scholarly journals Genome sequences hot and cold: a database of organisms with defined optimal growth temperatures

2021 ◽  
Author(s):  
Karla Helena-Bueno ◽  
Charlotte Rebecca Brown ◽  
Egor Konyk ◽  
Sergey Melnikov
Keyword(s):  
Growth Temperature ◽  
Bacterial Species ◽  
Protein Sequences ◽  
Optimal Growth ◽  
Growth Conditions ◽  
Biological Molecules ◽  
Genomic Sequences ◽  
Genome Sequences ◽  
Optimal Growth Temperature ◽  
Database Interface

Despite the rapidly increasing number of organisms with sequenced genomes, there is no existing resource that simultaneously contains information about genome sequences and the optimal growth conditions for a given species. In the absence of such a resource, we cannot immediately sort genomic sequences by growth conditions, making it difficult to study how organisms and biological molecules adapt to distinct environments. To address this problem, we have created a database called GSHC (Genome Sequences: Hot, Cold, and everything in between). This database, available at http://melnikovlab.com/gshc, brings together information about the genomic sequences and optimal growth temperatures for 25,324 species, including ~89% of the bacterial species with known genome sequences. Using this database, it is now possible to readily compare genomic sequences from thousands of species and correlate variations in genes and genomes with optimal growth temperatures, at the scale of the entire tree of life. The database interface allows users to retrieve protein sequences sorted by optimal growth temperature for their corresponding species, providing a tool to explore how organisms, genomes, and individual proteins and nucleic acids adapt to certain temperatures. We hope that this database will contribute to medicine and biotechnology by helping to create a better understanding of molecular adaptations to heat and cold, leading to new ways to preserve biological samples, engineer useful enzymes, and develop new biological materials and organisms with the desired tolerance to heat and cold.

Optimal growth temperature for the isolation of Plesiomonas shigelloides, using various selective and differential agars

1991 ◽  
Vol 37 (10) ◽  
pp. 800-802 ◽  
Author(s):  
Anwarul Huq ◽  
Anwari Akhtar ◽  
M. A. R. Chowdhury ◽  
David A. Sack
Keyword(s):  
Growth Temperature ◽  
Optimal Growth ◽  
Growth Conditions ◽  
Aeromonas Species ◽  
Growth Media ◽  
Optimal Growth Temperature ◽  
Environmental Waters ◽  
Temperature Growth ◽  
Plesiomonas Shigelloides ◽  
Incubation Temperatures

The growth characteristics of known strains of Plesiomonas shigelloides were compared with those of Aeromonas species (the major competing species in environmental waters) on plesiomonas differential agar, inositol brilliant green bile salt, and modified salmonella–shigella agar at incubation temperatures of 37, 42, and 44 °C. Using local isolates from clinical and environmental sources, optimal growth conditions, as determined by colony counts and the colony characteristics, plesiomonas differential agar proved to be ideal when incubated at 44 °C. Contrary to earlier recommendations for 48 h incubation, the colonies could be recognized readily after an incubation of 24 h. Key words: Plesiomonas, growth temperature, growth media.

scholarly journals Endosymbiotic adaptations in three new bacterial species associated with Dictyostelium discoideum: Burkholderia agricolaris sp. nov., Burkholderia hayleyella sp. nov., and Burkholderia bonniea sp. nov

2018 ◽  
Author(s):  
Debra A. Brock ◽  
Alicia N.M. Hubert ◽  
Suegene Noh ◽  
Susanne DiSalvo ◽  
Katherine S. Geist ◽  
...  
Keyword(s):  
New Species ◽  
Dictyostelium Discoideum ◽  
Growth Temperature ◽  
Bacterial Species ◽  
Optimal Growth ◽  
Cell Length ◽  
Phylogenetic Position ◽  
Optimal Growth Temperature ◽  
Eastern Usa

AbstractHere we name three species of Burkholderia that can defeat the mechanisms by which bacteria are normally excluded from the spores of a soil dwelling eukaryote Dictyostelium discoideum, which is predatory on bacteria. They are B. agricolaris sp. nov., B. hayleyella sp. nov., and B. bonniea sp. nov. These new species are widespread across the eastern USA and were isolated as internal symbionts of wild collected D. discoideum. Evidence that they are each a distinct new species comes from their phylogenetic position, carbon usage, reduced cell length, cooler optimal growth temperature, and ability to invade D. discoideum amoebae and remain there for generations.

scholarly journals A Positive Correlation between Bacterial GC Content and Growth Temperature

2021 ◽  
Author(s):  
En-Ze Hu ◽  
Xin-Ran Lan ◽  
Zhi-Ling Liu ◽  
Jie Gao ◽  
Deng-Ke Niu
Keyword(s):  
High Temperatures ◽  
Growth Temperature ◽  
Gc Content ◽  
Optimal Growth ◽  
Whole Genome ◽  
Genome Sequences ◽  
Optimal Growth Temperature ◽  
Positive Correlations ◽  
Rna Genes ◽  
Gc Contents

Abstract Background: Because GC pairs are more stable than AT pairs, GC-rich genomes were proposed to be more adapted to high temperatures than AT-rich genomes. Previous studies consistently showed positive correlations between growth temperature and the GC contents of structural RNA genes. However, for the whole genome sequences and the silent sites of the codons in protein-coding genes, the relationship between GC content and growth temperature is in a long-lasting debate. Results: With a dataset much larger than previous studies (681 bacteria and 155 archaea), our phylogenetic comparative analyses showed positive correlations between optimal growth temperature and GC content both in bacterial and archaeal structural RNA genes and in bacterial whole genome sequences, chromosomal sequences, plasmid sequences, core genes, and accessory genes. However, in the 155 archaea, we did not observe a significant positive correlation of optimal growth temperature with whole-genome GC content or GC content at four-fold degenerate sites. We randomly drew 155 samples from the 681 bacteria for 1000 rounds. In most cases (> 95%), the positive correlations between optimal growth temperature and genomic GC contents became statistically nonsignificant (P > 0.05). This result suggested that the small sample sizes might account for the lack of positive correlations between growth temperature and genomic GC content in the 155 archaea and the bacterial samples of previous studies. Conclusions: This study explains the previous contradictory observations and ends a long debate. Bacteria growing in high temperatures have higher GC contents. Thermal adaptation is one possible explanation for the positive association. Meanwhile, we should be open to other intricate explanations, including nonadaptive ones.

scholarly journals Significant Correlation between Growth Temperature and Guanine-Cytosine Content in Bacteria

2021 ◽  
Author(s):  
En-Ze Hu ◽  
Xin-Ran Lan ◽  
Zhi-Ling Liu ◽  
Jie Gao ◽  
Deng-Ke Niu
Keyword(s):  
Growth Temperature ◽  
Gc Content ◽  
Optimal Growth ◽  
Small Sample ◽  
Whole Genome ◽  
Genome Sequences ◽  
Optimal Growth Temperature ◽  
Positive Correlations ◽  
Rna Genes ◽  
Genomic Gc Content

AbstractBecause GC pairs are more stable than AT pairs, GC-rich sequences were proposed to be more adapted to high temperatures than AT-rich sequences. Previous studies consistently showed positive correlations between the growth temperature and the GC contents of structural RNA genes. However, for the whole genome sequences and the silent sites of the codons in protein-coding genes, the relationship between GC content and growth temperature is in a long-lasting debate. With a dataset much larger than previous studies (681 bacteria and 155 archaea), our phylogenetic comparative analyses showed positive correlations between optimal growth temperature and GC content exists, both in the structural RNA genes of bacteria and archaea and in bacterial whole genome sequences, chromosomal sequences, plasmid sequences, core genes, and accessory genes. However, in the 155 archaea, we did not observe a significant positive correlation of optimal growth temperature with whole-genome GC content or GC content at four-fold degenerate sites. We randomly drew 155 samples from the 681 bacteria for 1000 rounds. In most cases (> 95%), the positive correlations between optimal growth temperature and genomic GC content became statistically nonsignificant (P > 0.05). This result suggested that the small sample sizes might account for the lack of positive correlations between growth temperature and genomic GC content in the 155 archaea and the bacterial samples of previous studies.

scholarly journals Characteristics of Phomopsis juglandina (Sacc.) Hohn. Associated with Dieback of Walnut in the Climatic Conditions of Southern Romania

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Cristina Mihaescu ◽  
Daniel Dunea ◽  
Adrian Gheorghe Bășa ◽  
Loredana Neagu Frasin
Keyword(s):  
Growth Temperature ◽  
Potential Evapotranspiration ◽  
Optimal Growth ◽  
Climatic Conditions ◽  
Cultural Characteristics ◽  
Optimal Growth Temperature ◽  
Conidial State ◽  
Optimal Ph

Phomopsis juglandina (Sacc.) Höhn., which is the conidial state of Diaporthe juglandina (Fuckel) Nitschke, and the main pathogen causing the dieback of branches and twigs of walnut was recently detected in many orchards from Romania. The symptomatological, morphological, ultrastructural, and cultural characteristics, as well as the pathogenicity of an isolate of this lignicolous fungus, were described and illustrated. The optimum periods for infection, under the conditions prevailing in Southern Romania, mainly occur in the spring (April) and autumn months (late September-beginning of October). Strong inverse correlations (p < 0.001) were found between potential evapotranspiration and lesion lengths on walnut branches in 2019. The pathogen forms two types of phialospores: alpha and beta; the role of beta phialospores is not well known in pathogenesis. In Vitro, the optimal growth temperature of mycelial hyphae was in the range of 22–26 °C, and the optimal pH is 4.4–7. This pathogen should be monitored continuously due to its potential for damaging infestations of intensive plantations.

The correlation between genomic G + C and optimal growth temperature of prokaryotes is robust: A reply to Marashi and Ghalanbor

2005 ◽  
Vol 330 (2) ◽  
pp. 357-360 ◽  
Author(s):  
Hector Musto ◽  
Hugo Naya ◽  
Alejandro Zavala ◽  
Hector Romero ◽  
Fernando Alvarez-Valin ◽  
...  
Keyword(s):  
Growth Temperature ◽  
Optimal Growth ◽  
Optimal Growth Temperature

Genomic GC level, optimal growth temperature, and genome size in prokaryotes

2006 ◽  
Vol 347 (1) ◽  
pp. 1-3 ◽  
Author(s):  
Héctor Musto ◽  
Hugo Naya ◽  
Alejandro Zavala ◽  
Héctor Romero ◽  
Fernando Alvarez-Valín ◽  
...  
Keyword(s):  
Genome Size ◽  
Growth Temperature ◽  
Optimal Growth ◽  
Optimal Growth Temperature

scholarly journals Building a tRNA thermometer to access the world’s biochemical diversity

2020 ◽  
Author(s):  
Emre Cimen ◽  
Sarah E. Jensen ◽  
Edward S. Buckler
Keyword(s):  
Growth Temperature ◽  
Input Data ◽  
Prediction Models ◽  
Extreme Temperature ◽  
Protein Composition ◽  
Network Models ◽  
Optimal Growth ◽  
Optimal Growth Temperature ◽  
Neural Network Models ◽  
Thermophilic Organism

ABSTRACTBecause ambient temperature affects biochemical reactions, organisms living in extreme temperature conditions adapt protein composition and structure to maintain biochemical functions. While it is not feasible to experimentally determine optimal growth temperature (OGT) for every known microbial species, organisms adapted to different temperatures have measurable differences in DNA, RNA, and protein composition that allow OGT prediction from genome sequence alone. In this study, we built a model using tRNA sequence to predict OGT. We used tRNA sequences from 100 archaea and 683 bacteria species as input to train two Convolutional Neural Network models. The first pairs individual tRNA sequences from different species to predict which comes from a more thermophilic organism, with accuracy ranging from 0.538 to 0.992. The second uses the complete set of tRNAs in a species to predict optimal growth temperature, achieving a maximum r2 of 0.86; comparable with other prediction accuracies in the literature despite a significant reduction in the quantity of input data. This model improves on previous OGT prediction models by providing a model with minimum input data requirements, removing laborious feature extraction and data preprocessing steps, and widening the scope of valid downstream analyses.

scholarly journals Building a tRNA thermometer to estimate microbial adaptation to temperature

2020 ◽  
Vol 48 (21) ◽  
pp. 12004-12015
Author(s):  
Emre Cimen ◽  
Sarah E Jensen ◽  
Edward S Buckler
Keyword(s):  
Growth Temperature ◽  
Input Data ◽  
Prediction Models ◽  
Extreme Temperature ◽  
Protein Composition ◽  
Network Models ◽  
Optimal Growth ◽  
Optimal Growth Temperature ◽  
Neural Network Models ◽  
Thermophilic Organism

Abstract Because ambient temperature affects biochemical reactions, organisms living in extreme temperature conditions adapt protein composition and structure to maintain biochemical functions. While it is not feasible to experimentally determine optimal growth temperature (OGT) for every known microbial species, organisms adapted to different temperatures have measurable differences in DNA, RNA and protein composition that allow OGT prediction from genome sequence alone. In this study, we built a ‘tRNA thermometer’ model using tRNA sequence to predict OGT. We used sequences from 100 archaea and 683 bacteria species as input to train two Convolutional Neural Network models. The first pairs individual tRNA sequences from different species to predict which comes from a more thermophilic organism, with accuracy ranging from 0.538 to 0.992. The second uses the complete set of tRNAs in a species to predict optimal growth temperature, achieving a maximum ${r^2}$ of 0.86; comparable with other prediction accuracies in the literature despite a significant reduction in the quantity of input data. This model improves on previous OGT prediction models by providing a model with minimum input data requirements, removing laborious feature extraction and data preprocessing steps and widening the scope of valid downstream analyses.

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