Comparative Metagenomic Analysis of Electrogenic Microbial Communities in Differentially Inoculated Swine Wastewater-Fed Microbial Fuel Cells.

Bioelectrochemical systems such as microbial fuel cells (MFCs) are promising new technologies for efficient removal of organic compounds from industrial wastewaters, including that generated from swine farming. We inoculated two pairs of laboratory-scale MFCs with sludge granules from a beer wastewater-treating anaerobic digester (IGBS) or from sludge taken from the bottom of a tank receiving swine wastewater (SS). The SS-inoculated MFC outperformed the IGBS-inoculated MFC with regard to COD and VFA removal and electricity production. Using a metagenomic approach, we describe the microbial diversity of the MFC planktonic and anodic communities derived from the different inocula. Proteobacteria (mostly Deltaproteobacteria) became the predominant phylum in both MFC anodic communities with amplification of the electrogenic genus Geobacter being the most pronounced. Eight dominant and three minor species of Geobacter were found in both MFC anodic communities. The anodic communities of the SS-inoculated MFCs had a higher proportion of Clostridium and Bacteroides relative to those of the IGBS-inoculated MFCs, which were enriched with Pelobacter. The archaeal populations of the SS- and IGBS-inoculated MFCs were dominated by Methanosarcina barkeri and Methanothermobacter thermautotrophicus, respectively. Our results show a long-term influence of inoculum type on the performance and microbial community composition of swine wastewater-treating MFCs.

Taxonomic and functional metagenomic analysis of anodic communities in two pilot-scale microbial fuel cells treating different industrial wastewaters.

The combined processes of microbial biodegradation accompanied by extracellular electron transfer make microbial fuel cells (MFCs) a promising new technology for cost-effective and sustainable wastewater treatment. Although a number of microbial species that build biofilms on the anode surfaces of operating MFCs have been identified, studies on the metagenomics of entire electrogenic communities are limited. Here we present the results of whole-genome metagenomic analysis of electrochemically active robust anodic microbial communities, and their anaerobic digester (AD) sludge inocula, from two pilot-scale MFC bioreactors fed with different distillery wastewaters operated under ambient conditions in distinct climatic zones. Taxonomic analysis showed that Proteobacteria, Bacteroidetes and Firmicutes were abundant in AD sludge from distinct climatic zones, and constituted the dominant core of the MFC microbiomes. Functional analysis revealed species involved in degradation of organic compounds commonly present in food industry wastewaters. Also, accumulation of methanogenic Archaea was observed in the electrogenic biofilms, suggesting a possibility for simultaneous electricity and biogas recovery from one integrated wastewater treatment system. Finally, we found a range of species within the anode communities possessing the capacity for extracellular electron transfer, both via direct contact and electron shuttles, and show differential distribution of bacterial groups on the carbon cloth and activated carbon granules of the anode surface. Overall, this study provides insights into structural shifts that occur in the transition from an AD sludge to an MFC microbial community and the metabolic potential of electrochemically active microbial populations with wastewater-treating MFCs.

Magnet-Facilitated Selection of Electrogenic Bacteria from Marine Sediment.

Some bacteria can carry out anaerobic respiration by depositing electrons on external materials, such as electrodes, thereby creating an electrical current. Into the anode chamber of microbial fuel cells (MFCs) having abiotic air-cathodes we inoculated microorganisms cultured from a magnetic particle-enriched portion of a marine tidal sediment, reasoning that since some external electron acceptors are ferromagnetic, electrogenic bacteria should be found in their vicinity. Two MFCs, one inoculated with a mixed bacterial culture and the other with an axenic culture of a helical bacterium isolated from the magnetic particle enrichment, termed strain HJ, were operated for 65 d. Both MFCs produced power, with production from the mixed culture MFC exceeding that of strain HJ. Strain HJ was identified as a Thalassospira sp. by transmission electron microscopic analysis and 16S rRNA gene comparisons. An MFC inoculated with strain HJ and operated in open circuit produced 47% and 57% of the maximal power produced from MFCs inoculated with the known electrogen Geobacter daltonii and the magnetotactic bacterium Desulfamplus magnetomortis, respectively. Further investigation will be needed to determine whether bacterial populations associated with magnetic particles within marine sediments are enriched for electrogens.

Genome Sequence of the Electrogenic Petroleum-Degrading Thalassospira sp. Strain HJ.

We present the draft genome of the petroleum-degrading Thalassospira sp. strain HJ, isolated from tidal marine sediment. Knowledge of this genomic information will inform studies on electrogenesis and means to degrade environmental organic contaminants, including compounds found in petroleum.

HAMSTER: visualizing microarray experiments as a set of minimum spanning trees.

BACKGROUND: Visualization tools allow researchers to obtain a global view of the interrelationships between the probes or experiments of a gene expression (e.g. microarray) data set. Some existing methods include hierarchical clustering and k-means. In recent years, others have proposed applying minimum spanning trees (MST) for microarray clustering. Although MST-based clustering is formally equivalent to the dendrograms produced by hierarchical clustering under certain conditions; visually they can be quite different. METHODS: HAMSTER (Helpful Abstraction using Minimum Spanning Trees for Expression Relations) is an open source system for generating a set of MSTs from the experiments of a microarray data set. While previous works have generated a single MST from a data set for data clustering, we recursively merge experiments and repeat this process to obtain a set of MSTs for data visualization. Depending on the parameters chosen, each tree is analogous to a snapshot of one step of the hierarchical clustering process. We scored and ranked these trees using one of three proposed schemes. HAMSTER is implemented in C++ and makes use of Graphviz for laying out each MST. RESULTS: We report on the running time of HAMSTER and demonstrate using data sets from the NCBI Gene Expression Omnibus (GEO) that the images created by HAMSTER offer insights that differ from the dendrograms of hierarchical clustering. In addition to the C++ program which is available as open source, we also provided a web-based version (HAMSTER+) which allows users to apply our system through a web browser without any computer programming knowledge. CONCLUSION: Researchers may find it helpful to include HAMSTER in their microarray analysis workflow as it can offer insights that differ from hierarchical clustering. We believe that HAMSTER would be useful for certain types of gradient data sets (e.g time-series data) and data that indicate relationships between cells/tissues. Both the source and the web server variant of HAMSTER are available from http://hamster.cbrc.jp/.

Orthotopic implantation mouse model and cDNA microarray analysis indicates several genes potentially involved in lymph node metastasis of colorectal cancer.

In colorectal cancer (CRC) patients, metastasis to the regional lymph node (LN) is an important first step in the dissemination of cancers. To identify the genes possibly involved in LN metastasis of CRC, we analyzed LN metastases in an orthotopic implantation mouse model with 22 CRC cell lines using Matrigel, an extracellular matrix protein derived from mice sarcoma, and combined the data with gene expression profiles of cDNA microarray of those cell lines. With this implantation analysis, the incidence of LN metastasis was 60% in 228 orthotopically implanted mice and varied from 100% to 0% among the cell lines. KM12c and Clone A showed LN metastasis in all orthotopically implanted mice, but DLD-1, HCT-8, and SW948 did not show LN metastases at all. In contrast, the incidence of liver and lung metastasis in 22 CRC cell lines was 13% and 1%, respectively. Combining those data with cDNA microarray in vitro, we isolated 636 genes that were differentially expressed depending on the incidence of LN metastasis. Among those genes, the expression level of ring finger protein 125 (RNF125), previously known as an E3 ubiquitin ligase in T cell activation, was significantly different between primary tumors in Stage III CRC patients with LN metastasis and Stage II patients without LN metastasis. In conclusion, the orthotopic implantation mice model with Matrigel was useful, and we isolated candidate genes such as RNF125 that possibly play an important role in LN metastasis of CRC cells.

CellMontage: similar expression profile search server.

The establishment and rapid expansion of microarray databases has created a need for new search tools. Here we present CellMontage, the first server for expression profile similarity search over a large database-69 000 microarray experiments derived from NCBI's; GEO site. CellMontage provides a novel, content-based search engine for accessing gene expression data. Microarray experiments with similar overall expression to a user-provided expression profile (e.g. microarray experiment) are computed and displayed-usually within 20 s. The core search engine software is downloadable from the site.

RaPiDS: an algorithm for rapid expression profile database search.

In this paper we present a fast algorithm and implementation for computing the Spearman rank correlation (SRC) between a query expression profile and each expression profile in a database of profiles. The algorithm is linear in the size of the profile database with a very small constant factor. It is designed to efficiently handle multiple profile platforms and missing values. We show that our specialized algorithm and C++ implementation can achieve an approximately 100-fold speed-up over a reasonable baseline implementation using Perl hash tables. RaPiDS is designed for general similarity search rather than classification - but in order to attempt to classify the usefulness of SRC as a similarity measure we investigate the usefulness of this program as a classifier for classifying normal human cell types based on gene expression. Specifically we use the k nearest neighbor classifier with a t statistic derived from SRC as the similarity measure for profile pairs. We estimate the accuracy using a jackknife test on the microarray data with manually checked cell type annotation. Preliminary results suggest the measure is useful (64% accuracy on 1,685 profiles vs. the majority class classifier's 17.5%) for profiles measured under similar conditions (same laboratory and chip platform); but requires improvement when comparing profiles from different experimental series.