The Evolution of Gene-Specific Transcriptional Noise Is Driven by Selection at the Pathway Level

Unveiling cell-type-specific mode of evolution in comparative single-cell expression data

While methodology for determining the mode of evolution in coding sequences has been well established, evaluation of adaptation events in emerging types of phenotype data needs further development. Here we propose an analysis framework (expression variance decomposition, EVaDe) for comparative single-cell expression data based on phenotypic evolution theory. After decomposing the gene expression variance into separate components, we use two strategies to identify genes exhibiting large between-taxon expression divergence and small within-cell-type expression noise in certain cell types, attributing this pattern to putative adaptive evolution. In a dataset of primate prefrontal cortex, we find that such human-specific key genes enrich with neurodevelopment-related functions, while most other genes exhibit neutral evolution patterns. Specific neuron types are found to harbor more of these key genes than other cell types, thus likely to have experienced more extensive adaptation. Reassuringly, at molecular sequence level, the key genes are significantly associated with the rapidly evolving conserved non-coding elements. An additional case analysis comparing the naked mole-rat (NMR) with the mouse suggests that innate-immunity-related genes and cell types have undergone putative expression adaptation in NMR. Overall, the EVaDe framework may effectively probe adaptive evolution mode in single-cell expression data.

Disentangling variational bias: the roles of development, mutation, and selection

The extraordinary diversity and adaptive fit of organisms to their environment depends fundamentally on the availability of variation. While most population genetic frameworks assume that random mutations produce isotropic phenotypic variation, the distribution of variation available to natural selection is more restricted, as the distribution of phenotypic variation is affected by a range of factors in developmental systems. Here, we revisit the concept of developmental bias - the observation that the generation of phenotypic variation is biased due to the structure, character, composition, or dynamics of the developmental system - and argue that a more rigorous investigation into the role of developmental bias in the genotype-to-phenotype map will produce fundamental insights into evolutionary processes, with potentially important consequences on the relation between micro- and macro-evolution. We discuss the hierarchical relationships between different types of variational biases, including mutation bias and developmental bias, and their roles in shaping the realized phenotypic space. Furthermore, we highlight the challenges in studying variational bias and propose potential approaches to identify developmental bias using modern tools.

Stress-Induced Constraint on Expression Noise of Essential Genes in E. coli

Gene expression is an inherently noisy process that is constrained by natural selection. Yet the condition dependence of constraint on expression noise remains unclear. Here, we address this problem by studying constraint on expression noise of E. coli genes in eight diverse growth conditions. In particular, we use variation in expression noise as an analog for constraint, examining its relationships to expression level and to the number of regulatory inputs from transcription factors across and within conditions. We show that variation in expression noise is negatively associated with expression level, implicating constraint to minimize expression noise of highly expressed genes. However, this relationship is condition dependent, with the strongest constraint observed when E. coli are grown in the presence of glycerol or ciprofloxacin, which result in carbon or antibiotic stress, respectively. In contrast, we do not observe evidence of constraint on expression noise of highly regulated genes, suggesting that highly expressed and highly regulated genes represent distinct classes of genes. Indeed, we find that essential genes are often highly expressed but not highly regulated, with elevated expression noise in glycerol and ciprofloxacin conditions. Thus, our findings support the hypothesis that selective constraint on expression noise is condition dependent in E. coli, illustrating how it may play a critical role in ensuring expression stability of essential genes in unstable environments.

Molecular models of bidirectional promoter regulation

Approximately 11% of human genes are transcribed by a bidirectional promoter (BDP), defined as two genes with <1 kb between their transcription start sites. Despite their evolutionary conservation and enrichment for housekeeping genes and oncogenes, the regulatory role of BDPs remains unclear. BDPs have been suggested to facilitate gene coregulation and/or decrease expression noise. This review discusses these potential regulatory functions through the context of six prospective underlying mechanistic models: a single nucleosome free region, shared transcription factor/regulator binding, cooperative negative supercoiling, bimodal histone marks, joint activation by enhancer(s), and RNA-mediated recruitment of regulators. These molecular mechanisms may act independently and/or cooperatively to facilitate the coregulation and/or decreased expression noise predicted of BDPs.

Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder

The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little is known about the early dynamics of gene expression in these disorders due to their relatively late manifestation. To address this, we conducted a secondary analysis of post-mortem prefrontal cortex datasets using bioinformatics and machine learning techniques to identify differentially expressed gene modules associated with aging and the diseases, determine their time-perturbation points, and assess enrichment with expression quantitative trait loci (eQTL) genes. Our findings revealed early, mid, and late deregulation of expression of functional gene modules involved in neurodevelopment, plasticity, homeostasis, and immune response. This supports the hypothesis that multiple hits throughout life contribute to disease manifestation rather than a single early-life event. Moreover, the time-perturbed functional gene modules were associated with genetic loci affecting gene expression, highlighting the role of genetic factors in gene expression dynamics and the development of disease phenotypes. Our findings emphasize the importance of investigating time-dependent perturbations in gene expression before the age of onset in elucidating the molecular mechanisms of psychiatric disorders.

Stability in gene expression and body-plan development leads to evolutionary conservation

BACKGROUND

Phenotypic evolution is mainly explained by selection for phenotypic variation arising from factors including mutation and environmental noise. Recent theoretical and experimental studies have suggested that phenotypes with greater developmental stability tend to have a constant phenotype and gene expression level within a particular genetic and environmental condition, and this positively correlates with stronger evolutionary conservation, even after the accumulation of genetic changes. This could reflect a novel mechanism that contributes to evolutionary conservation; however, it remains unclear whether developmental stability is the cause, or whether at least it contributes to their evolutionary conservation. Here, using Japanese medaka lines, we tested experimentally whether developmental stages and gene expression levels with greater stability led to their evolutionary conservation.

RESULTS

We first measured the stability of each gene expression level and developmental stage (defined here as the whole embryonic transcriptome) in the inbred F0 medaka population. We then measured their evolutionary conservation in the F3 generation by crossing the F0 line with the distantly related Japanese medaka line (Teradomori), followed by two rounds of intra-generational crossings. The results indicated that the genes and developmental stages that had smaller variations in the F0 generation showed lower diversity in the hybrid F3 generation, which implies a causal relationship between stability and evolutionary conservation.

CONCLUSIONS

These findings suggest that the stability in phenotypes, including the developmental stages and gene expression levels, leads to their evolutionary conservation; this most likely occurs due to their low potential to generate phenotypic variation. In addition, since the highly stable developmental stages match with the body-plan-establishment stage, it also implies that the developmental stability potentially contributed to the strict conservation of animal body plan.

Transcriptional noise adjusted for expression levels reveals genes with high transcriptional noise that are highly expressed, functionally related, and co-regulated in yeast

Understanding the relationship between variability in single-cell and non-single-cell gene expression studies will aid in understanding the role of and mechanisms that lead to variability in biological systems. Studies on the variation of gene expression levels in yeast normally focus on single cells and use the coefficient of variance (CV) as a measure of noise. The CV is typically negatively correlated with gene expression levels, so most of the studies using yeast find that genes with high transcriptional noise are lowly expressed. We find adjusting noise for expression levels using linear/natural log polynomial, and local fits and analyzing many non-single-cell RNA-seq sets identifies genes with high median transcriptional noise that are different than those that have high median CVs. Interestingly, these genes are heavily regulated by transcription factors that are related to variability and stochastic processes based on single-cell studies, including Msn2p, Msn4p, Hsf1p, and Crz1p but are not associated with genes with high median CVs based on non-single-cell gene expression data. In addition, adjusting noise for expression levels in a single-cell RNA-seq data set adds value by finding genes that have noisy gene expression levels and their associated transcription factors that are not found to be associated with genes with high CVs in the single-cell expression data or a comparable non-single-cell gene expression data. Lastly, S. cerevisiae genes with noisy expression tend to have orthologs with noisy gene expression in C. albicans, indicating transcriptional noise is evolutionarily conserved.

Rhythmicity is linked to expression cost at the protein level but to expression precision at the mRNA level

Many genes have nycthemeral rhythms of expression, i.e. a 24-hours periodic variation, at either mRNA or protein level or both, and most rhythmic genes are tissue-specific. Here, we investigate and discuss the evolutionary origins of rhythms in gene expression. Our results suggest that rhythmicity of protein expression could have been favored by selection to minimize costs. Trends are consistent in bacteria, plants and animals, and are also supported by tissue-specific patterns in mouse. Unlike for protein level, cost cannot explain rhythm at the RNA level. We suggest that instead it allows to periodically reduce expression noise. Noise control had the strongest support in mouse, with limited evidence in other species. We have also found that genes under stronger purifying selection are rhythmically expressed at the mRNA level, and we propose that this is because they are noise sensitive genes. Finally, the adaptive role of rhythmic expression is supported by rhythmic genes being highly expressed yet tissue-specific. This provides a good evolutionary explanation for the observation that nycthemeral rhythms are often tissue-specific.

For many genes, their expression, i.e. the production of RNA and proteins, is rhythmic with a 24-hour period. Here, we study and discuss the evolutionary origins of these rhythms. Our analyses of data from different species suggest that the rhythmicity of protein level may have been favored by selection for cost minimization. Furthermore, we have shown that cost cannot explain the rhythmic variations in RNA levels. Instead, we suggest that it periodically reduces the stochasticity of gene expression. We also found that genes under stronger purifying selection are rhythmically expressed at the mRNA level, and propose that this is because they are noise-sensitive genes. Finally, rhythmic expression involves genes that are often highly expressed and tissue-specific. This provides a good evolutionary explanation for the tissue-specificity of these rhythms.

Comparative studies of genomic and epigenetic factors influencing transcriptional variation in two insect species

Different genes show different levels of expression variability. For example, highly expressed genes tend to exhibit less expression variability. Genes whose promoters have TATA box and initiator motifs tend to have increased expression variability. On the other hand, DNA methylation of transcriptional units, or gene body DNA methylation, is associated with reduced gene expression variability in many species. Interestingly, some insect lineages, most notably Diptera including the canonical model insect Drosophila melanogaster, have lost DNA methylation. Therefore, it is of interest to determine whether genomic features similarly influence gene expression variability in lineages with and without DNA methylation. We analyzed recently generated large-scale data sets in D. melanogaster and honey bee (Apis mellifera) to investigate these questions. Our analysis shows that increased gene expression levels are consistently associated with reduced expression variability in both species, while the presence of TATA box is consistently associated with increased gene expression variability. In contrast, initiator motifs and gene lengths have weak effects limited to some data sets. Importantly, we show that a sequence characteristics indicative of gene body DNA methylation is strongly and negatively associate with gene expression variability in honey bees, while it shows no such association in D. melanogaster. These results suggest the evolutionary loss of DNA methylation in some insect lineages has reshaped the molecular mechanisms concerning the regulation of gene expression variability.

Potential contribution of intrinsic developmental stability toward body plan conservation

BACKGROUND

Despite the morphological diversity of animals, their basic anatomical patterns-the body plans in each animal phylum-have remained highly conserved over hundreds of millions of evolutionary years. This is attributed to conservation of the body plan-establishing developmental period (the phylotypic period) in each lineage. However, the evolutionary mechanism behind this phylotypic period conservation remains under debate. A variety of hypotheses based on the concept of modern synthesis have been proposed, such as negative selection in the phylotypic period through its vulnerability to embryonic lethality. Here we tested a new hypothesis that the phylotypic period is developmentally stable; it has less potential to produce phenotypic variations than the other stages, and this has most likely led to the evolutionary conservation of body plans.

RESULTS

By analyzing the embryos of inbred Japanese medaka embryos raised under the same laboratory conditions and measuring the whole embryonic transcriptome as a phenotype, we found that the phylotypic period has greater developmental stability than other stages. Comparison of phenotypic differences between two wild medaka populations indicated that the phylotypic period and its genes in this period remained less variational, even after environmental and mutational modifications accumulated during intraspecies evolution. Genes with stable expression levels were enriched with those involved in cell-cell signalling and morphological specification such as Wnt and Hox, implying possible involvement in body plan development of these genes.

CONCLUSIONS

This study demonstrated the correspondence between the developmental stage with low potential to produce phenotypic variations and that with low diversity in micro- and macroevolution, namely the phylotypic period. Whereas modern synthesis explains evolution as a process of shaping of phenotypic variations caused by mutations, our results highlight the possibility that phenotypic variations are readily limited by the intrinsic nature of organisms, namely developmental stability, thus biasing evolutionary outcomes.

Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level

Background

Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made.

Results

Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes.

Conclusions

These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01264-9.

A computational exploration of resilience and evolvability of protein-protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

Different Patterns of mRNA Nuclear Retention during Meiotic Prophase in Larch Microsporocytes

Recent studies show a crucial role of post-transcriptional processes in the regulation of gene expression. Our research has shown that mRNA retention in the nucleus plays a significant role in such regulation. We studied larch microsporocytes during meiotic prophase, characterized by pulsatile transcriptional activity. After each pulse, the transcriptional activity is silenced, but the transcripts synthesized at this time are not exported immediately to the cytoplasm but are retained in the cell nucleus and especially in Cajal bodies, where non-fully-spliced transcripts with retained introns are accumulated. Analysis of the transcriptome of these cells and detailed analysis of the nuclear retention and transport dynamics of several mRNAs revealed two main patterns of nuclear accumulation and transport. The majority of studied transcripts followed the first one, consisting of a more extended retention period and slow release to the cytoplasm. We have shown this in detail for the pre-mRNA and mRNA encoding RNA pol II subunit 10. In this pre-mRNA, a second (retained) intron is posttranscriptionally spliced at a precisely defined time. Fully mature mRNA is then released into the cytoplasm, where the RNA pol II complexes are produced. These proteins are necessary for transcription in the next pulse to occur.mRNAs encoding translation factors and SERRATE followed the second pattern, in which the retention period was shorter and transcripts were rapidly transferred to the cytoplasm. The presence of such a mechanism in various cell types from a diverse range of organisms suggests that it is an evolutionarily conserved mechanism of gene regulation.

Hematopoietic Stem Cells: Nature and Niche Nurture

Like all cells, hematopoietic stem cells (HSCs) and their offspring, the hematopoietic progenitor cells (HPCs), are highly sociable. Their capacity to interact with bone marrow niche cells and respond to environmental cytokines orchestrates the generation of the different types of blood and immune cells. The starting point for engineering hematopoiesis ex vivo is the nature of HSCs, and a longstanding premise is that they are a homogeneous population of cells. However, recent findings have shown that adult bone marrow HSCs are really a mixture of cells, with many having lineage affiliations. A second key consideration is: Do HSCs "choose" a lineage in a random and cell-intrinsic manner, or are they instructed by cytokines? Since their discovery, the hematopoietic cytokines have been viewed as survival and proliferation factors for lineage committed HPCs. Some are now known to also instruct cell lineage choice. These fundamental changes to our understanding of hematopoiesis are important for placing niche support in the right context and for fabricating an ex vivo environment to support HSC development.

Capturing and Understanding the Dynamics and Heterogeneity of Gene Expression in the Living Cell

The regulation of gene expression is a fundamental process enabling cells to respond to internal and external stimuli or to execute developmental programs. Changes in gene expression are highly dynamic and depend on many intrinsic and extrinsic factors. In this review, we highlight the dynamic nature of transient gene expression changes to better understand cell physiology and development in general. We will start by comparing recent in vivo procedures to capture gene expression in real time. Intrinsic factors modulating gene expression dynamics will then be discussed, focusing on chromatin modifications. Furthermore, we will dissect how cell physiology or age impacts on dynamic gene regulation and especially discuss molecular insights into acquired transcriptional memory. Finally, this review will give an update on the mechanisms of heterogeneous gene expression among genetically identical individual cells. We will mainly focus on state-of-the-art developments in the yeast model but also cover higher eukaryotic systems.

Robust inference of positive selection on regulatory sequences in the human brain

A longstanding hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory level adaptations than by protein sequence adaptations. This has especially been suggested for regulatory adaptations in the evolution of the human brain. We present a new method to detect positive selection on transcription factor binding sites on the basis of measuring predicted affinity change with a machine learning model of binding. Unlike other methods, this approach requires neither defining a priori neutral sites nor detecting accelerated evolution, thus removing major sources of bias. We scanned the signals of positive selection for CTCF binding sites in 29 human and 11 mouse tissues or cell types. We found that human brain-related cell types have the highest proportion of positive selection. This result is consistent with the view that adaptive evolution to gene regulation has played an important role in evolution of the human brain.

Does Gene Expression Noise Play a Functional Role in Plants?

Gene expression in individual cells can be surprisingly noisy. In unicellular organisms this noise can be functional; for example, by allowing a subfraction of the population to prepare for environmental stress. The role of gene expression noise in multicellular organisms has, however, remained unclear. In this review, we discuss how new techniques are revealing an unexpected level of variability in gene expression between and within genetically identical plants. We describe recent progress as well as speculate on the function of transcriptional noise as a mechanism for generating functional phenotypic diversity in plants.

Inter-embryo gene expression variability recapitulates the hourglass pattern of evo-devo

BACKGROUND

The evolution of embryological development has long been characterized by deep conservation. In animal development, the phylotypic stage in mid-embryogenesis is more conserved than either early or late stages among species within the same phylum. Hypotheses to explain this hourglass pattern have focused on purifying the selection of gene regulation. Here, we propose an alternative-genes are regulated in different ways at different stages and have different intrinsic capacities to respond to perturbations on gene expression.

RESULTS

To eliminate the influence of natural selection, we quantified the expression variability of isogenetic single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that the expression variability is lower at the phylotypic stage, supporting that the underlying regulatory architecture in this stage is more robust to stochastic variation on gene expression. We present evidence that the phylotypic stage is also robust to genetic variations on gene expression. Moreover, chromatin regulation appears to play a key role in the variation and evolution of gene expression.

CONCLUSIONS

We suggest that a phylum-level pattern of embryonic conservation can be explained by the intrinsic difference of gene regulatory mechanisms in different stages.

Towards a New Understanding of Decision-Making by Hematopoietic Stem Cells

Cells within the hematopoietic stem cell compartment selectively express receptors for cytokines that have a lineage(s) specific role; they include erythropoietin, macrophage colony-stimulating factor, granulocyte colony-stimulating factor, granulocyte/macrophage colony-stimulating factor and the ligand for the fms-like tyrosine kinase 3. These hematopoietic cytokines can instruct the lineage fate of hematopoietic stem and progenitor cells in addition to ensuring the survival and proliferation of cells that belong to a particular cell lineage(s). Expression of the receptors for macrophage colony-stimulating factor and granulocyte colony-stimulating factor is positively autoregulated and the presence of the cytokine is therefore likely to enforce a lineage bias within hematopoietic stem cells that express these receptors. In addition to the above roles, macrophage colony-stimulating factor and granulocyte/macrophage colony-stimulating factor are powerful chemoattractants. The multiple roles of some hematopoietic cytokines leads us towards modelling hematopoietic stem cell decision-making whereby these cells can 'choose' just one lineage fate and migrate to a niche that both reinforces the fate and guarantees the survival and expansion of cells as they develop.

Temporal changes in the gene expression heterogeneity during brain development and aging

Cells in largely non-mitotic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause increased variability between cells and individuals over time. Although increased inter-individual heterogeneity in gene expression was previously reported, whether this process starts during development or if it is restricted to the aging period has not yet been studied. The regulatory dynamics and functional significance of putative aging-related heterogeneity are also unknown. Here we address these by a meta-analysis of 19 transcriptome datasets from three independent studies, covering diverse human brain regions. We observed a significant increase in inter-individual heterogeneity during aging (20 + years) compared to postnatal development (0 to 20 years). Increased heterogeneity during aging was consistent among different brain regions at the gene level and associated with lifespan regulation and neuronal functions. Overall, our results show that increased expression heterogeneity is a characteristic of aging human brain, and may influence aging-related changes in brain functions.

Unknown to Known: Advancing Knowledge of Coral Gene Function

Given the catastrophic changes befalling coral reefs, understanding coral gene function is essential to advance reef conservation. This has proved challenging due to the paucity of genomic data and genetic tools available for corals. Recently, CRISPR/Cas9 gene editing was applied to these species; however, a major bottleneck is the identification and prioritization of candidate genes for manipulation. This issue is exacerbated by the many unknown ('dark') coral genes that may play key roles in the stress response. We review the use of gene coexpression networks that incorporate both known and unknown genes to identify targets for reverse genetic analysis. This approach also provides a framework for the annotation of dark genes in established interaction networks to improve our fundamental knowledge of coral gene function.

Experimental evolution reveals a general role for the methyltransferase Hmt1 in noise buffering

Cell-to-cell heterogeneity within an isogenic population has been observed in prokaryotic and eukaryotic cells. Such heterogeneity often manifests at the level of individual protein abundance and may have evolutionary benefits, especially for organisms in fluctuating environments. Although general features and the origins of cellular noise have been revealed, details of the molecular pathways underlying noise regulation remain elusive. Here, we used experimental evolution of Saccharomyces cerevisiae to select for mutations that increase reporter protein noise. By combining bulk segregant analysis and CRISPR/Cas9-based reconstitution, we identified the methyltransferase Hmt1 as a general regulator of noise buffering. Hmt1 methylation activity is critical for the evolved phenotype, and we also show that two of the Hmt1 methylation targets can suppress noise. Hmt1 functions as an environmental sensor to adjust noise levels in response to environmental cues. Moreover, Hmt1-mediated noise buffering is conserved in an evolutionarily distant yeast species, suggesting broad significance of noise regulation.

Experimental evolution in yeast reveals that the methyltransferase Hmt1 functions as a mediator connecting environmental stimuli to cellular noise; Hmt1-mediated noise buffering is conserved in an evolutionarily distant yeast.

Black Swans of CRISPR: Stochasticity and Complexity of Genetic Regulation

Recent waves of controversies surrounding genetic engineering have spilled into popular science in Twitter battles between reputable scientists and their followers. Here, a cautionary perspective on the possible blind spots and risks of CRISPR and related biotechnologies is presented, focusing in particular on the stochastic nature of cellular control processes.

Widespread inter-individual gene expression variability in Arabidopsis thaliana

A fundamental question in biology is how gene expression is regulated to give rise to a phenotype. However, transcriptional variability is rarely considered although it could influence the relationship between genotype and phenotype. It is known in unicellular organisms that gene expression is often noisy rather than uniform, and this has been proposed to be beneficial when environmental conditions are unpredictable. However, little is known about inter-individual transcriptional variability in multicellular organisms. Using transcriptomic approaches, we analysed gene expression variability between individual Arabidopsis thaliana plants growing in identical conditions over a 24-h time course. We identified hundreds of genes that exhibit high inter-individual variability and found that many are involved in environmental responses, with different classes of genes variable between the day and night. We also identified factors that might facilitate gene expression variability, such as gene length, the number of transcription factors regulating the genes and the chromatin environment. These results shed new light on the impact of transcriptional variability in gene expression regulation in plants.

Single-cell expression noise and gene-body methylation in Arabidopsis thaliana

Gene-body methylation (gbM) refers to an increased level of methylated cytosines specifically in a CG sequence context within genes. gbM is found in plant genes with intermediate expression level, which evolve slowly, and is often broadly conserved across millions of years of evolution. Intriguingly however, some plants lack gbM, and thus it remains unclear whether gbM has a function. In animals, there is support for a role of gbM in reducing erroneous transcription and transcription noise, but so far most studies in plants have tested for an effect of gbM on expression level, not noise. Here, we therefore tested whether gbM was associated with reduced expression noise in Arabidopsis thaliana, using single-cell transcriptome sequencing data from root quiescent centre cells. We find that gbM genes have lower expression noise levels than unmethylated genes. However, an analysis of covariance revealed that, if other genomic features are taken into account, this association disappears. Nonetheless, gbM genes were more consistently expressed across single-cell samples, supporting previous inference that gbM genes are constitutively expressed. Finally, we observed that fewer RNAseq reads map to introns of gbM genes than to introns of unmethylated genes, which indicates that gbM might be involved in reducing erroneous transcription by reducing intron retention.

Fitness effects of altering gene expression noise in Saccharomyces cerevisiae

Gene expression noise is an evolvable property of biological systems that describes differences in expression among genetically identical cells in the same environment. Prior work has shown that expression noise is heritable and can be shaped by selection, but the impact of variation in expression noise on organismal fitness has proven difficult to measure. Here, we quantify the fitness effects of altering expression noise for the TDH3 gene in Saccharomyces cerevisiae. We show that increases in expression noise can be deleterious or beneficial depending on the difference between the average expression level of a genotype and the expression level maximizing fitness. We also show that a simple model relating single-cell expression levels to population growth produces patterns consistent with our empirical data. We use this model to explore a broad range of average expression levels and expression noise, providing additional insight into the fitness effects of variation in expression noise.