PaxDb, a database of protein abundance averages across all three domains of life

Co-adaption of tRNA gene copy number and amino acid usage influences translation rates in three life domains

Although more and more entangled participants of translation process were realized, how they cooperate and co-determine the final translation efficiency still lacks details. Here, we reasoned that the basic translation components, tRNAs and amino acids should be consistent to maximize the efficiency and minimize the cost. We firstly revealed that 310 out of 410 investigated genomes of three domains had significant co-adaptions between the tRNA gene copy numbers and amino acid compositions, indicating that maximum efficiency constitutes ubiquitous selection pressure on protein translation. Furthermore, fast-growing and larger bacteria are found to have significantly better co-adaption and confirmed the effect of this pressure. Within organism, highly expressed proteins and those connected to acute responses have higher co-adaption intensity. Thus, the better co-adaption probably speeds up the growing of cells through accelerating the translation of special proteins. Experimentally, manipulating the tRNA gene copy number to optimize co-adaption between enhanced green fluorescent protein (EGFP) and tRNA gene set of Escherichia coli indeed lifted the translation rate (speed). Finally, as a newly confirmed translation rate regulating mechanism, the co-adaption reflecting translation rate not only deepens our understanding on translation process but also provides an easy and practicable method to improve protein translation rates and productivity.

Evaluating the Possibility of Detecting Variants in Shotgun Proteomics via LeTE-Fusion Analysis Pipeline

In proteogenomic studies, many genome-annotated events, for example, single amino acid variation (SAAV) and short INDEL, are often unobserved in shotgun proteomics. Therefore, we propose an analysis pipeline called LeTE-fusion (Le, peptide length; T, theoretical values; E, experimental data) to first investigate whether peptides with certain lengths are observed more often in mass spectrometry (MS)-based proteomics, which may hinder peptide identification causing difficulty in detecting genome-annotated events. By applying LeTE-fusion on different MS-based proteome data sets, we found peptides within 7-20 amino acids are more frequently identified, possibly attributed to MS-related factors instead of proteases. We then further extended the usage of LeTE-fusion on four variant-containing-sequence data sets (SAAV-only) with various sample complexity up to the whole human proteome scale, which yields theoretically ∼70% variants observable in an ideal shotgun proteomics. However, only ∼40% of variants might be detectable in real shotgun proteomic experiments when LeTE-fusion utilizes the experimentally observed variant-site-containing wild-type peptides in PeptideAtlas to estimate the expected observable coverage of variants. Finally, we conducted a case study on HEK293 cell line with variants reported at genomic level that were also identified in shotgun proteomics to demonstrate the efficacy of LeTE-fusion on estimating expected observable coverage of variants. To the best of our knowledge, this is the first study to systematically investigate the detection limits of genome-annotated events via shotgun proteomics using such analysis pipeline.

Systems level expression correlation of Ras GTPase regulators

BACKGROUND

Proteins of the ubiquitously expressed core proteome are quantitatively correlated across multiple eukaryotic species. In addition, it was found that many protein paralogues exhibit expression anticorrelation, suggesting that the total level of protein with a given functionality must be kept constant.

METHODS

We performed Spearman's rank correlation analyses of gene expression levels for the RAS GTPase subfamily and their regulatory GEF and GAP proteins across tissues and across individuals for each tissue. A large set of published data for normal tissues from a wide range of species, human cancer tissues and human cell lines was analysed.

RESULTS

We show that although the multidomain regulatory proteins of Ras GTPases exhibit considerable tissue and individual gene expression variability, their total amounts are balanced in normal tissues. In a given tissue, the sum of activating (GEFs) and deactivating (GAPs) domains of Ras GTPases can vary considerably, but each person has balanced GEF and GAP levels. This balance is impaired in cell lines and in cancer tissues for some individuals.

CONCLUSIONS

Our results are relevant for critical considerations of knock out experiments, where functionally related homologs may compensate for the down regulation of a protein.

Redesigning thiamin synthesis: Prospects and potential payoffs

Thiamin is essential for plant growth but is short-lived in vivo and energetically very costly to produce - a combination that makes thiamin biosynthesis a prime target for improvement by redesign. Thiamin consists of thiazole and pyrimidine moieties. Its high biosynthetic cost stems from use of the suicide enzyme THI4 to form the thiazole and the near-suicide enzyme THIC to form the pyrimidine. These energetic costs lower biomass yield potential and are likely compounded by environmental stresses that destroy thiamin and hence increase the rate at which it must be made. The energy costs could be slashed by refactoring the thiamin biosynthesis pathway to eliminate the suicidal THI4 and THIC reactions. To substantiate this design concept, we first document the energetic costs of the THI4 and THIC steps in the pathway and explain how cutting these costs could substantially increase crop biomass and grain yields. We then show that a refactored pathway must produce thiamin itself rather than a stripped-down analog because the thiamin molecule cannot be simplified without losing biological activity. Lastly, we consider possible energy-efficient alternatives to the inefficient natural THI4- and THIC-mediated steps.

Functional Role of Fibrillin5 in Acclimation to Photooxidative Stress

The functional role of a lipid-associated soluble protein, fibrillin5 (FBN5), was determined with the Arabidopsis thaliana homozygous fbn5-knockout mutant line (SALK_064597) that carries a T-DNA insertion within the FBN5 gene. The fbn5 mutant remained alive, displaying a slow growth and a severe dwarf phenotype. The mutant grown even under growth light conditions at 80 µmol m-2 s-1 showed a drastic decrease in electron transfer activities around PSII, with little change in electron transfer activities around PSI, a phenomenon which was exaggerated under high light stress. The accumulation of plastoquinone-9 (PQ-9) was suppressed in the mutant, and >90% of the PQ-9 pool was reduced under growth light conditions. Non-photochemical quenching (NPQ) in the mutant functioned less efficiently, resulting from little contribution by energy-dependent quenching (qE). The ultrastructure of thylakoids in the mutant revealed that their grana were unstacked and transformed into loose and disordered structures. Light-harvesting complex (LHC)-containing large photosystem complexes and photosystem core complexes in the mutant were less abundant than those in wild-type plants. These results suggest that the lack of FBN5 causes a decrease in PQ-9 and imbalance of the redox state of PQ-9, resulting in misconducting both short-term and long-term control of the input of light energy to photosynthetic reaction centers. Furthermore, in the fbn5 mutant, the expression of genes involved in jasmonic acid biosynthesis was suppressed to ≤10% of that in the wild type under both growth-light and high-light conditions, suggesting that FBN5 functions as a transmitter of 1O2 in the stroma.

Tyrosine substitution of a conserved active-site histidine residue activates Plasmodium falciparum peroxiredoxin 6

Peroxiredoxins efficiently remove hydroperoxides and peroxynitrite in pro- and eukaryotes. However, isoforms of one subfamily of peroxiredoxins, the so-called Prx6-type enzymes, usually have very low activities in standard peroxidase assays in vitro. In contrast to other peroxiredoxins, Prx6 homologues share a conserved histidyl residue at the bottom of the active site. Here we addressed the role of this histidyl residue for redox catalysis using the Plasmodium falciparum homologue PfPrx6 as a model enzyme. Steady-state kinetics with tert-butyl hydroperoxide (tBuOOH) revealed that the histidyl residue is nonessential for Prx6 catalysis and that a replacement with tyrosine can even increase the enzyme activity four- to six-fold in vitro. Stopped-flow kinetics with reduced PfPrx6^WT , PfPrx6^C128A , and PfPrx6^H39Y revealed a preference for H₂ O₂ as an oxidant with second order rate constants for H₂ O₂ and tBuOOH around 2.5 × 10⁷ M^-1 s^-1 and 3 × 10⁶ M^-1 s^-1 , respectively. Differences between the oxidation kinetics of PfPrx6^WT , PfPrx6^C128A , and PfPrx6^H39Y were observed during a slower second-reaction phase. Our kinetic data support the interpretation that the reductive half-reaction is the rate-limiting step for PfPrx6 catalysis in steady-state measurements. Whether the increased activity of PfPrx6^H39Y is caused by a facilitated enzyme reduction because of a destabilization of the fully folded enzyme conformation remains to be analyzed. In summary, the conserved histidyl residue of Prx6-type enzymes is non-essential for catalysis, PfPrx6 is rapidly oxidized by hydroperoxides, and the gain-of-function mutant PfPrx6^H39Y might provide a valuable tool to address the influence of conformational changes on the reactivity of Prx6 homologues.

A study on multi-omic oscillations in Escherichia coli metabolic networks

BACKGROUND

Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels.

RESULTS

Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species.

CONCLUSIONS

The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome.

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Protein posttranslational modifications (PTMs) have typically been studied independently, yet many proteins are modified by more than one PTM type, and cell signaling pathways somehow integrate this information. We coupled immunoprecipitation using PTM-specific antibodies with tandem mass tag (TMT) mass spectrometry to simultaneously examine phosphorylation, methylation, and acetylation in 45 lung cancer cell lines compared to normal lung tissue and to cell lines treated with anticancer drugs. This simultaneous, large-scale, integrative analysis of these PTMs using a cluster-filtered network (CFN) approach revealed that cell signaling pathways were outlined by clustering patterns in PTMs. We used the t-distributed stochastic neighbor embedding (t-SNE) method to identify PTM clusters and then integrated each with known protein-protein interactions (PPIs) to elucidate functional cell signaling pathways. The CFN identified known and previously unknown cell signaling pathways in lung cancer cells that were not present in normal lung epithelial tissue. In various proteins modified by more than one type of PTM, the incidence of those PTMs exhibited inverse relationships, suggesting that molecular exclusive "OR" gates determine a large number of signal transduction events. We also showed that the acetyltransferase EP300 appears to be a hub in the network of pathways involving different PTMs. In addition, the data shed light on the mechanism of action of geldanamycin, an HSP90 inhibitor. Together, the findings reveal that cell signaling pathways mediated by acetylation, methylation, and phosphorylation regulate the cytoskeleton, membrane traffic, and RNA binding protein-mediated control of gene expression.

Protein intrinsic disorder negatively associates with gene age in different eukaryotic lineages

The emergence of new protein-coding genes in a specific lineage or species provides raw materials for evolutionary adaptations. Until recently, the biology of new genes emerging particularly from non-genic sequences remained unexplored. Although the new genes are subjected to variable selection pressure and face rapid deletion, some of them become functional and are retained in the gene pool. To acquire functional novelties, new genes often get integrated into the pre-existing ancestral networks. However, the mechanism by which young proteins acquire novel interactions remains unanswered till date. Since structural orientation contributes hugely to the mode of proteins' physical interactions, in this regard, we put forward an interesting question - Do new genes encode proteins with stable folds? Addressing the question, we demonstrated that the intrinsic disorder inversely correlates with the evolutionary gene ages - i.e. young proteins are richer in intrinsic disorder than the ancient ones. We further noted that young proteins, which are initially poorly connected hubs, prefer to be structurally more disordered than well-connected ancient proteins. The phenomenon strikingly defies the usual trend of well-connected proteins being highly disordered in structure. We justified that structural disorder might help poorly connected young proteins to undergo promiscuous interactions, which provides the foundation for novel protein interactions. The study focuses on the evolutionary perspectives of young proteins in the light of structural adaptations.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

An enrichment method based on synergistic and reversible covalent interactions for large-scale analysis of glycoproteins

Protein glycosylation is ubiquitous in biological systems and essential for cell survival. However, the heterogeneity of glycans and the low abundance of many glycoproteins complicate their global analysis. Chemical methods based on reversible covalent interactions between boronic acid and glycans have great potential to enrich glycopeptides, but the binding affinity is typically not strong enough to capture low-abundance species. Here, we develop a strategy using dendrimer-conjugated benzoboroxole to enhance the glycopeptide enrichment. We test the performance of several boronic acid derivatives, showing that benzoboroxole markedly increases glycopeptide coverage from human cell lysates. The enrichment is further improved by conjugating benzoboroxole to a dendrimer, which enables synergistic benzoboroxole–glycan interactions. This robust and simple method is highly effective for sensitive glycoproteomics analysis, especially capturing low-abundance glycopeptides. Importantly, the enriched glycopeptides remain intact, making the current method compatible with mass-spectrometry-based approaches to identify glycosylation sites and glycan structures.

Understanding the functions of protein glycosylation critically depends on methods to efficiently enrich glycoproteins from complex samples. Here, the authors develop a strategy using dendrimer-conjugated benzoboroxole to enhance glycopeptide enrichment, providing the basis for more comprehensive glycoprotein analyses.

Genome scale analysis of Escherichia coli with a comprehensive prokaryotic sequence-based biophysical model of translation initiation and elongation

Translation initiation in prokaryotes is affected by the mRNA folding and interaction of the ribosome binding site with the ribosomal RNA. The elongation rate is affected, among other factors, by the local biophysical properties of the coding regions, the decoding rates of different codons, and the interactions among ribosomes. Currently, there is no comprehensive biophysical model of translation that enables the prediction of mRNA translation dynamics based only on the transcript sequence and while considering all of these fundamental aspects of translation. In this study, we provide, for the first time, a computational simulative biophysical model of both translation initiation and elongation with all aspects mentioned above. We demonstrate our model performance and advantages focusing on Escherichia coli genes. We further show that the model enables prediction of translation rate, protein levels, and ribosome densities. In addition, our model enables quantifying the effect of silent mutations on translation rate in different parts of the transcript, the relative effect of mutations on translation initiation and elongation, and the effect of mutations on ribosome traffic jams. Thus, unlike previous models, the proposed one provides comprehensive information, facilitating future research in disciplines such as molecular evolution, synthetic biology, and functional genomics. A toolkit to estimate translation dynamics of transcripts is available at: https://www.cs.tau.ac.il/∼tamirtul/transim.

Zinc knuckle of TAF1 is a DNA binding module critical for TFIID promoter occupancy

The general transcription factor IID (TFIID) is the first component of the preinitiation complex (PIC) to bind the core promoter of RNA polymerase II transcribed genes. Despite its critical role in protein-encoded gene expression, how TFIID engages promoter DNA remains elusive. We have previously revealed a winged-helix DNA-binding domain in the N-terminal region of the largest TFIID subunit, TAF1. Here, we report the identification of a second DNA-binding module in the C-terminal half of human TAF1, which is encoded by a previously uncharacterized conserved zinc knuckle domain. We show that the TAF1 zinc knuckle aids in the recruit of TFIID to endogenous promoters vital for cellular proliferation. Mutation of the TAF1 zinc knuckle with defects in DNA binding compromises promoter occupancy of TFIID, which leads to a decrease in transcription and cell viability. Together, our studies provide a foundation to understand how TAF1 plays a central role in TFIID promoter binding and regulation of transcription initiation.

Role of Toll-like receptors and interferon regulatory factors in different experimental heart failure models of diverse etiology: IRF7 as novel cardiovascular stress-inducible factor

Heart failure (HF) is a leading cause of morbidity and mortality in the western world. Although optimal medical care and treatment is widely available, the prognosis of patients with HF is still poor.

Toll-like receptors (TLRs) are important compartments of the innate immunity. Current studies have identified TLRs as critical mediators in cardiovascular diseases. In the present study, we investigated the involvement of TLRs and interferon (IFN) regulatory factors (IRFs) in different experimental HF models including viral myocarditis, myocardial ischemia, diabetes mellitus, and cardiac hypertrophy. In addition, we investigated for the first time comprehensive TLR and IRF gene and protein expression under basal conditions in murine and human cardiac tissue. We found that Tlr4, Tlr9 and Irf7 displayed highest gene expression under basal conditions, indicating their significant role in first-line defense in the murine and human heart. Moreover, induction of TLRs and IRFs clearly differs between the various experimental HF models of diverse etiology and the concomitant inflammatory status. In the HF model of acute viral-induced myocarditis, TLR and IRF activation displayed the uppermost gene expression in comparison to the remaining experimental HF models, indicating the highest amount of myocardial inflammation in myocarditis. In detail, Irf7 displayed by far the highest gene expression during acute viral infection. Interestingly, post myocardial infarction TLR and IRF gene expression was almost exclusively increased in the infarct zone after myocardial ischemia (Tlr2, Tlr3, Tlr6, Tlr7, Tlr9, Irf3, Irf7). With one exception, Irf3 showed a decreased gene expression in the remote zone post infarction. Finally, we identified Irf7 as novel cardiovascular stress-inducible factor in the pathologically stressed heart. These findings on TLR and IRF function in the inflamed heart highlight the complexity of inflammatory immune response and raise more interesting questions for future investigation.

Ubiquitin Modulates Liquid-Liquid Phase Separation of UBQLN2 via Disruption of Multivalent Interactions

Under stress, certain eukaryotic proteins and RNA assemble to form membraneless organelles known as stress granules. The most well-studied stress granule components are RNA-binding proteins that undergo liquid-liquid phase separation (LLPS) into protein-rich droplets mediated by intrinsically disordered low-complexity domains (LCDs). Here we show that stress granules include proteasomal shuttle factor UBQLN2, an LCD-containing protein structurally and functionally distinct from RNA-binding proteins. In vitro, UBQLN2 exhibits LLPS at physiological conditions. Deletion studies correlate oligomerization with UBQLN2's ability to phase-separate and form stress-induced cytoplasmic puncta in cells. Using nuclear magnetic resonance (NMR) spectroscopy, we mapped weak, multivalent interactions that promote UBQLN2 oligomerization and LLPS. Ubiquitin or polyubiquitin binding, obligatory for UBQLN2's biological functions, eliminates UBQLN2 LLPS, thus serving as a switch between droplet and disperse phases. We postulate that UBQLN2 LLPS enables its recruitment to stress granules, where its interactions with ubiquitinated substrates reverse LLPS to enable shuttling of clients out of stress granules.

Control of Retrograde Signaling by Rapid Turnover of GENOMES UNCOUPLED1

The exchange of signals between cellular compartments coordinates development and differentiation, modulates metabolic pathways, and triggers responses to environmental conditions. The proposed central regulator of plastid-to-nucleus retrograde signaling, GENOMES UNCOUPLED1 (GUN1), is present at very low levels, which has hampered the discovery of its precise molecular function. Here, we show that the Arabidopsis (Arabidopsis thaliana) GUN1 protein accumulates to detectable levels only at very early stages of leaf development, where it functions in the regulation of chloroplast biogenesis. GUN1 mRNA is present at high levels in all tissues, but GUN1 protein undergoes rapid degradation (with an estimated half-life of ∼4 h) in all tissues where chloroplast biogenesis has been completed. The rapid turnover of GUN1 is controlled mainly by the chaperone ClpC1, suggesting degradation of GUN1 by the Clp protease. Degradation of GUN1 slows under stress conditions that alter retrograde signaling, thus ensuring that the plant has sufficient GUN1 protein. We also find that the pentatricopeptide repeat motifs of GUN1 are important determinants of GUN1 stability. Moreover, overexpression of GUN1 causes an early flowering phenotype, suggesting a function of GUN1 in developmental phase transitions beyond chloroplast biogenesis. Taken together, our results provide new insight into the regulation of GUN1 by proteolytic degradation, uncover its function in early chloroplast biogenesis, and suggest a role in developmental phase transitions.

eIF2B Mechanisms of Action and Regulation: A Thermodynamic View

Eukaryotic translation initiation factor 2B (eIF2B) is the guanine nucleotide exchange factor of the GTPase eIF2, which brings the initiator Met-tRNA_i to the ribosome in the form of the eIF2-GTP·Met-tRNA_i ternary complex (TC). The activity of eIF2B is inhibited by phosphorylation of its substrate eIF2 by several stress-induced kinases, which triggers the integrated stress response (ISR). The ISR plays a central role in maintaining homeostasis in the cell under various stress conditions, and its dysregulation is a causative factor in the pathology of a number of neurodegenerative disorders. Over the past three decades, virtually every aspect of eIF2B function has been the subject of uncertainty or controversy: from the catalytic mechanism of nucleotide exchange, to whether eIF2B only catalyzes nucleotide exchange on eIF2 or also promotes binding of Met-tRNA_i to eIF2-GTP to form the TC. Here, we provide the first complete thermodynamic analysis of the process of recycling of eIF2-GDP to the TC. The available evidence leads to the conclusion that eIF2 is channeled from the ribosome (as an eIF5·eIF2-GDP complex) to eIF2B, converted by eIF2B to the TC, which is then channeled back to eIF5 and the ribosome. The system has evolved to be regulated by multiple factors, including post-translational modifications of eIF2, eIF2B, and eIF5, as well as directly by the energy balance in the cell, through the GTP:GDP ratio.

The binding landscape of a partially-selective isopeptidase inhibitor with potent pro-death activity, based on the bis(arylidene)cyclohexanone scaffold

Diaryldienone derivatives with accessible β-carbons show strong anti-neoplastic properties, related to their ability to make covalent adducts with free thiols by Michael addition, and low toxicity in vivo. Accumulation of poly-ubiquitylated proteins, activation of the unfolded protein response (UPR) and induction of cell death are universal hallmarks of their activities. These compounds have been characterized as inhibitors of isopeptidases, a family of cysteine-proteases, which de-conjugate ubiquitin and ubiquitin-like proteins from their targets. However, it is unclear whether they can also react with additional proteins. In this work, we utilized the biotin-conjugated diaryldienone-derivative named 2c, as a bait to purify novel cellular targets of these small molecules. Proteomic analyses have unveiled that, in addition to isopeptidases, these inhibitors can form stable covalent adducts with different intracellular proteins, thus potentially impacting on multiple functions of the cells, from cytoskeletal organization to metabolism. These widespread activities can explain the ability of diaryldienone derivatives to efficiently trigger different cell death pathways.

In-Depth Proteomic Characterization of Classical and Non-Classical Monocyte Subsets

Monocytes are bone marrow-derived leukocytes that are part of the innate immune system. Monocytes are divided into three subsets: classical, intermediate and non-classical, which can be differentiated by their expression of some surface antigens, mainly CD14 and CD16. These cells are key players in the inflammation process underlying the mechanism of many diseases. Thus, the molecular characterization of these cells may provide very useful information for understanding their biology in health and disease. We performed a multicentric proteomic study with pure classical and non-classical populations derived from 12 healthy donors. The robust workflow used provided reproducible results among the five participating laboratories. Over 5000 proteins were identified, and about half of them were quantified using a spectral counting approach. The results represent the protein abundance catalogue of pure classical and enriched non-classical blood peripheral monocytes, and could serve as a reference dataset of the healthy population. The functional analysis of the differences between cell subsets supports the consensus roles assigned to human monocytes.

Disulfide Bonds Enable Accelerated Protein Evolution

The different proteins of any proteome evolve at enormously different rates. What factors contribute to this variability, and to what extent, is still a largely open question. We hypothesized that disulfide bonds, by increasing protein stability, should make proteins' structures relatively independent of their amino acid sequences, thus acting as buffers of deleterious mutations and enabling accelerated sequence evolution. In agreement with this hypothesis, we observed that membrane proteins with disulfide bonds evolved 88% faster than those without disulfide bonds, and that extracellular proteins with disulfide bonds evolved 49% faster than those without disulfide bonds. In addition, genes encoding proteins with disulfide bonds exhibit an increased likelihood of showing signatures of positive selection. Multivariate analyses indicate that the trend is independent of a number of potentially confounding factors. The effect, however, is not observed among the longest proteins, which can become stabilized by mechanisms other than disulfide bonds.

The extent of ribosome queuing in budding yeast

Ribosome queuing is a fundamental phenomenon suggested to be related to topics such as genome evolution, synthetic biology, gene expression regulation, intracellular biophysics, and more. However, this phenomenon hasn't been quantified yet at a genomic level. Nevertheless, methodologies for studying translation (e.g. ribosome footprints) are usually calibrated to capture only single ribosome protected footprints (mRPFs) and thus limited in their ability to detect ribosome queuing. On the other hand, most of the models in the field assume and analyze a certain level of queuing. Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of ribosomes (dRPFs) using a modified ribosome profiling protocol. We combine our approach with traditional ribosome profiling to generate a detailed profile of ribosome traffic. The data are analyzed using computational models of translation dynamics. The approach was implemented on the Saccharomyces cerevisiae transcriptome. Our data shows that ribosome queuing is more frequent than previously thought: the measured ratio of ribosomes within dRPFs to mRPFs is 0.2–0.35, suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods. We found that specific regions are enriched with queued ribosomes, such as the 5’-end of ORFs, and regions upstream to mRPF peaks, among others. While queuing is related to higher density of ribosomes on the transcript (characteristic of highly translated genes), we report cases where traffic jams are relatively more severe in lowly expressed genes and possibly even selected for. In addition, our analysis demonstrates that higher adaptation of the coding region to the intracellular tRNA levels is associated with lower queuing levels. Our analysis also suggests that the Saccharomyces cerevisiae transcriptome undergoes selection for eliminating traffic jams. Thus, our proposed approach is an essential tool for high resolution analysis of ribosome traffic during mRNA translation and understanding its evolution.

During translation, multiple ribosomes may translate the same mRNA. The density of ribosomal traffic across the transcript poses several open questions, such as how often a ribosome’s path is blocked by a second ribosome, do queues of multiple ribosomes typically form on mRNAs and what is their effect on the overall translation rate of an mRNA. However, this phenomenon hasn't been quantified yet at a genomic level. Nevertheless, methodologies for monitoring translation are limited in their ability to detect ribosome queuing. On the other hand, most of the models in the field assume and analyze a certain level of queuing. Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of adjacent translating ribosomes, and a computational model of translation dynamics. Our data shows that ribosome queuing in Saccharomyces cerevisiae is more frequent than previously thought, suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods. Our analysis also suggests that the S. cerevisiae transcriptome undergoes selection for eliminating traffic jams, while specific regions and genes may possibly be under selection for increased queuing.

Hsp70-associated chaperones have a critical role in buffering protein production costs

Proteins are necessary for cellular growth. Concurrently, however, protein production has high energetic demands associated with transcription and translation. Here, we propose that activity of molecular chaperones shape protein burden, that is the fitness costs associated with expression of unneeded proteins. To test this hypothesis, we performed a genome-wide genetic interaction screen in baker's yeast. Impairment of transcription, translation, and protein folding rendered cells hypersensitive to protein burden. Specifically, deletion of specific regulators of the Hsp70-associated chaperone network increased protein burden. In agreement with expectation, temperature stress, increased mistranslation and a chemical misfolding agent all substantially enhanced protein burden. Finally, unneeded protein perturbed interactions between key components of the Hsp70-Hsp90 network involved in folding of native proteins. We conclude that specific chaperones contribute to protein burden. Our work indicates that by minimizing the damaging impact of gratuitous protein overproduction, chaperones enable tolerance to massive changes in genomic expression.

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.

Defining tissue proteomes by systematic literature review

Defining protein composition is a key step in understanding the function of both healthy and diseased biological systems. There is currently little consensus between existing published proteomes in tissues such as the aorta, cartilage and organs such as skin. Lack of agreement as to both the number and identity of proteins may be due to issues in protein extraction, sensitivity/specificity of detection and the use of disparate tissue/cell sources. Here, we developed a method combining bioinformatics and systematic review to screen >32M articles from the Web of Science for evidence of proteins in healthy human skin. The resulting Manchester Proteome (www.manchesterproteome.manchester.ac.uk) collates existing evidence which characterises 2,948 skin proteins, 437 unique to our database and 2011 evidenced by both mass spectrometry and immune-based techniques. This approach circumvents the limitations of individual proteomics studies and can be applied to other species, organs, cells or disease-states. Accurate tissue proteomes will aid development of engineered constructs and offer insight into disease treatments by highlighting differences in proteomic composition.

Constitutive redox and phosphoproteome changes in multiple herbicide resistant Avena fatua L. are similar to those of systemic acquired resistance and systemic acquired acclimation

Plants are routinely confronted with numerous biotic and abiotic stressors, and in response have evolved highly effective strategies of systemic acquired resistance (SAR) and systemic acquired acclimation (SAA), respectively. A much more evolutionarily recent abiotic stress is the application of herbicides to control weedy plants, and their intensive use has selected for resistant weed populations that cause substantial crop yield losses and increase production costs. Non-target site resistance (NTSR) to herbicides is rapidly increasing worldwide and is associated with alterations in generalized stress defense networks. This work investigated protein post-translational modifications associated with NTSR in multiple herbicide resistant (MHR) Avena fatua, and their commonalities with those of SAR and SAA. We used proteomic, biochemical, and immunological approaches to compare constitutive protein profiles in MHR and herbicide susceptible (HS) A. fatua populations. Phosphoproteome and redox proteome surveys showed that post-translational modifications of proteins with functions in core cellular processes were reduced in MHR plants, while those involved in xenobiotic and stress response, reactive oxygen species detoxification and redox maintenance, heat shock response, and intracellular signaling were elevated in MHR as compared to HS plants. More specifically, MHR plants contained constitutively elevated levels of three protein kinases including the lectin S-receptor-like serine/threonine-protein kinase LecRK2, a well-characterized component of SAR. Analyses of superoxide dismutase enzyme activity and protein levels did not reveal constitutive differences between MHR and HS plants. The overall results support the idea that herbicide stress is perceived similarly to other abiotic stresses, and that A. fatua NTSR shares analogous features with SAR and SAA. We speculate that MHR A. fatua's previous exposure to sublethal herbicide doses, as well as earlier evolution under a diversity of abiotic and biotic stressors, has led to a heightened state of stress preparedness that includes NTSR to a number of unrelated herbicides.

Toward Large-Scale Computational Prediction of Protein Complexes

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein-protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein-protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

Protein Stability and Avoidance of Toxic Misfolding Do Not Explain the Sequence Constraints of Highly Expressed Proteins

The avoidance of cytotoxic effects associated with protein misfolding has been proposed as a dominant constraint on the sequence evolution and molecular clock of highly expressed proteins. Recently, Leuenberger et al. developed an elegant experimental approach to measure protein thermal stability at the proteome scale. The collected data allow us to rigorously test the predictions of the misfolding avoidance hypothesis that highly expressed proteins have evolved to be more stable, and that maintaining thermodynamic stability significantly constrains their evolution. Notably, reanalysis of the Leuenberger et al. data across four different organisms reveals no substantial correlation between protein stability and protein abundance. Therefore, the key predictions of the misfolding toxicity and related hypotheses are not supported by available empirical data. The data also suggest that, regardless of protein expression, protein stability does not substantially affect the protein molecular clock across organisms.

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes.

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).

S-Enantiomer of the Antitubercular Compound S006-830 Complements Activity of Frontline TB Drugs and Targets Biogenesis of Mycobacterium tuberculosis Cell Envelope

A synthetic molecule S006-830, belonging to the class of thiophene-containing trisubstituted methanes, had shown good in vitro and in vivo bactericidal activity against drug-sensitive and drug-resistant Mycobacterium tuberculosis (Mtb). The molecule had also shown good druglike pharmacokinetic properties. However, S006-830 is a racemic mixture of two enantiomers, one of which could possess a better pharmacological profile than the other. We purified both the enantiomers on a chiral column and observed that S-enantiomer has a significantly higher inhibitory and cidal activity against Mtb than the R-enantiomer. Action of S-S006-830 was "synergistic" for rifampicin and "additive" for isoniazid and ethambutol. The combination of S-S006-830 and rifampicin produced 100% kill of Mtb within 8 days. In a chemical proteomics approach using matrix-bound compound to pull down its target protein(s) from Mtb membrane, FabG4 (β-ketoacyl CoA reductase, EC 1.1.1.100) emerged as the most likely target for S-S006-830. In target validation assays, the compound exhibited 2-fold higher inhibitory concentration for an Mtb construct overexpressing FabG4. In addition, it inhibited mycolic acid biosynthesis and formation of biofilms by Mtb. Molecular docking of S-S006-830 with FabG4 was consistent with the experimental data. These results support the development of S-S006-830 as a novel lead against tuberculosis.

The Incomplete Glutathione Puzzle: Just Guessing at Numbers and Figures?

SIGNIFICANCE

Glutathione metabolism is comparable to a jigsaw puzzle with too many pieces. It is supposed to comprise (i) the reduction of disulfides, hydroperoxides, sulfenic acids, and nitrosothiols, (ii) the detoxification of aldehydes, xenobiotics, and heavy metals, and (iii) the synthesis of eicosanoids, steroids, and iron-sulfur clusters. In addition, glutathione affects oxidative protein folding and redox signaling. Here, I try to provide an overview on the relevance of glutathione-dependent pathways with an emphasis on quantitative data. Recent Advances: Intracellular redox measurements reveal that the cytosol, the nucleus, and mitochondria contain very little glutathione disulfide and that oxidative challenges are rapidly counterbalanced. Genetic approaches suggest that iron metabolism is the centerpiece of the glutathione puzzle in yeast. Furthermore, recent biochemical studies provide novel insights on glutathione transport processes and uncoupling mechanisms.

CRITICAL ISSUES

Which parts of the glutathione puzzle are most relevant? Does this explain the high intracellular concentrations of reduced glutathione? How can iron-sulfur cluster biogenesis, oxidative protein folding, or redox signaling occur at high glutathione concentrations? Answers to these questions not only seem to depend on the organism, cell type, and subcellular compartment but also on different ideologies among researchers.

FUTURE DIRECTIONS

A rational approach to compare the relevance of glutathione-dependent pathways is to combine genetic and quantitative kinetic data. However, there are still many missing pieces and too little is known about the compartment-specific repertoire and concentration of numerous metabolites, substrates, enzymes, and transporters as well as rate constants and enzyme kinetic patterns. Gathering this information might require the development of novel tools but is crucial to address potential kinetic competitions and to decipher uncoupling mechanisms to solve the glutathione puzzle. Antioxid. Redox Signal. 27, 1130-1161.

Physicochemical properties that control protein aggregation also determine whether a protein is retained or released from necrotic cells

Amyloidogenic protein aggregation impairs cell function and is a hallmark of many chronic degenerative disorders. Protein aggregation is also a major event during acute injury; however, unlike amyloidogenesis, the process of injury-induced protein aggregation remains largely undefined. To provide this insight, we profiled the insoluble proteome of several cell types after acute injury. These experiments show that the disulfide-driven process of nucleocytoplasmic coagulation (NCC) is the main form of injury-induced protein aggregation. NCC is mechanistically distinct from amyloidogenesis, but still broadly impairs cell function by promoting the aggregation of hundreds of abundant and essential intracellular proteins. A small proportion of the intracellular proteome resists NCC and is instead released from necrotic cells. Notably, the physicochemical properties of NCC-resistant proteins are contrary to those of NCC-sensitive proteins. These observations challenge the dogma that liberation of constituents during necrosis is anarchic. Rather, inherent physicochemical features including cysteine content, hydrophobicity and intrinsic disorder determine whether a protein is released from necrotic cells. Furthermore, as half of the identified NCC-resistant proteins are known autoantigens, we propose that physicochemical properties that control NCC also affect immune tolerance and other host responses important for the restoration of homeostasis after necrotic injury.

miRnalyze: an interactive database linking tool to unlock intuitive microRNA regulation of cell signaling pathways

The various pathophysiological processes occurring in living systems are known to be orchestrated by delicate interplays and cross-talks between different genes and their regulators. Among the various regulators of genes, there is a class of small non-coding RNA molecules known as microRNAs. Although, the relative simplicity of miRNAs and their ability to modulate cellular processes make them attractive therapeutic candidates, their presence in large numbers make it challenging for experimental researchers to interpret the intricacies of the molecular processes they regulate. Most of the existing bioinformatic tools fail to address these challenges. Here, we present a new web resource 'miRnalyze' that has been specifically designed to directly identify the putative regulation of cell signaling pathways by miRNAs. The tool integrates miRNA-target predictions with signaling cascade members by utilizing TargetScanHuman 7.1 miRNA-target prediction tool and the KEGG pathway database, and thus provides researchers with in-depth insights into modulation of signal transduction pathways by miRNAs. miRnalyze is capable of identifying common miRNAs targeting more than one gene in the same signaling pathway-a feature that further increases the probability of modulating the pathway and downstream reactions when using miRNA modulators. Additionally, miRnalyze can sort miRNAs according to the seed-match types and TargetScan Context ++ score, thus providing a hierarchical list of most valuable miRNAs. Furthermore, in order to provide users with comprehensive information regarding miRNAs, genes and pathways, miRnalyze also links to expression data of miRNAs (miRmine) and genes (TiGER) and proteome abundance (PaxDb) data. To validate the capability of the tool, we have documented the correlation of miRnalyze's prediction with experimental confirmation studies.

Database URL

http://www.mirnalyze.in.

The Rapid Evolution of an Ohnolog Contributes to the Ecological Specialization of Incipient Yeast Species

Identifying the molecular changes that lead to ecological specialization during speciation is one of the major goals of molecular evolution. One question that remains to be thoroughly investigated is whether ecological specialization derives strictly from adaptive changes and their associated trade-offs, or from conditionally neutral mutations that accumulate under relaxed selection. We used whole-genome sequencing, genome annotation and computational analyses to identify genes that have rapidly diverged between two incipient species of Saccharomyces paradoxus that occupy different climatic regions along a south-west to north-east gradient. As candidate loci for ecological specialization, we identified genes that show signatures of adaptation and accelerated rates of amino acid substitutions, causing asymmetric evolution between lineages. This set of genes includes a glycyl-tRNA-synthetase, GRS2, which is known to be transcriptionally induced under heat stress in the model and sister species S. cerevisiae. Molecular modelling, expression analysis and fitness assays suggest that the accelerated evolution of this gene in the Northern lineage may be caused by relaxed selection. GRS2 arose during the whole-genome duplication (WGD) that occurred 100 million years ago in the yeast lineage. While its ohnolog GRS1 has been preserved in all post-WGD species, GRS2 has frequently been lost and is evolving rapidly, suggesting that the fate of this ohnolog is still to be resolved. Our results suggest that the asymmetric evolution of GRS2 between the two incipient S. paradoxus species contributes to their restricted climatic distributions and thus that ecological specialization derives at least partly from relaxed selection rather than a molecular trade-off resulting from adaptive evolution.

Tagging and Enriching Proteins Enables Cell-Specific Proteomics

Cell-specific proteomics in multicellular systems and whole animals is a promising approach to understand the differentiated functions of cells and tissues. Here, we extend our stochastic orthogonal recoding of translation (SORT) approach for the co-translational tagging of proteomes with a cyclopropene-containing amino acid in response to diverse codons in genetically targeted cells, and create a tetrazine-biotin probe containing a cleavable linker that offers a way to enrich and identify tagged proteins. We demonstrate that SORT with enrichment, SORT-E, efficiently recovers and enriches SORT tagged proteins and enables specific identification of enriched proteins via mass spectrometry, including low-abundance proteins. We show that tagging at distinct codons enriches overlapping, but distinct sets of proteins, suggesting that tagging at more than one codon enhances proteome coverage. Using SORT-E, we accomplish cell-specific proteomics in the fly. These results suggest that SORT-E will enable the definition of cell-specific proteomes in animals during development, disease progression, and learning and memory.

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A tetrazine-biotin probe containing a cleavable linker was created

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Proteomes labeled with cyclopropene amino acids were enriched and identified

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Proteome coverage is increased by targeting the amino acids to multiple codons

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Cell-specific proteomics was accomplished in the fly

A Map of Human Mitochondrial Protein Interactions Linked to Neurodegeneration Reveals New Mechanisms of Redox Homeostasis and NF-κB Signaling

Mitochondrial protein (MP) dysfunction has been linked to neurodegenerative disorders (NDs); however, the discovery of the molecular mechanisms underlying NDs has been impeded by the limited characterization of interactions governing MP function. Here, using mass spectrometry (MS)-based analysis of 210 affinity-purified mitochondrial (mt) fractions isolated from 27 epitope-tagged human ND-linked MPs in HEK293 cells, we report a high-confidence MP network including 1,964 interactions among 772 proteins (>90% previously unreported). Nearly three-fourths of these interactions were confirmed in mouse brain and multiple human differentiated neuronal cell lines by primary antibody immunoprecipitation and MS, with many linked to NDs and autism. We show that the SOD1-PRDX5 interaction, critical for mt redox homeostasis, can be perturbed by amyotrophic lateral sclerosis-linked SOD1 allelic variants and establish a functional role for ND-linked factors coupled with IκBɛ in NF-κB activation. Our results identify mechanisms for ND-linked MPs and expand the human mt interaction landscape.

SH2 Superbinder Modified Monolithic Capillary Column for the Sensitive Analysis of Protein Tyrosine Phosphorylation

In this study, we present a method to specifically capture phosphotyrosine (pTyr) peptides from minute amount of sample for the sensitive analysis of protein tyrosine phosphorylation. We immobilized SH2 superbinder on a monolithic capillary column to construct a microreactor to enrich pTyr peptides. It was found that the synthetic pTyr peptide could be specifically enriched by the microreactor from the peptide mixture prepared by spiking of the synthetic pTyr peptide into the tryptic digests of α-casein and β-casein with molar ratios of 1:1000:1000. The microreactor was further applied to enrich pTyr peptides from pervanadate-treated HeLa cell digests for phosphoproteomics analysis, which resulted in the identification of 796 unique pTyr sites. In contrast, the conventional SH2 superbinder-based method identified 41 pTyr sites for the same sample, only 5.2% of the number achieved by the microreactor. Finally, this microreactor was also applied to analyze the pTyr in Shc1 complex, an immunopurified protein complex, which resulted in the identification of 15 pTyr sites. Together, this technique is best fitted to analyze the pTyr in minute amount of sample and will have broad application in fields where only a limited amount of sample is available.

Intensive herbicide use has selected for constitutively elevated levels of stress-responsive mRNAs and proteins in multiple herbicide-resistant Avena fatua L

BACKGROUND

Intensive use of herbicides has led to the evolution of two multiple herbicide-resistant (MHR) Avena fatua (wild oat) populations in Montana that are resistant to members of all selective herbicide families available for A. fatua control in US small grain crops. We used transcriptome and proteome surveys to compare constitutive changes in MHR and herbicide-susceptible (HS) plants associated with non-target site resistance.

RESULTS

Compared to HS plants, MHR plants contained constitutively elevated levels of differentially expressed genes (DEGs) with functions in xenobiotic catabolism, stress response, redox maintenance and transcriptional regulation that are similar to abiotic stress-tolerant phenotypes. Proteome comparisons identified similarly elevated proteins including biosynthetic and multifunctional enzymes in MHR plants. Of 25 DEGs validated by RT-qPCR assay, differential regulation of 21 co-segregated with flucarbazone-sodium herbicide resistance in F₃ families, and a subset of 10 of these were induced or repressed in herbicide-treated HS plants.

CONCLUSION

Although the individual and collective contributions of these DEGs and proteins to MHR remain to be determined, our results support the idea that intensive herbicide use has selected for MHR populations with altered, constitutively regulated patterns of gene expression that are similar to those in abiotic stress-tolerant plants. © 2017 Society of Chemical Industry.

Granzyme B Disrupts Central Metabolism and Protein Synthesis in Bacteria to Promote an Immune Cell Death Program

Human cytotoxic lymphocytes kill intracellular microbes. The cytotoxic granule granzyme proteases released by cytotoxic lymphocytes trigger oxidative bacterial death by disrupting electron transport, generating superoxide anion and inactivating bacterial oxidative defenses. However, they also cause non-oxidative cell death because anaerobic bacteria are also killed. Here, we use differential proteomics to identify granzyme B substrates in three unrelated bacteria: Escherichia coli, Listeria monocytogenes, and Mycobacteria tuberculosis. Granzyme B cleaves a highly conserved set of proteins in all three bacteria, which function in vital biosynthetic and metabolic pathways that are critical for bacterial survival under diverse environmental conditions. Key proteins required for protein synthesis, folding, and degradation are also substrates, including multiple aminoacyl tRNA synthetases, ribosomal proteins, protein chaperones, and the Clp system. Because killer cells use a multipronged strategy to target vital pathways, bacteria may not easily become resistant to killer cell attack.

stAIcalc: tRNA adaptation index calculator based on species-specific weights

Summary

The tRNA Adaptation Index (tAI) is a tRNA-centric measure of translation efficiency which includes weights that take into account the efficiencies of the different wobble interactions. To enable the calculation of the index based on a species-specific inference of these weights, we created the stAI calc . The calculator includes optimized tAI weights for 100 species from the three domains of life along with a standalone software package that optimizes the weights for new organisms. The tAI with the optimized weights should enable performing large scale studies in disciplines such as molecular evolution, genomics, systems biology and synthetic biology.

Availability and Implementation

The calculator is publicly available at http://www.cs.tau.ac.il/∼tamirtul/stAIcalc/stAIcalc.html.

Contact

tamirtul@post.tau.ac.il.

Proteogenomics in Aid of Host-Pathogen Interaction Studies: A Bacterial Perspective

By providing useful tools to study host-pathogen interactions, next-generation omics has recently enabled the study of gene expression changes in both pathogen and infected host simultaneously. However, since great discriminative power is required to study pathogen and host simultaneously throughout the infection process, the depth of quantitative gene expression profiling has proven to be unsatisfactory when focusing on bacterial pathogens, thus preferentially requiring specific strategies or the development of novel methodologies based on complementary omics approaches. In this review, we focus on the difficulties encountered when making use of proteogenomics approaches to study bacterial pathogenesis. In addition, we review different omics strategies (i.e., transcriptomics, proteomics and secretomics) and their applications for studying interactions of pathogens with their host.

Gene duplication can impart fragility, not robustness, in the yeast protein interaction network

The maintenance of duplicated genes is thought to protect cells from genetic perturbations, but the molecular basis of this robustness is largely unknown. By measuring the interaction of yeast proteins with their partners in wild-type cells and in cells lacking a paralog, we found that 22 out of 56 paralog pairs compensate for the lost interactions. An equivalent number of pairs exhibit the opposite behavior and require each other's presence for maintaining their interactions. These dependent paralogs generally interact physically, regulate each other's abundance, and derive from ancestral self-interacting proteins. This reveals that gene duplication may actually increase mutational fragility instead of robustness in a large number of cases.

Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the "Maximum Allowable mammalian Trade-Off-Weight" (MAmTOW)

Network complexity is required to lend cellular processes flexibility to respond timely to a variety of dynamic signals, while simultaneously warranting robustness to protect cellular integrity against perturbations. The cell cycle serves as a paradigm for such processes; it maintains its frequency and temporal structure (although these may differ among cell types) under the former, but accelerates under the latter. Cell cycle molecules act together in time and in different cellular compartments to execute cell type-specific programs. Strikingly, the timing at which molecular switches occur is controlled by abundance and stoichiometry of multiple proteins within complexes. However, traditional methods that investigate one effector at a time are insufficient to understand how modulation of protein complex dynamics at cell cycle transitions shapes responsiveness, yet preserving robustness. To overcome this shortcoming, we propose a multidisciplinary approach to gain a systems-level understanding of quantitative cell cycle dynamics in mammalian cells from a new perspective. By suggesting advanced experimental technologies and dedicated modeling approaches, we present innovative strategies (i) to measure absolute protein concentration in vivo, and (ii) to determine how protein dosage, e.g., altered protein abundance, and spatial (de)regulation may affect timing and robustness of phase transitions. We describe a method that we name “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW), which may be realized to determine the upper limit of gene copy numbers in mammalian cells. These aspects, not covered by current systems biology approaches, are essential requirements to generate precise computational models and identify (sub)network-centered nodes underlying a plethora of pathological conditions.

Determinants and Regulation of Protein Turnover in Yeast

Protein turnover maintains the recycling needs of the proteome, and its malfunction has been linked to aging and age-related diseases. However, not all proteins turnover equally, and the factors that contribute to accelerate or slow down turnover are mostly unknown. We measured turnover rates for 3,160 proteins in exponentially growing yeast and analyzed their dependence on physical, functional, and genetic properties. We found that functional characteristics, including protein localization, complex membership, and connectivity, have greater effect on turnover than sequence elements. We also found that protein turnover and mRNA turnover are correlated. Analysis under nutrient perturbation and osmotic stress revealed that protein turnover highly depends on cellular state and is faster when proteins are being actively used. Finally, stress-induced changes in protein and transcript abundance correlated with changes in protein turnover. This study provides a resource of protein turnover rates and principles to understand the recycling needs of the proteome under basal conditions and perturbation.

Tracking the evolution of 3D gene organization demonstrates its connection to phenotypic divergence

It has recently been shown that the organization of genes in eukaryotic genomes, and specifically in 3D, is strongly related to gene expression and function and partially conserved between organisms. However, previous studies of 3D genomic organization analyzed each organism independently from others. Here, we propose an approach for unified inter-organismal analysis of gene organization based on a network representation of Hi-C data. We define and detect four classes of spatially co-evolving orthologous modules (SCOMs), i.e. gene families that co-evolve in their 3D organization, based on patterns of divergence and conservation of distances. We demonstrate our methodology on Hi-C data from Saccharomyces cerevisiae and Schizosaccharomyces pombe, and identify, among others, modules relating to RNA splicing machinery and chromatin silencing by small RNA which are central to S. pombe's lifestyle. Our results emphasize the importance of 3D genomic organization in eukaryotes and suggest that the evolutionary mechanisms that shape gene organization affect the organism fitness and phenotypes. The proposed algorithms can be utilized in future studies of genome evolution and comparative analysis of spatial genomic organization in different tissues, conditions and single cells.