TEMPURA: Database of Growth TEMPeratures of Usual and RAre Prokaryotes.
Ontology highlight
ABSTRACT:
SUBMITTER: Sato Y
PROVIDER: S-EPMC7511790 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
TEMPURA: Database of Growth TEMPeratures of Usual and RAre Prokaryotes.
Sato Yu Y Okano Kenji K Kimura Hiroyuki H Honda Kohsuke K
Microbes and environments 20200101 3
Growth temperature is one of the most representative biological parameters for characterizing living organisms. Prokaryotes have been isolated from various temperature environments and show wide diversity in their growth temperatures. We herein constructed a database of growth TEMPeratures of Usual and RAre prokaryotes (TEMPURA, http://togodb.org/db/tempura), which contains the minimum, optimum, and maximum growth temperatures of 8,639 prokaryotic strains. Growth temperature information is linke ...[more]
Similar Datasets
Project description:MOTIVATION:Optimal growth temperature is a fundamental characteristic of all living organisms. Knowledge of this temperature is central to the study of a prokaryote, the thermal stability and temperature dependent activity of its genes, and the bioprospecting of its genome for thermally adapted proteins. While high throughput sequencing methods have dramatically increased the availability of genomic information, the growth temperatures of the source organisms are often unknown. This limits the study and technological application of these species and their genomes. Here, we present a novel method for the prediction of growth temperatures of prokaryotes using only genomic sequences. RESULTS:By applying the reverse ecology principle that an organism's genome includes identifiable adaptations to its native environment, we can predict a species' optimal growth temperature with an accuracy of 5.17°C root-mean-square error and a coefficient of determination of 0.835. The accuracy can be further improved for specific taxonomic clades or by excluding psychrophiles. This method provides a valuable tool for the rapid calculation of organism growth temperature when only the genome sequence is known. AVAILABILITY AND IMPLEMENTATION:Source code, genomes analyzed and features calculated are available at: https://github.com/DavidBSauer/OGT_prediction. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6748728 | biostudies-literature
Project description:We report here the finding of a highly significant inverse correlation of the uracil content of 16S rRNA and the optimum growth temperature (T(opt)) of cultured thermophilic and psychrophilic prokaryotes. This correlation was significantly different from the weaker correlations between the contents of other nucleotides and T(opt). Analysis of the 16S rRNA secondary structure regions revealed a fall in the A:U base-pair content in step with the increase in T(opt) that was much steeper than that of mismatched base-pairs, which are thermodynamically less stable. These findings indicate that the 16S rRNA sequences of thermophiles and psychrophiles are under a strong thermo-adaptive pressure, and that structure-function constraints play a crucial role in determining their 16S rRNA nucleotide composition. The derived relationship between uracil content and T(opt) was used to develop an algorithm to predict the T(opt) values of uncultured prokaryotes lacking cultured close relatives and belonging to the phyla predominantly containing thermophiles. This algorithm may be useful in guiding the design of cultivation conditions for hitherto uncultured microbes.
| S-EPMC1179731 | biostudies-literature
Project description:As one of the most important post-translational modifications, phosphorylation is highly involved in almost all of biological processes through temporally and spatially modifying substrate proteins. Recently, phosphorylation in prokaryotes attracted much attention for its critical roles in various cellular processes such as signal transduction. Thus, an integrative data resource of the prokaryotic phosphorylation will be useful for further analysis. In this study, we presented a curated database of phosphorylation sites in prokaryotes (dbPSP, Database URL: http://dbpsp.biocuckoo.org) for 96 prokaryotic organisms, which belong to 11 phyla in two domains including bacteria and archaea. From the scientific literature, we manually collected experimentally identified phosphorylation sites on seven types of residues, including serine, threonine, tyrosine, aspartic acid, histidine, cysteine and arginine. In total, the dbPSP database contains 7391 phosphorylation sites in 3750 prokaryotic proteins. With the dataset, the sequence preferences of the phosphorylation sites and functional annotations of the phosphoproteins were analyzed, while the results shows that there were obvious differences among the phosphorylation in bacteria, archaea and eukaryotes. All the phosphorylation sites were annotated with original references and other descriptions in the database, which could be easily accessed through user-friendly website interface including various search and browse options. Taken together, the dbPSP database provides a comprehensive data resource for further studies of protein phosphorylation in prokaryotes. Database URL: http://dbpsp.biocuckoo.org
| S-EPMC4385273 | biostudies-literature
Project description:CRISPR-Cas is an anti-viral mechanism of prokaryotes that has been widely adopted for genome editing. To make CRISPR-Cas genome editing more controllable and safer to use, anti-CRISPR proteins have been recently exploited to prevent excessive/prolonged Cas nuclease cleavage. Anti-CRISPR (Acr) proteins are encoded by (pro)phages/(pro)viruses, and have the ability to inhibit their host's CRISPR-Cas systems. We have built an online database AcrDB (http://bcb.unl.edu/AcrDB) by scanning ∼19 000 genomes of prokaryotes and viruses with AcrFinder, a recently developed Acr-Aca (Acr-associated regulator) operon prediction program. Proteins in Acr-Aca operons were further processed by two machine learning-based programs (AcRanker and PaCRISPR) to obtain numerical scores/ranks. Compared to other anti-CRISPR databases, AcrDB has the following unique features: (i) It is a genome-scale database with the largest collection of data (39 799 Acr-Aca operons containing Aca or Acr homologs); (ii) It offers a user-friendly web interface with various functions for browsing, graphically viewing, searching, and batch downloading Acr-Aca operons; (iii) It focuses on the genomic context of Acr and Aca candidates instead of individual Acr protein family and (iv) It collects data with three independent programs each having a unique data mining algorithm for cross validation. AcrDB will be a valuable resource to the anti-CRISPR research community.
| S-EPMC7778997 | biostudies-literature
Project description:In prokaryotes, protein phosphorylation plays a critical role in regulating a broad spectrum of biological processes and occurs mainly on various amino acids, including serine (S), threonine (T), tyrosine (Y), arginine (R), aspartic acid (D), histidine (H) and cysteine (C) residues of protein substrates. Through literature curation and public database integration, here we reported an updated database of phosphorylation sites (p-sites) in prokaryotes (dbPSP 2.0) that contains 19,296 experimentally identified p-sites in 8,586 proteins from 200 prokaryotic organisms, which belong to 12 phyla of two kingdoms, bacteria and archaea. To carefully annotate these phosphoproteins and p-sites, we integrated the knowledge from 88 publicly available resources that covers 9 aspects, namely, taxonomy annotation, genome annotation, function annotation, transcriptional regulation, sequence and structure information, family and domain annotation, interaction, orthologous information and biological pathway. In contrast to version 1.0 (~30 MB), dbPSP 2.0 contains ~9 GB of data, with a 300-fold increased volume. We anticipate that dbPSP 2.0 can serve as a useful data resource for further investigating phosphorylation events in prokaryotes. dbPSP 2.0 is free for all users to access at: http://dbpsp.biocuckoo.cn.
| S-EPMC7260176 | biostudies-literature
Project description:Concerted evolution, where paralogs in the same species show higher sequence similarity to each other than to orthologs in other species, is widely found in many species. However, cases of concerted evolution that last for hundreds of millions of years are very rare. By genome-wide analysis of a broad selection of prokaryotes, we provide strong evidence of recurrent concerted evolution in 26 genes, most of which have lasted more than ~500 million years. We find that most concertedly evolving genes are key members of important pathways, and encode proteins from the same complexes and/or pathways, suggesting coevolution of genes via concerted evolution to maintain gene balance. We also present LRCE-DB, a comprehensive online repository of long-lasting concerted evolution. Collectively, our study reveals that although most duplicated genes may diverge in sequence over a long period, on rare occasions this constraint can be breached, leading to unexpected long-lasting concerted evolution in a recurrent manner.
| S-EPMC6053082 | biostudies-literature
Project description:BACKGROUND:The ambient temperature of all habitats is a key physical property that shapes the biology of microbes inhabiting them. The optimal growth temperature (OGT) of a microbe, is therefore a key piece of data needed to understand evolutionary adaptations manifested in their genome sequence. Unfortunately there is no growth temperature database or easily downloadable dataset encompassing the majority of cultured microorganisms. We are thus limited in interpreting genomic data to identify temperature adaptations in microbes. RESULTS:In this work I significantly contribute to closing this gap by mining data from major culture collection centres to obtain growth temperature data for a nonredundant set of 21,498 microbes. The dataset ( https://doi.org/10.5281/zenodo.1175608 ) contains mainly bacteria and archaea and spans psychrophiles, mesophiles, thermophiles and hyperthermophiles. Using this data a full 43% of all protein entries in the UniProt database can be annotated with the growth temperature of the species from which they originate. I validate the dataset by showing a Pearson correlation of up to 0.89 between growth temperature and mean enzyme optima, a physiological property directly influenced by the growth temperature. Using the temperature dataset I correlate the genomic occurance of enzyme functional annotations with growth temperature. I identify 319 enzyme functions that either increase or decrease in occurrence with temperature. Eight metabolic pathways were statistically enriched for these enzyme functions. Furthermore, I establish a correlation between 33 domains of unknown function (DUFs) with growth temperature in microbes, four of which (DUF438, DUF1524, DUF1957 and DUF3458_C) were significant in both archaea and bacteria. CONCLUSIONS:The growth temperature dataset enables large-scale correlation analysis with enzyme function- and domain-level annotations. Growth-temperature dependent changes in their occurrence highlight potential evolutionary adaptations. A few of the identified changes are previously known, such as the preference for menaquinone biosynthesis through the futalosine pathway in bacteria growing at high temperatures. Others represent important starting points for future studies, such as DUFs where their occurrence change with temperature. The growth temperature dataset should become a valuable community resource and will find additional, important, uses in correlating genomic, transcriptomic, proteomic, metabolomic, phenotypic or taxonomic properties with temperature in future studies.
| S-EPMC6219164 | biostudies-literature
Project description:High-throughput sequencing is expanding our knowledge of microbial diversity in the environment. Still, understanding the metabolic potentials and ecological roles of rare and uncultured microbes in natural communities remains a major challenge. To this end, we applied a 'divide and conquer' strategy that partitioned a massive metagenomic data set (>100 Gbp) into subsets based on K-mer frequency in sequence assembly to a low-diversity acid mine drainage (AMD) microbial community and, by integrating with an additional metatranscriptomic assembly, successfully obtained 11 draft genomes most of which represent yet uncultured and/or rare taxa (relative abundance <1%). We report the first genome of a naturally occurring Ferrovum population (relative abundance >90%) and its metabolic potentials and gene expression profile, providing initial molecular insights into the ecological role of these lesser known, but potentially important, microorganisms in the AMD environment. Gene transcriptional analysis of the active taxa revealed major metabolic capabilities executed in situ, including carbon- and nitrogen-related metabolisms associated with syntrophic interactions, iron and sulfur oxidation, which are key in energy conservation and AMD generation, and the mechanisms of adaptation and response to the environmental stresses (heavy metals, low pH and oxidative stress). Remarkably, nitrogen fixation and sulfur oxidation were performed by the rare taxa, indicating their critical roles in the overall functioning and assembly of the AMD community. Our study demonstrates the potential of the 'divide and conquer' strategy in high-throughput sequencing data assembly for genome reconstruction and functional partitioning analysis of both dominant and rare species in natural microbial assemblages.
| S-EPMC4438317 | biostudies-literature
Project description:DNA methylation is an important regulator of genome function in the eukaryotes, but it is currently unclear if the same is true in prokaryotes. While regulatory functions have been demonstrated for a small number of bacteria, there have been no large-scale studies of prokaryotic methylomes and the full repertoire of targets and biological functions of DNA methylation remains unclear. Here we applied single-molecule, real-time sequencing to directly study the methylomes of 232 phylogenetically diverse prokaryotes. Collectively, we identified 834 methylated motifs, enabling the specific annotation of 415 DNA methyltransferases (MTases), and adding substantially to existing databases of MTase specificities. While the majority of MTases function as components of restriction-modification systems, 139 MTases have no cognate restriction enzyme in the genome, suggesting some other functional role. Several of these ‘orphan’ MTases are conserved across species and exhibit patterns of DNA methylation consistent with known regulatory MTases. Based on these patterns of methylation, we identify candidate novel regulators of gene expression in several phyla of bacteria, and candidate regulators of DNA replication in Haloarchaea. Together these data substantially advance our knowledge of DNA restriction-modification systems, and hint at a wider role for methylation in prokaryotic genome regulation.
2016-01-20 | GSE69872 | GEO
Project description:Membranes of vacuoles, the lysosomal organelles of Saccharomyces cerevisiae (budding yeast), undergo extraordinary changes during the cell's normal growth cycle. The cycle begins with a stage of rapid cell growth. Then, as glucose becomes scarce, growth slows, and vacuole membranes phase separate into micrometer-scale domains of two liquid phases. Recent studies suggest that these domains promote yeast survival by organizing membrane proteins that play key roles in a central signaling pathway conserved among eukaryotes (TORC1). An outstanding question in the field has been whether cells regulate phase transitions in response to new physical conditions and how this occurs. Here, we measure transition temperatures and find that after an increase of roughly 15 °C, vacuole membranes appear uniform, independent of growth temperature. Moreover, populations of cells grown at a single temperature regulate this transition to occur over a surprisingly narrow temperature range. Remarkably, the transition temperature scales linearly with the growth temperature, demonstrating that the cells physiologically adapt to maintain proximity to the transition. Next, we ask how yeast adjust their membranes to achieve phase separation. We isolate vacuoles from yeast during the rapid stage of growth, when their membranes do not natively exhibit domains. Ergosterol is the major sterol in yeast. We find that domains appear when ergosterol is depleted, contradicting the prevalent assumption that increases in sterol concentration generally cause membrane phase separation in vivo, but in agreement with previous studies using artificial and cell-derived membranes.
| S-EPMC8795566 | biostudies-literature