Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast

Omics analyses uncover host networks defining virus-permissive and -hostile cellular states

The capacity of host cells to sustain or restrict virus infection is influenced by their proteome. Understanding the compendium of proteins defining cellular permissiveness is key to many questions in fundamental virology. Here, we apply a multiomic approach to determine the proteins that are associated with highly permissive, intermediate, and hostile cellular states. We observed two groups of differentially regulated genes: i) with robust changes in mRNA and protein levels, and ii) with protein/RNA discordances. Whereas many of the latter are classified as interferon stimulated genes (ISGs), most exhibit no antiviral effects in overexpression screens. This suggest that IFN-dependent protein changes can be better indicators of antiviral function than mRNA levels. Phosphoproteomics revealed an additional regulatory layer involving non-signalling proteins with altered phosphorylation. Indeed, we confirmed that several permissiveness-associated proteins with changes in abundance or phosphorylation regulate infection fitness. Altogether, our study provides a comprehensive and systematic map of the cellular alterations driving virus susceptibility.

Systems genomics of salinity stress response in rice

Populations can adapt to stressful environments through changes in gene expression. However, the fitness effect of gene expression in mediating stress response and adaptation remains largely unexplored. Here, we use an integrative field dataset obtained from 780 plants of Oryza sativa ssp. indica (rice) grown in a field experiment under normal or moderate salt stress conditions to examine selection and evolution of gene expression variation under salinity stress conditions. We find that salinity stress induces increased selective pressure on gene expression. Further, we show that trans-eQTLs rather than cis-eQTLs are primarily associated with rice's gene expression under salinity stress, potentially via a few master-regulators. Importantly, and contrary to the expectations, we find that cis-trans reinforcement is more common than cis-trans compensation which may be reflective of rice diversification subsequent to domestication. We further identify genetic fixation as the likely mechanism underlying this compensation/reinforcement. Additionally, we show that cis- and trans-eQTLs are under balancing and purifying selection, respectively, giving us insights into the evolutionary dynamics of gene expression variation. By examining genomic, transcriptomic, and phenotypic variation across a rice population, we gain insights into the molecular and genetic landscape underlying adaptive salinity stress responses, which is relevant for other crops and other stresses.

Multilevel Gene Expression Changes in Lineages Containing Adaptive Copy Number Variants

Copy number variants (CNVs) are an important class of genetic variation that can mediate rapid adaptive evolution. Whereas, CNVs can increase the relative fitness of the organism, they can also incur a cost due to the associated increased gene expression and repetitive DNA. 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 gene 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- chemostats. We find CNV-amplified genes correlate with higher mRNA abundance; however, this effect is reduced at the level of the proteome, consistent with post-transcriptional dosage compensation. By normalizing each level of gene expression by the abundance of the preceding step we were able to identify widespread differences in the efficiency of each level of 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 genome evolution shapes gene expression.

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

Systems genomics of salinity stress response in rice

Populations can adapt to stressful environments through changes in gene expression. However, the fitness effect of gene expression in mediating stress response and adaptation remains largely unexplored. Here, we use an integrative field dataset obtained from 780 plants of Oryza sativa ssp. indica (rice) grown in a field experiment under normal or moderate salt stress conditions to examine selection and evolution of gene expression variation under salinity stress conditions. We find that salinity stress induces increased selective pressure on gene expression. Further, we show that trans-eQTLs rather than cis-eQTLs are primarily associated with rice's gene expression under salinity stress, potentially via a few master-regulators. Importantly, and contrary to the expectations, we find that cis-trans reinforcement is more common than cis-trans compensation which may be reflective of rice diversification subsequent to domestication. We further identify genetic fixation as the likely mechanism underlying this compensation/reinforcement. Additionally, we show that cis- and trans-eQTLs are under balancing and purifying selection, respectively, giving us insights into the evolutionary dynamics of gene expression variation. By examining genomic, transcriptomic, and phenotypic variation across a rice population, we gain insights into the molecular and genetic landscape underlying adaptive salinity stress responses, which is relevant for other crops and other stresses.

Regulating translation in aging: from global to gene-specific mechanisms

Aging is characterized by a decline in various biological functions that is associated with changes in gene expression programs. Recent transcriptome-wide integrative studies in diverse organisms and tissues have revealed a gradual uncoupling between RNA and protein levels with aging, which highlights the importance of post-transcriptional regulatory processes. Here, we provide an overview of multi-omics analyses that show the progressive uncorrelation of transcriptomes and proteomes during the course of healthy aging. We then describe the molecular changes leading to global downregulation of protein synthesis with age and review recent work dissecting the mechanisms involved in gene-specific translational regulation in complementary model organisms. These mechanisms include the recognition of regulated mRNAs by trans-acting factors such as miRNA and RNA-binding proteins, the condensation of mRNAs into repressive cytoplasmic RNP granules, and the pausing of ribosomes at specific residues. Lastly, we mention future challenges of this emerging field, possible buffering functions as well as potential links with disease.

Single-molecule tracking reveals dynamic regulation of ribosomal scanning

How eukaryotic ribosomes traverse messenger RNA (mRNA) leader sequences to search for protein-synthesis start sites remains one of the most mysterious aspects of translation and its regulation. While the search process is conventionally described by a linear "scanning" model, its exquisitely dynamic nature has restricted detailed mechanistic study. Here, we observed single Saccharomyces cerevisiae ribosomal scanning complexes in real time, finding that they scan diverse mRNA leaders at a rate of 10 to 20 nt s-1. We show that specific binding of a protein to its mRNA leader sequence substantially arrests scanning. Conversely, impairing scanning-complex guanosine 5'-triphosphate hydrolysis results in native start-site bypass. Our results illustrate an mRNA-centric, kinetically controlled regulatory model where the ribosomal pre-initiation complex amplifies a nuanced energetic landscape to regulate scanning and start-site selection fidelity.

Beyond Haldane's rule: Sex-biased hybrid dysfunction for all modes of sex determination

Haldane's rule occupies a special place in biology as one of the few 'rules' of speciation, with empirical support from hundreds of species. And yet, its classic purview is restricted taxonomically to the subset of organisms with heteromorphic sex chromosomes. I propose explicit acknowledgement of generalized hypotheses about Haldane's rule that frame sex bias in hybrid dysfunction broadly and irrespective of the sexual system. The consensus view of classic Haldane's rule holds that sex-biased hybrid dysfunction across taxa is a composite phenomenon that requires explanations from multiple causes. Testing of the multiple alternative hypotheses for Haldane's rule is, in many cases, applicable to taxa with homomorphic sex chromosomes, environmental sex determination, haplodiploidy, and hermaphroditism. Integration of a variety of biological phenomena about hybrids across diverse sexual systems, beyond classic Haldane's rule, will help to derive a more general understanding of the contributing forces and mechanisms that lead to predictable sex biases in evolutionary divergence and speciation.

Effects of sleep deprivation on anxiety-depressive-like behavior and neuroinflammation

BACKGROUND

Depression is defined by a persistent low mood and disruptions in sleep patterns, with the WHO forecasting that major depression will rank as the third most prevalent contributor to the global burden of disease by the year 2030. Sleep deprivation serves as a stressor that triggers inflammation within the central nervous system, a process known as neuroinflammation. This inflammatory response plays a crucial role in the development of depression by upregulating the expression of inflammatory mediators that contribute to symptoms such as anxiety, hopelessness, and loss of pleasure.

METHODS

In this study, sleep deprivation was utilized as a method to induce anxiety and depressive-like behaviors in mice. The behavioral changes in the mice were then evaluated using the EZM, EPM, TST, FST, and SPT. H&E staining and Nissl staining was used to detect morphological changes in the medial prefrontal cortical (mPFC) regions. Elisa to assess serum CORT levels. Detection of mRNA levels and protein expression of clock genes, high mobility genome box-1 (Hmgb1), silent message regulator 6 (Sirt6), and pro-inflammatory factors by RT-qPCR, Western blotting, and immunofluorescence techniques.

RESULTS

Sleep deprivation resulted in decreased exploration of unfamiliar territory, increased time spent in a state of despair, and lower sucrose water intake in mice. Additionally, sleep deprivation led to increased secretion of serum CORT and upregulation of clock genes, IL6, IL1β, TNFα, Cox-2, iNOS, Sirt6, and Hmgb1. Sleep.

CONCLUSIONS

Sleep deprivation induces anxiety-depressive-like behaviors and neuroinflammation in the brain. Transcription of clock genes and activation of the Sirt6/Hmgb1 pathway may contribute to inflammatory responses in the mPFC.

Untranslated yet indispensable-UTRs act as key regulators in the environmental control of gene expression

To survive and thrive in a dynamic environment, plants must continuously monitor their surroundings and adjust their development and physiology accordingly. Changes in gene expression underlie these developmental and physiological adjustments, and are traditionally attributed to widespread transcriptional reprogramming. Growing evidence, however, suggests that post-transcriptional mechanisms also play a vital role in tailoring gene expression to a plant's environment. Untranslated regions (UTRs) act as regulatory hubs for post-transcriptional control, harbouring cis-elements that affect an mRNA's processing, localization, translation, and stability, and thereby tune the abundance of the encoded protein. Here, we review recent advances made in understanding the critical function UTRs exert in the post-transcriptional control of gene expression in the context of a plant's abiotic environment. We summarize the molecular mechanisms at play, present examples of UTR-controlled signalling cascades, and discuss the potential that resides within UTRs to render plants more resilient to a changing climate.

Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress

Translational control is important in all life, but it remains a challenge to accurately quantify. When ribosomes translate messenger (m)RNA into proteins, they attach to the mRNA in series, forming poly(ribo)somes, and can co-localize. Here, we computationally model new types of co-localized ribosomal complexes on mRNA and identify them using enhanced translation complex profile sequencing (eTCP-seq) based on rapid in vivo crosslinking. We detect long disome footprints outside regions of non-random elongation stalls and show these are linked to translation initiation and protein biosynthesis rates. We subject footprints of disomes and other translation complexes to artificial intelligence (AI) analysis and construct a new, accurate and self-normalized measure of translation, termed stochastic translation efficiency (STE). We then apply STE to investigate rapid changes to mRNA translation in yeast undergoing glucose depletion. Importantly, we show that, well beyond tagging elongation stalls, footprints of co-localized ribosomes provide rich insight into translational mechanisms, polysome dynamics and topology. STE AI ranks cellular mRNAs by absolute translation rates under given conditions, can assist in identifying its control elements and will facilitate the development of next-generation synthetic biology designs and mRNA-based therapeutics.

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.

RiboGraph: an interactive visualization system for ribosome profiling data at read length resolution

MOTIVATION

Ribosome profiling is a widely-used technique for measuring ribosome occupancy at nucleotide resolution. However, the need to analyze this data at nucleotide resolution introduces unique challenges in data visualization and analyses.

RESULTS

In this study, we introduce RiboGraph, a dedicated visualization tool designed to work with .ribo files, a specialized and efficient format for ribosome occupancy data. Unlike existing solutions that rely on large alignment files and time-consuming preprocessing steps, RiboGraph operates on a purpose designed compact file type. This efficiency allows for interactive, real-time visualization at ribosome-protected fragment length resolution. By providing an integrated toolset, RiboGraph empowers researchers to conduct comprehensive visual analysis of ribosome occupancy data.

AVAILABILITY AND IMPLEMENTATION

Source code, step-by-step installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling/ribograph. On the same page, we provide test files and a step-by-step tutorial highlighting the key features of RiboGraph.

Natural proteome diversity links aneuploidy tolerance to protein turnover

Accessing the natural genetic diversity of species unveils hidden genetic traits, clarifies gene functions and allows the generalizability of laboratory findings to be assessed. One notable discovery made in natural isolates of Saccharomyces cerevisiae is that aneuploidy-an imbalance in chromosome copy numbers-is frequent1,2 (around 20%), which seems to contradict the substantial fitness costs and transient nature of aneuploidy when it is engineered in the laboratory3-5. Here we generate a proteomic resource and merge it with genomic1 and transcriptomic6 data for 796 euploid and aneuploid natural isolates. We find that natural and lab-generated aneuploids differ specifically at the proteome. In lab-generated aneuploids, some proteins-especially subunits of protein complexes-show reduced expression, but the overall protein levels correspond to the aneuploid gene dosage. By contrast, in natural isolates, more than 70% of proteins encoded on aneuploid chromosomes are dosage compensated, and average protein levels are shifted towards the euploid state chromosome-wide. At the molecular level, we detect an induction of structural components of the proteasome, increased levels of ubiquitination, and reveal an interdependency of protein turnover rates and attenuation. Our study thus highlights the role of protein turnover in mediating aneuploidy tolerance, and shows the utility of exploiting the natural diversity of species to attain generalizable molecular insights into complex biological processes.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Translation variation across genetic backgrounds reveals a post-transcriptional buffering signature in yeast

Gene expression is known to vary among individuals, and this variability can impact the phenotypic diversity observed in natural populations. While the transcriptome and proteome have been extensively studied, little is known about the translation process itself. Here, we therefore performed ribosome and transcriptomic profiling on a genetically and ecologically diverse set of natural isolates of the Saccharomyces cerevisiae yeast. Interestingly, we found that the Euclidean distances between each profile and the expression fold changes in each pairwise isolate comparison were higher at the transcriptomic level. This observation clearly indicates that the transcriptional variation observed in the different isolates is buffered through a phenomenon known as post-transcriptional buffering at the translation level. Furthermore, this phenomenon seemed to have a specific signature by preferentially affecting essential genes as well as genes involved in complex-forming proteins, and low transcribed genes. We also explored the translation of the S. cerevisiae pangenome and found that the accessory genes related to introgression events displayed similar transcription and translation levels as the core genome. By contrast, genes acquired through horizontal gene transfer events tended to be less efficiently translated. Together, our results highlight both the extent and signature of the post-transcriptional buffering.

Dissecting the sequence and structural determinants guiding m6A deposition and evolution via inter- and intra-species hybrids

BACKGROUND

N6-methyladenosine (m6A) is the most abundant mRNA modification, and controls mRNA stability. m6A distribution varies considerably between and within species. Yet, it is unclear to what extent this variability is driven by changes in genetic sequences ('cis') or cellular environments ('trans') and via which mechanisms.

RESULTS

Here we dissect the determinants governing RNA methylation via interspecies and intraspecies hybrids in yeast and mammalian systems, coupled with massively parallel reporter assays and m6A-QTL reanalysis. We find that m6A evolution and variability is driven primarily in 'cis', via two mechanisms: (1) variations altering m6A consensus motifs, and (2) variation impacting mRNA secondary structure. We establish that mutations impacting RNA structure - even when distant from an m6A consensus motif - causally dictate methylation propensity. Finally, we demonstrate that allele-specific differences in m6A levels lead to allele-specific changes in gene expression.

CONCLUSIONS

Our findings define the determinants governing m6A evolution and diversity and characterize the consequences thereof on gene expression regulation.

Four classic "de novo" genes all have plausible homologs and likely evolved from retro-duplicated or pseudogenic sequences

Despite being previously regarded as extremely unlikely, the idea that entirely novel protein-coding genes can emerge from non-coding sequences has gradually become accepted over the past two decades. Examples of "de novo origination", resulting in lineage-specific "orphan" genes, lacking coding orthologs, are now produced every year. However, many are likely cases of duplicates that are difficult to recognize. Here, I re-examine the claims and show that four very well-known examples of genes alleged to have emerged completely "from scratch"- FLJ33706 in humans, Goddard in fruit flies, BSC4 in baker's yeast and AFGP2 in codfish-may have plausible evolutionary ancestors in pre-existing genes. The first two are likely highly diverged retrogenes coding for regulatory proteins that have been misidentified as orphans. The antifreeze glycoprotein, moreover, may not have evolved from repetitive non-genic sequences but, as in several other related cases, from an apolipoprotein that could have become pseudogenized before later being reactivated. These findings detract from various claims made about de novo gene birth and show there has been a tendency not to invest the necessary effort in searching for homologs outside of a very limited syntenic or phylostratigraphic methodology. A robust approach is used for improving detection that draws upon similarities, not just in terms of statistical sequence analysis, but also relating to biochemistry and function, to obviate notable failures to identify homologs.

Decoding the Heterogeneity and Specialized Function of Translation Machinery Through Ribosome Profiling in Yeast Mutants of Initiation Factors

The nuanced heterogeneity and specialized functions of translation machinery are increasingly recognized as crucial for precise translational regulation. Here, high-throughput ribosomal profiling (ribo-seq) is used to analyze the specialized roles of eukaryotic initiation factors (eIFs) in the budding yeast. By examining changes in ribosomal distribution across the genome resulting from knockouts of eIF4A, eIF4B, eIF4G1, CAF20, or EAP1, or knockdowns of eIF1, eIF1A, eIF4E, or PAB1, two distinct initiation-factor groups, the "looping" and "scanning" groups are discerned, based on similarities in the ribosomal landscapes their perturbation induced. The study delves into the cis-regulatory sequence features of genes influenced predominantly by each group, revealing that genes more dependent on the looping-group factors generally have shorter transcripts and poly(A) tails. In contrast, genes more dependent on the scanning-group factors often possess upstream open reading frames and exhibit a higher GC content in their 5' untranslated regions. From the ribosomal RNA fragments identified in the ribo-seq data, ribosomal heterogeneity associated with perturbation of specific initiation factors is further identified, suggesting their potential roles in regulating ribosomal components. Collectively, the study illuminates the complexity of translational regulation driven by heterogeneity and specialized functions of translation machinery, presenting potential approaches for targeted gene translation manipulation.

RiboGraph: An interactive visualization system for ribosome profiling data at read length resolution

Ribosome profiling is a widely-used technique for measuring ribosome occupancy at nucleotide resolution. However, the need to analyze this data at nucleotide resolution introduces unique challenges in data visualization and analyses. In this study, we introduce RiboGraph, a dedicated visualization tool designed to work with .ribo files, a specialized and efficient format for ribosome occupancy data. Unlike existing solutions that rely on large alignment files and time-consuming preprocessing steps, RiboGraph operates on a purpose designed compact file type and eliminates the need for data preprocessing. This efficiency allows for interactive, real-time visualization at ribosome-protected fragment length resolution. By providing an integrated toolset, RiboGraph empowers researchers to conduct comprehensive visual analysis of ribosome occupancy data.

Availability and Implementation

Source code, step-by-step installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling/ribograph. On the same page, we provide test files and a step-by-step tutorial highlighting the key features of RiboGraph.

Translatomics to explore dynamic differences in immunocytes in the tumor microenvironment

Protein is an essential component of all living organisms and is primarily responsible for life activities; furthermore, its synthesis depends on a highly complex and accurate translation system. For proteins, the regulation at the translation level exceeds the sum of that during transcription, mRNA degradation, and protein degradation. Therefore, it is necessary to study regulation at the translation level. Imbalance in the translation process may change the cellular landscape, which not only leads to the occurrence, maintenance, progression, invasion, and metastasis of cancer but also affects the function of immune cells and changes the tumor microenvironment. Detailed analysis of transcriptional and protein atlases is needed to better understand how gene translation occurs. However, a more rigorous direct correlation between mRNA and protein levels is needed, which somewhat limits further studies. Translatomics is a technique for capturing and sequencing ribosome-related mRNAs that can effectively identify translation changes caused by ribosome stagnation and local translation abnormalities during cancer occurrence to further understand the changes in the translation landscape of cancer cells themselves and immune cells in the tumor microenvironment, which can provide new strategies and directions for tumor treatment.

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk

Background

The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood.

Results

We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level.

Conclusion

The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Highlights

At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ( ρ = 0.165). While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscapeWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populationsLoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover.

On the Decoupling of Evolutionary Changes in mRNA and Protein Levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

Evolutionary trade-off and mutational bias could favor transcriptional over translational divergence within paralog pairs

How changes in the different steps of protein synthesis-transcription, translation and degradation-contribute to differences of protein abundance among genes is not fully understood. There is however accumulating evidence that transcriptional divergence might have a prominent role. Here, we show that yeast paralogous genes are more divergent in transcription than in translation. We explore two causal mechanisms for this predominance of transcriptional divergence: an evolutionary trade-off between the precision and economy of gene expression and a larger mutational target size for transcription. Performing simulations within a minimal model of post-duplication evolution, we find that both mechanisms are consistent with the observed divergence patterns. We also investigate how additional properties of the effects of mutations on gene expression, such as their asymmetry and correlation across levels of regulation, can shape the evolution of paralogs. Our results highlight the importance of fully characterizing the distributions of mutational effects on transcription and translation. They also show how general trade-offs in cellular processes and mutation bias can have far-reaching evolutionary impacts.

Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning

Upstream open-reading frames (uORFs) are potent cis-acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆upf1 yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on expression. Machine learning regression modeling revealed that both uORF sequences and locations within transcript leaders predict their effect on gene expression. Indeed, alternative transcription start sites highly influenced uORF activity. These results define the scope of natural uORF activity, identify features associated with translational repression and NMD, and suggest that the locations of uORFs in transcript leaders are nearly as predictive as uORF sequences.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Decoupling of evolutionary changes in mRNA and protein levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level, which is true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and its translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic studies.

Ribosome profiling reveals the translational landscape and allele-specific translational efficiency in rice

Translational regulation is a critical step in the process of gene expression and governs the synthesis of proteins from mRNAs. Many studies have revealed translational regulation in plants in response to various environmental stimuli. However, there have been no studies documenting the comprehensive landscape of translational regulation and allele-specific translational efficiency in multiple plant tissues, especially those of rice, a main staple crop that feeds nearly half of the world's population. Here we used RNA sequencing and ribosome profiling data to analyze the transcriptome and translatome of an elite hybrid rice, Shanyou 63 (SY63), and its parental varieties Zhenshan 97 and Minghui 63. The results revealed that gene expression patterns varied more among tissues than among varieties at the transcriptional and translational levels. We identified 3392 upstream open reading frames (uORFs), and the uORF-containing genes were enriched in transcription factors. Only 668 of 13 492 long non-coding RNAs could be translated into peptides. Finally, we discovered numerous genes with allele-specific translational efficiency in SY63 and demonstrated that some cis-regulatory elements may contribute to allelic divergence in translational efficiency. Overall, these findings may improve our understanding of translational regulation in rice and provide information for molecular breeding research.

A dynamical stochastic model of yeast translation across the cell cycle

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in S. cerevisiae in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

The missing link between genetic association and regulatory function

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'

The landscape of transcriptional and translational changes over 22 years of bacterial adaptation

Organisms can adapt to an environment by taking multiple mutational paths. This redundancy at the genetic level, where many mutations have similar phenotypic and fitness effects, can make untangling the molecular mechanisms of complex adaptations difficult. Here, we use the Escherichia coli long-term evolution experiment (LTEE) as a model to address this challenge. To understand how different genomic changes could lead to parallel fitness gains, we characterize the landscape of transcriptional and translational changes across 12 replicate populations evolving in parallel for 50,000 generations. By quantifying absolute changes in mRNA abundances, we show that not only do all evolved lines have more mRNAs but that this increase in mRNA abundance scales with cell size. We also find that despite few shared mutations at the genetic level, clones from replicate populations in the LTEE are remarkably similar in their gene expression patterns at both the transcriptional and translational levels. Furthermore, we show that the majority of the expression changes are due to changes at the transcriptional level with very few translational changes. Finally, we show how mutations in transcriptional regulators lead to consistent and parallel changes in the expression levels of downstream genes. These results deepen our understanding of the molecular mechanisms underlying complex adaptations and provide insights into the repeatability of evolution.

The Origins and Functions of De Novo Genes: Against All Odds?

"De novo" genes evolve from previously non-genic DNA. This strikes many of us as remarkable, because it seems extraordinarily unlikely that random sequence would produce a functional gene. How is this possible? In this two-part review, I first summarize what is known about the origins and molecular functions of the small number of de novo genes for which such information is available. I then speculate on what these examples may tell us about how de novo genes manage to emerge despite what seem like enormous opposing odds.

Development of a ribosome profiling protocol to study translation in Kluyveromyces marxianus

Kluyveromyces marxianus is an interesting and important yeast because of particular traits such as thermotolerance and rapid growth, and for applications in food and industrial biotechnology. For both understanding its biology and developing bioprocesses, it is important to understand how K. marxianus responds and adapts to changing environments. For this, a full suite of omics tools to measure and compare global patterns of gene expression and protein synthesis is needed. We report here the development of a ribosome profiling method for K. marxianus, which allows codon resolution of translation on a genome-wide scale by deep sequencing of ribosome locations on mRNAs. To aid in the analysis and sharing of ribosome profiling data, we added the K. marxianus genome as well as transcriptome and ribosome profiling data to the publicly accessible GWIPS-viz and Trips-Viz browsers. Users are able to upload custom ribosome profiling and RNA-Seq data to both browsers, therefore allowing easy analysis and sharing of data. We also provide a set of step-by-step protocols for the experimental and bioinformatic methods that we developed.

Heterosis of fitness and phenotypic variance in the evolution of a diploid gene regulatory network

Heterosis describes the phenomenon, whereby a hybrid population has higher fitness than an inbred population, which has previously been explained by either Mendelian dominance or overdominance under the general assumption of a simple genotype-phenotype relationship. However, recent studies have demonstrated that genes interact through a complex gene regulatory network (GRN). Furthermore, phenotypic variance is reportedly lower for heterozygotes, and the origin of such variance-related heterosis remains elusive. Therefore, a theoretical analysis linking heterosis to GRN evolution and stochastic gene expression dynamics is required. Here, we investigated heterosis related to fitness and phenotypic variance in a system with interacting genes by numerically evolving diploid GRNs. According to the results, the heterozygote population exhibited higher fitness than the homozygote population, indicating fitness-related heterosis resulting from evolution. In addition, the heterozygote population exhibited lower noise-related phenotypic variance in expression levels than the homozygous population, implying that the heterozygote population is more robust to noise. Furthermore, the distribution of the ratio of heterozygote phenotypic variance to homozygote phenotypic variance exhibited quantitative similarity with previous experimental results. By applying dominance and differential gene expression rather than only a single gene expression model, we confirmed the correlation between heterosis and differential gene expression. We explain our results by proposing that the convex high-fitness region is evolutionarily shaped in the genetic space to gain noise robustness under genetic mixing through sexual reproduction. These results provide new insights into the effects of GRNs on variance-related heterosis and differential gene expression.

Selection of Cashmere Fineness Functional Genes by Translatomics

Cashmere fineness is an important index to evaluate cashmere quality. Liaoning Cashmere Goat (LCG) has a large cashmere production and long cashmere fiber, but its fineness is not ideal. Therefore, it is important to find genes involved in cashmere fineness that can be used in future endeavors aiming to improve this phenotype. With the continuous advancement of research, the regulation of cashmere fineness has made new developments through high-throughput sequencing and genome-wide association analysis. It has been found that translatomics can identify genes associated with phenotypic traits. Through translatomic analysis, the skin tissue of LCG sample groups differing in cashmere fineness was sequenced by Ribo-seq. With these data, we identified 529 differentially expressed genes between the sample groups among the 27197 expressed genes. From these, 343 genes were upregulated in the fine LCG group in relation to the coarse LCG group, and 186 were downregulated in the same relationship. Through GO enrichment analysis and KEGG enrichment analysis of differential genes, the biological functions and pathways of differential genes can be found. In the GO enrichment analysis, 491 genes were significantly enriched, and the functional region was mainly in the extracellular region. In the KEGG enrichment analysis, the enrichment of the human papillomavirus infection pathway was seen the most. We found that the COL6A5 gene may affect cashmere fineness.

Toward improved terpenoids biosynthesis: strategies to enhance the capabilities of cell factories

Terpenoids form the most diversified class of natural products, which have gained application in the pharmaceutical, food, transportation, and fine and bulk chemical industries. Extraction from naturally occurring sources does not meet industrial demands, whereas chemical synthesis is often associated with poor enantio-selectivity, harsh working conditions, and environmental pollutions. Microbial cell factories come as a suitable replacement. However, designing efficient microbial platforms for isoprenoid synthesis is often a challenging task. This has to do with the cytotoxic effects of pathway intermediates and some end products, instability of expressed pathways, as well as high enzyme promiscuity. Also, the low enzymatic activity of some terpene synthases and prenyltransferases, and the lack of an efficient throughput system to screen improved high-performing strains are bottlenecks in strain development. Metabolic engineering and synthetic biology seek to overcome these issues through the provision of effective synthetic tools. This review sought to provide an in-depth description of novel strategies for improving cell factory performance. We focused on improving transcriptional and translational efficiencies through static and dynamic regulatory elements, enzyme engineering and high-throughput screening strategies, cellular function enhancement through chromosomal integration, metabolite tolerance, and modularization of pathways.

Genetic architecture of protein expression and its regulation in the mouse brain

BACKGROUND

Natural variation in protein expression is common in all organisms and contributes to phenotypic differences among individuals. While variation in gene expression at the transcript level has been extensively investigated, the genetic mechanisms underlying variation in protein expression have lagged considerably behind. Here we investigate genetic architecture of protein expression by profiling a deep mouse brain proteome of two inbred strains, C57BL/6 J (B6) and DBA/2 J (D2), and their reciprocal F1 hybrids using two-dimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) technology.

RESULTS

By comparing protein expression levels in the four mouse strains, we observed 329 statistically significant differentially expressed proteins between the two parental strains and characterized the genetic basis of protein expression. We further applied a proteogenomic approach to detect variant peptides and define protein allele-specific expression (pASE), identifying 33 variant peptides with cis-effects and 17 variant peptides showing trans-effects. Comparison of regulation at transcript and protein levels show a significant divergence.

CONCLUSIONS

The results provide a comprehensive analysis of genetic architecture of protein expression and the contribution of cis- and trans-acting regulatory differences to protein expression.

Determinants of efficient modulation of ribosomal traffic jams

mRNA translation is the process which consumes most of the cellular energy. Thus, this process is under strong evolutionary selection for its optimization and rational optimization or reduction of the translation efficiency can impact the cell growth rate. Algorithms for modulating cell growth rate can have various applications in biotechnology, medicine, and agriculture. In this study, we demonstrate that the analysis of these algorithms can also be used for understanding translation.

We specifically describe and analyze various generic algorithms, based on comprehensive computational models and whole cell simulations of translation, for introducing silent mutations that can either reduce or increase ribosomal traffic jams along the mRNA. As a result, more or less resources are available, for the cell, promoting improved or reduced cells growth-rate, respectively. We then explore the cost of these algorithms' performance, in terms of their computational time, the number of mutations they introduce, the modified genomic region, the effect on local translation rates, and the properties of the modified genes.

Among others, we show that mRNA levels of a gene are much stronger predictors for the effect of its engineering on the ribosomal pool than the ribosomal density of the gene. We also demonstrate that the mutations at the ends of the coding regions have a stronger effect on the ribosomal pool. Furthermore, we report two optimization algorithms that exhibit a tread-off between the number of mutations they introduce and their executing time.

The reported results here are fundamental both for understanding the biophysics and evolution of translation, as well as for developing efficient approaches for its engineering.

Osteochondral Repair and Electromechanical Evaluation of Custom 3D Scaffold Microstructured by Direct Laser Writing Lithography

OBJECTIVE

The objective of this study was to assess a novel 3D microstructured scaffold seeded with allogeneic chondrocytes (cells) in a rabbit osteochondral defect model.

DESIGN

Direct laser writing lithography in pre-polymers was employed to fabricate custom silicon-zirconium containing hybrid organic-inorganic (HOI) polymer SZ2080 scaffolds of a predefined morphology. Hexagon-pored HOI scaffolds were seeded with chondrocytes (cells), and tissue-engineered cartilage biocompatibility, potency, efficacy, and shelf-life in vitro was assessed by morphological, ELISA (enzyme-linked immunosorbent assay) and PCR (polymerase chain reaction) analysis. Osteochondral defect was created in the weight-bearing area of medial femoral condyle for in vivo study. Polymerized fibrin was added to every defect of 5 experimental groups. Cartilage repair was analyzed after 6 months using macroscopical (Oswestry Arthroscopy Score [OAS]), histological, and electromechanical quantitative potential (QP) scores. Collagen scaffold (CS) was used as a positive comparator for in vitro and in vivo studies.

RESULTS

Type II collagen gene upregulation and protein secretion was maintained up to 8 days in seeded HOI. In vivo analysis revealed improvement in all scaffold treatment groups. For the first time, electromechanical properties of a cellular-based scaffold were analyzed in a preclinical study. Cell addition did not enhance OAS but improved histological and QP scores in HOI groups.

CONCLUSIONS

HOI material is biocompatible for up to 8 days in vitro and is supportive of cartilage formation at 6 months in vivo. Electromechanical measurement offers a reliable quality assessment of repaired cartilage.

Proteome-wide Association Study Provides Insights Into the Genetic Component of Protein Abundance in Psychiatric Disorders

BACKGROUND

Genome-wide association studies have identified multiple risk variants for psychiatric disorders. Nevertheless, how the risk variants confer risk of psychiatric disorders remains largely unknown.

METHODS

We performed proteome-wide association studies to identify genes whose cis-regulated protein abundance change in the human brain were associated with psychiatric disorders.

RESULTS

By integrating genome-wide associations of four common psychiatric disorders and two independent brain proteomes (n = 376 and n = 152, respectively) from the dorsolateral prefrontal cortex, we identified 61 genes (including 48 genes for schizophrenia, 12 genes for bipolar disorder, 5 genes for depression, and 2 genes for attention-deficit/hyperactivity disorder) whose genetically regulated protein abundance levels were associated with risk of psychiatric disorders. Comparison with transcriptome-wide association studies identified 18 overlapping genes that showed significant associations with psychiatric disorders at both proteome-wide and transcriptome-wide levels, suggesting that genetic risk variants likely confer risk of psychiatric disorders by regulating messenger RNA expression and protein abundance of these genes.

CONCLUSIONS

Our study not only provides new insights into the genetic component of protein abundance in psychiatric disorders but also highlights several high-confidence risk proteins (including CNNM2 and CTNND1) for schizophrenia and depression. These high-confidence risk proteins represent promising therapeutic targets for future drug development.

Regulation of gene expression via translational buffering

Translation of an mRNA represents a critical step during the expression of protein-coding genes. As mechanisms governing post-transcriptional regulation of gene expression are progressively unveiled, it is becoming apparent that transcriptional programs are not fully reflected in the proteome. Herein, we highlight a previously underappreciated post-transcriptional mode of regulation of gene expression termed translational buffering. In principle, translational buffering opposes the impact of alterations in mRNA levels on the proteome. We further describe three types of translational buffering: compensation, which maintains protein levels e.g. across species or individuals; equilibration, which retains pathway stoichiometry; and offsetting, which acts as a reversible mechanism that maintains the levels of selected subsets of proteins constant despite genetic alteration and/or stress-induced changes in corresponding mRNA levels. While mechanisms underlying compensation and equilibration have been reviewed elsewhere, the principal focus of this review is on the less-well understood mechanism of translational offsetting. Finally, we discuss potential roles of translational buffering in homeostasis and disease.

Ribosome profiling elucidates differential gene expression in bundle sheath and mesophyll cells in maize

The efficiencies offered by C4 photosynthesis have motivated efforts to understand its biochemical, genetic, and developmental basis. Reactions underlying C4 traits in most C4 plants are partitioned between two cell types, bundle sheath (BS), and mesophyll (M) cells. RNA-seq has been used to catalog differential gene expression in BS and M cells in maize (Zea mays) and several other C4 species. However, the contribution of translational control to maintaining the distinct proteomes of BS and M cells has not been addressed. In this study, we used ribosome profiling and RNA-seq to describe translatomes, translational efficiencies, and microRNA abundance in BS- and M-enriched fractions of maize seedling leaves. A conservative interpretation of our data revealed 182 genes exhibiting cell type-dependent differences in translational efficiency, 31 of which encode proteins with core roles in C4 photosynthesis. Our results suggest that non-AUG start codons are used preferentially in upstream open reading frames of BS cells, revealed mRNA sequence motifs that correlate with cell type-dependent translation, and identified potential translational regulators that are differentially expressed. In addition, our data expand the set of genes known to be differentially expressed in BS and M cells, including genes encoding transcription factors and microRNAs. These data add to the resources for understanding the evolutionary and developmental basis of C4 photosynthesis and for its engineering into C3 crops.

Uncovering Divergence in Gene Expression Regulation in the Adaptation of Yeast to Nitrogen Scarcity

Saccharomyces cerevisiae rewires its transcriptional output to survive stressful environments, such as nitrogen scarcity under fermentative conditions. Although divergence in nitrogen metabolism among natural yeast populations has been reported, the impact of regulatory genetic variants modulating gene expression and nitrogen consumption remains to be investigated. Here, we employed an F1 hybrid from two contrasting S. cerevisiae strains, providing a controlled genetic environment to map cis factors involved in the divergence of gene expression regulation in response to nitrogen scarcity. We used a dual approach to obtain genome-wide allele-specific profiles of chromatin accessibility, transcription factor binding, and gene expression through ATAC-seq (assay for transposase accessible chromatin) and RNA-seq (transcriptome sequencing). We observed large variability in allele-specific expression and accessibility between the two genetic backgrounds, with a third of these differences specific to a deficient nitrogen environment. Furthermore, we discovered events of allelic bias in gene expression correlating with allelic bias in transcription factor binding solely under nitrogen scarcity, where the majority of these transcription factors orchestrates the nitrogen catabolite repression regulatory pathway and demonstrates a cis × environment-specific response. Our approach allowed us to find cis variants modulating gene expression, chromatin accessibility, and allelic differences in transcription factor binding in response to low nitrogen culture conditions. IMPORTANCE Historically, coding variants were prioritized when searching for causal mechanisms driving adaptation of natural populations to stressful environments. However, the recent focus on noncoding variants demonstrated their ubiquitous role in adaptation. Here, we performed genome-wide regulatory variation profiles between two divergent yeast strains when facing nitrogen nutritional stress. The open chromatin availability of several regulatory regions changes in response to nitrogen scarcity. Importantly, we describe regulatory events that deviate between strains. Our results demonstrate a widespread variation in gene expression regulation between naturally occurring populations in response to stressful environments.

Testing the Genomic Shock Hypothesis Using Transposable Element Expression in Yeast Hybrids

Transposable element (TE) insertions are a source of structural variation and can cause genetic instability and gene expression changes. A host can limit the spread of TEs with various repression mechanisms. Many examples of plant and animal interspecific hybrids show disrupted TE repression leading to TE propagation. Recent studies in yeast did not find any increase in transposition rate in hybrids. However, this does not rule out the possibility that the transcriptional or translational activity of TEs increases following hybridization because of a disruption of the host TE control mechanisms. Thus, whether total expression of a TE family is higher in hybrids than in their parental species remains to be examined. We leveraged publically available RNA-seq and ribosomal profiling data on yeast artificial hybrids of the Saccharomyces genus and performed differential expression analysis of their LTR retrotransposons (Ty elements). Our analyses of total mRNA levels show that Ty elements are generally not differentially expressed in hybrids, even when the hybrids are exposed to a low temperature stress condition. Overall, only 2/26 Ty families show significantly higher expression in the S. cerevisiae × S. uvarum hybrids while there are 3/26 showing significantly lower expression in the S. cerevisiae x S. paradoxus hybrids. Our analysis of ribosome profiling data of S. cerevisiae × S. paradoxus hybrids shows similar translation efficiency of Ty in both parents and hybrids, except for Ty1_cer showing higher translation efficiency. Overall, our results do not support the hypothesis that hybridization could act as a systematic trigger of TE expression in yeast and suggest that the impact of hybridization on TE activity is strain and TE specific.

Genome-Scale Analysis of Acetobacterium woodii Identifies Translational Regulation of Acetogenesis

Acetogens synthesize acetyl-CoA via the CO₂-fixing Wood-Ljungdahl pathway. Despite their ecological and biotechnological importance, their translational regulation of carbon and energy metabolisms remains unclear. Here, we report how carbon and energy metabolisms in the model acetogen Acetobacterium woodii are translationally controlled under different growth conditions. Data integration of genome-scale transcriptomic and translatomic analyses revealed that the acetogenesis genes, including those of the Wood-Ljungdahl pathway and energy metabolism, showed changes in translational efficiency under autotrophic growth conditions. In particular, genes encoding the Wood-Ljungdahl pathway are translated at similar levels to achieve efficient acetogenesis activity under autotrophic growth conditions, whereas genes encoding the carbonyl branch present increased translation levels in comparison to those for the methyl branch under heterotrophic growth conditions. The translation efficiency of genes in the pathways is differentially regulated by 5′ untranslated regions and ribosome-binding sequences under different growth conditions. Our findings provide potential strategies to optimize the metabolism of syngas-fermenting acetogenic bacteria for better productivity.

IMPORTANCE Acetogens are capable of reducing CO₂ to multicarbon compounds (e.g., ethanol or 2,3-butanediol) via the Wood-Ljungdahl pathway. Given that protein synthesis in bacteria is highly energy consuming, acetogens living at the thermodynamic limit of life are inevitably under translation control. Here, we dissect the translational regulation of carbon and energy metabolisms in the model acetogen Acetobacterium woodii under heterotrophic and autotrophic growth conditions. The latter may be experienced when acetogen is used as a cell factory that synthesizes products from CO₂ during the gas fermentation process. We found that the methyl and carbonyl branches of the Wood-Ljungdahl pathway are activated at similar translation levels during autotrophic growth. Translation is mainly regulated by the 5′-untranslated-region structure and ribosome-binding-site sequence. This work reveals novel translational regulation for coping with autotrophic growth conditions and provides the systematic data set, including the transcriptome, translatome, and promoter/5′-untranslated-region bioparts.

Frequent Assembly of Chimeric Complexes in the Protein Interaction Network of an Interspecies Yeast Hybrid

Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein–protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein–protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein–protein interactions in the hybrid, for instance, in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteomes.

Codon-based indices for modeling gene expression and transcript evolution

Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related and can affect all gene expression steps. Starting from the 1980s, codon-based indices have been used for answering different questions in all biomedical fields, including systems biology, agriculture, medicine, and biotechnology. In general, codon usage bias indices weigh each codon or a small set of codons to estimate the fitting of a certain coding sequence to a certain phenomenon (e.g., bias in codons, adaptation to the tRNA pool, frequencies of certain codons, transcription elongation speed, etc.) and are usually easy to implement. Today there are dozens of such indices; thus, this paper aims to review and compare the different codon usage bias indices, their applications, and advantages. In addition, we perform analysis that demonstrates that most indices tend to correlate even though they aim to capture different aspects. Due to the centrality of codon usage bias on different gene expression steps, it is important to keep developing new indices that can capture additional aspects that are not modeled with the current indices.

Reduced Translational Efficiency of Eukaryotic Genes after Duplication Events

Control of gene expression has been found to be predominantly determined at the level of protein translation. However, to date, reduced expression from duplicated genes in eukaryotes for dosage maintenance has only been linked to transcriptional control involving epigenetic mechanisms. Here, we hypothesize that dosage maintenance following gene duplication also involves regulation at the protein level. To test this hypothesis, we compared transcriptome and proteome data of yeast models, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and worm models, Caenorhabditis elegans and Caenorhabditis briggsae, to investigate lineage-specifically duplicated genes. Duplicated genes in both eukaryotic models exhibited a reduced protein-to-mRNA abundance ratio. Moreover, dosage sensitive genes, represented by genes encoding protein complex subunits, reduced their protein-to-mRNA abundance ratios more significantly than the other genes after duplication events. An analysis of ribosome profiling (Ribo-Seq) data further showed that reduced translational efficiency was more prominent for dosage sensitive genes than for the other genes. Meanwhile, no difference in protein degradation rate was associated with duplication events. Translationally repressed duplicated genes were also more likely to be inhibited at the level of transcription. Taken together, these results suggest that translation-mediated dosage control is partially contributed by natural selection and it enhances transcriptional control in maintaining gene dosage after gene duplication events during eukaryotic genome evolution.

Determinants of genome-wide distribution and evolution of uORFs in eukaryotes

Upstream open reading frames (uORFs) play widespread regulatory functions in modulating mRNA translation in eukaryotes, but the principles underlying the genomic distribution and evolution of uORFs remain poorly understood. Here, we analyze ~17 million putative canonical uORFs in 478 eukaryotic species that span most of the extant taxa of eukaryotes. We demonstrate how positive and purifying selection, coupled with differences in effective population size (Ne), has shaped the contents of uORFs in eukaryotes. Besides, gene expression level is important in influencing uORF occurrences across genes in a species. Our analyses suggest that most uORFs might play regulatory roles rather than encode functional peptides. We also show that the Kozak sequence context of uORFs has evolved across eukaryotic clades, and that noncanonical uORFs tend to have weaker suppressive effects than canonical uORFs in translation regulation. This study provides insights into the driving forces underlying uORF evolution in eukaryotes.