Leonard Lipovich

The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression

The human genome contains many thousands of long noncoding RNAs (lncRNAs). While several studies have demonstrated compelling biological and disease roles for individual examples, analytical and experimental approaches to investigate these genes have been hampered by the lack of comprehensive lncRNA annotation. Here, we present and analyze the most complete human lncRNA annotation to date, produced by the GENCODE consortium within the framework of the ENCODE project and comprising 9277 manually annotated genes producing 14,880 transcripts. Our analyses indicate that lncRNAs are generated through pathways similar to that of protein-coding genes, with similar histone-modification profiles, splicing signals, and exon/intron lengths. In contrast to protein-coding genes, however, lncRNAs display a striking bias toward two-exon transcripts, they are predominantly localized in the chromatin and nucleus, and a fraction appear to be preferentially processed into small RNAs. They are under stronger selective pressure than neutrally evolving sequences-particularly in their promoter regions, which display levels of selection comparable to protein-coding genes. Importantly, about one-third seem to have arisen within the primate lineage. Comprehensive analysis of their expression in multiple human organs and brain regions shows that lncRNAs are generally lower expressed than protein-coding genes, and display more tissue-specific expression patterns, with a large fraction of tissue-specific lncRNAs expressed in the brain. Expression correlation analysis indicates that lncRNAs show particularly striking positive correlation with the expression of antisense coding genes. This GENCODE annotation represents a valuable resource for future studies of lncRNAs.

The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells

Oct4 and Nanog are transcription factors required to maintain the pluripotency and self-renewal of embryonic stem (ES) cells. Using the chromatin immunoprecipitation paired-end ditags method, we mapped the binding sites of these factors in the mouse ES cell genome. We identified 1,083 and 3,006 high-confidence binding sites for Oct4 and Nanog, respectively. Comparative location analyses indicated that Oct4 and Nanog overlap substantially in their targets, and they are bound to genes in different configurations. Using de novo motif discovery algorithms, we defined the cis-acting elements mediating their respective binding to genomic sites. By integrating RNA interference–mediated depletion of Oct4 and Nanog with microarray expression profiling, we demonstrated that these factors can activate or suppress transcription. We further showed that common core downstream targets are important to keep ES cells from differentiating. The emerging picture is one in which Oct4 and Nanog control a cascade of pathways that are intricately connected to govern pluripotency, self-renewal, genome surveillance and cell fate determination.

Mapping and genome organization of microsatellite sequences in rice (Oryza sativa L.).

Abstractu2002In order to enhance the resolution of an existing genetic map of rice, and to obtain a comprehensive picture of marker utility and genomic distribution of microsatellites in this important grain species, rice DNA sequences containing simple sequence repeats (SSRs) were extracted from several small-insert genomic libraries and from the database. One hundred and eighty eight new microsatellite markers were developed and evaluated for allelic diversity. The new simple sequence length polymorphisms (SSLPs) were incorporated into the existing map previously containing 124 SSR loci. The 312 microsatellite markers reported here provide whole-genome coverage with an average density of one SSLP per 6 cM. In this study, 26 SSLP markers were identified in published sequences of known genes, 65 were developed based on partial cDNA sequences available in GenBank, and 97 were isolated from genomic libraries. Microsatellite markers with different SSR motifs are relatively uniformly distributed along rice chromosomes regardless of whether they were derived from genomic clones or cDNA sequences. However, the distribution of polymorphism detected by these markers varies between different regions of the genome.

Diversity of microsatellites derived from genomic libraries and GenBank sequences in rice (Oryza sativa L.)

Abstractu2002The growing number of rice microsatellite markers warrants a comprehensive comparison of allelic variability between the markers developed using different methods, with various sequence repeat motifs, and from coding and non-coding portions of the genome. We have performed such a comparison over a set of 323 microsatellite markers; 194 were derived from genomic library screening and 129 were derived from the analysis of rice-expressed sequence tags (ESTs) available in public DNA databases. We have evaluated the frequency of polymorphism between parental pairs of six inter- subspecific crosses and one inter-specific cross widely used for mapping in rice. Microsatellites derived from genomic libraries detected a higher level of polymorphism than those derived from ESTs contained in the GenBank database (83.8% versus 54.0%). Similarly, the other measures of genetic variability [the number of alleles per locus, polymorphism information content (PIC), and allele size ranges] were all higher in genomic library-derived microsatellites than in their EST-database counterparts. The highest overall degree of genetic diversity was seen in GA-containing microsatellites of genomic library origin, while the most conserved markers contained CCG- or CAG-trinucleotide motifs and were developed from GenBank sequences. Preferential location of specific motifs in coding versus non-coding regions of known genes was related to observed levels of microsatellite diversity. A strong positive correlation was observed between the maximum length of a microsatellite motif and the standard deviation of the molecular-weight of amplified fragments. The reliability of molecular weight standard deviation (SDmw) as an indicator of genetic variability of microsatellite loci is discussed.

Gene identification signature (GIS) analysis for transcriptome characterization and genome annotation

We have developed a DNA tag sequencing and mapping strategy called gene identification signature (GIS) analysis, in which 5′ and 3′ signatures of full-length cDNAs are accurately extracted into paired-end ditags (PETs) that are concatenated for efficient sequencing and mapped to genome sequences to demarcate the transcription boundaries of every gene. GIS analysis is potentially 30-fold more efficient than standard cDNA sequencing approaches for transcriptome characterization. We demonstrated this approach with 116,252 PET sequences derived from mouse embryonic stem cells. Initial analysis of this dataset identified hundreds of previously uncharacterized transcripts, including alternative transcripts of known genes. We also uncovered several intergenically spliced and unusual fusion transcripts, one of which was confirmed as a trans-splicing event and was differentially expressed. The concept of paired-end ditagging described here for transcriptome analysis can also be applied to whole-genome analysis of cis-regulatory and other DNA elements and represents an important technological advance for genome annotation.

Genome-wide computational identification and manual annotation of human long noncoding RNA genes.

Experimental evidence suggests that half or more of the mammalian transcriptome consists of noncoding RNA. Noncoding RNAs are divided into short noncoding RNAs (including microRNAs) and long noncoding RNAs (lncRNAs). We defined complementary DNAs (cDNAs) lacking any positive-strand open reading frames (ORFs) longer than 30 amino acids, as well as cDNAs lacking any evidence of interspecies conservation of their longer-than-30-amino acid ORFs, as noncoding. We have identified 5446 lncRNA genes in the human genome from approximately 24,000 full-length cDNAs, using our new ORF-prediction pipeline. We combined them nonredundantly with lncRNAs from four published sources to derive 6736 lncRNA genes. In an effort to distinguish standalone and antisense lncRNA genes from database artifacts, we stratified our catalog of lncRNAs according to the distance between each lncRNA gene candidate and its nearest known protein-coding gene. We concurrently examined the protein-coding capacity of known genes overlapping with lncRNAs. Remarkably, 62% of known genes with hypothetical protein names actually lacked protein-coding capacity. This study has greatly expanded the known human lncRNA catalog, increased its accuracy through manual annotation of cDNA-to-genome alignments, and revealed that a large set of hypothetical-protein genes in GenBank lacks protein-coding capacity. In addition, we have developed, independently of existing NCBI tools, command-line programs with high-throughput ORF-finding and BLASTP-parsing functionality, suitable for future automated assessments of protein-coding capacity of novel transcripts.

Long noncoding RNAs are rarely translated in two human cell lines

Data from the Encyclopedia of DNA Elements (ENCODE) project show over 9640 human genome loci classified as long noncoding RNAs (lncRNAs), yet only ~100 have been deeply characterized to determine their role in the cell. To measure the protein-coding output from these RNAs, we jointly analyzed two recent data sets produced in the ENCODE project: tandem mass spectrometry (MS/MS) data mapping expressed peptides to their encoding genomic loci, and RNA-seq data generated by ENCODE in long polyA+ and polyA- fractions in the cell lines K562 and GM12878. We used the machine-learning algorithm RuleFit3 to regress the peptide data against RNA expression data. The most important covariate for predicting translation was, surprisingly, the Cytosol polyA- fraction in both cell lines. LncRNAs are ~13-fold less likely to produce detectable peptides than similar mRNAs, indicating that ~92% of GENCODE v7 lncRNAs are not translated in these two ENCODE cell lines. Intersecting 9640 lncRNA loci with 79,333 peptides yielded 85 unique peptides matching 69 lncRNAs. Most cases were due to a coding transcript misannotated as lncRNA. Two exceptions were an unprocessed pseudogene and a bona fide lncRNA gene, both with open reading frames (ORFs) compromised by upstream stop codons. All potentially translatable lncRNA ORFs had only a single peptide match, indicating low protein abundance and/or false-positive peptide matches. We conclude that with very few exceptions, ribosomes are able to distinguish coding from noncoding transcripts and, hence, that ectopic translation and cryptic mRNAs are rare in the human lncRNAome.

Conserved long noncoding RNAs transcriptionally regulated by Oct4 and Nanog modulate pluripotency in mouse embryonic stem cells

The genetic networks controlling stem cell identity are the focus of intense interest, due to their obvious therapeutic potential as well as exceptional relevance to models of early development. Genome-wide mapping of transcriptional networks in mouse embryonic stem cells (mESCs) reveals that many endogenous noncoding RNA molecules, including long noncoding RNAs (lncRNAs), may play a role in controlling the pluripotent state. We performed a genome-wide screen that combined full-length mESC transcriptome genomic mapping data with chromatin immunoprecipitation genomic location maps of the key mESC transcription factors Oct4 and Nanog. We henceforth identified four mESC-expressed, conserved lncRNA-encoding genes residing proximally to active genomic binding sites of Oct4 and Nanog. Accordingly, these four genes have potential roles in pluripotency. We show that two of these lncRNAs, AK028326 (Oct4-activated) and AK141205 (Nanog-repressed), are direct targets of Oct4 and Nanog. Most importantly, we demonstrate that these lncRNAs are not merely controlled by mESC transcription factors, but that they themselves regulate developmental state: knockdown and overexpression of these transcripts lead to robust changes in Oct4 and Nanog mRNA levels, in addition to alterations in cellular lineage-specific gene expression and in the pluripotency of mESCs. We further characterize AK028326 as a co-activator of Oct4 in a regulatory feedback loop. These results for the first time implicate lncRNAs in the modulation of mESC pluripotency and expand the established mESC regulatory network model to include functional lncRNAs directly controlled by key mESC transcription factors.

Sall4 Regulates Distinct Transcription Circuitries in Different Blastocyst-Derived Stem Cell Lineages

Stem cells self-renew or differentiate under the governance of a stem-cell-specific transcriptional program, with each transcription factor orchestrating the activities of a particular set of genes. Here we demonstrate that a single transcription factor is able to regulate distinct core circuitries in two different blastocyst-derived stem cell lines, embryonic stem cells (ESCs) and extraembryonic endoderm (XEN) cells. The transcription factor Sall4 is required for early embryonic development and for ESC pluripotency. Sall4 is also expressed in XEN cells, and depletion of Sall4 disrupts self-renewal and induces differentiation. Genome-wide analysis reveals that Sall4 is regulating different gene sets in ESCs and XEN cells, and depletion of Sall4 targets in the respective cell types induces differentiation. With Oct4, Sox2, and Nanog, Sall4 forms a crucial interconnected autoregulatory network in ESCs. In XEN cells, Sall4 regulates the key XEN lineage-associated genes Gata4, Gata6, Sox7, and Sox17. Our findings demonstrate how Sall4 functions as an essential stemness factor for two different stem cell lines.

REST regulates distinct transcriptional networks in embryonic and neural stem cells.

The maintenance of pluripotency and specification of cellular lineages during embryonic development are controlled by transcriptional regulatory networks, which coordinate specific sets of genes through both activation and repression. The transcriptional repressor RE1-silencing transcription factor (REST) plays important but distinct regulatory roles in embryonic (ESC) and neural (NSC) stem cells. We investigated how these distinct biological roles are effected at a genomic level. We present integrated, comparative genome- and transcriptome-wide analyses of transcriptional networks governed by REST in mouse ESC and NSC. The REST recruitment profile has dual components: a developmentally independent core that is common to ESC, NSC, and differentiated cells; and a large, ESC-specific set of target genes. In ESC, the REST regulatory network is highly integrated into that of pluripotency factors Oct4-Sox2-Nanog. We propose that an extensive, pluripotency-specific recruitment profile lends REST a key role in the maintenance of the ESC phenotype.