gjing Jin

PLncDB: plant long non-coding RNA database

SUMMARY Plant long non-coding RNA database (PLncDB) attempts to provide the following functions related to long non-coding RNAs (lncRNAs): (i) Genomic information for a large number of lncRNAs collected from various resources; (ii) an online genome browser for plant lncRNAs based on a platform similar to that of the UCSC Genome Browser; (iii) Integration of transcriptome datasets derived from various samples including different tissues, developmental stages, mutants and stress treatments; and (iv) A list of epigenetic modification datasets and small RNA datasets. Currently, our PLncDB provides a comprehensive genomic view of Arabidopsis lncRNAs for the plant research community. This database will be regularly updated with new plant genome when available so as to greatly facilitate future investigations on plant lncRNAs. AVAILABILITY PLncDB is freely accessible at http://chualab.rockefeller.edu/gbrowse2/homepage.html and all results can be downloaded for free at the website.

Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions

BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs.ResultsWe develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both host and pathogen proteins involved in host-pathogen PPIs tend to have lower charge, and tend to be more hydrophilic.ConclusionsOur stringent homology-based prediction approach provides a better strategy in predicting PPIs between eukaryotic hosts and prokaryotic pathogens than a conventional homology-based approach. The properties we have observed from the predicted H. sapiens-M. tuberculosis H37Rv PPI network are useful for understanding inter-species host-pathogen PPI networks and provide novel insights for host-pathogen interaction studies.ReviewersThis article was reviewed by Michael Gromiha, Narayanaswamy Srinivasan and Thomas Dandekar.

Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization.

PROGRESS IN COMPUTATIONAL STUDIES OF HOST{PATHOGEN INTERACTIONS

Host-pathogen interactions are important for understanding infection mechanism and developing better treatment and prevention of infectious diseases. Many computational studies on host-pathogen interactions have been published. Here, we review recent progress and results in this field and provide a systematic summary, comparison and discussion of computational studies on host-pathogen interactions, including prediction and analysis of host-pathogen protein-protein interactions; basic principles revealed from host-pathogen interactions; and database and software tools for host-pathogen interaction data collection, integration and analysis.

Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited.ResultsWe develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces.We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI.ConclusionsThe stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.

Next generation sequencing unravels the biosynthetic ability of spearmint (Mentha spicata) peltate glandular trichomes through comparative transcriptomics.

BackgroundPlant glandular trichomes are chemical factories with specialized metabolic capabilities to produce diverse compounds. Aromatic mint plants produce valuable essential oil in specialised glandular trichomes known as peltate glandular trichomes (PGT). Here, we performed next generation transcriptome sequencing of different tissues of Mentha spicata (spearmint) to identify differentially expressed transcripts specific to PGT. Our results provide a comprehensive overview of PGT’s dynamic metabolic activities which will help towards pathway engineering.ResultsSpearmint RNAs from 3 different tissues: PGT, leaf and leaf stripped of PGTs (leaf-PGT) were sequenced by Illumina paired end sequencing. The sequences were assembled de novo into 40,587 non-redundant unigenes; spanning a total of 101 Mb. Functions could be assigned to 27,025 (67%) unigenes and among these 3,919 unigenes were differentially expressed in PGT relative to leaf - PGT. Lack of photosynthetic transcripts in PGT transcriptome indicated the high levels of purity of isolated PGT, as mint PGT are non-photosynthetic. A significant number of these unigenes remained unannotated or encoded hypothetical proteins. We found 16 terpene synthases (TPS), 18 cytochrome P450s, 5 lipid transfer proteins and several transcription factors that were preferentially expressed in PGT. Among the 16 TPSs, two were characterized biochemically and found to be sesquiterpene synthases.ConclusionsThe extensive transcriptome data set renders a complete description of genes differentially expressed in spearmint PGT. This will facilitate the metabolic engineering of mint terpene pathway to increase yield and also enable the development of strategies for sustainable production of novel or altered valuable compounds in mint.

The floral transcriptome of ylang ylang (Cananga odorata var. fruticosa) uncovers biosynthetic pathways for volatile organic compounds and a multifunctional and novel sesquiterpene synthase

Highlight Combined RNA sequencing and chemical analysis led to the identification of biosynthetic pathway genes for volatile organic compounds and the discovery of novel terpene synthases in ylang ylang flowers.

A consensus linkage map of oil palm and a major QTL for stem height

Oil palm (Elaeis guinensis Jacquin) is the most important source of vegetable oil and fat. Several linkage maps had been constructed using dominant and co-dominant markers to facilitate mapping of QTL. However, dominant markers are not easily transferable among different laboratories. We constructed a consensus linkage map for oil palm using co-dominant markers (i.e. microsatellite and SNPs) and two F1 breeding populations generated by crossing Dura and Pisifera individuals. Four hundreds and forty-four microsatellites and 36 SNPs were mapped onto 16 linkage groups. The map length was 1565.6 cM, with an average marker space of 3.72 cM. A genome-wide scan of QTL identified a major QTL for stem height on the linkage group 5, which explained 51% of the phenotypic variation. Genes in the QTL were predicted using the palm genome sequence and bioinformatic tools. The linkage map supplies a base for mapping QTL for accelerating the genetic improvement, and will be also useful in the improvement of the assembly of the genome sequences. Markers linked to the QTL may be used in selecting dwarf trees. Genes within the QTL will be characterized to understand the mechanisms underlying dwarfing.

Draft genome sequence of an elite Dura palm and whole-genome patterns of DNA variation in oil palm

Oil palm is the world’s leading source of vegetable oil and fat. Dura, Pisifera and Tenera are three forms of oil palm. The genome sequence of Pisifera is available whereas the Dura form has not been sequenced yet. We sequenced the genome of one elite Dura palm, and re-sequenced 17 palm genomes. The assemble genome sequence of the elite Dura tree contained 10,971 scaffolds and was 1.701 Gb in length, covering 94.49% of the oil palm genome. 36,105 genes were predicted. Re-sequencing of 17 additional palm trees identified 18.1 million SNPs. We found high genetic variation among palms from different geographical regions, but lower variation among Southeast Asian Dura and Pisifera palms. We mapped 10,000 SNPs on the linkage map of oil palm. In addition, high linkage disequilibrium (LD) was detected in the oil palms used in breeding populations of Southeast Asia, suggesting that LD mapping is likely to be practical in this important oil crop. Our data provide a valuable resource for accelerating genetic improvement and studying the mechanism underlying phenotypic variations of important oil palm traits.

Transcriptome and functional analysis reveals hybrid vigor for oil biosynthesis in oil palm

Oil palm is the most productive oil crop in the world and composes 36% of the world production. However, the molecular mechanisms of hybrids vigor (or heterosis) between Dura, Pisifera and their hybrid progeny Tenera has not yet been well understood. Here we compared the temporal and spatial compositions of lipids and transcriptomes for two oil yielding organs mesocarp and endosperm from Dura, Pisifera and Tenera. Multiple lipid biosynthesis pathways are highly enriched in all non-additive expression pattern in endosperm, while cytokinine biosynthesis and cell cycle pathways are highly enriched both in endosperm and mesocarp. Compared with parental palms, the high oil content in Tenera was associated with much higher transcript levels of EgWRI1, homolog of Arabidopsis thaliana WRINKLED1. Among 338 identified genes in lipid synthesis, 207 (61%) has been identified to contain the WRI1 specific binding AW motif. We further functionally identified EgWRI1-1, one of three EgWRI1 orthologs, by genetic complementation of the Arabidopsis wri1 mutant. Ectopic expression of EgWRI1-1 in plant produced dramatically increased seed mass and oil content, with oil profile changed. Our findings provide an explanation for EgWRI1 as an important gene contributing hybrid vigor in lipid biosynthesis in oil palm.