Massively parallel tag sequencing reveals the complexity of anaerobic marine protistan communities

Background  

Recent advances in sequencing strategies make possible unprecedented depth and scale of sampling for molecular detection of microbial diversity. Two major paradigm-shifting discoveries include the detection of bacterial diversity that is one to two orders of magnitude greater than previous estimates, and the discovery of an exciting 'rare biosphere' of molecular signatures ('species') of poorly understood ecological significance. We applied a high-throughput parallel tag sequencing (454 sequencing) protocol adopted for eukaryotes to investigate protistan community complexity in two contrasting anoxic marine ecosystems (Framvaren Fjord, Norway; Cariaco deep-sea basin, Venezuela). Both sampling sites have previously been scrutinized for protistan diversity by traditional clone library construction and Sanger sequencing. By comparing these clone library data with 454 amplicon library data, we assess the efficiency of high-throughput tag sequencing strategies. We here present a novel, highly conservative bioinformatic analysis pipeline for the processing of large tag sequence data sets.     

Key biosynthetic gene subfamily recruited for pheromone production prior to the extensive radiation of Lepidoptera

Background  

Moths have evolved highly successful mating systems, relying on species-specific mixtures of sex pheromone components for long-distance mate communication. Acyl-CoA desaturases are key enzymes in the biosynthesis of these compounds and to a large extent they account for the great diversity of pheromone structures in Lepidoptera. A novel desaturase gene subfamily that displays Δ11 catalytic activities has been highlighted to account for most of the unique pheromone signatures of the taxonomically advanced ditrysian species. To assess the mechanisms driving pheromone evolution, information is needed about the signalling machinery of primitive moths. The currant shoot borer, Lampronia capitella, is the sole reported primitive non-ditrysian moth known to use unsaturated fatty-acid derivatives as sex-pheromone. By combining biochemical and molecular approaches we elucidated the biosynthesis paths of its main pheromone component, the (Z,Z)-9,11-tetradecadien-1-ol and bring new insights into the time point of the recruitment of the key Δ11-desaturase gene subfamily in moth pheromone biosynthesis.     

Arsenal of plant cell wall degrading enzymes reflects host preference among plant pathogenic fungi

Background  

The discovery and development of novel plant cell wall degrading enzymes is a key step towards more efficient depolymerization of polysaccharides to fermentable sugars for the production of liquid transportation biofuels and other bioproducts. The industrial fungus Trichoderma reesei is known to be highly cellulolytic and is a major industrial microbial source for commercial cellulases, xylanases and other cell wall degrading enzymes. However, enzyme-prospecting research continues to identify opportunities to enhance the activity of T. reesei enzyme preparations by supplementing with enzymatic diversity from other microbes. The goal of this study was to evaluate the enzymatic potential of a broad range of plant pathogenic and non-pathogenic fungi for their ability to degrade plant biomass and isolated polysaccharides.     

Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays

Purpose

To evaluate the accuracy of the sub-classification of renal cortical neoplasms using molecular signatures.

Experimental Design

A search of publicly available databases was performed to identify microarray datasets with multiple histologic sub-types of renal cortical neoplasms. Meta-analytic techniques were utilized to identify differentially expressed genes for each histologic subtype. The lists of genes obtained from the meta-analysis were used to create predictive signatures through the use of a pair-based method. These signatures were organized into an algorithm to sub-classify renal neoplasms. The use of these signatures according to our algorithm was validated on several independent datasets.

Results

We identified three Gene Expression Omnibus datasets that fit our criteria to develop a training set. All of the datasets in our study utilized the Affymetrix platform. The final training dataset included 149 samples represented by the four most common histologic subtypes of renal cortical neoplasms: 69 clear cell, 41 papillary, 16 chromophobe, and 23 oncocytomas. When validation of our signatures was performed on external datasets, we were able to correctly classify 68 of the 72 samples (94%). The correct classification by subtype was 19/20 (95%) for clear cell, 14/14 (100%) for papillary, 17/19 (89%) for chromophobe, 18/19 (95%) for oncocytomas.

Conclusions

Through the use of meta-analytic techniques, we were able to create an algorithm that sub-classified renal neoplasms on a molecular level with 94% accuracy across multiple independent datasets. This algorithm may aid in selecting molecular therapies and may improve the accuracy of subtyping of renal cortical tumors.     

A high-throughput pipeline for the design of real-time PCR signatures

Background  

Pathogen diagnostic assays based on polymerase chain reaction (PCR) technology provide high sensitivity and specificity. However, the design of these diagnostic assays is computationally intensive, requiring high-throughput methods to identify unique PCR signatures in the presence of an ever increasing availability of sequenced genomes.     

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

Background  

A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.     

Relative extended haplotype homozygosity signals across breeds reveal dairy and beef specific signatures of selection

Background

A number of methods are available to scan a genome for selection signatures by evaluating patterns of diversity within and between breeds. Among these, “extended haplotype homozygosity” (EHH) is a reliable approach to detect genome regions under recent selective pressure. The objective of this study was to use this approach to identify regions that are under recent positive selection and shared by the most representative Italian dairy and beef cattle breeds.

Results

A total of 3220 animals from Italian Holstein (2179), Italian Brown (775), Simmental (493), Marchigiana (485) and Piedmontese (379) breeds were genotyped with the Illumina BovineSNP50 BeadChip v.1. After standard quality control procedures, genotypes were phased and core haplotypes were identified. The decay of linkage disequilibrium (LD) for each core haplotype was assessed by measuring the EHH. Since accurate estimates of local recombination rates were not available, relative EHH (rEHH) was calculated for each core haplotype. Genomic regions that carry frequent core haplotypes and with significant rEHH values were considered as candidates for recent positive selection. Candidate regions were aligned across to identify signals shared by dairy or beef cattle breeds. Overall, 82 and 87 common regions were detected among dairy and beef cattle breeds, respectively. Bioinformatic analysis identified 244 and 232 genes in these common genomic regions. Gene annotation and pathway analysis showed that these genes are involved in molecular functions that are biologically related to milk or meat production.

Conclusions

Our results suggest that a multi-breed approach can lead to the identification of genomic signatures in breeds of cattle that are selected for the same production goal and thus to the localisation of genomic regions of interest in dairy and beef production.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0113-9) contains supplementary material, which is available to authorized users.     

Expanding the understanding of biases in development of clinical-grade molecular signatures: a case study in acute respiratory viral infections

Background

The promise of modern personalized medicine is to use molecular and clinical information to better diagnose, manage, and treat disease, on an individual patient basis. These functions are predominantly enabled by molecular signatures, which are computational models for predicting phenotypes and other responses of interest from high-throughput assay data. Data-analytics is a central component of molecular signature development and can jeopardize the entire process if conducted incorrectly. While exploratory data analysis may tolerate suboptimal protocols, clinical-grade molecular signatures are subject to vastly stricter requirements. Closing the gap between standards for exploratory versus clinically successful molecular signatures entails a thorough understanding of possible biases in the data analysis phase and developing strategies to avoid them.

Methodology and Principal Findings

Using a recently introduced data-analytic protocol as a case study, we provide an in-depth examination of the poorly studied biases of the data-analytic protocols related to signature multiplicity, biomarker redundancy, data preprocessing, and validation of signature reproducibility. The methodology and results presented in this work are aimed at expanding the understanding of these data-analytic biases that affect development of clinically robust molecular signatures.

Conclusions and Significance

Several recommendations follow from the current study. First, all molecular signatures of a phenotype should be extracted to the extent possible, in order to provide comprehensive and accurate grounds for understanding disease pathogenesis. Second, redundant genes should generally be removed from final signatures to facilitate reproducibility and decrease manufacturing costs. Third, data preprocessing procedures should be designed so as not to bias biomarker selection. Finally, molecular signatures developed and applied on different phenotypes and populations of patients should be treated with great caution.     

Fused eco29kIR- and M genes coding for a fully functional hybrid polypeptide as a model of molecular evolution of restriction-modification systems

Background  

The discovery of restriction endonucleases and modification DNA methyltransferases, key instruments of genetic engineering, opened a new era of molecular biology through development of the recombinant DNA technology. Today, the number of potential proteins assigned to type II restriction enzymes alone is beyond 6000, which probably reflects the high diversity of evolutionary pathways. Here we present experimental evidence that a new type IIC restriction and modification enzymes carrying both activities in a single polypeptide could result from fusion of the appropriate genes from preexisting bipartite restriction-modification systems.     

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Background  

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain.     

Direct ethanol production from cellulosic materials using a diploid strain of <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis>with optimized cellulase expression

Background  

Hydrolysis of cellulose requires the action of the cellulolytic enzymes endoglucanase, cellobiohydrolase and β-glucosidase. The expression ratios and synergetic effects of these enzymes significantly influence the extent and specific rate of cellulose degradation. In this study, using our previously developed method to optimize cellulase-expression levels in yeast, we constructed a diploid Saccharomyces cerevisiae strain optimized for expression of cellulolytic enzymes, and attempted to improve the cellulose-degradation activity and enable direct ethanol production from rice straw, one of the most abundant sources of lignocellulosic biomass.     

Glioblastoma models reveal the connection between adult glial progenitors and the proneural phenotype

Background

Tumor heterogeneity is a major obstacle for finding effective treatment of Glioblastoma (GBM). Based on global expression analysis, GBM can be classified into distinct subtypes: Proneural, Neural, Classical and Mesenchymal. The signatures of these different tumor subtypes may reflect the phenotypes of cells giving rise to them. However, the experimental evidence connecting any specific subtype of GBM to particular cells of origin is lacking. In addition, it is unclear how different genetic alterations interact with cells of origin in determining tumor heterogeneity. This issue cannot be addressed by studying end-stage human tumors.

Methodology/Principal Findings

To address this issue, we used retroviruses to deliver transforming genetic lesions to glial progenitors in adult mouse brain. We compared the resulting tumors to human GBM. We found that different initiating genetic lesions gave rise to tumors with different growth rates. However all mouse tumors closely resembled the human Proneural GBM. Comparative analysis of these mouse tumors allowed us to identify a set of genes whose expression in humans with Proneural GBM correlates with survival.

Conclusions/Significance

This study offers insights into the relationship between adult glial progenitors and Proneural GBM, and allows us to identify molecular alterations that lead to more aggressive tumor growth. In addition, we present a new preclinical model that can be used to test treatments directed at a specific type of GBM in future studies.     

Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

Background  

Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature.     

Identification of <Emphasis Type="Italic">Lactobacillus plantarum</Emphasis> genes modulating the cytokine response of human peripheral blood mononuclear cells

Background  

Modulation of the immune system is one of the most plausible mechanisms underlying the beneficial effects of probiotic bacteria on human health. Presently, the specific probiotic cell products responsible for immunomodulation are largely unknown. In this study, the genetic and phenotypic diversity of strains of the Lactobacillus plantarum species were investigated to identify genes of L. plantarum with the potential to influence the amounts of cytokines interleukin 10 (IL-10) and IL-12 and the ratio of IL-10/IL-12 produced by peripheral blood mononuclear cells (PBMCs).     

Recent artificial selection in U.S. Jersey cattle impacts autozygosity levels of specific genomic regions

Background

Genome signatures of artificial selection in U.S. Jersey cattle were identified by examining changes in haplotype homozygosity for a resource population of animals born between 1953 and 2007. Genetic merit of this population changed dramatically during this period for a number of traits, especially milk yield. The intense selection underlying these changes was achieved through extensive use of artificial insemination (AI), which also increased consanguinity of the population to a few superior Jersey bulls. As a result, allele frequencies are shifted for many contemporary animals, and in numerous cases to a homozygous state for specific genomic regions. The goal of this study was to identify those selection signatures that occurred after extensive use of AI since the 1960, using analyses of shared haplotype segments or Runs of Homozygosity. When combined with animal birth year information, signatures of selection associated with economically important traits were identified and compared to results from an extended haplotype homozygosity analysis.

Results

Overall, our results reveal that more recent selection increased autozygosity across the entire genome, but some specific regions increased more than others. A genome-wide scan identified more than 15 regions with a substantial change in autozygosity. Haplotypes found to be associated with increased milk, fat and protein yield in U.S. Jersey cattle also consistently increased in frequency.

Conclusions

The analyses used in this study was able to detect directional selection over the last few decades when individual production records for Jersey animals were available.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1500-x) contains supplementary material, which is available to authorized users.     

Incorporating significant amino acid pairs to identify O-linked glycosylation sites on transmembrane proteins and non-transmembrane proteins

Background  

While occurring enzymatically in biological systems, O-linked glycosylation affects protein folding, localization and trafficking, protein solubility, antigenicity, biological activity, as well as cell-cell interactions on membrane proteins. Catalytic enzymes involve glycotransferases, sugar-transferring enzymes and glycosidases which trim specific monosaccharides from precursors to form intermediate structures. Due to the difficulty of experimental identification, several works have used computational methods to identify glycosylation sites.     

Predicting conserved protein motifs with Sub-HMMs

Background  

Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins.     

Culture-independent analysis of bacterial diversity in a child-care facility

Background  

Child-care facilities appear to provide daily opportunities for exposure and transmission of bacteria and viruses. However, almost nothing is known about the diversity of microbial contamination in daycare facilities or its public health implications. Recent culture-independent molecular studies of bacterial diversity in indoor environments have revealed an astonishing diversity of microorganisms, including opportunistic pathogens and many uncultured bacteria. In this study, we used culture and culture-independent methods to determine the viability and diversity of bacteria in a child-care center over a six-month period.