Testing a series of modifications on genomic library preparation methods for ancient or degraded DNA.

Technological advancements have revolutionized ancient and degraded DNA analysis, moving the field to the Next Generation Sequencing era. One of the advancements, the ancient DNA-oriented high-throughput library preparation methods, enabled the sequencing of more endogenous molecules. Although fairly optimized, both single- and double-stranded library preparation methods hold the potential for further improvement. Here, we test a series of modifications made at different steps of both single- and double-stranded library preparation methods. Given all the modifications tested, we found that two of them provide further benefits, including the use of Endonuclease VIII as a pre-treatment step before preparing single-stranded libraries and the use of a modified second adapter of the single stranded-libraries as an alternative option to enable sequencing of single stranded-libraries with the standard Illumina sequencing primer instead of the custom designed as described in the single stranded library preparation method. Furthermore, we propose uracil-DNA-glycosylase (UDG) could also be considered for both single- and double-stranded library preparation methods, although additional parameters should be taken into account depending on the sequencing strategy and the sample characteristics. Further modifications were also tested and although they were not advantageous, they could be considered as equivalent to the published options.

Molecular identification and geographic origin of a post-Medieval elephant finding from southwestern Portugal using high-throughput sequencing.

Molecular species identification plays a crucial role in archaeology and palaeontology, especially when diagnostic morphological characters are unavailable. Molecular markers have been used in forensic science to trace the geographic origin of wildlife products, such as ivory. So far, only a few studies have applied genetic methods to both identify the species and circumscribe the provenance of historic wildlife trade material. Here, by combining ancient DNA methods and genome skimming on a historical elephantid tooth found in southwestern Portugal, we aimed to identify its species, infer its placement in the elephantid phylogenetic tree, and triangulate its geographic origin. According to our results the specimen dates back to the eighteenth century CE and belongs to a female African forest elephant (non-hybrid Loxodonta cyclotis individual) geographically originated from west-west-central Africa, from areas where one of the four major mitochondrial clades of L. cyclotis is distributed. Historical evidence supports our inference, pointing out that the tooth should be considered as post-Medieval raw ivory trade material between West Africa and Portugal. Our study provides a comprehensive approach to study historical products and artefacts using archaeogenetics and contributes towards enlightening cultural and biological historical aspects of ivory trade in western Europe.

A Novel Hantavirus of the European Mole, Bruges Virus, Is Involved in Frequent Nova Virus Coinfections.

Hantaviruses are zoonotic viruses with a complex evolutionary history of virus-host coevolution and cross-species transmission. Although hantaviruses have a broad reservoir host range, virus-host relationships were previously thought to be strict, with a single virus species infecting a single host species. Here, we describe Bruges virus, a novel hantavirus harbored by the European mole (Talpa europaea), which is the well-known host of Nova virus. Phylogenetic analyses of all three genomic segments showed tree topology inconsistencies, suggesting that Bruges virus has emerged from cross-species transmission and ancient reassortment events. A high number of coinfections with Bruges and Nova viruses was detected, but no evidence was found for reassortment between these two hantaviruses. These findings highlight the complexity of hantavirus evolution and the importance of further investigation of hantavirus-reservoir relationships.

Ras mutants enhance the ability of cells to anticipate future lethal stressors.

Organisms integrate information of current environmental stressors and can adjust themselves against harmful events that might occur in the future. The molecular processes that lead to such "anticipatory" behaviors, although of great interest, are mostly unexplored and the minimal genetic requirements for reconfiguring key signaling networks in order either to create or to strengthen such vital "anticipatory" capabilities is largely unknown. We identified new "anticipatory" phenotypes in yeast cells by evolving yeast strains that strongly associate a present modest stress with a future deadly one. Whole genome sequencing and classic genetic analysis revealed that two dominant negative ras2 alleles (ras2-K23N and ras2-G17C) displayed a strong "anticipatory" ability being highly resistant to oxidative stress, extremely thermotolerant and long lived only following an initial mild heat shock. We suggest that such "anticipatory" phenotypes can be easily evolved by a single point mutation in a key signaling protein, the Ras2 small GTPase, and we propose a molecular model describing how specific ras2 alleles, and not null ras2 mutants, or mutations in other components of the Ras/cAMP pathway, can enhance the "predictive ability" of cells for future lethal stressors.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Comparative transcriptome analysis of two olive cultivars in response to NaCl-stress.

BACKGROUND: Olive (Olea europaea L.) cultivation is rapidly expanding and low quality saline water is often used for irrigation. The molecular basis of salt tolerance in olive, though, has not yet been investigated at a system level. In this study a comparative transcriptomics approach was used as a tool to unravel gene regulatory networks underlying salinity response in olive trees by simulating as much as possible olive growing conditions in the field. Specifically, we investigated the genotype-dependent differences in the transcriptome response of two olive cultivars, a salt-tolerant and a salt-sensitive one. METHODOLOGY/PRINCIPAL FINDINGS: A 135-day long salinity experiment was conducted using one-year old trees exposed to NaCl stress for 90 days followed by 45 days of post-stress period during the summer. A cDNA library made of olive seedling mRNAs was sequenced and an olive microarray was constructed. Total RNA was extracted from root samples after 15, 45 and 90 days of NaCl-treatment as well as after 15 and 45 days of post-treatment period and used for microarray hybridizations. SAM analysis between the NaCl-stress and the post-stress time course resulted in the identification of 209 and 36 differentially expressed transcripts in the salt-tolerant and salt-sensitive cultivar, respectively. Hierarchical clustering revealed two major, distinct clusters for each cultivar. Despite the limited number of probe sets, transcriptional regulatory networks were constructed for both cultivars while several hierarchically-clustered interacting transcription factor regulators such as JERF and bZIP homologues were identified. CONCLUSIONS/SIGNIFICANCE: A systems biology approach was used and differentially expressed transcripts as well as regulatory interactions were identified. The comparison of the interactions among transcription factors in olive with those reported for Arabidopsis might indicate similarities in the response of a tree species with Arabidopsis at the transcriptional level under salinity stress.

Multi-platform data integration in microarray analysis.

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among-platforms scale implemented by Shen et al. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.

Complementary gene signature integration in multiplatform microarray experiments.

The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. In this paper, we verify the aforementioned fact, but we also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature. We proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions, we derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution.

Detection of DNA mutations using a capacitive micro-membrane array.

The detection of DNA hybridization using capacitive readout and a biosensor array of ultrathin Si membranes is presented. The biosensor exploits the ability of the ultrathin membranes to deflect upon surface stress variations caused by biological interactions. Probe DNA molecules are immobilized on the membrane surface and the surface stress variations during hybridization with their complementary strands force the membrane to deflect and effectively change the capacitance between the flexible membrane and the fixed substrate. The sensor array comprises 256 such sensing sites thus allowing the concurrent sensing of multiple DNA mutations. The biosensor and its performance for the detection of complementary DNA strands are demonstrated using beta-thalassemia oligonucleotides. The experimental results show that the presented sensors are able to detect DNA hybridization and to discriminate single nucleotide mismatches.

Supporting genotype-to-phenotype association studies with grid-enabled knowledge discovery workflows.

Web Services and Grid-enabled scientific workflows are of paramount importance for the realization of efficient and secure knowledge discovery scenarios. This paper presents a Grid-enabled Genotype-to-Phenotype discovery scenario (GG2P), which is realized by a respective scientific workflow. GG2P supports the seamless integration of SNP genotype data sources, and the discovery of indicative and predictive genotype-to-phenotype association models - all wrapped around custom-made Web Services. GG2P is applied on a whole-genome SNP-genotyping experiment (breast cancer vs. normal/control phenotypes). A set of about 100 indicative SNPs are induced with very high classification performance. The biological relevance of the findings is supported by the relevant literature.

Outcome prediction based on microarray analysis: a critical perspective on methods.

BACKGROUND: Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation. RESULTS: A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance. CONCLUSION: Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

Transcriptome analysis approaches for the isolation of trichome-specific genes from the medicinal plant Cistus creticus subsp. creticus.

Cistus creticus subsp. creticus is a plant of intrinsic scientific interest due to the distinctive pharmaceutical properties of its resin. Labdane-type diterpenes, the main constituents of the resin, exhibit considerable antibacterial and cytotoxic activities. In this study chemical analysis of isolated trichomes from different developmental stages revealed that young leaves of 1-2 cm length displayed the highest content of labdane-type diterpenes (80 mg/g fresh weight) whereas trichomes from older leaves (2-3 or 3-4 cm) exhibited gradual decreased concentrations. A cDNA library was constructed enriched in transcripts from trichomes isolated from young leaves, which are characterized by high levels of labdane-type diterpenes. Functional annotation of 2,022 expressed sequence tags (ESTs) from the trichome cDNA library based on homology to A. thaliana genes suggested that 8% of the putative identified sequences were secondary metabolism-related and involved primarily in flavonoid and terpenoid biosynthesis. A significant proportion of the ESTs (38%) displayed no significant similarity to any other DNA deposited in databases, indicating a yet unknown function. Custom DNA microarrays constructed with 1,248 individual clones from the cDNA library facilitated transcriptome comparisons between trichomes and trichome-free tissues. In addition, gene expression studies in various Cistus tissues and organs for one of the genes highlighted as the most differentially expressed by the microarray experiments revealed a putative sesquiterpene synthase with a trichome-specific expression pattern. Full length cDNA isolation and heterologous expression in E. coli followed by biochemical analysis, led to the characterization of the produced protein as germacrene B synthase.

Dominant and redundant functions of TFIID involved in the regulation of hepatic genes.

To study the in vivo role of TFIID in the transcriptional regulation of hepatic genes, we generated mice with liver-specific disruption of the TAF10 gene. Inactivation of TAF10 in hepatocytes resulted in the dissociation of TFIID into individual components. This correlated with the downregulation of most hepatocyte-specific genes during embryonic life and a defect in liver organogenesis. Unexpectedly, however, the transcription of less than 5% of active genes was affected by TAF10 inactivation and TFIID disassembly in adult liver. The extent of changes in transcription of the affected genes was dependent on the timing of their activation during liver development, relative to that of TAF10 inactivation. Furthermore, TFIID dissociation from promoters leads to the re-expression of several postnatally silenced hepatic genes. Promoter occupancy analyses, combined with expression profiling, demonstrate that TFIID is required for the initial activation or postnatal repression of genes, while it is dispensable for maintaining ongoing transcription.

A semantic grid infrastructure enabling integrated access and analysis of multilevel biomedical data in support of postgenomic clinical trials on cancer.

This paper reports on original results of the Advancing Clinico-Genomic Trials on Cancer integrated project focusing on the design and development of a European biomedical grid infrastructure in support of multicentric, postgenomic clinical trials (CTs) on cancer. Postgenomic CTs use multilevel clinical and genomic data and advanced computational analysis and visualization tools to test hypothesis in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment. This paper provides a presentation of the needs of users involved in postgenomic CTs, and presents such needs in the form of scenarios, which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented, and its services are classified and discussed. A key set of such services are those used for wrapping heterogeneous clinical trial management systems and other public biological databases. Also, the main technological challenge, i.e. the design and development of semantically rich grid services is discussed. In achieving such an objective, extensive use of ontologies and metadata are required. The Master Ontology on Cancer, developed by the project, is presented, and our approach to develop the required metadata registries, which provide semantically rich information about available data and computational services, is provided. Finally, a short discussion of the work lying ahead is included.

Stage-specific expression of the chitin synthase DmeChSA and DmeChSB genes during the onset of Drosophila metamorphosis.

Chitin, the major structural polysaccharide of arthropods, is an important constituent of the insect extracellular structures, cuticle and gut peritrophic matrix. Synthesis of cuticular chitin is strictly coordinated with the ecdysone-regulated molting cycle of insect development (the term "ecdysone" is used in this paper instead of "ecdysteroids" since the exact ratio of various hormonal forms changes during metamorphosis). Based on observed similarities between the fungal chitin synthases and other processive beta-glycosyltransferases, we have identified the first insect chitin synthase genes, DmeChSA and DmeChSB (Database accession numbers: EMBL/GenBank/DDBJ A83122, A83126, AJ309488, AJ309489), from Drosophila melanogaster. Chromosomal localization has identified these genes close to and on either side of the centromere of the third chromosome. Partial cDNA clones of both genes have been isolated from a pupal cDNA library. To obtain the first insight into the transcriptional regulation of chitin synthesis, we have monitored the expression of DmeChSA and DmeChSB during the periods of the late-larval and prepupal ecdysone pulses that direct metamorphosis. Transcripts of either gene are barely detected prior to and during the late-larval ecdysone pulse. Once the late-larval ecdysone pulse is ceased completely, both DmeChSA and DmeChSB genes show a remarkable up-regulation.