Gata2, Nkx2-2 and Skor2 form a transcription factor network regulating development of a midbrain GABAergic neuron subtype with characteristics of REM-sleep regulatory neurons.

The midbrain reticular formation (MRF) is a mosaic of diverse GABAergic and glutamatergic neurons that have been associated with a variety of functions, including sleep regulation. However, the molecular characteristics and development of MRF neurons are poorly understood. As the transcription factor, Gata2 is required for the development of all GABAergic neurons derived from the embryonic mouse midbrain, we hypothesized that the genes expressed downstream of Gata2 could contribute to the diversification of GABAergic neuron subtypes in this brain region. Here, we show that Gata2 is required for the expression of several GABAergic lineage-specific transcription factors, including Nkx2-2 and Skor2, which are co-expressed in a restricted group of post-mitotic GABAergic precursors in the MRF. Both Gata2 and Nkx2-2 function is required for Skor2 expression in GABAergic precursors. In the adult mouse and rat midbrain, Nkx2-2-and Skor2-expressing GABAergic neurons locate at the boundary of the ventrolateral periaqueductal gray and the MRF, an area containing REM-off neurons regulating REM sleep. In addition to the characteristic localization, Skor2+ cells increase their activity upon REM-sleep inhibition, send projections to the dorsolateral pons, a region associated with sleep control, and are responsive to orexins, consistent with the known properties of midbrain REM-off neurons.

Range of chromatin accessibility configurations are permissive of GABAergic fate acquisition in developing mouse brain.

In recent single-cell -omics studies, both the differential activity of transcription factors regulating cell fate determination and differential genome activation have been tested for utility as descriptors of cell types. Naturally, genome accessibility and gene expression are interlinked. To understand the variability in genomic feature activation in the GABAergic neurons of different spatial origins, we have mapped accessible chromatin regions and mRNA expression in single cells derived from the developing mouse central nervous system (CNS). We first defined a reference set of open chromatin regions for scATAC-seq read quantitation across samples, allowing comparison of chromatin accessibility between brain regions and cell types directly. Second, we integrated the scATAC-seq and scRNA-seq data to form a unified resource of transcriptome and chromatin accessibility landscape for the cell types in di- and telencephalon, midbrain and anterior hindbrain of E14.5 mouse embryo. Importantly, we implemented resolution optimization at the clustering, and automatized the cell typing step. We show high level of concordance between the cell clustering based on the chromatin accessibility and the transcriptome in analyzed neuronal lineages, indicating that both genome and transcriptome features can be used for cell type definition. Hierarchical clustering by the similarity in accessible chromatin reveals that the genomic feature activation correlates with neurotransmitter phenotype, selector gene expression, cell differentiation stage and neuromere origins.

Transcriptomic analyses reveal three distinct molecular subgroups of Merkel cell carcinoma with differing prognoses.

Merkel cell carcinoma (MCC) is a cutaneous neuroendocrine malignancy with a poor prognosis and an unknown cell of origin. Proffered cells of origin include epithelial stem cells of the hair follicle or interfollicular epidermis, dermal stem cells and pro/pre- or pre-B cells. MCC has also been proposed to have more than one cell of origin and indeed to represent more than one type of carcinoma, currently grouped together due to phenotypic similarities. We explored the heterogeneous nature of MCC by studying the most variably expressed genes with the goal of identifying gene expression patterns that are either clinically relevant or have implications regarding the cell(s) of origin. We performed RNA sequencing on primary tumor samples from 102 patients and identified the top 200 most variably expressed genes. These genes and the tumor samples were hierarchically clustered based on their expression. The functions of three gene clusters exhibiting clearly divergent expression between samples were studied by cross-referencing the lists of genes with online databases. High expression of a gene cluster related to embryonic developmental processes and low expression of a gene cluster related to neuroendocrine processes distinguished Merkel cell polyomavirus (MCPyV)-negative tumors from MCPyV-positive tumors. Furthermore, two prognostically relevant subgroups of MCPyV-positive MCC were identified based on dichotomic expression of genes related to epidermal structures and processes. We identified three distinct molecular subgroups of MCC with prognostic relevance. We propose that the dichotomic expression of epidermis-related genes might reflect both an epidermal and a nonepidermal origin for MCPyV-positive MCC.

Application of active and kinase-deficient kinome collection for identification of kinases regulating hedgehog signaling.

To allow genome-scale identification of genes that regulate cellular signaling, we cloned >90% of all human full-length protein kinase cDNAs and constructed the corresponding kinase activity-deficient mutants. To establish the utility of this resource, we tested the effect of expression of the kinases on three different cellular signaling models. In all screens, many kinases had a modest but significant effect, apparently due to crosstalk between signaling pathways. However, the strongest effects were found with known regulators and novel components, such as MAP3K10 and DYRK2, which we identified in a mammalian Hedgehog (Hh) signaling screen. DYRK2 directly phosphorylated and induced the proteasome-dependent degradation of the key Hh pathway-regulated transcription factor, GLI2. MAP3K10, in turn, affected GLI2 indirectly by modulating the activity of DYRK2 and the known Hh pathway component, GSK3beta. Our results establish kinome expression screening as a highly effective way to identify physiological signaling pathway components and genes involved in pathological signaling crosstalk.

A novel transcript, VNN1-AB, as a biomarker for colorectal cancer.

Colorectal cancer is a global health challenge with high incidence rate and mortality. The patients' prognosis is strongly associated with disease stage and currently there is a need for improved prognostic and predictive biomarkers. In this study, novel colorectal cancer-specific transcript structures were nominated from whole transcriptome sequencing of seven colorectal cancer cell lines, two primary colorectal carcinomas with corresponding normal colonic mucosa and 16 normal tissues. The nominated transcripts were combined with gene level outlier expression analyses in a cohort of 505 colorectal cancers to identify biomarkers with capacity to stratify colorectal cancer subgroups. The transcriptome sequencing data and outlier expression analysis revealed 11 novel colorectal cancer-specific exon-exon junctions, of which 3 were located in the gene VNN1. The junctions within VNN1 were further characterized using rapid amplification of cDNA ends (RACE) and the prevalence of the subsequently characterized novel transcript, VNN1-AB, was investigated by real-time RT-PCR in 291 samples of miscellaneous origins. VNN1-AB was not present in any of the 43 normal colorectal tissue samples investigated, but in 5 of the 6 polyps, and 102 of the 136 (75%) colorectal cancers. We have identified a novel transcript of the VNN1 gene, with an organ-confined complete specificity for colorectal neoplasia.

LGI2 truncation causes a remitting focal epilepsy in dogs.

One quadrillion synapses are laid in the first two years of postnatal construction of the human brain, which are then pruned until age 10 to 500 trillion synapses composing the final network. Genetic epilepsies are the most common neurological diseases with onset during pruning, affecting 0.5% of 2-10-year-old children, and these epilepsies are often characterized by spontaneous remission. We previously described a remitting epilepsy in the Lagotto romagnolo canine breed. Here, we identify the gene defect and affected neurochemical pathway. We reconstructed a large Lagotto pedigree of around 34 affected animals. Using genome-wide association in 11 discordant sib-pairs from this pedigree, we mapped the disease locus to a 1.7 Mb region of homozygosity in chromosome 3 where we identified a protein-truncating mutation in the Lgi2 gene, a homologue of the human epilepsy gene LGI1. We show that LGI2, like LGI1, is neuronally secreted and acts on metalloproteinase-lacking members of the ADAM family of neuronal receptors, which function in synapse remodeling, and that LGI2 truncation, like LGI1 truncations, prevents secretion and ADAM interaction. The resulting epilepsy onsets at around seven weeks (equivalent to human two years), and remits by four months (human eight years), versus onset after age eight in the majority of human patients with LGI1 mutations. Finally, we show that Lgi2 is expressed highly in the immediate post-natal period until halfway through pruning, unlike Lgi1, which is expressed in the latter part of pruning and beyond. LGI2 acts at least in part through the same ADAM receptors as LGI1, but earlier, ensuring electrical stability (absence of epilepsy) during pruning years, preceding this same function performed by LGI1 in later years. LGI2 should be considered a candidate gene for common remitting childhood epilepsies, and LGI2-to-LGI1 transition for mechanisms of childhood epilepsy remission.

PDE3A Is a Highly Expressed Therapy Target in Myxoid Liposarcoma.

Liposarcomas (LPSs) are a heterogeneous group of malignancies that arise from adipose tissue. Although LPSs are among the most common soft-tissue sarcoma subtypes, precision medicine treatments are not currently available. To discover LPS-subtype-specific therapy targets, we investigated RNA sequenced transcriptomes of 131 clinical LPS tissue samples and compared the data with a transcriptome database that contained 20,218 samples from 95 healthy tissues and 106 cancerous tissue types. The identified genes were referred to the NCATS BioPlanet library with Enrichr to analyze upregulated signaling pathways. PDE3A protein expression was investigated with immunohistochemistry in 181 LPS samples, and PDE3A and SLFN12 mRNA expression with RT-qPCR were investigated in 63 LPS samples. Immunoblotting and cell viability assays were used to study LPS cell lines and their sensitivity to PDE3A modulators. We identified 97, 247, and 37 subtype-specific, highly expressed genes in dedifferentiated, myxoid, and pleomorphic LPS subtypes, respectively. Signaling pathway analysis revealed a highly activated hedgehog signaling pathway in dedifferentiated LPS, phospholipase c mediated cascade and insulin signaling in myxoid LPS, and pathways associated with cell proliferation in pleomorphic LPS. We discovered a strong association between high PDE3A expression and myxoid LPS, particularly in high-grade tumors. Moreover, myxoid LPS samples showed elevated expression levels of SLFN12 mRNA. In addition, PDE3A- and SLFN12-coexpressing LPS cell lines SA4 and GOT3 were sensitive to PDE3A modulators. Our results indicate that PDE3A modulators are promising drugs to treat myxoid LPS. Further studies are required to develop these drugs for clinical use.

Tumor-independent Detection of Inherited Mismatch Repair Deficiency for the Diagnosis of Lynch Syndrome with High Specificity and Sensitivity.

Lynch syndrome (LS) is the most common hereditary cancer syndrome. Early diagnosis improves prognosis and reduces health care costs, through existing cancer surveillance methods. The problem is finding and diagnosing the cancer predisposing genetic condition. The current workup involves a complex array of tests that combines family cancer history and clinical phenotypes with tumor characteristics and sequencing data, followed by a challenging task to interpret the found variant(s). On the basis of the knowledge that an inherited mismatch repair (MMR) deficiency is a hallmark of LS, we have developed and validated a functional MMR test, DiagMMR, that detects inherited MMR deficiency directly from healthy tissue without need of tumor and variant information. The validation included 119 skin biopsies collected from clinically pathogenic MMR variant carriers (MSH2, MSH6) and controls, and was followed by a small clinical pilot study. The repair reaction was performed on proteins extracted from primary fibroblasts and the interpretation was based on the MMR capability of the sample in relation to cutoff, which distinguishes MMR proficient (non-LS) from MMR deficient (LS) function. The results were compared with the reference standard (germline NGS). The test was shown to have exceptional specificity (100%) with high sensitivity (89%) and accuracy (97%). The ability to efficiently distinguish LS carriers from controls was further shown with a high area under the receiving operating characteristic (AUROC) value (0.97). This test offers an excellent tool for detecting inherited MMR deficiency linked to MSH2 or MSH6 and can be used alone or with conventional tests to recognize genetically predisposed individuals. Significance: Clinical validation of DiagMMR shows high accuracy in distinguishing individuals with hereditary MSH2 or MSH6 MMR deficiency (i.e., LS). The method presented overcomes challenges faced by the complexity of current methods and can be used alone or with conventional tests to improve the ability to recognize genetically predisposed individuals.

Activation of Oncogenic and Immune-Response Pathways Is Linked to Disease-Specific Survival in Merkel Cell Carcinoma.

BACKGROUND: Merkel cell carcinoma (MCC) is a rare but highly aggressive neuroendocrine carcinoma of the skin with a poor prognosis. Improving the prognosis of MCC by means of targeted therapies requires further understanding of the mechanisms that drive tumor progression. In this study, we aimed to identify the genes, processes, and pathways that play the most crucial roles in determining MCC outcomes. METHODS: We investigated transcriptomes generated by RNA sequencing of formalin-fixed paraffin-embedded tissue samples of 102 MCC patients and identified the genes that were upregulated among survivors and in patients who died from MCC. We subsequently cross-referenced these genes with online databases to investigate the functions and pathways they represent. We further investigated differential gene expression based on viral status in patients who died from MCC. RESULTS: We found several novel genes associated with MCC-specific survival. Genes upregulated in patients who died from MCC were most notably associated with angiogenesis and the PI3K-Akt and MAPK pathways; their expression predominantly had no association with viral status in patients who died from MCC. Genes upregulated among survivors were largely associated with antigen presentation and immune response. CONCLUSION: This outcome-based discrepancy in gene expression suggests that these pathways and processes likely play crucial roles in determining MCC outcomes.

First analysis of a bacterial collagen-binding protein with collagen Toolkits: promiscuous binding of YadA to collagens may explain how YadA interferes with host processes.

The Yersinia adhesin YadA mediates the adhesion of the human enteropathogen Yersinia enterocolitica to collagens and other components of the extracellular matrix. Though YadA has been proposed to bind to a specific site in collagens, the exact binding determinants for YadA in native collagen have not previously been elucidated. We investigated the binding of YadA to collagen Toolkits, which are libraries of triple-helical peptides spanning the sequences of type II and III human collagens. YadA bound to many of them, in particular to peptides rich in hydroxyproline but with few charged residues. We were able to block the binding of YadA to collagen type IV with the triple-helical peptide (Pro-Hyp-Gly)(10), suggesting that the same site in YadA binds to triple-helical regions in network-forming collagens as well. We showed that a single Gly-Pro-Hyp triplet in a triple-helical peptide was sufficient to support YadA binding, but more than six triplets were required to form a tight YadA binding site. This is significantly longer than the case for eukaryotic collagen-binding proteins. YadA-expressing bacteria bound promiscuously to Toolkit peptides. Promiscuous binding could be advantageous for pathogenicity in Y. enterocolitica and, indeed, for other pathogenic bacteria. Many of the tightly binding peptides are also targets for eukaryotic collagen-binding proteins, and YadA was able to inhibit the interaction between selected Toolkit peptides and platelets. This leads to the intriguing possibility that YadA may interfere in vivo with host processes mediated by endogenous collagen-binding proteins.

Array-based gene expression, CGH and tissue data defines a 12q24 gain in neuroblastic tumors with prognostic implication.

BACKGROUND: Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma. METHODS: A single-gene resolution aCGH profiling was integrated with microarray-based gene expression profiling data to distinguish genetic copy number alterations that were strongly associated with transcriptional changes in two neuroblastoma cell lines. FISH analysis using a hotspot tumor tissue microarray of 37 paraffin-embedded neuroblastoma samples and in silico data mining for gene expression information obtained from previously published studies including up to 445 healthy nervous system samples and 123 neuroblastoma samples were used to evaluate the clinical significance and transcriptional consequences of the detected alterations and to identify subsequently activated gene(s). RESULTS: In addition to the anticipated high-level amplification and subsequent overexpression of MYCN, MEIS1, CDK4 and MDM2 oncogenes, the aCGH analysis revealed numerous other genetic alterations, including microamplifications at 2p and 12q24.11. Most interestingly, we identified and investigated the clinical relevance of a previously poorly characterized amplicon at 12q24.31. FISH analysis showed low-level gain of 12q24.31 in 14 of 33 (42%) neuroblastomas. Patients with the low-level gain had an intermediate prognosis in comparison to patients with MYCN amplification (poor prognosis) and to those with no MYCN amplification or 12q24.31 gain (good prognosis) (P = 0.001). Using the in silico data mining approach, we identified elevated expression of five genes located at the 12q24.31 amplicon in neuroblastoma (DIABLO, ZCCHC8, RSRC2, KNTC1 and MPHOSPH9). Among these, DIABLO showed the strongest activation suggesting a putative role in neuroblastoma progression. CONCLUSIONS: The presented systematic and rapid framework, which integrates aCGH, gene expression and tissue data to obtain novel targets and biomarkers for cancer, identified a low-level gain of the 12q24.31 as a potential new biomarker for neuroblastoma progression. Furthermore, results of in silico data mining suggest a new neuroblastoma target gene, DIABLO, within this region, whose functional and therapeutic role remains to be elucidated in follow-up studies.

Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations.

BACKGROUND: Gene expression microarray technologies are widely used across most areas of biological and medical research. Comparing and integrating microarray data from different experiments would be very useful, but is currently very challenging due to the experimental and hybridization conditions, as well as data preprocessing and normalization methods. Furthermore, even in the case of the widely-used, industry-standard Affymetrix oligonucleotide microarrays, the various array generations have different probe sets representing different genes, hindering the data integration. RESULTS: In this study our objective is to find systematic approaches to normalize the data emerging from different Affymetrix array generations and from different laboratories. We compare and assess the accuracy of five normalization methods for Affymetrix gene expression data using 6,926 Affymetrix experiments from five array generations. The methods that we compare include 1) standardization, 2) housekeeping gene based normalization, 3) equalized quantile normalization, 4) Weibull distribution based normalization and 5) array generation based gene centering. Our results indicate that the best results are achieved when the data is normalized first within a sample and then between-samples with Array Generation based gene Centering (AGC) normalization. CONCLUSION: We conclude that with the AGC method integrating different Affymetrix datasets results in values that are significantly more comparable across the array generations than in the cases where no array generation based normalization is used. The AGC method was found to be the best method for normalizing the data from several different array generations, and achieve comparable gene values across thousands of samples.

High-resolution copy number and gene expression microarray analyses of head and neck squamous cell carcinoma cell lines of tongue and larynx.

Gene amplifications and deletions are frequent in head and neck squamous cell carcinomas (SCC) but the association of these alterations with gene expression is mostly unknown. Here, we characterized genome-wide copy number and gene expression changes on microarrays for 18 oral tongue SCC (OTSCC) cell lines. We identified a number of altered regions including nine high-level amplifications such as 6q12-q14 (CD109, MYO6), 9p24 (JAK2, CD274, SLC1A1, RLN1), 11p12-p13 (TRAF6, COMMD9, TRIM44, FJX1, CD44, PDHX, APIP), 11q13 (FADD, PPFIA1, CTTN), and 14q24 (ABCD4, HBLD1, LTBP2, ZNF410, COQ6, ACYP1, JDP2) where 9% to 64% of genes showed overexpression. Across the whole genome, 26% of the amplified genes had associated overexpression in OTSCC. Furthermore, our data implicated that OTSCC cell lines harbored similar genomic alterations as laryngeal SCC cell lines We have previously analyzed, suggesting that despite differences in clinicopathological features there are no marked differences in molecular genetic alterations of these two HNSCC sites. To identify genes whose expression was associated with copy number increase in head and neck SCC, a statistical analysis for oral tongue and laryngeal SCC cell line data were performed. We pinpointed 1,192 genes that had a statistically significant association between copy number and gene expression. These results suggest that genomic alterations with associated gene expression changes play an important role in the malignant behavior of head and neck SCC. The identified genes provide a basis for further functional validation and may lead to the identification of novel candidates for targeted therapies. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

TMPRSS2 fusions with oncogenic ETS factors in prostate cancer involve unbalanced genomic rearrangements and are associated with HDAC1 and epigenetic reprogramming.

Translocations fusing the strong androgen-responsive gene, TMPRSS2, with ERG or other oncogenic ETS factors may facilitate prostate cancer development. Here, we studied 18 advanced prostate cancers for ETS factor alterations, using reverse transcription-PCR and DNA and RNA array technologies, and identified putative ERG downstream gene targets from the microarray data of 410 prostate samples. Out of the 27 ETS factors, ERG was most frequently overexpressed. Seven cases showed TMPRSS2:ERG gene fusions, whereas the TMPRSS2:ETV4 fusion was seen in one case. In five out of six tumors with high ERG expression, array-CGH analysis revealed interstitial 2.8 Mb deletions between the TMPRSS2 and ERG loci, or smaller, unbalanced rearrangements. In silico analysis of the ERG gene coexpression patterns revealed an association with high expression of the histone deacetylase 1 gene, and low expression of its target genes. Furthermore, we observed increased expression of WNT-associated pathways and down-regulation of tumor necrosis factor and cell death pathways. In summary, our data indicate that the TMPRSS2:ERG translocation is common in advanced prostate cancer and occurs by virtue of unbalanced genomic rearrangements. Activation of ERG by fusion with TMPRSS2 may lead to epigenetic reprogramming, WNT signaling, and down-regulation of cell death pathways, implicating ERG in several hallmarks of cancer with potential therapeutic importance.

NMD microarray analysis for rapid genome-wide screen of mutated genes in cancer.

Gene mutations play a critical role in cancer development and progression, and their identification offers possibilities for accurate diagnostics and therapeutic targeting. Finding genes undergoing mutations is challenging and slow, even in the post-genomic era. A new approach was recently developed by Noensie and Dietz to prioritize and focus the search, making use of nonsense-mediated mRNA decay (NMD) inhibition and microarray analysis (NMD microarrays) in the identification of transcripts containing nonsense mutations. We combined NMD microarrays with array-based CGH (comparative genomic hybridization) in order to identify inactivation of tumor suppressor genes in cancer. Such a "mutatomics" screening of prostate cancer cell lines led to the identification of inactivating mutations in the EPHB2 gene. Up to 8% of metastatic uncultured prostate cancers also showed mutations of this gene whose loss of function may confer loss of tissue architecture. NMD microarray analysis could turn out to be a powerful research method to identify novel mutated genes in cancer cell lines, providing targets that could then be further investigated for their clinical relevance and therapeutic potential.

Biclustering methods: biological relevance and application in gene expression analysis.

DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical) methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1) we examine how well the considered (bi)clustering methods differentiate various sample types; (2) we evaluate how well the groups of genes discovered by the (bi)clustering methods are annotated with similar Gene Ontology categories; (3) we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4) we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such as Plaid and SAMBA are useful for discovering relevant subsets of genes and samples.

Extracellular membrane vesicles from umbilical cord blood-derived MSC protect against ischemic acute kidney injury, a feature that is lost after inflammatory conditioning.

BACKGROUND: Mesenchymal stromal cells (MSC) are shown to have a great therapeutic potential in many immunological disorders. Currently the therapeutic effect of MSCs is considered to be mediated via paracrine interactions with immune cells. Umbilical cord blood is an attractive but still less studied source of MSCs. We investigated the production of extracellular membrane vesicles (MVs) from human umbilical cord blood derived MSCs (hUCBMSC) in the presence (MVstim) or absence (MVctrl) of inflammatory stimulus. METHODS: hUCBMSCs were cultured in serum free media with or without IFN-γ and MVs were collected from conditioned media by ultracentrifugation. The protein content of MVs were analyzed by mass spectrometry. Hypoxia induced acute kidney injury rat model was used to analyze the in vivo therapeutic potential of MVs and T-cell proliferation and induction of regulatory T cells were analyzed by co-culture assays. RESULTS: Both MVstim and MVctrl showed similar T-cell modulation activity in vitro, but only MVctrls were able to protect rat kidneys from reperfusion injury in vivo. To clarify this difference in functionality we made a comparative mass spectrometric analysis of the MV protein contents. The IFN-γ stimulation induced dramatic changes in the protein content of the MVs. Complement factors (C3, C4A, C5) and lipid binding proteins (i.e apolipoproteins) were only found in the MVctrls, whereas the MVstim contained tetraspanins (CD9, CD63, CD81) and more complete proteasome complex accompanied with MHCI. We further discovered that differently produced MV pools contained specific Rab proteins suggesting that same cells, depending on external signals, produce vesicles originating from different intracellular locations. CONCLUSIONS: We demonstrate by both in vitro and in vivo models accompanied with a detailed analysis of molecular characteristics that inflammatory conditioning of MSCs influence on the protein content and functional properties of MVs revealing the complexity of the MSC paracrine regulation.

Facilitates chromatin transcription complex is an "accelerator" of tumor transformation and potential marker and target of aggressive cancers.

The facilitates chromatin transcription (FACT) complex is involved in chromatin remodeling during transcription, replication, and DNA repair. FACT was previously considered to be ubiquitously expressed and not associated with any disease. However, we discovered that FACT is the target of a class of anticancer compounds and is not expressed in normal cells of adult mammalian tissues, except for undifferentiated and stem-like cells. Here, we show that FACT expression is strongly associated with poorly differentiated aggressive cancers with low overall survival. In addition, FACT was found to be upregulated during in vitro transformation and to be necessary, but not sufficient, for driving transformation. FACT also promoted survival and growth of established tumor cells. Genome-wide mapping of chromatin-bound FACT indicated that FACT's role in cancer most likely involves selective chromatin remodeling of genes that stimulate proliferation, inhibit cell death and differentiation, and regulate cellular stress responses.

Classification of unknown primary tumors with a data-driven method based on a large microarray reference database.

We present a new method to analyze cancer of unknown primary origin (CUP) samples. Our method achieves good results with classification accuracy (88% leave-one-out cross validation for primary tumors from 56 categories, 78% for CUP samples), and can also be used to study CUP samples on a gene-by-gene basis. It is not tied to any a priori defined gene set as many previous methods, and is adaptable to emerging new information.

Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data.

BACKGROUND: Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as developed as e.g. in the field of nucleotide sequence comparisons. We have created a rapid and powerful approach for the alignment of microarray gene expression profiles (AGEP) from test samples with those contained in a large annotated public reference database and demonstrate here how this can facilitate interpretation of microarray data from individual samples. METHODS: AGEP is based on the calculation of kernel density distributions for the levels of expression of each gene in each reference tissue type and provides a quantitation of the similarity between the test sample and the reference tissue types as well as the identity of the typical and atypical genes in each comparison. As a reference database, we used 1654 samples from 44 normal tissues (extracted from the Genesapiens database). RESULTS: Using leave-one-out validation, AGEP correctly defined the tissue of origin for 1521 (93.6%) of all the 1654 samples in the original database. Independent validation of 195 external normal tissue samples resulted in 87% accuracy for the exact tissue type and 97% accuracy with related tissue types. AGEP analysis of 10 Duchenne muscular dystrophy (DMD) samples provided quantitative description of the key pathogenetic events, such as the extent of inflammation, in individual samples and pinpointed tissue-specific genes whose expression changed (SAMD4A) in DMD. AGEP analysis of microarray data from adipocytic differentiation of mesenchymal stem cells and from normal myeloid cell types and leukemias provided quantitative characterization of the transcriptomic changes during normal and abnormal cell differentiation. CONCLUSIONS: The AGEP method is a widely applicable method for the rapid comprehensive interpretation of microarray data, as proven here by the definition of tissue- and disease-specific changes in gene expression as well as during cellular differentiation. The capability to quantitatively compare data from individual samples against a large-scale annotated reference database represents a widely applicable paradigm for the analysis of all types of high-throughput data. AGEP enables systematic and quantitative comparison of gene expression data from test samples against a comprehensive collection of different cell/tissue types previously studied by the entire research community.