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This document is a correction notice for an article titled "Intestinal microbiome profiles in broiler chickens raised without antibiotics exhibit altered microbiome dynamics relative to conventionally raised chickens." The correction states that the first author's name was spelled incorrectly and provides the correct spelling as Seyed Hossein Karimi. It also corrects the author contributions, specifying the roles of each author in the study. The correction is reported by Seyed Hossein Karimi, Khaled Abdelaziz, Heidi Spahany, Jake Astill, David Trott, Blake Wang, Alice Wang, John Parkinson, and Shayan Sharif. [Extracted from the article]

Most filarial parasites in the subfamilies Onchocercinae and Dirofilariinae depend on Wolbachia endobacteria to successfully carry out their life cycle. Recently published data indicate that the few Wolbachia-free species in these subfamilies were infected in the distant past and have subsequently shed their endosymbionts. We used an integrated transcriptomic and proteomic analysis of Onchocerca flexuosa to explore the molecular mechanisms that allow worms of this species to survive without a bacterial partner. Roche/454 sequencing of the adult transcriptome produced 16,814 isogroup and 47,252 singleton sequences that are estimated to represent approximately 41% of the complete gene set. Sequences similar to 97 Wolbachia genes were identified from the transcriptome, some of which appear on the same transcripts as sequences similar to nematode genes. Computationally predicted peptides, including those with similarity to Wolbachia proteins, were classified at the domain and pathway levels in order to assess the metabolic capabilities of O. flexuosa and compare against the Wolbachia-dependent model filaria, Brugia malayi. Transcript data further facilitated a shotgun proteomic analysis of O. flexuosa adult worm lysate, resulting in the identification of 1,803 proteins. Three of the peptides detected by mass spectroscopy map to two ABC transport-related proteins from Wolbachia. Antibodies raised to one of the Wolbachia-like peptides labeled a single 38 kDa band on Western blots of O. flexuosa lysate and stained specific worm tissues by immunohistology. Future studies will be required to determine the exact functions of Wolbachia-like peptides and proteins in O. flexuosa and to assess their roles in worm biology. [ABSTRACT FROM AUTHOR]

We describe the reconstruction of a genome-scale metabolic model of the crenarchaeon Sulfolobus solfataricus,a hyperthermoacidophilic microorganism. It grows in terrestrial volcanic hot springs with growth occurring at pH 2-4 (optimum 3.5) and a temperature of 75-80°C (optimum 80°C). The genome of Sulfolobus solfataricus P2 contains 2,992,245 bp on a single circular chromosome and encodes 2,977 proteins and a number of RNAs. The network comprises 718 metabolic and 58 transport/exchange reactions and 705 unique metabolites, based on the annotated genome and available biochemical data. Using the model in conjunction with constraint-based methods, we simulated the metabolic fluxes induced by different environmental and genetic conditions. The predictions were compared to experimental measurements and phenotypes of S. solfataricus. Furthermore, the performance of the network for 35 different carbon sources known for S. solfataricus from the literature was simulated. Comparing the growth on different carbon sources revealed that glycerol is the carbon source with the highest biomass flux per imported carbon atom (75% higher than glucose). Experimental data was also used to fit the model to phenotypic observations. In addition to the commonly known heterotrophic growth of S. solfataricus, the crenarchaeon is also able to grow autotrophically using the hydroxypropionate-hydroxybutyrate cycle for bicarbonate fixation. We integrated this pathway into our model and compared bicarbonate fixation with growth on glucose as sole carbon source. Finally, we tested the robustness of the metabolism with respect to gene deletions using the method of Minimization of Metabolic Adjustment (MOMA), which predicted that 18% of all possible single gene deletions would be lethal for the organism. [ABSTRACT FROM AUTHOR]

Although there has been extensive debate about whether Trichuris suis and Trichuris trichiura are separate species, only one species of the whipworm T. trichiura has been considered to infect humans and non-human primates. In order to investigate potential cross infection of Trichuris sp. between baboons and humans in the Cape Peninsula, South Africa, we sequenced the ITS1-5.8S-ITS2 region of adult Trichuris sp. worms isolated from five baboons from three different troops, namely the Cape Peninsula troop, Groot Olifantsbos troop and Da Gama Park troop. This region was also sequenced from T. trichiura isolated from a human patient from central Africa (Cameroon) for comparison. By combining this dataset with Genbank records for Trichuris isolated from other humans, non- human primates and pigs from several different countries in Europe, Asia, and Africa, we confirmed the identification of two distinct Trichuris genotypes that infect primates. Trichuris sp. isolated from the Peninsula baboons fell into two distinct clades that were found to also infect human patients from Cameroon, Uganda and Jamaica (named the CP-GOB clade) and China, Thailand, the Czech Republic, and Uganda (named the DG clade), respectively. The divergence of these Trichuris clades is ancient and precedes the diversification of T. suis which clustered closely to the CP-GOB clade. The identification of two distinct Trichuris genotypes infecting both humans and non-human primates is important for the ongoing treatment of Trichuris which is estimated to infect 600 million people worldwide. Currently baboons in the Cape Peninsula, which visit urban areas, provide a constant risk of infection to local communities. A reduction in spatial overlap between humans and baboons is thus an important measure to reduce both cross-transmission and zoonoses of helminthes in Southern Africa. [ABSTRACT FROM AUTHOR]

Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them. [ABSTRACT FROM AUTHOR]

With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics. [ABSTRACT FROM AUTHOR]

Chromatin modification (CM) plays a key role in regulating transcription, DNA replication, repair and recombination. However, our knowledge of these processes in humans remains very limited. Here we use computational approaches to study proteins and functional domains involved in CM in humans. We analyze the abundance and the pair-wise domain-domain co-occurrences of 25 well-documented CM domains in 5 model organisms: yeast, worm, fly, mouse and human. Results show that domains involved in histone methylation, DNA methylation, and histone variants are remarkably expanded in metazoan, reflecting the increased demand for cell type-specific gene regulation. We find that CM domains tend to co-occur with a limited number of partner domains and are hence not promiscuous. This property is exploited to identify 47 potentially novel CM domains, including 24 DNA-binding domains, whose role in CM has received little attention so far. Lastly, we use a consensus Machine Learning approach to predict 379 novel CM genes (coding for 329 proteins) in humans based on domain compositions. Several of these predictions are supported by very recent experimental studies and others are slated for experimental verification. Identification of novel CM genes and domains in humans will aid our understanding of fundamental epigenetic processes that are important for stem cell differentiation and cancer biology. Information on all the candidate CM domains and genes reported here is publicly available. [ABSTRACT FROM AUTHOR]