GOBASE: the organelle genome database

Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes

Gastric cancer (GC) is a common digestive system cancer, characterized by a significant mortality rate. Mitochondria is an indispensable organelle in eukaryotic cells. It was previously revealed that a series of nucleus-encoded mitochondrial genes (NMG) mutations and dysfunctions potentially contribute to the initiation and progression of GC. However, the correlation between NMG mutations and survival outcomes for GC patients is still unclear. In this study, NMG expression profile and clinical information in GC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through consistent clustering and functional enrichment analysis, we have identified three NMG clusters and three gene clusters that are associated with patterns of immune cell infiltration. Prognostic genes were identified through Univariate Cox regression analysis. The principal component analysis was conducted to set up a scoring system. Subsequently, the Single‑cell RNA sequencing (scRNA-seq) data of GC patients and cancer cell drug sensitivity data were retrieved from the GEO database. Patients with high NMG scores exhibited increased microsatellite instability status and a heightened tumor mutation rate compared to those with low NMG scores. Survival analysis revealed that GC samples with high NMG scores could achieve a better prognosis. Additionally, These patients were observed to be more responsive to immunotherapy. Moreover, we delved into prognostic genes at the level of single cells, revealing that MRPL4 and MRPL37 exhibit high expression in epithelial cells, while TPM1 demonstrates high expression in tissue stem cells. Utilizing cancer cell drug sensitivity data from the Drug Sensitivity in Cancer (GDSC) database, we noted a heightened sensitivity to chemotherapy in the high NMG group. Furthermore, we discovered a significant enrichment of cuproptosis-related genes in clusters with high NMG scores. Consequently, employing the scoring system could facilitate the prediction of GC patients' sensitivity to cuproptosis-induced therapy. Our study confirmed the potency of this scoring system as a therapeutic response biomarker for gastric cancer, potentially informing clinical treatment strategies.

Sequencing and phylogenomics of the complete mitochondrial genome of Allodiplogaster sp. (Rhabditida: Diplogasteridae): A new gene order and its phylogenetic implications

Gene order has been utilized as a phylogenetic signal for many taxa. However, its phylogenetic performance has not been evaluated in Nematoda. As there is only one nematode mitogenome available to date, in the Diplogasteridae family, we sequenced the mitogenome of Allodiplogaster sp. and constructed a phylogeny for Nematoda using this updated mitogenome dataset. We then compared this phylogeny to one constructed using gene order. The complete mitochondrial genome of Allodiplogaster sp. was 13,953 bp in size and included 22 tRNAs, two rRNAs, and 12 protein-coding genes. To assess how Allodiplogaster sp. is related to other nematode species, we used Bayesian inference and maximum likelihood algorithms to construct phylogenetic trees of the Nematoda. We found that: 1) The target species Allodiplogaster sp. is closely related to Allodiplogaster sudhausi. The topology of the mitogenome based phylogeny was nearly identical to previous phylogenies created using 18S rRNA data, except for the placement of the Strongyloididae family. 2) The maximum likelihood tree constructed using gene order was roughly consistent with the mitogenome-based tree at the family level, but not at the species level. 3) Protein-coding genes were ordered differently in Allodiplogaster sp. versus Allodiplogaster sudhausi; this represents the first report of such a reordering in the class Chromadorea in our study. Our study confirms that gene order represents useful phylogenetic information for the Nematoda: the maximum likelihood tree based on gene order provided additional support for the nematode phylogeny constructed using molecular data.

Intron Derived Size Polymorphism in the Mitochondrial Genomes of Closely Related Chrysoporthe Species

In this study, the complete mitochondrial (mt) genomes of Chrysoporthe austroafricana (190,834 bp), C. cubensis (89,084 bp) and C. deuterocubensis (124,412 bp) were determined. Additionally, the mitochondrial genome of another member of the Cryphonectriaceae, namely Cryphonectria parasitica (158,902 bp), was retrieved and annotated for comparative purposes. These genomes showed high levels of synteny, especially in regions including genes involved in oxidative phosphorylation and electron transfer, unique open reading frames (uORFs), ribosomal RNAs (rRNAs) and transfer RNAs (tRNAs), as well as intron positions. Comparative analyses revealed signatures of duplication events, intron number and length variation, and varying intronic ORFs which highlighted the genetic diversity of mt genomes among the Cryphonectriaceae. These mt genomes showed remarkable size polymorphism. The size polymorphism in the mt genomes of these closely related Chrysoporthe species was attributed to the varying number and length of introns, coding sequences and to a lesser extent, intergenic sequences. Compared to publicly available fungal mt genomes, the C. austroafricana mt genome is the second largest in the Ascomycetes thus far.

Energy matters: mitochondrial proteomics for biomedicine

This review compiles results of medical relevance from mitochondrial proteomics, grouped either according to the type of disease - genetic or degenerative - or to the involved mechanism - oxidative stress or apoptosis. The findings are commented in the light of our current understanding of uniformity/variability in cell responses to different stimuli. Specificities in the conceptual and technical approaches to human mitochondrial proteomics are also outlined.

Genetic engineering of algae for enhanced biofuel production

There are currently intensive global research efforts aimed at increasing and modifying the accumulation of lipids, alcohols, hydrocarbons, polysaccharides, and other energy storage compounds in photosynthetic organisms, yeast, and bacteria through genetic engineering. Many improvements have been realized, including increased lipid and carbohydrate production, improved H(2) yields, and the diversion of central metabolic intermediates into fungible biofuels. Photosynthetic microorganisms are attracting considerable interest within these efforts due to their relatively high photosynthetic conversion efficiencies, diverse metabolic capabilities, superior growth rates, and ability to store or secrete energy-rich hydrocarbons. Relative to cyanobacteria, eukaryotic microalgae possess several unique metabolic attributes of relevance to biofuel production, including the accumulation of significant quantities of triacylglycerol; the synthesis of storage starch (amylopectin and amylose), which is similar to that found in higher plants; and the ability to efficiently couple photosynthetic electron transport to H(2) production. Although the application of genetic engineering to improve energy production phenotypes in eukaryotic microalgae is in its infancy, significant advances in the development of genetic manipulation tools have recently been achieved with microalgal model systems and are being used to manipulate central carbon metabolism in these organisms. It is likely that many of these advances can be extended to industrially relevant organisms. This review is focused on potential avenues of genetic engineering that may be undertaken in order to improve microalgae as a biofuel platform for the production of biohydrogen, starch-derived alcohols, diesel fuel surrogates, and/or alkanes.

GOBASE: an organelle genome database

The organelle genome database GOBASE, now in its 21st release (June 2008), contains all published mitochondrion-encoded sequences (approximately 913,000) and chloroplast-encoded sequences (approximately 250,000) from a wide range of eukaryotic taxa. For all sequences, information on related genes, exons, introns, gene products and taxonomy is available, as well as selected genome maps and RNA secondary structures. Recent major enhancements to database functionality include: (i) addition of an interface for RNA editing data, with substitutions, insertions and deletions displayed using multiple alignments; (ii) addition of medically relevant information, such as haplotypes, SNPs and associated disease states, to human mitochondrial sequence data; (iii) addition of fully reannotated genome sequences for Escherichia coli and Nostoc sp., for reference and comparison; and (iv) a number of interface enhancements, such as the availability of both genomic and gene-coding sequence downloads, and a more sophisticated literature reference search functionality with links to PubMed where available. Future projects include the transfer of GOBASE features to NCBI/GenBank, allowing long-term preservation of accumulated expert information. The GOBASE database can be found at http://gobase.bcm.umontreal.ca/. Queries about custom and large-scale data retrievals should be addressed to gobase@bch.umontreal.ca.

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.

GOBASE--a database of organelle and bacterial genome information

The organelle genome database GOBASE is now in its twelfth release, and includes 350,000 mitochondrial sequences and 118,000 chloroplast sequences, roughly a 3-fold expansion since previously documented. GOBASE also includes a fully reannotated genome sequence of Rickettsia prowazekii, one of the closest bacterial relatives of mitochondria, and will shortly expand to contain more data from bacteria from which organelles originated. All these sequences are now accessible through a single unified interface. Enhancements to the functionality of GOBASE include addition of pages for RNA structures and a page compiling data about the taxonomic distribution of organelle-encoded genes; incorporation of Gene Ontology terms; addition of features deduced from incomplete annotations to sequences in GenBank; marking of type examples in cases where single genes in single species are oversampled within GenBank; and addition of graphics illustrating gene structure and the position of neighbouring genes on a sequence. The database has been reimplemented in PostgreSQL to facilitate development and maintenance, and structural modifications have been made to speed up queries, particularly those related to taxonomy. The GOBASE database can be queried at http://gobase.bcm.umontreal.ca/ and inquiries should be directed to gobase@bch.umontreal.ca.

AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis

Background

Sequence data analyses such as gene identification, structure modeling or phylogenetic tree inference involve a variety of bioinformatics software tools. Due to the heterogeneity of bioinformatics tools in usage and data requirements, scientists spend much effort on technical issues including data format, storage and management of input and output, and memorization of numerous parameters and multi-step analysis procedures.

Results

In this paper, we present the design and implementation of AnaBench, an interactive, Web-based bioinformatics Analysis workBench allowing streamlined data analysis. Our philosophy was to minimize the technical effort not only for the scientist who uses this environment to analyze data, but also for the administrator who manages and maintains the workbench. With new bioinformatics tools published daily, AnaBench permits easy incorporation of additional tools. This flexibility is achieved by employing a three-tier distributed architecture and recent technologies including CORBA middleware, Java, JDBC, and JSP. A CORBA server permits transparent access to a workbench management database, which stores information about the users, their data, as well as the description of all bioinformatics applications that can be launched from the workbench.

Conclusion

AnaBench is an efficient and intuitive interactive bioinformatics environment, which offers scientists application-driven, data-driven and protocol-driven analysis approaches. The prototype of AnaBench, managed by a team at the Université de Montréal, is accessible on-line at: . Please contact the authors for details about setting up a local-network AnaBench site elsewhere.

GOBASE--a database of mitochondrial and chloroplast information

GOBASE is a relational database containing integrated sequence, RNA secondary structure and biochemical and taxonomic information about organelles. GOBASE release 6 (summer 2002) contains over 130 000 mitochondrial sequences, an increase of 37% over the previous release, and more than 30 000 chloroplast sequences in a new auxiliary database. To handle this flood of new data, we have designed and implemented GOpop, a Java system for population and verification of the database. We have also implemented a more powerful and flexible user interface using the PHP programming language. http://megasun.bch.umontreal.ca/gobase/gobase.html.

OGRe: a relational database for comparative analysis of mitochondrial genomes

Organellar Genome Retrieval (OGRe) is a relational database of complete mitochondrial genome sequences for over 250 Metazoan species. OGRe provides a resource for the comparative analysis of mitochondrial genomes at several levels. At the sequence level, OGRe allows the retrieval of any selected set of mitochondrial genes from any selected set of species. Species are classified using a taxonomic system that allows easy selection of related groups of species. Sequence alignments are also available for some species. At the level of individual nucleotides, the system contains information on base frequencies and codon usage frequencies that can be compared between organisms. At the level of whole genomes, OGRe provides several ways of visualizing information on gene order. Diagrams illustrating the genome arrangement can be generated for any selected set of species automatically from the information in the database. Searches can be done based on gene arrangement to find sets of species that have the same order as one another. Diagrams for pairwise comparison of species can be produced that show the positions of break-points in the gene order and use colour to highlight the sections of the genome that have moved. OGRe is available from http://www.bioinf.man.ac.uk/ogre.

MitoNuc: a database of nuclear genes coding for mitochondrial proteins. Update 2002

Mitochondria, besides their central role in energy metabolism, have recently been found to be involved in a number of basic processes of cell life and to contribute to the pathogenesis of many degenerative diseases. All functions of mitochondria depend on the interaction of nuclear and organelle genomes. Mitochondrial genomes have been extensively sequenced and analysed and data have been collected in several specialised databases. In order to collect information on nuclear coded mitochondrial proteins we developed MitoNuc, a database containing detailed information on sequenced nuclear genes coding for mitochondrial proteins in Metazoa. The MitoNuc database can be retrieved through SRS and is available via the web site http://bighost.area.ba.cnr.it/mitochondriome where other mitochondrial databases developed by our group, the complete list of the sequenced mitochondrial genomes, links to other mitochondrial sites and related information, are available. The MitoAln database, related to MitoNuc in the previous release, reporting the multiple alignments of the relevant homologous protein coding regions, is no longer supported in the present release. In order to keep the links among entries in MitoNuc from homologous proteins, a new field in the database has been defined: the cluster identifier, an alpha numeric code used to identify each cluster of homologous proteins. A comment field derived from the corresponding SWISS-PROT entry has been introduced; this reports clinical data related to dysfunction of the protein. The logic scheme of MitoNuc database has been implemented in the ORACLE DBMS. This will allow the end-users to retrieve data through a friendly interface that will be soon implemented.

FUGOID: functional genomics of organellar introns database

FUGOID is a web-based, taxonomically broad organelle intron database that collects and integrates various functional and structural data on organellar (mitochondrial and chloroplast) introns. The main information provided by FUGOID includes intron sequence, subclass, resident ORF, self-splicing capability, host gene, protein factor(s) involved in splicing, mobility, insertion site, twintron, seminal references and taxonomic position of host organism. It is implemented in a relational database management system, allowing sophisticated, user-friendly searching, data entry and revision. Users can access the database by any common web browser using a variety of operating systems. The main page of the database is available at http://wnt.cc.utexas.edu/~ifmr530/introndata/main.htm.

Does the evolutionary history of aminoacyl-tRNA synthetases explain the loss of mitochondrial tRNA genes?

The importation of cytosolic tRNAs is required for protein synthesis in the mitochondria of the wide variety of eukaryotes that lack a complete set of mitochondrial tRNA genes. The evolutionary history of the process, however, is still enigmatic. The analysis presented here suggests that the loss of distinct mitochondrial tRNA genes was not random and that it might be explained by the differential capabilities of mitochondrial aminoacyl-tRNA synthetases to charge imported eukaryotic-type tRNAs with amino acid.