Biology subjects

Partha, R.

Publications and source records attributed to Partha, R..

6 recordsLinked to original sources

bioRxiv2025

ERC 2.0 - evolutionary rate covariation update provides more powerful inference of functional interactions across large phylogenies

Evolutionary Rate Covariation (ERC) is an established comparative genomics method that identifies sets of genes sharing patterns of sequence evolution, which suggests shared function. Whereas many functional predictions of ERC have been empirically validated, its predictive power has hitherto been limited by its inability to tackle the large numbers of species in contemporary comparative genomics datasets. This study introduces ERC2.0, an enhanced methodology for studying ERC across phylogenies with hundreds of species and tens of thousands of genes. ERC2.0 improves upon previous iterations of ERC in algorithm speed, normalizing for heteroskedasticity, and normalizing correlations via Fisher transformations. These improvements have resulted in greater statistical power to predict biological function. In exemplar yeast and mammalian datasets, we demonstrate that the predictive power of ERC2.0 is improved relative to the previous method, ERC1.0, and that further improvements are obtained by using larger yeast and mammalian phylogenies. We attribute the improvements to both the larger datasets and improved rate normalization. We demonstrate that ERC2.0 has high predictive accuracy for known annotations and can predict the functions of genes in non-model systems. Our findings underscore the potential for ERC2.0 to be used as a single-pass computational tool in candidate gene screening and functional predictions.

evolutionary biology↗

bioRxiv2024

Evolutionary rate covariation is pervasive between glycosylation pathways and points to potential disease modifiers

Mutations in glycosylation pathways, such as N-linked glycosylation, O-linked glycosylation, and GPI anchor synthesis, lead to Congenital Disorders of Glycosylation (CDG). CDGs typically present with seizures, hypotonia, and developmental delay but display large clinical variability with symptoms affecting every system in the body. This variability suggests modifier genes might influence the phenotypes. Because of the similar physiology and clinical symptoms, there are likely common genetic modifiers between CDGs. Here, we use evolution as a tool to identify common modifiers between CDG and glycosylation genes. Protein glycosylation is evolutionarily conserved from yeast to mammals. Evolutionary rate covariation (ERC) identifies proteins with similar evolutionary rates that indicate shared biological functions and pathways. Using ERC, we identified strong evolutionary rate signatures between proteins in the same and different glycosylation pathways. Genome-wide analysis of proteins showing significant ERC with GPI anchor synthesis proteins revealed strong signatures with ncRNA modification proteins and DNA repair proteins. We also identified strong patterns of ERC based on cellular sub-localization of the GPI anchor synthesis enzymes. Functional testing of the highest scoring candidates validated genetic interactions and identified novel genetic modifiers of CDG genes. ERC analysis of disease genes and biological pathways allows for rapid prioritization of potential genetic modifiers, which can provide a better understanding of disease pathophysiology and novel therapeutic targets. AUTHOR SUMMARYCongenital Disorders of Glycosylation (CDGs) are a group of rare disorders resulting from impaired protein glycosylation. Glycosylation is the addition of sugar chains onto proteins and is required for proper protein function. CDG patients typically present with seizures and hypotonia. However, they can have a large amount of clinical variability, which is likely influenced by modifier genes. Modifier genes are genes that affect a phenotype without causing the disease. Using an evolutionary method that examines proteins that evolve at similar rates, we identified proteins within glycosylation pathways and among other unexpected pathways, such as ncRNA modification and DNA repair, that could be potential genetic modifiers of CDG genes. We also tested top protein pairs using the Drosophila eye as a model and identified novel genetic modifiers of CDG genes. Broadening our understanding of CDG modifiers can help us to better understand why loss of glycosylation results in specific patient symptoms and could provide new treatment targets.

genetics↗

bioRxiv2020

A Drosophila natural variation screen identifies NKCC1 as a substrate of NGLY1 deglycosylation and a modifier of NGLY1 deficiency

N-Glycanase 1 (NGLY1) is a cytoplasmic deglycosylating enzyme. Loss-of-function mutations in the NGLY1 gene cause NGLY1 deficiency, which is characterized by developmental delay, seizures, and a lack of sweat and tears. To model the phenotypic variability observed among patients, we crossed a Drosophila model of NGLY1 deficiency onto a panel of genetically diverse strains. The resulting progeny showed a phenotypic spectrum from 0-100% lethality. Association analysis on the lethality phenotype as well as an evolutionary rate covariation analysis generated lists of modifying genes, providing insight into NGLY1 function and disease. The top association hit was Ncc69 (human NKCC1/2), a conserved ion transporter. Analyses in NGLY1 -/- mouse cells demonstrated that NKCC1 is misglycosylated and has reduced function, making it only the second confirmed NGLY1 enzymatic substrate. The misregulation of this ion transporter may explain the observed defects in secretory epithelium function in NGLY1 deficiency patients.

genomics↗

bioRxiv2019

Cancer control is a key functionality underlying evolution of extended lifespan in mammals

The biological origin of life expectancy remains a fundamental and unanswered scientific question with important ramifications for human health, especially as the bulk of burden of human healthcare shifts from infectious to age-related diseases. The striking variability in life-span among animals occupying similar ecological niches1 and the numerous mutations that have been shown to increase lifespan in model organisms2-5 point to a considerable genetic contribution. Using mammalian comparative genomics, we correlate lifespan phenotypes with relative evolutionary rates, a measure of evolutionary selective pressure6. Our analysis demonstrates that many genes and pathways are under increased evolutionary constraint in both Long-Lived Large-bodied mammals (3L) and mammals Exceptionally Long-Lived given their size (ELL), suggesting that these genes and pathways contribute to the maintenance of both traits. For 3L species, we find strong evolutionary constraint on multiple pathways involved in controlling carcinogenesis, including cell cycle, apoptosis, and immune pathways. These findings provide additional perspective on the well-known Petos Paradox that large animals with large numbers of cells do not get cancer at higher rates than smaller animals with fewer cells7. For the ELL phenotype, our analysis strongly implicates pathways involved in DNA repair, further supporting the importance of DNA repair processes in aging8-12. Moreover, these correlations with lifespan phenotypes are consistent across the entire mammalian phylogeny, suggesting that additional constraint on these pathways is a universal requirement for long lifespan.

evolutionary biology↗

bioRxiv2018

Robust methods for detecting convergent shifts in evolutionary rates

Identifying genomic elements underlying phenotypic adaptations is an important problem in evolutionary biology. Comparative analyses learning from convergent evolution of traits are gaining momentum in accurately detecting such elements. We previously developed a method for predicting phenotypic associations of genetic elements by contrasting patterns of sequence evolution in species showing a phenotype with those that do not. Using this method, we successfully demonstrated convergent evolutionary rate shifts in genetic elements associated with two phenotypic adaptations, namely the independent subterranean and marine transitions of terrestrial mammalian lineages. Our method calculates gene-specific rates of evolution on branches of phylogenetic trees using linear regression. These rates represent the extent of sequence divergence on a branch after removing the expected divergence on the branch due to background factors. The rates calculated using this regression analysis exhibit an important statistical limitation, namely heteroscedasticity. We observe that the rates on branches that are longer on average show higher variance, and describe how this problem adversely affects the confidence with which we can make inferences about rate shifts. Using a combination of data transformation and weighted regression, we have developed an updated method that corrects this heteroscedasticity in the rates. We additionally illustrate the improved performance offered by the updated method at robust detection of convergent rate shifts in phylogenetic trees of protein-coding genes across mammals, as well as using simulated tree datasets. Overall, we present an important extension to our evolutionary-rates-based method that performs more robustly and consistently at detecting convergent shifts in evolutionary rates.

evolutionary biology↗

bioRxiv2018

RERconverge: an R package for associating evolutionary rates with convergent traits

Motivation: When different lineages of organisms independently adapt to similar environments, selection often acts repeatedly upon the same genes, leading to signatures of convergent evolutionary rate shifts at these genes. With the increasing availability of genome sequences for organisms displaying a variety of convergent traits, the ability to identify genes with such convergent rate signatures would enable new insights into the molecular basis of these traits.\n\nResults: Here we present the R package RERconverge, which tests for association between relative evolutionary rates of genes and the evolution of traits across a phylogeny. RERconverge can perform associations with binary and continuous traits, and it contains tools for visualization and enrichment analyses of association results.\n\nAvailability: RERconverge source code, documentation, and a detailed usage walk-through are freely available at https://github.com/nclark-lab/RERconverge. Datasets for mammals, Drosophila, and yeast are available at https://bit.ly/2J2QBnj.\n\nContact: mchikina@pitt.edu\n\nSupplementary information: Supplementary information, containing detailed vignettes for usage of RERconverge, are available at Bioinformatics online.

evolutionary biology↗