A microRNA feedback loop regulates global microRNA abundance during aging

Expression levels of many microRNAs (miRNAs) change during aging, notably declining globally in a number of organisms and tissues across taxa. However, little is known about the mechanisms or the biological relevance for this change. We investigated the network of genes that controls miRNA transcription and processing during C. elegans aging. We found that miRNA biogenesis genes are highly networked with transcription factors and aging-associated miRNAs. In particular, miR-71, known to influence life span and itself up-regulated during aging, represses alg-1/Argonaute expression post-transcriptionally during aging. Increased ALG-1 abundance in mir-71 loss-of-function mutants led to globally increased miRNA expression. Interestingly, these mutants demonstrated widespread mRNA expression dysregulation and diminished levels of variability both in gene expression and in overall life span. Thus, the progressive molecular decline often thought to be the result of accumulated damage over an organism's life may be partially explained by a miRNA-directed mechanism of age-associated decline.</jats:p

Survey of variation in human transcription factors reveals prevalent DNA binding changes

Published in final edited form as: Science. 2016 Mar 25; 351(6280): 1450–1454. Published online 2016 Mar 24. doi: 10.1126/science.aad2257Sequencing of exomes and genomes has revealed abundant genetic variation affecting the coding sequences of human transcription factors (TFs), but the consequences of such variation remain largely unexplored. We developed a computational, structure-based approach to evaluate TF variants for their impact on DNA binding activity and used universal protein-binding microarrays to assay sequence-specific DNA binding activity across 41 reference and 117 variant alleles found in individuals of diverse ancestries and families with Mendelian diseases. We found 77 variants in 28 genes that affect DNA binding affinity or specificity and identified thousands of rare alleles likely to alter the DNA binding activity of human sequence-specific TFs. Our results suggest that most individuals have unique repertoires of TF DNA binding activities, which may contribute to phenotypic variation.National Institutes of Health; NHGRI R01 HG003985; P50 HG004233; A*STAR National Science Scholarship; National Science Foundatio

miR-34a Silences c-SRC to Attenuate Tumor Growth in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype with no clinically proven biologically targeted treatment options. The molecular heterogeneity of TNBC and lack of high frequency driver mutations other than TP53 have hindered the development of new and effective therapies that significantly improve patient outcomes. miRNAs, global regulators of survival and proliferation pathways important in tumor development and maintenance, are becoming promising therapeutic agents. We performed miRNA-profiling studies in different TNBC subtypes to identify miRNAs that significantly contribute to disease progression. We found that miR-34a was lost in TNBC, specifically within mesenchymal and mesenchymal stem cell-like subtypes, whereas expression of miR-34a targets was significantly enriched. Furthermore, restoration of miR-34a in cell lines representing these subtypes inhibited proliferation and invasion, activated senescence, and promoted sensitivity to dasatinib by targeting the proto-oncogene c-SRC. Notably, SRC depletion in TNBC cell lines phenocopied the effects of miR-34a reintroduction, whereas SRC overexpression rescued the antitumorigenic properties mediated by miR-34a. miR-34a levels also increased when cells were treated with c-SRC inhibitors, suggesting a negative feedback exists between miR-34a and c-SRC. Moreover, miR-34a administration significantly delayed tumor growth of subcutaneously and orthotopically implanted tumors in nude mice, and was accompanied by c-SRC downregulation. Finally, we found that miR-34a and SRC levels were inversely correlated in human tumor specimens. Together, our results demonstrate that miR-34a exerts potent antitumorigenic effects in vitro and in vivo and suggests that miR-34a replacement therapy, which is currently being tested in human clinical trials, represents a promising therapeutic strategy for TNBC. Cancer Res; 76(4); 1-13. (c)2015 AACR

miRNAGE-34 induces cardiac damAGE

Small non-coding microRNAs (miRNAs, miRs) regulate gene expression in virtually all cells, and they have been implicated in cardiovascular disease and aging. In a paper recently published in Nature, miR-34a was identified as an aging-associated apoptotic and overall damaging factor for the heart

Novel MicroRNAs Differentially Expressed during Aging in the Mouse Brain

<div><p>MicroRNAs (miRNAs) are endogenous small RNA molecules that regulate gene expression post-transcriptionally. Work in <em>Caenorhabditis elegans</em> has shown that specific miRNAs function in lifespan regulation and in a variety of age-associated pathways, but the roles of miRNAs in the aging of vertebrates are not well understood. We examined the expression of small RNAs in whole brains of young and old mice by deep sequencing and report here on the expression of 558 known miRNAs and identification of 41 novel miRNAs. Of these miRNAs, 75 known and 18 novel miRNAs exhibit greater than 2.0-fold expression changes. The majority of expressed miRNAs in our study decline in relative abundance in the aged brain, in agreement with trends observed in other miRNA studies in aging tissues and organisms. Target prediction analysis suggests that many of our novel aging-associated miRNAs target genes in the insulin signaling pathway, a central node of aging-associated genetic networks. These novel miRNAs may thereby regulate aging-related functions in the brain. Since many mouse miRNAs are conserved in humans, the aging-affected brain miRNAs we report here may represent novel regulatory genes that also function during aging in the human brain.</p> </div

Novel miRNA candidates.

<p>(<b>a</b>) Novel miRNA candidates that change more than 2.0-fold in expression in old versus young mouse brains. MiRNA frequency was normalized by all reads that matched to the mouse genome (mm9) (Old/Young  = 1.472107). †: candidates validated by qRT-PCR. ‡: candidates with sequence overlap with known miRNAs (but have distinct mature miRNA sequences: isomiRs). ^A: Novel miRNA candidates that map to regions overlapping snoRNA and rRNA sequences (see main text). Blue font: miRNA novel to mouse. Black font: completely novel miRNA sequence, excluding seed sequence matches. Differentially expressed miRNAs with P-values <0.05 (calculated using DEGseq <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040028#pone.0040028-Wang1" target="_blank">[20]</a>) indicated in bold. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040028#pone.0040028.s005" target="_blank">Table S4</a>. (<b>b</b>) Secondary structures of putative precursor hairpins corresponding to nine novel miRNA candidates identified in this study. The predicted miRNA mature sequences are highlighted in red. Four of these novel miRNAs were found to be up-regulated (top) in aged mouse brain while five others were down-regulated (bottom). (See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040028#pone.0040028.s005" target="_blank">Table S4</a>). (<b>c</b>) Comparison of qRT-PCR data with deep sequencing data for the nine novel miRNA candidates shown in (b). Values shown are log2 ratios of old versus young brain expression levels. qPCR results were normalized to U6 snRNA expression levels. Inset: Correlation of expression changes as measured by deep sequencing versus qPCR (Pearson correlation coefficient  = 0.78). Plot for miR-5620 (isomiR) was taken out as the sequence was not reliably detected by qPCR. (<b>d</b>) Sequence alignment of novel miRNA candidates with known miRNAs of other species. *: conserved nucleotide. age: <i>Ateles geoffroyi</i>. bta: <i>Bos taurus</i>. dan: <i>Drosophila ananassae</i>. der: <i>Drosophila erecta</i>. dgr: <i>Drosophila grimshawi</i>. dme: <i>Drosophila melanogaster</i>. dmo: <i>Drosophila mojavensis</i>. dpe: <i>Drosophila persimilis</i>. dps: <i>Drosophila pseudoobscura</i>. dse: <i>Drosophila sechellia</i>. dsi: <i>Drosophila simulans</i>. dvi: <i>Drosophila virilis</i>. dwi: <i>Drosophila willistoni</i>. dya: <i>Drosophila yakuba</i>. gga: <i>Gallus gallus</i>. ggo: <i>Gorilla gorilla</i>. hsa: <i>Homo sapiens</i>. lla: <i>Lagothrix lagotricha</i>. mdo: <i>Monodelphis domestica</i>. mml: <i>Macaca mulatta</i>. mne: <i>Macaca nemestrina</i>. ppa: <i>Pan paniscus</i>. ppy: <i>Pongo pygmaeus</i>. ptr: <i>Pan troglodytes</i>. rno: <i>Rattus norvegicus</i>. sla: <i>Saguinus labiatus</i>. sme: <i>Schmidtea mediterranea.</i> xtr: <i>Xenopus tropicalis</i>.</p

Brain-expressed miRNAs.

<p>(<b>a</b>) Expression changes of miRNAs in mouse brain with aging. Distribution of individual miRNA expression changes are ranked by those miRNAs that exhibit the greatest increase in expression with aging (log2 Ratio (Old/Young)). Blue: known miRNAs, red: novel miRNAs. (<b>b</b>) Known miRNAs that change more than 2.0-fold in expression in old versus young mouse brains. Only miRNAs with at least 10 sequence reads at one time point are shown and P-value <0.05 are in bold. MiRNA frequency was normalized by all reads that matched to the mouse genome (mm9) (Old/Young  = 1.472107). (<b>c</b>) Comparison of qRT-PCR data with deep sequencing data for three known miRNAs. Values shown are fold changes in old versus young brain expression levels. qPCR results were normalized to U6 snRNA expression levels; error bars indicate standard deviation for technical triplicate. Statistically significant difference from U6 control denoted by asterisks (*: two-tailed P-value <0.01).</p