Specific changes in the mammalian gut microbiome as a biomarker for oxytocin-induced behavioral changes

Humans are colonized by bacteria, fungi, archaea, and viruses, which are collectively referred to as the microbiome. Most of the microbiome resides in the gut and may easily be investigated via stool sampling and subsequent metagenomic DNA sequencing. Prolonged exposure to psychiatric pharmacological agents is often associated with marked gastrointestinal phenomena, including changes in food intake, bowel motility, gastric emptying, and transit time [1–3]. Unlike the relatively objective measurement of the microbiota composition, accurate assessment of patients’ therapy adherence and treatment outcomes represent a challenge in psychiatric medical care [4]. This is partly because, for most psychopharmacological agents, compliance and response to treatments are subjectively assessed based on self-reporting and physicians’ evaluations [5,6]. An interesting alternative is having changes in the psychiatric patients’ gut microbiota composition serve as a measurable proxy for monitoring patients’ compliance and the therapeutic effects of some drugs. It is yet unclear how behavioral changes and drug intake affect the microbiota; however, mounting evidence suggests that physical and mental disturbances may lead to changes in gastrointestinal (GI) motility [7,8] in both animals and humans [9–11]. Indeed, in humans, anger, fear, pain, and anxiety, as well as intensive exercise, results in changes in GI activity [8]. In rats, chronic stress results in initial delayed gastric emptying followed by acceleration later on [12]. Medication intake [13,14] and changes in stool consistency, gastric transit, and emptying time [15,16] also have a great impact on microbial composition

Antibiotic Cocktail for Pediatric Acute Severe Colitis and the Microbiome : The PRASCO Randomized Controlled Trial

Background: Alterations in the microbiome have been postulated to drive inflammation in IBD. In this pilot randomized controlled trial, we evaluated the effectiveness of quadruple antibiotic cocktail in addition to intravenous-corticosteroids (IVCSs) in acute severe colitis (ASC). Methods: Hospitalized children with ASC (pediatric ulcerative colitis activity index [PUCAI] >= 65) were randomized into 2 arms: the first received antibiotics in addition to IVCS (amoxicillin, vancomycin, metronidazole, doxycycline/ciprofloxacin [IVCS+AB]), whereas the other received only IVCS for 14 days. The primary outcome was disease activity (PUCAI) at day 5. Microbiome was analyzed using 16S rRNA gene and metagenome. Results: Twenty-eight children were included: 16 in the AB + IVCS arm and 12 in the IVCS arm (mean age 13.9 +/- 4.1 years and 23 [82%] with extensive colitis). The mean day-5 PUCAI was 25 +/- 16.7 vs 40.4 +/- 20.4, respectively (P = 0.037). Only 3 and 2 children, respectively, required colectomy during 1-year follow-up (P = 0.89). Microbiome data at time of admission were analyzed for 25 children, of whom 17 (68%) had a predominant bacterial species (>33% abundance); response was not associated with the specific species, whereas decreased microbiome diversity at admission was associated with day-5 response in the IVCS arm. Conclusion: Patients with ASC have alterations in the microbiome characterized by loss of diversity and presence of predominant bacterial species. Quadruple therapy in addition to IVCS improved disease activity on day 5, but larger studies are needed to determine whether this is associated with improved long-term outcomes.Peer reviewe

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Effect of Solar Radiation on Skin Microbiome: Study of Two Populations

Here, we examined the skin microbiome of two groups of healthy volunteers living on the Mediterranean coast with different exposures to sun radiation. One group, exposed to the sun in the summer, was compared with a group covered with clothing throughout the year. The seasonal effects on the skin microbiome of three body sites were determined before and after summer. Surprisingly, at the phyla level, there were no significant differences in microbiome diversity between the groups. Furthermore, within each group, there were no significant seasonal differences in high-abundance species at any of the sampling sites. These results suggest that the skin microbiome, developed over years, remains stable even after several months of exposure to summer weather, direct sunlight and humidity. However, in the group exposed to the sun during the summer months, there were significant differences in low-abundance species in sun-exposed areas of the skin (the inner and outer arm). These subtle changes in low-abundance species are interesting, and their effect on skin physiology should be studied further

A Novel Nutritional Predictor Links Microbial Fastidiousness with Lowered Ubiquity, Growth Rate, and Cooperativeness

<div><p>Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited; (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks; and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect the adaptation of fastidious microorganisms to unique niches with few cohabitating species. They also explain how non-fastidious species inhabit many ecological niches with high abundance rates. Taken together, these results advance our understanding microbial nutrition on a large scale, by presenting new nutrition-related associations that govern the distribution of microorganisms in nature.</p></div

Relationships of the gut microbiome with cognitive development among healthy school-age children

BackgroundThe gut microbiome might play a role in neurodevelopment, however, evidence remains elusive. We aimed to examine the relationship between the intestinal microbiome and cognitive development of school-age children.MethodsThis cross-sectional study included healthy Israeli Arab children from different socioeconomic status (SES). The microbiome was characterized in fecal samples by implementing 16S rRNA gene sequencing. Cognitive function was measured using Stanford-Binet test, yielding full-scale Intelligence Quotient (FSIQ) score. Sociodemographics and anthropometric and hemoglobin measurements were obtained. Multivariate models were implemented to assess adjusted associations between the gut microbiome and FSIQ score, while controlling for age, sex, SES, physical growth, and hemoglobin levels.ResultsOverall, 165 children (41.2% females) aged 6–9 years were enrolled. SES score was strongly related to both FSIQ score and the gut microbiome. Measures of α-diversity were significantly associated with FSIQ score, demonstrating a more diverse, even, and rich microbiome with increased FSIQ score. Significant differences in fecal bacterial composition were found; FSIQ score explained the highest variance in bacterial β-diversity, followed by SES score. Several taxonomic differences were significantly associated with FSIQ score, including Prevotella, Dialister, Sutterella, Ruminococcus callidus, and Bacteroides uniformis.ConclusionsWe demonstrated significant independent associations between the gut microbiome and cognitive development in school-age children

Nutritional fastidiousness scales negatively with breadth of ecological environments.

<p>This trend is shown (A) between species of <i>Mycoplasma</i> and <i>Pseudomonas</i>, (B) between five lifestyle categorizations with ascending environmental breadth, and (C) in environmental distributions in Greengenes. (D) shows a histogram of the number of organisms with different sized minenvs. Species on the extreme ends are listed.</p

Nutritional fastidiousness scales with various ecological and lifestyle factors.

<p>The plots compare minenv size to (A) the number of metabolic reactions, (B) the number of metabolic genes, (C) the size of the genome, (D) cooperation score, (E) competition score, and (F) growth rate for all organisms we were able to map. Cooperation and competition scores were computed as averages per organism of all organism-organism scores from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003726#pcbi.1003726-Freilich1" target="_blank">[7]</a>, and empirical growth rates were taken from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003726#pcbi.1003726-VieiraSilva1" target="_blank">[38]</a>. A higher cooperation(competition) score denotes more cooperation(competition). Lines in each plot denote robust Lowess fits. (G) shows the Spearman rhos for the cooperation score against various other measures; ** denotes significance with p<1e-4. Notably, cooperation score correlates better with lifestyle class than the other metrics tested. (H) shows organism max abundance of each organism (y-axis) versus the count of environments in which the organism was present (i.e., ubiquitiousness; x-axis) in the Human Microbiome Project (HMP) dataset (from the OTU abundance table in the 2010 HMP data freeze). Dots represent individual OTUs. (I) depicts average abundances of all organisms with a given ubiquitiousness score in a given environment (dots), with a different color and a trendline for each environment. All lines in (H) and (I) are exponential fits.</p

Metabolite prevalence in u-minenvs across organisms.

<p>(A) Frequencies of appearance are compared for all metabolites shared between the DSMZ known lab media and minenvs. Each metabolite is classed into one of 4 categories, as noted, and a trendline is shown. (B) The most common metabolites in minenvs and DSMZ media are listed, along with their compound class and their frequency in both spaces. Metabolites are sorted based on the sum of DSMZ media usage and minenv usage.</p

Nutritional requirements are more similar among ecologically co-distributed organisms.

<p>(A) Similarity of ecological distributions (in Greengenes) and also similarity of MINENVs were calculated for each of ∼3 million organism-organism pairs, using jaccard metrics (see methods). Org-org pairs were then binned by ecological distance (x-axis), and the ratio of pairs in each bin with a MINENV similarity above some percentage threshold (i.e., 100*jaccard similarity) was determined (y-axis). A range of MINENV similarity thresholds were explored; three are shown on the plot, along with the correlation coefficients between ecological and MINENV similarities. Each dot on the plot represents all org-org pairs falling into a given ecological distance bin. (B) microorganisms from within each Greengenes environment are split into two groups 100 times, and distances between aggregate minenvs for each group are compared (each green dot is an environment; see methods). This was repeated each time for the same number of organisms but not from within the given environment (orange dots). Errorbars denote standard deviations over multiple trials and dots denote means. (C) FBA is performed to determine what percentage of organisms from within an environment (green dots) or outside an environment (orange dots) are able to grow <i>in silico</i> on aggregated MINENVs built from half of the organisms within an environment (organisms used to build environments are never used for the test). (D) The test of growth from (C) is repeated, but aggregate environments are composed from unions of DSMZ media of 50% of organisms from within an environment, and ‘growth’ is assessed by whether an organism's DSMZ medium is contained fully within the environmental aggregate.</p