Empirical maximum lifespan of earthworms is twice that of mice

We considered a Gompertzian model for the population dynamics of Eisenia andrei case-cohorts in artificial OECD soil under strictly controlled conditions. The earthworm culture was kept between 18 and 22°C at a constant pH of 5.0. In all, 77 lumbricids were carefully followed for almost 9 years, until the oldest died. The Eisenia median longevity is 4.25 years and the oldest specimen was 8.73 years. Eisenia cocoons were hand-sorted every 3 weeks, washed in distilled water, placed in Petri dishes, and counted. Regular removal did not reduce breeding. Each fertile cocoon contained on average two or three embryos. The failure rates (mortality and infertility percentages) are smooth power functions where the rate at time (n + 1) captured most of the phenomenology of the previous rate at time n, as expected by the considered law, but not at both the beginning and the end of this long-term laboratory study

Interplay between distribution of live cells and growth dynamics of solid tumours

Experiments show that simple diffusion of nutrients and waste molecules is not sufficient to explain the typical multilayered structure of solid tumours, where an outer rim of proliferating cells surrounds a layer of quiescent but viable cells and a central necrotic region. These experiments challenge models of tumour growth based exclusively on diffusion. Here we propose a model of tumour growth that incorporates the volume dynamics and the distribution of cells within the viable cell rim. The model is suggested by in silico experiments and is validated using in vitro data. The results correlate with in vivo data as well, and the model can be used to support experimental and clinical oncology

Differential Adaptation of Human Gut Microbiota to Bariatric Surgery–Induced Weight Loss: Links With Metabolic and Low-Grade Inflammation Markers

International audienceOBJECTIVE Obesity alters gut microbiota ecology and associates with low-grade inflammation in humans. Roux-en-Y gastric bypass (RYGB) surgery is one of the most efficient procedures for the treatment of morbid obesity resulting in drastic weight loss and improvement of metabolic and inflammatory status. We analyzed the impact of RYGB on the modifications of gut microbiota and examined links with adaptations associated with this procedure. RESEARCH DESIGN AND METHODS Gut microbiota was profiled from fecal samples by real-time quantitative PCR in 13 lean control subjects and in 30 obese individuals (with seven type 2 diabetics) explored before (M0), 3 months (M3), and 6 months (M6) after RYGB. RESULTS Four major findings are highlighted: 1) Bacteroides/Prevotella group was lower in obese subjects than in control subjects at MO and increased at M3. It was negatively correlated with corpulence, but the correlation depended highly on caloric intake; 2) Escherichia coli species increased at M3 and inversely correlated with fat mass and leptin levels independently of changes in food intake; 3) lactic acid bacteria including Lacto-bacillus/Leuconostoc/Pediococcus group and Bifidobacterium genus decreased at M3; and 4) Faecalibacterium prausnitzii species was lower in subjects with diabetes and associated negatively with inflammatory markers at MO and throughout the follow-up after surgery independently of changes in food intake. CONCLUSIONS These results suggest that components of the dominant gut microbiota rapidly adapt in a starvation-like situation induced by RYGB while the F. prausnitzii species is directly linked to the reduction in low-grade inflammation state in obesity and diabetes independently of calorie intake. Diabetes 59:3049-3057, 201

Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models

Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients. We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simple mathematical models.We developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient's training set and his validation set. The training set, used for model personalization, contained the patient's initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set. The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients (the coefficient of determination between the predicted and observed PSA values was R(2) = 0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Using a few initial measurements, we constructed robust patient-specific models of PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols

Effect of macromolecular crowding on the rate of diffusion-limited enzymatic reaction

The cytoplasm of a living cell is crowded with several macromolecules of different shapes and sizes. Molecular diffusion in such a medium becomes anomalous due to the presence of macromolecules and diffusivity is expected to decrease with increase in macromolecular crowding. Moreover, many cellular processes are dependent on molecular diffusion in the cell cytosol. The enzymatic reaction rate has been shown to be affected by the presence of such macromolecules. A simple numerical model is proposed here based on percolation and diffusion in disordered systems to study the effect of macromolecular crowding on the enzymatic reaction rates. The model explains qualitatively some of the experimental observations.Comment: 6 pages, 4 figure

Antibiotic resistance determinants in the interplay between food and gut microbiota

A complex and heterogeneous microflora performs sugar and lactic acid fermentations in food products. Depending on the fermentable food matrix (dairy, meat, vegetable etc.) as well as on the species composition of the microbiota, specific combinations of molecules are produced that confer unique flavor, texture, and taste to each product. Bacterial populations within such “fermented food microbiota” are often of environmental origin, they persist alive in foods ready for consumption, eventually reaching the gastro-intestinal tract where they can interact with the resident gut microbiota of the host. Although this interaction is mostly of transient nature, it can greatly contribute to human health, as several species within the food microbiota also display probiotic properties. Such an interplay between food and gut microbiota underlines the importance of the microbiological quality of fermented foods, as the crowded environment of the gut is also an ideal site for genetic exchanges among bacteria. Selection and spreading of antibiotic resistance genes in foodborne bacteria has gained increasing interest in the past decade, especially in light of the potential transferability of antibiotic resistance determinants to opportunistic pathogens, natural inhabitants of the human gut but capable of acquiring virulence in immunocompromised individuals. This review aims at describing major findings and future prospects in the field, especially after the use of antibiotics as growth promoters was totally banned in Europe, with special emphasis on the application of genomic technologies to improve quality and safety of fermented foods

Translational research into gut microbiota: new horizons on obesity treatment: updated 2014

Obesity is currently a pandemic of worldwide proportions affecting millions of people. Recent studies have proposed the hypothesis that mechanisms not directly related to the human genome could be involved in the genesis of obesity, due to the fact that, when a population undergoes the same nutritional stress, not all individuals present weight gain related to the diet or become hyperglycemic. The human intestine is colonized by millions of bacteria which form the intestinal flora, known as gut flora. Studies show that lean and overweight human may present a difference in the composition of their intestinal flora; these studies suggest that the intestinal flora could be involved in the development of obesity. Several mechanisms explain the correlation between intestinal flora and obesity. The intestinal flora would increase the energetic extraction of non-digestible polysaccharides. In addition, the lipopolysaccharide from intestinal flora bacteria could trigger a chronic sub-clinical inflammatory process, leading to obesity and diabetes. Another mechanism through which the intestinal flora could lead to obesity would be through the regulation of genes of the host involved in energy storage and expenditure. In the past five years data coming from different sources established causal effects between intestinal microbiota and obesity/insulin resistance, and it is clear that this area will open new avenues of therapeutic to obesity, insulin resistance and DM2

Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/human​n. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.National Institutes of Health (U.S.) (U54HG004968