Effects of probiotics and antibiotics on the intestinal homeostasis in a computer controlled model of the large intestine

Background: Antibiotic associated diarrhea and Clostridium difficile infection are frequent complications of broad spectrum antibiotic therapy. Probiotic bacteria are used as therapeutic and preventive agents in these disorders, but the exact functional mechanisms and the mode of action are poorly understood. The effects of clindamycin and the probiotic mixture VSL#3 (containing the 8 bacterial strains Streptococcus thermophilus, Bifidobacterium breve, Bifidobacterium longum, Bifidobacterium infantis, Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus paracasei and Lactobacillus delbrueckii subsp. Bulgaricus) consecutively or in combination were investigated and compared to controls without therapy using a standardized human fecal microbiota in a computer-controlled in vitro model of large intestine. Microbial metabolites (short chain fatty acids, lactate, branched chain fatty acids, and ammonia) and the intestinal microbiota were analyzed. Results: Compared to controls and combination therapy, short chain fatty acids and lactate, but also ammonia and branched chain fatty acids, were increased under probiotic therapy. The metabolic pattern under combined therapy with antibiotics and probiotics had the most beneficial and consistent effect on intestinal metabolic profiles. The intestinal microbiota showed a decrease in several indigenous bacterial groups under antibiotic therapy, there was no significant recovery of these groups when the antibiotic therapy was followed by administration of probiotics. Simultaneous application of anti- and probiotics had a stabilizing effect on the intestinal microbiota with increased bifidobacteria and lactobacilli. Conclusions: Administration of VSL#3 parallel with the clindamycin therapy had a beneficial and stabilizing effect on the intestinal metabolic homeostasis by decreasing toxic metabolites and protecting the endogenic microbiota from destruction. Probiotics could be a reasonable strategy in prevention of antibiotic associated disturbances of the intestinal homeostasis and disorders. © 2012 Rehman et al; licensee BioMed Central Lt

Analysis and Computational Dissection of Molecular Signature Multiplicity

Molecular signatures are computational or mathematical models created to diagnose disease and other phenotypes and to predict clinical outcomes and response to treatment. It is widely recognized that molecular signatures constitute one of the most important translational and basic science developments enabled by recent high-throughput molecular assays. A perplexing phenomenon that characterizes high-throughput data analysis is the ubiquitous multiplicity of molecular signatures. Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally predictive signatures. This phenomenon has far-reaching implications for biological discovery and development of next generation patient diagnostics and personalized treatments. Currently the causes and interpretation of signature multiplicity are unknown, and several, often contradictory, conjectures have been made to explain it. We present a formal characterization of signature multiplicity and a new efficient algorithm that offers theoretical guarantees for extracting the set of maximally predictive and non-redundant signatures independent of distribution. The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in human microarray gene expression datasets. Our results shed light on the causes of signature multiplicity, provide computational tools for studying it empirically and introduce a framework for in silico bioequivalence of this important new class of diagnostic and personalized medicine modalities

Harnessing naturally randomized transcription to infer regulatory relationships among genes

An approach is developed that utilizes randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level. The approach is applied to an experiment in yeast, yielding new insights into the topology of the yeast transcriptional regulatory network

A nitty-gritty aspect of correlation and network inference from gene expression data

<p>Abstract</p> <p>Background</p> <p>All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels.</p> <p>Results</p> <p>This paper provides a theoretical consideration of the random effect of signal aggregation and its implications for correlation analysis and network inference. An attempt is made to quantitatively assess the magnitude of this effect from real data. Some preliminary ideas are offered to mitigate the consequences of random signal aggregation in the analysis of gene expression data.</p> <p>Conclusion</p> <p>Resulting from the summation of expression intensities over a random number of individual cells, the observed signals may not adequately reflect the true dependence structure of intra-cellular gene expression levels needed as a source of information for network reconstruction. Whether the reported effect is extrime or not, the important point, is to reconize and incorporate such signal source for proper inference. The usefulness of inference on genetic regulatory structures from microarray data depends critically on the ability of investigators to overcome this obstacle in a scientifically sound way.</p> <p>Reviewers</p> <p>This article was reviewed by Byung Soo KIM, Jeanne Kowalski and Geoff McLachlan</p

A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays

Motivation: One approach to inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization is to estimate the associations of gene expression levels measured in repeated samples. The associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. Although these procedures have been successfully applied to other areas, their validity when applied to microarray measurements has yet to be tested. Results: This paper describes an elementary statistical difficulty for all such procedures, no matter whether based on Bayesian updating, conditional independence testing, or other machine learning procedures such as simulated annealing or neural net pruning. The difficulty obtains if a number of cells from a common population are aggregated in a measurement of expression levels. Although there are special cases where the conditional associations are preserved under aggregation, in general inference of genetic regulatory structure based on conditional association is unwarranted