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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.

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)

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.

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

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.