Complex gene-environment interactions are considered important in the development of obesity. The composition of the gut microbiota can determine the efficacy of energy harvest from food and changes in dietary composition have been associated with changes in the composition of gut microbial populations. The capacity to explore microbiota composition was markedly improved by the development of metagenomic approaches, which have already allowed production of the first human gut microbial gene catalogue and stratifying individuals by their gut genomic profile into different enterotypes, but the analyses were carried out mainly in non-intervention settings. To investigate the temporal relationships between food intake, gut microbiota and metabolic and inflammatory phenotypes, we conducted diet-induced weight-loss and weight-stabilization interventions in a study sample of 38 obese and 11 overweight individuals. Here we report that individuals with reduced microbial gene richness (40%) present more pronounced dys-metabolism and low-grade inflammation, as observed concomitantly in the accompanying paper. Dietary intervention improves low gene richness and clinical phenotypes, but seems to be less efficient for inflammation variables in individuals with lower gene richness. Low gene richness may therefore have predictive potential for the efficacy of intervention.

In this paper, two inferential procedures for selecting the significant predictors in the PLS1 regression model are introduced. The significant PLS components are first obtained and the two predictor selection methods, called PLS-Forward and PLS-Bootstrap, are applied to the PLS model obtained. They are also compared empirically to two other methods that exist in the literature with respect to the quality of fit of the model and to their predictive ability. Although none of the four methods is uniformly best, it is seen that PLS-Forward and PLS-Bootstrap perform well and can be very useful in practical situations in identifying the important explanatory variables.

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Psychrotrophic bacteria in raw milk are most well known for their spoilage potential and cause significant economic losses in the dairy industry. Despite their ability to produce several exoenzyme types at low temperatures, psychrotrophs that dominate the microflora at the time of spoilage are generally considered benign bacteria. It was recently reported that raw milk-spoiling Gram-negative-psychrotrophs frequently carried antibiotic resistance (AR) features. The present study evaluated AR to four antibiotics (ABs) (gentamicin, ceftazidime, levofloxacin, and trimethoprim-sulfamethoxazole) in mesophilic and psychrotrophic bacterial populations recovered from 18 raw milk samples, after four days storage at C or C. Robust analysis of variance and non parametric statistics (e.g., REGW and NPS) revealed that AR prevalence among psychrotrophs, for milk samples stored at C, often equalled the initial levels and equalled or increased during the cold storage at C, depending on the AB. The study performed at C with an intermediate sampling point at day 2 suggested that (1) different psychrotrophic communities with varying AR levels dominate over time and (2) that AR (determined from relative amounts) was most prevalent, transiently, after 2-day storage in psychrotrophic or mesophilic populations, most importantly at a stage where total counts were below or around  CFU/mL, at levels at which the milk is acceptable for industrial dairy industrial processes.