Current Issue : July - September Volume : 2020 Issue Number : 3 Articles : 5 Articles
Background: MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative\ndiseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited\nfor analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature\nselection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data\nfrom a knock-in mouse model (Hdh mice) of Huntingtonâ??s disease (HD), a disease caused by CAG repeat expansion\nin huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh\nmice.\nResults: Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach\nretained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat\ndependence over time, among which 5 pairs with a strong change of target expression levels. Several of these\npairs were previously associated with neuronal homeostasis or HD pathogenesis, or both. Such miRNA-mRNA pairs\nwere not detected in cortex.\nConclusions: These data suggest that miRNA regulation has a limited global role in HD while providing accuratelyselected\nmiRNA-target pairs to study how the brain may compute molecular responses to HD over time. These data\nalso provide a methodological framework for researchers to explore how shape analysis can enhance\nmultidimensional data analytics in biology and disease....
Background: The term triple-negative breast cancer (TNBC) is used to describe breast cancers without expression\nof estrogen receptor, progesterone receptor or HER2 amplification. To advance targeted treatment options for\nTNBC, it is critical that the subtypes within this classification be described in regard to their characteristic biology\nand gene expression. The Cancer Genome Atlas (TCGA) dataset provides not only clinical and mRNA expression\ndata but also expression data for microRNAs.\nResults: In this study, we applied the Lehmann classifier to TCGA-derived TNBC cases which also contained\nmicroRNA expression data and derived subtype-specific microRNA expression patterns. Subsequent analyses\nintegrated known and predicted microRNA-mRNA regulatory nodes as well as patient survival data to identify key\nnetworks. Notably, basal-like 1 (BL1) TNBCs were distinguished from basal-like 2 TNBCs through up-regulation of\nmembers of the miR-17-92 cluster of microRNAs and suppression of several known miR-17-92 targets including\ninositol polyphosphate 4-phosphatase type II, INPP4B.\nConclusions: These data demonstrate TNBC subtype-specific microRNA and target mRNA expression which may be\napplied to future biomarker and therapeutic development studies....
Background: MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene expression\nregulation and have been described as key regulators of carcinogenesis. Aberrant miRNA expression has been\nfrequently reported in sporadic breast cancers, but few studies have focused on profiling hereditary breast cancers.\nIn this study, we aimed to identify specific miRNA signatures in hereditary breast tumors and to compare with\nsporadic breast cancer and normal breast tissues.\nMethods: Global miRNA expression profiling using NanoString technology was performed on 43 hereditary breast\ntumors (15 BRCA1, 14 BRCA2, and 14 BRCAX), 23 sporadic breast tumors and 8 normal breast tissues. These normal\nbreast tissues derived from BRCA1- and BRCA2- mutation carriers (n = 5) and non-mutation carriers (n = 3).\nSubsequently, we performed receiver operating characteristic (ROC) curve analyses to evaluate the diagnostic\nperformance of differentially expressed miRNAs. Putative target genes of each miRNAs considered as potential\nbiomarkers were identified using miRDIP platform and used for pathway enrichment analysis.\nResults: miRNA expression analyses identified several profiles that were specific to hereditary breast cancers. A total\nof 25 miRNAs were found to be differentially expressed (fold change: > 2.0 and p < 0.05) and considered as\npotential biomarkers (area under the curve > 0.75) in hereditary breast tumors compared to normal breast tissues,\nwith an expressive upregulation among BRCAX cases. Furthermore, bioinformatic analysis revealed that these\nmiRNAs shared target genes involved in ErbB, FoxO, and PI3K-Akt signaling pathways.\nConclusions: Our results showed that miRNA expression profiling can differentiate hereditary from sporadic breast\ntumors and normal breast tissues. These miRNAs were remarkably deregulated in BRCAX hereditary breast cancers.\nTherefore, miRNA signatures can be used as potential novel diagnostic biomarkers for the prediction of BRCA1/2-\ngermline mutations and may be useful for future clinical management....
Background: Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional\ncapacity of a community towards its most abundant members. For an unbiased assessment of community function,\nshort reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read\nsequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to\ndetermine how library size selection, read length and format, protein database, e-value threshold, and sequencing\ndepth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is\nup to 20,000 times faster than BLASTX.\nResults: Using metagenomes simulated from a database of experimentally verified protein sequences, we find that\nread length, e-value threshold, and the choice of protein database dramatically impact detection of a known target,\nwith best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using\npublicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing\ndepth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected\nto enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are\nindependently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of\nabundance estimates of specific target genes, including an antimicrobial resistance gene.\nConclusions: Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina\nplatform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150\nformat with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target\ngenes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND\nwould then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will\nbe best obtained using small databases customized for the research question....
Background: Aberrant JAK/STAT activation has been detected in many types of human cancers. The role of JAK/\nSTAT activation in cancer has been mostly attributed to direct transcriptional regulation of target genes by\nphosphorylated STAT (pSTAT), while the unphosphorylated STAT (uSTAT) is believed to be dormant and reside in\nthe cytoplasm. However, several studies have shown that uSTATs can be found in the nucleus. In addition, it has\nbeen shown that tissue-specific loss of STAT3 or STAT5 in mice promotes cancer growth in certain tissues, and thus\nthese STAT proteins can act as tumor suppressors. However, no unifying mechanism has been shown for the tumor\nsuppressor function of STATs to date. We have previously demonstrated a non-canonical mode of JAK/STAT\nsignaling for Drosophila STAT and human STAT5A, where a fraction of uSTAT is in the nucleus and associated with\nHeterochromatin Protein 1 (HP1); STAT activation (by phosphorylation) causes its dispersal, leading to HP1\ndelocalization and heterochromatin loss.\nMethods: We used a combination of imaging, cell biological assays, and mouse xenografts to investigate the role\nof STAT3 in lung cancer development.\nResults: We found that uSTAT3 has a function in promoting heterochromatin formation in lung cancer cells,\nsuppressing cell proliferation in vitro, and suppressing tumor growth in mouse xenografts.\nConclusions: Thus, uSTAT3 possesses noncanonical function in promoting heterochromatin formation, and the\ntumor suppressor function of STAT3 is likely attributable to the heterochromatin-promoting activity of uSTAT3 in\nthe non-canonical JAK/STAT pathway....
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