Project description:In this study, mutations present in a series of human melanomas (stage IV disease) will be determined, using autologous blood cells to obtain a reference genome. From each of the samples that are analyzed, tumour-infiltrating T lymphocytes have also been isolated. This offers a unique opportunity to determine which (fraction of) mutations in human cancer leads to epitopes that are recognized by T cells. The resulting information is likely to be of value to understand how T cell activating drugs exert their action.
Project description:In order to study molecular changes in the stroma from tissue samples it is recommended to separate tumor tissue from stromal tissue. This is particularly relevant to mouse tumor xenograft models where tumor, particularly metastatic tumors, can be small and difficult to separate from the host tissue. In our research we compared qualitatively the ability of high-throughput mRNA sequencing, RNA-Seq, and microarrays to detect tumor (human) and stromal (mouse) expression from mixed tumor-stromal samples in terms of the genes and pathways that are involved in cross-alignment (RNA-Seq) and cross-hybridization (microarrays). Human samples consisted of total RNA obtained from MDA-MB-231 human breast carcinoma cell line and isolated from three independent cultures of sub-confluent MDA-MB-231 cell lines in exponential phase of growth. Mouse samples were obtained from NOD scid gamma mice, and normal lung tissue was harvested from three independent age-matched mice.
Project description:miR-Blood is a high-quality, small RNA expression atlas for the major components of human peripheral blood (plasma, erythrocytes, thrombocytes, monocytes, neutrophils, eosinophils, basophils, natural killer cells, CD4+ T cells, CD8+ T cells, and B cells). *** The data provided in this GEO dataset is licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ***
Project description:Whole-transcriptome sequencing ('RNA-Seq') has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification. The SEQC project generated over 30 billion sequence reads representing the largest RNA-Seq data ever generated by a single project on individual RNA samples. This extraordinarily ultradeep transcriptomic data set and the known truths built into the study design provide many opportunities for further research and development to advance the improvement and application of RNA-Seq.
Project description:MOTIVATION: High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction. RESULTS: We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed Integer Programming. MITIE can (i) take advantage of known transcripts, (ii) reconstruct and quantify transcripts simultaneously in multiple samples, and (iii) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction. We present an extensive study based on realistic simulated RNA-Seq data. When compared with state-of-the-art approaches, MITIE proves to be significantly more sensitive and overall more accurate. Moreover, MITIE yields substantial performance gains when used with multiple samples. We applied our system to 38 Drosophila melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Our results corroborate that a well-motivated objective paired with appropriate optimization techniques lead to significant improvements over the state-of-the-art in transcriptome reconstruction. AVAILABILITY: MITIE is implemented in C++ and is available from http://bioweb.me/mitie under the GPL license.
Project description:Numerous studies have described the altered expression and the causal role of miRNAs in human cancer. However, to date efforts to modulate miRNA levels for therapeutic purposes have been challenging to implement. Here, we find that Nucleolin (NCL), a major nucleolar protein, post-transcriptionally regulates the expression of a specific subset of miRNAs, including miR-21, miR-221, miR-222, and miR-103, causally involved in breast cancer initiation, progression and drug-resistance. We also show that NCL is commonly overexpressed in human breast tumors, and its expression correlates with that of NCL-dependent miRNAs. Finally, this study indicates that NCL-binding guanosine-rich aptamers affect the levels of NCL-dependent miRNAs and their target genes, reducing breast cancer cell aggressiveness, both in vitro and in vivo. These findings illuminate a path to novel therapeutic approaches based on NCL-targeting aptamers for the modulation of miRNA expression in the treatment of breast cancer. Identification of NCL regulated miRNAs by using miRNA high-throughput sequencing of HeLa cells stably expressing double-strand (ds) interfering RNA against NCL or scrambled sequences (sh-NCL or sh-Scr).
Project description:Alveolar echinococcosis caused by Echinococcus multilocularis is an important zoonotic disease. In the infected mice, emu-miR-4989-3p is present in sera, but its role remains unknown. Using high-throughput sequencing and qPCR, emu-miR-4989-3p was herein confirmed to be encapsulated into E. multilocularis extracellular vesicles. In the transfected macrophages, emu-miR-4989-3p was demonstrated to significantly inhibit NO production compared to the control (p < 0.05). Moreover, transfection of emu-miR-4989-3p also gave rise to the increased expression of TNF-? (p < 0.01). Furthermore, emu-miR-4989-3p induced the dysregulation of several key components in the LPS/TLR4 signaling pathway compared with the control, especially TLR4 and NF-?B that both were upregulated. Conversely, the NO production and the expression of TNF-?, TLR4 and NF-?B tended to be increased and decreased in the mimics-transfected cells upon emu-miR-4989-3p low expression, respectively. These results suggest that emu-miR-4989-3p is one of 'virulence' factors encapsulated into the extracellular vesicles, potentially playing a role in the pathogenesis of E. multilocularis.
Project description:OBJECTIVE:Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns. RESULTS:We present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to "functional groups". We hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). We test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method's ability to accurately reflect global miRNA expression profiles. We define accuracy as total counts close to the total number of input reads originating from miRNAs. We find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.