Independent evolution of transcript abundance and gene regulatory dynamics

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Mechanisms of regulatory evolution in yeast

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.

Evolution of transcription factor binding through sequence variations and turnover of binding sites

Variations in noncoding regulatory sequences play a central role in evolution. Interpreting such variations, however, remains difficult even in the context of defined attributes such as transcription factor (TF) binding sites. Here, we systematically link variations in cis-regulatory sequences to TF binding by profiling the allele-specific binding of 27 TFs expressed in a yeast hybrid, in which two related genomes are present within the same nucleus. TFs localize preferentially to sites containing their known consensus motifs but occupy only a small fraction of the motif-containing sites available within the genomes. Differential binding of TFs to the orthologous alleles was well explained by variations that alter motif sequence, whereas differences in chromatin accessibility between alleles were of little apparent effect. Motif variations that abolished binding when present in only one allele were still bound when present in both alleles, suggesting evolutionary compensation, with a potential role for sequence conservation at the motif's vicinity. At the level of the full promoter, we identify cases of binding-site turnover, in which binding sites are reciprocally gained and lost, yet most interspecific differences remained uncompensated. Our results show the flexibility of TFs to bind imprecise motifs and the fast evolution of TF binding sites between related species.

Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal data sets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA-Seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways, as well as impaired type I IFN (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

Systematic temporal analysis of peripheral blood transcriptomes using TrendCatcher identifies early and persistent neutrophil activation as a hallmark of severe COVID-19

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there is a lack of methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic datasets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal datasets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk RNA-seq and scRNA-seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways as well as impaired type I interferon (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

Interspecific hybridization as a driver of fungal evolution and adaptation

Cross-species gene transfer is often associated with bacteria, which have evolved several mechanisms that facilitate horizontal DNA exchange. However, the increased availability of whole-genome sequences has revealed that fungal species also exchange DNA, leading to intertwined lineages, blurred species boundaries or even novel species. In contrast to prokaryotes, fungal DNA exchange originates from interspecific hybridization, where two genomes are merged into a single, often highly unstable, polyploid genome that evolves rapidly into stabler derivatives. The resulting hybrids can display novel combinations of genetic and phenotypic variation that enhance fitness and allow colonization of new niches. Interspecific hybridization led to the emergence of important pathogens of humans and plants (for example, various Candida and 'powdery mildew' species, respectively) and industrially important yeasts, such as Saccharomyces hybrids that are important in the production of cold-fermented lagers or cold-cellared Belgian ales. In this Review, we discuss the genetic processes and evolutionary implications of fungal interspecific hybridization and highlight some of the best-studied examples. In addition, we explain how hybrids can be used to study molecular mechanisms underlying evolution, adaptation and speciation, and serve as a route towards development of new variants for industrial applications.

Accumulation of cis- and trans-regulatory variations is associated with phenotypic divergence of a complex trait between yeast species

Gene regulatory variations accumulate during evolution and alter gene expression. While the importance of expression variation in phenotypic evolution is well established, the molecular basis remains largely unknown. Here, we examine two closely related yeast species, Saccharomyces cerevisiae and Saccharomyces paradoxus, which show phenotypical differences in morphology and cell cycle progression when grown in the same environment. By profiling the cell cycle transcriptome and binding of key transcription factors (TFs) in the two species and their hybrid, we show that changes in expression levels and dynamics of oscillating genes are dominated by upstream trans-variations. We find that multiple cell cycle regulators show both cis- and trans-regulatory variations, which alters their expression in favor of the different cell cycle phenotypes. Moreover, we show that variations in the cell cycle TFs, Fkh1, and Fkh2 affect both the expression of target genes, and the binding specificity of an interacting TF, Ace2. Our study reveals how multiple variations accumulate and propagate through the gene regulatory network, alter TFs binding, contributing to phenotypic changes in cell cycle progression.

Speed-Specificity Trade-Offs in the Transcription Factors Search for Their Genomic Binding Sites

Transcription factors (TFs) regulate gene expression by binding DNA sequences recognized by their DNA-binding domains (DBDs). DBD-recognized motifs are short and highly abundant in genomes. The ability of TFs to bind a specific subset of motif-containing sites, and to do so rapidly upon activation, is fundamental for gene expression in all eukaryotes. Despite extensive interest, our understanding of the TF-target search process is fragmented; although binding specificity and detection speed are two facets of this same process, trade-offs between them are rarely addressed. In this opinion article, we discuss potential speed-specificity trade-offs in the context of existing models. We further discuss the recently described 'distributed specificity' paradigm, suggesting that intrinsically disordered regions (IDRs) promote specificity while reducing the TF-target search time.