The coevolution of insect muscle TpnT and TpnI gene isoforms

In bilaterians, the main regulator of muscle contraction is the troponin (Tpn) complex, comprising three closely interacting subunits (C, T, and I). To understand how evolutionary forces drive molecular change in protein complexes, we have compared the gene structures and expression patterns of Tpn genes in insects. In this class, while TpnC is encoded by multiple genes, TpnT and TpnI are encoded by single genes. Their isoform expression pattern is highly conserved within the Drosophilidae, and single orthologous genes were identified in the sequenced genomes of Drosophila pseudoobscura, Anopheles gambiae, and Apis mellifera. Apis expression patterns also support the equivalence of their exon organization throughout holometabolous insects. All TpnT genes include a previously unidentified indirect flight muscle (IFM)-specific exon (10A) that has evolved an expression pattern similar to that of exon 9 in TpnI. Thus, expression patterns, sequence evolution trends, and structural data indicate that Tpn genes and their isoforms have coevolved, building species- and muscle-specific troponin complexes. Furthermore, a clear case can be made for independent evolution of the IFM-specific isoforms containing alanine/proline-rich sequences. Dipteran genomes contain one tropomyosin gene that encodes one or two high-molecular weight isoforms (TmH) incorporating APPAEGA-rich sequences, specifically expressed in IFM. Corresponding exons do not exist in the Apis tropomyosin gene, but equivalent sequences occur in a high-molecular weight Apis IFM-specific TpnI isoform (TnH). Overall, our approach to comparatively analyze supramolecular complexes reveals coevolutionary trends not only in gene families but in isoforms generated by alternative splicing.     

Multiple isoforms of beta-TrCP display differential activities in the regulation of Wnt signaling

The F-box proteins beta-TrCP1 and 2 (beta-transducin repeat containing protein) have 2 and 3 isoforms, respectively, due to alternative splicing of exons encoding the N-terminal region. We identified an extra exon in between the previously known exons 1 and 2 of beta-TrCP1 and beta-TrCP2. Interestingly, sequence analysis suggested that many more isoforms are produced than previously identified, via the alternative splicing of all possible combination of exons II to V of beta-TrCP1 and exons II to IV of beta-TrCP2. Different mouse tissues show specific expression patterns of the isoforms, and the level of expression of the isoform that has been used in most published papers was very low. Yeast two-hybrid assays show that beta-TrCP1 isoforms containing exon III, which are the most highly expressed isoforms in most tissues, do not interact with Skp1. Indirect immunofluorescence analysis of transiently expressed beta-TrCP1 isoforms suggests that the presence of exon III causes beta-TrCP1 to localize in nuclei. Consistent with the above findings, isoforms including exon III showed a reduced ability to block ectopic embryonic axes induced via injection of Wnt8 or beta-catenin in Xenopus embryos. Overall, our data suggest that isoforms of beta-TrCPs generated by alternative splicing may have different biological roles.     

A Method for Construction, Cloning and Expression of Intron-Less Gene from Unannotated Genomic DNA

The present century has witnessed an unprecedented rise in genome sequences owing to various genome-sequencing programs. However, the same has not been replicated with cDNA or expressed sequence tags (ESTs). Hence, prediction of protein coding sequence of genes from this enormous collection of genomic sequences presents a significant challenge. While robust high throughput methods of cloning and expression could be used to meet protein requirements, lack of intron information creates a bottleneck. Computational programs designed for recognizing intron–exon boundaries for a particular organism or group of organisms have their own limitations. Keeping this in view, we describe here a method for construction of intron-less gene from genomic DNA in the absence of cDNA/EST information and organism-specific gene prediction program. The method outlined is a sequential application of bioinformatics to predict correct intron–exon boundaries and splicing by overlap extension PCR for spliced gene synthesis. The gene construct so obtained can then be cloned for protein expression. The method is simple and can be used for any eukaryotic gene expression.     

Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach

Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64–79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome.     

SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples

Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.     

Quantitative Profiling of Drosophila melanogaster Dscam1 Isoforms Reveals No Changes in Splicing after Bacterial Exposure

The hypervariable Dscam1 (Down syndrome cell adhesion molecule 1) gene can produce thousands of different ectodomain isoforms via mutually exclusive alternative splicing. Dscam1 appears to be involved in the immune response of some insects and crustaceans. It has been proposed that the diverse isoforms may be involved in the recognition of, or the defence against, diverse parasite epitopes, although evidence to support this is sparse. A prediction that can be generated from this hypothesis is that the gene expression of specific exons and/or isoforms is influenced by exposure to an immune elicitor. To test this hypothesis, we for the first time, use a long read RNA sequencing method to directly investigate the Dscam1 splicing pattern after exposing adult Drosophila melanogaster and a S2 cell line to live Escherichia coli. After bacterial exposure both models showed increased expression of immune-related genes, indicating that the immune system had been activated. However there were no changes in total Dscam1 mRNA expression. RNA sequencing further showed that there were no significant changes in individual exon expression and no changes in isoform splicing patterns in response to bacterial exposure. Therefore our studies do not support a change of D. melanogaster Dscam1 isoform diversity in response to live E. coli. Nevertheless, in future this approach could be used to identify potentially immune-related Dscam1 splicing regulation in other host species or in response to other pathogens.     

Clathrin light chain B: gene structure and neuron-specific splicing.

The clathrin light chains are components of clathrin coated vesicles, structural constituents involved in endocytosis and membrane recycling. The clathrin light chain B (LCB) gene encodes two isoforms, termed LCB2 and LCB3, via an alternative RNA splicing mechanism. We have determined the structure of the rat clathrin light chain B gene. The gene consists of six exons that extend over 11.9 kb. The first four exons and the last exon are common to the LCB2 and LCB3 isoforms. The fifth exon, termed EN, is included in the mRNA in brain, giving rise to the brain specific form LCB2 but is excluded in other tissues, generating the LCB3 isoform. Primary rat neuronal cell cultures express predominantly the brain specific LCB2 isoform, whereas primary rat cultures of glia express only the LCB3 isoform, suggesting that expression of the brain-specific LCB2 form is limited to neurons. Further evidence for neuronal localization of the LCB2 form is provided using a teratocarcinoma cell line, P19, which can be induced by retinoic acid to express a neuronal phenotype, concomitant with the induction of the LCB2 form. In order to determine the sequences involved in alternative splice site selection, we constructed a minigene containing the alternative spliced exon EN and its flanking intron and exon sequences. This minigene reflects the splicing pattern of the endogenous gene upon transfection in HeLa cell and primary neuronal cell cultures, indicating that this region of the LCB gene contains all the necessary information for neuron-specific splicing.     

Prediction of treatment response using gene expression profiles

This paper concerns prediction of clinical outcome from gene expression profiles using work in a different area, nonlinear system identification. In particular, the approach can predict long-term treatment response from data of a landmark article by Golub et al. (Golub, T. R.; Slonim, D. K.; Tamayo, P.; Huard, C.; Gaasenbeek, M.; Mesirov, J. P. et al. Science 1999, 286, 531-537) that has not previously been achieved with these data. The present paper shows that, for these data, gene expression profiles taken at time of diagnosis of acute myeloid leukemia contain information predictive of eventual response to chemotherapy. This was not evident in previous work; indeed, the Golub et al. article did not find a set of genes strongly correlated with clinical outcome. However, the present approach can accurately predict outcome class of gene expression profiles even when the genes do not have large differences in expression levels between the classes.