Protein abundances are more conserved than mRNA abundances across diverse taxa

Quantitative proteome dynamics across embryogenesis in a model chordate

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

In vivo identification of astrocyte and neuron subproteomes by proximity-dependent biotinylation

The central nervous system (CNS) comprises diverse and morphologically complex cells. To understand the molecular basis of their physiology, it is crucial to assess proteins expressed within intact cells. Commonly used methods utilize cell dissociation and sorting to isolate specific cell types such as neurons and astrocytes, the major CNS cells. Proteins purified from isolated cells are identified by mass spectrometry-based proteomics. However, dissociation and cell-sorting methods lead to near total loss of cellular morphology, thereby losing proteins from key relevant subcompartments such as processes, end feet, dendrites and axons. Here we provide a systematic protocol for cell- and subcompartment-specific labeling and identification of proteins found within intact astrocytes and neurons in vivo. This protocol utilizes the proximity-dependent biotinylation system BioID2, selectively expressed in either astrocytes or neurons, to label proximal proteins in a cell-specific manner. BioID2 is targeted genetically to assess the subproteomes of subcellular compartments such as the plasma membrane and sites of cell-cell contacts. We describe in detail the expression methods (variable timing), stereotaxic surgeries for expression (1-2 d and then 3 weeks), in vivo protein labeling (7 d), protein isolation (2-3 d), protein identification methods (2-3 d) and data analysis (1 week). The protocol can be applied to any area of the CNS in mouse models of physiological processes and for disease-related research.

Analysis of the expression and mechanism of follistatin‑like protein 1 in cervical cancer

The abnormal expression of follistatin‑like protein 1 (FSTL1) in various tumors is a crucial regulator of the biological process of tumorigenesis. Nonetheless, the regulatory role of FSTL1 in cervical cancer is yet to be elucidated. Hence, the present study aimed to explore the expression, function, and molecular mechanism of FSTL1 in cervical cancer. The expression of FSTL1 in normal and cervical cancer tissues was examined using quantitative reverse transcription‑polymerase chain reaction and immunohistochemistry assays. The effects of abnormal expression of FSTL1 on cervical cancer cells were assessed using colony formation, MTT, wound‑healing, Transwell, apoptosis, and nude mouse tumorigenicity assays. FSTL1‑related molecular mechanisms were screened using gene chip analysis. Western blotting analysis was used to verify the regulatory mechanisms of FSTL1 in cervical cancer. The results indicated that the expression of FSTL1 was downregulated in cervical cancer tissues and that its downregulation was associated with tumor differentiation, pathologic type, and infiltration depth. Moreover, FSTL1 inhibited the proliferation, migration, and invasion of cervical cancer cells as well as xenograft tumor growth and promoted cell apoptosis. In addition, the findings of gene chip analysis suggested that the differentially expressed genes of FSTL1 were predominantly enriched in multiple signaling pathways, of which the insulin‑like growth factor (IGF)‑1 signaling pathway was significantly activated. Western blotting suggested the involvement of FSTL1 in the regulation of the IGF‑1R/PI3K/AKT/BCL‑2 signaling pathway. These data establish the downregulation of FSTL1 in cervical cancer tissues. FSTL1 inhibited the proliferation, migration, and invasion of cervical cancer cells and promoted their apoptosis. Furthermore, xenograft tumor growth in nude mice was inhibited. FSTL1 may be involved in the regulation of the IGF‑1R/PI3K/AKT/BCL‑2 signaling pathway in cervical cancer. Therefore, FSTL1 may be employed as a novel biomarker to determine the extent of disease progression in patients with cervical cancer.

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.

Changing turn-over rates regulate abundance of tryptophan, GS biosynthesis, IAA transport and photosynthesis proteins in Arabidopsis growth defense transitions

BACKGROUND

Shifts in dynamic equilibria of the abundance of cellular molecules in plant-pathogen interactions need further exploration. We induced PTI in optimally growing Arabidopsis thaliana seedlings for 16 h, returning them to growth conditions for another 16 h.

METHODS

Turn-over and abundance of 99 flg22 responding proteins were measured chronologically using a stable heavy nitrogen isotope partial labeling strategy and targeted liquid chromatography coupled to mass spectrometry (PRM LC-MS). These experiments were complemented by measurements of mRNA and phytohormone levels.

RESULTS

Changes in synthesis and degradation rate constants (Ks and Kd) regulated tryptophane and glucosinolate, IAA transport, and photosynthesis-associated protein (PAP) homeostasis in growth/PTI transitions independently of mRNA levels. Ks values increased after elicitation while protein and mRNA levels became uncorrelated. mRNA returned to pre-elicitation levels, yet protein abundance remained at PTI levels even 16 h after media exchange, indicating protein levels were robust and unresponsive to transition back to growth. The abundance of 23 PAPs including FERREDOXIN-NADP( +)-OXIDOREDUCTASE (FNR1) decreased 16 h after PAMP exposure, their depletion was nearly abolished in the myc234 mutant. FNR1 Kd increased as mRNA levels decreased early in PTI, its Ks decreased in prolonged PTI. FNR1 Kd was lower in myc234, mRNA levels decreased as in wild type.

CONCLUSIONS

Protein Kd and Ks values change in response to flg22 exposure and constitute an additional layer of protein abundance regulation in growth defense transitions next to changes in mRNA levels. Our results suggest photosystem remodeling in PTI to direct electron flow away from the photosynthetic carbon reaction towards ROS production as an active defense mechanism controlled post-transcriptionally and by MYC2 and homologs. Target proteins accumulated later and PAP and auxin/IAA depletion was repressed in myc234 indicating a positive effect of the transcription factors in the establishment of PTI.

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.

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.

Multiparametric senescent cell phenotyping reveals targets of senolytic therapy in the aged murine skeleton

Senescence drives organismal aging, yet the deep characterization of senescent cells in vivo remains incomplete. Here, we apply mass cytometry by time-of-flight using carefully validated antibodies to analyze senescent cells at single-cell resolution. We use multiple criteria to identify senescent mesenchymal cells that are growth-arrested and resistant to apoptosis. These p16 + Ki67-BCL-2+ cells are highly enriched for senescence-associated secretory phenotype and DNA damage markers, are strongly associated with age, and their percentages are increased in late osteoblasts/osteocytes and CD24high osteolineage cells. Moreover, both late osteoblasts/osteocytes and CD24high osteolineage cells are robustly cleared by genetic and pharmacologic senolytic therapies in aged mice. Following isolation, CD24+ skeletal cells exhibit growth arrest, senescence-associated β-galactosidase positivity, and impaired osteogenesis in vitro. These studies thus provide an approach using multiplexed protein profiling to define senescent mesenchymal cells in vivo and identify specific skeletal cell populations cleared by senolytics.

What do we mean by "aging"?: Questions and perspectives revealed by studies in Drosophila

What is the nature of aging, and how best can we study it? Here, using a series of questions that highlight differing perspectives about the nature of aging, we ask how data from Drosophila melanogaster at the organismal, tissue, cellular, and molecular levels shed light on the complex interactions among the phenotypes associated with aging. Should aging be viewed as an individual's increasing probability of mortality over time or as a progression of physiological states? Are all age-correlated changes in physiology detrimental to vigor or are some compensatory changes that maintain vigor? Why do different age-correlated functions seem to change at different rates in a single individual as it ages? Should aging be considered as a single, integrated process across the scales of biological resolution, from organismal to molecular, or must we consider each level of biological scale as a separate, distinct entity? Viewing aging from these differing perspectives yields distinct but complementary interpretations about the properties and mechanisms of aging and may offer a path through the complexities related to understanding the nature of aging.

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.

Multiparametric senescent cell phenotyping reveals CD24 osteolineage cells as targets of senolytic therapy in the aged murine skeleton

Senescence drives organismal aging, yet the deep characterization of senescent cells in vivo remains incomplete. Here, we applied mass cytometry by time-of-flight (CyTOF) using carefully validated antibodies to analyze senescent cells at single-cell resolution. We used multiple criteria to identify senescent mesenchymal cells that were growth arrested and resistant to apoptosis (p16+/Ki67-/BCL-2+; "p16KB" cells). These cells were highly enriched for senescence-associated secretory phenotype (SASP) and DNA damage markers and were strongly associated with age. p16KB cell percentages were also increased in CD24+ osteolineage cells, which exhibited an inflammatory SASP in aged mice and were robustly cleared by both genetic and pharmacologic senolytic therapies. Following isolation, CD24+ skeletal cells exhibited growth arrest, SA-βgal positivity, and impaired osteogenesis in vitro . These studies thus provide a new approach using multiplexed protein profiling by CyTOF to define senescent mesenchymal cells in vivo and identify a highly inflammatory, senescent CD24+ osteolineage population cleared by senolytics.

Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria

Protein concentrations are set by a complex interplay between gene-specific regulatory processes and systemic factors, including cell volume and shared gene expression machineries. Elucidating this interplay is crucial for discerning and designing gene regulatory systems. We quantitatively characterized gene-specific and systemic factors that affect transcription and translation genome-wide for Escherichia coli across many conditions. The results revealed two design principles that make regulation of gene expression insulated from concentrations of shared machineries: RNA polymerase activity is fine-tuned to match translational output, and translational characteristics are similar across most messenger RNAs (mRNAs). Consequently, in bacteria, protein concentration is set primarily at the promoter level. A simple mathematical formula relates promoter activities and protein concentrations across growth conditions, enabling quantitative inference of gene regulation from omics data.

Low protein expression enhances phenotypic evolvability by intensifying selection on folding stability

Protein abundance affects the evolution of protein genotypes, but we do not know how it affects the evolution of protein phenotypes. Here we investigate the role of protein abundance in the evolvability of green fluorescent protein (GFP) towards the novel phenotype of cyan fluorescence. We evolve GFP in E. coli through multiple cycles of mutation and selection and show that low GFP expression facilitates the evolution of cyan fluorescence. A computational model whose predictions we test experimentally helps explain why: lowly expressed proteins are under stronger selection for proper folding, which facilitates their evolvability on short evolutionary time scales. The reason is that high fluorescence can be achieved by either few proteins that fold well or by many proteins that fold less well. In other words, we observe a synergy between a protein's scarcity and its stability. Because many proteins meet the essential requirements for this scarcity-stability synergy, it may be a widespread mechanism by which low expression helps proteins evolve new phenotypes and functions.

Principles of brain aging: Status and challenges of modeling human molecular changes in mice

Due to the extension of human life expectancy, the prevalence of cognitive impairment is rising in the older portion of society. Developing new strategies to delay or attenuate cognitive decline is vital. For this purpose, it is imperative to understand the cellular and molecular events at the basis of brain aging. While several organs are directly accessible to molecular analysis through biopsies, the brain constitutes a notable exception. Most of the molecular studies are performed on postmortem tissues, where cell death and tissue damage have already occurred. Hence, the study of the molecular aspects of cognitive decline largely relies on animal models and in particular on small mammals such as mice. What have we learned from these models? Do these animals recapitulate the changes observed in humans? What should we expect from future mouse studies? In this review we answer these questions by summarizing the state of the research that has addressed cognitive decline in mice from several perspectives, including genetic manipulation and omics strategies. We conclude that, while extremely valuable, mouse models have limitations that can be addressed by the optimal design of future studies and by ensuring that results are cross-validated in the human context.

Protein Abundance Prediction Through Machine Learning Methods

Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing the protein pool. Protein abundance is impacted by translation kinetics, which rely on features of codons. In this study, we evaluated the effect of codon usage bias of genes on protein abundance. Notably, we observed differences regarding codon usage patterns between genes coding for highly abundant proteins and genes coding for less abundant proteins. Analysis of synonymous codon usage and evolutionary selection showed a clear split between the two groups. Our machine learning models predicted protein abundances from codon usage metrics with remarkable accuracy, achieving strong correlation with experimental data. Upon integration of the predicted protein abundance in enzyme-constrained genome-scale metabolic models, the simulated phenotypes closely matched experimental data, which demonstrates that our predictive models are valuable tools for systems metabolic engineering approaches.

Regulation of gene expression via translational buffering

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

Novel perspectives of environmental proteomics

The connection among genome expression, proteome alteration, metabolism regulation and phenotype change under environmental stresses is very vague. It is a tough task for the traditional research approaches to reveal the related scientific mechanisms of the above connection at molecular and systematic levels. Proteomics approach is an insightful tool for revealing the biological functions, metabolic networks and functional protein interaction networks of cells and organisms under stresses at the systematic level. The purpose of this review is to provide an insightful guideline on how to set up a proteomic investigation for revealing biomolecule mechanisms, protein biomarkers and metabolism networks related to stress response, pollutant recognition, transport and biodegradation, and providing an insightful high-throughput approach for screening functional enzymes and effective microbes based on bioinformatics and functional verification method. Furthermore, the toxicity evaluation of pollutants and byproducts by proteomics approaches provides a scientific insight for early diagnosis of ecological risk and determination of the effectiveness of pollutant treatment techniques.

Alcohol Dependence in Rats Is Associated with Global Changes in Gene Expression in the Central Amygdala

Alcohol dependence is associated with adverse consequences of alcohol (ethanol) use and is evident in most severe cases of alcohol use disorder (AUD). The central nucleus of the amygdala (CeA) plays a critical role in the development of alcohol dependence and escalation of alcohol consumption in dependent subjects. Molecular mechanisms underlying the CeA-driven behavioral changes are not well understood. Here, we examined the effects of alcohol on global gene expression in the CeA using a chronic intermittent ethanol (CIE) vapor model in rats and RNA sequencing (RNA-Seq). The CIE procedure resulted in robust changes in CeA gene expression during intoxication, as the number of differentially expressed genes (DEGs) was significantly greater than those expected by chance. Over-representation analysis of cell types, functional groups and molecular pathways revealed biological categories potentially important for the development of alcohol dependence in our model. Genes specific for astrocytes, myelinating oligodendrocytes, and endothelial cells were over-represented in the DEG category, suggesting that these cell types were particularly affected by the CIE procedure. The majority of the over-represented functional groups and molecular pathways were directly related to the functions of glial and endothelial cells, including extracellular matrix (ECM) organization, myelination, and the regulation of innate immune response. A coordinated regulation of several ECM metalloproteinases (e.g., Mmp2; Mmp14), their substrates (e.g., multiple collagen genes and myelin basic protein; Mbp), and a metalloproteinase inhibitor, Reck, suggests a specific mechanism for ECM re-organization in response to chronic alcohol, which may modulate neuronal activity and result in behavioral changes, such as an escalation of alcohol drinking. Our results highlight the importance of glial and endothelial cells in the effects of chronic alcohol exposure on the CeA, and demonstrate further insight into the molecular mechanisms of alcohol dependence in rats. These molecular targets may be used in future studies to develop therapeutics to treat AUD.

Large-scale translatome profiling annotates the functional genome and reveals the key role of genic 3' untranslated regions in translatomic variation in plants

The translatome, a profile of the translational status of genetic information within cells, provides a new perspective on gene expression. Although many plant genomes have been sequenced, comprehensive translatomic annotations are not available for plants due to a lack of efficient translatome profiling techniques. Here, we developed a new technique termed 3' ribosome-profiling sequencing (3'Ribo-seq) for reliable, robust translatomic profiling. 3'Ribo-seq combines polysome profiling and 3' selection with a barcoding and pooling strategy. Systematic translatome profiling of different tissues of Arabidopsis, rice, and maize using conventional ribosome profiling (Ribo-seq) and 3'Ribo-seq revealed many novel translational genomic loci, thereby complementing functional genome annotation in plants. Using the low-cost, efficient 3'Ribo-seq technique and genome-wide association mapping of translatome expression (eGWAS), we performed a population-level dissection of the translatomes of 159 diverse maize inbred lines and identified 1,777 translational expression quantitative trait loci (eQTLs). Notably, local eQTLs are significantly enriched in the 3' untranslated regions of genes. Detailed eQTL analysis suggested that sequence variation around the polyadenylation (polyA) signal motif plays a key role in translatomic variation. Our study provides a comprehensive translatome annotation of plant functional genomes and introduces 3'Ribo-seq, which paves the way for deep translatomic analysis at the population level.

Comparing mRNA expression and protein abundance in MDR Mycobacterium tuberculosis: Novel protein candidates, Rv0443, Rv0379 and Rv0147 as TB potential diagnostic or therapeutic targets

Tuberculosis (TB) is a sizable public health threat in the world. This study was conducted to determine the differential protein composition between susceptible and MDRTB strains. Tuberculosis proteins were extracted by Triton™ X-114 and ammonium sulfate. Two-dimensional gel electrophoresis protein spots were selected for identification by mass spectrometry and mRNA expression levels were measured by real- time PCR. 2DE-Western blot and T cell epitope prediction for identified proteins were made by the IEDB server. The result shows at least six protein spots (Rv0147, Rv3597c, Rv0379, Rv3699, Rv1392 and Rv0443) were differentially expressed in MDRTB isolates. However, difference in mRNA gene expression was not found in the six mRNA genes. 2DE-Western blot procedures indicated strong reaction against MDRTB proteins corresponds to 13, 16 and 55 kDa areas that might be used as new diagnostic tools. In conclusion, these MDRTB proteins identified in this study could be reliable TB diagnostic candidates or therapeutic targets.

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

BACKGROUND

Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research.

RESULTS

We optimized and employed a pipeline integrating various "guilt-by-association" (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum. Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum. In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions.

CONCLUSIONS

This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

Genetic control of the human brain proteome

We generated an online brain pQTL resource for 7,376 proteins through the analysis of genetic and proteomic data derived from post-mortem samples of the dorsolateral prefrontal cortex of 330 older adults. The identified pQTLs tend to be non-synonymous variation, are over-represented among variants associated with brain diseases, and replicate well (77%) in an independent brain dataset. Comparison to a large study of brain eQTLs revealed that about 75% of pQTLs are also eQTLs. In contrast, about 40% of eQTLs were identified as pQTLs. These results are consistent with lower pQTL mapping power and greater evolutionary constraint on protein abundance. The latter is additionally supported by observations of pQTLs with large effects' tending to be rare, deleterious, and associated with proteins that have evidence for fewer protein-protein interactions. Mediation analyses using matched transcriptomic and proteomic data provided additional evidence that pQTL effects are often, but not always, mediated by mRNA. Specifically, we identified roughly 1.6 times more mRNA-mediated pQTLs than mRNA-independent pQTLs (550 versus 341). Our pQTL resource provides insight into the functional consequences of genetic variation in the human brain and a basis for novel investigations of genetics and disease.

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.

Comparison of protein and peptide fractionation approaches in protein identification and quantification from Saccharomyces cerevisiae

Shotgun proteomics is a high-throughput technology which has been developed with the aim of investigating the maximum number of proteins in cells in a given experiment. However, protein discovery and data generation vary in depth and coverage when different technical strategies are selected. In this study, three different sample preparation approaches, and peptide or protein fractionation methods, were applied to identify and quantify proteins from log-phase yeast lysate: sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), filter-aided sample preparation coupled with gas phase fractionation (FASP-GPF), and FASP - high pH reversed phase fractionation (HpH). Fractions were initially analyzed and compared using nanoflow liquid chromatography - tandem mass spectrometry (nanoLC-MS/MS) employing data dependent acquisition on a linear ion trap instrument. The number of fractions and analytical replicates was adjusted so that each experiment used a similar amount of mass spectrometric instrument time. A second set of experiments was performed, comparing FASP-GPF, SDS-PAGE and FASP-HpH using a Q Exactive Orbitrap mass spectrometer. Compared with results from the linear ion trap mass spectrometer, the use of a Q Exactive Orbitrap mass spectrometer enabled a substantial increase in protein identifications, and an even greater increase in peptide identifications. This shows that the main advantage of using the higher resolution instrument is in increased proteome coverage. A total of 1035, 1357 and 2134 proteins were separately identified by FASP-GPF, SDS-PAGE and FASP-HpH. Combining results from the Orbitrap experiments, there were a total of 2269 proteins found, with 94% of them identified using the FASP-HpH method. Therefore, the FASP-HpH method is the optimal choice among these approaches, when applied to this type of sample.

Human and Nonhuman Primate Lineage-Specific Footprints in the Salivary Proteome

Proteins in saliva are needed for preprocessing food in the mouth, maintenance of tooth mineralization, and protection from microbial pathogens. Novel insights into human lineage-specific functions of salivary proteins and clues to their involvement in human disease can be gained through evolutionary studies, as recently shown for salivary amylase AMY1 and salivary agglutinin DMBT1/gp340. However, the entirety of proteins in saliva, the salivary proteome, has not yet been investigated from an evolutionary perspective. Here, we compared the proteomes of human saliva and the saliva of our closest extant evolutionary relatives, chimpanzees and gorillas, using macaques as an outgroup, with the aim to uncover features in saliva protein composition that are unique to each species. We found that humans produce a waterier saliva, containing less than half total protein than great apes and Old World monkeys. For all major salivary proteins in humans, we could identify counterparts in chimpanzee and gorilla saliva. However, we discovered unique protein profiles in saliva of humans that were distinct from those of nonhuman primates. These findings open up the possibility that dietary differences and pathogenic pressures may have shaped a distinct salivary proteome in the human lineage.

Susceptibility of Protein Methionine Oxidation in Response to Hydrogen Peroxide Treatment-Ex Vivo Versus In Vitro: A Computational Insight

Methionine oxidation plays a relevant role in cell signaling. Recently, we built a database containing thousands of proteins identified as sulfoxidation targets. Using this resource, we have now developed a computational approach aimed at characterizing the oxidation of human methionyl residues. We found that proteins oxidized in both cell-free preparations (in vitro) and inside living cells (ex vivo) were enriched in methionines and intrinsically disordered regions. However, proteins oxidized ex vivo tended to be larger and less abundant than those oxidized in vitro. Another distinctive feature was their subcellular localizations. Thus, nuclear and mitochondrial proteins were preferentially oxidized ex vivo but not in vitro. The nodes corresponding with ex vivo and in vitro oxidized proteins in a network based on gene ontology terms showed an assortative mixing suggesting that ex vivo oxidized proteins shared among them molecular functions and biological processes. This was further supported by the observation that proteins from the ex vivo set were co-regulated more often than expected by chance. We also investigated the sequence environment of oxidation sites. Glutamate and aspartate were overrepresented in these environments regardless the group. In contrast, tyrosine, tryptophan and histidine were clearly avoided but only in the environments of the ex vivo sites. A hypothetical mechanism of methionine oxidation accounts for these observations presented.

A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes

How do the cells of an organism—each with an identical genome—give rise to tissues of incredible phenotypic diversity? Key to answering this question is the transcriptome: the set of genes expressed in a given tissue. We would clearly benefit from the ability to identify qualitative differences in expression (whether a gene is active or inactive in a given tissue/species). Inferring the expression state of genes is surprisingly difficult, owing to the complex biological processes that give rise to transcriptomes and to the vagaries of techniques used to generate transcriptomic datasets. We develop a hierarchical Bayesian mixture model that—by describing those biological and technical processes—allows us to infer the expression state of genes from replicate transcriptomic datasets.

Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets—where we demonstrate its ability to correctly infer true (known) expression states—and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.

Towards understanding the cell surface phenotype, metabolic properties and immune functions of resident macrophages of the peritoneal cavity and splenic red pulp using high resolution quantitative proteomics

Background:Resident macrophages (Mϕs) are distributed throughout the body and are important for maintaining tissue homeostasis and for defence against infections. Tissue Mϕs are highly adapted to their microenvironment and thought to mediate tissue-specific functions involving metabolism and immune defence that are not fully elucidated. Methods:We have used high resolution quantitative proteomics to gain insights into the functions of two types of resident tissue Mϕs: peritoneal cavity Mϕs and splenic red pulp Mϕs.  The cellular expression levels of many proteins were validated by flow cytometry and were consistently in agreement with the proteomics data.Results:Peritoneal and splenic red pulp macrophages displayed major differences in cell surface phenotype reflecting their adaptation to different tissue microenvironments and tissue-specific functions. Peritoneal Mϕs were shown to be enriched in a number of key enzymes and metabolic pathways normally associated with the liver, such as metabolism of fructose, detoxification, nitrogen homeostasis and the urea cycle. Supporting these observations, we show that peritoneal Mϕs are able to utilise glutamine and glutamate which are rich in peritoneum for urea generation.  In comparison, splenic red pulp Mϕs were enriched in proteins important for adaptive immunity such as antigen presenting MHC molecules, in addition to proteins required for erythrocyte homeostasis and iron turnover. We also show that these tissue Mϕs may utilise carbon and nitrogen substrates for different metabolic fates to support distinct tissue-specific roles.Conclusions:This study provides new insights into the functions of tissue Mϕs in immunity and homeostasis.  The comprehensive proteomics data sets are a valuable resource for biologists and immunologists.

Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods

BACKGROUND

Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate.

RESULTS

We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric.

CONCLUSIONS

Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.

The pathogen Moniliophthora perniciosa promotes differential proteomic modulation of cacao genotypes with contrasting resistance to witches´ broom disease

BACKGROUND

Witches' broom disease (WBD) of cacao (Theobroma cacao L.), caused by Moniliophthora perniciosa, is the most important limiting factor for the cacao production in Brazil. Hence, the development of cacao genotypes with durable resistance is the key challenge for control the disease. Proteomic methods are often used to study the interactions between hosts and pathogens, therefore helping classical plant breeding projects on the development of resistant genotypes. The present study compared the proteomic alterations between two cacao genotypes standard for WBD resistance and susceptibility, in response to M. perniciosa infection at 72 h and 45 days post-inoculation; respectively the very early stages of the biotrophic and necrotrophic stages of the cacao x M. perniciosa interaction.

RESULTS

A total of 554 proteins were identified, being 246 in the susceptible Catongo and 308 in the resistant TSH1188 genotypes. The identified proteins were involved mainly in metabolism, energy, defense and oxidative stress. The resistant genotype showed more expressed proteins with more variability associated with stress and defense, while the susceptible genotype exhibited more repressed proteins. Among these proteins, stand out pathogenesis related proteins (PRs), oxidative stress regulation related proteins, and trypsin inhibitors. Interaction networks were predicted, and a complex protein-protein interaction was observed. Some proteins showed a high number of interactions, suggesting that those proteins may function as cross-talkers between these biological functions.

CONCLUSIONS

We present the first study reporting the proteomic alterations of resistant and susceptible genotypes in the T. cacao x M. perniciosa pathosystem. The important altered proteins identified in the present study are related to key biologic functions in resistance, such as oxidative stress, especially in the resistant genotype TSH1188, that showed a strong mechanism of detoxification. Also, the positive regulation of defense and stress proteins were more evident in this genotype. Proteins with significant roles against fungal plant pathogens, such as chitinases, trypsin inhibitors and PR 5 were also identified, and they may be good resistance markers. Finally, important biological functions, such as stress and defense, photosynthesis, oxidative stress and carbohydrate metabolism were differentially impacted with M. perniciosa infection in each genotype.

Molecular docking and mechanisms of fusaric acid induced mitochondrial sirtuin aberrations in glycolytically and oxidatively poised human hepatocellular carcinoma (HepG2) cells

Fusaric acid (FA) is a ubiquitous yet neglected mycotoxin. The toxicity of FA is associated with mitochondrial dysfunction and oxidative stress. Sirtuins (SIRTs) are key mediators of cell stress responses through deacetylation of antioxidant, mitochondrial maintenance and energy metabolism proteins. Dietary bioactive compounds have profound effects on SIRT activity, however little is known regarding common foodborne toxins and SIRTs. In this study the interaction of FA with mitochondrial SIRTs - SIRT3 and SIRT5, were firstly studied by molecular docking. Thereafter we substantiated the in silico findings by investigating the effect of FA on expression profiles of SIRT3 and SIRT5, and transcriptional and post-transcriptional regulators, PGC-1α and miRNA-30c using western blots and qPCR in vitro. FA was predicted to bind to the active site of SIRT3 and SIRT5 having implications for biological activity. Furthermore, protein expression of SIRT3 and SIRT5 was down-regulated despite elevated mRNA levels. Further experimentation revealed post-transcriptional regulation of both SIRTs as evidenced by elevated miRNA-30c despite induction of PGC-1α. This study highlights the potential of a diet contaminated with FA to dysregulate mitochondrial specific proteins that can lead to initiation and progression of sirtuin related diseases including cancer and insulin resistance.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis

Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

Polysome-profiling in small tissue samples

Polysome-profiling is commonly used to study translatomes and applies laborious extraction of efficiently translated mRNA (associated with >3 ribosomes) from a large volume across many fractions. This property makes polysome-profiling inconvenient for larger experimental designs or samples with low RNA amounts. To address this, we optimized a non-linear sucrose gradient which reproducibly enriches for efficiently translated mRNA in only one or two fractions, thereby reducing sample handling 5-10-fold. The technique generates polysome-associated RNA with a quality reflecting the starting material and, when coupled with smart-seq2 single-cell RNA sequencing, translatomes in small tissues from biobanks can be obtained. Translatomes acquired using optimized non-linear gradients resemble those obtained with the standard approach employing linear gradients. Polysome-profiling using optimized non-linear gradients in serum starved HCT-116 cells with or without p53 showed that p53 status associates with changes in mRNA abundance and translational efficiency leading to changes in protein levels. Moreover, p53 status also induced translational buffering whereby changes in mRNA levels are buffered at the level of mRNA translation. Thus, here we present a polysome-profiling technique applicable to large study designs, primary cells and frozen tissue samples such as those collected in biobanks.

Rational targeting of a NuRD subcomplex guided by comprehensive in situ mutagenesis

Developmental silencing of fetal globins serves as both a paradigm of spatiotemporal gene regulation and an opportunity for therapeutic intervention of β-hemoglobinopathy. The nucleosome remodeling and deacetylase (NuRD) chromatin complex participates in γ-globin repression. We used pooled CRISPR screening to disrupt NuRD protein coding sequences comprehensively in human adult erythroid precursors. Essential for fetal hemoglobin (HbF) control is a non-redundant subcomplex of NuRD protein family paralogs, whose composition we corroborated by affinity chromatography and proximity labeling mass spectrometry proteomics. Mapping top functional guide RNAs identified key protein interfaces where in-frame alleles resulted in loss-of-function due to destabilization or altered function of subunits. We ascertained mutations of CHD4 that dissociate its requirement for cell fitness from HbF repression in both primary human erythroid precursors and transgenic mice. Finally we demonstrated that sequestering CHD4 from NuRD phenocopied these mutations. These results indicate a generalizable approach to discover protein complex features amenable to rational biochemical targeting.

Characterization of Proteome Variation During Modern Maize Breeding

The success of modern maize breeding has been demonstrated by remarkable increases in productivity with tremendous modification of agricultural phenotypes over the last century. Although the underlying genetic changes of the maize adaptation from tropical to temperate regions have been extensively studied, our knowledge is limited regarding the accordance of protein and mRNA expression levels accompanying such adaptation. Here we conducted an integrative analysis of proteomic and transcriptomic changes in a maize association panel. The minimum extent of correlation between protein and RNA levels suggests that variation in mRNA expression is often not indicative of protein expression at a population scale. This is corroborated by the observation that mRNA- and protein-based coexpression networks are relatively independent of each other, and many pQTLs arise without the presence of corresponding eQTLs. Importantly, compared with transcriptome, the subtypes categorized by the proteome show a markedly high accuracy to resemble the genomic subpopulation. These findings suggest that proteome evolved under a greater evolutionary constraint than transcriptome during maize adaptation from tropical to temperate regions. Overall, the integrated multi-omics analysis provides a functional context to interpret gene expression variation during modern maize breeding.

Identification of Early Salinity Stress-Responsive Proteins in Dunaliella salina by isobaric tags for relative and absolute quantitation (iTRAQ)-Based Quantitative Proteomic Analysis

Salt stress is one of the most serious abiotic factors that inhibit plant growth. Dunaliella salina has been recognized as a model organism for stress response research due to its high capacity to tolerate extreme salt stress. A proteomic approach based on isobaric tags for relative and absolute quantitation (iTRAQ) was used to analyze the proteome of D. salina during early response to salt stress and identify the differentially abundant proteins (DAPs). A total of 141 DAPs were identified in salt-treated samples, including 75 upregulated and 66 downregulated DAPs after 3 and 24 h of salt stress. DAPs were annotated and classified into gene ontology functional groups. The Kyoto Encyclopedia of Genes and Genomes pathway analysis linked DAPs to tricarboxylic acid cycle, photosynthesis and oxidative phosphorylation. Using search tool for the retrieval of interacting genes (STRING) software, regulatory protein⁻protein interaction (PPI) networks of the DAPs containing 33 and 52 nodes were built at each time point, which showed that photosynthesis and ATP synthesis were crucial for the modulation of early salinity-responsive pathways. The corresponding transcript levels of five DAPs were quantified by quantitative real-time polymerase chain reaction (qRT-PCR). These results presented an overview of the systematic molecular response to salt stress. This study revealed a complex regulatory mechanism of early salt tolerance in D. salina and potentially contributes to developing strategies to improve stress resilience.

Increased Virulence of Bloodstream Over Peripheral Isolates of P. aeruginosa Identified Through Post-transcriptional Regulation of Virulence Factors

The factors influencing the virulence of P. aeruginosa in the development of invasive infection remain poorly understood. Here, we investigated the role of the host microenvironment in shaping pathogen virulence and investigated the mechanisms involved. Comparing seven paired genetically indistinguishable clinical bloodstream and peripheral isolates of P. aeruginosa, we demonstrate that isolates derived from bloodstream infections are more virulent than their peripheral counterparts (p = 0.025). Bloodstream and peripheral isolates elicited similar NF-kB responses in a THP-1 monocyte NF-kappaB reporter cell line implicating similar immunogenicity. Proteomic analysis by mass spectrometry identified multiple virulence and virulence-related factors including LecA and RpoN in significantly greater abundance in the bacterial supernatant from the bloodstream isolate in comparison to that from the corresponding peripheral isolate. Investigation by qPCR revealed that control of expression of these virulence factors was not due to altered levels of transcription. Based on these data, we hypothesize a post-transcriptional mechanism of virulence regulation in P. aeruginosa bloodstream infections influenced by surrounding microenvironmental conditions.

Row versus column correlations: avoiding the ecological fallacy in RNA/protein expression studies

Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the ecological fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the ecological fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.

Post-transcriptional regulation of the oxidative stress response in plants

Due to their sessile lifestyle, plants can be exposed to several kinds of stresses that will increase the production of reactive oxygen species (ROS), such as hydrogen peroxide, singlet oxygen, and hydroxyl radicals, in the plant cells and activate several signaling pathways that cause alterations in the cellular metabolism. Nevertheless, when ROS production outreaches a certain level, oxidative damage to nucleic acids, lipids, metabolites, and proteins will occur, finally leading to cell death. Until now, the most comprehensive and detailed readout of oxidative stress responses is undoubtedly obtained at the transcriptome level. However, transcript levels often do not correlate with the corresponding protein levels. Indeed, together with transcriptional regulations, post-transcriptional, translational, and/or post-translational regulations will shape the active proteome. Here, we review the current knowledge on the post-transcriptional gene regulation during the oxidative stress responses in planta.

Post-translational buffering leads to convergent protein expression levels between primates

BACKGROUND

Differences in gene regulation between human and closely related species influence phenotypes that are distinctly human. While gene regulation is a multi-step process, the majority of research concerning divergence in gene regulation among primates has focused on transcription.

RESULTS

To gain a comprehensive view of gene regulation, we surveyed genome-wide ribosome occupancy, which reflects levels of protein translation, in lymphoblastoid cell lines derived from human, chimpanzee, and rhesus macaque. We further integrated messenger RNA and protein level measurements collected from matching cell lines. We find that, in addition to transcriptional regulation, the major factor determining protein level divergence between human and closely related species is post-translational buffering. Inter-species divergence in transcription is generally propagated to the level of protein translation. In contrast, gene expression divergence is often attenuated post-translationally, potentially mediated through post-translational modifications.

CONCLUSIONS

Results from our analysis indicate that post-translational buffering is a conserved mechanism that led to relaxation of selective constraint on transcript levels in humans.

Translational profiling of dorsal root ganglia and spinal cord in a mouse model of neuropathic pain

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Translational landscape in DRG and spinal cord in SNI assay of neuropathic pain was established.

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ERK is a central hub of both transcriptionally and translationally controlled genes.

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Changes in translation efficiency and mRNA levels occur in the opposite direction for multiple mRNAs.

Acute pain serves as a protective mechanism, guiding the organism away from actual or potential tissue injury. In contrast, chronic pain is a debilitating condition without any obvious physiological function. The transition to, and the maintenance of chronic pain require new gene expression to support biochemical and structural changes within the pain pathway. The regulation of gene expression at the level of mRNA translation has emerged as an important step in the control of protein expression in the cell. Recent studies show that signaling pathways upstream of mRNA translation, such as mTORC1 and ERK, are upregulated in chronic pain conditions, and their inhibition effectively alleviates pain in several animal models. Despite this progress, mRNAs whose translation is altered in chronic pain conditions remain largely unknown. Here, we performed genome-wide translational profiling of dorsal root ganglion (DRG) and spinal cord dorsal horn tissues in a mouse model of neuropathic pain, spared nerve injury (SNI), using the ribosome profiling technique. We identified distinct subsets of mRNAs that are differentially translated in response to nerve injury in both tissues. We discovered key converging upstream regulators and pathways linked to mRNA translational control and neuropathic pain. Our data are crucial for the understanding of mechanisms by which mRNA translation promotes persistent hypersensitivity after nerve injury.

PTEN expression is a prognostic marker for patients with non-small cell lung cancer: a systematic review and meta-analysis of the literature

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a known tumor suppressor in non-small cell lung cancer (NSCLC). By performing a systematic review and meta-analysis of the literature, we determined the prognostic value of decreased PTEN expression in patients with NSCLC. We comprehensively and systematically searched through multiple online databases up to May 22, 2016 for NSCLC studies reporting on PTEN expression and patient survival outcome. Several criteria, including the Newcastle-Ottawa Quality Assessment Scale (NOS), were used to discriminate between studies. In total, 23 eligible studies with a total of 2,505 NSCLC patients were included in our meta-analysis. Our results demonstrated that decreased expression of PTEN correlated with poor overall survival in NSCLC patients and was indicative of a poor prognosis for disease-free survival and progression-free survival in patients with NSCLC.

Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Protein activity is the ultimate arbiter of function in most cellular pathways, and protein concentration is fundamentally connected to protein action. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukaryote, existing datasets are difficult to compare, and there is no consensus abundance value for each protein. We evaluated 21 quantitative analyses of the S. cerevisiae proteome, normalizing and converting all measurements of protein abundance into the intuitive measurement of absolute molecules per cell. We estimate the cellular abundance of 92% of the proteins in the yeast proteome and assess the variation in each abundance measurement. Using our protein abundance dataset, we find that a global response to diverse environmental stresses is not detected at the level of protein abundance, we find that protein tags have only a modest effect on protein abundance, and we identify proteins that are differentially regulated at the mRNA abundance, mRNA translation, and protein abundance levels.

Transcriptome-wide Identification and Validation of Interactions between the miRNA Machinery and HuR on mRNA Targets

The 3' untranslated region (UTR) of mRNAs is the primary regulatory region that mediates post-transcriptional control by microRNAs and RNA-binding proteins in the cytoplasm. Aside from individual sequence-specific binding and regulation, examples of interaction between these factors at particular 3' UTR sites have emerged. However, the whole picture of such higher-order regulatory modules across the transcriptome is lacking. Here, we investigate the interactions between HuR, a ubiquitous RNA-binding protein, and Ago2, a core effector of the miRNA pathway, at the transcriptome-wide level. Using HITS-CLIP, we map HuR and miRNA binding sites on human 3' UTRs and assess their co-occurrence. In addition, we demonstrate global effects of HuR knockdown on Ago2 occupancy, suggesting a co-regulatory relationship. Focusing on sites of Ago2-HuR overlap, 13 candidates were screened in luciferase reporter assays. Eleven sites showed miRNA-dependent repression, as confirmed in Dicer-null cells. To test for HuR's role in co-regulation, we measured the reporters in HuR KO cells. Three of the miRNA sites demonstrated altered activities, indicating that HuR has an effect on miRNA repression at those sites. Our study presents an efficient search and validation system for studying miRNA-HuR interactions, which expands our understanding of the combinatorial post-transcriptional control of gene expression at the 3' UTR.

Dietary Selenium Deficiency or Excess Reduces Sperm Quality and Testicular mRNA Abundance of Nuclear Glutathione Peroxidase 4 in Rats

Background: Glutathione peroxidase (GPX) 4 and selenoprotein P (SELENOP) are abundant, and several variants are expressed in the testis.Objective: We determined the effects of dietary selenium deficiency or excess on sperm quality and expressions of GPX4 and SELENOP variants in rat testis and liver.Methods: After weaning, male Sprague-Dawley rats were fed a Se-deficient basal diet (BD) for 5 wk until they were 9 wk old [mean ± SEM body weight (BW) = 256 ± 5 g]. They were then fed the BD diet alone (deficient) or with 0.25 (adequate), 3 (excess), or 5 (excess) mg Se/kg for 4 wk. Testis, liver, blood, and semen were collected to assay for selenoprotein mRNA and protein abundances, selenium concentration, GPX activity, 8-hydroxy-deoxyguanosine concentration, and sperm quality.Results: Dietary selenium supplementations elevated (P < 0.05) tissue selenium concentrations and GPX activities. Compared with those fed BD + 0.25 mg Se/kg, rats fed BD showed lower (P < 0.05) BW gain (86%) and sperm density (57%) but higher (P < 0.05) plasma 8-hydroxy-deoxyguanosine concentrations (189%), and nonprogressive sperm motility (4.4-fold). Likewise, rats fed BD + 5 mg Se/kg had (P = 0.06) lower BW gain and higher (1.9-fold) sperm deformity rates than those in the selenium-adequate group. Compared with the selenium-adequate group, dietary selenium deficiency (BD) or excess (BD + 3 or 5 mg Se/kg) resulted in 45-77% lower (P < 0.05) nuclear Gpx4 (nGpx4) mRNA abundance in the testis. Rats fed BD had lower (P < 0.05) mRNA levels of 2 Selenop variants in both testis and liver than those in the other groups. Testicular SELENOP was 155-170% higher (P < 0.05) in rats fed BD + 5 mg Se/kg and hepatic c/mGPX4 was 13-15% lower (P < 0.05) in rats fed BD than in the other groups.Conclusions: The mRNA abundance of rat testicular nGPX4 responded to dietary selenium concentrations in similar ways to sperm parameters and may be used as a sensitive marker to assess appropriate Se status for male function.

Intra and Interspecific Variations of Gene Expression Levels in Yeast Are Largely Neutral: (Nei Lecture, SMBE 2016, Gold Coast)

It is commonly, although not universally, accepted that most intra and interspecific genome sequence variations are more or less neutral, whereas a large fraction of organism-level phenotypic variations are adaptive. Gene expression levels are molecular phenotypes that bridge the gap between genotypes and corresponding organism-level phenotypes. Yet, it is unknown whether natural variations in gene expression levels are mostly neutral or adaptive. Here we address this fundamental question by genome-wide profiling and comparison of gene expression levels in nine yeast strains belonging to three closely related Saccharomyces species and originating from five different ecological environments. We find that the transcriptome-based clustering of the nine strains approximates the genome sequence-based phylogeny irrespective of their ecological environments. Remarkably, only ∼0.5% of genes exhibit similar expression levels among strains from a common ecological environment, no greater than that among strains with comparable phylogenetic relationships but different environments. These and other observations strongly suggest that most intra and interspecific variations in yeast gene expression levels result from the accumulation of random mutations rather than environmental adaptations. This finding has profound implications for understanding the driving force of gene expression evolution, genetic basis of phenotypic adaptation, and general role of stochasticity in evolution.

Reduced changes in protein compared to mRNA levels across non-proliferating tissues

BACKGROUND

The quantitative relations between RNA and protein are fundamental to biology and are still not fully understood. Across taxa, it was demonstrated that the protein-to-mRNA ratio in steady state varies in a direction that lessens the change in protein levels as a result of changes in the transcript abundance. Evidence for this behavior in tissues is sparse. We tested this phenomenon in new data that we produced for the mouse auditory system, and in previously published tissue datasets. A joint analysis of the transcriptome and proteome was performed across four datasets: inner-ear mouse tissues, mouse organ tissues, lymphoblastoid primate samples and human cancer cell lines.

RESULTS

We show that the protein levels are more conserved than the mRNA levels in all datasets, and that changes in transcription are associated with translational changes that exert opposite effects on the final protein level, in all tissues except cancer. Finally, we observe that some functions are enriched in the inner ear on the mRNA level but not in protein.

CONCLUSIONS

We suggest that partial buffering between transcription and translation ensures that proteins can be made rapidly in response to a stimulus. Accounting for the buffering can improve the prediction of protein levels from mRNA levels.

Genome-scale transcriptional study of hybrid effects and regulatory divergence in an F1 hybrid Ruellia (Wild Petunias: Acanthaceae) and its parents

BACKGROUND

New combinations of divergent genomes can give rise to novel genetic functions in resulting hybrid progeny. Such functions may yield opportunities for ecological divergence, contributing ultimately to reproductive isolation and evolutionary longevity of nascent hybrid lineages. In plants, the degree to which transgressive genotypes contribute to floral novelty remains a question of key interest. Here, we generated an F1 hybrid plant between the red-flowered Ruellia elegans and yellow flowered R. speciosa. RNA-seq technology was used to explore differential gene expression between the hybrid and its two parents, with emphasis on genetic elements involved in the production of floral anthocyanin pigments.

RESULTS

The hybrid was purple flowered and produced novel floral delphinidin pigments not manufactured by either parent. We found that nearly a fifth of all 86,475 unigenes expressed were unique to the hybrid. The majority of hybrid unigenes (80.97%) showed a pattern of complete dominance to one parent or the other although this ratio was uneven, suggesting asymmetrical influence of parental genomes on the progeny transcriptome. However, 8.87% of all transcripts within the hybrid were expressed at significantly higher or lower mean levels than observed for either parent. A total of 28 unigenes coding putatively for eight core enzymes in the anthocyanin pathway were recovered, along with three candidate MYBs involved in anthocyanin regulation.

CONCLUSION

Our results suggest that models of gene evolution that explain phenotypic novelty and hybrid establishment in plants may need to include transgressive effects. Additionally, our results lend insight into the potential for floral novelty that derives from unions of divergent genomes. These findings serve as a starting point to further investigate molecular mechanisms involved in flower color transitions in Ruellia.