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Genomic sequence mutations in both the germline and somatic cells can be pathogenic. Several authors have observed that often the same genes are involved in cancer when mutated in somatic cells and in genetic diseases when mutated in the germline. Recent advances in high-throughput sequencing techniques have provided us with large databases of both types of mutations, allowing us to investigate this issue in a systematic way. Here we show that high-throughput data about the frequency of somatic mutations in the most common cancers can be used to predict the genes involved in abnormal phenotypes and diseases. The predictive power of somatic mutation patterns is largely independent of that of methods based on germline mutation frequency, so that they can be fruitfully integrated into algorithms for the prioritization of causal variants. Our results confirm the deep relationship between pathogenic mutations in somatic and germline cells, provide new insight into the common origin of cancer and genetic diseases and can be used to improve the identification of new disease genes.

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre-diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case-control study nested within the EPIC-Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case-control pairs). We validated the top signals in 429 case-control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p-valuepooled =4 10-17 ), cg03636183 in the F2RL3 gene (p-valuepooled =2 10 - 13 ), cg21566642 and cg05951221 in 2q37.1 (p-valuepooled =7 10-16 and 1 10-11 respectively), cg06126421 in 6p21.33 (p-valuepooled =2 10-15 ) and cg23387569 in 12q14.1 (p-valuepooled =5 10-7 ). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p-valuesheterogeneity 1.8 x10 - 7 ), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p-values0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack-years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk. (VLID)2220247

MCF7/MCF10 comparisons vs Fu - density plots of FPKM PRE values for different classes of genes: “Common” are genes detected as shortened by both approaches, “Only roar” and “Only Fu” are genes identified by only either one of the approaches, “N.S.” are genes not significantly identified as shortened. FPKM PRE values are averaged between the MCF7 and MCF10 samples. (PDF 456 kb)

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre‐diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case‐control study nested within the EPIC‐Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case‐control pairs). We validated the top signals in 429 case‐control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p‐valuepooled = 4 × 10−17), cg03636183 in the F2RL3 gene (p‐valuepooled = 2 × 10 − 13), cg21566642 and cg05951221 in 2q37.1 (p‐valuepooled = 7 × 10−16 and 1 × 10−11 respectively), cg06126421 in 6p21.33 (p‐valuepooled = 2 × 10−15) and cg23387569 in 12q14.1 (p‐valuepooled = 5 × 10−7). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p‐valuesheterogeneity ≤ 1.8 x10 − 7), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p‐values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack‐years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk., What's new? It is well known that smoking can cause lung cancer but the concept that it might do so by changing DNA methylation is only emerging. Here the authors identify six sites of methylation (CpGs), where methylation levels were associated with lung cancer risk after adjusting for smoking, current or former. Methylation of five of the CpGs was lowest in current smokers and increased in former smokers with time since quitting, supporting the growing evidence that smoking may lead to DNA methylation changes measurable in peripheral blood and useful as predictive markers for lung cancer risk, especially in former smokers.

Metastasis depends on the ability of tumor cells to establish a relationship with the newly seeded tissue that is conducive to their survival and proliferation. However, the factors that render tissues permissive for metastatic tumor growth have yet to be fully elucidated. Breast tumors arising during pregnancy display early metastatic proclivity, raising the possibility that pregnancy may constitute a physiological condition of permissiveness for tumor dissemination. Here we have shown that during murine gestation, metastasis is enhanced regardless of tumor type, and that decreased NK cell activity is responsible for the observed increase in experimental metastasis. Gene expression changes in pregnant mouse lung and liver were shown to be similar to those detected in premetastatic sites and indicative of myeloid cell infiltration. Indeed, myeloid-derived suppressor cells (MDSCs) accumulated in pregnant mice and exerted an inhibitory effect on NK cell activity, providing a candidate mechanism for the enhanced metastatic tumor growth observed in gestant mice. Although the functions of MDSCs are not yet understood in the context of pregnancy, our observations suggest that they may represent a shared mechanism of immune suppression occurring during gestation and tumor growth.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

The inference of gene regulatory networks from expression data is an important area of research that provides insight to the inner workings of a biological system. In this paper we present a new reverse-engineering framework for gene regulatory network reconstruction. It exploits some interesting characteristics of genetic programming, like the ability of solving complex regression problems with little or no information about the underlying data and the ability of performing an automatic selection of features at learning time. Our framework not only reconstructs the topology of the network, but it is also able to simulate its dynamic behaviour, forecasting the gene expression values in the timepoints subsequent to the initial one. In this paper, we test the proposed framework on the well-known IRMA gene regulatory network, and we show that its performances are promising.

BACKGROUND: Transcriptional regulation is a key mechanism in the functioning of the cell, and is mostly effected through transcription factors binding to specific recognition motifs located upstream of the coding region of the regulated gene. The computational identification of such motifs is made easier by the fact that they often appear several times in the upstream region of the regulated genes, so that the number of occurrences of relevant motifs is often significantly larger than expected by pure chance. RESULTS: To exploit this fact, we construct sets of genes characterized by the statistical overrepresentation of a certain motif in their upstream regions. Then we study the functional characterization of these sets by analyzing their annotation to Gene Ontology terms. For the sets showing a statistically significant specific functional characterization, we conjecture that the upstream motif characterizing the set is a binding site for a transcription factor involved in the regulation of the genes in the set. CONCLUSIONS: The method we propose is able to identify many known binding sites in S. cerevisiae and new candidate targets of regulation by known transcription factors. Its application to less well studied organisms is likely to be valuable in the exploration of their regulatory interaction network., 19 pages, 1 figure. Published version with several improvements. Supplementary material available from the authors

Background Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation. Results We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest. Conclusions CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques.

Background When generating transformed plants, a first step in their characterization is to obtain, for each new line, an estimate of how many copies of the transgene have been integrated in the plant genome because this can deeply influence the level of transgene expression and the ease of stabilizing expression in following generations. This task is normally achieved by Southern analysis, a procedure that requires relatively large amounts of plant material and is both costly and labour-intensive. Moreover, in the presence of rearranged copies the estimates are not correct. New approaches to the problem could be of great help for plant biotechnologists. Results By using a quantitative real-time PCR method that requires limited preliminary optimisation steps, we achieved statistically significant estimates of 1, 2 and 3 copies of a transgene in the primary transformants. Furthermore, by estimating the copy number of both the gene of interest and the selectable marker gene, we show that rearrangements of the T-DNA are not the exception, and probably happen more often than usually recognised. Conclusions We have developed a rapid and reliable method to estimate the number of integrated copies following genetic transformation. Unlike other similar procedures, this method is not dependent on identical amplification efficiency between the PCR systems used and does not need preliminary information on a calibrator. Its flexibility makes it appropriate in those situations where an accurate optimisation of all reaction components is impossible or impractical. Finally, the quality of the information produced is higher than what can be obtained by Southern blot analysis.

Gene regulation in eukaryotes is mainly effected through transcription factors binding to rather short recognition motifs generally located upstream of the coding region. We present a novel computational method to identify regulatory elements in the upstream region of eukaryotic genes. The genes are grouped in sets sharing an overrepresented short motif in their upstream sequence. For each set, the average expression level from a microarray experiment is determined: If this level is significantly higher or lower than the average taken over the whole genome, then the overerpresented motif shared by the genes in the set is likely to play a role in their regulation. The method was tested by applying it to the genome of Saccharomyces cerevisiae, using the publicly available results of a DNA microarray experiment, in which expression levels for virtually all the genes were measured during the diauxic shift from fermentation to respiration. Several known motifs were correctly identified, and a new candidate regulatory sequence was determined., Comment: Published version available from http://www.biomedcentral.com/1471-2105/3/7

We consider the three--dimensional BF--model with planar boundary in the axial gauge. We find two--dimensional conserved chiral currents living on the boundary and satisfying Kac--Moody algebras., Comment: 18 pages

Ewing's sarcoma is a member of Ewing's family tumors (EFTs) and the second most common solid bone and soft tissue malignancy of children and young adults. It is associated in 85% of cases with the t(11;22)(q24:q12) chromosomal translocation that generates fusion of the 5' segment of the EWS gene with the 3' segment of the ETS family gene FLI-1. The EWS-FLI-1 fusion protein behaves as an aberrant transcriptional activator and is believed to contribute to EFT development. However, EWS-FLI-1 induces growth arrest and apoptosis in normal fibroblasts, and primary cells that are permissive for its putative oncogenic properties have not been discovered, hampering basic understanding of EFT biology. Here, we show that EWS-FLI-1 alone can transform primary bone marrow-derived mesenchymal progenitor cells and generate tumors that display hallmarks of Ewing's sarcoma, including a small round cell phenotype, expression of EFT-associated markers, insulin like growth factor-I dependence, and induction or repression of numerous EWS-FLI-1 target genes. These observations provide the first identification of candidate primary cells from which EFTs originate and suggest that EWS-FLI-1 expression may constitute the initiating event in EFT pathogenesis.

Aim: To investigate the transcriptional changes induced by Fingolimod (FTY) in T cells of relapsing remitting multiple sclerosis patients. Patients & methods: Transcriptomic changes after 6 months of FTY therapy were evaluated on T cells from 24 relapsing remitting multiple sclerosis patients through RNA-sequencing, followed by technical validation and pathway analysis. Results: Among differentially expressed genes, CX3CR1 and CCR7 resulted strongly up- and downregulated, respectively. Two relevant genes were validated with quantitative PCR and we largely confirmed findings from two previous microarray-based studies with similar design. Pathway analysis pointed to an involvement of processes related to immune function and cell migration. Conclusion: Our data support the evidence that FTY induces major transcriptional changes in genes involved in immune response and cell trafficking in T lymphocytes.

With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods—logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)–as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines.

Breast cancer is a complex disease in which heterogeneity makes clinical management very challenging. Although breast cancer subtypes classified according to specific molecular features are associated to better or worse prognosis, the identification of specific markers predicting disease outcome within the single subtypes still lags behind. Both the non-canonical WNT and the STAT3 pathways are often constitutively activated in breast tumors, and both can induce the small GTPase RhoU gene transcription. Here we show that RhoU transcription can be triggered by both canonical and non-canonical WNT ligands via the activation of JNK and the recruitment of the SP1 transcription factor to the RhoU promoter, identifying for the first time SP1 as a JNK-dependent mediator of WNT signaling. RhoU down-regulation by silencing or treatment with JNK, SP1 or STAT3 inhibitors lead to impaired cell migration in basal-like MDA-MB-231 cells, which display constitutive activation of both the non-canonical WNT and STAT3 pathways. These data suggest that STAT3 and SP1 can cooperate to induce high RhoU expression and enhance migration of breast cancer cells.In vivobinding of both factors characterizes a group of SP1/STAT3 responsive genes belonging to the non-canonical WNT and the IL-6/STAT3 pathways. High expression of this signature is significantly correlated with poor prognosis across all profiled patients. Thus, concomitant binding of both STAT3 and SP1 defines a subclass of genes contributing to breast cancer aggressiveness, suggesting the relevance of developing novel targeted therapies combining inhibitors of the STAT3 and WNT pathways or of their downstream mediators.Novelty and ImpactThe WNT and STAT3 pathways are often activated in breast tumors, but whether they can cooperate towards aggressiveness is not presently known. Here the authors show that WNT ligands can elicit the activation of the transcription factor SP1, which cooperates with STAT3 to induce a subset of non-canonical WNT and IL-6/STAT3 genes. Expression of this gene signature correlates with bad prognosis in breast cancer, suggesting coordinated interference with both TFs as a novel therapeutic option.