Prediction of Optimal Growth Temperature using only Genome Derived Features

AbstractOptimal growth temperature is a fundamental characteristic of all living organisms. Knowledge of this temperature is central to the study the organism, 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. 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 4.69 °C root-mean-square error and a correlation coefficient of 0.908. 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.

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Predicting the optimal growth temperatures of prokaryotes using only genome derived features

Bioinformatics ◽

10.1093/bioinformatics/btz059 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3224-3231 ◽

Cited By ~ 8

Author(s):

David B Sauer ◽

Da-Neng Wang

Keyword(s):

Growth Temperature ◽

High Throughput Sequencing ◽

Optimal Growth ◽

Supplementary Information ◽

Coefficient Of Determination ◽

Optimal Growth Temperature ◽

Organism Growth ◽

Novel Method ◽

Living Organisms

Abstract 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.

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

Agronomy ◽

10.3390/agronomy11010046 ◽

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.

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

Biochemical and Biophysical Research Communications ◽

10.1016/j.bbrc.2005.02.133 ◽

2005 ◽

Vol 330 (2) ◽

pp. 357-360 ◽

Cited By ~ 11

Author(s):

Hector Musto ◽

Hugo Naya ◽

Alejandro Zavala ◽

Hector Romero ◽

Fernando Alvarez-Valin ◽

...

Keyword(s):

Growth Temperature ◽

Optimal Growth ◽

Optimal Growth Temperature

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On the correlation between genomic G+C content and optimal growth temperature in prokaryotes: Data quality and confounding factors

Biochemical and Biophysical Research Communications ◽

10.1016/j.bbrc.2006.02.037 ◽

2006 ◽

Vol 342 (3) ◽

pp. 681-684 ◽

Cited By ~ 49

Author(s):

Huai-Chun Wang ◽

Edward Susko ◽

Andrew J. Roger

Keyword(s):

Data Quality ◽

Growth Temperature ◽

Optimal Growth ◽

Confounding Factors ◽

Optimal Growth Temperature

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

Biochemical and Biophysical Research Communications ◽

10.1016/j.bbrc.2006.06.054 ◽

2006 ◽

Vol 347 (1) ◽

pp. 1-3 ◽

Cited By ~ 77

Author(s):

Héctor Musto ◽

Hugo Naya ◽

Alejandro Zavala ◽

Héctor Romero ◽

Fernando Alvarez-Valín ◽

...

Keyword(s):

Genome Size ◽

Growth Temperature ◽

Optimal Growth ◽

Optimal Growth Temperature

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

10.1101/2020.07.01.179846 ◽

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.

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Growth temperature-dependent activity of glycerol dehydratase inEscherichia coliexpressing theCitrobacter freundii dharegulon

FEMS Microbiology Letters ◽

10.1111/j.1574-6968.1992.tb14053.x ◽

1992 ◽

Vol 100 (1-3) ◽

pp. 281-285 ◽

Cited By ~ 26

Author(s):

Rolf Daniel ◽

Gerhard Gottschalk

Keyword(s):

Growth Temperature ◽

Temperature Dependent ◽

Glycerol Dehydratase ◽

Dependent Activity

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Building a tRNA thermometer to estimate microbial adaptation to temperature

Nucleic Acids Research ◽

10.1093/nar/gkaa1030 ◽

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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Elevation of the Heat Resistance of Vegetative Cells and Spores of Clostridium perfringens Type A by Sublethal Heat Shock

Journal of Food Protection ◽

10.4315/0362-028x-60.8.998 ◽

1997 ◽

Vol 60 (8) ◽

pp. 998-1000 ◽

Cited By ~ 35

Author(s):

NORMA L. HEREDIA ◽

GERARDO A. GARCÍA ◽

RAMIRO LUÉVANOS ◽

RONALD G. LABBÉ ◽

J. SANTOS GARCÍA-ALVARADO

Keyword(s):

Heat Shock ◽

Heat Resistance ◽

Clostridium Perfringens ◽

Growth Temperature ◽

Optimal Growth ◽

Shock Treatment ◽

Heat Shock Treatment ◽

Optimal Growth Temperature ◽

Vegetative Cells ◽

Kinetics Of

The degree of heat resistance conferred on Clostridium perfringens by a heat shock, the kinetics of this development, and its duration were determined. A sublethal heat shock at 55°C for 30 min increased the heat tolerance of vegetative cells at least two- to threefold. The acquired tolerance was maintained for 2 h after the heat shock treatment. Heat shock applied for the first hour of incubation produced spores more tolerant to heat than the spores of the control. Acquired thermotolerance is of importance in the case of this organism because of its inherently high optimal growth temperature.

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