Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs

Functional annotation transfer across multi-gene family orthologs can lead to functional misannotations. We hypothesised that co-expression network will help predict functional orthologs amongst complex homologous gene families. To explore the use of transcriptomic data available in public domain to identify functionally equivalent ones from all predicted orthologs, we collected genome wide expression data in mouse and rat liver from over 1500 experiments with varied treatments. We used a hyper-graph clustering method to identify clusters of orthologous genes co-expressed in both mouse and rat. We validated these clusters by analysing expression profiles in each species separately, and demonstrating a high overlap. We then focused on genes in 18 homology groups with one-to-many or many-to-many relationships between two species, to discriminate between functionally equivalent and non-equivalent orthologs. Finally, we further applied our method by collecting heart transcriptomic data (over 1400 experiments) in rat and mouse to validate the method in an independent tissue

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson's disease: a comparison of 33 human and animal studies.

BACKGROUND: As the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition. METHODS: In a comparison of 33 microarray studies of Parkinson's disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson's disease. RESULTS: Concordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson's disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson's disease; while comparison with other brain diseases (Alzheimer's disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson's disease. CONCLUSIONS: The observed clustering and concordance results suggest the existence of a 'characteristic' signal of Parkinson's disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson's disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease

A COMPARISON OF META-ANALYSIS METHODS FOR DETECTING DIFFERENTIALLY EXPRESSED GENES IN MICROARRAY EXPERIMENTS: AN APPLICATION TO MALIGNANT PLEURAL MESOTHELIOMA DATA

The proliferation of microarray experiments and the increasing availability of relevant amount of data in public repositories have created a need for meta-analysis methods to efficiently integrate and validate microarray results from independent but related studies. Despite its increasing popularity, meta-analysis of microarray data is not without problems. In fact, although it shares many features with traditional meta-analysis, most classical meta-analysis methods cannot be directly applied to microarray experiments because of their unique issues. Several meta-analysis techniques have been proposed in the context of microarrays. However, only recently a comprehensive framework to carry out microarray data meta-analysis has been proposed. Moreover very few software packages for microarray meta-analysis implementation exist and most of them either have unclear manuals or are not easy to apply. We applied four meta-analysis methods, the Stouffer’s method, the moderated effect size combination approach, the t-based hierarchical modeling and the rank product method, to a set of three microarray studies on malignant pleural mesothelioma. We focused on differential expression analysis between normal and malignant mesothelioma pleural tissues. Both unfiltered and filtered data were analyzed. The lists of differentially expressed genes provided by each method for either kind of data were compared, also by pathway analysis. These comparisons highlighted a poor overlap between the lists of differentially expressed genes and the related pathways obtained using the unfiltered data. Conversely, a higher concordance of the results, both at the gene and the pathway level, was observed when filtered data were considered. The fact that a significant number of genes were identified by only one of the tested methods shows that the gene ranking is based on different perspectives. In fact, the analyzed methods are based on different assumptions and focus on diverse aspects in selecting significant genes. Since so far there is no consensus on what is (are) the ‘best’ meta-analysis method(s), it may be useful to select candidate genes for further analysis using a combination of different meta-analysis methods. In particular, differentially expressed genes detected by more than one method may be considered as the most reliable ones while genes identified by only a single method may be further explored to expand the knowledge of the biological phenomenon of interest

In vitro homology search array comprehensively reveals highly conserved genes and their functional characteristics in non-sequenced species

<p>Abstract</p> <p>Background</p> <p>With the increase in genomic and transcriptomic data produced by the recent advancements in next generation sequencers and microarrays, it is now easier than ever to conduct large-scale comparative genomic studies for familiar species. However, there are more than ten million species on earth, and the study of all remaining species is not realistic in terms of cost and time. There have been a number of attempts at using microarrays for cross-species hybridization; however, those approaches only utilized the same probes for each species or different probes designed from orthologous genes. To establish easier and cheaper methods for the large-scale comparative genomic study of non-sequenced species, we developed an <it>in vitro</it> homology search array with the aid of a bioinformatic approach to probe design.</p> <p>Results</p> <p>To perform large-scale genomic comparisons of non-sequenced species, we chose squid, one of the most intelligent species among Protostomes, for comparison with human genes. We designed a microarray using human single copy genes and conducted microarray experiments with mRNAs extracted from the squid. Multi-copy genes could not be detected using the microarray in this study because their sequence similarity caused cross-hybridization. A search for squid homologous genes among human genes revealed that 68% of the human probes tested showed the expression of squid homolog genes and 95 genes were confirmed to be expressed highly in squid. Functional classification analysis showed that these highly expressed genes comprise DNA binding proteins, which are under pressure of DNA level mutation and, consequently, show high similarity at the nucleotide level.</p> <p>Conclusions</p> <p>Our array could detect homologous genes in squids and humans in spite of the distant phylogenic relationships between the species. This experimental method will be useful for identifying homologs in non-sequenced species, for the development of genetic resources and for the collection of information on biodiversity, particularly when using the genome of sibling or closely related species.</p

Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome

The ability to quantify differentiation potential of single cells is a task of critical importance. Here we demonstrate, using over 7,000 single-cell RNA-Seq profiles, that differentiation potency of a single cell can be approximated by computing the signalling promiscuity, or entropy, of a cell's transcriptome in the context of an interaction network, without the need for feature selection. We show that signalling entropy provides a more accurate and robust potency estimate than other entropy-based measures, driven in part by a subtle positive correlation between the transcriptome and connectome. Signalling entropy identifies known cell subpopulations of varying potency and drug resistant cancer stem-cell phenotypes, including those derived from circulating tumour cells. It further reveals that expression heterogeneity within single-cell populations is regulated. In summary, signalling entropy allows in silico estimation of the differentiation potency and plasticity of single cells and bulk samples, providing a means to identify normal and cancer stem-cell phenotypes

A Conserved Mito-Cytosolic Translational Balance Links Two Longevity Pathways.

Slowing down translation in either the cytosol or the mitochondria is a conserved longevity mechanism. Here, we found a non-interventional natural correlation of mitochondrial and cytosolic ribosomal proteins (RPs) in mouse population genetics, suggesting a translational balance. Inhibiting mitochondrial translation in C. elegans through mrps-5 RNAi repressed cytosolic translation. Transcriptomics integrated with proteomics revealed that this inhibition specifically reduced translational efficiency of mRNAs required in growth pathways while increasing stress response mRNAs. The repression of cytosolic translation and extension of lifespan from mrps-5 RNAi were dependent on atf-5/ATF4 and independent from metabolic phenotypes. We found the translational balance to be conserved in mammalian cells upon inhibiting mitochondrial translation pharmacologically with doxycycline. Lastly, extending this in vivo, doxycycline repressed cytosolic translation in the livers of germ-free mice. These data demonstrate that inhibiting mitochondrial translation initiates an atf-5/ATF4-dependent cascade leading to coordinated repression of cytosolic translation, which could be targeted to promote longevity