Brandi Cantarel: Crohn's disease insights. Swedish identical twins - expect similar microbiota from genetic identity and common environment. CSI metaphor. 454-16S and whole-genome sequancing and meta-proteomics.
Previous data (16S) showed different clustering of bacteria with Crohn's at different colon sites, away form non-Crohn's. 1 healthy pair, 1 both-Crohn's pair, and two discordant pairs (one twin healthy, other with Crohn's). Now looking at whole-genome sequence data for same twins.
Look at predicted proteins and at proteome... Lists of categories that were and were not informative... More genes expressed in healthy than in disease communities. Two proteins differ significantly: thiosulfate somethingtransferase and something else (fits previous info about sulfur metabolism in Crohn's). Crohn's has Bacteroides proteins, healthy have Faecalis??? From genome sequences, not much correlation with enzymes for use of different energy sources, but see some correlation (genes present but not expressed) in proteome.
No correlation with twin phenotype = no effect of genes. This must be more surprising than she sounded.
~30% of proteins were from host. But we don't learn anything (at least not yet).
Sarkis Mazmanian: Unlocking the evolutionary mysteries of microbiome-immune symbiosis. Experimentomics!
Hypothesis: Probmen in irritable bowel diesases (including Crohn's) is lack of immune tolerance to 'normal' flora, causes 'dysbiosis' = imbalance.
Lab studies: Bacteroides fragilis (Gram-neg, obligate anaerobe, makes > 8 capsule types, two have zwitterionic - pos-charge unusual. Treat model organism with 'polysaccharide A' (capsular polysaccharide of one type), reduce inflammatory response, reduce symptoms. See PSA (but not PSG = another capsular type) is packaged into vesicles. These vesicles protect against colon disease.
Role of type 6 secretion system: Bacteria can secrete immunomodulators. If this is disrupted, get overactivation of host T-cell-induced inflammation.
Elhanen Borenstein: Gut microbiome metabolic dependencies and determinants.
Two studies: 1. Systems biology. 2. Metabolic dependencies. Using network theory: oversimplified but very messy. But easy to create on a large scale, and lots of tools for analyzing topologies.
A. Reverse ecology: Predict ecology from genomics. Identify the set of exogenously acquired compounds (network seeds). B..Topological markers of environmental adaptation.
Nice ecology-based analysis of evidence for competitive and cooperative interactions in the oral and gut microbiomes. See coocurrence correlating with competition, not cooperation. Suggests role of niche-selection, more than species interaction.
Microbiome-wide metabolic modeling produces a network 'hairball'!
Markus Hilty: The nasopharyngeal microbiome in infants with acute otitis media (AOM) in the era of PCV7 vaccine. From cultuyre-based studies: S. pneumoniae, H. influenzae, Moraxella catarrhalis. All also commensal.
Microbiome study: healthy vs AOM, antibiotic use, pneumococcal vaccine. Infants < 2 yr old, nasopharyngal swabs, four winter seasons at a pediatrics hospital in Bern. Half ind aycare, most vaccinated. 153 with AOM, 10 healthy. Used 16S PCR, plus culture of stereptococci to distinguish species v. similar by 16S sequences.
AOM microbiome has very low species richness/diversity compared to healthy microbiome. Total population, mostly Moraxella and Streptococcus, H. influenzae 16%. Pasteurellaceae absent from healthy infants. Antibiotic exposure effects: increases Pasteurellaceae! Effect of PCV7 vaccination: not much. Generally lower species richness when S. pneumoniae is present. No effects of sex, age, or daycare attendance.
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