On the Decoupling of Evolutionary Changes in mRNA and Protein Levels

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Molecular concepts to explain heterosis in crops

Heterosis describes the superior performance of hybrid plants compared with their genetically distinct parents and is a pillar of global food security. Here we review the current status of the molecular dissection of heterosis. We discuss how extensive intraspecific structural genomic variation between parental genotypes leads to heterosis by genetic complementation in hybrids. Moreover, we survey how global gene expression complementation contributes to heterosis by hundreds of additionally active genes in hybrids and how overdominant single genes mediate heterosis in several species. Furthermore, we highlight the prominent role of the microbiome in improving the performance of hybrids. Taken together, the molecular understanding of heterosis will pave the way to accelerate hybrid productivity and a more sustainable agriculture.

Genetic and selective constraints on the optimization of gene product diversity

RNA and protein expressed from the same gene can have diverse isoforms due to various post-transcriptional and post-translational modifications. For the vast majority of alternative isoforms, It is unknown whether they are adaptive or simply biological noise. As we cannot experimentally probe the function of each isoform, we can ask whether the distribution of isoforms across genes and across species is consistent with expectations from different evolutionary processes. However, there is currently no theoretical framework that can generate such predictions. To address this, we developed a mathematical model where isoform abundances are determined collectively by cis-acting loci, trans-acting factors, gene expression levels, and isoform decay rates to predict isoform abundance distributions across species and genes in the face of mutation, genetic drift, and selection. We found that factors beyond selection, such as effective population size and the number of cis-acting loci, significantly influence evolutionary outcomes. Notably, suboptimal phenotypes are more likely to evolve when the population is small and/or when the number of cis-loci is large. We also explored scenarios where modification processes have both beneficial and detrimental effects, revealing a non-monotonic relationship between effective population size and optimization, demonstrating how opposing selection pressures on cis- and trans-acting loci can constrain the optimization of gene product diversity. As a demonstration of the power of our theory, we compared the expected distribution of A-to-I RNA editing levels in coleoids and found this to be largely consistent with non-adaptive explanations.

Multilevel gene expression changes in lineages containing adaptive copy number variants

Copy-number variants (CNVs) are an important class of recurrent variants that mediate adaptive evolution. While CNVs can increase the relative fitness of the organism, they can also incur a cost. We previously evolved populations of Saccharomyces cerevisiae over hundreds of generations in glutamine-limited (Gln-) chemostats and observed the recurrent evolution of CNVs at the GAP1 locus. To understand the role that expression plays in adaptation, both in relation to the adaptation of the organism to the selective condition, and as a consequence of the CNV, we measured the transcriptome, translatome, and proteome of 4 strains of evolved yeast, each with a unique CNV, and their ancestor in Gln- conditions. We find CNV-amplified genes correlate with higher RNA abundance; however, this effect is reduced at the level of the proteome, consistent with post-transcriptional dosage compensation. By normalizing each level of expression by the abundance of the preceding step we were able to identify widespread divergence in the efficiency of each step in the gene in the efficiency of each step in gene expression. Genes with significantly different translational efficiency were enriched for potential regulatory mechanisms including either upstream open reading frames, RNA binding sites for SSD1, or both. Genes with lower protein expression efficiency were enriched for genes encoding proteins in protein complexes. Taken together, our study reveals widespread changes in gene expression at multiple regulatory levels in lineages containing adaptive CNVs highlighting the diverse ways in which adaptive evolution shapes gene expression.

Expression of WNT Signaling Genes in the Dorsolateral Prefrontal Cortex in Schizophrenia

Gene expression alterations in postmortem schizophrenia tissue are well-documented and are influenced by genetic, medication, and epigenetic factors. The Wingless/Integrated (WNT) signaling pathway, critical for cell growth and development, is involved in various cellular processes including neurodevelopment and synaptic plasticity. Despite its importance, WNT signaling remains understudied in schizophrenia, a disorder characterized by metabolic and bioenergetic defects in cortical regions. In this study, we examined the gene expression of 10 key WNT signaling pathway transcripts: IQGAP1, CTNNβ1, GSK3β, FOXO1, LRP6, MGEA5, TCF4, βTRC, PPP1Cβ, and DVL2 in the dorsolateral prefrontal cortex (DLPFC) using postmortem tissue from schizophrenia subjects (n = 20, 10 males, 10 females) compared to age, pH, and postmortem interval (PMI)-matched controls (n = 20, 10 males, 10 females). Employing the R-shiny application Kaleidoscope, we conducted in silico "lookup" studies from published transcriptomic datasets to examine cell- and region-level expression of these WNT genes. In addition, we investigated the impact of antipsychotics on the mRNA expression of the WNT genes of interest in rodent brain transcriptomic datasets. Our findings revealed no significant changes in region-level WNT transcript expression; however, analyses of previously published cell-level datasets indicated alterations in WNT transcript expression and antipsychotic-specific modulation of certain genes. These results suggest that WNT signaling transcripts may be variably expressed at the cellular level and influenced by antipsychotic treatment, providing novel insights into the role of WNT signaling in the pathophysiology of schizophrenia.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Serum cytokine profile of neonatal broiler chickens infected with Salmonella Typhimurium

The avian immune system responds to Salmonella infection by expressing cytokines and chemokines. We hypothesized that the immune status of Salmonella Typhimurium (ST) challenged neonatal broilers would differ from the uninfected treatment. The objective of this experiment was to evaluate 12 cytokines. Day of hatch male chicks were randomly allocated into a control or ST challenged group. At day three of age, sterile diluent or 5.0 × 108 CFU of ST was given orally to each chick. Blood was obtained 24 h post challenge and serum separated for later analysis (n = 30 chicks/treatment). Significant (p ≤ 0.05) increases in pro-inflammatory cytokines-interleukin-6 (IL-6), IL-16, and IL-21; anti-inflammatory cytokines- IL-10; chemokines-regulated on activation, normal T cell expressed and secreted (RANTES), macrophage inflammatory protein-1β (MIP-1β), and MIP-3α; colony stimulating factors-macrophage colony-stimulating factor (M-CSF); and growth factors-vascular endothelial growth factor (VEGF) were observed in the serum of the challenged chicks when compared to the control. No significant differences were observed in IL-2, interferon gamma (IFNγ), and IFNα. These data indicate the detection of mucosal immune responses in broiler chickens following ST infection. The heightened levels of pro-inflammatory cytokines, chemokines, and colony stimulating factors align with known inflammatory mechanisms, like the influx of immune cells. However, the elevation of IL-10 was unexpected, due to its immunoregulatory properties. Notably, the rise in VEGF levels is compelling, as it suggests the possibility of tissue repair and angiogenesis in ST infected birds.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Therapeutic Potential of P110 Peptide: New Insights into Treatment of Alzheimer's Disease

Mitochondrial degeneration in various neurodegenerative diseases, specifically in Alzheimer's disease, involves excessive mitochondrial fission and reduced fusion, leading to cell damage. P110 is a seven-amino acid peptide that restores mitochondrial dynamics by acting as an inhibitor of mitochondrial fission. However, the role of P110 as a neuroprotective agent in AD remains unclear. Therefore, we performed cell culture studies to evaluate the neuroprotective effect of P110 on amyloid-β accumulation and mitochondrial functioning. Human SH-SY5Y neuronal cells were incubated with 1 µM and 10 µM of P110, and Real-Time PCR and Western blot analysis were done to quantify the expression of genes pertaining to AD and neuronal health. Exposure of SH-SY5Y cells to P110 significantly increased APP mRNA levels at 1 µM, while BACE1 mRNA levels were increased at both 1 µM and 10 µM. However, protein levels of both APP and BACE1 were significantly reduced at 10 µM of P110. Further, P110 treatment significantly increased ADAM10 and Klotho protein levels at 10 µM. In addition, P110 exposure significantly increased active mitochondria and reduced ROS in live SH-SY5Y cells at both 1 µM and 10 µM concentrations. Taken together, our results indicate that P110 might be useful in attenuating amyloid-β generation and improving neuronal health by maintaining mitochondrial function in neurons.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.