Core promoter information content correlates with optimal growth temperature

Aptekmann, Ariel; Nadra, Alejandro D.

doi:10.1038/s41598-018-19495-8

Cited by 15 publications

(17 citation statements)

References 20 publications

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“…One notable result is the positive correlation between optimal temperature and GC content (0.22 posterior mean, [0.08, 0.37] 95% highest posterior density interval) that the thermal adaptation hypothesis predicts (Bernardi and Bernardi 1986). Researchers have discussed this hypothesis for years with mixed support (Hurst and Merchant 2001;Musto et al 2004;Wang et al 2006;Wu et al 2012;Sabath et al 2013;Aptekmann and Nadra 2018). Our analysis, however, includes 435 taxa with measurements for both GC content and optimal growth temperature, making it the largest study we are aware of that accounts for phylogenetic relationships.…”

Section: Prokaryote Evolutionmentioning

confidence: 99%

Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

Hassler

Tolkoff

Allen

et al. 2020

Journal of the American Statistical Association

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Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or nonheritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale.

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Section: Prokaryote Evolutionmentioning

confidence: 99%

Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

Hassler

Tolkoff

Allen

et al. 2020

Journal of the American Statistical Association

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“…Less-well studied is how the temperature of the environment shapes molecular evolution across the proteome. Interactions between biochemistry and temperature affect DNA, RNA, and protein composition, and are consistent across species and even across phylogenetic domains [8][9][10][11][12]. In plants, genome size, GC content, and proteome composition have been correlated with environmental temperature [13,14].…”

Section: Introductionmentioning

confidence: 89%

Pfam domain adaptation profiles reflect plant species’ evolutionary history

Buckler

2021

Preprint

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The increase in global temperatures predicted by climate change models presents a serious problem for agriculture because high temperatures reduce crop yields. Protein biochemistry is at the core of plant heat stress response, and understanding the interactions between protein biochemistry and temperature will be key to developing heat-tolerant crop varieties. Current experimental studies of proteome-wide plant thermostability are limited by the complexity of plant proteomes: evaluating function for thousands of proteins across a variety of temperatures is simply not feasible with existing technologies. In this paper, we use homologous prokaryote sequences to predict plant Pfam temperature adaptation and gain insights into how thermostability varies across the proteome for three species: maize, Arabidopsis, and poplar. We find that patterns of Pfam domain adaptation across organelles are consistent and highly significant between species, with cytosolic proteins having the largest range of predicted Pfam stabilities and a long tail of highly-stable ribosomal proteins. Pfam adaptation in leaf and root organs varies between species, and maize root proteins have more low-temperature Pfam domains than do Arabidopsis or poplar root proteins. Both poplar and maize populations have an excess of low-temperature mutations in Pfam domains, but only the mutations identified in poplar accessions have a negative effect on Pfam temperature adaptation overall. These Pfam domain adaptation profiles provide insight into how different plant structures adapt to their surrounding environment and can help inform breeding or protein editing strategies to produce heat-tolerant crops.

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“…The optimal growth temperature for 19,474 microorganisms was manually curated from multiple sources: BacDive 48 , DSMZ 49 , Pasteur Institute (PI), the National Institute for Environmental Studies (NIES) 50 , and a curated list from a previous work 51 .…”

Section: Dataset Generationmentioning

confidence: 99%

Shedding Light on Microbial Dark Matter with A Universal Language of Life

Hoarfrost

Aptekmann

Farfañuk

et al. 2020

Preprint

Self Cite

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The majority of microbial genomes have yet to be cultured, and most proteins predicted from microbial genomes or sequenced from the environment cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the full functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. The scientific community needs a means to capture the functionally and evolutionarily relevant features underlying biology, independent of our incomplete reference databases. Such a model can form the basis for transfer learning tasks, enabling downstream applications in environmental microbiology, medicine, and bioengineering. Here we present LookingGlass, a deep learning model capturing a “universal language of life”. LookingGlass encodes contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, distinguishing reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass is the first contextually-aware, general purpose pre-trained “biological language” representation model for short-read DNA sequences. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.AvailabilityThe pretrained LookingGlass model and the transfer learning-derived models demonstrated in this paper are available in the LookingGlass release v1.01. The open source fastBio Github repository and python package provides classes and functions for training and fine tuning deep learning models with biological data2. Code for reproducing analyses presented in this paper are available as an open source Github repository3.

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Core promoter information content correlates with optimal growth temperature

Cited by 15 publications

References 20 publications

Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

Pfam domain adaptation profiles reflect plant species’ evolutionary history

Shedding Light on Microbial Dark Matter with A Universal Language of Life

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