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

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9151 ◽  
Author(s):  
Debra A. Brock ◽  
Suegene Noh ◽  
Alicia N.M. Hubert ◽  
Tamara S. Haselkorn ◽  
Susanne DiSalvo ◽  
...  
Keyword(s):  
New Species ◽  
Dictyostelium Discoideum ◽  
Bacterial Species ◽  
Optimal Growth ◽  
Phylogenetic Position ◽  
Optimal Growth Temperature ◽  
Eastern Usa ◽  
Three New Species ◽  
Food Bacteria ◽  
Genome Distance

Here we give names to three new species of Paraburkholderia that can remain in symbiosis indefinitely in the spores of a soil dwelling eukaryote, Dictyostelium discoideum. The new species P. agricolaris sp. nov., P. hayleyella sp. nov., and P. bonniea sp. nov. are widespread across the eastern USA and were isolated as internal symbionts of wild-collected D. discoideum. We describe these sp. nov. using several approaches. Evidence that they are each a distinct new species comes from their phylogenetic position, average nucleotide identity, genome-genome distance, carbon usage, reduced length, cooler optimal growth temperature, metabolic tests, and their previously described ability to invade D. discoideum amoebae and form a symbiotic relationship. All three of these new species facilitate the prolonged carriage of food bacteria by D. discoideum, though they themselves are not food. Further studies of the interactions of these three new species with D. discoideum should be fruitful for understanding the ecology and evolution of symbioses.

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.

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.

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

1997 ◽  
Vol 60 (8) ◽  
pp. 998-1000 ◽  
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.

scholarly journals Reconstruction of Ancestral 16S rRNA Reveals Mutation Bias in the Evolution of Optimal Growth Temperature in the Thermotogae Phylum

2013 ◽  
Vol 30 (11) ◽  
pp. 2463-2474 ◽  
Author(s):  
Anna G. Green ◽  
Kristen S. Swithers ◽  
Jan F. Gogarten ◽  
Johann Peter Gogarten
Keyword(s):  
16S Rrna ◽  
Growth Temperature ◽  
Optimal Growth ◽  
Optimal Growth Temperature ◽  
Mutation Bias
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