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"Kelly, Steven"
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The economics of organellar gene loss and endosymbiotic gene transfer
Background
The endosymbiosis of the bacterial progenitors of the mitochondrion and the chloroplast are landmark events in the evolution of life on Earth. While both organelles have retained substantial proteomic and biochemical complexity, this complexity is not reflected in the content of their genomes. Instead, the organellar genomes encode fewer than 5% of the genes found in living relatives of their ancestors. While many of the 95% of missing organellar genes have been discarded, others have been transferred to the host nuclear genome through a process known as endosymbiotic gene transfer.
Results
Here, we demonstrate that the difference in the per-cell copy number of the organellar and nuclear genomes presents an energetic incentive to the cell to either delete organellar genes or transfer them to the nuclear genome. We show that, for the majority of transferred organellar genes, the energy saved by nuclear transfer exceeds the costs incurred from importing the encoded protein into the organelle where it can provide its function. Finally, we show that the net energy saved by endosymbiotic gene transfer can constitute an appreciable proportion of total cellular energy budgets and is therefore sufficient to impart a selectable advantage to the cell.
Conclusion
Thus, reduced cellular cost and improved energy efficiency likely played a role in the reductive evolution of mitochondrial and chloroplast genomes and the transfer of organellar genes to the nuclear genome.
Journal Article
The Amount of Nitrogen Used for Photosynthesis Modulates Molecular Evolution in Plants
Genome and transcript sequences are composed of long strings of nucleotide monomers (A, C, G, and T/U) that require different quantities of nitrogen atoms for biosynthesis. Here, it is shown that the strength of selection acting on transcript nitrogen content is influenced by the amount of nitrogen plants require to conduct photosynthesis. Specifically, plants that require more nitrogen to conduct photosynthesis experience stronger selection on transcript sequences to use synonymous codons that cost less nitrogen to biosynthesize. It is further shown that the strength of selection acting on transcript nitrogen cost constrains molecular sequence evolution such that genes experiencing stronger selection evolve at a slower rate. Together these findings reveal that the plant molecular clock is set by photosynthetic efficiency, and provide a mechanistic explanation for changes in plant speciation rates that occur concomitant with improvements in photosynthetic efficiency and changes in the environment such as light, temperature, and atmospheric CO2 concentration.
Journal Article
OrthoFinder: phylogenetic orthology inference for comparative genomics
Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at
https://github.com/davidemms/OrthoFinder
.
Journal Article
OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy
Identifying homology relationships between sequences is fundamental to biological research. Here we provide a novel orthogroup inference algorithm called OrthoFinder that solves a previously undetected gene length bias in orthogroup inference, resulting in significant improvements in accuracy. Using real benchmark datasets we demonstrate that OrthoFinder is more accurate than other orthogroup inference methods by between 8 % and 33 %. Furthermore, we demonstrate the utility of OrthoFinder by providing a complete classification of transcription factor gene families in plants revealing 6.9 million previously unobserved relationships.
Journal Article
SHOOT: phylogenetic gene search and ortholog inference
Determining the evolutionary relationships between genes is fundamental to comparative biological research. Here, we present SHOOT. SHOOT searches a user query sequence against a database of phylogenetic trees and returns a tree with the query sequence correctly placed within it. We show that SHOOT performs this analysis with comparable speed to a BLAST search. We demonstrate that SHOOT phylogenetic placements are as accurate as conventional tree inference, and it can identify orthologs with high accuracy. In summary, SHOOT is a fast and accurate tool for phylogenetic analyses of novel query sequences. It is available online at
www.shoot.bio
.
Journal Article
STRIDE: Species Tree Root Inference from Gene Duplication Events
The correct interpretation of any phylogenetic tree is dependent on that tree being correctly rooted. We present STRIDE, a fast, effective, and outgroup-free method for identification of gene duplication events and species tree root inference in large-scale molecular phylogenetic analyses. STRIDE identifies sets of well-supported in-group gene duplication events from a set of unrooted gene trees, and analyses these events to infer a probability distribution over an unrooted species tree for the location of its root. We show that STRIDE correctly identifies the root of the species tree in multiple large-scale molecular phylogenetic data sets spanning a wide range of timescales and taxonomic groups. We demonstrate that the novel probability model implemented in STRIDE can accurately represent the ambiguity in species tree root assignment for data sets where information is limited. Furthermore, application of STRIDE to outgroup-free inference of the origin of the eukaryotic tree resulted in a root probability distribution that provides additional support for leading hypotheses for the origin of the eukaryotes.
Journal Article
Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data
Identifying co-expressed gene clusters can provide evidence for genetic or physical interactions. Thus, co-expression clustering is a routine step in large-scale analyses of gene expression data. We show that commonly used clustering methods produce results that substantially disagree and that do not match the biological expectations of co-expressed gene clusters. We present
clust
, a method that solves these problems by extracting clusters matching the biological expectations of co-expressed genes and outperforms widely used methods. Additionally,
clust
can simultaneously cluster multiple datasets, enabling users to leverage the large quantity of public expression data for novel comparative analysis.
Clust
is available at
https://github.com/BaselAbujamous/clust
.
Journal Article
