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

Uniform spatial distribution of collagen fibril radii within tendon implies local activation of pC-collagen at individual fibrils

Collagen fibril cross-sectional radii show no systematic variation between the interior and the periphery of fibril bundles, indicating an effectively constant rate of collagen incorporation into fibrils throughout the bundle. Such spatially homogeneous incorporation constrains the extracellular diffusion of collagen precursors from sources at the bundle boundary to sinks at the growing fibrils. With a coarse-grained diffusion equation we determine stringent bounds, using parameters extracted from published experimental measurements of tendon development. From the lack of new fibril formation after birth, we further require that the concentration of diffusing precursors stays below the critical concentration for fibril nucleation. We find that the combination of the diffusive bound, which requires larger concentrations to ensure homogeneous fibril radii, and lack of nucleation, which requires lower concentrations, is only marginally consistent with fully processed collagen using conservative bounds. More realistic bounds may leave no consistent concentrations. Therefore, we propose that unprocessed pC-collagen diffuses from the bundle periphery followed by local C-proteinase activity and subsequent collagen incorporation at each fibril. We suggest that C-proteinase is localized within bundles, at fibril surfaces, during radial fibrillar growth. The much greater critical concentration of pC-collagen, as compared to fully processed collagen, then provides broad consistency between homogeneous fibril radii and the lack of fibril nucleation during fibril growth.

Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus

The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this description is neither necessary nor sufficient. It does not cover all cases of PTS1 proteins, and some proteins in accordance with this consensus do not target to the peroxisome. In order to find new PTS proteins in yeast and to arrive at a more complete description of the PTS1 consensus motif, we developed a machine learning approach that involves orthologue expansion of the set of known peroxisomal proteins. We performed a genome-wide in silico screen, characterised several PTS1-containing peptides and identified two new peroxisomal matrix proteins, which we named Pxp1 (Yel020c) and Pxp2 (Yjr111c). Based on these in silico and in vivo analyses, we revised the yeast PTS1 consensus which now includes all known PTS1 proteins.

Role of mRNA structure in the control of protein folding

Specific structures in mRNA modulate translation rate and thus can affect protein folding. Using the protein structures from two eukaryotes and three prokaryotes, we explore the connections between the protein compactness, inferred from solvent accessibility, and mRNA structure, inferred from mRNA folding energy (ΔG). In both prokaryotes and eukaryotes, the ΔG value of the most stable 30 nucleotide segment of the mRNA (ΔGmin) strongly, positively correlates with protein solvent accessibility. Thus, mRNAs containing exceptionally stable secondary structure elements typically encode compact proteins. The correlations between ΔG and protein compactness are much more pronounced in predicted ordered parts of proteins compared to the predicted disordered parts, indicative of an important role of mRNA secondary structure elements in the control of protein folding. Additionally, ΔG correlates with the mRNA length and the evolutionary rate of synonymous positions. The correlations are partially independent and were used to construct multiple regression models which explain about half of the variance of protein solvent accessibility. These findings suggest a model in which the mRNA structure, particularly exceptionally stable RNA structural elements, act as gauges of protein co-translational folding by reducing ribosome speed when the nascent peptide needs time to form and optimize the core structure.

Channel crossing: how are proteins shipped across the bacterial plasma membrane?

The structure of the first protein-conducting channel was determined more than a decade ago. Today, we are still puzzled by the outstanding problem of protein translocation—the dynamic mechanism underlying the consignment of proteins across and into membranes. This review is an attempt to summarize and understand the energy transducing capabilities of protein-translocating machines, with emphasis on bacterial systems: how polypeptides make headway against the lipid bilayer and how the process is coupled to the free energy associated with ATP hydrolysis and the transmembrane protein motive force. In order to explore how cargo is driven across the membrane, the known structures of the protein-translocation machines are set out against the background of the historic literature, and in the light of experiments conducted in their wake. The paper will focus on the bacterial general secretory (Sec) pathway (SecY-complex), and its eukaryotic counterpart (Sec61-complex), which ferry proteins across the membrane in an unfolded state, as well as the unrelated Tat system that assembles bespoke channels for the export of folded proteins.

Sequence- and Structure-Based Analysis of Tissue-Specific Phosphorylation Sites

Phosphorylation is the most widespread and well studied reversible posttranslational modification. Discovering tissue-specific preferences of phosphorylation sites is important as phosphorylation plays a role in regulating almost every cellular activity and disease state. Here we present a comprehensive analysis of global and tissue-specific sequence and structure properties of phosphorylation sites utilizing recent proteomics data. We identified tissue-specific motifs in both sequence and spatial environments of phosphorylation sites. Target site preferences of kinases across tissues indicate that, while many kinases mediate phosphorylation in all tissues, there are also kinases that exhibit more tissue-specific preferences which, notably, are not caused by tissue-specific kinase expression. We also demonstrate that many metabolic pathways are differentially regulated by phosphorylation in different tissues.

Exome sequencing of extreme clopidogrel response phenotypes identifies B4GALT2 as a determinant of on-treatment platelet reactivity

Interindividual variability in platelet aggregation is common among patients treated with clopidogrel and both high on-treatment platelet reactivity (HTPR) and low on-treatment platelet reactivity (LTPR) increase risks for adverse clinical outcomes. CYP2C19 influences clopidogrel response but only accounts for ∼12% of the variability in platelet reactivity. To identify novel variants implicated in on-treatment platelet reactivity, patients with coronary artery disease (CAD) with extreme pharmacodynamic responses to clopidogrel and wild-type CYP2C19 were subjected to exome sequencing. Candidate variants that clustered in the LTPR subgroup subsequently were genotyped across the discovery cohort (n = 636). Importantly, carriers of B4GALT2 c.909C>T had lower on-treatment P2Y12 reaction units (PRUs; P = 0.0077) and residual platelet aggregation (P = 0.0008) compared with noncarriers, which remained significant after adjusting for CYP2C19 and other clinical variables in both the discovery (P = 0.0298) and replication (n = 160; PRU: P = 0.0001) cohorts. B4GALT2 is a platelet-expressed galactosyltransferase, indicating that B4GALT2 c.909C>T may influence clopidogrel sensitivity through atypical cell-surface glycoprotein processing and platelet adhesion.

Inferring the determinants of protein evolutionary rates in mammals

Understanding the determinants of protein evolutionary rates is one of the most fundamental evolutionary questions. Previous studies have revealed that many biological variables are tightly associated with protein evolutionary rates in mammals. However, the dominant role of these biological variables and their combinatorial effects to evolutionary rates of mammalian proteins are still less understood. In this work, we derived a quantitative model to correlate protein evolutionary rates with the levels of these variables. The result showed that only a small number of variables are necessary to accurately predict protein evolutionary rates, among which miRNA regulation plays the most important role. Our result suggested that biological variables are extensively interrelated and suffer from hidden redundancies in determining protein evolutionary rates. Various variables should be considered in a natural ensemble to comprehensively assess the determinants of protein evolutionary rate.

Comparative analysis of contextual bias around the translation initiation sites in plant genomes

Nucleotide distribution around translation initiation site (TIS) is thought to play an important role in determining translation efficiency. Kozak in vertebrates and later Joshi et al. in plants identified context sequence having a key role in translation efficiency, but a great variation regarding this context sequence has been observed among different taxa. The present study aims to refine the context sequence around initiation codon in plants and addresses the sampling error problem by using complete genomes of 7 monocots and 7 dicots separately. Besides positions -3 and +4, significant conservation at -2 and +5 positions was also found and nucleotide bias at the latter two positions was shown to directly influence translation efficiency in the taxon studied. About 1.8% (monocots) and 2.4% (dicots) of the total sequences fit the context sequence from positions -3 to +5, which might be indicative of lower number of housekeeping genes in the transcriptome. A three base periodicity was observed in 5' UTR and CDS of monocots and only in CDS of dicots as confirmed against random occurrence and annotation errors. Deterministic enrichment of GCNAUGGC in monocots, AANAUGGC in dicots and GCNAUGGC in plants around TIS was also established (where AUG denotes the start codon), which can serve as an arbiter of putative TIS with efficient translation in plants.

Coevolution between Stop Codon Usage and Release Factors in Bacterial Species

Three stop codons in bacteria represent different translation termination signals, and their usage is expected to depend on their differences in translation termination efficiency, mutation bias, and relative abundance of release factors (RF1 decoding UAA and UAG, and RF2 decoding UAA and UGA). In 14 bacterial species (covering Proteobacteria, Firmicutes, Cyanobacteria, Actinobacteria and Spirochetes) with cellular RF1 and RF2 quantified, UAA is consistently over-represented in highly expressed genes (HEGs) relative to lowly expressed genes (LEGs), whereas UGA usage is the opposite even in species where RF2 is far more abundant than RF1. UGA usage relative to UAG increases significantly with P_RF2 [=RF2/(RF1 + RF2)] as expected from adaptation between stop codons and their decoders. P_RF2 is > 0.5 over a wide range of AT content (measured by P_AT3 as the proportion of AT at third codon sites), but decreases rapidly toward zero at the high range of P_AT3. This explains why bacterial lineages with high P_AT3 often have UGA reassigned because of low RF2. There is no indication that UAG is a minor stop codon in bacteria as claimed in a recent publication. The claim is invalid because of the failure to apply the two key criteria in identifying a minor codon: (1) it is least preferred by HEGs (or most preferred by LEGs) and (2) it corresponds to the least abundant decoder. Our results suggest a more plausible explanation for why UAA usage increases, and UGA usage decreases, with P_AT3, but UAG usage remains low over the entire P_AT3 range.

Selection for reduced translation costs at the intronic 5' end in fungi

It is generally believed that introns are not translated; therefore, the potential intronic features that may be related to the translation step (occurring after splicing) have yet to be thoroughly studied. Here, focusing on four fungi, we performed for the first time a comprehensive study aimed at characterizing how translation efficiency is encoded in introns and affects their evolution. By analysing their intronome we provide evidence of selection for STOP codons close to the intronic 5′ end, and show that the beginning of introns are selected for significantly high translation, presumably to reduce translation and metabolic costs in cases of non-spliced introns. Ribosomal profiling data analysis in Saccharomyces cerevisiae supports the conjecture that in this organism intron retention frequently occurs, introns are partially translated, and their translation efficiency affects organismal fitness. We show that the reported results are more significant in highly translated and highly spliced genes, but are not associated only with genes with a specific function. We also discuss the potential relation of the reported signals to efficient nonsense-mediated decay due to splicing errors. These new discoveries are supported by population-genetics considerations. In addition, they are contributory steps towards a broader understanding of intron evolution and the effect of silent mutations on gene expression and organismal fitness.

Identifying network-based biomarkers of complex diseases from high-throughput data

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

In order to achieve selective targeting of affinity-ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor-ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.

Confirmation of a Protein-Protein Interaction in the Pantothenate Biosynthetic Pathway by Using Sortase-Mediated Labelling

High-throughput studies have been widely used to identify protein-protein interactions; however, few of these candidate interactions have been confirmed in vitro. We have used a combination of isothermal titration calorimetry and fluorescence anisotropy to screen candidate interactions within the pantothenate biosynthetic pathway. In particular, we observed no interaction between the next enzyme in the pathway, pantothenate synthetase (PS), and aspartate decarboxylase, but did observe an interaction between PS and the putative Nudix hydrolase, YfcD. Confirmation of the interaction by fluorescence anisotropy was dependent upon labelling an adventitiously formed glycine on the protein N-terminal affinity purification tag by using Sortase. Subsequent formation of the protein-protein complex led to apparent restriction of the dynamics of this tag, thus suggesting that this approach could be generally applied to a subset of other protein-protein interaction complexes.

Genic insights from integrated human proteomics in GeneCards

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite’s next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein–RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome.

Database URL: http://www.genecards.org/

Activation of G Proteins by Guanine Nucleotide Exchange Factors Relies on GTPase Activity

G proteins are an important family of signalling molecules controlled by guanine nucleotide exchange and GTPase activity in what is commonly called an 'activation/inactivation cycle'. The molecular mechanism by which guanine nucleotide exchange factors (GEFs) catalyse the activation of monomeric G proteins is well-established, however the complete reversibility of this mechanism is often overlooked. Here, we use a theoretical approach to prove that GEFs are unable to positively control G protein systems at steady-state in the absence of GTPase activity. Instead, positive regulation of G proteins must be seen as a product of the competition between guanine nucleotide exchange and GTPase activity--emphasising a central role for GTPase activity beyond merely signal termination. We conclude that a more accurate description of the regulation of G proteins via these processes is as a 'balance/imbalance' mechanism. This result has implications for the understanding of intracellular signalling processes, and for experimental strategies that rely on modulating G protein systems.

Natural Genetic Variation Influences Protein Abundances in C. elegans Developmental Signalling Pathways

Complex traits, including common disease-related traits, are affected by many different genes that function in multiple pathways and networks. The apoptosis, MAPK, Notch, and Wnt signalling pathways play important roles in development and disease progression. At the moment we have a poor understanding of how allelic variation affects gene expression in these pathways at the level of translation. Here we report the effect of natural genetic variation on transcript and protein abundance involved in developmental signalling pathways in Caenorhabditis elegans. We used selected reaction monitoring to analyse proteins from the abovementioned four pathways in a set of recombinant inbred lines (RILs) generated from the wild-type strains N2 (Bristol) and CB4856 (Hawaii) to enable quantitative trait locus (QTL) mapping. About half of the cases from the 44 genes tested showed a statistically significant change in protein abundance between various strains, most of these were however very weak (below 1.3-fold change). We detected a distant QTL on the left arm of chromosome II that affected protein abundance of the phosphatidylserine receptor protein PSR-1, and two separate QTLs that influenced embryonic and ionizing radiation-induced apoptosis on chromosome IV. Our results demonstrate that natural variation in C. elegans is sufficient to cause significant changes in signalling pathways both at the gene expression (transcript and protein abundance) and phenotypic levels.

Targeting the replisome with transduced monoclonal antibodies triggers lethal DNA replication stress in cancer cells

Although chemical inhibition of the DNA damage response (DDR) in cancer cells triggers cell death, it is not clear if the fork blockade achieved with inhibitors that neutralise proteins of the replisome is sufficient on its own to overcome the DDR. Monoclonal antibodies to PCNA, which block the DNA elongation process in vitro, have been developed. When these antibodies were transduced into cancer cells, they are able to inhibit the incorporation of nucleoside analogues. When co-delivered with anti-PCNA siRNA, the cells were flattened and the size of their nuclei increased by up to 3-fold, prior to cell death. Analysis of these nuclei by super-resolution microscopy revealed the presence of large numbers of phosphorylated histone H2AX foci. A senescence-like phenotype of the transduced cells was also observed upon delivery of the corresponding Fab molecules or following PCNA gene disruption or when the Fab fragment of an antibody that neutralises DNA polymerase alpha was used. Primary melanoma cells and leukaemia cells that are resistant to chemical inhibitors were similarly affected by these antibody treatments. These results demonstrate that transduced antibodies can trigger a lethal DNA replication stress, which kills cancer cells by abolishing the biological activity of several constituents of the replisome.

Natural Genetic Variation Differentially Affects the Proteome and Transcriptome in Caenorhabditis elegans

Natural genetic variation is the raw material of evolution and influences disease development and progression. An important question is how this genetic variation translates into variation in protein abundance. To analyze the effects of the genetic background on gene and protein expression in the nematode Caenorhabditis elegans, we quantitatively compared the two genetically highly divergent wild-type strains N2 and CB4856. Gene expression was analyzed by microarray assays, and proteins were quantified using stable isotope labeling by amino acids in cell culture. Among all transcribed genes, we found 1,532 genes to be differentially transcribed between the two wild types. Of the total 3,238 quantified proteins, 129 proteins were significantly differentially expressed between N2 and CB4856. The differentially expressed proteins were enriched for genes that function in insulin-signaling and stress-response pathways, underlining strong divergence of these pathways in nematodes. The protein abundance of the two wild-type strains correlates more strongly than protein abundance versus transcript abundance within each wild type. Our findings indicate that in C. elegans only a fraction of the changes in protein abundance can be explained by the changes in mRNA abundance. These findings corroborate with the observations made across species.

Metabolic Trade-offs in Yeast are Caused by F1F0-ATP synthase

Intermediary metabolism provides living cells with free energy and precursor metabolites required for synthesizing proteins, lipids, RNA and other cellular constituents, and it is highly conserved among living species. Only a fraction of cellular protein can, however, be allocated to enzymes of intermediary metabolism and consequently metabolic trade-offs may take place. One such trade-off, aerobic fermentation, occurs in both yeast (the Crabtree effect) and cancer cells (the Warburg effect) and has been a scientific challenge for decades. Here we show, using flux balance analysis combined with in vitro measured enzyme specific activities, that fermentation is more catalytically efficient than respiration, i.e. it produces more ATP per protein mass. And that the switch to fermentation at high growth rates therefore is a consequence of a high ATP production rate, provided by a limited pool of enzymes. The catalytic efficiency is also higher for cells grown on glucose compared to galactose and ethanol, which may explain the observed differences in their growth rates. The enzyme F1F0-ATP synthase (Complex V) was found to have flux control over respiration in the model, and since it is evolutionary conserved, we expect the trade-off to occur in organisms from all kingdoms of life.

Mutations reducing replication from R-loops suppress the defects of growth, chromosome segregation and DNA supercoiling in cells lacking topoisomerase I and RNase HI activity

R-loop formation occurs when the nascent RNA hybridizes with the template DNA strand behind the RNA polymerase. R-loops affect a wide range of cellular processes and their use as origins of replication was the first function attributed to them. In Escherichia coli, R-loop formation is promoted by the ATP-dependent negative supercoiling activity of gyrase (gyrA and gyrB) and is inhibited by topoisomerase (topo) I (topA) relaxing transcription-induced negative supercoiling. RNase HI (rnhA) degrades the RNA moiety of R-loops. The depletion of RNase HI activity in topA null mutants was previously shown to lead to extensive DNA relaxation, due to DNA gyrase inhibition, and to severe growth and chromosome segregation defects that were partially corrected by overproducing topo III (topB). Here, DNA gyrase assays in crude cell extracts showed that the ATP-dependent activity (supercoiling) of gyrase but not its ATP-independent activity (relaxation) was inhibited in topA null cells lacking RNase HI. To characterize the cellular event(s) triggered by the absence of RNase HI, we performed a genetic screen for suppressors of the growth defect of topA rnhA null cells. Suppressors affecting genes in replication (holC2::aph and dnaT18::aph) nucleotide metabolism (dcd49::aph), RNA degradation (rne59::aph) and fimbriae synthesis (fimD22::aph) were found to reduce replication from R-loops and to restore supercoiling, thus pointing to a correlation between R-loop-dependent replication in topA rnhA mutants and the inhibition of gyrase activity and growth. Interestingly, the position of fimD on the E. coli chromosome corresponds to the site of one of the five main putative origins of replication from R-loops in rnhA null cells recently identified by next-generation sequencing, thus suggesting that the fimD22::aph mutation inactivated one of these origins. Furthermore, we show that topo III overproduction is unable to complement the growth defect of topA rnhA null mutants at low temperatures that stabilizes hyper-negatively supercoiled DNA.

Structural hot spots for the solubility of globular proteins

Natural selection shapes protein solubility to physiological requirements and recombinant applications that require higher protein concentrations are often problematic. This raises the question whether the solubility of natural protein sequences can be improved. We here show an anti-correlation between the number of aggregation prone regions (APRs) in a protein sequence and its solubility, suggesting that mutational suppression of APRs provides a simple strategy to increase protein solubility. We show that mutations at specific positions within a protein structure can act as APR suppressors without affecting protein stability. These hot spots for protein solubility are both structure and sequence dependent but can be computationally predicted. We demonstrate this by reducing the aggregation of human α-galactosidase and protective antigen of Bacillus anthracis through mutation. Our results indicate that many proteins possess hot spots allowing to adapt protein solubility independently of structure and function.

Mutations in aggregation prone regions of recombinant proteins often improve their solubility, although they might cause negative effects on their structure and function. Here, the authors identify proteins hot spots that can be exploited to optimize solubility without compromising stability.

Mass spectrometric imaging of in vivo protein and lipid adsorption on biodegradable vascular replacement systems

Cardiovascular diseases present amongst the highest mortality risks in Western civilization and are frequently caused by arteriosclerotic vessel failure. Coronary artery and peripheral vessel reconstruction necessitates the use of small diameter systems that are mechanically stress-resistant and biocompatible. Expanded polytetrafluorethylene (ePTFE) is amongst the materials used most frequently for non-degradable and bio-degradable vessel reconstruction procedures, with thermoplastic polyurethanes (TPU) representing a promising substitute. The present study describes and compares the biological adsorption and diffusion occurring with both materials following implantation in rat models. Gel electrophoresis and thin-layer chromatography, combined with mass spectrometry and mass spectrometry imaging, were utilized to identify the adsorbed lipids and proteins. The results were compared with the analytes present in native aorta tissue. It was revealed that both polymers were severely affected by biological adsorption after 10 min in vivo. Proteins associated with cell growth and migration were identified, especially on the luminal graft surface, while lipids were found to be located on both the luminal and abluminal surfaces. Lipid adsorption and cholesterol diffusion were found to be correlated with the polymer modifications identified on degradable thermoplastic urethane graft samples, with the latter revealing extensive cholesterol adsorption. The present study demonstrates an interaction between biological matter and both graft materials, and provides insights into polymer changes, in particular, those observed with thermoplastic urethanes already after 10 min in vivo exposure. ePTFE demonstrated minor polymer modifications, whereas several different polymer signals were observed for TPU, all were co-localized with biological signals.

RePLiCal: A QconCAT Protein for Retention Time Standardization in Proteomics Studies

This study introduces a new reversed-phase liquid chromatography retention time (RT) standard, RePLiCal (Reversed-phase liquid chromatography calibrant), produced using QconCAT technology. The synthetic protein contains 27 lysine-terminating calibrant peptides, meaning that the same complement of standards can be generated using either Lys-C or trypsin-based digestion protocols. RePLiCal was designed such that each constituent peptide is unique with respect to all eukaryotic proteomes, thereby enabling integration into a wide range of proteomic analyses. RePLiCal has been benchmarked against three commercially available peptide RT standard kits and outperforms all in terms of LC gradient coverage. RePLiCal also provides a higher number of calibrant points for chromatographic retention time standardization and normalization. The standard provides stable RTs over long analysis times and can be readily transferred between different LC gradients and nUHPLC instruments. Moreover, RePLiCal can be used to predict RTs for other peptides in a timely manner. Furthermore, it is shown that RePLiCal can be used effectively to evaluate trapping column performance for nUHPLC instruments using trap-elute configurations, to optimize gradients to maximize peptide and protein identification rates, and to recalibrate the m/z scale of mass spectrometry data post-acquisition.

The structure of the PanD/PanZ protein complex reveals negative feedback regulation of pantothenate biosynthesis by coenzyme A

Coenzyme A (CoA) is an ubiquitous and essential cofactor, synthesized from the precursor pantothenate. Vitamin biosynthetic pathways are normally tightly regulated, including the pathway from pantothenate to CoA. However, no regulation of pantothenate biosynthesis has been identified. We have recently described an additional component in the pantothenate biosynthetic pathway, PanZ, which promotes the activation of the zymogen, PanD, to form aspartate α-decarboxylase (ADC) in a CoA-dependent manner. Here we report the structure of PanZ in complex with PanD, which reveals the structural basis for the CoA dependence of this interaction and activation. In addition, we show that PanZ acts as a CoA-dependent inhibitor of ADC catalysis. This inhibitory effect can effectively regulate the biosynthetic pathway to pantothenate, and thereby also regulate CoA biosynthesis. This represents a previously unobserved mode of metabolic regulation whereby a cofactor-utilizing protein negatively regulates the biosynthesis of the same cofactor.

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Structure of the PanD-PanZ.AcCoA complex is reported at a resolution of 1.6 Å

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Binding of AcCoA to PanZ is required to form the PanZ/PanD interface

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PanZ.AcCoA activates PanD via selection of a reactive conformation of PanD

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PanZ.AcCoA inhibits the activated enzyme, regulating pantothenate biosynthesis

Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring

Defining intracellular protein concentration is critical in molecular systems biology. Although strategies for determining relative protein changes are available, defining robust absolute values in copies per cell has proven significantly more challenging. Here we present a reference data set quantifying over 1800 Saccharomyces cerevisiae proteins by direct means using protein-specific stable-isotope labeled internal standards and selected reaction monitoring (SRM) mass spectrometry, far exceeding any previous study. This was achieved by careful design of over 100 QconCAT recombinant proteins as standards, defining 1167 proteins in terms of copies per cell and upper limits on a further 668, with robust CVs routinely less than 20%. The selected reaction monitoring-derived proteome is compared with existing quantitative data sets, highlighting the disparities between methodologies. Coupled with a quantification of the transcriptome by RNA-seq taken from the same cells, these data support revised estimates of several fundamental molecular parameters: a total protein count of ∼100 million molecules-per-cell, a median of ∼1000 proteins-per-transcript, and a linear model of protein translation explaining 70% of the variance in translation rate. This work contributes a “gold-standard” reference yeast proteome (including 532 values based on high quality, dual peptide quantification) that can be widely used in systems models and for other comparative studies.

Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways

Cellular stress responses require exquisite coordination between intracellular signaling molecules to integrate multiple stimuli and actuate specific cellular behaviors. Deciphering the web of complex interactions underlying stress responses is a key challenge in understanding robust biological systems and has the potential to lead to the discovery of targeted therapeutics for diseases triggered by dysregulation of stress response pathways. We constructed large-scale molecular interaction maps of six major stress response pathways in Saccharomyces cerevisiae (baker's or budding yeast). Biological findings from over 900 publications were converted into standardized graphical formats and integrated into a common framework. The maps are posted at http://www.yeast-maps.org/yeast-stress-response/ for browse and curation by the research community. On the basis of these maps, we undertook systematic analyses to unravel the underlying architecture of the networks. A series of network analyses revealed that yeast stress response pathways are organized in bow-tie structures, which have been proposed as universal sub-systems for robust biological regulation. Furthermore, we demonstrated a potential role for complexes in stabilizing the conserved core molecules of bow-tie structures. Specifically, complex-mediated reversible reactions, identified by network motif analyses, appeared to have an important role in buffering the concentration and activity of these core molecules. We propose complex-mediated reactions as a key mechanism mediating robust regulation of the yeast stress response. Thus, our comprehensive molecular interaction maps provide not only an integrated knowledge base, but also a platform for systematic network analyses to elucidate the underlying architecture in complex biological systems.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

Mining Large Scale Tandem Mass Spectrometry Data for Protein Modifications Using Spectral Libraries

Experimental improvements in post-translational modification (PTM) detection by tandem mass spectrometry (MS/MS) has allowed the identification of vast numbers of PTMs. Open modification searches (OMSs) of MS/MS data, which do not require prior knowledge of the modifications present in the sample, further increased the diversity of detected PTMs. Despite much effort, there is still a lack of functional annotation of PTMs. One possibility to narrow the annotation gap is to mine MS/MS data deposited in public repositories and to correlate the PTM presence with biological meta-information attached to the data. Since the data volume can be quite substantial and contain tens of millions of MS/MS spectra, the data mining tools must be able to cope with big data. Here, we present two tools, Liberator and MzMod, which are built using the MzJava class library and the Apache Spark large scale computing framework. Liberator builds large MS/MS spectrum libraries, and MzMod searches them in an OMS mode. We applied these tools to a recently published set of 25 million spectra from 30 human tissues and present tissue specific PTMs. We also compared the results to the ones obtained with the OMS tool MODa and the search engine X!Tandem.

Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets

Background

Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities.

Results

We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis.

Conclusions

In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2280-z) contains supplementary material, which is available to authorized users.

Improvement of lactic acid production in Saccharomyces cerevisiae by a deletion of ssb1

Polylactic acid (PLA) is an important renewable polymer, but current processes for producing its precursor, lactic acid, suffer from process inefficiencies related to the use of bacterial hosts. Therefore, improving the capacity of Saccharomyces cerevisiae to produce lactic acid is a promising approach to improve industrial production of lactic acid. As one such improvement required, the lactic acid tolerance of yeast must be significantly increased. To enable improved tolerance, we employed an RNAi-mediated genome-wide expression knockdown approach as a means to rapidly identify potential genetic targets. In this approach, several gene knockdown targets were identified which confer increased acid tolerance to S. cerevisiae BY4741, of which knockdown of the ribosome-associated chaperone SSB1 conferred the highest increase (52%). This target was then transferred into a lactic acid-overproducing strain of S. cerevisiae CEN.PK in the form of a knockout and the resulting strain demonstrated up to 33% increased cell growth, 58% increased glucose consumption, and 60% increased L-lactic acid production. As SSB1 contains a close functional homolog SSB2 in yeast, this result was counterintuitive and may point to as-yet-undefined functional differences between SSB1 and SSB2 related to lactic acid production. The final strain produced over 50 g/L of lactic acid in under 60 h of fermentation.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

RNA Silencing of Exocyst Genes in the Stigma Impairs the Acceptance of Compatible Pollen in Arabidopsis

Initial pollen-pistil interactions in the Brassicaceae are regulated by rapid communication between pollen grains and stigmatic papillae and are fundamentally important, as they are the first step toward successful fertilization. The goal of this study was to examine the requirement of exocyst subunits, which function in docking secretory vesicles to sites of polarized secretion, in the context of pollen-pistil interactions. One of the exocyst subunit genes, EXO70A1, was previously identified as an essential factor in the stigma for the acceptance of compatible pollen in Arabidopsis (Arabidopsis thaliana) and Brassica napus. We hypothesized that EXO70A1, along with other exocyst subunits, functions in the Brassicaceae dry stigma to deliver cargo-bearing secretory vesicles to the stigmatic papillar plasma membrane, under the pollen attachment site, for pollen hydration and pollen tube entry. Here, we investigated the functions of exocyst complex genes encoding the remaining seven subunits, SECRETORY3 (SEC3), SEC5, SEC6, SEC8, SEC10, SEC15, and EXO84, in Arabidopsis stigmas following compatible pollinations. Stigma-specific RNA-silencing constructs were used to suppress the expression of each exocyst subunit individually. The early postpollination stages of pollen grain adhesion, pollen hydration, pollen tube penetration, seed set, and overall fertility were analyzed in the transgenic lines to evaluate the requirement of each exocyst subunit. Our findings provide comprehensive evidence that all eight exocyst subunits are necessary in the stigma for the acceptance of compatible pollen. Thus, this work implicates a fully functional exocyst complex as a component of the compatible pollen response pathway to promote pollen acceptance.

Complex stability and dynamic subunit interchange modulates the disparate activities of the yeast moonlighting proteins Hal3 and Vhs3

Saccharomyces cerevisiae Hal3 and Vhs3 are moonlighting proteins, acting both as inhibitors of the serine/threonine protein phosphatase Ppz1 and as subunits (together with Cab3) of the unique heterotrimeric phosphopantothenoylcysteine decarboxylase (PPCDC) enzyme of Hemiascomycetous yeast. Both these roles are essential: PPCDC catalyses the third step of coenzyme A biosynthesis, while Ppz1 inhibition is required for regulation of monovalent cation homeostasis. However, the mechanisms by which these proteins’ disparate activities are regulated are not well understood. The PPCDC domains (PDs) of Hal3, Vhs3 and Cab3 constitute the minimum requirement for these proteins to show both PPCDC activity and, in the case of Hal3 and Vhs3, to bind to Ppz1. Using these PD proteins as a model system to study the possibility of dynamic interchange between these roles, we provide evidence that Hal3 binds Ppz1 as a monomer (1:1 stoichiometry), requiring it to de-oligomerize from its usual homo- and heterotrimeric states (the latter having PPCDC activity). This de-oligomerization is made possible by structural features that set Hal3 apart from Vhs3, increasing its ability to undergo monomer exchange. These findings suggest that oligomer interchange may be a significant factor in the functional regulation of these proteins and their various unrelated (moonlighting) functions.

Neurodegeneration and Cancer: Where the Disorder Prevails

It has been reported that genes up-regulated in cancer are often down-regulated in neurodegenerative disorders and vice versa. The fact that apparently unrelated diseases share functional pathways suggests a link between their etiopathogenesis and the properties of molecules involved. Are there specific features that explain the exclusive association of proteins with either cancer or neurodegeneration? We performed a large-scale analysis of physico-chemical properties to understand what characteristics differentiate classes of diseases. We found that structural disorder significantly distinguishes proteins up-regulated in neurodegenerative diseases from those linked to cancer. We also observed high correlation between structural disorder and age of onset in Frontotemporal Dementia, Parkinson's and Alzheimer's diseases, which strongly supports the role of protein unfolding in neurodegenerative processes.

The Low Noise Limit in Gene Expression

Protein noise measurements are increasingly used to elucidate biophysical parameters. Unfortunately noise analyses are often at odds with directly measured parameters. Here we show that these inconsistencies arise from two problematic analytical choices: (i) the assumption that protein translation rate is invariant for different proteins of different abundances, which has inadvertently led to (ii) the assumption that a large constitutive extrinsic noise sets the low noise limit in gene expression. While growing evidence suggests that transcriptional bursting may set the low noise limit, variability in translational bursting has been largely ignored. We show that genome-wide systematic variation in translational efficiency can–and in the case of E. coli does–control the low noise limit in gene expression. Therefore constitutive extrinsic noise is small and only plays a role in the absence of a systematic variation in translational efficiency. These results show the existence of two distinct expression noise patterns: (1) a global noise floor uniformly imposed on all genes by expression bursting; and (2) high noise distributed to only a select group of genes.

Nucleotide sequence composition adjacent to intronic splice sites improves splicing efficiency via its effect on pre-mRNA local folding in fungi

RNA splicing is the central process of intron removal in eukaryotes known to regulate various cellular functions such as growth, development, and response to external signals. The canonical sequences indicating the splicing sites needed for intronic boundary recognition are well known. However, the roles and evolution of the local folding of intronic and exonic sequence features adjacent to splice sites has yet to be thoroughly studied. Here, focusing on four fungi (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Aspergillus nidulans, and Candida albicans), we performed for the first time a comprehensive high-resolution study aimed at characterizing the encoding of intronic splicing efficiency in pre-mRNA transcripts and its effect on intron evolution. Our analysis supports the conjecture that pre-mRNA local folding strength at intronic boundaries is under selective pressure, as it significantly affects splicing efficiency. Specifically, we show that in the immediate region of 12-30 nucleotides (nt) surrounding the intronic donor site there is a preference for weak pre-mRNA folding; similarly, in the region of 15-33 nt surrounding the acceptor and branch sites there is a preference for weak pre-mRNA folding. We also show that in most cases there is a preference for strong pre-mRNA folding further away from intronic splice sites. In addition, we demonstrate that these signals are not associated with gene-specific functions, and they correlate with splicing efficiency measurements (r = 0.77, P = 2.98 × 10(-21)) and with expression levels of the corresponding genes (P = 1.24 × 10(-19)). We suggest that pre-mRNA folding strength in the above-mentioned regions has a direct effect on splicing efficiency by improving the recognition of intronic boundaries. These new discoveries are contributory steps toward a broader understanding of splicing regulation and intronic/transcript evolution.

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC-based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs.

Systematic and site-specific analysis of N-sialoglycosylated proteins on the cell surface by integrating click chemistry and MS-based proteomics

Glycoproteins on the cell surface are ubiquitous and essential for cells to interact with the extracellular matrix, communicate with other cells, and respond to environmental cues. Although surface sialoglycoproteins can dramatically impact cell properties and represent different cellular statuses, global and site-specific analysis of sialoglycoproteins only on the cell surface is extraordinarily challenging. An effective method integrating metabolic labeling, copper-free click chemistry and mass spectrometry-based proteomics was developed to globally and site-specifically analyze surface N-sialoglycoproteins. Surface sialoglycoproteins metabolically labeled with a functional group were specifically tagged through copper-free click chemistry, which is ideal because it is quick, specific and occurs under physiological conditions. Sequentially tagged sialoglycoproteins were enriched for site-specific identification by mass spectrometry. Systematic and quantitative analysis of the surface N-sialoglycoproteome in cancer cells with distinctive invasiveness demonstrated many N-sialoglycoproteins up-regulated in invasive cells, the majority of which contained cell adhesion-related domains. This method is very effective to globally and site-specifically analyze N-sialoglycoproteins on the cell surface, and will have extensive applications in the biological and biomedical research communities. Site-specific information regarding surface sialoglycoproteins can serve as biomarkers for disease detection, targets for vaccine development and drug treatment.

Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability

Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.

Polymorphism Analysis Reveals Reduced Negative Selection and Elevated Rate of Insertions and Deletions in Intrinsically Disordered Protein Regions

Intrinsically disordered protein regions are abundant in eukaryotic proteins and lack stable tertiary structures and enzymatic functions. Previous studies of disordered region evolution based on interspecific alignments have revealed an increased propensity for indels and rapid rates of amino acid substitution. How disordered regions are maintained at high abundance in the proteome and across taxa, despite apparently weak evolutionary constraints, remains unclear. Here, we use single nucleotide and indel polymorphism data in yeast and human populations to survey the population variation within disordered regions. First, we show that single nucleotide polymorphisms in disordered regions are under weaker negative selection compared with more structured protein regions and have a higher proportion of neutral non-synonymous sites. We also confirm previous findings that nonframeshifting indels are much more abundant in disordered regions relative to structured regions. We find that the rate of nonframeshifting indel polymorphism in intrinsically disordered regions resembles that of noncoding DNA and pseudogenes, and that large indels segregate in disordered regions in the human population. Our survey of polymorphism confirms patterns of evolution in disordered regions inferred based on longer evolutionary comparisons.

Profiling of urinary proteins in Karan Fries cows reveals more than 1550 proteins

Urine is a non-invasive source of biological fluid, which reflects the physiological status of the mammals. We have profiled the cow urinary proteome and analyzed its functional significance. The urine collected from three healthy cows was concentrated by diafiltration (DF) followed by protein extraction using three methods, namely methanol, acetone, and ammonium sulphate (AS) precipitation and Proteo Spin urine concentration kit (PS). The quality of the protein was assessed by two-dimensional gel electrophoresis (2DE). In-gel digestion method revealed more proteins (1191) in comparison to in-solution digestion method (541). Collectively, 938, 606 and 444 proteins were identified in LC-MS/MS after in-gel and in-solution tryptic digestion of proteins prepared by AS, PS and DF methods, respectively resulting in identification of a total of 1564 proteins. Gene ontology (GO) using Panther7.0 grouped the majority of the proteins into cytoplasmic (location), catalytic activity (function), and metabolism (biological processes), while Cytoscape grouped proteins into complement and coagulation cascades; protease inhibitor activity and wound healing. Functional significance of few selected proteins seems to play important role in their physiology. Comparative analysis with human urine revealed 315 overlapping proteins. This study reports for the first time evidence of more than 1550 proteins in urine of healthy cow donors. This article is part of a Special Issue entitled: Proteomics in India.

Improving 3D Genome Reconstructions Using Orthologous and Functional Constraints

The study of the 3D architecture of chromosomes has been advancing rapidly in recent years. While a number of methods for 3D reconstruction of genomic models based on Hi-C data were proposed, most of the analyses in the field have been performed on different 3D representation forms (such as graphs). Here, we reproduce most of the previous results on the 3D genomic organization of the eukaryote Saccharomyces cerevisiae using analysis of 3D reconstructions. We show that many of these results can be reproduced in sparse reconstructions, generated from a small fraction of the experimental data (5% of the data), and study the properties of such models. Finally, we propose for the first time a novel approach for improving the accuracy of 3D reconstructions by introducing additional predicted physical interactions to the model, based on orthologous interactions in an evolutionary-related organism and based on predicted functional interactions between genes. We demonstrate that this approach indeed leads to the reconstruction of improved models.

Understanding the importance of genome architecture, the arrangement of genes within the genome and how this organization evolved has been intensively studied in recent years. Despite rapid progress in the field, accurate 3D modeling of genome organization remains a challenge. While a number of methods for 3D reconstruction of genomic models based on genome-wide experimental data were proposed, most of the analyses in the field have been performed on different 3D representation forms (such as graphs). Here, we reproduce most of the previous results on the 3D genome organization of the eukaryote Saccharomyces cerevisiae using analysis of 3D reconstructions. We show that many of these results can be reproduced in sparse reconstructions, generated from a small fraction of the experimental data (5% of the data), and study the properties of such models. Finally, we propose for the first time a novel approach for improving the accuracy of 3D reconstructions by introducing additional predicted physical interactions to the model, based on orthologous interactions in a different organism and based on predicted functional interactions between genes. Our proposed approach can facilitate future studies of 3D genome organization via improved models.

The 4E-BP Caf20p Mediates Both eIF4E-Dependent and Independent Repression of Translation

Translation initiation factor eIF4E mediates mRNA selection for protein synthesis via the mRNA 5'cap. A family of binding proteins, termed the 4E-BPs, interact with eIF4E to hinder ribosome recruitment. Mechanisms underlying mRNA specificity for 4E-BP control remain poorly understood. Saccharomyces cerevisiae 4E-BPs, Caf20p and Eap1p, each regulate an overlapping set of mRNAs. We undertook global approaches to identify protein and RNA partners of both 4E-BPs by immunoprecipitation of tagged proteins combined with mass spectrometry or next-generation sequencing. Unexpectedly, mass spectrometry indicated that the 4E-BPs associate with many ribosomal proteins. 80S ribosome and polysome association was independently confirmed and was not dependent upon interaction with eIF4E, as mutated forms of both Caf20p and Eap1p with disrupted eIF4E-binding motifs retain ribosome interaction. Whole-cell proteomics revealed Caf20p mutations cause both up and down-regulation of proteins and that many changes were independent of the 4E-binding motif. Investigations into Caf20p mRNA targets by immunoprecipitation followed by RNA sequencing revealed a strong association between Caf20p and mRNAs involved in transcription and cell cycle processes, consistent with observed cell cycle phenotypes of mutant strains. A core set of over 500 Caf20p-interacting mRNAs comprised of both eIF4E-dependent (75%) and eIF4E-independent targets (25%), which differ in sequence attributes. eIF4E-independent mRNAs share a 3' UTR motif. Caf20p binds all tested motif-containing 3' UTRs. Caf20p and the 3'UTR combine to influence ERS1 mRNA polysome association consistent with Caf20p contributing to translational control. Finally ERS1 3'UTR confers Caf20-dependent repression of expression to a heterologous reporter gene. Taken together, these data reveal conserved features of eIF4E-dependent Caf20p mRNA targets and uncover a novel eIF4E-independent mode of Caf20p binding to mRNAs that extends the regulatory role of Caf20p in the mRNA-specific repression of protein synthesis beyond its interaction with eIF4E.

A dynamic DNA-repair complex observed by correlative single-molecule nanomanipulation and fluorescence

We characterize in real time the composition and catalytic state of the initial Escherichia coli transcription-coupled repair (TCR) machinery by using correlative single-molecule methods. TCR initiates when RNA polymerase (RNAP) stalled by a lesion is displaced by the Mfd DNA translocase, thus giving repair components access to the damage. We previously used DNA nanomanipulation to obtain a nanomechanical readout of protein-DNA interactions during TCR initiation. Here we correlate this signal with simultaneous single-molecule fluorescence imaging of labeled components (RNAP, Mfd or RNA) to monitor the composition and localization of the complex. Displacement of stalled RNAP by Mfd results in loss of nascent RNA but not of RNAP, which remains associated with Mfd as a long-lived complex on the DNA. This complex translocates at ∼4 bp/s along the DNA, in a manner determined by the orientation of the stalled RNAP on the DNA.

Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.