An Alignment-Free Metapeptide Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing

In principle, tandem mass spectrometry can be used to detect and quantify the peptides present in a microbiome sample, enabling functional and taxonomic insight into microbiome metabolic activity. However, the phylogenetic diversity constituting a particular microbiome is often unknown, and many of the organisms present may not have assembled genomes. In ocean microbiome samples, with particularly diverse and uncultured bacterial communities, it is difficult to construct protein databases that contain the bulk of the peptides in the sample without losing detection sensitivity due to the overwhelming number of candidate peptides for each tandem mass spectrum. We describe a method for deriving metapeptides (short amino acid sequences that may be represented in multiple organisms) from shotgun metagenomic sequencing of microbiome samples. In two ocean microbiome samples, we constructed site-specific metapeptide databases to detect more than one and a half times as many peptides as by searching against predicted genes from an assembled metagenome and roughly three times as many peptides as by searching against the NCBI environmental proteome database. The increased peptide yield has the potential to enrich the taxonomic and functional characterization of sample metaproteomes

Metaproteomics Reveal That Rapid Perturbations in Organic Matter Prioritize Functional Restructuring Over Taxonomy In Western Arctic Ocean Microbiomes

We examined metaproteome profiles from two Arctic microbiomes during 10-day shipboard incubations to directly track early functional and taxonomic responses to a simulated algal bloom and an oligotrophic control. Using a novel peptide-based enrichment analysis, significant changes (p-value \u3c 0.01) in biological and molecular functions associated with carbon and nitrogen recycling were observed. Within the first day under both organic matter conditions, Bering Strait surface microbiomes increased protein synthesis, carbohydrate degradation, and cellular redox processes while decreasing C1 metabolism. Taxonomic assignments revealed that the core microbiome collectively responded to algal substrates by assimilating carbon before select taxa utilize and metabolize nitrogen intracellularly. Incubations of Chukchi Sea bottom water microbiomes showed similar, but delayed functional responses to identical treatments. Although 24 functional terms were shared between experimental treatments, the timing, and degree of the remaining responses were highly variable, showing that organic matter perturbation directs community functionality prior to alterations to the taxonomic distribution at the microbiome class level. The dynamic responses of these two oceanic microbial communities have important implications for timing and magnitude of responses to organic perturbations within the Arctic Ocean and how community-level functions may forecast biogeochemical gradients in oceans

MetaGOmics: A Web-Based Tool for Peptide-Centric Functional and Taxonomic Analysis of Metaproteomics Data

Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the proteins that perform those functions? However, traditional protein-centric functional analysis is greatly complicated by the large size, redundancy, and lack of biological annotations for the protein sequences in the database used to search the data. To help address these issues, we have developed an algorithm and web application (dubbed MetaGOmics ) that automates the quantitative functional (using Gene Ontology) and taxonomic analysis of metaproteomics data and subsequent visualization of the results. MetaGOmics is designed to overcome the shortcomings of traditional proteomics analysis when used with metaproteomics data. It is easy to use, requires minimal input, and fully automates most steps of the analysis-including comparing the functional makeup between samples

Catchment-wide interactive effects of anthropogenic structures and river levels on fish spawning migrations

Worldwide, rivers are extensively fragmented by anthropogenic structures, reducing longitudinal connectivity, inhibiting migration and leading to severe declines in many fish populations, especially for diadromous species. However, few studies have determined the effects of annual differences in hydrology on catchment penetration past barriers to spawning habitats. We investigated the upstream spawning migration of 120 (n = 61 & 59) acoustic tagged river lamprey (Lampetra fluviatilis) across two contrasting (dry and wet) years in the River Yorkshire Ouse, England. Overall, significantly more lamprey reached spawning habitat (76% vs 39%) and penetrated significantly further upstream (median [km] from release, 53.9 vs 16.8) in the wet year than the dry year. Passage at weirs was almost exclusively during elevated river levels, which directly and collectively influenced catchment-wide distribution, especially in the dry year. Indeed, higher proportions entered two upper tributaries in the wet year (9.8% vs 27.1% and 9.8% vs 30.5%), due to increased passage efficiencies at the two main river weirs (60.5–87.5% and 54.5–83.8%), and reached assumed spawning locations 66.5% and 10.9% quicker. By contrast, there was no difference in numbers of lamprey entering, or time taken to arrive at assumed spawning location, in the two lower river tributaries between years. Our study supports the landscape-scale paradigm for ecosystem restoration because of the observed catchment-level effects of hydrology and barrier distribution on fish migration. Connectivity restoration for migratory fish should be implemented at a catchment scale, with planning incorporating spatial information regarding accessibility to key habitats to reap the largest gains

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

<p>Abstract</p> <p>Background</p> <p>Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual <it>C. elegans </it>genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (<it>i.e</it>., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.</p> <p>Results</p> <p>In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at <url>http://starrynite.sourceforge.net</url>.</p> <p>Conclusions</p> <p>We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task.</p

A dynamical model reveals gene co-localizations in nucleus

Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency-or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes

Randomised trial of glutamine and selenium supplemented parenteral nutrition for critically ill patients

Background: Mortality rates in the Intensive Care Unit and subsequent hospital mortality rates in the UK remain high. Infections in Intensive Care are associated with a 2–3 times increased risk of death. It is thought that under conditions of severe metabolic stress glutamine becomes "conditionally essential". Selenium is an essential trace element that has antioxidant and anti-inflammatory properties. Approximately 23% of patients in Intensive Care require parenteral nutrition and glutamine and selenium are either absent or present in low amounts. Both glutamine and selenium have the potential to influence the immune system through independent biochemical pathways. Systematic reviews suggest that supplementing parenteral nutrition in critical illness with glutamine or selenium may reduce infections and mortality. Pilot data has shown that more than 50% of participants developed infections, typically resistant organisms. We are powered to show definitively whether supplementation of PN with either glutamine or selenium is effective at reducing new infections in critically ill patients. Methods/design: 2 × 2 factorial, pragmatic, multicentre, double-blind, randomised controlled trial. The trial has an enrolment target of 500 patients. Inclusion criteria include: expected to be in critical care for at least 48 hours, aged 16 years or over, patients who require parenteral nutrition and are expected to have at least half their daily nutritional requirements given by that route. Allocation is to one of four iso-caloric, iso-nitrogenous groups: glutamine, selenium, both glutamine & selenium or no additional glutamine or selenium. Trial supplementation is given for up to seven days on the Intensive Care Unit and subsequent wards if practicable. The primary outcomes are episodes of infection in the 14 days after starting trial nutrition and mortality. Secondary outcomes include antibiotic usage, length of hospital stay, quality of life and cost-effectiveness. Discussion: To date more than 285 patients have been recruited to the trial from 10 sites in Scotland. Recruitment is due to finish in August 2008 with a further six months follow up. We expect to report the results of the trial in summer 2009. Trial registration: This trial is registered with the International Standard Randomised Controlled Trial Number system. ISRCTN87144826Not peer reviewedPublisher PD