Defining characteristics and conservation of poorly annotated genes in Caenorhabditis elegans using WormCat 2.0

Omics tools provide broad datasets for biological discovery. However, the computational tools for identifying important genes or pathways in RNA-seq, proteomics, or GWAS (Genome-Wide Association Study) data depend on Gene Ontogeny annotations and are biased toward well-described pathways. This limits their utility as poorly annotated genes, which could have novel functions, are often passed over. Recently, we developed an annotation and category enrichment tool for Caenorhabditis elegans genomic data, WormCat, which provides an intuitive visualization output. Unlike Gene Ontogeny-based enrichment tools, which exclude genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and can include genes with biological functions identified in published datasets. Poorly annotated genes are often considered to be potentially species-specific and thus, of reduced interest to the biomedical community. Instead, we find that around 3% of the UNASSIGNED genes have human orthologs, including some linked to human diseases. These human orthologs themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that the failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes. This suggests that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows the association of UNASSIGNED genes with specific phenotypes and known pathways. Building these associations in C. elegans, with its robust genetic tools, provides a path to further functional study and insight into these understudied genes.     

Aging of the cells: Insight into cellular senescence and detection Methods

Cellular theory of aging states that human aging is the result of cellular aging, in which an increasing proportion of cells reach senescence. Senescence, from the Latin word senex, means “growing old,” is an irreversible growth arrest which occurs in response to damaging stimuli, such as DNA damage, telomere shortening, telomere dysfunction and oncogenic stress leading to suppression of potentially dysfunctional, transformed, or aged cells. Cellular senescence is characterized by irreversible cell cycle arrest, flattened and enlarged morphology, resistance to apoptosis, alteration in gene expression and chromatin structure, expression of senescence associated- β-galactosidase (SA-β-gal) and acquisition of senescence associated secretory phenotype (SASP). In this review paper, different types of cellular senescence including replicative senescence (RS) which occurs due to telomere shortening and stress induced premature senescence (SIPS) which occurs in response to different types of stress in cells, are discussed. Biomarkers of cellular senescence and senescent assays including BrdU incorporation assay, senescence associated- β-galactosidase (SA-β-gal) and senescence-associated heterochromatin foci assays to detect senescent cells are also addressed.     

Expression of senescence-associated genes in multipotent mesenchymal stromal cells during long-term cultivation at various hypoxic levels

The expression of several genes which functions are associated with cellular senescence was analyzed in multipotent mesenchymal stromal cells during long-term cultivation at different oxygen levels (20, 5, and 1%) using the RT² Profiler? PCR Array Human Cellular Senescence system (Qiagen, United States). It was established that replicative senescence processes develop most actively in the cells cultured under the standard conditions (20% O₂). The most significant changes were observed in the expression of CCND1, ID1, IGF1, PIK3CA, and SERPINE1 genes.     

Gene expression profiling of replicative and induced senescence

Cellular senescence is a cell cycle arrest accompanied by high expression of cyclin dependent kinase inhibitors which counteract overactive growth signals, which serves as a tumor suppressive mechanism. Senescence can be a result of telomere shortening (natural or replicative senescence) or DNA damage resulting from exogenous stressors (induced senescence). Here, we performed gene expression profiling through RNA-seq of replicative senescence, adriamycin-induced senescence, H₂O₂-induced senescence, and 5-aza-2-deoxycytidine-induced senescence in order to profile the pathways controlling various types of senescence. Overall, the pathways common to all 4 types of senescence were related to inflammation and the innate immune system. It was also evident that 5-aza-induced senescence mirrors natural replicative senescence due to telomere shortening. We also examined the prevalence of senescence-associated secretory phenotype (SASP) factors in the RNA-seq data, showing that it is a common characteristic of all 4 types of senescence. In addition, we could discriminate changes in gene expression due to quiescence during cellular senescence from those that were specific to senescence.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Remodeling of chromatin structure in senescent cells and its potential impact on tumor suppression and aging

Cellular senescence is an important tumor suppression process, and a possible contributor to tissue aging. Senescence is accompanied by extensive changes in chromatin structure. In particular, many senescent cells accumulate specialized domains of facultative heterochromatin, called Senescence-Associated Heterochromatin Foci (SAHF), which are thought to repress expression of proliferation-promoting genes, thereby contributing to senescence-associated proliferation arrest. This article reviews our current understanding of the structure, assembly and function of these SAHF at a cellular level. The possible contribution of SAHF to tumor suppression and tissue aging is also critically discussed.     

The institute for genomic research Osa1 rice genome annotation database

We have developed a rice (Oryza sativa) genome annotation database (Osa1) that provides structural and functional annotation for this emerging model species. Using the sequence of O. sativa subsp. japonica cv Nipponbare from the International Rice Genome Sequencing Project, pseudomolecules, or virtual contigs, of the 12 rice chromosomes were constructed. Our most recent release, version 3, represents our third build of the pseudomolecules and is composed of 98% finished sequence. Genes were identified using a series of computational methods developed for Arabidopsis (Arabidopsis thaliana) that were modified for use with the rice genome. In release 3 of our annotation, we identified 57,915 genes, of which 14,196 are related to transposable elements. Of these 43,719 non-transposable element-related genes, 18,545 (42.4%) were annotated with a putative function, 5,777 (13.2%) were annotated as encoding an expressed protein with no known function, and the remaining 19,397 (44.4%) were annotated as encoding a hypothetical protein. Multiple splice forms (5,873) were detected for 2,538 genes, resulting in a total of 61,250 gene models in the rice genome. We incorporated experimental evidence into 18,252 gene models to improve the quality of the structural annotation. A series of functional data types has been annotated for the rice genome that includes alignment with genetic markers, assignment of gene ontologies, identification of flanking sequence tags, alignment with homologs from related species, and syntenic mapping with other cereal species. All structural and functional annotation data are available through interactive search and display windows as well as through download of flat files. To integrate the data with other genome projects, the annotation data are available through a Distributed Annotation System and a Genome Browser. All data can be obtained through the project Web pages at http://rice.tigr.org.     

Information theory applied to the sparse gene ontology annotation network to predict novel gene function

MOTIVATION: Despite advances in the gene annotation process, the functions of a large portion of gene products remain insufficiently characterized. In addition, the in silico prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or functional genomic approaches. To our knowledge, no prediction method has been demonstrated to be highly accurate for sparsely annotated GO terms (those associated to fewer than 10 genes). RESULTS: We propose a novel approach, information theory-based semantic similarity (ITSS), to automatically predict molecular functions of genes based on existing GO annotations. Using a 10-fold cross-validation, we demonstrate that the ITSS algorithm obtains prediction accuracies (precision 97%, recall 77%) comparable to other machine learning algorithms when compared in similar conditions over densely annotated portions of the GO datasets. This method is able to generate highly accurate predictions in sparsely annotated portions of GO, where previous algorithms have failed. As a result, our technique generates an order of magnitude more functional predictions than previous methods. A 10-fold cross validation demonstrated a precision of 90% at a recall of 36% for the algorithm over sparsely annotated networks of the recent GO annotations (about 1400 GO terms and 11,000 genes in Homo sapiens). To our knowledge, this article presents the first historical rollback validation for the predicted GO annotations, which may represent more realistic conditions than more widely used cross-validation approaches. By manually assessing a random sample of 100 predictions conducted in a historical rollback evaluation, we estimate that a minimum precision of 51% (95% confidence interval: 43-58%) can be achieved for the human GO Annotation file dated 2003. AVAILABILITY: The program is available on request. The 97,732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset and other supplementary information is available at http://phenos.bsd.uchicago.edu/ITSS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Hallmarks for senescence in carcinogenesis: novel signaling players

Cellular senescence is a potent anti-cancer mechanism controlled by tumor suppressor genes, particularly p53 and pRb, which is characterized by the irreversible loss of proliferation. Senescence induced by DNA damage, oncogenic stimulation, or excessive mitogenic input, serves as a barrier that counteracts cancer progression. Emerging evidence in cellular and in in vivo models revealed the involvement of additional signaling players in senescence, including PML, CK2, Bcl-2, PI3K effectors such as Rheb, Rho small GTPases, and cytokines. Recent studies have also implicated protein kinase C (PKC) isozymes as modulators of senescence phenotypes and showed that phorbol esters, widely used PKC activators, can induce senescence in a number of cancer cells. These novel findings suggest a complex array of cross-talks between senescence pathways and may have significant implications in cancer therapy.     

Phospholipase D in cellular senescence.

Cellular senescence appears to be an important part of organismal aging. Cellular senescence is characterized by flattened enlarged morphology, inhibition of DNA replication in response to growth factors, inability to phosphorylate the pRb tumor suppressor protein, inability to produce c-fos or AP-1 and overexpression of a variety of genes, notably p21 (CIP-1/WAF-1) and p16(INK). It is now clear that certain early mitotic signals become defective with the onset of senescence. Among these is the PLD/PKC pathway. Evidence suggests that activation of PLD and PKC is critical for mitogenesis. Recent data suggest that the defect in PLD/PKC in cellular senescence is a result of elevated cellular ceramide levels which inhibit PLD activation. It appears that the elevated ceramide is a result of neutral sphingomyelinase activation. Ceramide acts to inhibit the activation of PLD by possibly three mechanisms, inhibiting activation by Rho, translocation to the membrane and gene expression. Addition of ceramide to young cells not only inhibits PLD but also recapitulates all the standard measures of cellular senescence as described above.