蛋白质丰度调控及整体分布的规律性认识

蛋白质是生命的重要物质基础之一,也是生命活动的主要承担者.蛋白质丰度与其执行的生物学功能息息相关,受基因表达各个过程严格精密的调控.蛋白质丰度的直接影响因素包括相应mRNA初始量、蛋白质合成速率和降解速率.细胞对此3因素的调控将决定蛋白质最终的丰度.得益于定量蛋白质组学的飞速发展,规模化蛋白质丰度数据的产出,使得研究者可致力于发掘蛋白质丰度与其内在性质(如进化特征、结构特征、功能类型等)间规律性的相关性,这对于深入认识生命系统组成的基本原则具有重要意义.本文总结了蛋白质丰度调控及蛋白质丰度与其内在性质相关性的最新研究进展,及对这些规律性现象反映的生物学意义的解读.     

Abundance trends for river macroinvertebrates vary across taxa,trophic group and river typology

There is mounting evidence that terrestrial arthropods are declining rapidly in many areas of the world. It is unclear whether freshwater invertebrates, which are key providers of ecosystem services, are also declining. We addressed this question by analysing a long-term dataset of macroinvertebrate abundance collected from 2002 to 2019 across 5009 sampling sites in English rivers. Patterns varied markedly across taxonomic groups. Within trophic groups we detected increases in the abundance of carnivores by 19% and herbivores by 14.8%, while we estimated decomposers have declined by 21.7% in abundance since 2002. We also found heterogeneity in trends across rivers belonging to different typologies based on geological dominance and catchment altitude, with organic lowland rivers having generally higher rates of increase in abundance across taxa and trophic groups, with siliceous lowland rivers having the most declines. Our results reveal a complex picture of change in freshwater macroinvertebrate abundance between taxonomic groups, trophic levels and river typologies. Our analysis helps with identifying priority regions for action on potential environmental stressors where we discover macroinvertebrate abundance declines.     

Low Contents of Carbon and Nitrogen in Highly Abundant Proteins: Evidence of Selection for the Economy of Atomic Composition

Proteins that assimilate particular elements were found to avoid using amino acids containing the element, which indicates that the metabolic constraints of amino acids may influence the evolution of proteins. We suspected that low contents of carbon, nitrogen, and sulfur may also be selected for economy in highly abundant proteins that consume large amounts of the resources of cells. By analyzing recently available proteomic data in Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe, we found that at least the carbon and nitrogen contents in amino acid side chains are negatively correlated with protein abundance. An amino acid with a high number of carbon atoms in its side chain generally requires relatively more energy for its synthesis. Thus, it may be selected against in highly abundant proteins either because of economy in building blocks or because of economy in energy. Previous studies showed that highly abundant proteins preferentially use cheap (in terms of energy) amino acids. We found that the carbon content is still negatively correlated with protein abundance after controlling for the energetic cost of the amino acids. However, the negative correlation between protein abundance and energetic cost disappeared after controlling for carbon content. Building blocks seem to be more restricted than energy. It seems that the amino acid sequences of highly abundant proteins have to compromise between optimization for their biological functions and reducing the consumption of limiting resources. By contrast, the amino acid sequences of weakly expressed proteins are more likely to be optimized for their biological functions. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Eschericia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.     

Absolute Quantification of Endogenous Ras Isoform Abundance

Ras proteins are important signalling hubs situated near the top of networks controlling cell proliferation, differentiation and survival. Three almost identical isoforms, HRAS, KRAS and NRAS, are ubiquitously expressed yet have differing biological and oncogenic properties. In order to help understand the relative biological contributions of each isoform we have optimised a quantitative proteomics method for accurately measuring Ras isoform protein copy number per cell. The use of isotopic protein standards together with selected reaction monitoring for diagnostic peptides is sensitive, robust and suitable for application to sub-milligram quantities of lysates. We find that in a panel of isogenic SW48 colorectal cancer cells, endogenous Ras proteins are highly abundant with ≥260,000 total Ras protein copies per cell and the rank order of isoform abundance is KRAS>NRAS≥HRAS. A subset of oncogenic KRAS mutants exhibit increased total cellular Ras abundance and altered the ratio of mutant versus wild type KRAS protein. These data and methodology are significant because Ras protein copy number is required to parameterise models of signalling networks and informs interpretation of isoform-specific Ras functional data.     

Abundance patterns of lily pollen cDNAs: characterization of three pollen-preferential cDNA clones

Twenty-five clones were randomly selected from a mature pollen cDNA library of Easter lily (Lilium longiflorum Thunb.) in order to study the abundance of pollen-expressed mRNAs and the functional roles of the proteins encoded by these mRNAs. Plaque hybridization experiments were conducted to estimate indirectly the expression level of the mRNAs. Based on the hybridization frequency in the mature pollen library, the cDNA clones were divided into three abundance groups. Eight clones belonged to a high abundance class in which each cDNA clone was present in the mature lily pollen library at a frequency between 0.3 and 3%. Six of these clones were not found in cDNA libraries made from carpel, leaf, or root, suggesting that they are preferentially expressed in pollen. Fourteen clones belonged to a medium abundance class and were present in the mature pollen library at a frequency between 0.01 and 0.08%. The remaining three clones, which were present at a frequency below 0.01%, were grouped as a low abundance class. Almost all of the cDNA clones which belong to either the medium or low abundance class were also detected in the leaf library. Northern blot hybridization with three of the highly abundant cDNA clones confirmed their preferential expression in anther. In situ hybridization experiment with one of the clones showed the pollen-specific expression of the clone in mature anther. DNA sequence analysis revealed that the clone LMP131 encodes a peptide which is highly homologous to the tomato pollen-preferential gene, LAT59, which encodes a putative pectate lyase. The clone LMP134 encodes a peptide that shows an extensive similarity to a variety of thioredoxins. The third clone LMP132 encodes a 182-residue protein that has no significant homology to known sequences.     

Multiple marker abundance profiling: combining selected reaction monitoring and data‐dependent acquisition for rapid estimation of organelle abundance in subcellular samples

Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently‐used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower‐abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox ( http://suba.live ).     

The Plasma Proteome: High Abundance versus Low Abundance

The field of proteomics is rapidly turning towards targeted mass spectrometry (MS) methods to quantify putative markers or known proteins of biological interest. Historically, the enzyme-linked immunosorbent assay (ELISA) has been used for targeted protein analysis, but, unfortunately, it is limited by the excessive time required for antibody preparation, as well as concerns over selectivity. Despite the ability of proteomics to deliver increasingly quantitative measurements, owing to limited sensitivity, the leads generated are in the microgram per milliliter range. This stands in stark contrast to ELISA, which is capable of quantifying proteins at low picogram per milliliter levels. To bridge this gap, targeted liquid chromatography (LC) tandem MS (MS/MS) analysis of tryptic peptide surrogates using selected reaction monitoring detection has emerged as a viable option for rapid quantification of target proteins. The precision of this approach has been enhanced by the use of stable isotope-labeled peptide internal standards to compensate for variation in recovery and the influence of differential matrix effects. Unfortunately, the complexity of proteinaceous matrices, such as plasma, limits the usefulness of this approach to quantification in the mid-nanogram per milliliter range (medium-abundance proteins). This article reviews the current status of LC/MS/MS using selected reaction monitoring for protein quantification, and specifically considers the use of a single antibody to achieve superior enrichment of either the protein target or the released tryptic peptide. Examples of immunoaffinity-assisted LC/MS/MS are reviewed that demonstrate quantitative analysis of low-abundance proteins (subnanogram per milliliter range). A strategy based on this technology is proposed for the expedited evaluation of novel protein biomarkers, which relies on the synergy created from the complementary nature of MS and ELISA.     

Proteomic analysis reveals perturbed energy metabolism and elevated oxidative stress in hearts of rats with inborn low aerobic capacity

Selection on running capacity has created rat phenotypes of high-capacity runners (HCRs) that have enhanced cardiac function and low-capacity runners (LCRs) that exhibit risk factors of metabolic syndrome. We analysed hearts of HCRs and LCRs from generation 22 of selection using DIGE and identified proteins from MS database searches. The running capacity of HCRs was six-fold greater than LCRs. DIGE resolved 957 spots and proteins were unambiguously identified in 369 spots. Protein expression profiling detected 67 statistically significant (p<0.05; false discovery rate <10%, calculated using q-values) differences between HCRs and LCRs. Hearts of HCR rats exhibited robust increases in the abundance of each enzyme of the β-oxidation pathway. In contrast, LCR hearts were characterised by the modulation of enzymes associated with ketone body or amino acid metabolism. LCRs also exhibited enhanced expression of antioxidant enzymes such as catalase and greater phosphorylation of α B-crystallin at serine 59, which is a common point of convergence in cardiac stress signalling. Thus, proteomic analysis revealed selection on low running capacity is associated with perturbations in cardiac energy metabolism and provided the first evidence that the LCR cardiac proteome is exposed to greater oxidative stress.     

Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance

Understanding and predicting how amino acid substitutions affect proteins are keys to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analyzed 6,749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyze the structural and mechanistic origins of loss of function and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.     

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.     

Aminoacylation of Plasmodium falciparum tRNAAsn and Insights in the Synthesis of Asparagine Repeats

Genome sequencing revealed an extreme AT-rich genome and a profusion of asparagine repeats associated with low complexity regions (LCRs) in proteins of the malarial parasite Plasmodium falciparum. Despite their abundance, the function of these LCRs remains unclear. Because they occur in almost all families of plasmodial proteins, the occurrence of LCRs cannot be associated with any specific metabolic pathway; yet their accumulation must have given selective advantages to the parasite. Translation of these asparagine-rich LCRs demands extraordinarily high amounts of asparaginylated tRNA^Asn. However, unlike other organisms, Plasmodium codon bias is not correlated to tRNA gene copy number. Here, we studied tRNA^Asn accumulation as well as the catalytic capacities of the asparaginyl-tRNA synthetase of the parasite in vitro. We observed that asparaginylation in this parasite can be considered standard, which is expected to limit the availability of asparaginylated tRNA^Asn in the cell and, in turn, slow down the ribosomal translation rate when decoding asparagine repeats. This observation strengthens our earlier hypothesis considering that asparagine rich sequences act as “tRNA sponges” and help cotranslational folding of parasite proteins. However, it also raises many questions about the mechanistic aspects of the synthesis of asparagine repeats and about their implications in the global control of protein expression throughout Plasmodium life cycle.     

Abundance and dominance become less predictable as species richness decreases

Aim To test the hypothesis that communities with higher diversity have more predictable properties by examining patterns of community structure along a species richness gradient. Location Trinidad and Tobago (11°00 N, 61°00 W), on the South American continental shelf, opposite the Orinoco River delta, north‐east Venezuela. Methods We used quantile regressions to investigate how three total abundance, absolute and relative dominance measures – numerical abundance, biomass and energy use, respectively – change across a species richness gradient. We investigated which allocation rule best mimics community assembly in this species richness gradient by examining the abundance of the dominant species and comparing it with predictions of niche apportionment models. Results All measures of total abundance increase on average across the gradient, but the upper limit remains constant. On average, absolute dominance is constant, but the distance between the upper and lower limits decreases along the gradient. Relative dominance decreases with species richness. Observed dominance patterns are best described by Tokeshi's random fraction model. Main conclusions Our results show that both total abundance and absolute dominance become increasingly variable as biodiversity decreases. Consequently, our study suggests that ecosystem properties are less predictable when biodiversity is lower.