The MISOPRED score: Development and validation of a clinical scoring system to predict the effectiveness of Misoprostol treatment for early pregnancy loss.

Background: Misoprostol treatment for early pregnancy loss has varied success demonstrated in previous studies. Incorporating predictors in a single clinical scoring system would be highly beneficial in clinical practice.

Objective: To develop and evaluate the accuracy of a scoring system to predict misoprostol treatment outcomes for managing early pregnancy loss.

Comparative study of bacterial community dynamics in different soils following application of the herbicide atrazine.

Microbial communities in cultivated soils control the fate of pollutants associated with agricultural practice. The present study was designed to explore the response of bacterial communities to the application of the widely-used herbicide atrazine in three different crop fields that differ significantly in their physicochemical structure and nutritional content: the nutrient-rich (with relatively high carbon and nitrogen content) Newe Yaar (NY) and Ha-Ogen (HO) soils and the nutrient-poor, sandy Sde-Eliyahu (SE) soil. The 16 S rRNA gene amplicon sequencing revealed the nutrient poor HO soil differs in its response to atrazine in comparison to the two nutrient-rich soils both in the shortest persistence of atrazine and its effect on community structure and composition.

Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil.

Extensive use of agrochemicals is emerging as a serious environmental issue coming at the cost of the pollution of soil and water resources. Bioremediation techniques such as biostimulation are promising strategies used to remove pollutants from agricultural soils by supporting the indigenous microbial degraders. Though considered cost-effective and eco-friendly, the success rate of these strategies typically varies, and consequently, they are rarely integrated into commercial agricultural practices.

Strategies for Enhancing Degradation of Linuron by sp. Strain SRS 16 Under the Guidance of Metabolic Modeling.

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment.

Genome-scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation.

Atrazine is an herbicide and a pollutant of great environmental concern that is naturally biodegraded by microbial communities. Paenarthrobacter aurescens TC1 is one of the most studied degraders of this herbicide. Here, we developed a genome scale metabolic model for P.

Prebiotic-Induced Anti-tumor Immunity Attenuates Tumor Growth.

Growing evidence supports the importance of gut microbiota in the control of tumor growth and response to therapy. Here, we select prebiotics that can enrich bacterial taxa that promote anti-tumor immunity. Addition of the prebiotics inulin or mucin to the diet of C57BL/6 mice induces anti-tumor immune responses and inhibition of BRAF mutant melanoma growth in a subcutaneously implanted syngeneic mouse model.

Gut microbiota dependent anti-tumor immunity restricts melanoma growth in Rnf5 mice.

Accumulating evidence points to an important role for the gut microbiome in anti-tumor immunity. Here, we show that altered intestinal microbiota contributes to anti-tumor immunity, limiting tumor expansion. Mice lacking the ubiquitin ligase RNF5 exhibit attenuated activation of the unfolded protein response (UPR) components, which coincides with increased expression of inflammasome components, recruitment and activation of dendritic cells and reduced expression of antimicrobial peptides in intestinal epithelial cells.

Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions.

Microbial communities play a vital role in biogeochemical cycles, allowing the biodegradation of a wide range of pollutants. The composition of the community and the interactions between its members affect degradation rate and determine the identity of the final products. Here, we demonstrate the application of sequencing technologies and metabolic modeling approaches towards enhancing biodegradation of atrazine-a herbicide causing environmental pollution.

Diet-induced changes of redox potential underlie compositional shifts in the rumen archaeal community.

Dietary changes are known to affect gut community structure, but questions remain about the mechanisms by which diet induces shifts in microbiome membership. Here, we addressed these questions in the rumen microbiome ecosystem - a complex microbial community that resides in the upper digestive tract of ruminant animals and is responsible for the degradation of the ingested plant material. Our dietary intervention experiments revealed that diet affects the most abundant taxa within the microbiome and that a specific group of methanogenic archaea of the order Methanomicrobiales is highly sensitive to its changes.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes.

Harnessing the landscape of microbial culture media to predict new organism-media pairings.

Culturing microorganisms is a critical step in understanding and utilizing microbial life. Here we map the landscape of existing culture media by extracting natural-language media recipes into a Known Media Database (KOMODO), which includes >18,000 strain-media combinations, >3300 media variants and compound concentrations (the entire collection of the Leibniz Institute DSMZ repository). Using KOMODO, we show that although media are usually tuned for individual strains using biologically common salts, trace metals and vitamins/cofactors are the most differentiating components between defined media of strains within a genus.

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways.

Glycan degradation (GlyDeR) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations.

Unlabelled: Glycans form the primary nutritional source for microbes in the human gut, and understanding their metabolism is a critical yet understudied aspect of microbiome research. Here, we present a novel computational pipeline for modeling glycan degradation (GlyDeR) which predicts the glycan degradation potency of 10,000 reference glycans based on either genomic or metagenomic data. We first validated GlyDeR by comparing degradation profiles for genomes in the Human Microbiome Project against KEGG reaction annotations.

A novel nutritional predictor links microbial fastidiousness with lowered ubiquity, growth rate, and cooperativeness.

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.

Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms.

Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious.

Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes.

Competitive and cooperative metabolic interactions in bacterial communities.

Revealing the ecological principles that shape communities is a major challenge of the post-genomic era. To date, a systematic approach for describing inter-species interactions has been lacking. Here we independently predict the competitive and cooperative potential between 6,903 bacterial pairs derived from a collection of 118 species' metabolic models.