Public understanding of science and common sense: Social representations of the human microbiome among the expert and non-expert public

The aim of this investigation is to examine the structure and the content of different social groups’ representations of the human microbiome. We employed a non-probabilistic sample comprising two groups of participants. The first group (n = 244) included university students. The second group included lay people (n = 355). We chose a mixed-method approach. The data obtained were processed using IRaMuTeQ software. The results allow us to identify the anchoring and objectification processes activated by the two different groups of interviewees. The results could be useful to those in charge of implementing campaigns aimed at promoting health literac

Quantifying biosynthetic network robustness across the human oral microbiome

Metabolic interactions, such as cross-feeding, play a prominent role in microbial communitystructure. For example, they may underlie the ubiquity of uncultivated microorganisms. We investigated this phenomenon in the human oral microbiome, by analyzing microbial metabolic networks derived from sequenced genomes. Specifically, we devised a probabilistic biosynthetic network robustness metric that describes the chance that an organism could produce a given metabolite, and used it to assemble a comprehensive atlas of biosynthetic capabilities for 88 metabolites across 456 human oral microbiome strains. A cluster of organisms characterized by reduced biosynthetic capabilities stood out within this atlas. This cluster included several uncultivated taxa and three recently co-cultured Saccharibacteria (TM7) phylum species. Comparison across strains also allowed us to systematically identify specific putative metabolic interdependences between organisms. Our method, which provides a new way of converting annotated genomes into metabolic predictions, is easily extendible to other microbial communities and metabolic products.https://www.biorxiv.org/content/10.1101/392621v1First author draf

A comparative analysis of water collected from Koel River & water released from Rourkela Steel Plant (RSP)

The expected increase in the use of coal as an energy source for the production of steel has resulted in several investigations into environmental cycling of coal unleashes pollutants. Among these is the release of various liquid effluents that are associated with coal throughout the carbonization, cleaning and combustion processes. The industries, like coal, by-product coke-plants, coal washeries and thermal power plants unleash their liquid effluents that are required urgent attention for the treatment, before they are discharged into the contemporary water streams. There’s also the release of ash pond decant into the local water bodies from the coal-based industries. Such unleash pond decant tends to deposit ash all along its path thereby inflicting fugitive dust nuisance when it dries up. Additionally when such water mixes with a water body, it will increase the turbidity of the water body thereby decreasing the primary productivity. This is often harmful to the fisheries and other aquatic biota within the water body. The objective of this project work is to investigate the environmental impacts of waste water discharged from coal based industries and need to recognize the consequences. The Rourkela Steel Plant (RSP) releases the polluted water to Koel River which is the main river in Rourkela & most of the people are dependent on it for different purpose like drinking, washing, bathing etc.The present study indicated that most bacteria which were isolated from the Koel River are Bacillus subtilis, Bacillus megaterium&Bacillus pumilis

Foliar lead uptake by lettuce exposed to atmospheric fallouts

Metal uptake by plants occurs by soil−root transfer but also by direct transfer of contaminants from the atmosphere to the shoots. This second pathway may be particularly important in kitchen gardens near industrial plants. The mechanisms of foliar uptake of lead by lettuce (Lactuca sativa) exposed to the atmospheric fallouts of a lead-recycling plant were studied. After 43 days of exposure, the thoroughly washed leaves contained 335 ± 50 mg Pb kg−1 (dry weight). Micro-X-ray fluorescence mappings evidenced Pb-rich spots of a few hundreds of micrometers in diameter located in necrotic zones. These spots were more abundant at the base of the central nervure. Environmental scanning electron microscopy coupled with energy dispersive X-ray microanalysis showed that smaller particles (a few micrometers in diameter) were also present in other regions of the leaves, often located beneath the leaf surface. In addition, submicrometric particles were observed inside stomatal openings. Raman microspectrometry analyses of the leaves identified smelter-originated Pb minerals but also secondary phases likely resulting from the weathering of original particles. On the basis of these observations, several pathways for foliar lead uptake are discussed. A better understanding of these mechanisms may be of interest for risk assessment of population exposure to atmospheric metal contamination

Automation on the generation of genome scale metabolic models

Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed.Comment: 24 pages, 2 figures, 2 table

Recovering complete and draft population genomes from metagenome datasets.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a better understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identification of ncRNAs and functional inference of identified ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species

BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism