Igor Ulitsky

lincRNAs: Genomics, Evolution, and Mechanisms

Long intervening noncoding RNAs (lincRNAs) are transcribed from thousands of loci in mammalian genomes and might play widespread roles in gene regulation and other cellular processes. This Review outlines the emerging understanding of lincRNAs in vertebrate animals, with emphases on how they are being identified and current conclusions and questions regarding their genomics, evolution and mechanisms of action.

Network-based prediction of protein function

Functional annotation of proteins is a fundamental problem in the post‐genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module‐assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

Dynamic Changes in the Copy Number of Pluripotency and Cell Proliferation Genes in Human ESCs and iPSCs during Reprogramming and Time in Culture

Genomic stability is critical for the clinical use of human embryonic and induced pluripotent stem cells. We performed high-resolution SNP (single-nucleotide polymorphism) analysis on 186 pluripotent and 119 nonpluripotent samples. We report a higher frequency of subchromosomal copy number variations in pluripotent samples compared to nonpluripotent samples, with variations enriched in specific genomic regions. The distribution of these variations differed between hESCs and hiPSCs, characterized by large numbers of duplications found in a few hESC samples and moderate numbers of deletions distributed across many hiPSC samples. For hiPSCs, the reprogramming process was associated with deletions of tumor-suppressor genes, whereas time in culture was associated with duplications of oncogenic genes. We also observed duplications that arose during a differentiation protocol. Our results illustrate the dynamic nature of genomic abnormalities in pluripotent stem cells and the need for frequent genomic monitoring to assure phenotypic stability and clinical safety.

Conserved Function of lincRNAs in Vertebrate Embryonic Development despite Rapid Sequence Evolution

Thousands of long intervening noncoding RNAs (lincRNAs) have been identified in mammals. To better understand the evolution and functions of these enigmatic RNAs, we used chromatin marks, poly(A)-site mapping and RNA-Seq data to identify more than 550 distinct lincRNAs in zebrafish. Although these shared many characteristics with mammalian lincRNAs, only 29 had detectable sequence similarity with putative mammalian orthologs, typically restricted to a single short region of high conservation. Other lincRNAs had conserved genomic locations without detectable sequence conservation. Antisense reagents targeting conserved regions of two zebrafish lincRNAs caused developmental defects. Reagents targeting splice sites caused the same defects and were rescued by adding either the mature lincRNA or its human or mouse ortholog. Our study provides a roadmap for identification and analysis of lincRNAs in model organisms and shows that lincRNAs play crucial biological roles during embryonic development with functionality conserved despite limited sequence conservation.Thousands of long intervening noncoding RNAs (lincRNAs) have been identified in mammals. To better understand the evolution and functions of these enigmatic RNAs, we used chromatin marks, poly(A)-site mapping and RNA-Seq data to identify more than 550 distinct lincRNAs in zebrafish. Although these shared many characteristics with mammalian lincRNAs, only 29 had detectable sequence similarity with putative mammalian orthologs, typically restricted to a single short region of high conservation. Other lincRNAs had conserved genomic locations without detectable sequence conservation. Antisense reagents targeting conserved regions of two zebrafish lincRNAs caused developmental defects. Reagents targeting splice sites caused the same defects and were rescued by adding either the mature lincRNA or its human or mouse ortholog. Our study provides a roadmap for identification and analysis of lincRNAs in model organisms and shows that lincRNAs play crucial biological roles during embryonic development with functionality conserved despite limited sequence conservation.

Regulatory networks define phenotypic classes of human stem cell lines

Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal and adult sources have been called stem cells, even though they range from pluripotent cells—typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation—to adult stem cell lines, which can generate a far more limited repertoire of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine has highlighted the need for a general, reproducible method for classification of these cells. We report here the creation and analysis of a database of global gene expression profiles (which we call the ‘stem cell matrix’) that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent and differentiated cell types. Using an unsupervised clustering method to categorize a collection of ∼150 cell samples, we discovered that pluripotent stem cell lines group together, whereas other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis we uncovered a protein–protein network (PluriNet) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas and induced pluripotent cells). Analysis of published data showed that the PluriNet seems to be a common characteristic of pluripotent cells, including mouse embryonic stem and induced pluripotent cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks.

Identification of functional modules using network topology and high-throughput data

BackgroundWith the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data.ResultsWe describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity.ConclusionWe have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

Comprehensive MicroRNA Profiling Reveals a Unique Human Embryonic Stem Cell Signature Dominated by a Single Seed Sequence

Embryonic stem cells are unique among cultured cells in their ability to self‐renew and differentiate into a wide diversity of cell types, suggesting that a specific molecular control network underlies these features. Human embryonic stem cells (hESCs) are known to have distinct mRNA expression, global DNA methylation, and chromatin profiles, but the involvement of high‐level regulators, such as microRNAs (miRNA), in the hESC‐specific molecular network is poorly understood. We report that global miRNA expression profiling of hESCs and a variety of stem cell and differentiated cell types using a novel microarray platform revealed a unique set of miRNAs differentially regulated in hESCs, including numerous miRNAs not previously linked to hESCs. These hESC‐associated miRNAs were more likely to be located in large genomic clusters, and less likely to be located in introns of coding genes. hESCs had higher expression of oncogenic miRNAs and lower expression of tumor suppressor miRNAs than the other cell types. Many miRNAs upregulated in hESCs share a common consensus seed sequence, suggesting that there is cooperative regulation of a critical set of target miRNAs. We propose that miRNAs are coordinately controlled in hESCs, and are key regulators of pluripotence and differentiation.

Beyond Secondary Structure: Primary-Sequence Determinants License Pri-miRNA Hairpins for Processing

To use microRNAs to downregulate mRNA targets, cells must first process these ~22 nt RNAs from primary transcripts (pri-miRNAs). These transcripts form RNA hairpins important for processing, but additional determinants must distinguish pri-miRNAs from the many other hairpin-containing transcripts expressed in each cell. Illustrating the complexity of this recognition, we show that most Caenorhabditis elegans pri-miRNAs lack determinants required for processing in human cells. To find these determinants, we generated many variants of four human pri-miRNAs, sequenced millions that retained function, and compared them with the starting variants. Our results confirmed the importance of pairing in the stem and revealed three primary-sequence determinants, including an SRp20-binding motif (CNNC) found downstream of most pri-miRNA hairpins in bilaterian animals, but not in nematodes. Adding this and other determinants to C. elegans pri-miRNAs imparted efficient processing in human cells, thereby confirming the importance of primary-sequence determinants for distinguishing pri-miRNAs from other hairpin-containing transcripts.

Expander: from expression microarrays to networks and functions

A major challenge in the analysis of gene expression microarray data is to extract meaningful biological knowledge out of the huge volume of raw data. Expander (EXPression ANalyzer and DisplayER) is an integrated software platform for the analysis of gene expression data, which is freely available for academic use. It is designed to support all the stages of microarray data analysis, from raw data normalization to inference of transcriptional regulatory networks. The microarray analysis described in this protocol starts with importing the data into Expander 5.0 and is followed by normalization and filtering. Then, clustering and network-based analyses are performed. The gene groups identified are tested for enrichment in function (based on Gene Ontology), co-regulation (using transcription factor and microRNA target predictions) or co-location. The results of each analysis step can be visualized in a number of ways. The complete protocol can be executed in ≈1 h.

Identifying functional modules using expression profiles and confidence-scored protein interactions

MOTIVATION Microarray-based gene expression studies have great potential but are frequently difficult to interpret due to their overwhelming dimensions. Recent studies have shown that the analysis of expression data can be improved by its integration with protein interaction networks, but the performance of these analyses has been hampered by the uneven quality of the interaction data. RESULTS We present Co-Expression Zone ANalysis using NEtworks (CEZANNE), a novel confidence-based method for extraction of functionally coherent co-expressed gene sets. CEZANNE uses probabilities for individual interactions, which can be computed by any available method. We propose a probabilistic model and a weighting scheme in which the likelihood of the connectivity of a subnetwork is related to the weight of its minimum cut. Applying CEZANNE to an expression dataset of DNA damage response in Saccharomyces cerevisiae, we recover both known and novel modules and predict novel protein functions. We show that CEZANNE outperforms previous methods for analysis of expression and interaction data. AVAILABILITY CEZANNE is available as part of the MATISSE software at http://acgt.cs.tau.ac.il/matisse. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.