Central dogma rates and the trade-off between precision and economy in gene expression

Molecular correlates of childhood adversity - a multi-omics perspective on stress regulation

The experience of adversity in childhood can have life-long consequences on health outcomes. In search of mediators of this relationship, alterations of bio-behavioral and cellular regulatory systems came into focus, including those dealing with basic gene regulatory processes. System biology oriented approaches have been proposed to gain a more comprehensive understanding of the complex multiple interrelations between and within layers of analysis. Here, we used co-expression based, supervised and unsupervised single and multi-omics systems approaches to investigate the association between childhood adversity and gene expression, protein expression and DNA methylation in CD14+ monocytes in the context of psychosocial stress exposure, in a sample of healthy adults with (n = 29) or without (n = 27) a history of childhood adversity. Childhood adversity explained some variance at the single analyte level and within gene and protein co-expression structures. A single-omics, post-stress gene expression model differentiated best between participants with a history of childhood adversity and control participants in supervised analyses. In unsupervised analyses, a multi-omics based model showed best performance but separated participants based on sex only. Multi-omics analyses are a promising concept but might yield different results based on the specific approach taken and the omics-datasets supplied. We found that stress associated gene-expression pattern were most strongly associated with childhood adversity, and integrating multiple cellular layers did not results in better discriminatory performance in our rather small sample. The capacity and yield of different omics-profiling methods might currently limit the full potential of integrative approaches.

The hidden costs of imperfection: transcription errors in protein aggregation diseases

At first glance, biological systems appear to operate with remarkable precision and order. Yet, closer examination reveals that this perfection is an illusion, biological processes are inherently prone to errors. Here, we describe recent evidence that indicates that errors that occur during transcription play an important role in neurological diseases. These errors, though transient, can have lasting consequences when they generate mutant proteins with amyloid or prion-like properties. Such proteins can seed aggregation cascades, converting wild-type counterparts into misfolded conformations, ultimately leading to toxic deposits seen in diseases like Alzheimer's and amyotrophic lateral sclerosis. These observations help to paint a fuller picture of the origins of neurodegenerative diseases in aging humans and suggest a unified mechanism by which they may arise.

Female-biased vascular smooth muscle cell gene regulatory networks predict MYH9 as a key regulator of fibrous plaque phenotype

Atherosclerosis, a chronic inflammatory condition driving coronary artery disease (CAD), manifests in two primary plaque types: unstable atheromatous plaques and stable fibrous plaques. While significant research has focused on atheromatous plaques, recent studies emphasize the growing importance of fibrous plaques, particularly in females under 50 years of age, where erosion on fibrous plaques significantly contributes to coronary thrombosis. The molecular mechanisms underlying sex differences in atherosclerotic plaque characteristics, including vascular smooth muscle cell (VSMC) contributions, remain understudied. Therefore, we utilized sex-specific gene regulatory networks (GRNs) derived from VSMC gene expression data from 119 male and 32 female heart transplant donors to identify molecular drivers of fibrous plaques. GRN analysis revealed two female-biased networks in VSMC, GRN floralwhite and GRN yellowgreen , enriched for inflammatory signaling and actin remodeling pathways, respectively. Single-cell RNA sequencing of carotid plaques from female and male patients confirmed the sex specificity of these networks in VSMCs. Further sub cellular phenotyping of the single-cell RNA sequencing revealed a sex-specific gene expression signature within GRN yellowgreen for VSMCs enriched for contractile and vasculature development pathways. Bayesian network modeling of the GRN yellowgreen identified MYH9 as a key driver gene. Indeed, elevated MYH9 protein expression in atherosclerotic plaques was associated with higher smooth muscle cell content and lower lipid content in female plaques, suggesting its involvement in fibrous plaque formation. Further proteomic analysis confirmed MYH9's upregulation in female fibrous plaques only and its correlation with stable plaque features. These findings provide novel insights into sex-specific molecular mechanisms regulating fibrous plaque formation.

Macroevolutionary divergence of gene expression driven by selection on protein abundance

The regulation of messenger RNA (mRNA) and protein abundances is well-studied, but less is known about the evolutionary processes shaping their relationship. To address this, we derived a new phylogenetic model and applied it to multispecies mammalian data. Our analyses reveal (i) strong stabilizing selection on protein abundances over macroevolutionary time, (ii) mutations affecting mRNA abundances minimally impact protein abundances, (iii) mRNA abundances evolve under selection to align with protein abundances, and (iv) mRNA abundances adapt faster than protein abundances owing to greater mutational opportunity. These conclusions are supported by comparisons of model parameters with independent functional genomic data. By decomposing mutational and selective influences on mRNA-protein dynamics, our approach provides a framework for discovering the evolutionary rules that drive divergence in gene expression.

Mechanistic computational modeling of sFLT1 secretion dynamics

Constitutively secreted by endothelial cells, soluble FLT1 (sFLT1 or sVEGFR1) binds and sequesters extracellular vascular endothelial growth factors (VEGF), thereby reducing VEGF binding to VEGF receptor tyrosine kinases and their downstream signaling. In doing so, sFLT1 plays an important role in vascular development and in the patterning of new blood vessels in angiogenesis. Here, we develop multiple mechanistic models of sFLT1 secretion and identify a minimal mechanistic model that recapitulates key qualitative and quantitative features of temporal experimental datasets of sFLT1 secretion from multiple studies. We show that the experimental data on sFLT1 secretion is best represented by a delay differential equation (DDE) system including a maturation term, reflecting the time required between synthesis and secretion. Using optimization to identify appropriate values for the key mechanistic parameters in the model, we show that two model parameters (extracellular degradation rate constant and maturation time) are very strongly constrained by the experimental data, and that the remaining parameters are related by two strongly constrained constants. Thus, only one degree of freedom remains, and measurements of the intracellular levels of sFLT1 would fix the remaining parameters. Comparison between simulation predictions and additional experimental data of the outcomes of chemical inhibitors and genetic perturbations suggest that intermediate values of the secretion rate constant best match the simulation with experiments, which would completely constrain the model. However, some of the inhibitors tested produce results that cannot be reproduced by the model simulations, suggesting that additional mechanisms not included here are required to explain those inhibitors. Overall, the model reproduces most available experimental data and suggests targets for further quantitative investigation of the sFLT1 system.

How energy determines spatial localisation and copy number of molecules in neurons

In neurons, the quantities of mRNAs and proteins are traditionally assumed to be determined by functional, electrical or genetic factors. Yet, there may also be global, currently unknown computational rules that are valid across different molecular species inside a cell. Surprisingly, our results show that the energy for molecular turnover is a significant cellular expense, en par with spiking cost, and which requires energy-saving strategies. We show that the drive to save energy determines transcript quantities and their location while acting differently on each molecular species depending on the length, longevity and other features of the respective molecule. We combined our own data and experimental reports from five other large-scale mRNA and proteomics screens, comprising more than ten thousand molecular species to reveal the underlying computational principles of molecular localisation. We found that energy minimisation principles explain experimentally-reported exponential rank distributions of mRNA and protein copy numbers. Our results further reveal robust energy benefits when certain mRNA classes are moved into dendrites, for example mRNAs of proteins with long amino acid chains or mRNAs with large non-coding regions and long half-lives proving surprising insights at the level of molecular populations.

Optimisation of gene expression noise for cellular persistence against lethal events

Bacterial cell persistence, crucial for survival under adverse conditions like antibiotic exposure, is intrinsically linked to stochastic fluctuations in gene expression. Certain genes, while inhibiting growth under normal circumstances, confer tolerance to antibiotics at elevated expression levels. The occurrence of antibiotic events lead to instantaneous cellular responses with varied survival probabilities correlated with gene expression levels. Notably, cells with lower protein concentrations face higher mortality rates. This study aims to elucidate an optimal strategy for protein expression conducive to cellular survival. Through comprehensive mathematical analysis, we determine the optimal burst size and frequency that maximise cell proliferation. Furthermore, we explore how the optimal expression distribution changes as the cost of protein expression to growth escalates. Our model reveals a hysteresis phenomenon, characterised by discontinuous transitions between deterministic and stochastic optima. Intriguingly, stochastic optima possess a noise floor, representing the minimal level of fluctuations essential for optimal cellular resilience.

Structural determinants of co-translational protein complex assembly

Protein assembly into functional complexes is critical to life's processes. While complex assembly is classically described as occurring between fully synthesized proteins, recent work showed that co-translational assembly is prevalent in human cells. However, the biological basis for the existence of this process and the identity of protein pairs that assemble co-translationally remain unknown. We show that co-translational assembly is governed by structural characteristics of complexes and involves mutually stabilized subunits. Accordingly, co-translationally assembling subunits are unstable in isolation and exhibit synchronized proteostasis with their partner. By leveraging structural signatures and AlphaFold2-based predictions, we accurately predicted co-translational assembly, including pair identities, at proteome scale and across species. We validated our predictions by ribosome profiling, stoichiometry perturbations, and single-molecule RNA-fluorescence in situ hybridization (smFISH) experiments that revealed co-localized mRNAs. This work establishes a fundamental connection between protein structure and the translation process, highlighting the overarching impact of three-dimensional structure on gene expression, mRNA localization, and proteostasis.

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.

Deciphering the role of LOC124905135-related non-coding RNA cluster in human cancers: A comprehensive review

Non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and long ncRNAs (lncRNAs), are essential regulators of processes, such as the cell cycle and apoptosis. In addition to interacting with intracellular complexes and participating in diverse molecular pathways, ncRNAs can be used as clinical diagnostic biomarkers and therapeutic targets for fighting cancer. Studying ncRNA gene clusters is crucial for understanding their role in cancer and developing new treatments. LOC124905135 is a protein-coding gene encoding a collagen alpha-1(III) chain-like protein, and also acts as a gene for several ncRNAs, including miR-3619, PRR34 antisense RNA 1 (PRR34-AS1), PRR34, long intergenic ncRNA 2939 (LINC02939), LOC112268288, and MIRLET7BHG. It also serves as a host gene for three miRNAs (hsa-let7-A3, hsa-miR-4763, and hsa-let-7b). Notably, the ncRNAs derived from this particular genomic region significantly affect various cell functions, including the cell cycle and apoptosis. This cluster of ncRNAs is dysregulated in several types of cancer, exhibiting mutations, alterations in copy number, and being subject to DNA methylation and histone modification. In summary, the ncRNAs derived from the LOC124905135 cluster could be used as targets for diagnosis, therapy monitoring, and drug discovery in human cancers.

Sequestration of gene products by decoys enhances precision in the timing of intracellular events

Expressed gene products often interact ubiquitously with binding sites at nucleic acids and macromolecular complexes, known as decoys. The binding of transcription factors (TFs) to decoys can be crucial in controlling the stochastic dynamics of gene expression. Here, we explore the impact of decoys on the timing of intracellular events, as captured by the time taken for the levels of a given TF to reach a critical threshold level, known as the first passage time (FPT). Although nonlinearity introduced by binding makes exact mathematical analysis challenging, employing suitable approximations and reformulating FPT in terms of an alternative variable, we analytically assess the impact of decoys. The stability of the decoy-bound TFs against degradation impacts FPT statistics crucially. Decoys reduce noise in FPT, and stable decoy-bound TFs offer greater timing precision with less expression cost than their unstable counterparts. Interestingly, when both bound and free TFs decay at the same rate, decoy binding does not directly alter FPT noise. We verify these results by performing exact stochastic simulations. These results have important implications for the precise temporal scheduling of events involved in the functioning of biomolecular clocks, development processes, cell-cycle control, and cell-size homeostasis.

Link Between Individual Codon Frequencies and Protein Expression: Going Beyond Codon Adaptation Index

An important role of a particular synonymous codon composition of a gene in its expression level is well known. There are a number of algorithms optimizing codon usage of recombinant genes to maximize their expression in host cells. Nevertheless, the underlying mechanism remains unsolved and is of significant relevance. In the realm of modern biotechnology, directing protein production to a specific level is crucial for metabolic engineering, genome rewriting and a growing number of other applications. In this study, we propose two new simple statistical and empirical methods for predicting the protein expression level from the nucleotide sequence of the corresponding gene: Codon Expression Index Score (CEIS) and Codon Productivity Score (CPS). Both of these methods are based on the influence of each individual codon in the gene on the overall expression level of the encoded protein and the frequencies of isoacceptors in the species. Our predictions achieve a correlation level of up to r = 0.7 with experimentally measured quantitative proteome data of Escherichia coli, which is superior to any previously proposed methods. Our work helps understand how codons determine protein abundances. Based on these methods, it is possible to design proteins optimized for expression in a particular organism.

Transcript errors generate amyloid-like proteins in huwman cells

Aging is characterized by the accumulation of proteins that display amyloid-like behavior. However, the molecular mechanisms by which these proteins arise remain unclear. Here, we demonstrate that amyloid-like proteins are produced in a variety of human cell types, including stem cells, brain organoids and fully differentiated neurons by mistakes that occur in messenger RNA molecules. Some of these mistakes generate mutant proteins already known to cause disease, while others generate proteins that have not been observed before. Moreover, we show that these mistakes increase when cells are exposed to DNA damage, a major hallmark of human aging. When taken together, these experiments suggest a mechanistic link between the normal aging process and age-related diseases.

Unveiling the role of transgelin as a prognostic and therapeutic target in kidney fibrosis via a proteomic approach

Chronic kidney disease (CKD) progression involves tubulointerstitial fibrosis, a process characterized by excessive extracellular matrix accumulation. To identify potential biomarkers for kidney fibrosis, we performed mass spectrometry-based proteomic profiling of human kidney tubular epithelial cells and kidney tissue from a 5/6 nephrectomy rat model. Multidisciplinary analysis across kidney fibrosis models revealed 351 differentially expressed proteins associated with kidney fibrosis, and they were enriched in processes related to the extracellular matrix, kidney aging, and mitochondrial functions. Network analysis of the selected proteins revealed five crucial proteins, of which transgelin emerged as a candidate protein that interacts with known fibrosis-related proteins. Concordantly, the gene expression of transgelin in the kidney tissue from the 5/6 nephrectomy model was elevated. Transgelin expression in kidney tissue gradually increased from intermediate to advanced fibrosis stages in 5/6 Nx rats and mice with unilateral ureteral obstruction. Subsequent validation in kidney tissue and urine samples from patients with CKD confirmed the upregulation of transgelin, particularly under advanced disease stages. Moreover, we investigated whether blocking TAGLN ameliorated kidney fibrosis and reduced reactive oxygen species levels in cellular models. In conclusion, our proteomic approach identified TAGLN as a potential noninvasive biomarker and therapeutic target for CKD-associated kidney fibrosis, suggesting its role in modulating mitochondrial dysfunction and oxidative stress responses.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Protein overabundance is driven by growth robustness

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

Transcription Attenuation in Synthetic Promoters in Nonoverlapping Tandem Formation

Closely spaced promoters are ubiquitous in prokaryotic and eukaryotic genomes. How their structure and dynamics relate remains unclear, particularly for tandem formations. To study their transcriptional interference, we engineered two pairs and one trio of synthetic promoters in nonoverlapping, tandem formation, in single-copy plasmids transformed into Escherichia coli cells. From in vivo measurements, we found that these promoters in tandem formation can have attenuated transcription rates. The attenuation strength can be widely fine-tuned by the promoters' positioning, natural regulatory mechanisms, and other factors, including the antibiotic rifampicin, which is known to hamper RNAP promoter escape. From this, and supported by in silico models, we concluded that the attenuation in these constructs emerges from premature terminations generated by collisions between RNAPs elongating from upstream promoters and RNAPs occupying downstream promoters. Moreover, we found that these collisions can cause one or both RNAPs to falloff. Finally, the broad spectrum of possible, externally regulated, attenuation strengths observed in our synthetic tandem promoters suggests that they could become useful as externally controllable regulators of future synthetic circuits.

Protein overabundance is driven by growth robustness

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Codon usage bias in yeasts and its correlation with gene expression, growth temperature, and protein structure

Codon usage bias (CUB) has been described in viruses, prokaryotes, and eukaryotes and has been linked to several cellular and environmental factors, such as the organism's growth temperature, gene expression levels, and regulation of protein synthesis and folding. Most of the studies in this area have been conducted in bacteria and higher eukaryotes, in some cases with different results. In this study, a comparative analysis of CUB in yeasts isolated from cold and template environments was performed in order to evaluate the correlation of CUB with yeast optimal temperature of growth (OTG), gene expression levels, cellular function, and structure of encoded proteins. Among the main findings, highly expressed ORFs tend to have a more similar CUB within and between yeasts, and a direct correlation between codons ending in C and expression level was generally found. A low correspondence between CUB and OTG was observed, with an inverse correlation for some codons ending in C. The clustering of yeasts based on their CUB partially aligns with their OTG, being more consistent for yeasts with lower OTG. In most yeasts, the abundance of preferred codons was generally lower at the 5' end of ORFs, higher in segments encoding beta strand, lower in segments encoding extracellular and transmembrane regions, and higher in "translation" and "energy metabolism" pathways, especially in highly expressed ORFs. Based on our findings, it is suggested that the abundance and distribution of preferred and non-preferred codons along mRNAs contribute to proper protein folding and functionality by regulating protein synthesis rates, becoming a more important factor under conditions that require faster protein synthesis in yeasts.

A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM

Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.

Gene activity fully predicts transcriptional bursting dynamics

Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict individual alleles' average ON and OFF periods. Lowly transcribing alleles predominantly modulate OFF periods (burst frequency), while highly transcribing alleles primarily tune ON periods (burst size). These relationships persist even under perturbations of cis-regulatory elements or trans-factors and account for bursting dynamics measured in other species. Our results suggest a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes.

Modeling 0.6 million genes for the rational design of functional cis-regulatory variants and de novo design of cis-regulatory sequences

Rational design of plant cis-regulatory DNA sequences without expert intervention or prior domain knowledge is still a daunting task. Here, we developed PhytoExpr, a deep learning framework capable of predicting both mRNA abundance and plant species using the proximal regulatory sequence as the sole input. PhytoExpr was trained over 17 species representative of major clades of the plant kingdom to enhance its generalizability. Via input perturbation, quantitative functional annotation of the input sequence was achieved at single-nucleotide resolution, revealing an abundance of predicted high-impact nucleotides in conserved noncoding sequences and transcription factor binding sites. Evaluation of maize HapMap3 single-nucleotide polymorphisms (SNPs) by PhytoExpr demonstrates an enrichment of predicted high-impact SNPs in cis-eQTL. Additionally, we provided two algorithms that harnessed the power of PhytoExpr in designing functional cis-regulatory variants, and de novo creation of species-specific cis-regulatory sequences through in silico evolution of random DNA sequences. Our model represents a general and robust approach for functional variant discovery in population genetics and rational design of regulatory sequences for genome editing and synthetic biology.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

Central dogma rates in human mitochondria

In human cells, the nuclear and mitochondrial genomes engage in a complex interplay to produce dual-encoded oxidative phosphorylation (OXPHOS) complexes. The coordination of these dynamic gene expression processes is essential for producing matched amounts of OXPHOS protein subunits. This review focuses on our current understanding of the mitochondrial central dogma rates, highlighting the striking differences in gene expression rates between mitochondrial and nuclear genes. We synthesize a coherent model of mitochondrial gene expression kinetics, highlighting the emerging principles and emphasizing where more precise measurements would be beneficial. Such an understanding is pivotal for grasping the unique aspects of mitochondrial function and its role in cellular energetics, and it has profound implications for aging, metabolic disorders, and neurodegenerative diseases.

Pinobanksin ameliorated DSS-induced acute colitis mainly through modulation of SLC7A11/glutathione-mediated intestinal epithelial ferroptosis

Inhibition of ferroptosis in intestinal epithelial cells serves as an attractive target for the development of therapeutic strategies for colitis. Pinobanksin, one of the main flavonoids derived from propolis, possesses significant anti-inflammatory effects and inhibits the cell death of several cell lines. Here, we evaluated whether pinobanksin influenced colitis by modulation of epithelial ferroptosis. Mice treated with 2.5% DSS dissolved in sterile distilled water were established for an acute colitis model. The mitochondrial morphology, colonic iron level, lipid peroxidation products MDA/4-HNE, and lipid reactive oxygen species levels were measured to assess ferroptosis in epithelial cells. RNA-seq and functional analyses were performed to reveal key genes mediating pinobanksin-exerted modulation of ferroptosis. We found that pinobanksin, at different doses, induced significant anti-colitis effects and inhibited the elevated ferroptosis in colonic epithelial cells isolated from DSS-treated mice largely by activating GPX4 (negative regulator of ferroptosis). Furthermore, RNA-seq assays indicated that pinobanksin significantly increased the cystine transporter SLC7A11 in colonic tissues from mice with colitis. Depletion of SLC7A11 largely blocked pinobanksin-induced promotion of cystine uptake/glutathione biosynthesis and suppression of ferroptosis in epithelial cells from mice with colitis or IEC-6 cells pretreated with RSL3. Altogether, pinobanksin alleviated DSS-induced colitis largely by inhibition of ferroptosis in epithelial cells. Activation of SLC7A11 by pinobanksin resulted in the promotion of cystine uptake and enhancement of glutathione biosynthesis. This work will provide novel guidance for the clinical use of pinobanksin to treat colitis through inhibition of epithelial ferroptosis.

Mutational biases favor complexity increases in protein interaction networks after gene duplication

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

Quantitative proteome dynamics across embryogenesis in a model chordate

The evolution of gene expression programs underlying the development of vertebrates remains poorly characterized. Here, we present a comprehensive proteome atlas of the model chordate Ciona, covering eight developmental stages and ∼7,000 translated genes, accompanied by a multi-omics analysis of co-evolution with the vertebrate Xenopus. Quantitative proteome comparisons argue against the widely held hourglass model, based solely on transcriptomic profiles, whereby peak conservation is observed during mid-developmental stages. Our analysis reveals maximal divergence at these stages, particularly gastrulation and neurulation. Together, our work provides a valuable resource for evaluating conservation and divergence of multi-omics profiles underlying the diversification of vertebrates.

A kinetic dichotomy between mitochondrial and nuclear gene expression processes

Oxidative phosphorylation (OXPHOS) complexes, encoded by both mitochondrial and nuclear DNA, are essential producers of cellular ATP, but how nuclear and mitochondrial gene expression steps are coordinated to achieve balanced OXPHOS subunit biogenesis remains unresolved. Here, we present a parallel quantitative analysis of the human nuclear and mitochondrial messenger RNA (mt-mRNA) life cycles, including transcript production, processing, ribosome association, and degradation. The kinetic rates of nearly every stage of gene expression differed starkly across compartments. Compared with nuclear mRNAs, mt-mRNAs were produced 1,100-fold more, degraded 7-fold faster, and accumulated to 160-fold higher levels. Quantitative modeling and depletion of mitochondrial factors LRPPRC and FASTKD5 identified critical points of mitochondrial regulatory control, revealing that the mitonuclear expression disparities intrinsically arise from the highly polycistronic nature of human mitochondrial pre-mRNA. We propose that resolving these differences requires a 100-fold slower mitochondrial translation rate, illuminating the mitoribosome as a nexus of mitonuclear co-regulation.

Mitigating transcription noise via protein sharing in syncytial cells

Bursty transcription allows nuclei to concentrate the work of transcribing mRNA into short, intermittent intervals, potentially reducing transcriptional interference. However, bursts of mRNA production can increase noise in protein abundances. Here, we formulate models for gene expression in syncytia, or multinucleate cells, showing that protein abundance noise may be mitigated locally via spatial averaging of diffuse proteins. Our modeling shows a universal reduction in protein noise, which increases with the average number of nuclei per cell and persists even when the number of nuclei is itself a random variable. Experimental data comparing distributions of a cyclin mRNA that is conserved between brewer's yeast and a closely related filamentous fungus Ashbya gossypii confirm that syncytism is permissive of greater levels of transcriptional noise. Our findings suggest that division of transcriptional labor between nuclei allows syncytia to sidestep tradeoffs between efficiency and precision of gene expression.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Decoding semiotic minimal genome: a non-genocentric approach

The search for the minimum information required for an organism to sustain a cellular system network has rendered both the identification of a fixed number of known genes and those genes whose function remains to be identified. The approaches used in such search generally focus their analysis on coding genomic regions, based on the genome to proteic-product perspective. Such approaches leave other fundamental processes aside, mainly those that include higher-level information management. To cope with this limitation, a non-genocentric approach based on genomic sequence analysis using language processing tools and gene ontology may prove an effective strategy for the identification of those fundamental genomic elements for life autonomy. Additionally, this approach will provide us with an integrative analysis of the information value present in all genomic elements, regardless of their coding status.

Repurposing the mammalian RNA-binding protein Musashi-1 as an allosteric translation repressor in bacteria

The RNA recognition motif (RRM) is the most common RNA-binding protein domain identified in nature. However, RRM-containing proteins are only prevalent in eukaryotic phyla, in which they play central regulatory roles. Here, we engineered an orthogonal post-transcriptional control system of gene expression in the bacterium Escherichia coli with the mammalian RNA-binding protein Musashi-1, which is a stem cell marker with neurodevelopmental role that contains two canonical RRMs. In the circuit, Musashi-1 is regulated transcriptionally and works as an allosteric translation repressor thanks to a specific interaction with the N-terminal coding region of a messenger RNA and its structural plasticity to respond to fatty acids. We fully characterized the genetic system at the population and single-cell levels showing a significant fold change in reporter expression, and the underlying molecular mechanism by assessing the in vitro binding kinetics and in vivo functionality of a series of RNA mutants. The dynamic response of the system was well recapitulated by a bottom-up mathematical model. Moreover, we applied the post-transcriptional mechanism engineered with Musashi-1 to specifically regulate a gene within an operon, implement combinatorial regulation, and reduce protein expression noise. This work illustrates how RRM-based regulation can be adapted to simple organisms, thereby adding a new regulatory layer in prokaryotes for translation control.

Ribosomal profiling of human endogenous retroviruses in healthy tissues

Human endogenous retroviruses (HERVs) are the germline embedded proviral fragments of ancient retroviral infections that make up roughly 8% of the human genome. Our understanding of HERVs in physiology primarily surrounds their non-coding functions, while their protein coding capacity remains virtually uncharacterized. Therefore, we applied the bioinformatic pipeline "hervQuant" to high-resolution ribosomal profiling of healthy tissues to provide a comprehensive overview of translationally active HERVs. We find that HERVs account for 0.1-0.4% of all translation in distinct tissue-specific profiles. Collectively, our study further supports claims that HERVs are actively translated throughout healthy tissues to provide sequences of retroviral origin to the human proteome.

Syncytial Assembly Lines: Consequences of Multinucleate Cellular Compartments for Fungal Protein Synthesis

Fast growth and prodigious cellular outputs make fungi powerful tools in biotechnology. Recent modeling work has exposed efficiency gains associated with dividing the labor of transcription over multiple nuclei, and experimental innovations are opening new windows on the capacities and adaptations that allow nuclei to behave autonomously or in coordination while sharing a single, common cytoplasm. Although the motivation of our review is to motivate and connect recent work toward a greater understanding of fungal factories, we use the analogy of the assembly line as an organizing idea for studying coordinated gene expression, generally.

Comparative spatial proteomics of Plasmodium-infected erythrocytes

Plasmodium parasites contribute to one of the highest global infectious disease burdens. To achieve this success, the parasite has evolved a range of specialized subcellular compartments to extensively remodel the host cell for its survival. The information to fully understand these compartments is likely hidden in the so far poorly characterized Plasmodium species spatial proteome. To address this question, we determined the steady-state subcellular location of more than 12,000 parasite proteins across five different species by extensive subcellular fractionation of erythrocytes infected by Plasmodium falciparum, Plasmodium knowlesi, Plasmodium yoelii, Plasmodium berghei, and Plasmodium chabaudi. This comparison of the pan-species spatial proteomes and their expression patterns indicates increasing species-specific proteins associated with the more external compartments, supporting host adaptations and post-transcriptional regulation. The spatial proteome offers comprehensive insight into the different human, simian, and rodent Plasmodium species, establishing a powerful resource for understanding species-specific host adaptation processes in the parasite.

Responses and modulation of the white shrimp Litopenaeus vannamei glutathione peroxidases 2 and 4 during hypoxia, reoxygenation and GPx4 knock-down

Glutathione peroxidases (GPxs) are important antioxidant enzymes that act at distinct levels of the antioxidant defense. In vertebrates, there are several glutathione peroxidase (GPx) isoforms with different cellular and tissue distribution, but little is known about their interrelationships. The shrimp Litopenaeus vannamei is the main crustacean cultivated worldwide. It is affected by environmental stressors, including hypoxia and reoxygenation that cause reactive oxygen species accumulation. Thus, the antioxidant response modulation is key for shrimp resilience. Recently, several GPx isoforms genes were identified in the L. vannamei genome sequence, but their functions are just beginning to be studied. As in vertebrates, shrimp GPx isoforms can present differences in their antioxidant responses. Also, there could be interrelationships among the isoforms that may influence their responses. We evaluated shrimp GPx2 and GPx4 expressions during hypoxia, reoxygenation, and GPx4 knock-down using RNAi for silencing, as well as the enzymatic activity of total GPx and GPx4. Also, glutathione content in hepatopancreas was evaluated. GPx2 and GPx4 presented similar expression patterns during hypoxia and reoxygenation. Their expressions decreased during hypoxia and were reestablished in reoxygenation at 6 h in non-silenced shrimp. GPx2 expression was down-regulated by GPx4 knock-down, suggesting that GPx4 affects GPx2 expression. Total GPx activity changed in hypoxia and reoxygenation at 6 h but not at 12 h, while GPx4 activity was not affected by any stressor. The GSH/GSSG ratio in hepatopancreas indicated that at early hours, the redox status remains well-modulated but at 12 h it is impaired by hypoxia and reoxygenation.

Gene expression flux analysis reveals specific regulatory modalities of gene expression

The level of a given protein is determined by the synthesis and degradation rates of its mRNA and protein. While several studies have quantified the contribution of different gene expression steps in regulating protein levels, these are limited by using equilibrium approximations in out-of-equilibrium biological systems. Here, we introduce gene expression flux analysis to quantitatively dissect the dynamics of the expression level for specific proteins and use it to analyze published transcriptomics and proteomics datasets. Our analysis reveals distinct regulatory modalities shared by sets of genes with clear functional signatures. We also find that protein degradation plays a stronger role than expected in the adaptation of protein levels. These findings suggest that shared regulatory strategies can lead to versatile responses at the protein level and highlight the importance of going beyond equilibrium approximations to dissect the quantitative contribution of different steps of gene expression to protein dynamics.

Studying stochastic systems biology of the cell with single-cell genomics data

Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.

Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia

The effect of schizophrenia (SCZ) genetic risk on gene expression in brain remains elusive. A popular approach to this problem has been the application of gene co-expression network algorithms (e.g., WGCNA). To improve reliability with this method it is critical to remove unwanted sources of variance while also preserving biological signals of interest. In this WCGNA study of RNA-Seq data from postmortem prefrontal cortex (78 neurotypical donors, EUR ancestry), we tested the effects of SCZ genetic risk on co-expression networks. Specifically, we implemented a novel design in which gene expression was adjusted by linear regression models to preserve or remove variance explained by biological signal of interest (GWAS genomic scores for SCZ risk-(GS-SCZ), and genomic scores- GS of height (GS-Ht) as a negative control), while removing variance explained by covariates of non-interest. We calculated co-expression networks from adjusted expression (GS-SCZ and GS-Ht preserved or removed), and consensus between them (representative of a "background" network free of genomic scores effects). We then tested the overlap between GS-SCZ preserved modules and background networks reasoning that modules with reduced overlap would be most affected by GS-SCZ biology. Additionally, we tested these modules for convergence of SCZ risk (i.e., enrichment in PGC3 SCZ GWAS priority genes, enrichment in SCZ risk heritability and relevant biological ontologies. Our results highlight key aspects of GS-SCZ effects on brain co-expression networks, specifically: 1) preserving/removing SCZ genetic risk alters the co-expression modules; 2) biological pathways enriched in modules affected by GS-SCZ implicate processes of transcription, translation and metabolism that converge to influence synaptic transmission; 3) priority PGC3 SCZ GWAS genes and SCZ risk heritability are enriched in modules associated with GS-SCZ effects. Overall, our results indicate that gene co-expression networks that selectively integrate information about genetic risk can reveal novel combinations of biological pathways involved in schizophrenia.

Engineering low-salt growth Halomonas Bluephagenesis for cost-effective bioproduction combined with adaptive evolution

Halophilic Halomonas bluephagenesis has been engineered to produce various added-value bio-compounds with reduced costs. However, the salt-stress regulatory mechanism remained unclear. H. bluephagenesis was randomly mutated to obtain low-salt growing mutants via atmospheric and room temperature plasma (ARTP). The resulted H. bluephagenesis TDH4A1B5 was constructed with the chromosomal integration of polyhydroxyalkanoates (PHA) synthesis operon phaCAB and deletion of phaP1 gene encoding PHA synthesis associated protein phasin, forming H. bluephagenesis TDH4A1B5P, which led to increased production of poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-4-hydrobutyrate) (P34HB) by over 1.4-fold. H. bluephagenesis TDH4A1B5P also enhanced production of ectoine and threonine by 50% and 77%, respectively. A total 101 genes related to salinity tolerance was identified and verified via comparative genomic analysis among four ARTP mutated H. bluephagenesis strains. Recombinant H. bluephagenesis TDH4A1B5P was further engineered for PHA production utilizing sodium acetate or gluconate as sole carbon source. Over 33% cost reduction of PHA production could be achieved using recombinant H. bluephagenesis TDH4A1B5P. This study successfully developed a low-salt tolerant chassis H. bluephagenesis TDH4A1B5P and revealed salt-stress related genes of halophilic host strains.

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

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.

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor

Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural network (CNN) models to predict host adaptation of swine CoVs. Decomposing of each ORF1ab and Spike sequence was performed with dinucleotide composition representation (DCR) and other traits. The relationship between CoVs from different adaptive hosts was analyzed by unsupervised learning, and CNN models based on DCR of ORF1ab and Spike were built to predict the host adaptation of swine CoVs. The rationality of the models was verified with phylogenetic analysis. Unsupervised learning showed that there is a multiple host adaptation of different swine CoVs. According to the adaptation prediction of CNN models, swine acute diarrhea syndrome CoV (SADS-CoV) and porcine epidemic diarrhea virus (PEDV) are adapted to Chiroptera, swine transmissible gastroenteritis virus (TGEV) is adapted to Carnivora, porcine hemagglutinating encephalomyelitis (PHEV) might be adapted to Primate, Rodent, and Lagomorpha, and porcine deltacoronavirus (PDCoV) might be adapted to Chiroptera, Artiodactyla, and Carnivora. In summary, the DCR trait has been confirmed to be representative for the CoV genome, and the DCR-based deep learning model works well to assess the adaptation of swine CoVs to other mammals. Suiformes might be intermediate hosts for human CoVs and other mammalian CoVs. The present study provides a novel approach to assess the risk of adaptation and transmission to humans and other mammals of swine CoVs.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.