Evolutionary-based methods for predicting genotype-phenotype associations in the mammalian genome

Phenotypic and genotypic variation between species are the result of millions of experiments performed by nature. Understanding why and how phenotypic complexity arises is a central goal of evolutionary biology. Technological advancements enabling whole genome sequencing have laid the foundation for developing comparative genomics-based tools for inferring genetic elements underlying phenotypic adaptations. The work covered as part of this thesis will develop these tools drawing from principles of convergent evolution, aimed at generating specific functional hypotheses that can help focus experimental efforts. These tools will be relevant for characterizing context-specific functions of cis-regulatory elements as well as protein-coding genes, where a large number lack functional annotation beyond domain homology. Expanding from one-dimensional approaches studying proteins in isolation, we propose to build an integrated co-evolutionary framework that will serve as a powerful tool for protein interaction prediction. In this dissertation, we discuss these ideas through the following three projects. In chapter 1, we perform a genome-wide scan for genes showing convergent rate changes in four subterranean mammals, and study the underlying changes in selective pressure causing these convergent shifts in rate. Using a new variant of our rates-based method, we demonstrate that eye-specific regulatory regions show strong rate accelerations in the subterranean mammals. This study demonstrates the potential of convergent evolution-based tools in the functional annotation of eye-specific genetic elements. In chapter 2, we build a robust method to infer shifts in rate associated with a wide range of evolutionary scenarios. We investigate the statistical underpinnings of our rates-based framework and identify the best performing variant of our method across real and simulated phylogenetic datasets. We distribute these tools to the research community, enabling large scale generation of specific functional hypotheses for regulatory regions. In chapter 3, we propose to construct a powerful framework for protein interaction prediction using integration of proteome-wide co-evolutionary signatures. We systematically benchmark the predictions of our coevolutionary framework using known functional interactions among proteins across various scales. We make the predictions of the framework publicly available, useful for functional annotation of less well-characterized genes

A Drosophila screen identifies NKCC1 as 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 to 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 has an altered average molecular weight and reduced function. The misregulation of this ion transporter may explain the observed defects in secretory epithelium function in NGLY1 deficiency patients

Ancient convergent losses of Paraoxonase 1 yield potential risks for modern marine mammals

Mammals diversified by colonizing drastically different environments, with each transition yielding numerous molecular changes, including losses of protein function. Though not initially deleterious, these losses could subsequently carry deleterious pleiotropic consequences. We have used phylogenetic methods to identify convergent functional losses across independent marine mammal lineages. In one extreme case, Paraoxonase 1 (PON1) accrued lesions in all marine lineages, while remaining intact in all terrestrial mammals. These lesions coincide with PON1 enzymatic activity loss in marine species’ blood plasma. This convergent loss is likely explained by parallel shifts in marine ancestors’ lipid metabolism and/or bloodstream oxidative environment affecting PON1’s role in fatty acid oxidation. PON1 loss also eliminates marine mammals’ main defense against neurotoxicity from specific man-made organophosphorus compounds, implying potential risks in modern environment

Ancient convergent losses of Paraoxonase 1 yield potential risks for modern marine mammals

Mammals diversified by colonizing drastically different environments, with each transition yielding numerous molecular changes, including losses of protein function. Though not initially deleterious, these losses could subsequently carry deleterious pleiotropic consequences. We have used phylogenetic methods to identify convergent functional losses across independent marine mammal lineages. In one extreme case, Paraoxonase 1 (PON1) accrued lesions in all marine lineages, while remaining intact in all terrestrial mammals. These lesions coincide with PON1 enzymatic activity loss in marine species’ blood plasma. This convergent loss is likely explained by parallel shifts in marine ancestors’ lipid metabolism and/or bloodstream oxidative environment affecting PON1’s role in fatty acid oxidation. PON1 loss also eliminates marine mammals’ main defense against neurotoxicity from specific man-made organophosphorus compounds, implying potential risks in modern environment

Panax ginseng Modulates Cytokines in Bone Marrow Toxicity and Myelopoiesis: Ginsenoside Rg1 Partially Supports Myelopoiesis

In this study, we have demonstrated that Korean Panax ginseng (KG) significantly enhances myelopoiesis in vitro and reconstitutes bone marrow after 5-flurouracil-induced (5FU) myelosuppression in mice. KG promoted total white blood cell, lymphocyte, neutrophil and platelet counts and improved body weight, spleen weight, and thymus weight. The number of CFU-GM in bone marrow cells of mice and serum levels of IL-3 and GM-CSF were significantly improved after KG treatment. KG induced significant c-Kit, SCF and IL-1 mRNA expression in spleen. Moreover, treatment with KG led to marked improvements in 5FU-induced histopathological changes in bone marrow and spleen, and partial suppression of thymus damage. The levels of IL-3 and GM-CSF in cultured bone marrow cells after 24 h stimulation with KG were considerably increased. The mechanism underlying promotion of myelopoiesis by KG was assessed by monitoring gene expression at two time-points of 4 and 8 h. Treatment with Rg1 (0.5, 1 and 1.5 µmol) specifically enhanced c-Kit, IL-6 and TNF-α mRNA expression in cultured bone marrow cells. Our results collectively suggest that the anti-myelotoxicity activity and promotion of myelopoiesis by KG are mediated through cytokines. Moreover, the ginsenoside, Rg1, supports the role of KG in myelopoiesis to some extent

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Revisiting Robustness and Evolvability: Evolution in Weighted Genotype Spaces

<div><p>Robustness and evolvability are highly intertwined properties of biological systems. The relationship between these properties determines how biological systems are able to withstand mutations and show variation in response to them. Computational studies have explored the relationship between these two properties using neutral networks of RNA sequences (genotype) and their secondary structures (phenotype) as a model system. However, these studies have assumed every mutation to a sequence to be equally likely; the differences in the likelihood of the occurrence of various mutations, and the consequence of probabilistic nature of the mutations in such a system have previously been ignored. Associating probabilities to mutations essentially results in the <i>weighting</i> of genotype space. We here perform a comparative analysis of weighted and unweighted neutral networks of RNA sequences, and subsequently explore the relationship between robustness and evolvability. We show that assuming an equal likelihood for all mutations (as in an unweighted network), underestimates robustness and overestimates evolvability of a system. In spite of discarding this assumption, we observe that a negative correlation between sequence (genotype) robustness and sequence evolvability persists, and also that structure (phenotype) robustness promotes structure evolvability, as observed in earlier studies using unweighted networks. We also study the effects of base composition bias on robustness and evolvability. Particularly, we explore the association between robustness and evolvability in a sequence space that is AU-rich – sequences with an AU content of 80% or higher, compared to a normal (unbiased) sequence space. We find that evolvability of both sequences and structures in an AU-rich space is lesser compared to the normal space, and robustness higher. We also observe that AU-rich populations evolving on neutral networks of phenotypes, can access less phenotypic variation compared to normal populations evolving on neutral networks.</p></div

Minimum Free Energies of structures formed by RNA sequences of varied GC content.

<p>Data shown are for one million sequences of varied GC-content in comparison to normal unbiased sequences. The MFEs were calculated using viennaRNAFold routine of Vienna RNA package <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112792#pone.0112792-Gruber1" target="_blank">[10]</a>. We observe that the Minimum Free Energies of sequences become less negative as their GC content decreases, reflecting a decrease in thermal stability.</p

Mean genotype robustness and evolvability of 10⁶ AU-rich sequences and 10⁶ normal sequences.

<p>The genotype space was weighted using κ = 2.5. We observed that the mean genotype robustness is higher for AU-rich sequences, while mean genotype evolvability is lesser, in comparison to normal space. In a pair-wise Wilcoxon signed rank test between the two datasets, the <i>p</i>-value was less than 10⁻⁸. Spearman rank correlation values mentioned are between genotype robustness and genotype evolvability.</p><p>Mean genotype robustness and evolvability of 10⁶ AU-rich sequences and 10⁶ normal sequences.</p