Publications

The global effect of copy number and epigenetic alterations on miRNA expression in cancer is poorly understood. In the present study, we integrate genome-wide DNA methylation, copy number and miRNA expression and identify genetic mechanisms underlying miRNA dysregulation in breast cancer.

RESULTS:
We identify 70 miRNAs whose expression was associated with alterations in copy number or methylation, or both. Among these, five miRNA families are represented. Interestingly, the members of these families are encoded on different chromosomes and are complementarily altered by gain or hypomethylation across the patients. In an independent breast cancer cohort of 123 patients, 41 of the 70 miRNAs were confirmed with respect to aberration pattern and association to expression. In vitro functional experiments were performed in breast cancer cell lines with miRNA mimics to evaluate the phenotype of the replicated miRNAs. let-7e-3p, which in tumors is found associated with hypermethylation, is shown to induce apoptosis and reduce cell viability, and low let-7e-3p expression is associated with poorer prognosis. The overexpression of three other miRNAs associated with copy number gain, miR-21-3p, miR-148b-3p and miR-151a-5p, increases proliferation of breast cancer cell lines. In addition, miR-151a-5p enhances the levels of phosphorylated AKT protein.

CONCLUSIONS:
Our data provide novel evidence of the mechanisms behind miRNA dysregulation in breast cancer. The study contributes to the understanding of how methylation and copy number alterations influence miRNA expression, emphasizing miRNA functionality through redundant encoding, and suggests novel miRNAs important in breast cancer.

Cell-lineage-specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage-specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell-lineage-specific expression, even in lineages not separable by experimental microdissection. Our machine-learning-based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. In a systematic evaluation of our predictions by immunohistochemistry, our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23%). Our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsies, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to apply this method to any cell-lineage or tissue of interest. Identified cell-lineage-specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.

We present Unveiling RNA Sample Annotation (URSA) that leverages the complex tissue/cell-type relationships and simultaneously estimates the probabilities associated with hundreds of tissues/cell-types for any given gene expression profile. URSA provides accurate and intuitive probability values for expression profiles across independent studies and outperforms other methods, irrespective of data preprocessing techniques. Moreover, without re-training, URSA can be used to classify samples from diverse microarray platforms and even from next-generation sequencing technology. Finally, we provide a molecular interpretation for the tissue and cell-type models as the biological basis for URSA’s classifications.

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.

A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction.

We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.

Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.

The histone demethylase LSD1, a component of the CoREST (corepressor for element 1-silencing transcription factor) corepressor complex, plays an important role in the downregulation of gene expression during development. However, the activities of LSD1 in mediating short-time-scale gene expression changes have not been well understood. To reveal the mechanisms underlying these two distinct functions of LSD1, we performed genome-wide mapping and cellular localization studies of LSD1 and its dimethylated histone 3 lysine 4 (substrate H3K4me2) in mouse embryonic stem cells (ES cells). Our results showed an extensive overlap between the LSD1 and H3K4me2 genomic regions and a correlation between the genomic levels of LSD1/H3K4me2 and gene expression, including many highly expressed ES cell genes. LSD1 is recruited to the chromatin of cells in the G(1)/S/G(2) phases and is displaced from the chromatin of M-phase cells, suggesting that LSD1 or H3K4me2 alternatively occupies LSD1 genomic regions during cell cycle progression. LSD1 knockdown by RNA interference or its displacement from the chromatin by antineoplastic agents caused an increase in the levels of a subset of LSD1 target genes. Taken together, these results suggest that cell cycle-dependent association and dissociation of LSD1 with chromatin mediates short-time-scale gene expression changes during embryonic stem cell cycle progression.

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

The development of technology capable of inexpensively performing large-scale measurements of biological systems has generated a wealth of data. Integrative analysis of these data holds the promise of uncovering gene function, regulation, and, in the longer run, understanding complex disease. However, their analysis has proved very challenging, as it is difficult to quickly and effectively assess the relevance and accuracy of these data for individual biological questions. Here, we identify biases that present challenges for the assessment of functional genomics data and methods. We then discuss evaluation methods that, taken together, begin to address these issues. We also argue that the funding of systematic data-driven experiments and of high-quality curation efforts will further improve evaluation metrics so that they more-accurately assess functional genomics data and methods. Such metrics will allow researchers in the field of functional genomics to continue to answer important biological questions in a data-driven manner.