocation
• Gastrointestinal surgery
• Mesenteric lymph node

• L. casei Shirota
• Probiotics
• gut microbiota
• health

• antioxidants
• cohort
• fruits
• legumes
• meta-analysis
• mortality
• nuts
• prospective studies
• vegetables
• whole grains

• Antioxidants/metabolism
• Antioxidants/therapeutic use
• Biomarkers
• Cardiovascular Diseases/mortality
• Cardiovascular Diseases/prevention & control
• Cause of Death
• Diet, Vegetarian
• Energy Intake
• Feeding Behavior
• Fruit
• Humans
• Neoplasms/mortality
• Neoplasms/prevention & control
• Nutritional Status
• Nuts
• Plants/chemistry
• Vegetables
• Whole Grains

• aging
• constipation
• defecation
• exercise intensity
• gut microbiota
• intestinal motility
• probiotics
• step count