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1. Transcriptomic analysis provides insights into molecular mechanisms of thermal physiology

   https://doi.org/10.1186/s12864-022-08653-y  

   Drown, Melissa K.; Crawford, Douglas L.; Oleksiak, Marjorie F. (December 2022, BMC Genomics)

   Abstract Physiological trait variation underlies health, responses to global climate change, and ecological performance. Yet, most physiological traits are complex, and we have little understanding of the genes and genomic architectures that define their variation. To provide insight into the genetic architecture of physiological processes, we related physiological traits to heart and brain mRNA expression using a weighted gene co-expression network analysis. mRNA expression was used to explain variation in six physiological traits (whole animal metabolism (WAM), critical thermal maximum (CT max ), and four substrate specific cardiac metabolic rates (CaM)) under 12 °C and 28 °C acclimation conditions. Notably, the physiological trait variations among the three geographically close (within 15 km) and genetically similar F. heteroclitus populations are similar to those found among 77 aquatic species spanning 15–20° of latitude (~ 2,000 km). These large physiological trait variations among genetically similar individuals provide a powerful approach to determine the relationship between mRNA expression and heritable fitness related traits unconfounded by interspecific differences. Expression patterns explained up to 82% of metabolic trait variation and were enriched for multiple signaling pathways known to impact metabolic and thermal tolerance ( e.g. , AMPK, PPAR, mTOR, FoxO, and MAPK) but also contained several unexpected pathways ( e.g. , apoptosis, cellular senescence), suggesting that physiological trait variation is affected by many diverse genes. 

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2. Genome-wide association mapping of transcriptome variation in Mimulus guttatus indicates differing patterns of selection on cis - versus trans -acting mutations

   https://doi.org/10.1093/genetics/iyab189  

   Brown, Keely_E; Kelly, John_K; Wright, ed., S. (November 2021, Genetics)

   Abstract We measured the floral bud transcriptome of 151 fully sequenced lines of Mimulus guttatus from one natural population. Thousands of single nucleotide polymorphisms (SNPs) are implicated as transcription regulators, but there is a striking difference in the allele frequency spectrum of cis-acting and trans-acting mutations. Cis-SNPs have intermediate frequencies (consistent with balancing selection) while trans-SNPs exhibit a rare-alleles model (consistent with purifying selection). This pattern only becomes clear when transcript variation is normalized on a gene-to-gene basis. If a global normalization is applied, as is typically in RNAseq experiments, asymmetric transcript distributions combined with “rarity disequilibrium” produce a superabundance of false positives for trans-acting SNPs. To explore the cause of purifying selection on trans-acting mutations, we identified gene expression modules as sets of coexpressed genes. The extent to which trans-acting mutations influence modules is a strong predictor of allele frequency. Mutations altering expression of genes with high “connectedness” (those that are highly predictive of the representative module expression value) have the lowest allele frequency. The expression modules can also predict whole-plant traits such as flower size. We find that a substantial portion of the genetic (co)variance among traits can be described as an emergent property of genetic effects on expression modules. 

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3. The quantitative genetics of gene expression in Mimulus guttatus

   https://doi.org/10.1371/journal.pgen.1011072  

   Veltsos, Paris; Kelly, John K. (April 2024, PLOS Genetics)

   Lasky, Jesse R. (Ed.)

   Gene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population ofMimulus guttatusand scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05). cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTLs make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of “gene expression modules,” which has important implications for the evolution of gene expression and how it is studied by geneticists. 

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4. Mechanisms of regulatory evolution in yeast

   https://doi.org/10.1016/j.gde.2022.101998  

   Siddiq, Mohammad A; Wittkopp, Patricia J (December 2022, Current Opinion in Genetics & Development)

   Studies of regulatory variation in yeast -- at the level of new mutations, polymorphisms within a species, and divergence between species -- have provided great insight into the molecular and evolutionary processes responsible for the evolution of gene expression in eukaryotes. The increasing ease with which yeast genomes can be manipulated and expression quantified in a high-throughput manner has recently accelerated mechanistic studies of cis- and trans-regulatory variation at multiple evolutionary timescales. These studies have, for example, identified differences in the properties of cis- and trans-acting mutations that affect their evolutionary fate, experimentally characterized the molecular mechanisms through which cis- and trans-regulatory variants act, and illustrated how regulatory networks can diverge between species with or without changes in gene expression. 

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5. Learning gene networks under SNP perturbation using SNP and allele-specific expression data

   https://doi.org/10.1101/2023.10.23.563661  

   Yoon, Jun Ho; Kim, Seyoung (October 2023, bioRxiv)

   Abstract Allele-specific expression quantification from RNA-seq reads provides opportunities to study the control of gene regulatory networks bycis-acting andtrans-acting genetic variants. Many existing methods performed a single-gene and single-SNP association analysis to identify expression quantitative trait loci (eQTLs), and placed the eQTLs against known gene networks for functional interpretation. Instead, we view eQTL data as a capture of the effects of perturbation of gene regulatory system by a large number of genetic variants and reconstruct a gene network perturbed by eQTLs. We introduce a statistical framework called CiTruss for simultaneously learning a gene network andcis-acting andtrans-acting eQTLs that perturb this network, given population allele-specific expression and SNP data. CiTruss uses a multi-level conditional Gaussian graphical model to modeltrans-acting eQTLs perturbing the expression of both alleles in gene network at the top level andcis-acting eQTLs perturbing the expression of each allele at the bottom level. We derive a transformation of this model that allows efficient learning for large-scale human data. Our analysis of the GTEx and LG×SM advanced intercross line mouse data for multiple tissue types with CiTruss provides new insights into genetics of gene regulation. CiTruss revealed that gene networks consist of local subnetworks over proximally located genes and global subnetworks over genes scattered across genome, and that several aspects of gene regulation by eQTLs such as the impact of genetic diversity, pleiotropy, tissue-specific gene regulation, and local and long-range linkage disequilibrium among eQTLs can be explained through these local and global subnetworks. 

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