Determinants and Regulation of Protein Turnover in Yeast

Mitochondrial protein heterogeneity stems from the stochastic nature of co-translational protein targeting in cell senescence

A decline in mitochondrial function is a hallmark of aging and neurodegenerative diseases. It has been proposed that changes in mitochondrial morphology, including fragmentation of the tubular mitochondrial network, can lead to mitochondrial dysfunction, yet the mechanism of this loss of function is unclear. Most proteins contained within mitochondria are nuclear-encoded and must be properly targeted to the mitochondria. Here, we report that sustained mRNA localization and co-translational protein delivery leads to a heterogeneous protein distribution across fragmented mitochondria. We find that age-induced mitochondrial fragmentation drives a substantial increase in protein expression noise across fragments. Using a translational kinetic and molecular diffusion model, we find that protein expression noise is explained by the nature of stochastic compartmentalization and that co-translational protein delivery is the main contributor to increased heterogeneity. We observed that cells primarily reduce the variability in protein distribution by utilizing mitochondrial fission-fusion processes rather than relying on the mitophagy pathway. Furthermore, we are able to reduce the heterogeneity of the protein distribution by inhibiting co-translational protein targeting. This research lays the framework for a better understanding of the detrimental impact of mitochondrial fragmentation on the physiology of cells in aging and disease.

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

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.

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.

Quantitative and Kinetic Proteomics Reveal ApoE Isoform-dependent Proteostasis Adaptations in Mouse Brain

Apolipoprotein E (ApoE) polymorphisms modify the risk of neurodegenerative disease with the ApoE4 isoform increasing and ApoE2 isoform decreasing risk relative to the 'wild-type control' ApoE3 isoform. To elucidate how ApoE isoforms alter the proteome, we measured relative protein abundance and turnover in transgenic mice expressing a human ApoE gene (isoform 2, 3, or 4). This data provides insight into how ApoE isoforms affect the in vivo synthesis and degradation of a wide variety of proteins. We identified 4849 proteins and tested for ApoE isoform-dependent changes in the homeostatic regulation of ~2700 ontologies. In the brain, we found that ApoE4 and ApoE2 both lead to modified regulation of mitochondrial membrane proteins relative to the wild-type control ApoE3. In ApoE4 mice, this regulation is not cohesive suggesting that aerobic respiration is impacted by proteasomal and autophagic dysregulation. ApoE2 mice exhibited a matching change in mitochondrial matrix proteins and the membrane which suggests coordinated maintenance of the entire organelle. In the liver, we did not observe these changes suggesting that the ApoE-effect on proteostasis is amplified in the brain relative to other tissues. Our findings underscore the utility of combining protein abundance and turnover rates to decipher proteome regulatory mechanisms and their potential role in biology.

Genome dilution by cell growth drives starvation-like proteome remodeling in mammalian and yeast cells

Cell size is tightly controlled in healthy tissues and single-celled organisms, but it remains unclear how cell size influences physiology. Increasing cell size was recently shown to remodel the proteomes of cultured human cells, demonstrating that large and small cells of the same type can be compositionally different. In the present study, we utilize the natural heterogeneity of hepatocyte ploidy and yeast genetics to establish that the ploidy-to-cell size ratio is a highly conserved determinant of proteome composition. In both mammalian and yeast cells, genome dilution by cell growth elicits a starvation-like phenotype, suggesting that growth in large cells is restricted by genome concentration in a manner that mimics a limiting nutrient. Moreover, genome dilution explains some proteomic changes ascribed to yeast aging. Overall, our data indicate that genome concentration drives changes in cell composition independently of external environmental cues.

Partitioning into ER membrane microdomains impacts autophagic protein turnover during cellular aging

Eukaryotic membranes are compartmentalized into distinct micro- and nanodomains that rearrange dynamically in response to external and internal cues. This lateral heterogeneity of the lipid bilayer and associated clustering of distinct membrane proteins contribute to the spatial organization of numerous cellular processes. Here, we show that membrane microdomains within the endoplasmic reticulum (ER) of yeast cells are reorganized during metabolic reprogramming and aging. Using biosensors with varying transmembrane domain length to map lipid bilayer thickness, we demonstrate that in young cells, microdomains of increased thickness mainly exist within the nuclear ER, while progressing cellular age drives the formation of numerous microdomains specifically in the cortical ER. Partitioning of biosensors with long transmembrane domains into these microdomains increased protein stability and prevented autophagic removal. In contrast, reporters with short transmembrane domains progressively accumulated at the membrane contact site between the nuclear ER and the vacuole, the so-called nucleus-vacuole junction (NVJ), and were subjected to turnover via selective microautophagy occurring specifically at these sites. Reporters with long transmembrane domains were excluded from the NVJ. Our data reveal age-dependent rearrangement of the lateral organization of the ER and establish transmembrane domain length as a determinant of membrane contact site localization and autophagic degradation.

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.

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.

Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO

The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.

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.

Role of the San1 ubiquitin ligase in the heat stress-induced degradation of nonnative Nup1 in the nuclear pore complex

The nuclear pore complex (NPC) mediates the selective exchange of macromolecules between the nucleus and the cytoplasm. Neurodegenerative diseases such as amyotrophic lateral sclerosis are characterized by mislocalization of nucleoporins (Nups), transport receptors, and Ras-related nuclear proteins into nucleoplasmic or cytosolic aggregates, underscoring the importance of precise assembly of the NPC. The assembly state of large protein complexes is strictly monitored by the protein quality control system. The ubiquitin-proteasome system may eliminate aberrant, misfolded, and/or orphan components; however, the involvement of the ubiquitin-proteasome system in the degradation of nonnative Nups in the NPC remains unclear. Here, we show that in Saccharomyces cerevisiae, although Nup1 (the FG-Nup component of the central core of the NPC) was stable, C-terminally green fluorescent protein-tagged Nup1, which had been incorporated into the NPC, was degraded by the proteasome especially under heat stress conditions. The degradation was dependent on the San1 ubiquitin ligase and Cdc48/p97, as well as its cofactor Doa1. We also demonstrate that San1 weakly but certainly contributes to the degradation of nontagged endogenous Nup1 in cells defective in NPC biogenesis by the deletion of NUP120. In addition, the overexpression of SAN1 exacerbated the growth defect phenotype of nup120Δ cells, which may be caused by excess degradation of defective Nups due to the deletion of NUP120. These biochemical and genetic data suggest that San1 is involved in the degradation of nonnative Nups generated by genetic mutation or when NPC biogenesis is impaired.

Multi-omics view of recombinant Yarrowia lipolytica: Enhanced ketogenic amino acid catabolism increases polyketide-synthase-driven docosahexaenoic production to high selectivity at the gram scale

DHA is a marine PUFA of commercial value, given its multiple health benefits. The worldwide emerging shortage in DHA supply has increased interest in microbial cell factories that can provide the compound de novo. In this regard, the present work aimed to improve DHA production in the oleaginous yeast strain Y. lipolytica Af4, which synthetized the PUFA via a heterologous myxobacterial polyketide synthase (PKS)-like gene cluster. As starting point, we used transcriptomics, metabolomics, and 13C-based metabolic pathway profiling to study the cellular dynamics of Y. lipolytica Af4. The shift from the growth to the stationary DHA-production phase was associated with fundamental changes in carbon core metabolism, including a strong upregulation of the PUFA gene cluster, as well as an increase in citrate and fatty acid degradation. At the same time, the intracellular levels of the two DHA precursors acetyl-CoA and malonyl-CoA dropped by up to 98% into the picomolar range. Interestingly, the degradation pathways for the ketogenic amino acids l-lysine, l-leucine, and l-isoleucine were transcriptionally activated, presumably to provide extra acetyl-CoA. Supplementation with small amounts of these amino acids at the beginning of the DHA production phase beneficially increased the intracellular CoA-ester pools and boosted the DHA titer by almost 40%. Isotopic 13C-tracer studies revealed that the supplements were efficiently directed toward intracellular CoA-esters and DHA. Hereby, l-lysine was found to be most efficient, as it enabled long-term activation, due to storage within the vacuole and continuous breakdown. The novel strategy enabled DHA production in Y. lipolytica at the gram scale for the first time. DHA was produced at a high selectivity (27% of total fatty acids) and free of the structurally similar PUFA DPA, which facilitates purification for high-value medical applications that require API-grade DHA. The assembled multi-omics picture of the central metabolism of Y. lipolytica provides valuable insights into this important yeast. Beyond our work, the enhanced catabolism of ketogenic amino acids seems promising for the overproduction of other compounds in Y. lipolytica, whose synthesis is limited by the availability of CoA ester precursors.

Genome dilution by cell growth drives starvation-like proteome remodeling in mammalian and yeast cells

Cell size is tightly controlled in healthy tissues and single-celled organisms, but it remains unclear how size influences cell physiology. Increasing cell size was recently shown to remodel the proteomes of cultured human cells, demonstrating that large and small cells of the same type can be biochemically different. Here, we corroborate these results in mouse hepatocytes and extend our analysis using yeast. We find that size-dependent proteome changes are highly conserved and mostly independent of metabolic state. As eukaryotic cells grow larger, the dilution of the genome elicits a starvation-like proteome phenotype, suggesting that growth in large cells is limited by the genome in a manner analogous to a limiting nutrient. We also demonstrate that the proteomes of replicatively-aged yeast are primarily determined by their large size. Overall, our data suggest that genome concentration is a universal determinant of proteome content in growing cells.

Loss of N-terminal acetyltransferase A activity induces thermally unstable ribosomal proteins and increases their turnover in Saccharomyces cerevisiae

Protein N-terminal (Nt) acetylation is one of the most abundant modifications in eukaryotes, covering ~50-80 % of the proteome, depending on species. Cells with defective Nt-acetylation display a wide array of phenotypes such as impaired growth, mating defects and increased stress sensitivity. However, the pleiotropic nature of these effects has hampered our understanding of the functional impact of protein Nt-acetylation. The main enzyme responsible for Nt-acetylation throughout the eukaryotic kingdom is the N-terminal acetyltransferase NatA. Here we employ a multi-dimensional proteomics approach to analyze Saccharomyces cerevisiae lacking NatA activity, which causes global proteome remodeling. Pulsed-SILAC experiments reveals that NatA-deficient strains consistently increase degradation of ribosomal proteins compared to wild type. Explaining this phenomenon, thermal proteome profiling uncovers decreased thermostability of ribosomes in NatA-knockouts. Our data are in agreement with a role for Nt-acetylation in promoting stability for parts of the proteome by enhancing the avidity of protein-protein interactions and folding.

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.

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.

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.

Directed Evolution of Aerotolerance in Sulfide-Dependent Thiazole Synthases

Sulfide-dependent THI4 thiazole synthases could potentially be used to replace plant cysteine-dependent suicide THI4s, whose high protein turnover rates make thiamin synthesis exceptionally energy-expensive. However, sulfide-dependent THI4s are anaerobic or microoxic enzymes and hence unadapted to the aerobic conditions in plants; they are also slow enzymes (kcat < 1 h-1). To improve aerotolerance and activity, we applied continuous directed evolution under aerobic conditions in the yeast OrthoRep system to two sulfide-dependent bacterial THI4s. Seven beneficial single mutations were identified, of which five lie in the active-site cleft predicted by structural modeling and two recapitulate features of naturally aerotolerant THI4s. That single mutations gave substantial improvements suggests that further advance under selection will be possible by stacking mutations. This proof-of-concept study established that the performance of sulfide-dependent THI4s in aerobic conditions is evolvable and, more generally, that yeast OrthoRep provides a plant-like bridge to adapt nonplant enzymes to work better in plants.

Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)

The fluctuation assay remains an important tool for analyzing the levels of mutagenesis in microbial populations. The mutant counts originating from some average number of mutations are usually assumed to obey the Luria-Delbrück distribution. While several tools for estimating mutation rates are available, they sometimes lack accuracy or versatility under non-standard conditions. In this work, extensions to the Luria-Delbrück protocol to account for phenotypic lag and cellular death with either perfect or partial plating were developed. Hence, the novel MLE MUtation Rate calculator, or mlemur, is the first tool that provides a user-friendly graphical interface allowing the researchers to model their data with consideration for partial plating, differential growth of mutants and non-mutants, phenotypic lag, cellular death, variability of the final number of cells, post-exponential-phase mutations, and the size of the inoculum. Additionally, mlemur allows the users to incorporate most of these special conditions at the same time to obtain highly accurate estimates of mutation rates and P values, confidence intervals for an arbitrary function of data (such as fold), and perform power analysis and sample size determination for the likelihood ratio test. The accuracy of point and interval estimates produced by mlemur against historical and simulated fluctuation experiments are assessed. Both mlemur and the analyses in this work might be of great help when evaluating fluctuation experiments and increase the awareness of the limitations of the widely-used Lea-Coulson formulation of the Luria-Delbrück distribution in the more realistic biological contexts.

Turnover and replication analysis by isotope labeling (TRAIL) reveals the influence of tissue context on protein and organelle lifetimes

The lifespans of proteins range from minutes to years within mammalian tissues. Protein lifespan is relevant to organismal aging, as long-lived proteins accrue damage over time. It is unclear how protein lifetime is shaped by tissue context, where both cell turnover and proteolytic degradation contribute to protein turnover. We develop turnover and replication analysis by ¹⁵ N isotope labeling (TRAIL) to quantify protein and cell lifetimes with high precision and demonstrate that cell turnover, sequence-encoded features, and environmental factors modulate protein lifespan across tissues. Cell and protein turnover flux are comparable in proliferative tissues, while protein turnover outpaces cell turnover in slowly proliferative tissues. Physicochemical features such as hydrophobicity, charge, and disorder influence protein turnover in slowly proliferative tissues, but protein turnover is much less sequence-selective in highly proliferative tissues. Protein lifetimes vary nonrandomly across tissues after correcting for cell turnover. Multiprotein complexes such as the ribosome have consistent lifetimes across tissues, while mitochondria, peroxisomes, and lipid droplets have variable lifetimes. TRAIL can be used to explore how environment, aging, and disease affect tissue homeostasis.

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased "hypothesis-free" analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.

Estimating Total Quantitative Protein Content in Escherichia coli, Saccharomyces cerevisiae, and HeLa Cells

The continuous improvement of proteomic techniques, most notably mass spectrometry, has generated quantified proteomes of many organisms with unprecedented depth and accuracy. However, there is still a significant discrepancy in the reported numbers of total protein molecules per specific cell type. In this article, we explore the results of proteomic studies of Escherichia coli, Saccharomyces cerevisiae, and HeLa cells in terms of total protein copy numbers per cell. We observe up to a ten-fold difference between reported values. Investigating possible reasons for this discrepancy, we conclude that neither an unmeasured fraction of the proteome nor biases in the quantification of individual proteins can explain the observed discrepancy. We normalize protein copy numbers in each study using a total protein amount per cell as reported in the literature and create integrated proteome maps of the selected model organisms. Our results indicate that cells contain from one to three million protein molecules per µm³ and that protein copy density decreases with increasing organism complexity.

Identification of Deubiquitinase Substrates in Saccharomyces cerevisiae by Systematic Overexpression

A significant hurdle to understanding the functions of deubiquitinases (DUBs) is the identification of their in vivo substrates. Substrate identification can be difficult for two reasons. First, many proteins that are degraded by the ubiquitin-proteasome system are expressed at relatively low levels in the cell, and second, redundancy between DUBs complicates loss of function screening approaches. Here, we describe a systematic overexpression approach that takes advantage of genome-wide resources available in S. cerevisiae to overcome these challenges and identify DUB substrates in cells.

A brain atlas of synapse protein lifetime across the mouse lifespan

The lifetime of proteins in synapses is important for their signaling, maintenance, and remodeling, and for memory duration. We quantified the lifetime of endogenous PSD95, an abundant postsynaptic protein in excitatory synapses, at single-synapse resolution across the mouse brain and lifespan, generating the Protein Lifetime Synaptome Atlas. Excitatory synapses have a wide range of PSD95 lifetimes extending from hours to several months, with distinct spatial distributions in dendrites, neurons, and brain regions. Synapses with short protein lifetimes are enriched in young animals and in brain regions controlling innate behaviors, whereas synapses with long protein lifetimes accumulate during development, are enriched in the cortex and CA1 where memories are stored, and are preferentially preserved in old age. Synapse protein lifetime increases throughout the brain in a mouse model of autism and schizophrenia. Protein lifetime adds a further layer to synapse diversity and enriches prevailing concepts in brain development, aging, and disease.

Meiotic DNA breaks activate a streamlined phospho-signaling response that largely avoids protein-level changes

Meiotic cells introduce a numerous programmed DNA breaks into their genome to stimulate meiotic recombination and ensure controlled chromosome inheritance and fertility. A checkpoint network involving key kinases and phosphatases coordinates the repair of these DNA breaks, but the precise phosphorylation targets remain poorly understood. It is also unknown whether meiotic DNA breaks change gene expression akin to the canonical DNA-damage response. To address these questions, we analyzed the meiotic DNA break response in Saccharomyces cerevisiae using multiple systems-level approaches. We identified 332 DNA break-dependent phosphorylation sites, vastly expanding the number of known events during meiotic prophase. Less than half of these events occurred in recognition motifs for the known meiotic checkpoint kinases Mec1 (ATR), Tel1 (ATM), and Mek1 (CHK2), suggesting that additional kinases contribute to the meiotic DNA-break response. We detected a clear transcriptional program but detected only very few changes in protein levels. We attribute this dichotomy to a decrease in transcript levels after meiotic entry that dampens the effects of break-induced transcription sufficiently to cause only minimal changes in the meiotic proteome.

Acquired resistance to severe ethanol stress-induced inhibition of proteasomal proteolysis in Saccharomyces cerevisiae

BACKGROUND

Although the budding yeast, Saccharomyces cerevisiae, produces ethanol via alcoholic fermentation, high-concentration ethanol is harmful to yeast cells. Severe ethanol stress (> 9% v/v) inhibits protein synthesis and increases the level of intracellular protein aggregates. However, its effect on proteolysis in yeast cells remains largely unknown.

METHODS

We examined the effects of ethanol on proteasomal proteolysis in yeast cells through the cycloheximide-chase analysis of short-lived proteins. We also assayed protein degradation in the auxin-inducible degron system and the ubiquitin-independent degradation of Spe1 under ethanol stress conditions.

RESULTS

We demonstrated that severe ethanol stress strongly inhibited the degradation of the short-lived proteins Rim101 and Gic2. Severe ethanol stress also inhibited protein degradation in the auxin-inducible degron system (Paf1-AID*-6FLAG) and the ubiquitin-independent degradation of Spe1. Proteasomal degradation of these proteins, which was inhibited by severe ethanol stress, resumed rapidly once the ethanol was removed. These results suggested that proteasomal proteolysis in yeast cells is reversibly inhibited by severe ethanol stress. Furthermore, yeast cells pretreated with mild ethanol stress (6% v/v) showed proteasomal proteolysis even with 10% (v/v) ethanol, indicating that yeast cells acquired resistance to proteasome inhibition caused by severe ethanol stress. However, yeast cells failed to acquire sufficient resistance to severe ethanol stress-induced proteasome inhibition when new protein synthesis was blocked with cycloheximide during pretreatment, or when Rpn4 was lost.

CONCLUSIONS AND GENERAL SIGNIFICANCE

Our results provide novel insights into the adverse effects of severe ethanol stress on proteasomal proteolysis and ethanol adaptability in yeast.

Evidence that conserved essential genes are enriched for pro-longevity factors

At the cellular level, many aspects of aging are conserved across species. This has been demonstrated by numerous studies in simple model organisms like Saccharomyces cerevisiae, Caenorhabdits elegans, and Drosophila melanogaster. Because most genetic screens examine loss of function mutations or decreased expression of genes through reverse genetics, essential genes have often been overlooked as potential modulators of the aging process. By taking the approach of increasing the expression level of a subset of conserved essential genes, we found that 21% of these genes resulted in increased replicative lifespan in S. cerevisiae. This is greater than the ~ 3.5% of genes found to affect lifespan upon deletion, suggesting that activation of essential genes may have a relatively disproportionate effect on increasing lifespan. The results of our experiments demonstrate that essential gene overexpression is a rich, relatively unexplored means of increasing eukaryotic lifespan.

The MyLO CRISPR-Cas9 toolkit: a markerless yeast localization and overexpression CRISPR-Cas9 toolkit

The genetic tractability of the yeast Saccharomyces cerevisiae has made it a key model organism for basic research and a target for metabolic engineering. To streamline the introduction of tagged genes and compartmental markers with powerful Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) - CRISPR-associated protein 9 (Cas9)-based genome editing tools, we constructed a Markerless Yeast Localization and Overexpression (MyLO) CRISPR-Cas9 toolkit with 3 components: (1) a set of optimized Streptococcus pyogenes Cas9-guide RNA expression vectors with 5 selectable markers and the option to either preclone or cotransform the gRNAs; (2) vectors for the one-step construction of integration cassettes expressing an untagged or green fluorescent protein/red fluorescent protein/hemagglutinin-tagged gene of interest at one of 3 levels, supporting localization and overexpression studies; and (3) integration cassettes containing moderately expressed green fluorescent protein- or red fluorescent protein-tagged compartmental markers for colocalization experiments. These components allow rapid, high-efficiency genomic integrations and modifications with only transient selection for the Cas9 vector, resulting in markerless transformations. To demonstrate the ease of use, we applied our complete set of compartmental markers to colabel all target subcellular compartments with green fluorescent protein and red fluorescent protein. Thus, the MyLO toolkit packages CRISPR-Cas9 technology into a flexible, optimized bundle that allows the stable genomic integration of DNA with the ease of use approaching that of transforming plasmids.

Degron masking outlines degronons, co-degrading functional modules in the proteome

Effective organization of proteins into functional modules (networks, pathways) requires systems-level coordination between transcription, translation and degradation. Whereas the cooperation between transcription and translation was extensively studied, the cooperative degradation regulation of protein complexes and pathways has not been systematically assessed. Here we comprehensively analyzed degron masking, a major mechanism by which cellular systems coordinate degron recognition and protein degradation. For over 200 substrates with characterized degrons (E3 ligase targeting motifs, ubiquitination sites and disordered proteasomal entry sequences), we demonstrate that degrons extensively overlap with protein-protein interaction sites. Analysis of binding site information and protein abundance comparisons show that regulatory partners effectively outcompete E3 ligases, masking degrons from the ubiquitination machinery. Protein abundance variations between normal and cancer cells highlight the dynamics of degron masking components. Finally, integrative analysis of gene co-expression, half-life correlations and functional relationships between interacting proteins point towards higher-order, co-regulated degradation modules (‘degronons’) in the proteome.

Systematic bioinformatics analysis of cooperative degradation of protein complexes indicates that degrons extensively overlap with protein-protein interaction sites, hiding degrons from ubiquitination machinery and suggesting the existence of co-degrading functional modules in the proteome.

Protein degradation sets the fraction of active ribosomes at vanishing growth

Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such “growth laws” neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of “maintenance” ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from E. coli and S. cerevisiae. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms.

The idea that simple quantitative relationships relate cell physiology to cellular composition dates back to the 1950s, but the recent years saw a leap in our understanding of such “growth laws”, with relevant implications regarding the interdependence between growth, metabolism and biochemical networks. However, recent works on nutrient-limited growth mainly focused on laboratory conditions that are favourable to growth. Thus, our current mathematical understanding of the growth laws neglects protein degradation, under the argument that it occurs slowly compared to the timescale of the cell cycle. Instead, at slow growth the timescales of mass loss from protein degradation and dilution become comparable. In this work, we propose that protein degradation shapes the quantitative relationships between ribosome allocation and growth rate, and determines a fraction of ribosomes that do not contribute to growth and need to remain active to balance degradation.

Yeast Secretes High Amounts of Human Calreticulin without Cellular Stress

The ER chaperone calreticulin (CALR) also has extracellular functions and can exit the mammalian cell in response to various factors, although the mechanism by which this takes place is unknown. The yeast Saccharomyces cerevisiae efficiently secretes human CALR, and the analysis of this process in yeast could help to clarify how it gets out of eukaryotic cells. We have achieved a secretion titer of about 140 mg/L CALR in our S. cerevisiae system. Here, we present a comparative quantitative whole proteome study in CALR-secreting yeast using non-equilibrium pH gradient electrophoresis (NEPHGE)-based two-dimensional gel electrophoresis (2DE) as well as liquid chromatography mass spectrometry in data-independent analysis mode (LC-MSE). A reconstructed carrier ampholyte (CA) composition of NEPHGE-based first-dimension separation for 2DE could be used instead of formerly commercially available gels. Using LC-MSE, we identified 1574 proteins, 20 of which exhibited differential expression. The largest group of differentially expressed proteins were structural ribosomal proteins involved in translation. Interestingly, we did not find any signs of cellular stress which is usually observed in recombinant protein-producing yeast, and we did not identify any secretory pathway proteins that exhibited changes in expression. Taken together, high-level secretion of human recombinant CALR protein in S. cerevisiae does not induce cellular stress and does not burden the cellular secretory machinery. There are only small changes in the cellular proteome of yeast secreting CALR at a high level.

Design principles that protect the proteasome from self-destruction

The proteasome is a powerful intracellular protease that can degrade effectively any protein, self or foreign, for regulation, quality control, or immune response. Proteins are targeted for degradation by localizing them to the proteasome, typically by ubiquitin tags. At the same time, the proteasome is built from ~33 subunits, and their assembly into the complex and activity are tuned by post-translational modifications on long disordered regions on the subunits. Molecular modeling and biochemical experiments show that some of the disordered regions of proteasomal subunits can access the substrate recognition sites. All disordered regions tested, independent of location, are constructed from amino acid sequences that escape recognition. Replacing a disordered region with a sequence that is recognized by the proteasome leads to self-degradation and, in the case of an essential subunit, cell death.

Linking post-translational modifications and protein turnover by site-resolved protein turnover profiling

Proteome-wide measurements of protein turnover have largely ignored the impact of post-translational modifications (PTMs). To address this gap, we employ stable isotope labeling and mass spectrometry to measure the turnover of >120,000 peptidoforms including >33,000 phosphorylated, acetylated, and ubiquitinated peptides for >9,000 native proteins. This site-resolved protein turnover (SPOT) profiling discloses global and site-specific differences in turnover associated with the presence or absence of PTMs. While causal relationships may not always be immediately apparent, we speculate that PTMs with diverging turnover may distinguish states of differential protein stability, structure, localization, enzymatic activity, or protein-protein interactions. We show examples of how the turnover data may give insights into unknown functions of PTMs and provide a freely accessible online tool that allows interrogation and visualisation of all turnover data. The SPOT methodology is applicable to many cell types and modifications, offering the potential to prioritize PTMs for future functional investigations.

Post-translational modifications (PTMs) can regulate cellular protein function but their global impact on protein turnover is largely unknown. Here, the authors develop proteomic workflows to profile PTM-resolved protein turnover and analyze the effects of phosphorylation, acetylation and ubiquitination.

The economics of organellar gene loss and endosymbiotic gene transfer

BACKGROUND

The endosymbiosis of the bacterial progenitors of the mitochondrion and the chloroplast are landmark events in the evolution of life on Earth. While both organelles have retained substantial proteomic and biochemical complexity, this complexity is not reflected in the content of their genomes. Instead, the organellar genomes encode fewer than 5% of the genes found in living relatives of their ancestors. While many of the 95% of missing organellar genes have been discarded, others have been transferred to the host nuclear genome through a process known as endosymbiotic gene transfer.

RESULTS

Here, we demonstrate that the difference in the per-cell copy number of the organellar and nuclear genomes presents an energetic incentive to the cell to either delete organellar genes or transfer them to the nuclear genome. We show that, for the majority of transferred organellar genes, the energy saved by nuclear transfer exceeds the costs incurred from importing the encoded protein into the organelle where it can provide its function. Finally, we show that the net energy saved by endosymbiotic gene transfer can constitute an appreciable proportion of total cellular energy budgets and is therefore sufficient to impart a selectable advantage to the cell.

CONCLUSION

Thus, reduced cellular cost and improved energy efficiency likely played a role in the reductive evolution of mitochondrial and chloroplast genomes and the transfer of organellar genes to the nuclear genome.

Proteome-wide mapping of short-lived proteins in human cells

Rapid protein degradation enables cells to quickly modulate protein abundance. Dysregulation of short-lived proteins plays essential roles in disease pathogenesis. A focused map of short-lived proteins remains understudied. Cycloheximide, a translational inhibitor, is widely used in targeted studies to measure degradation kinetics for short-lived proteins. Here, we combined cycloheximide chase assays with advanced quantitative proteomics to map short-lived proteins under translational inhibition in four human cell lines. Among 11,747 quantified proteins, we identified 1,017 short-lived proteins (half-lives ≤ 8 h). These short-lived proteins are less abundant, evolutionarily younger, and less thermally stable than other proteins. We quantified 103 proteins with different stabilities among cell lines. We showed that U2OS and HCT116 cells express truncated forms of ATRX and GMDS, respectively, which have lower stability than their full-length counterparts. This study provides a large-scale resource of human short-lived proteins under translational arrest, leading to untapped avenues of protein regulation for therapeutic interventions.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

iTARGEX analysis of yeast deletome reveals novel regulators of transcriptional buffering in S phase and protein turnover

Integrating omics data with quantification of biological traits provides unparalleled opportunities for discovery of genetic regulators by in silico inference. However, current approaches to analyze genetic-perturbation screens are limited by their reliance on annotation libraries for prioritization of hits and subsequent targeted experimentation. Here, we present iTARGEX (identification of Trait-Associated Regulatory Genes via mixture regression using EXpectation maximization), an association framework with no requirement of a priori knowledge of gene function. After creating this tool, we used it to test associations between gene expression profiles and two biological traits in single-gene deletion budding yeast mutants, including transcription homeostasis during S phase and global protein turnover. For each trait, we discovered novel regulators without prior functional annotations. The functional effects of the novel candidates were then validated experimentally, providing solid evidence for their roles in the respective traits. Hence, we conclude that iTARGEX can reliably identify novel factors involved in given biological traits. As such, it is capable of converting genome-wide observations into causal gene function predictions. Further application of iTARGEX in other contexts is expected to facilitate the discovery of new regulators and provide observations for novel mechanistic hypotheses regarding different biological traits and phenotypes.

The number of catalytic cycles in an enzyme's lifetime and why it matters to metabolic engineering

The continuous replacement of enzymes and other proteins appropriates up to half the maintenance energy budget in microorganisms and plants. High enzyme replacement rates therefore cut the productivity of biosystems ranging from microbial fermentations to crops. However, yardsticks to assess what drives enzyme protein replacement and guidelines on how to reduce it are lacking. Accordingly, we compared enzymes’ life spans across kingdoms using a new yardstick (catalytic cycles until replacement [CCR]) and related CCR to enzyme reaction chemistry. We concluded that 1) many enzymes fail due to collateral damage from the reaction they catalyze, and 2) such damage and its attendant enzyme replacement costs are mitigable by engineering and are therefore promising targets for synthetic biology.

Metabolic engineering uses enzymes as parts to build biosystems for specified tasks. Although a part’s working life and failure modes are key engineering performance indicators, this is not yet so in metabolic engineering because it is not known how long enzymes remain functional in vivo or whether cumulative deterioration (wear-out), sudden random failure, or other causes drive replacement. Consequently, enzymes cannot be engineered to extend life and cut the high energy costs of replacement. Guided by catalyst engineering, we adopted catalytic cycles until replacement (CCR) as a metric for enzyme functional life span in vivo. CCR is the number of catalytic cycles that an enzyme mediates in vivo before failure or replacement, i.e., metabolic flux rate/protein turnover rate. We used estimated fluxes and measured protein turnover rates to calculate CCRs for ∼100–200 enzymes each from Lactococcus lactis, yeast, and Arabidopsis. CCRs in these organisms had similar ranges (<10³ to >10⁷) but different median values (3–4 × 10⁴ in L. lactis and yeast versus 4 × 10⁵ in Arabidopsis). In all organisms, enzymes whose substrates, products, or mechanisms can attack reactive amino acid residues had significantly lower median CCR values than other enzymes. Taken with literature on mechanism-based inactivation, the latter finding supports the proposal that 1) random active-site damage by reaction chemistry is an important cause of enzyme failure, and 2) reactive noncatalytic residues in the active-site region are likely contributors to damage susceptibility. Enzyme engineering to raise CCRs and lower replacement costs may thus be both beneficial and feasible.

Protein interaction potential landscapes for yeast replicative aging

We proposed a novel interaction potential landscape approach to map the systems-level profile changes of gene networks during replicative aging in Saccharomyces cerevisiae. This approach enabled us to apply quasi-potentials, the negative logarithm of the probabilities, to calibrate the elevation of the interaction landscapes with young cells as a reference state. Our approach detected opposite landscape changes based on protein abundances from transcript levels, especially for intra-essential gene interactions. We showed that essential proteins play different roles from hub proteins on the age-dependent interaction potential landscapes. We verified that hub proteins tend to avoid other hub proteins, but essential proteins prefer to interact with other essential proteins. Overall, we showed that the interaction potential landscape is promising for inferring network profile change during aging and that the essential hub proteins may play an important role in the uncoupling between protein and transcript levels during replicative aging.

Toward Understanding the Biochemical Determinants of Protein Degradation Rates

Protein degradation is a key component of the regulation of gene expression and is at the center of several pathogenic processes. Proteins are regularly degraded, but there is large variation in their lifetimes, and the kinetics of protein degradation are not well understood. Many different factors can influence protein degradation rates, painting a highly complex picture. This has been partially unravelled in recent years thanks to invaluable advances in proteomics techniques. In this Mini-Review, we give a global vision of the determinants of protein degradation rates with the backdrop of the current understanding of proteolytic systems to give a contemporary view of the field.

Auxin-mediated protein depletion for metabolic engineering in terpene-producing yeast

In metabolic engineering, loss-of-function experiments are used to understand and optimise metabolism. A conditional gene inactivation tool is required when gene deletion is lethal or detrimental to growth. Here, we exploit auxin-inducible protein degradation as a metabolic engineering approach in yeast. We demonstrate its effectiveness using terpenoid production. First, we target an essential prenyl-pyrophosphate metabolism protein, farnesyl pyrophosphate synthase (Erg20p). Degradation successfully redirects metabolic flux toward monoterpene (C10) production. Second, depleting hexokinase-2, a key protein in glucose signalling transduction, lifts glucose repression and boosts production of sesquiterpene (C15) nerolidol to 3.5 g L-1 in flask cultivation. Third, depleting acetyl-CoA carboxylase (Acc1p), another essential protein, delivers growth arrest without diminishing production capacity in nerolidol-producing yeast, providing a strategy to decouple growth and production. These studies demonstrate auxin-mediated protein degradation as an advanced tool for metabolic engineering. It also has potential for broader metabolic perturbation studies to better understand metabolism.

Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation

Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation.

Harnessing Machine Learning To Unravel Protein Degradation in Escherichia coli

Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (stable isotope labeling with amino acids in cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins and identified dozens of fast-degrading novel proteins. Finally, we used structural, physicochemical, and protein-protein interaction network descriptors to train a machine learning classifier to discriminate fast-degrading proteins from the rest of the proteome, achieving an area under the receiver operating characteristic curve (AUC) of 0.72.IMPORTANCE Bacteria use protein degradation to control proliferation, dispose of misfolded proteins, and adapt to physiological and environmental shifts, but the factors that dictate which proteins are prone to degradation are mostly unknown. In this study, we have used a combined computational-experimental approach to explore protein degradation in E. coli We discovered that the proteome of E. coli is composed of three protein populations that are distinct in terms of stability and functionality, and we show that fast-degrading proteins can be identified using a combination of various protein properties. Our findings expand the understanding of protein degradation in bacteria and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings may help to identify new potential antibacterial drug targets.

Tune instead of destroy: How proteolysis keeps OXPHOS in shape

Mitochondria are highly dynamic and stress-responsive organelles that are renewed, maintained and removed by a number of different mechanisms. Recent findings bring more evidence for the focused, defined, and regulatory function of the intramitochondrial proteases extending far beyond the traditional concepts of damage control and stress responses. Until recently, the macrodegradation processes, such as mitophagy, were promoted as the major regulator of OXPHOS remodelling and turnover. However, the spatiotemporal dynamics of the OXPHOS system can be greatly modulated by the intrinsic mitochondrial mechanisms acting apart from changes in the global mitochondrial dynamics. This, in turn, may substantially contribute to the shaping of the metabolic status of the cell.

Unveiling complex responses at the molecular level: Transcriptional alterations by mixtures of bisphenol A, octocrylene, and 2'-ethylhexyl 4- (dimethylamino)benzoate on Chironomus riparius

Living organisms are exposed to mixtures of pollutants in the wild. Inland aquatic ecosystems contain many compounds from different sources that pollute the water column and the sediment. However, majority of toxicological research is focused on the effects of single exposures to toxicants. Furthermore, studies have been principally oriented toward ecologically relevant effects of intoxication, and lack an analysis of the cellular and molecular mechanisms involved in the response to toxicants. Effects of single, binary, and ternary mixtures of three compounds, bisphenol A, octocrylene, and 2'-ethylhexyl 4- (dimethylamino)benzoate, were assessed using a Real-Time PCR array. Forty genes, and additional six reference genes, were included in the array. The genes were selected based on their association with hormone responses, detoxification mechanisms, the stress response, DNA repair, and the immune system. The study was performed on Chironomus riparius, a benthic dipteran with an essential role in the food web. Transcriptional responses were assessed both 24 and 96 h post-exposure, to determinate short- and medium-term cellular responses. Individual fourth instar larvae were exposed to 0.1 and 1 mg/L of each of the toxic compounds and compound mixtures. A weak response was detected at 24 h, which was stronger in larvae exposed to mixtures than to individual toxicants. The response at 96 h was complex and principally involved genes related to the endocrine system, detoxification mechanisms, and the stress response. Furthermore, exposure to mixtures of compounds altered the expression patterns of an increased number of genes than did individual compound exposures, which suggested complex interactions between compounds affected the regulation of transcriptional activity. The results obtained highlight the importance of analyzing the mechanisms involved in the response to mixtures of compounds over extended periods and offer new insights into the basis of the physiological responses to pollution.

Proteome Turnover in the Spotlight: Approaches, Applications, and Perspectives

In all cells, proteins are continuously synthesized and degraded to maintain protein homeostasis and modify gene expression levels in response to stimuli. Collectively, the processes of protein synthesis and degradation are referred to as protein turnover. At a steady state, protein turnover is constant to maintain protein homeostasis, but in dynamic responses, proteins change their rates of synthesis and degradation to adjust their proteomes to internal or external stimuli. Thus, probing the kinetics and dynamics of protein turnover lends insight into how cells regulate essential processes such as growth, differentiation, and stress response. Here, we outline historical and current approaches to measuring the kinetics of protein turnover on a proteome-wide scale in both steady-state and dynamic systems, with an emphasis on metabolic tracing using stable isotope-labeled amino acids. We highlight important considerations for designing proteome turnover experiments, key biological findings regarding the conserved principles of proteome turnover regulation, and future perspectives for both technological and biological investigation.