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

Mitochondrial metabolism and hypoxic signaling in differentiated human cardiomyocyte AC16 cell line

Cardiovascular diseases are associated with an altered cardiomyocyte metabolism. Because of a shortage of human heart tissue, experimental studies mostly rely on alternative approaches including animal and cell culture models. Since the use of isolated primary cardiomyocytes is limited, immortalized cardiomyocyte cell lines may represent a useful tool as they closely mimic human cardiomyocytes. This study is focused on the AC16 cell line generated from adult human ventricular cardiomyocytes. Despite an increasing number of studies employing AC16 cells, a comprehensive proteomic, bioenergetic, and oxygen-sensing characterization of proliferating vs. differentiated cells is still lacking. Here, we provide a comparison of these two stages, particularly emphasizing cell metabolism, mitochondrial function, and hypoxic signaling. Label-free quantitative mass spectrometry revealed a decrease in autophagy and cytoplasmic translation in differentiated AC16, confirming their phenotype. Cell differentiation led to global increase in mitochondrial proteins [e.g. oxidative phosphorylation (OXPHOS) proteins, TFAM, VWA8] reflected by elevated mitochondrial respiration. Fatty acid oxidation proteins were increased in differentiated cells, whereas the expression levels of proteins associated with fatty acid synthesis were unchanged and glycolytic proteins were decreased. There was a profound difference between proliferating and differentiated cells in their response to hypoxia and anoxia-reoxygenation. We conclude that AC16 differentiation leads to proteomic and metabolic shifts and altered cell response to oxygen deprivation. This underscores the requirement for proper selection of the particular differentiation state during experimental planning.NEW & NOTEWORTHY Proliferating and differentiated AC16 cell lines exhibit distinct proteomic and metabolic profiles with critical implications for experimental design. Proliferating cells predominantly utilize glycolysis and are highly sensitive to hypoxia, whereas differentiated cells display enhanced mitochondrial biogenesis, oxidative phosphorylation, and resistance to anoxia-reoxygenation. These findings provide novel insights into the metabolic adaptations during differentiation and highlight the necessity of selecting the appropriate cellular stage to ensure accurate experimental outcomes.

Determination of ADP/ATP translocase isoform ratios in malignancy and cellular senescence

Cellular senescence has recently been recognized as a significant contributor to the poor prognosis of glioblastoma, one of the most aggressive brain tumors. Consequently, effectively eliminating senescent glioblastoma cells could benefit patients. Human ADP/ATP translocases (ANTs) play a role in oxidative phosphorylation in both normal and tumor cells. Previous research has shown that the sensitivity of senescent cells to mitochondria-targeted senolytics depends on the level of ANT2. Here, we systematically mapped the transcript and protein levels of ANT isoforms in various types of senescence and glioblastoma tumorigenesis. We employed bioinformatics analysis, targeted mass spectrometry, RT-PCR, immunoblotting, and assessment of cellular energy state to elucidate how individual ANT isoforms are expressed during the development of senescence in noncancerous and glioblastoma cells. We observed a consistent elevation of ANT1 protein levels across all tested senescence types, while ANT2 and ANT3 exhibited variable changes. Alterations in ANT protein isoform levels correlated with shifts in the cellular oxygen consumption rate. Our findings suggest that ANT isoforms are mutually interchangeable for oxidative phosphorylation and manipulating individual ANT isoforms could have potential for senolytic therapy.

Turnover atlas of proteome and phosphoproteome across mouse tissues and brain regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications such as phosphorylation are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites in eight mouse tissues and various brain regions using advanced proteomics and stable isotope labeling. We reveal tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discover a remarkable pattern of turnover changes for peroxisome proteins in specific tissues and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides fundamental insights into protein stability, tissue dynamic proteotypes, and functional protein phosphorylation and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

Crystal structure of Isthmin-1 and reassessment of its functional role in pre-adipocyte signaling

Isthmin-1 (ISM1) is a recently described adipokine with insulin-like properties that can control hyperglycemia and liver steatosis. Additionally, ISM1 is proposed to play critical roles in patterning, angiogenesis, vascular permeability, and apoptosis. A key feature of ISM1 is its AMOP (adhesion-associated domain in MUC4 (Mucin-4) and other proteins) domain which is essential for many of its functions. However, the molecular details of AMOP domains remain elusive as there are no descriptions of their structure. Here we determined the crystal structure of ISM1 including its thrombospondin type I repeat (TSR) and AMOP domain. Interestingly, ISM1's AMOP domain exhibits a distinct fold with similarities to bacterial streptavidin. When comparing our structure to predicted structures of other AMOP domains, we observed that while the core streptavidin-like barrel is conserved, the surface helices and loops vary greatly. Thus, the AMOP domain fold allows for structural plasticity that may underpin its diverse functions. Furthermore, and contrary to prior studies, we show that highly purified ISM1 does not stimulate AKT phosphorylation on 3T3-F442A pre-adipocytes. Rather, we find that co-purifying growth factors are responsible for this activity. Together, our data reveal the structure and clarify functional studies of this enigmatic protein.

Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling

Deep molecular phenotyping of cells at transcriptomic and proteomic levels is an essential first step to understanding cellular contributions to development, aging, injury, and disease. Since proteome and transcriptome level abundances only modestly correlate with each other, complementary profiling of both is needed. We report a novel method called simultaneous protein and RNA -omics (SPARO) to capture the cell type-specific transcriptome and proteome simultaneously from both in vitro and in vivo experimental model systems. This method leverages the ability of biotin ligase, TurboID, to biotinylate cytosolic proteins including ribosomal and RNA-binding proteins, which allows enrichment of biotinylated proteins for proteomics as well as protein-associated RNA for transcriptomics. We validated this approach first using well-controlled in vitro systems to verify that the proteomes and transcriptomes obtained reflect the ground truth, bulk proteomes and transcriptomes. We also show that the effect of a biological stimulus (e.g., neuroinflammatory activation by lipopolysaccharide) can be faithfully captured. We also applied this approach to obtain native-state proteomes and transcriptomes from two key neural cell types, astrocytes and neurons, thereby validating the in vivo application of SPARO. Next, we used these data to interrogate protein-mRNA concordance and discordance across these cell types, providing insights into groups of molecular processes that exhibit uniform or cell type-specific patterns of mRNA-protein discordance.

A Comprehensive and Robust Multiplex-DIA Workflow Profiles Protein Turnover Regulations Associated with Cisplatin Resistance

Measuring protein turnover is essential for understanding cellular biological processes and advancing drug discovery. The multiplex DIA mass spectrometry (DIA-MS) approach, combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC), has proven to be a reliable method for analyzing protein turnover and degradation kinetics. Previous multiplex DIA-MS workflows have employed various strategies, including leveraging the highest isotopic labeling channels of peptides to enhance the detection of isotopic MS signal pairs or clusters. In this study, we introduce an improved and robust workflow that integrates a novel machine learning strategy and channel-specific statistical filtering, enabling dynamic adaptation to systematic or temporal variations in channel ratios. This allows comprehensive profiling of protein turnover throughout the pSILAC experiment without relying solely on the highest channel signals. Additionally, we developed KdeggeR , a data processing and analysis package optimized for pSILAC-DIA experiments, which estimates and visualizes peptide and protein degradation rates and dynamic profiles. Our integrative workflow was benchmarked on both 2-channel and 3-channel standard DIA datasets and across two mass spectrometry platforms, demonstrating its broad applicability. Finally, applying this workflow to an aneuploid cancer cell model before and after cisplatin resistance development demonstrated a strong negative correlation between transcript regulation and protein degradation for major protein complex subunits. We also identified specific protein turnover signatures associated with cisplatin resistance.

Microbiome-induced reprogramming in post-transcriptional landscape using nanopore direct RNA sequencing

It has been widely recognized that the microbiota has the capacity to shape host gene expression and physiological functions. However, there remains a paucity of comprehensive study revealing the host transcriptional landscape regulated by the microbiota. Here, we comprehensively examined mRNA landscapes in mouse tissues (brain and cecum) from specific-pathogen-free and germ-free mice using nanopore direct RNA sequencing. Our results show that the microbiome has global influence on a host's RNA modifications (m6A, m5C, Ψ), isoform generation, poly(A) tail length, and transcript abundance in both brain and cecum tissues. Moreover, the microbiome exerts tissue-specific effects on various post-transcriptional regulatory processes. In addition, the microbiome impacts the coordination of multiple RNA modifications in host brain and cecum tissues. In conclusion, we establish the relationship between microbial regulation and gene expression. Our results help the understanding of the mechanisms by which the microbiome reprograms host gene expression.

An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications like phosphorylation, are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites across eight mouse tissues and various brain regions, using advanced proteomics and stable isotope labeling. We revealed tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discovered that peroxisomes are regulated by protein turnover across tissues, and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides new fundamental insights into protein stability, tissue dynamic proteotypes, and the role of protein phosphorylation, and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

The proteomic landscape and temporal dynamics of mammalian gastruloid development

Gastrulation is the highly coordinated process by which the early embryo breaks symmetry, establishes germ layers and a body plan, and sets the stage for organogenesis. As early mammalian development is challenging to study in vivo, stem cell-derived models have emerged as powerful surrogates, e.g. human and mouse gastruloids. However, although single cell RNA-seq (scRNA-seq) and high-resolution imaging have been extensively applied to characterize such in vitro embryo models, a paucity of measurements of protein dynamics and regulation leaves a major gap in our understanding. Here, we sought to address this by applying quantitative proteomics to human and mouse gastruloids at four key stages of their differentiation (naïve ESCs, primed ESCs, early gastruloids, late gastruloids). To the resulting data, we perform network analysis to map the dynamics of expression of macromolecular protein complexes and biochemical pathways, including identifying cooperative proteins that associate with them. With matched RNA-seq and phosphosite data from these same stages, we investigate pathway-, stage- and species-specific aspects of translational and post-translational regulation, e.g. finding peri-gastrulation stages of human and mice to be discordant with respect to the mitochondrial transcriptome vs. proteome, and nominating novel kinase-substrate relationships based on phosphosite dynamics. Finally, we leverage correlated dynamics to identify conserved protein networks centered around congenital disease genes. Altogether, our data (https://gastruloid.brotmanbaty.org/) and analyses showcase the potential of intersecting in vitro embryo models and proteomics to advance our understanding of early mammalian development in ways not possible through transcriptomics alone.

Playing pin-the-tail-on-the-protein in extracellular vesicle (EV) proteomics

Extracellular vesicles (EVs) are anucleate particles enclosed by a lipid bilayer that are released from cells via exocytosis or direct budding from the plasma membrane. They contain an array of important molecular cargo such as proteins, nucleic acids, and lipids, and can transfer these cargoes to recipient cells as a means of intercellular communication. One of the overarching paradigms in the field of EV research is that EV cargo should reflect the biological state of the cell of origin. The true relationship or extent of this correlation is confounded by many factors, including the numerous ways one can isolate or enrich EVs, overlap in the biophysical properties of different classes of EVs, and analytical limitations. This presents a challenge to research aimed at detecting low-abundant EV-encapsulated nucleic acids or proteins in biofluids for biomarker research and underpins technical obstacles in the confident assessment of the proteomic landscape of EVs that may be affected by sample-type specific or disease-associated proteoforms. Improving our understanding of EV biogenesis, cargo loading, and developments in top-down proteomics may guide us towards advanced approaches for selective EV and molecular cargo enrichment, which could aid EV diagnostics and therapeutics research.

Role of AMPK in Atrial Metabolic Homeostasis and Substrate Preference

Atrial fibrillation is the most common clinical arrhythmia and may be due in part to metabolic stress. Atrial specific deletion of the master metabolic sensor, AMP-activated protein kinase (AMPK), induces atrial remodeling culminating in atrial fibrillation in mice, implicating AMPK signaling in the maintenance of atrial electrical and structural homeostasis. However, atrial substrate preference for mitochondrial oxidation and the role of AMPK in regulating atrial metabolism are unknown. Here, using LC-MS/MS methodology combined with infusions of [ 13 C 6 ]glucose and [ 13 C 4 ]β-hydroxybutyrate in conscious mice, we demonstrate that conditional deletion of atrial AMPK catalytic subunits shifts mitochondrial atrial metabolism away from fatty acid oxidation and towards pyruvate oxidation. LC-MS/MS-based quantification of acyl-CoAs demonstrated decreased atrial tissue content of long-chain fatty acyl-CoAs. Proteomic analysis revealed a broad downregulation of proteins responsible for fatty acid uptake (LPL, CD36, FABP3), acylation and oxidation. Atrial AMPK deletion reduced expression of atrial PGC1-α and downstream PGC1-α/PPARα/RXR regulated gene transcripts. In contrast, atrial [ 14 C]2-deoxyglucose uptake and GLUT1 expression increased with fasting in mice with AMPK deletion, while the expression of glycolytic enzymes exhibited heterogenous changes. Thus, these results highlight the crucial homeostatic role of AMPK in the atrium, with loss of atrial AMPK leading to downregulation of the PGC1-α/PPARα pathway and broad metabolic reprogramming with a loss of fatty acid oxidation, which may contribute to atrial remodeling and arrhythmia.

IsoBayes: a Bayesian approach for single-isoform proteomics inference

Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

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.

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

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

Reciprocal antagonism of PIN1-APC/CCDH1 governs mitotic protein stability and cell cycle entry

Induced oncoproteins degradation provides an attractive anti-cancer modality. Activation of anaphase-promoting complex (APC/CCDH1) prevents cell-cycle entry by targeting crucial mitotic proteins for degradation. Phosphorylation of its co-activator CDH1 modulates the E3 ligase activity, but little is known about its regulation after phosphorylation and how to effectively harness APC/CCDH1 activity to treat cancer. Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1)-catalyzed phosphorylation-dependent cis-trans prolyl isomerization drives tumor malignancy. However, the mechanisms controlling its protein turnover remain elusive. Through proteomic screens and structural characterizations, we identify a reciprocal antagonism of PIN1-APC/CCDH1 mediated by domain-oriented phosphorylation-dependent dual interactions as a fundamental mechanism governing mitotic protein stability and cell-cycle entry. Remarkably, combined PIN1 and cyclin-dependent protein kinases (CDKs) inhibition creates a positive feedback loop of PIN1 inhibition and APC/CCDH1 activation to irreversibly degrade PIN1 and other crucial mitotic proteins, which force permanent cell-cycle exit and trigger anti-tumor immunity, translating into synergistic efficacy against triple-negative breast cancer.

EGFR targeting PhosTACs as a dual inhibitory approach reveals differential downstream signaling

We recently developed a heterobifunctional approach [phosphorylation targeting chimeras (PhosTACs)] to achieve the targeted protein dephosphorylation (TPDephos). Here, we envisioned combining the inhibitory effects of receptor tyrosine kinase inhibitors (RTKIs) and the active dephosphorylation by phosphatases to achieve dual inhibition of kinases. We report an example of tyrosine phosphatase-based TPDephos and the effective epidermal growth factor receptor (EGFR) tyrosine dephosphorylation. We also used phosphoproteomic approaches to study the signaling transductions affected by PhosTAC-related molecules at the proteome-wide level. This work demonstrated the differential signaling pathways inhibited by PhosTAC compared with the TKI, gefitinib. Moreover, a covalent PhosTAC selective for mutated EGFR was developed and showed its inhibitory potential for dysregulated EGFR. Last, EGFR PhosTACs, consistent with EGFR dephosphorylation profiles, induced apoptosis and inhibited cancer cell viability during prolonged PhosTAC treatment. PhosTACs showcased their potential of modulating RTKs activity, expanding the scope of bifunctional molecule utility.

Wnt5 controls splenic myelopoiesis and neutrophil functional ambivalency during DSS-induced colitis

Neutrophils are important innate immune cells with plasticity, heterogenicity, and functional ambivalency. While bone marrow is often regarded as the primary source of neutrophil production, the roles of extramedullary production in regulating neutrophil plasticity and heterogenicity in autoimmune diseases remain poorly understood. Here, we report that the lack of wingless-type MMTV integration site family member 5 (WNT5) unleashes anti-inflammatory protection against colitis in mice, accompanied by reduced colonic CD8+ T cell activation and enhanced splenic extramedullary myelopoiesis. In addition, colitis upregulates WNT5 expression in splenic stromal cells. The ablation of WNT5 leads to increased splenic production of hematopoietic niche factors, as well as elevated numbers of splenic neutrophils with heightened CD8+ T cell suppressive capability, in part due to elevated CD101 expression and attenuated pro-inflammatory activities. Thus, our study reveals a mechanism by which neutrophil plasticity and heterogenicity are regulated in colitis through WNT5 and highlights the role of splenic neutrophil production in shaping inflammatory outcomes.

A Systematic Investigation of Proteoforms with N-Terminal Glycine and Their Dynamics Reveals Its Impacts on Protein Stability

The N-termini of proteins can regulate their degradation, and the same protein with different N-termini may have distinct dynamics. Recently, it was found that N-terminal glycine can serve as a degron recognized by two E3 ligases, but N-terminal glycine was also reported to stabilize proteins. Here we developed a chemoenzymatic method for selective enrichment of proteoforms with N-terminal glycine and integrated dual protease cleavage to further improve the enrichment specificity. Over 2000 unique peptides with protein N-terminal glycine were analyzed from >1000 proteins, and most of them are previously unknown, indicating the effectiveness of the current method to capture low-abundance proteoforms with N-terminal glycine. The degradation rates of proteoforms with N-terminal glycine were quantified along with those of proteins from the whole proteome. Bioinformatic analyses reveal that proteoforms with N-terminal glycine with the fastest and slowest degradation rates have different functions and localizations. Membrane proteins with N-terminal glycine and proteins with N-terminal glycine from the N-terminal methionine excision degrade more rapidly. Furthermore, the secondary structures, adjacent amino acid residues, and protease specificities for N-terminal glycine are also vital for protein degradation. The results advance our understanding of the effects of N-terminal glycine on protein properties and functions.

The CUL5 E3 ligase complex negatively regulates central signaling pathways in CD8+ T cells

CD8+ T cells play an important role in anti-tumor immunity. Better understanding of their regulation could advance cancer immunotherapies. Here we identify, via stepwise CRISPR-based screening, that CUL5 is a negative regulator of the core signaling pathways of CD8+ T cells. Knocking out CUL5 in mouse CD8+ T cells significantly improves their tumor growth inhibiting ability, with significant proteomic alterations that broadly enhance TCR and cytokine signaling and their effector functions. Chemical inhibition of neddylation required by CUL5 activation, also enhances CD8 effector activities with CUL5 validated as a major target. Mechanistically, CUL5, which is upregulated by TCR stimulation, interacts with the SOCS-box-containing protein PCMTD2 and inhibits TCR and IL2 signaling. Additionally, CTLA4 is markedly upregulated by CUL5 knockout, and its inactivation further enhances the anti-tumor effect of CUL5 KO. These results together reveal a negative regulatory mechanism for CD8+ T cells and have strong translational implications in cancer immunotherapy.

An integrated workflow for quantitative analysis of the newly synthesized proteome

The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight into the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for the enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we report the optimization of multiple steps in sample preparation, mass spectrometry and data analysis, and we integrate them into a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enable the analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We apply QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of targets 2 h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight into complex cellular processes.

Workability of mRNA Sequencing for Predicting Protein Abundance

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

A Role of Multi-Omics Technologies in Sheep and Goat Meats: Progress and Way Ahead

Sheep and goat meats are increasingly popular worldwide due to their superior nutritional properties and distinctive flavor profiles. In recent decades, substantial progress in meat science has facilitated in-depth examinations of ovine and caprine muscle development during the antemortem phase, as well as post-mortem changes influencing meat attributes. To elucidate the intrinsic molecular mechanisms and identify potential biomarkers associated with meat quality, the methodologies employed have evolved from traditional physicochemical parameters (such as color, tenderness, water holding capacity, flavor, and pH) to some cutting-edge omics technologies, including transcriptomics, proteomics, and metabolomics approaches. This review provides a comprehensive analysis of multi-omics techniques and their applications in unraveling sheep and goat meat quality attributes. In addition, the challenges and future perspectives associated with implementing multi-omics technologies in this area of study are discussed. Multi-omics tools can contribute to deciphering the molecular mechanism responsible for the altered the meat quality of sheep and goats across transcriptomic, proteomic, and metabolomic dimensions. The application of multi-omics technologies holds great potential in exploring and identifying biomarkers for meat quality and quality control, thereby promoting the optimization of production processes in the sheep and goat meat industry.

Multi-Omics Approaches to Improve Meat Quality and Taste Characteristics

With rapid advances in meat science in recent decades, changes in meat quality during the pre-slaughter phase of muscle growth and the post-slaughter process from muscle to meat have been investigated. Commonly used techniques have evolved from early physicochemical indicators such as meat color, tenderness, water holding capacity, flavor, and pH to various omic tools such as genomics, transcriptomics, proteomics, and metabolomics to explore fundamental molecular mechanisms and screen biomarkers related to meat quality and taste characteristics. This review highlights the application of omics and integrated multi-omics in meat quality and taste characteristics studies. It also discusses challenges and future perspectives of multi-omics technology to improve meat quality and taste. Consequently, multi-omics techniques can elucidate the molecular mechanisms responsible for changes of meat quality at transcriptome, proteome, and metabolome levels. In addition, the application of multi-omics technology has great potential for exploring and identifying biomarkers for meat quality and quality control that can make it easier to optimize production processes in the meat industry.

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

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

Highlights

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

An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells

The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

Qingyihuaji Formula promotes apoptosis and autophagy through inhibition of MAPK/ERK and PI3K/Akt/mTOR signaling pathway on pancreatic cancer in vivo and in vitro

ETHNOPHARMACOLOGICAL RELEVANCE

Qingyihuaji Formula (QYHJ), a widely used traditional Chinese medicine (TCM), has been used to treat patients with cancer in China. However, the effect and mechanism of QYHJ on pancreatic ductal adenocarcinoma (PDAC) remains unclear.

AIM OF THE STUDY

This study aimed to explore the roles and evaluate the possible underlying molecular mechanisms of QYHJ and its core component in PDAC using label-free quantitative proteomics in conjunction with network pharmacology-based analysis.

MATERIALS AND METHODS

By screening differentially expressed proteins (DEPs) in proteomics and QYHJ-predicted gene sets, we identified QYHJ-related PDAC targets annotated with bioinformatic analysis. A subcutaneous tumor model was established to assess the role of QYHJ in vivo. The effects of quercetin (Que), a core component of QYHJ, on cell proliferation, migration, invasion, apoptosis, and autophagy in SW1990 and PANC-1 cells were investigated in vitro. Immunohistochemistry, western blotting, mRFP-GFP-LC3 adenovirus, and kinase analysis were used to determine the underlying mechanisms.

RESULTS

Bioinformatics analysis revealed that 41 QYHJ-related PDAC targets were closely related to the cellular response to nitrogen compounds, positive regulation of cell death, regulation of epithelial cell apoptotic processes, and chemokine signaling pathways. CASP3, SRC, STAT1, PTPN11, PKM, and PAK1 with high expression were identified as hub DEPs in the PPI network, and these DEPs were associated with poor overall survival and STAT 1, MAPK/ERK, and PI3K/Akt/mTOR signaling pathways in PDAC patients. QYHJ significantly promoted tumor death in nude mice. Moreover, quercetin inhibited the proliferation, migration, and invasion of PDAC cells. Additionally, Que induced apoptosis and autophagy in PDAC cells. Mechanistically, QYHJ and Que significantly activated STAT 1 and remarkably inhibited the MAPK/ERK and PI3K/Akt/mTOR signaling pathways in vivo and in vitro, respectively. Importantly, ERK1/2 inactivation contributes to que-induced apoptosis in SW1990 and PANC-1 cells.

CONCLUSIONS

These results suggest that QYHJ and Que are promising anti-PDAC avenues that benefit from their multiform mechanisms.

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

IRF4 as a novel target involved in malignant transformation of oral submucous fibrosis into oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) in the context of oral submucous fibrosis (OSF) has a high incidence owing to undefined pathogenesis. Identifying key genes and exploring the underlying molecular mechanisms involved in the conversion of OSF into OSCC are in urgent need. Differentially expressed genes (DEGs) between OSCC and OSF were dug from GEO databases and a total of 170 DEGs were acquired. Functional association of DEGs were analyzed by GO and KEGG. Protein-protein interactions (PPIs) analysis was carried out and candidate biomarkers were identified by Gene co-expression analysis and Cox analyses. Hub genes were confirmed by qRT-PCR in tissues and cell lines, of which we found that IRF4 mRNA was successively up-regulated from Normal to OSF and then to OSCC and associated with immune infiltrating levels. In addition, Immunohistochemical (IHC) and Immunofluorescence (IF) assays were conducted to validate the consistent upregulation of IRF4 and the oncogene role of IRF4 in OSF and OSCC at translation level. IRF4 may be indicative biomarker in transformation of OSF into OSCC. High IRF4 expression contribute to increased immune infiltration of OSCC and may provide a novel diagnostic marker for OSCC patients translated from OSF.

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.

Reciprocal inhibition of PIN1 and APC/CCDH1 controls timely G1/S transition and creates therapeutic vulnerability

Cyclin-dependent kinases (CDKs) mediated phosphorylation inactivates the anaphase-promoting complex (APC/CCDH1), an E3 ubiquitin ligase that contains the co-activator CDH1, to promote G1/S transition. PIN1 is a phosphorylation-directed proline isomerase and a master cancer signaling regulator. However, little are known about APC/CCDH1 regulation after phosphorylation and about PIN1 ubiquitin ligases. Here we uncover a domain-oriented reciprocal inhibition that controls the timely G1/S transition: The non-phosphorylated APC/CCDH1 E3 ligase targets PIN1 for degradation in G1 phase, restraining G1/S transition; APC/CCDH1 itself, after phosphorylation by CDKs, is inactivated by PIN1-catalyzed isomerization, promoting G1/S transition. In cancer, PIN1 overexpression and APC/CCDH1 inactivation reinforce each other to promote uncontrolled proliferation and tumorigenesis. Importantly, combined PIN1- and CDK4/6-inhibition reactivates APC/CCDH1 resulting in PIN1 degradation and an insurmountable G1 arrest that translates into synergistic anti-tumor activity against triple-negative breast cancer in vivo. Reciprocal inhibition of PIN1 and APC/CCDH1 is a novel mechanism to control timely G1/S transition that can be harnessed for synergistic anti-cancer therapy.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Advances in stable isotope labeling: dynamic labeling for spatial and temporal proteomic analysis

The field of proteomics is continually improving, requiring the development of new quantitative methods. Stable isotope labeling in cell culture (SILAC) is a metabolic labeling technique originating in the early 2000s. By incorporating isotopically labeled amino acids into the media used for cell culture, unlabeled versus labeled cells can be differentiated by the mass spectrometer. Traditional SILAC labeling has been expanded to pulsed applications allowing for a new quantitative dimension of proteomics - temporal analysis. The complete introduction of Heavy SILAC labeling chased with surplus unlabeled medium mimics traditional pulse-chase experiments and allows for the loss of heavy signal to track proteomic changes over time. In a similar fashion, pulsed SILAC (pSILAC) monitors the initial incorporation of a heavy label across a period of time, which allows for the rate of protein label integration to be assessed. These innovative techniques have aided in inspiring numerous SILAC-based temporal and spatial labeling applications, including super SILAC, spike-in SILAC, spatial SILAC, and a revival in label multiplexing. This review reflects upon the evolution of SILAC and the pulsed SILAC application, introduces advances in SILAC labeling, and proposes future perspectives for this novel and exciting field.

Enhanced protein isoform characterization through long-read proteogenomics

BACKGROUND

The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms.

RESULTS

We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.

CONCLUSIONS

Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.

Characterization of protein isoform diversity in human umbilical vein endothelial cells via long-read proteogenomics

Endothelial cells (ECs) comprise the lumenal lining of all blood vessels and are critical for the functioning of the cardiovascular system. Their phenotypes can be modulated by alternative splicing of RNA to produce distinct protein isoforms. To characterize the RNA and protein isoform landscape within ECs, we applied a long read proteogenomics approach to analyse human umbilical vein endothelial cells (HUVECs). Transcripts delineated from PacBio sequencing serve as the basis for a sample-specific protein database used for downstream mass-spectrometry (MS) analysis to infer protein isoform expression. We detected 53,863 transcript isoforms from 10,426 genes, with 22,195 of those transcripts being novel. Furthermore, the predominant isoform in HUVECs does not correspond with the accepted "reference isoform" 25% of the time, with vascular pathway-related genes among this group. We found 2,597 protein isoforms supported through unique peptides, with an additional 2,280 isoforms nominated upon incorporation of long-read transcript evidence. We characterized a novel alternative acceptor for endothelial-related gene CDH5, suggesting potential changes in its associated signalling pathways. Finally, we identified novel protein isoforms arising from a diversity of RNA splicing mechanisms supported by uniquely mapped novel peptides. Our results represent a high-resolution atlas of known and novel isoforms of potential relevance to endothelial phenotypes and function.[Figure: see text].

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.

Ribosome-Inactivating Proteins of Bougainvillea glabra Uncovered Polymorphism and Active Site Divergence

Ribosome-inactivating proteins (RIPs) are toxic proteins that can inhibit protein synthesis. RIPs purified from Bougainvillea have low nonspecific toxicity, showing promise for processing applications in the agricultural and medical fields. However, systematic research on the polymorphism of Bougainvillea RIPs is lacking, and it is worth exploring whether different isoforms differ in their active characteristics. The transcriptional and translational expression of type I RIPs in Bougainvillea glabra leaves was investigated in this study. Seven RIPs exhibited seasonal variation at both the mRNA and protein levels. The isoforms BI4 and BI6 showed the highest transcriptional expression in both the summer and autumn samples. Interestingly, BI6 was not detected in the protein level in any of the samples. However, the bioinformatics analysis showed that RIPs derived from the same species were gathered in a different cluster, and that the active sites changed among the isoforms during evolution. The significant discrepancy in Bougainvillea RIPs mainly locates at both termini of the amino acid sequence, particularly at the C terminus. Post-translational modifications may also exist in Bougainvillea RIPs. It is concluded that the reason for the polymorphism of Bougainvillea RIPs may be that these proteins are encoded by multiple genes due to genetic processes such as gene duplication and mutation. According to the results of sequence analysis, the possible functional differences of B. glabra RIP isoforms are discussed with regard to the observed discrepancy in both active sites and structures.

BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples

The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

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.

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.

Global and Site-Specific Effect of Phosphorylation on Protein Turnover

To date, the effects of specific modification types and sites on protein lifetime have not been systematically illustrated. Here, we describe a proteomic method, DeltaSILAC, to quantitatively assess the impact of site-specific phosphorylation on the turnover of thousands of proteins in live cells. Based on the accurate and reproducible mass spectrometry-based method, a pulse labeling approach using stable isotope-labeled amino acids in cells (pSILAC), phosphoproteomics, and a unique peptide-level matching strategy, our DeltaSILAC profiling revealed a global, unexpected delaying effect of many phosphosites on protein turnover. We further found that phosphorylated sites accelerating protein turnover are functionally selected for cell fitness, enriched in Cyclin-dependent kinase substrates, and evolutionarily conserved, whereas the glutamic acids surrounding phosphosites significantly delay protein turnover. Our method represents a generalizable approach and provides a rich resource for prioritizing the effects of phosphorylation sites on protein lifetime in the context of cell signaling and disease biology.

Proteomics in Non-model Organisms: A New Analytical Frontier

For the last century we have relied on model organisms to help understand fundamental biological processes. Now, with advancements in genome sequencing, assembly, and annotation, non-model organisms may be studied with the same advanced bioanalytical toolkit as model organisms. Proteomics is one such technique, which classically relies on predicted protein sequences to catalog and measure complex proteomes across tissues and biofluids. Applying proteomics to non-model organisms can advance and accelerate biomimicry studies, biomedical advancements, veterinary medicine, agricultural research, behavioral ecology, and food safety. In this postmodel organism era, we can study almost any species, meaning that many non-model organisms are, in fact, important emerging model organisms. Herein we specifically focus on eukaryotic organisms and discuss the steps to generate sequence databases, analyze proteomic data with or without a database, and interpret results as well as future research opportunities. Proteomics is more accessible than ever before and will continue to rapidly advance in the coming years, enabling critical research and discoveries in non-model organisms that were hitherto impossible.

mRNAs, proteins and the emerging principles of gene expression control

Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.