Yunlong Liu

miR2Disease: A manually curated database for microRNA deregulation in human disease

‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.

Predicting intrinsic disorder in proteins: an overview

The discovery of intrinsically disordered proteins (IDP) (i.e., biologically active proteins that do not possess stable secondary and/or tertiary structures) came as an unexpected surprise, as the existence of such proteins is in contradiction to the traditional “sequence→structure→function” paradigm. Accurate prediction of a proteins predisposition to be intrinsically disordered is a necessary prerequisite for the further understanding of principles and mechanisms of protein folding and function, and is a key for the elaboration of a new structural and functional hierarchy of proteins. Therefore, prediction of IDPs has attracted the attention of many researchers, and a number of prediction tools have been developed. Predictions of disorder, in turn, are playing major roles in directing laboratory experiments that are leading to the discovery of ever more disordered proteins, and thereby leading to a positive feedback loop in the investigation of these proteins. In this review of algorithms for intrinsic disorder prediction, the basic concepts of various prediction methods for IDPs are summarized, the strengths and shortcomings of many of the methods are analyzed, and the difficulties and directions of future development of IDP prediction techniques are discussed.

Estradiol-regulated microRNAs control estradiol response in breast cancer cells

Estradiol (E2) regulates gene expression at the transcriptional level by functioning as a ligand for estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ). E2-inducible proteins c-Myc and E2Fs are required for optimal ERα activity and secondary estrogen responses, respectively. We show that E2 induces 21 microRNAs and represses seven microRNAs in MCF-7 breast cancer cells; these microRNAs have the potential to control 420 E2-regulated and 757 non-E2-regulated mRNAs at the post-transcriptional level. The serine/threonine kinase, AKT, alters E2-regulated expression of microRNAs. E2 induced the expression of eight Let-7 family members, miR-98 and miR-21 microRNAs; these microRNAs reduced the levels of c-Myc and E2F2 proteins. Dicer, a ribonuclease III enzyme required for microRNA processing, is also an E2-inducible gene. Several E2-regulated microRNA genes are associated with ERα-binding sites or located in the intragenic region of estrogen-regulated genes. We propose that the clinical course of ERα-positive breast cancers is dependent on the balance between E2-regulated tumor-suppressor microRNAs and oncogenic microRNAs. Additionally, our studies reveal a negative-regulatory loop controlling E2 response through microRNAs as well as differences in E2-induced transcriptome and proteome.

MicroRNA Cluster 221-222 and Estrogen Receptor α Interactions in Breast Cancer

BACKGROUND Several lines of evidence have suggested that estrogen receptor alpha (ERalpha)-negative breast tumors, which are highly aggressive and nonresponsive to hormonal therapy, arise from ERalpha-positive precursors through different molecular pathways. Because microRNAs (miRNAs) modulate gene expression, we hypothesized that they may have a role in ER-negative tumor formation. METHODS Gene expression profiles were used to highlight the global changes induced by miRNA modulation of ERalpha protein. miRNA transfection and luciferase assays enabled us to identify new targets of miRNA 206 (miR-206) and miRNA cluster 221-222 (miR-221-222). Northern blot, luciferase assays, estradiol treatment, and chromatin immunoprecipitation were performed to identify the miR-221-222 transcription unit and the mechanism implicated in its regulation. RESULTS Different global changes in gene expression were induced by overexpression of miR-221-222 and miR-206 in ER-positive cells. miR-221 and -222 increased proliferation of ERalpha-positive cells, whereas miR-206 had an inhibitory effect (mean absorbance units [AU]: miR-206: 500 AU, 95% confidence interval [CI]) = 480 to 520; miR-221: 850 AU, 95% CI = 810 to 873; miR-222: 879 AU, 95% CI = 850 to 893; P < .05). We identified hepatocyte growth factor receptor and forkhead box O3 as new targets of miR-206 and miR-221-222, respectively. We demonstrated that ERalpha negatively modulates miR-221 and -222 through the recruitment of transcriptional corepressor partners: nuclear receptor corepressor and silencing mediator of retinoic acid and thyroid hormone receptor. CONCLUSIONS These findings suggest that the negative regulatory loop involving miR-221-222 and ERalpha may confer proliferative advantage and migratory activity to breast cancer cells and promote the transition from ER-positive to ER-negative tumors.

Splicing factor SFRS1 recognizes a functionally diverse landscape of RNA transcripts

Metazoan genes are encrypted with at least two superimposed codes: the genetic code to specify the primary structure of proteins and the splicing code to expand their proteomic output via alternative splicing. Here, we define the specificity of a central regulator of pre-mRNA splicing, the conserved, essential splicing factor SFRS1. Cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq) identified 23,632 binding sites for SFRS1 in the transcriptome of cultured human embryonic kidney cells. SFRS1 was found to engage many different classes of functionally distinct transcripts including mRNA, miRNA, snoRNAs, ncRNAs, and conserved intergenic transcripts of unknown function. The majority of these diverse transcripts share a purine-rich consensus motif corresponding to the canonical SFRS1 binding site. The consensus site was not only enriched in exons cross-linked to SFRS1 in vivo, but was also enriched in close proximity to splice sites. mRNAs encoding RNA processing factors were significantly overrepresented, suggesting that SFRS1 may broadly influence the post-transcriptional control of gene expression in vivo. Finally, a search for the SFRS1 consensus motif within the Human Gene Mutation Database identified 181 mutations in 82 different genes that disrupt predicted SFRS1 binding sites. This comprehensive analysis substantially expands the known roles of human SR proteins in the regulation of a diverse array of RNA transcripts.

Alcohol exposure alters DNA methylation profiles in mouse embryos at early neurulation

Alcohol exposure during development can cause variable neurofacial deficit and growth retardation known as fetal alcohol spectrum disorders (FASD). The mechanism underlying FASD is not fully understood. However, alcohol, which is known to affect methyl donor metabolism, may induce aberrant epigenetic changes contributing to FASD. Using a tightly controlled whole-embryo culture, we investigated the effect of alcohol exposure (88mM) at early embryonic neurulation on genome-wide DNA methylation and gene expression in the C57BL/6 mouse. The DNA methylation landscape around promoter CpG islands at early mouse development was analyzed using MeDIP (methylated DNA immunoprecipitation) coupled with microarray (MeDIP-chip). At early neurulation, genes associated with high CpG promoters (HCP) had a lower ratio of methylation but a greater ratio of expression. Alcohol-induced alterations in DNA methylation were observed, particularly in genes on chromosomes 7, 10, and X; remarkably, a >10 fold increase in the number of genes with increased methylation on chromosomes 10 and X was observed in alcohol-exposed embryos with a neural tube defect phenotype compared to embryos without a neural tube defect. Significant changes in methylation were seen in imprinted genes, genes known to play roles in cell cycle, growth, apoptosis, cancer, and in a large number of genes associated with olfaction. Altered methylation was associated with significant (p

Lessons learned from whole exome sequencing in multiplex families affected by a complex genetic disorder, intracranial aneurysm

Genetic risk factors for intracranial aneurysm (IA) are not yet fully understood. Genomewide association studies have been successful at identifying common variants; however, the role of rare variation in IA susceptibility has not been fully explored. In this study, we report the use of whole exome sequencing (WES) in seven densely-affected families (45 individuals) recruited as part of the Familial Intracranial Aneurysm study. WES variants were prioritized by functional prediction, frequency, predicted pathogenicity, and segregation within families. Using these criteria, 68 variants in 68 genes were prioritized across the seven families. Of the genes that were expressed in IA tissue, one gene (TMEM132B) was differentially expressed in aneurysmal samples (n=44) as compared to control samples (n=16) (false discovery rate adjusted p-value=0.023). We demonstrate that sequencing of densely affected families permits exploration of the role of rare variants in a relatively common disease such as IA, although there are important study design considerations for applying sequencing to complex disorders. In this study, we explore methods of WES variant prioritization, including the incorporation of unaffected individuals, multipoint linkage analysis, biological pathway information, and transcriptome profiling. Further studies are needed to validate and characterize the set of variants and genes identified in this study.

Prioritization of disease microRNAs through a human phenome-microRNAome network

BackgroundThe identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.ResultsHerein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.ConclusionsWe presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.

Gene expression patterns in bone following mechanical loading.

The advent of high‐throughput measurements of gene expression and bioinformatics analysis methods offers new ways to study gene expression patterns. The primary goal of this study was to determine the time sequence for gene expression in a bone subjected to mechanical loading during key periods of the bone‐formation process, including expression of matrix‐related genes, the appearance of active osteoblasts, and bone desensitization. A standard model for bone loading was employed in which the right forelimb was loaded axially for 3 minutes per day, whereas the left forearm served as a nonloaded contralateral control. We evaluated loading‐induced gene expression over a time course of 4 hours to 32 days after the first loading session. Six distinct time‐dependent patterns of gene expression were identified over the time course and were categorized into three primary clusters: genes upregulated early in the time course, genes upregulated during matrix formation, and genes downregulated during matrix formation. Genes then were grouped based on function and/or signaling pathways. Many gene groups known to be important in loading‐induced bone formation were identified within the clusters, including AP‐1‐related genes in the early‐response cluster, matrix‐related genes in the upregulated gene clusters, and Wnt/β‐catenin signaling pathway inhibitors in the downregulated gene clusters. Several novel gene groups were identified as well, including chemokine‐related genes, which were upregulated early but downregulated later in the time course; solute carrier genes, which were both upregulated and downregulated; and muscle‐related genes, which were primarily downregulated.

Control of EVI-1 oncogene expression in metastatic breast cancer cells through microRNA miR-22

Metastasis in breast cancer carries a disproportionately worse prognosis than localized primary disease. To identify microRNAs (miRNA) involved in metastasis, the expression of 254 miRNAs was measured across the following cell lines using microarray analysis: MDA-MB-231 breast cancer cells, cells that grew as a tumor in the mammary fat pad of nude mice (TMD-231), metastatic disease to the lungs (LMD-231), bone (BMD-231) and adrenal gland (ADMD-231). A brain-seeking variant of this cell line (231-BR) was used additionally in validation studies. Twenty miRNAs were upregulated and seven were downregulated in metastatic cancer cells compared with TMD-231 cells. The expression of the tumor suppressor miRNAs let-7 and miR-22 was consistently downregulated in metastatic cancer cells. These metastatic cells expressed higher levels of putative/proven miR-22 target oncogenes ERBB3, CDC25C and EVI-1. Introduction of miR-22 into cancer cells reduced the levels of ERBB3 and EVI-1 as well as phospho-AKT, an EVI-1 downstream target. The miR-22 primary transcript is located in the 5′-untranslated region of an open reading frame C17orf91, and the promoter/enhancer of C17orf91 drives miR-22 expression. We observed elevated C17orf91 expression in non-basal subtype compared with basal subtype breast cancers. In contrast, elevated expression of EVI-1 was observed in basal subtype and was associated with poor outcome in estrogen receptor-negative breast cancer patients. These results suggest that metastatic cancer cells increase specific oncogenic signaling proteins through downregulation of miRNAs. Identifying such metastasis-specific oncogenic pathways may help to manipulate tumor behavior and aid in the design of more effective targeted therapies.