Mitochondria are key to energy conversion in virtually all eukaryotes. Intriguingly, despite billions of years of evolution inside the eukaryote, mitochondria have retained their own small set of genes involved in the regulation of oxidative phosphorylation (OXPHOS) and protein translation. Although there was a long-standing assumption that the genetic variation found within the mitochondria would be selectively neutral, research over the past 3 decades has challenged this assumption. This research has provided novel insight into the genetic and evolutionary forces that shape mitochondrial evolution and broader implications for evolutionary ecological processes. Many of the seminal studies in this field, from the inception of the research field to current studies, have been conducted using Drosophila flies, thus establishing the species as a model system for studies in mitochondrial evolutionary biology. In this review, we comprehensively review these studies, from those focusing on genetic processes shaping evolution within the mitochondrial genome, to those examining the evolutionary implications of interactions between genes spanning mitochondrial and nuclear genomes, and to those investigating the dynamics of mitochondrial heteroplasmy. We synthesize the contribution of these studies to shaping our understanding of the evolutionary and ecological implications of mitochondrial genetic variation.

One powerful strategy of how to increase the complexity of cellular proteomes is through posttranslational modifications (PTMs) of proteins. Currently, there are ∼400 types of PTMs, the different combinations of which yield a large variety of protein isoforms with distinct biochemical properties. Although mitochondrial proteins undergoing PTMs were identified nearly 6 decades ago, studies on the roles and extent of PTMs on mitochondrial functions lagged behind the other cellular compartments. The application of mass spectrometry for the characterization of the mitochondrial proteome as well as for the detection of various PTMs resulted in the identification of thousands of amino acid positions that can be modified by different chemical groups. However, the data on mitochondrial PTMs are scattered in several data sets, and the available databases do not contain a complete list of modified residues. To integrate information on PTMs of the mitochondrial proteome of the yeast Saccharomyces cerevisiae, we built the yeast mitochondrial posttranslational modification (y-mtPTM) database (http://compbio.fmph.uniba.sk/y-mtptm/). It lists nearly 20,000 positions on mitochondrial proteins affected by ∼20 various PTMs, with phosphorylated, succinylated, acetylated, and ubiquitylated sites being the most abundant. A simple search of a protein of interest reveals the modified amino acid residues, their position within the primary sequence as well as on its 3D structure, and links to the source reference(s). The database will serve yeast mitochondrial researchers as a comprehensive platform to investigate the functional significance of the PTMs of mitochondrial proteins.

A striking characteristic of the molecular techniques of genetics is that they are derived from natural occurring systems. RNA interference, for example, utilizes a mechanism that evolved in eukaryotes to destroy foreign nucleic acid. Other case studies I highlight are restriction enzymes, DNA sequencing, polymerase chain reaction, gene targeting, fluorescent proteins (such as, green fluorescent protein), induced pluripotent stem cells, and clustered regularly interspaced short palindromic repeats-CRISPR associated 9. The natural systems' strategy for technique development means that biologists utilize the activity of a mechanism's effector (protein or RNA) and exploit biological specificity (protein or nucleic acid can cause precise reactions). I also argue that the developmental trajectory of novel molecular techniques, such as RNA interference, has 4 characteristic phases. The first phase is discovery of a biological phenomenon. The second phase is identification of the biological mechanism's trigger(s): the effector and biological specificity. The third phase is the application of the trigger(s) as a technique. The final phase is the maturation and refinement of the technique. Developing new molecular techniques from nature is crucial for future genetic research.

Numerous organismal traits, particularly at the cellular level, are likely to be under persistent directional selection across phylogenetic lineages. Unless all mutations affecting such traits have large enough effects to be efficiently selected in all species, gradients in mean phenotypes are expected to arise as a consequence of differences in the power of random genetic drift, which varies by approximately five orders of magnitude across the Tree of Life. Prior theoretical work examining the conditions under which such gradients can arise focused on the simple situation in which all genomic sites affecting the trait have identical and constant mutational effects. Here, we extend this theory to incorporate the more biologically realistic situation in which mutational effects on a trait differ among nucleotide sites. Pursuit of such modifications leads to the development of semi-analytic expressions for the ways in which selective interference arises via linkage effects in single-effects models, which then extend to more complex scenarios. The theory developed clarifies the conditions under which mutations of different selective effects mutually interfere with each others' fixation and shows how variance in effects among sites can substantially modify and extend the expected scaling relationships between mean phenotypes and effective population sizes.

Stochastic differences among clonal cells can initiate cell fate decisions in development or cause cell-to-cell differences in the responses to drugs or extracellular ligands. One hypothesis is that some of this phenotypic variability is caused by stochastic fluctuations in the activities of transcription factors (TFs). We tested this hypothesis in NIH3T3-CG cells using the response to Hedgehog signaling as a model cellular response. Here, we present evidence for the existence of distinct fast- and slow-responding substates in NIH3T3-CG cells. These two substates have distinct expression profiles, and fluctuations in the Prrx1 TF underlie some of the differences in expression and responsiveness between fast and slow cells. Our results show that fluctuations in TFs can contribute to cell-to-cell differences in Hedgehog signaling.

Bidirectional flow of information shapes the outcome of the host-pathogen interactions and depends on the genetics of each organism. Recent work has begun to use co-transcriptomic studies to shed light on this bidirectional flow, but it is unclear how plastic the co-transcriptome is in response to genetic variation in both the host and pathogen. To study co-transcriptome plasticity, we conducted transcriptomics using natural genetic variation in the pathogen, Botrytis cinerea, and large-effect genetic variation abolishing defense signaling pathways within the host, Arabidopsis thaliana. We show that genetic variation in the pathogen has a greater influence on the co-transcriptome than mutations that abolish defense signaling pathways in the host. Genome-wide association mapping using the pathogens' genetic variation and both organisms' transcriptomes allowed an assessment of how the pathogen modulates plasticity in response to the host. This showed that the differences in both organism's responses were linked to trans-expression quantitative trait loci (eQTL) hotspots within the pathogen's genome. These hotspots control gene sets in either the host or pathogen and show differential allele sensitivity to the host's genetic variation rather than qualitative host specificity. Interestingly, nearly all the trans-eQTL hotspots were unique to the host or pathogen transcriptomes. In this system of differential plasticity, the pathogen mediates the shift in the co-transcriptome more than the host.

Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been developed to tackle this task, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies of natural populations, only a single period of time can be sampled. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics. Serial sampling of organisms with shorter generation times has also become more feasible due to improvements in the cost and throughput of sequencing. With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper analyzes population genomic time-series data by first simulating training data under a demographic model appropriate for the data of interest, training a one-dimensional convolutional neural network on said simulations, and inferring which polymorphisms in this serialized data set were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, identifies selected variants with high resolution, and estimates selection coefficients more accurately than existing methods. In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times, as well as experimentally evolved populations where time-series data are often generated. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper as a Python package for use by the community.

Epistasis, commonly defined as interaction effects between alleles of different loci, is an important genetic component of the variation of phenotypic traits in natural and breeding populations. In addition to its impact on variance, epistasis can also affect the expected performance of a population and is then referred to as directional epistasis. Before the advent of genomic data, the existence of epistasis (both directional and non-directional) was investigated based on complex and expensive mating schemes involving several generations evaluated for a trait of interest. In this study, we propose a methodology to detect the presence of epistasis based on simple inbred biparental populations, both genotyped and phenotyped, ideally along with their parents. Thanks to genomic data, parental proportions as well as shared parental proportions between inbred individuals can be estimated. They allow the evaluation of epistasis through a test of the expected performance for directional epistasis or the variance of genetic values. This methodology was applied to two large multiparental populations, i.e. the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e.g. the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic associations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any bi/multiparental inbred population evaluated for a trait of interest.

Members of genetically admixed populations possess ancestry from multiple source groups, and studies of human genetic admixture frequently estimate ancestry components corresponding to fractions of individual genomes that trace to specific ancestral populations. However, the same numerical ancestry fraction can represent a wide array of admixture scenarios within an individual's genealogy. Using a mechanistic model of admixture, we consider admixture genealogically: how many ancestors from the source populations does the admixture represent? We consider African-Americans, for whom continent-level estimates produce a 75-85% value for African ancestry on average and 15-25% for European ancestry. Genetic studies together with key features of African-American demographic history suggest ranges for parameters of a simple three-epoch model. Considering parameter sets compatible with estimates of current ancestry levels, we infer that if all genealogical lines of a random African-American born during 1960-1965 are traced back until they reach members of source populations, the mean over parameter sets of the expected number of genealogical lines terminating with African individuals is 314 (interquartile range 240-376), and the mean of the expected number terminating in Europeans is 51 (interquartile range 32-69). Across discrete generations, the peak number of African genealogical ancestors occurs in birth cohorts from the early 1700s, and the probability exceeds 50% that at least one European ancestor was born more recently than 1835. Our genealogical perspective can contribute to further understanding the admixture processes that underlie admixed populations. For African-Americans, the results provide insight both on how many of the ancestors of a typical African-American might have been forcibly displaced in the Transatlantic Slave Trade and on how many separate European admixture events might exist in a typical African-American genealogy.

I outline a streamlined method to insert large, single-copy transgenes into the Caenorhabditis elegans genome using recombination-mediated cassette exchange (RMCE) that relies solely on drug selection yielding a homozygous fluorescent protein (FP) marked transgene in 3 generations (8 days) at high efficiency (>1 insertion per 2 injected P0 animals). Landing sites for this approach are available on four chromosomes in several configurations which yield lines marked in distinct cell types. An array of vectors permit creating transgenes using a variety of selection methods (HygR, NeoR, PuroR, and unc-119) that yield lines expressing different colored FPs (BFP, GFP, mNG, and Scarlet). Although these transgenes retain a plasmid backbone and a selection marker, the inclusion of these sequences typically does not alter the expression of several cell-specific promoters tested. However, in certain orientations, promoters exhibit crosstalk with adjacent transcription units. In cases where crosstalk is problematic, the loxP-flanked fluorescent marker, plasmid backbone, and hygR gene can be excised by crossing through germline Cre expressing lines also created using this technique. Finally, genetic and molecular reagents designed to facilitate customization of both targeting vectors and landing sites are also described. Together, the rapid RMCE toolbox provides a platform for developing further innovative uses of RMCE to create complex genetically engineered tools.

Ataxin-7 maintains the integrity of Spt-Ada-Gcn5-Acetyltransferase (SAGA), an evolutionarily conserved coactivator in stimulating preinitiation complex (PIC) formation for transcription initiation, and thus, its upregulation or downregulation is associated with various diseases. However, it remains unknown how ataxin-7 is regulated that could provide new insights into disease pathogenesis and therapeutic interventions. Here, we show that ataxin-7's yeast homologue, Sgf73, undergoes ubiquitylation and proteasomal degradation. Impairment of such regulation increases Sgf73's abundance, which enhances recruitment of TATA box-binding protein (TBP) (that nucleates PIC formation) to the promoter but impairs transcription elongation. Further, decreased Sgf73 level reduces PIC formation and transcription. Thus, Sgf73 is fine-tuned by ubiquitin-proteasome system (UPS) in orchestrating transcription. Likewise, ataxin-7 undergoes ubiquitylation and proteasomal degradation, alteration of which changes ataxin-7's abundance that is associated with altered transcription and cellular pathologies/diseases. Collectively, our results unveil a novel UPS regulation of Sgf73/ataxin-7 for normal cellular health and implicate alteration of such regulation in diseases.

Ribosomal proteins (Rps) are essential for viability. Genetic mutations affecting Rp genes were first discovered in Drosophila, where they represent a major class of haploinsufficient mutations. One mutant copy gives rise to the dominant "Minute" phenotype, characterized by slow growth and small, thin bristles. Wild-type (WT) and Minute cells compete in mosaics, that is, Rp+/- are preferentially lost when their neighbors are of the wild-type genotype. Many features of Rp gene haploinsufficiency (i.e. Rp+/- phenotypes) are mediated by a transcriptional program. In Drosophila, reduced translation and slow growth are under the control of Xrp1, a bZip-domain transcription factor induced in Rp mutant cells that leads ultimately to the phosphorylation of eIF2α and consequently inhibition of most translation. Rp mutant phenotypes are also mediated transcriptionally in yeast and in mammals. In mammals, the Impaired Ribosome Biogenesis Checkpoint activates p53. Recent findings link Rp mutant phenotypes to other cellular stresses, including the DNA damage response and endoplasmic reticulum stress. We suggest that cell competition results from nonautonomous inputs to stress responses, bringing decisions between adaptive and apoptotic outcomes under the influence of nearby cells. In Drosophila, cell competition eliminates aneuploid cells in which loss of chromosome leads to Rp gene haploinsufficiency. The effects of Rp gene mutations on the whole organism, in Minute flies or in humans with Diamond-Blackfan Anemia, may be inevitable consequences of pathways that are useful in eliminating individual cells from mosaics. Alternatively, apparently deleterious whole organism phenotypes might be adaptive, preventing even more detrimental outcomes. In mammals, for example, p53 activation appears to suppress oncogenic effects of Rp gene haploinsufficiency.

Ultraviolet (UV) light primarily causes C > T substitutions in lesion-forming dipyrimidine sequences. However, many of the key driver mutations in melanoma do not fit this canonical UV signature, but are instead caused by T > A, T > C, or C > A substitutions. To what extent exposure to the UVB or UVA spectrum of sunlight can induce these noncanonical mutation classes, and the molecular mechanism involved is unclear. Here, we repeatedly exposed wild-type or repair-deficient yeast (Saccharomyces cerevisiae) to UVB or UVA light and characterized the resulting mutations by whole genome sequencing. Our data indicate that UVB induces C > T and T > C substitutions in dipyrimidines, and T > A substitutions that are often associated with thymine-adenine (TA) sequences. All of these mutation classes are induced in nucleotide excision repair-deficient cells and show transcriptional strand asymmetry, suggesting they are caused by helix-distorting UV photoproducts. In contrast, UVA exposure induces orders of magnitude fewer mutations with a distinct mutation spectrum. UVA-induced mutations are elevated in Ogg1-deficient cells, and the resulting spectrum consists almost entirely of C > A/G > T mutations, indicating they are likely derived from oxidative guanine lesions. These mutations show replication asymmetry, with elevated G > T mutations on the leading strand, suggesting there is a strand bias in the removal or bypass of guanine lesions during replication. Finally, we develop a mutation reporter to show that UVA induces a G > T reversion mutation in yeast that mimics the oncogenic NRAS Q61K mutation in melanoma. Taken together, these findings indicate that UVA and UVB exposure can induce many of the noncanonical mutation classes that cause driver mutations in melanoma.

Haplodiploidy and paternal genome elimination (PGE) are examples of asymmetric inheritance, where males transmit only maternally inherited chromosomes to their offspring. Under haplodiploidy, this results from males being haploid, whereas under PGE, males inherit but subsequently exclude paternally inherited chromosomes from sperm. Their evolution involves changes in the mechanisms of meiosis and sex determination and sometimes also dosage compensation. As a result, these systems are thought to be an evolutionary trap, meaning that once asymmetric chromosome transmission evolves, it is difficult to transition back to typical Mendelian transmission. We assess whether there is evidence for this idea in the scale insect family Eriococcidae, a lineage with PGE and the only clade with a suggestion that asymmetric inheritance has transitioned back to Mendelian inheritance. We conduct a cytological survey of 13 eriococcid species, and a cytological, genetic, and gene expression analysis of species in the genus Cystococcus, to investigate whether there is evidence for species in this family evolving Mendelian chromosome transmission. Although we find that all species we examined exhibit PGE, the mechanism is extremely variable within Eriococcidae. Within Cystococcus, in fact, we uncover a previously undiscovered type of PGE in scale insects that acts exclusively in meiosis, where paternally inherited chromosomes in males are present, uncondensed, and expressed in somatic cells but eliminated prior to meiosis. Broadly, we fail to find evidence for a reversion from PGE to Mendelian inheritance in Eriococcidae, supporting the idea that asymmetric inheritance systems such as PGE may be an evolutionary trap.

Heterochromatin is characterized by an enrichment of repetitive elements and low gene density and is often maintained in a repressed state across cell division and differentiation. The silencing is mainly regulated by repressive histone marks such as H3K9 and H3K27 methylated forms and the heterochromatin protein 1 (HP1) family. Here, we analyzed in a tissue-specific manner the binding profile of the two HP1 homologs in Caenorhabditis elegans, HPL-1 and HPL-2, at the L4 developmental stage. We identified the genome-wide binding profile of intestinal and hypodermal HPL-2 and intestinal HPL-1 and compared them with heterochromatin marks and other features. HPL-2 associated preferentially to the distal arms of autosomes and correlated positively with the methylated forms of H3K9 and H3K27. HPL-1 was also enriched in regions containing H3K9me3 and H3K27me3 but exhibited a more even distribution between autosome arms and centers. HPL-2 showed a differential tissue-specific enrichment for repetitive elements conversely with HPL-1, which exhibited a poor association. Finally, we found a significant intersection of genomic regions bound by the BLMP-1/PRDM1 transcription factor and intestinal HPL-1, suggesting a corepressive role during cell differentiation. Our study uncovers both shared and singular properties of conserved HP1 proteins, providing information about genomic binding preferences in relation to their role as heterochromatic markers.

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a genome-wide association studies in three genes: dachsous, extra-macrochaete, and neuralized, using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the dachsous shape change vector, we observe genetic differentiation of variants in dachsous and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the extra-macrochaete shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of dachsous, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.

Transcription factors activate gene expression in development, homeostasis, and stress with DNA binding domains and activation domains. Although there exist excellent computational models for predicting DNA binding domains from protein sequence (Stormo 2013), models for predicting activation domains from protein sequence have lagged behind (Ravarani et al. 2018; Erijman et al. 2020; Sanborn et al. 2021), particularly in metazoans. We recently developed a simple and accurate predictor of acidic activation domains on human transcription factors (Staller et al. 2022). Here, we show how the accuracy of this human predictor arises from the clustering of aromatic, leucine, and acidic residues, which together are necessary for acidic activation domain function. When we combine our predictor with the predictions of convolutional neural network models trained in yeast, the intersection is more accurate than individual models, emphasizing that each approach carries orthogonal information. We synthesize these findings into a new set of activation domain predictions on human transcription factors.

The classical infinitesimal model is a simple and robust model for the inheritance of quantitative traits. In this model, a quantitative trait is expressed as the sum of a genetic and an environmental component, and the genetic component of offspring traits within a family follows a normal distribution around the average of the parents' trait values, and has a variance that is independent of the parental traits. In previous work, we showed that when trait values are determined by the sum of a large number of additive Mendelian factors, each of small effect, one can justify the infinitesimal model as a limit of Mendelian inheritance. In this paper, we show that this result extends to include dominance. We define the model in terms of classical quantities of quantitative genetics, before justifying it as a limit of Mendelian inheritance as the number, M, of underlying loci tends to infinity. As in the additive case, the multivariate normal distribution of trait values across the pedigree can be expressed in terms of variance components in an ancestral population and probabilities of identity by descent determined by the pedigree. Now, with just first order dominance effects, we require two, three and four way identities. We also show that, even if we condition on parental trait values, the `shared' and `residual' components of trait values within each family will be asymptotically normally distributed as the number of loci tends to infinity, with an error of order 1/√M. We illustrate our results with some numerical examples.

Variation in the rates and characteristics of germline and somatic mutations across the genome of an organism is informative about DNA damage and repair processes and can also shed light on aspects of organism physiology and evolution. We adapted a recently developed method for inferring somatic mutations from bulk RNA-seq data and applied it to a large collection of Arabidopsis thaliana accessions. The wide range of genomic data types available for A. thaliana enabled us to investigate the relationships of multiple genomic features with the variation in the somatic mutation rate across the genome of this model plant. We observed that late replicated regions showed evidence of an elevated rate of somatic mutation compared to genomic regions that are replicated early. We identified transcriptional strand asymmetries, consistent with the effects of transcription-coupled damage and/or repair. We also observed a negative relationship between the inferred somatic mutation count and the H3K36me3 histone mark which is well-documented in the literature of human systems. In addition, we were able to support previous reports of an inverse relationship between inferred somatic mutation count and GC content as well as a positive relationship between inferred somatic mutation count and DNA methylation for both cytosine and non-cytosine mutations.

The process of gametogenesis is orchestrated by a dynamic gene expression program, where a vital subset constitutes the early meiotic genes (EMGs). In budding yeast, the transcription factor Ume6 represses EMG expression during mitotic growth. However, during the transition from mitotic to meiotic cell fate, EMGs are activated in response to the transcriptional regulator Ime1 through its interaction with Ume6. While it is known that binding of Ime1 to Ume6 promotes EMG expression, the mechanism of EMG activation remains elusive. Two competing models have been proposed whereby Ime1 either forms an activator complex with Ume6 or promotes Ume6 degradation. Here, we resolve this controversy. First, we identify the set of genes that are directly regulated by Ume6, including UME6 itself. While Ume6 protein levels increase in response to Ime1, Ume6 degradation occurs much later in meiosis. Importantly, we found that depletion of Ume6 shortly before meiotic entry is detrimental to EMG activation and gamete formation, whereas tethering of Ume6 to a heterologous activation domain is sufficient to trigger EMG expression and produce viable gametes in the absence of Ime1. We conclude that Ime1 and Ume6 form an activator complex. While Ume6 is indispensable for EMG expression, Ime1 primarily serves as a transactivator for Ume6.