Correlation between protein and mRNA abundance in yeast

Method to link medicines to diseases using multiplex networks

The reuse of well-established medicines using computational modeling has gained a lot of attention due to its tremendous benefits. Based on this perspective, a new method for linking known medicines to diseases is proposed. The creation of a new treatment or medicine can be financially and temporally costly and the reuse of medicines is one possibility to accelerate this process efficiently. The main purpose of the reuse of medicines is to reduce some stages of the development of new medicines, motivating the proposition of several methods nowadays. In this work, a new method is developed aiming to connect known medicines to diseases based on available networks of protein interactions and available lists of medicines that affect protein action. The concepts of multiplex networks are used to connect subgraphs of vertices that represent medicines and proteins. The core of the procedure is determined by a weighting strategy constructed to define precisely the more relevant connections. The method was compared to other network link methods in the literature and a case study was presented and evaluated by the proposed method.

Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

Gene Expression Analysis of Yeast Strains with a Nonsense Mutation in the eRF3-Coding Gene Highlights Possible Mechanisms of Adaptation

In yeast Saccharomyces cerevisiae, there are two translation termination factors, eRF1 (Sup45) and eRF3 (Sup35), which are essential for viability. Previous studies have revealed that presence of nonsense mutations in these genes leads to amplification of mutant alleles (sup35-n and sup45-n), which appears to be necessary for the viability of such cells. However, the mechanism of this phenomenon remained unclear. In this study, we used RNA-Seq and proteome analysis to reveal the complete set of gene expression changes that occur during cellular adaptation to the introduction of the sup35-218 nonsense allele. Our analysis demonstrated significant changes in the transcription of genes that control the cell cycle: decreases in the expression of genes of the anaphase promoting complex APC/C (APC9, CDC23) and their activator CDC20, and increases in the expression of the transcription factor FKH1, the main cell cycle kinase CDC28, and cyclins that induce DNA biosynthesis. We propose a model according to which yeast adaptation to nonsense mutations in the translation termination factor genes occurs as a result of a delayed cell cycle progression beyond the G2-M stage, which leads to an extension of the S and G2 phases and an increase in the number of copies of the mutant sup35-n allele.

Single-cell proteomics by mass spectrometry: Advances and implications in cancer research

Cancer harbours extensive proteomic heterogeneity. Inspired by the prior success of single-cell RNA sequencing (scRNA-seq) in characterizing minute transcriptomics heterogeneity in cancer, researchers are now actively searching for information regarding the proteomics counterpart. Therefore recently, single-cell proteomics by mass spectrometry (SCP) has rapidly developed into state-of-the-art technology to cater the need. This review aims to summarize application of SCP in cancer research, while revealing current development progress of SCP technology. The review also aims to contribute ideas into research gaps and future directions, ultimately promoting the application of SCP in cancer research.

Next generation sequencing technologies to address aberrant mRNA translation in cancer

In this review, we explore the transformative impact of next generation sequencing technologies in the realm of translatomics (the study of how translational machinery acts on a genome-wide scale). Despite the expectation of a direct correlation between mRNA and protein content, the complex regulatory mechanisms that affect this relationship remark the limitations of standard RNA-seq approaches. Then, the review characterizes crucial techniques such as polysome profiling, ribo-seq, trap-seq, proximity-specific ribosome profiling, rnc-seq, tcp-seq, qti-seq and scRibo-seq. All these methods are summarized within the context of cancer research, shedding light on their applications in deciphering aberrant translation in cancer cells. In addition, we encompass databases and bioinformatic tools essential for researchers that want to address translatome analysis in the context of cancer biology.

Establishing a conceptual framework for holistic cell states and state transitions

Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.

Aqueous humor perturbations in chronic smokers: a proteomic study

The detrimental effects of smoking are multisystemic and its effects on the eye health are significant. Smoking is a strong risk factor for age-related nuclear cataract, age-related macular degeneration, glaucoma, delayed corneal epithelial healing and increased risk of cystoid macular edema in patients with intermediate uveitis among others. We aimed to characterize the aqueous humor (AH) proteome in chronic smokers to gain insight into its perturbations and to identify potential biomarkers for smoking-associated ocular pathologies. Compared to the control group, chronic smokers displayed 67 (37 upregulated, 30 downregulated) differentially expressed proteins (DEPs). Analysis of DEPs from the biological point of view revealed that they were proteins involved in complement activation, lymphocyte mediated immunity, innate immune response, cellular oxidant detoxification, bicarbonate transport and platelet degranulation. From the molecular function point of view, DEPs were involved in oxygen binding, oxygen carrier activity, hemoglobin binding, peptidase/endopeptidase/cysteine-type endopeptidase inhibitory activity. Several of the upregulated proteins were acute phase reactant proteins such as clusterin, alpha-2-HS-glycoprotein, fibrinogen, alpha-1-antitrypsin, C4b-binding protein and serum amyloid A-2. Further research should confirm if these proteins might serve as biomarkers or therapeutic target for smoking-associated ocular diseases.

Genomic context as well as sequence of both psr and penicillin-binding protein 5 contributes to β-lactam resistance in Enterococcus faecium

Penicillin-binding protein 5 (PBP5) of Enterococcus faecium (Efm) is vital for ampicillin resistance (AMP-R). We previously designated three forms of PBP5, namely, PBP5-S in Efm clade B strains [ampicillin susceptible (AMP-S)], PBP5-S/R (AMP-S or R), and PBP5-R (AMP-R) in clade A strains. Here, pbp5 deletion resulted in a marked reduction in AMP minimum inhibitory concentrations (MICs) to 0.01-0.09 µg/mL for clade B and 0.12-0.19 µg/mL for clade A strains; in situ complementation restored parental AMP MICs. Using D344SRF (lacking ftsW/psr/pbp5), constructs with ftsWA/psrA (from a clade A1 strain) cloned upstream of pbp5-S and pbp5-S/R alleles resulted in modest increases in MICs to 3-8 µg/mL, while high MICs (>64 µg/mL) were seen using pbp5 from A1 strains. Next, using ftsW ± psr from clade B and clade A/B and B/A hybrid constructs, the presence of psrB, even alone or in trans, resulted in much lower AMP MICs (3-8 µg/mL) than when psrA was present (MICs >64 µg/mL). qRT PCR showed relatively greater pbp5 expression (P = 0.007) with pbp5 cloned downstream of clade A1 ftsW/psr (MIC >128 µg/mL) vs when cloned downstream of clade B ftsW/psr (MIC 4-16 µg/mL), consistent with results in western blots. In conclusion, we report the effect of clade A vs B psr on AMP MICs as well as the impact of pbp5 alleles from different clades. While previously, Psr was not thought to contribute to AMP MICs in Efm, our results showed that the presence of psrB resulted in a major decrease in Efm AMP MICs.

IMPORTANCE

The findings of this study shed light on ampicillin resistance in Enterococcus faecium clade A strains. They underscore the significance of alterations in the amino acid sequence of penicillin-binding protein 5 (PBP5) and the pivotal role of the psr region in PBP5 expression and ampicillin resistance. Notably, the presence of a full-length psrB leads to reduced PBP5 expression and lower minimum inhibitory concentrations (MICs) of ampicillin compared to the presence of a shorter psrA, regardless of the pbp5 allele involved. Additionally, clade B E. faecium strains exhibit lower AMP MICs when both psr alleles from clades A and B are present, although it is important to consider other distinctions between clade A and B strains that may contribute to this effect. It is intriguing to note that the divergence between clade A and clade B E. faecium and the subsequent evolution of heightened AMP MICs in hospital-associated strains appear to coincide with changes in Pbp5 and psr. These changes in psr may have resulted in an inactive Psr, facilitating increased PBP5 expression and greater ampicillin resistance. These results raise the possibility that a mimicker of PsrB, if one could be designed, might be able to lower MICs of ampicillin-resistant E. faecium, thus potentially resorting ampicillin to our therapeutic armamentarium for this species.

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.

Proteomic Analysis of Domestic Cat Blastocysts and Their Secretome Produced in an In Vitro Culture System without the Presence of the Zona Pellucida

Domestic cat blastocysts cultured without the zona pellucida exhibit reduced implantation capacity. However, the protein expression profile has not been evaluated in these embryos. The objective of this study was to evaluate the protein expression profile of domestic cat blastocysts cultured without the zona pellucida. Two experimental groups were generated: (1) domestic cat embryos generated by IVF and cultured in vitro (zona intact, (ZI)) and (2) domestic cat embryos cultured in vitro without the zona pellucida (zona-free (ZF group)). The cleavage, morula, and blastocyst rates were estimated at days 2, 5 and 7, respectively. Day 7 blastocysts and their culture media were subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS). The UniProt Felis catus database was used to identify the standard proteome. No significant differences were found in the cleavage, morula, or blastocyst rates between the ZI and ZF groups (p > 0.05). Proteomic analysis revealed 22 upregulated and 20 downregulated proteins in the ZF blastocysts. Furthermore, 14 proteins involved in embryo development and implantation were present exclusively in the culture medium of the ZI blastocysts. In conclusion, embryo culture without the zona pellucida did not affect in vitro development, but altered the protein expression profile and release of domestic cat blastocysts.

A Ratiometric Catalog of Protein Isoform Shifts in the Cardiac Fetal Gene Program

The fetal genetic program orchestrates cardiac development and the re-expression of fetal genes is thought to underlie cardiac disease and adaptation. Here, a proteomics ratio test using mass spectrometry is applied to find protein isoforms with statistically significant usage differences in the fetal vs. postnatal mouse heart. Changes in isoform usage ratios are pervasive at the protein level, with 104 significant events observed, including 88 paralog-derived isoform switching events and 16 splicing-derived isoform switching events between fetal and postnatal hearts. The ratiometric proteomic comparisons rediscovered hallmark fetal gene signatures including a postnatal switch from fetal β (MYH7) toward ɑ (MYH6) myosin heavy chains and from slow skeletal muscle (TNNI1) toward cardiac (TNNI3) troponin I. Altered usages in metabolic proteins are prominent, including a platelet to muscle phosphofructokinase (PFKP - PFKM), enolase 1 to 3 (ENO1 - ENO3), and alternative splicing of pyruvate kinase M2 toward M1 (PKM2 - PKM1) isoforms in glycolysis. The data also revealed a parallel change in mitochondrial proteins in cardiac development, suggesting the shift toward aerobic respiration involves also a remodeling of the mitochondrial protein isoform proportion. Finally, a number of glycolytic protein isoforms revert toward their fetal forms in adult hearts under pathological cardiac hypertrophy, suggesting their functional roles in adaptive or maladaptive response, but this reversal is partial. In summary, this work presents a catalog of ratiometric protein markers of the fetal genetic program of the mouse heart, including previously unreported splice isoform markers.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding

Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

Creation of Knock-In Alleles of Insulin Receptor Tagged by Fluorescent Proteins mCherry or EYFP in Fruit Fly Drosophila melanogaster

The insulin/insulin-like growth factor-like signaling (IIS) pathway is highly conserved across metazoans and regulates numerous physiological functions, including development, metabolism, fecundity, and lifespan. The insulin receptor (InR), a crucial membrane receptor in the IIS pathway, is known to be ubiquitously expressed in various tissues, albeit at generally low levels, and its subcellular localization remains incompletely characterized. In this study, we employed CRISPR-mediated mutagenesis in the fruit fly Drosophila to create knock-in alleles of InR tagged with fluorescent proteins (InR::mCherry or InR::EYFP). By inserting the coding sequence of the fluorescent proteins mCherry or EYFP near the end of the coding sequence of the endogenous InR gene, we could trace the natural InR protein through their fluorescence. As an example, we investigated epithelial cells of the male accessory gland (AG), an internal reproductive organ, and identified two distinct patterns of InR::mCherry localization. In young AG, InR::mCherry accumulated on the basal plasma membrane between cells, whereas in mature AG, it exhibited intracellular localization as multiple puncta, indicating endocytic recycling of InR during cell growth. In the AG senescence accelerated by the mutation of Diuretic hormone 31 (Dh31), the presence of InR::mCherry puncta was more pronounced compared to the wild type. These findings raise expectations for the utility of the newly created InR::mCherry/EYFP alleles for studying the precise expression levels and subcellular localization of InR. Furthermore, this fluorescently tagged allele approach can be extended to investigate other membrane receptors with low abundance, facilitating the direct examination of their true expression and localization.

Fasting as a precursor to high-fat diet enhances mitochondrial resilience in Drosophila melanogaster

Changes in diet type and nutrient availability can impose significant environmental stress on organisms, potentially compromising physiological functions and reproductive success. In nature, dramatic fluctuations in dietary resources are often observed and adjustments to restore cellular homeostasis are crucial to survive this type of stress. In this study, we exposed male Drosophila melanogaster to two modulated dietary treatments: one without a fasting period before exposure to a high-fat diet and the other with a 24-h fasting period. We then investigated mitochondrial metabolism and molecular responses to these treatments. Exposure to a high-fat diet without a preceding fasting period resulted in disrupted mitochondrial respiration, notably at the level of complex I. On the other hand, a short fasting period before the high-fat diet maintained mitochondrial respiration. Generally, transcript abundance of genes associated with mitophagy, heat-shock proteins, mitochondrial biogenesis, and nutrient sensing pathways increased either slightly or significantly following a fasting period and remained stable when flies were subsequently put on a high-fat diet, whereas a drastic decrease of almost all transcript abundances was observed for all these pathways when flies were exposed directly to a high-fat diet. Moreover, mitochondrial enzymatic activities showed less variation after the fasting period than the treatment without a fasting period. Overall, our study sheds light on the mechanistic protective effects of fasting prior to a high-fat diet and highlights the metabolic flexibility of Drosophila mitochondria in response to abrupt dietary changes and have implication for adaptation of species to their changing environment.

Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis

Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy.

Expression profile of FASN gene and association of its polymorphisms with intramuscular fat content in Hu sheep

The content of intramuscular fat (IMF) is one of the most important factors that has a large impact on meat quality, and it is an effective way to improve IMF according to marker-assisted selection (MAS). Fatty-acid synthase (FASN) is a key gene in meat lipid deposition and fatty acid composition. Thus, this study was conducted to investigate the expression profile of FASN in mRNA and protein levels using real-time quantitative PCR (RT-qPCR) and western-blot methods. In addition, single nucleotide polymorphisms (SNPs) within FASN in 921 Hu rams with IMF content records were investigated using DNA-pooling sequencing and improved multiple ligase detection reaction (iMLDR) methods. Consequently, the highest mRNA expression level of FASN was observed in the perinephric fat, and the lowest in the liver among the 11 tissues analyzed, while no significant difference was found in mRNA and protein expression levels in longissimus dorsi among individuals with different IMF contents. A total of 10 putative SNPs were identified within FASN, and 9 of them can be genotyped by iMLDR method. Notably, two SNPs were significantly associated with IMF content, including NC_040262.1: g.5157 A > G in intron 5 (p = 0.046) and NC_040262.1: g.9413 T > C in intron 16 (p = 0.041), which supply molecular markers for improving meat quality in sheep breeding.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration

Next-generation risk assessment (NGRA) for environmental chemicals involves a weight of evidence (WoE) framework integrating a suite of new approach methodologies (NAMs) based on points of departure (PoD) obtained from in vitro assays. Among existing NAMs, the omic-based technologies are of particular importance based on the premise that any apical endpoint change indicative of impaired health must be underpinned by some alterations at the omics level, such as transcriptome, proteome, metabolome, epigenome and genome. Transcriptomic assay plays a leading role in providing relatively conservative PoDs compared with apical endpoints. However, it is unclear whether and how parameters measured with other omics techniques predict the cellular response to chemical perturbations, especially at exposure levels below the transcriptomically defined PoD. Multi-omics coverage may provide additional sensitive or confirmative biomarkers to complement and reduce the uncertainty in safety decisions made using targeted and transcriptomics assays. In the present study, we conducted multi-omics studies of transcriptomics, proteomics and phosphoproteomics on two prototype compounds, coumarin and 2,4-dichlorophenoxyacetic acid (2,4-D), with multiple chemical concentrations and time points, to understand the sensitivity of the three omics techniques in response to chemically-induced changes in HepG2. We demonstrated that, phosphoproteomics alterations occur not only earlier in time, but also more sensitive to lower concentrations than proteomics and transcriptomics when the HepG2 cells were exposed to various chemical treatments. The phosphoproteomics changes appear to approach maximum when the transcriptomics alterations begin to initiate. Therefore, it is proximal to the very early effects induced by chemical exposure. We concluded that phosphoproteomics can be utilized to provide a more complete coverage of chemical-induced cellular alteration and supplement transcriptomics-based health safety decision making.

Multi-Omics Analysis Reveals Intricate Gene Networks Involved in Female Development in Melon

Sexual differentiation is an important developmental phenomenon in cucurbits that directly affects fruit yield. The natural existence of multiple flower types in melon offers an inclusive structure for studying the molecular basis of sexual differentiation. The current study aimed to identify and characterize the molecular network involved in sex determination and female development in melon. Male and female pools separated by the F2 segregated generation were used for sequencing. The comparative multi-omics data revealed 551 DAPs and 594 DEGs involved in multiple pathways of melon growth and development, and based on functional annotation and enrichment analysis, we summarized four biological process modules, including ethylene biosynthesis, flower organ development, plant hormone signaling, and ubiquitinated protein metabolism, that are related to female development. Furthermore, the detailed analysis of the female developmental regulatory pathway model of ethylene biosynthesis, signal transduction, and target gene regulation identified some important candidates that might have a crucial role in female development. Two CMTs ((cytosine-5)-methyltransferase), one AdoHS (adenosylhomocysteinase), four ACSs (1-aminocyclopropane-1-carboxylic acid synthase), three ACOs (ACC oxidase), two ARFs (auxin response factor), four ARPs (auxin-responsive protein), and six ERFs (Ethylene responsive factor) were identified based on various female developmental regulatory models. Our data offer new and valuable insights into female development and hold the potential to offer a deeper comprehension of sex differentiation mechanisms in melon.

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.

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data

MOTIVATION

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges.

RESULTS

We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits.

AVAILABILITY AND IMPLEMENTATION

The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE-a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

The RNA binding proteins ZFP36L1 and ZFP36L2 are dysregulated in airway epithelium in human and a murine model of asthma

Introduction: Asthma is the most common chronic inflammatory disease of the airways. The airway epithelium is a key driver of the disease, and numerous studies have established genome-wide differences in mRNA expression between health and asthma. However, the underlying molecular mechanisms for such differences remain poorly understood. The human TTP family is comprised of ZFP36, ZFP36L1 and ZFP36L2, and has essential roles in immune regulation by determining the stability and translation of myriad mRNAs encoding for inflammatory mediators. We investigated the expression and possible role of the tristetraprolin (TTP) family of RNA binding proteins (RBPs), poorly understood in asthma. Methods: We analysed the levels of ZFP36, ZFP36L1 and ZFP36L2 mRNA in several publicly available asthma datasets, including single cell RNA-sequencing. We also interrogated the expression of known targets of these RBPs in asthma. We assessed the lung mRNA expression and cellular localization of Zfp36l1 and Zfp36l2 in precision cut lung slices in murine asthma models. Finally, we determined the expression in airway epithelium of ZFP36L1 and ZFP36L2 in human bronchial biopsies and performed rescue experiments in primary bronchial epithelium from patients with severe asthma. Results: We found ZFP36L1 and ZFP36L2 mRNA levels significantly downregulated in the airway epithelium of patients with very severe asthma in different cohorts (5 healthy vs. 8 severe asthma; 36 moderate asthma vs. 37 severe asthma on inhaled steroids vs. 26 severe asthma on oral corticoids). Integrating several datasets allowed us to infer that mRNAs potentially targeted by these RBPs are increased in severe asthma. Zfp36l1 was downregulated in the lung of a mouse model of asthma, and immunostaining of ex vivo lung slices with a dual antibody demonstrated that Zfp36l1/l2 nuclear localization was increased in the airway epithelium of an acute asthma mouse model, which was further enhanced in a chronic model. Immunostaining of human bronchial biopsies showed that airway epithelial cell staining of ZFP36L1 was decreased in severe asthma as compared with mild, while ZFP36L2 was upregulated. Restoring the levels of ZFP36L1 and ZFP36L2 in primary bronchial epithelial cells from patients with severe asthma decreased the mRNA expression of IL6, IL8 and CSF2. Discussion: We propose that the dysregulation of ZFP36L1/L2 levels as well as their subcellular mislocalization contributes to changes in mRNA expression and cytoplasmic fate in asthma.

Circadian ribosome profiling reveals a role for the Period2 upstream open reading frame in sleep

Many mammalian proteins have circadian cycles of production and degradation, and many of these rhythms are altered posttranscriptionally. We used ribosome profiling to examine posttranscriptional control of circadian rhythms by quantifying RNA translation in the liver over a 24-h period from circadian-entrained mice transferred to constant darkness conditions and by comparing ribosome binding levels to protein levels for 16 circadian proteins. We observed large differences in ribosome binding levels compared to protein levels, and we observed delays between peak ribosome binding and peak protein abundance. We found extensive binding of ribosomes to upstream open reading frames (uORFs) in circadian mRNAs, including the core clock gene Period2 (Per2). An increase in the number of uORFs in the 5'UTR was associated with a decrease in ribosome binding in the main coding sequence and a reduction in expression of synthetic reporter constructs. Mutation of the Per2 uORF increased luciferase and fluorescence reporter expression in 3T3 cells and increased luciferase expression in PER2:LUC MEF cells. Mutation of the Per2 uORF in mice increased Per2 mRNA expression, enhanced ribosome binding on Per2, and reduced total sleep time compared to that in wild-type mice. These results suggest that uORFs affect mRNA posttranscriptionally, which can impact physiological rhythms and sleep.

Differences in the expression of multidrug resistance proteins in chronic rhinosinusitis according to endotype

BACKGROUND

Chronic rhinosinusitis is a common disease of the nasal cavity and is classified into two major endotypes, which are neutrophilic and eosinophilic. Some patients with neutrophilic and eosinophilic chronic rhinosinusitis are refractory to treatment, and the mechanism of drug resistance is not completely understood.

METHODS

Nasal polyp samples were collected from patients with non-eosinophilic chronic rhinosinusitis (nECRS) and eosinophilic chronic rhinosinusitis (ECRS). Transcriptomic and proteomic analyses were performed simultaneously. Gene Ontology (GO) analysis was conducted to extract genes involved in drug resistance. Then, GO analysis results were validated via real-time polymerase chain reaction and immunohistochemistry analysis.

RESULTS

The nasal polyps of patients with ECRS were enriched with 110 factors in the genes and 112 in the proteins, unlike in those of patients with nECRS. GO analysis on the combined results of both showed that the factors involved in extracellular transportation were enriched. Our analysis focused on multidrug resistance protein 1-5 (MRP1-5). Real-time polymerase chain reaction revealed that the MRP4 expression was significantly upregulated in ECRS polyps. Immunohistochemical staining showed that the MRP3 and MRP4 expressions significantly increased in nECRS and ECRS, respectively. MRP3 and MRP4 expressions were positively correlated with the number of neutrophil and eosinophil infiltrates in polyps and associated with the tendency to relapse in patients with ECRS.

CONCLUSIONS

MRP is associated with treatment resistance and is expressed in nasal polyps. The expression pattern had different features based on chronic rhinosinusitis endotype. Therefore, drug resistance factors can be associated with therapeutic outcomes.

Distinct laccase expression and activity profiles of Trametes versicolor facilitate degradation of benzo[a]pyrene

A Trametes versicolor isolate from the Changbai Mountain showed promising activity in degrading benzo[a]pyrene (BaP), which is a high molecular weight (HMW) polycyclic aromatic hydrocarbon (PAH) compound. It was hypothesized that the T. versicolor isolate encode BaP-degrading enzymes, among which laccase is mostly sought after due to significant commercial potential. Genome of the T. versicolor isolate was sequenced and assembled, and seven laccase homologues were identified (TvLac1-7) as candidate genes potentially contributing to BaP degradation. In order to further identify the BaP responsive laccases, time-course transcriptomic and proteomic analyses were conducted in parallel on the T. versicolor isolate upon BaP treatment. Homologous laccases showed distinct expression patterns. Most strikingly, TvLac5 was rapidly induced in the secreted proteomes (secretomes), while TvLac2 was repressed. Recombinant laccase expression and biochemical characterization further showed corresponding enzymatic activity profiles, where TvLac5 was 21-fold more effective in BaP degradation compared to TvLac2. Moreover, TvLac5 also showed 3.6-fold higher BaP degrading activity compared to a commercial laccase product of T. versicolor origin. Therefore, TvLac5 was concluded to be a BaP-responsive enzyme from T. versicolor showing effective BaP degradation activity.

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.

Clinicopathological significance of SOX9 and β-catenin expression in pre-neoadjuvant chemotherapy cases of osteosarcoma: molecular and immunohistochemical study

The molecular pathogenesis of osteosarcoma (OS), the most frequent primary malignant bone tumor of all age groups, is still obscure. Since multidrug chemotherapeutic regimens were introduced in the 1970s, survival rates have been stationary. The Wnt-β-catenin signaling cascade and SOX9 have a significant contribution to skeletal growth, development, and tumorigenesis. In the present work, an attempt was made to examine the role and clinicopathological significance of β-catenin and SOX9 in 46 cases of pre-neoadjuvant chemotherapy OS tissues compared to 10 cases of non-neoplastic bone. The mRNA levels of both markers were assessed by qRT-PCR, and protein levels of β-catenin were analyzed by immunohistochemistry. The results were correlated with different clinicopathological parameters. SOX9 mRNA levels were significantly elevated in OS compared to non-neoplastic bone, and higher levels were significantly associated with the occurrence of fluid-fluid levels (indicating blood-containing cystic spaces) and osteolytic radiological pattern. Although β-catenin mRNA and protein levels were higher in OS compared to non-neoplastic bone, only the protein levels reached statistical significance. Higher β-catenin mRNA levels were significantly associated with tumor size, while higher protein levels were significantly associated with the histologic subtype, mitotic count, and radiological pattern. No significant association was noted with any of the other evaluated parameters. OS showing higher SOX9 mRNA expression and lower β-catenin mRNA and protein expression exhibited longer estimated overall survival times approaching statistical significance. To conclude, while high expression of β-catenin and SOX9 suggests their possible involvement in OS development, their prognostic role may need further research.

Spatiotemporal protein dynamics during early organogenesis in mouse conceptuses treated with valproic acid

Valproic acid (VPA) is an anti-epileptic medication that increases the risk of neural tube defect (NTD) outcomes in infants exposed during gestation. Previous studies into VPA's mechanism of action have focused on alterations in gene expression and metabolism but have failed to consider how exposure changes the abundance of critical developmental proteins over time. This study evaluates the effects of VPA on protein abundance in the developmentally distinct tissues of the mouse visceral yolk sac (VYS) and embryo proper (EMB) using mouse whole embryo culture. Embryos were exposed to 600 μM VPA at 2 h intervals over 10 h during early organogenesis with the aim of identifying protein pathways relevant to VPA's mechanism of action in failed NTC. Protein abundance was measured through tandem mass tag (TMT) labeling followed by liquid chromatography and mass spectrometry. Overall, there were over 1500 proteins with altered abundance after VPA exposure in the EMB or VYS with 428 of these proteins showing previous gene expression associations with VPA exposure. Limited overlap of significant proteins between tissues supported the conclusion of independent roles for the VYS and EMB in response to VPA. Pathway analysis of proteins with increased or decreased abundance identified multiple pathways with mechanistic relevance to NTC and embryonic development including convergent extension, Wnt Signaling/planar cell polarity, cellular migration, cellular proliferation, cell death, and cytoskeletal organization processes as targets of VPA. Clustering of co-regulated proteins to identify shared patterns of protein abundance over time highlighted 4 h and 6/10 h as periods of divergent protein abundance between control and VPA-treated samples in the VYS and EMB, respectively. Overall, this study demonstrated that VPA temporally alters protein content in critical developmental pathways in the VYS and the EMB during early organogenesis in mice.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Multi-omic stratification of the missense variant cysteinome

Cancer genomes are rife with genetic variants; one key outcome of this variation is gain-ofcysteine, which is the most frequently acquired amino acid due to missense variants in COSMIC. Acquired cysteines are both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain uncharacterized. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine acquisition. For both cancer and healthy genomes, we find that cysteine acquisition is a ubiquitous consequence of genetic variation that is further elevated in the context of decreased DNA repair. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized 2-stage false discovery rate (FDR) error controlled proteomic search, further enhanced with a user-friendly FragPipe interface. Integration of CADD predictions of deleteriousness revealed marked enrichment for likely damaging variants that result in acquisition of cysteine. By deploying chemoproteogenomics across eleven cell lines, we identify 116 gain-of-cysteines, of which 10 were liganded by electrophilic druglike molecules. Reference cysteines proximal to missense variants were also found to be pervasive, 791 in total, supporting heretofore untapped opportunities for proteoform-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.

Transcriptome profiling of the Caenorhabditis elegans intestine reveals that ELT-2 negatively and positively regulates intestinal gene expression within the context of a gene regulatory network

ELT-2 is the major transcription factor (TF) required for Caenorhabditis elegans intestinal development. ELT-2 expression initiates in embryos to promote development and then persists after hatching through the larval and adult stages. Though the sites of ELT-2 binding are characterized and the transcriptional changes that result from ELT-2 depletion are known, an intestine-specific transcriptome profile spanning developmental time has been missing. We generated this dataset by performing Fluorescence Activated Cell Sorting on intestine cells at distinct developmental stages. We analyzed this dataset in conjunction with previously conducted ELT-2 studies to evaluate the role of ELT-2 in directing the intestinal gene regulatory network through development. We found that only 33% of intestine-enriched genes in the embryo were direct targets of ELT-2 but that number increased to 75% by the L3 stage. This suggests additional TFs promote intestinal transcription especially in the embryo. Furthermore, only half of ELT-2's direct target genes were dependent on ELT-2 for their proper expression levels, and an equal proportion of those responded to elt-2 depletion with over-expression as with under-expression. That is, ELT-2 can either activate or repress direct target genes. Additionally, we observed that ELT-2 repressed its own promoter, implicating new models for its autoregulation. Together, our results illustrate that ELT-2 impacts roughly 20-50% of intestine-specific genes, that ELT-2 both positively and negatively controls its direct targets, and that the current model of the intestinal regulatory network is incomplete as the factors responsible for directing the expression of many intestinal genes remain unknown.

Natural variation in codon bias and mRNA folding strength interact synergistically to modify protein expression in Saccharomyces cerevisiae

Codon bias and mRNA folding strength (mF) are hypothesized molecular mechanisms by which polymorphisms in genes modify protein expression. Natural patterns of codon bias and mF across genes as well as effects of altering codon bias and mF suggest that the influence of these 2 mechanisms may vary depending on the specific location of polymorphisms within a transcript. Despite the central role codon bias and mF may play in natural trait variation within populations, systematic studies of how polymorphic codon bias and mF relate to protein expression variation are lacking. To address this need, we analyzed genomic, transcriptomic, and proteomic data for 22 Saccharomyces cerevisiae isolates, estimated protein accumulation for each allele of 1,620 genes as the log of protein molecules per RNA molecule (logPPR), and built linear mixed-effects models associating allelic variation in codon bias and mF with allelic variation in logPPR. We found that codon bias and mF interact synergistically in a positive association with logPPR, and this interaction explains almost all the effects of codon bias and mF. We examined how the locations of polymorphisms within transcripts influence their effects and found that codon bias primarily acts through polymorphisms in domain-encoding and 3' coding sequences, while mF acts most significantly through coding sequences with weaker effects from untranslated regions. Our results present the most comprehensive characterization to date of how polymorphisms in transcripts influence protein expression.

Proteomic profiling across breast cancer cell lines and models

We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.

On the Decoupling of Evolutionary Changes in mRNA and Protein Levels

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

Recent advances in proteomic-based diagnostics of cystic fibrosis

INTRODUCTION

Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF.

AREAS COVERED

We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers.

EXPERT OPINION

Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

Protein biomarkers for radiation injury and testing of medical countermeasure efficacy: promises, pitfalls, and future directions

INTRODUCTION

Radiological/nuclear accidents, hostile military activity, or terrorist strikes have the potential to expose a large number of civilians and military personnel to high doses of radiation resulting in the development of acute radiation syndrome and delayed effects of exposure. Thus, there is an urgent need for sensitive and specific assays to assess the levels of radiation exposure to individuals. Such radiation exposures are expected to alter primary cellular proteomic processes, resulting in multifaceted biological responses.

AREAS COVERED

This article covers the application of proteomics, a promising and fast developing technology based on quantitative and qualitative measurements of protein molecules for possible rapid measurement of radiation exposure levels. Recent advancements in high-resolution chromatography, mass spectrometry, high-throughput, and bioinformatics have resulted in comprehensive (relative quantitation) and precise (absolute quantitation) approaches for the discovery and accuracy of key protein biomarkers of radiation exposure. Such proteome biomarkers might prove useful for assessing radiation exposure levels as well as for extrapolating the pharmaceutical dose of countermeasures for humans based on efficacy data generated using animal models.

EXPERT OPINION

The field of proteomics promises to be a valuable asset in evaluating levels of radiation exposure and characterizing radiation injury biomarkers.

Newly synthesized glycoprotein profiling to identify molecular signatures of warm ischemic injury in donor lungs

Despite recent technological advances such as ex vivo lung perfusion (EVLP), the outcome of lung transplantation remains unsatisfactory with ischemic injury being a common cause for primary graft dysfunction. New therapeutic developments are hampered by limited understanding of pathogenic mediators of ischemic injury to donor lung grafts. Here, to identify novel proteomic effectors underlying the development of lung graft dysfunction, using bioorthogonal protein engineering, we selectively captured and identified newly synthesized glycoproteins (NewS-glycoproteins) produced during EVLP with unprecedented temporal resolution of 4 h. Comparing the NewS-glycoproteomes in lungs with and without warm ischemic injury, we discovered highly specific proteomic signatures with altered synthesis in ischemic lungs, which exhibited close association to hypoxia response pathways. Inspired by the discovered protein signatures, pharmacological modulation of the calcineurin pathway during EVLP of ischemic lungs offered graft protection and improved posttransplantation outcome. In summary, the described EVLP-NewS-glycoproteomics strategy delivers an effective new means to reveal molecular mediators of donor lung pathophysiology and offers the potential to guide future therapeutic development.NEW & NOTEWORTHY This study developed and implemented a bioorthogonal strategy to chemoselectively label, enrich, and characterize newly synthesized (NewS-)glycoproteins during 4-h ex vivo lung perfusion (EVLP). Through this approach, the investigators uncovered specific proteomic signatures associated with warm ischemic injury in donor lung grafts. These signatures exhibit high biological relevance to ischemia-reperfusion injury, validating the robustness of the presented approach.

A proteome-wide atlas of drug mechanism of action

Defining the cellular response to pharmacological agents is critical for understanding the mechanism of action of small molecule perturbagens. Here, we developed a 96-well-plate-based high-throughput screening infrastructure for quantitative proteomics and profiled 875 compounds in a human cancer cell line with near-comprehensive proteome coverage. Examining the 24-h proteome changes revealed ligand-induced changes in protein expression and uncovered rules by which compounds regulate their protein targets while identifying putative dihydrofolate reductase and tankyrase inhibitors. We used protein-protein and compound-compound correlation networks to uncover mechanisms of action for several compounds, including the adrenergic receptor antagonist JP1302, which we show disrupts the FACT complex and degrades histone H1. By profiling many compounds with overlapping targets covering a broad chemical space, we linked compound structure to mechanisms of action and highlighted off-target polypharmacology for molecules within the library.

Cisplatin toxicity is counteracted by the activation of the p38/ATF-7 signaling pathway in post-mitotic C. elegans

Cisplatin kills proliferating cells via DNA damage but also has profound effects on post-mitotic cells in tumors, kidneys, and neurons. However, the effects of cisplatin on post-mitotic cells are still poorly understood. Among model systems, C. elegans adults are unique in having completely post-mitotic somatic tissues. The p38 MAPK pathway controls ROS detoxification via SKN-1/NRF and immune responses via ATF-7/ATF2. Here, we show that p38 MAPK pathway mutants are sensitive to cisplatin, but while cisplatin exposure increases ROS levels, skn-1 mutants are resistant. Cisplatin exposure leads to phosphorylation of PMK-1/MAPK and ATF-7 and the IRE-1/TRF-1 signaling module functions upstream of the p38 MAPK pathway to activate signaling. We identify the response proteins whose increased abundance depends on IRE-1/p38 MAPK activity as well as cisplatin exposure. Four of these proteins are necessary for protection from cisplatin toxicity, which is characterized by necrotic death. We conclude that the p38 MAPK pathway-driven proteins are crucial for adult cisplatin resilience.

A proteomic study of the effect of UV-B on the regulatory mechanism of flavonoids metabolism in pea seedlings

This study aimed to investigate the mechanism of response of pea seedlings to UV-B stress from a proteomic perspective. In this experiment, we measured the growth of pea seedlings in two groups affected by UV-B and unaffected by UV-B and conducted proteomic analysis. The results showed that the ascorbic acid content of UV-B-irradiated pea seedlings increased by 19.0%; the relative content of flavonoids increased by 112.4%; the length of edible parts decreased by 14.2%, and the elongation of roots increased by 11.4%. Proteomics studies showed a significant increase in the levels of CHI, F3'5'H, F3H, F3'H, C4H, and CHR, which are key enzymes for flavonoid synthesis. RT-qPCR indicated that the expression of the regulatory genes of these enzymes was significantly upregulated. This study provided a basis for further studies on the flavonoid response mechanism in pea seedlings during UV stress.

Quantification of SARS-CoV-2 spike protein expression from mRNA vaccines using isotope dilution mass spectrometry

The advent of mRNA vaccine technology has been vital in rapidly creating and manufacturing COVID-19 vaccines at an industrial scale. To continue to accelerate this leading vaccine technology, an accurate method is needed to quantify antigens produced by the transfection of cells with a mRNA vaccine product. This will allow monitoring of protein expression during mRNA vaccine development and provide information on how changes to vaccine components affects the expression of the desired antigen. Developing novel approaches that allow for high-throughput screening of vaccines to detect changes in antigen production in cell culture prior to in vivo studies could aid vaccine development. We have developed and optimized an isotope dilution mass spectrometry method to detect and quantify the spike protein expressed after transfection of baby hamster kidney cells with expired COVID-19 mRNA vaccines. Five peptides of the spike protein are simultaneously quantified and provide assurance that protein digestion in the region of the target peptides is complete since results between the five peptides had a relative standard deviation of less than 15 %. In addition, two housekeeping proteins, actin and GAPDH, are quantified in the same analytical run to account for any variation in cell growth within the experiment. IDMS allows a precise and accurate means to quantify protein expression by mammalian cells transfected with an mRNA vaccine.

Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients

BACKGROUND

Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC.

OBJECTIVE

To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients.

DESIGN, SETTING, AND PARTICIPANTS

We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples.

INTERVENTION

Radical or partial nephrectomy on untreated ccRCC patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters.

RESULTS AND LIMITATIONS

We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients.

CONCLUSIONS

We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management.

PATIENT SUMMARY

We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.

Genetic dissection of the pluripotent proteome through multi-omics data integration

Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.

Decoupling of evolutionary changes in mRNA and protein levels

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

Cold-adapted amphipod species upon heat stress: Proteomic responses and their correlation with transcriptomic responses

The cellular heat shock response (HSR) comprises transcriptomic and proteomic reactions to thermal stress. It was here addressed, how the proteomic, together with the transcriptomic HSR, relate to the thermal sensitivities of three cold-adapted but differently thermo-sensitive freshwater amphipod species. The proteomes of thermosensitive Eulimnogammarus verrucosus and thermotolerant Eulimnogammarus cyaneus, both endemic to Lake Baikal, and of thermotolerant Holarctic Gammarus lacustris were investigated upon 24 h exposure to the species-specific 10 % lethal temperatures (LT10). Furthermore, correlations of heat stress induced changes in proteomes (this study) and transcriptomes (previous study with identical experimental design) were examined. Proteomes indicated that the HSR activated processes encompassed (i) proteostasis maintenance, (ii) maintenance of cell adhesion, (iii) oxygen transport, (iv) antioxidant response, and (v) regulation of protein synthesis. Thermo-sensitive E. verrucosus showed the most pronounced proteomic HSR and the lowest correlation of transcriptomic and proteomic HSRs. For proteins related to translation (ribosomal proteins, elongation factors), transcriptomic and proteomic changes were inconsistent: transcripts were downregulated in many cases, with levels of corresponding proteins remaining unchanged. In the Eulimnogammarus species, levels of hemocyanin protein but not transcript were increased upon heat stress, suggesting a HSR also directed to enhance oxygen transport. Thermosensitive E. verrucosus showed the most pronounced relocation of transcription/translation activity to proteostasis maintenance, which may indicate that the general species-specific stability of protein structure could be a fundamental determinant of thermotolerance. By combining transcriptomic and proteomic response data, this study provides a comprehensive picture of the cellular HSR components in the studied amphipods.

Multi-Omics Studies in Historically Excluded Populations: The Road to Equity

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.