Codon usage variability determines the correlation between proteome and transcriptome fold changes

Integrated Omic Approaches Reveal Molecular Mechanisms of Tolerance during Soybean and Meloidogyne incognita Interactions

The root-knot nematode (RKN), Meloidogyne incognita, is a devastating soybean pathogen worldwide. The use of resistant cultivars is the most effective method to prevent economic losses caused by RKNs. To elucidate the mechanisms involved in resistance to RKN, we determined the proteome and transcriptome profiles from roots of susceptible (BRS133) and highly tolerant (PI 595099) Glycine max genotypes 4, 12, and 30 days after RKN infestation. After in silico analysis, we described major defense molecules and mechanisms considered constitutive responses to nematode infestation, such as mTOR, PI3K-Akt, relaxin, and thermogenesis. The integrated data allowed us to identify protein families and metabolic pathways exclusively regulated in tolerant soybean genotypes. Among them, we highlighted the phenylpropanoid pathway as an early, robust, and systemic defense process capable of controlling M. incognita reproduction. Associated with this metabolic pathway, 29 differentially expressed genes encoding 11 different enzymes were identified, mainly from the flavonoid and derivative pathways. Based on differential expression in transcriptomic and proteomic data, as well as in the expression profile by RT-qPCR, and previous studies, we selected and overexpressed the GmPR10 gene in transgenic tobacco to assess its protective effect against M. incognita. Transgenic plants of the T₂ generation showed up to 58% reduction in the M. incognita reproduction factor. Finally, data suggest that GmPR10 overexpression can be effective against the plant parasitic nematode M. incognita, but its mechanism of action remains unclear. These findings will help develop new engineered soybean genotypes with higher performance in response to RKN infections.

Proteomic dissection of the rice-Fusarium fujikuroi interaction and the correlation between the proteome and transcriptome under disease stress

Background

Bakanae disease, caused by the fungus Fusarium fujikuroi, occurs widely throughout Asia and Europe and sporadically in other rice production areas. Recent changes in climate and cropping patterns have aggravated this disease. To gain a better understanding of the molecular mechanisms of rice bakanae disease resistance, we employed a 6-plex tandem mass tag approach for relative quantitative proteomic comparison of infected and uninfected rice seedlings 7 days post-inoculation with two genotypes: the resistant genotype 93–11 and the susceptible genotype Nipponbare.

Results

In total, 123 (77.2% up-regulated, 22.8% down-regulated) and 91 (94.5% up-regulated, 5.5% down-regulated) differentially expressed proteins (DEPs) accumulated in 93–11 and Nipponbare, respectively. Only 11 DEPs were both shared by the two genotypes. Clustering results showed that the protein regulation trends for the two genotypes were highly contrasting, which suggested obviously different interaction mechanisms of the host and the pathogen between 93 and 11 and Nipponbare. Further analysis showed that a noticeable aquaporin, PIP2–2, was sharply upregulated with a fold change (FC) of 109.2 in 93–11, which might be related to pathogen defense and the execution of bakanae disease resistance. Certain antifungal proteins were regulated in both 93–11 and Nipponbare with moderate FCs. These proteins might participate in protecting the cellular integrity required for basic growth of the susceptible genotype. Correlation analysis between the transcriptome and proteome revealed that Pearson correlation coefficients of R = 0.677 (P = 0.0005) and R = − 0.097 (P = 0.702) were obtained for 93–11 and Nipponbare, respectively. Our findings raised an intriguing result that a significant positive correlation only in the resistant genotype, while no correlation was found in the susceptible genotype. The differences in codon usage was hypothesized for the cause of the result.

Conclusions

Quantitative proteomic analysis of the rice genotypes 93-11and Nipponbare after F. fujikuroi infection revealed that the aquaporin protein PIP2–2 might execute bakanae disease resistance. The difference in the correlation between the transcriptome and proteome might be due to the differences in codon usage between 93-11and Nipponbare. Overall, the protein regulation trends observed under bakanae disease stress are highly contrasting, and the molecular mechanisms of disease defense are obviously different between 93 and 11 and Nipponbare. In summary, these findings deepen our understanding of the functions of proteins induced by bakanae disease and the mechanisms of rice bakanae disease resistance.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5435-5) contains supplementary material, which is available to authorized users.

Codon optimality, bias and usage in translation and mRNA decay

The advent of ribosome profiling and other tools to probe mRNA translation has revealed that codon bias - the uneven use of synonymous codons in the transcriptome - serves as a secondary genetic code: a code that guides the efficiency of protein production, the fidelity of translation and the metabolism of mRNAs. Recent advancements in our understanding of mRNA decay have revealed a tight coupling between ribosome dynamics and the stability of mRNA transcripts; this coupling integrates codon bias into the concept of codon optimality, or the effects that specific codons and tRNA concentrations have on the efficiency and fidelity of the translation machinery. In this Review, we first discuss the evidence for codon-dependent effects on translation, beginning with the basic mechanisms through which translation perturbation can affect translation efficiency, protein folding and transcript stability. We then discuss how codon effects are leveraged by the cell to tailor the proteome to maintain homeostasis, execute specific gene expression programmes of growth or differentiation and optimize the efficiency of protein production.

Screening the Molecular Framework Underlying Local Dendritic mRNA Translation

In the last decade, bioinformatic analyses of high-throughput proteomics and transcriptomics data have enabled researchers to gain insight into the molecular networks that may underlie lasting changes in synaptic efficacy. Development and utilization of these techniques have advanced the field of learning and memory significantly. It is now possible to move from the study of activity-dependent changes of a single protein to modeling entire network changes that require local protein synthesis. This data revolution has necessitated the development of alternative computational and statistical techniques to analyze and understand the patterns contained within. Thus, the focus of this review is to provide a synopsis of the journey and evolution toward big data techniques to address still unanswered questions regarding how synapses are modified to strengthen neuronal circuits. We first review the seminal studies that demonstrated the pivotal role played by local mRNA translation as the mechanism underlying the enhancement of enduring synaptic activity. In the interest of those who are new to the field, we provide a brief overview of molecular biology and biochemical techniques utilized for sample preparation to identify locally translated proteins using RNA sequencing and proteomics, as well as the computational approaches used to analyze these data. While many mRNAs have been identified, few have been shown to be locally synthesized. To this end, we review techniques currently being utilized to visualize new protein synthesis, a task that has proven to be the most difficult aspect of the field. Finally, we provide examples of future applications to test the physiological relevance of locally synthesized proteins identified by big data approaches.

Post-translational processing targets functionally diverse proteins in Mycoplasma hyopneumoniae

Mycoplasma hyopneumoniae is a genome-reduced, cell wall-less, bacterial pathogen with a predicted coding capacity of less than 700 proteins and is one of the smallest self-replicating pathogens. The cell surface of M. hyopneumoniae is extensively modified by processing events that target the P97 and P102 adhesin families. Here, we present analyses of the proteome of M. hyopneumoniae-type strain J using protein-centric approaches (one- and two-dimensional GeLC–MS/MS) that enabled us to focus on global processing events in this species. While these approaches only identified 52% of the predicted proteome (347 proteins), our analyses identified 35 surface-associated proteins with widely divergent functions that were targets of unusual endoproteolytic processing events, including cell adhesins, lipoproteins and proteins with canonical functions in the cytosol that moonlight on the cell surface. Affinity chromatography assays that separately used heparin, fibronectin, actin and host epithelial cell surface proteins as bait recovered cleavage products derived from these processed proteins, suggesting these fragments interact directly with the bait proteins and display previously unrecognized adhesive functions. We hypothesize that protein processing is underestimated as a post-translational modification in genome-reduced bacteria and prokaryotes more broadly, and represents an important mechanism for creating cell surface protein diversity.

Integrative Omics Analysis Reveals Post-Transcriptionally Enhanced Protective Host Response in Colorectal Cancers with Microsatellite Instability

Microsatellite instability (MSI) is a frequent and clinically relevant molecular phenotype in colorectal cancer. MSI cancers have favorable survival compared with microsatellite stable cancers (MSS), possibly due to the pronounced tumor-infiltrating lymphocytes observed in MSI cancers. Consistent with the strong immune response that MSI cancers trigger in the host, previous transcriptome expression studies have identified mRNA signatures characteristic of immune response in MSI cancers. However, proteomics features of MSI cancers and the extent to which the mRNA signatures are reflected at the protein level remain largely unknown. Here, we performed a comprehensive comparison of global proteomics profiles between MSI and MSS colorectal cancers in The Cancer Genome Atlas (TCGA) cohort. We found that protein signatures of MSI are also associated with increased immunogenicity. To reliably quantify post-transcription regulation in MSI cancers, we developed a resampling-based regression method by integrative modeling of transcriptomics and proteomics data sets. Compared with the popular simple method, which detects post-transcriptional regulation by either identifying genes differentially expressed at the mRNA level but not at the protein level or vice versa, our method provided a quantitative, more sensitive, and accurate way to identify genes subject to differential post-transcriptional regulation. With this method, we demonstrated that post-transcriptional regulation, coordinating protein expression with key players, initiates de novo and enhances protective host response in MSI cancers.

Elucidation of the CHO Super-Ome (CHO-SO) by Proteoinformatics

Chinese hamster ovary (CHO) cells are the preferred host cell line for manufacturing a variety of complex biotherapeutic drugs including monoclonal antibodies. We performed a proteomics and bioinformatics analysis on the spent medium from adherent CHO cells. Supernatant from CHO-K1 culture was collected and subjected to in-solution digestion followed by LC/LC-MS/MS analysis, which allowed the identification of 3281 different host cell proteins (HCPs). To functionally categorize them, we applied multiple bioinformatics tools to the proteins identified in our study including SignalP, TargetP, SecretomeP, TMHMM, WoLF PSORT, and Phobius. This analysis provided information on the presence of signal peptides, transmembrane domains, and cellular localization and showed that both secreted and intracellular proteins were constituents of the supernatant. Identified proteins were shown to be localized to the secretory pathway including ones playing roles in cell growth, proliferation, and folding as well as those involved in protein degradation and removal. After combining proteins predicted to be secreted or having a signal peptide, we identified 1015 proteins, which we termed as CHO supernatant-ome (CHO-SO), or superome. As a part of this effort, we created a publically accessible web-based tool called GO-CHO to functionally categorize proteins found in CHO-SO and identify enriched molecular functions, biological processes, and cellular components. We also used a tool to evaluate the immunogenicity potential of high-abundance HCPs. Among enriched functions were catalytic activity and structural constituents of the cytoskeleton. Various transport related biological processes, such as vesicle mediated transport, were found to be highly enriched. Extracellular space and vesicular exosome associated proteins were found to be the most enriched cellular components. The superome also contained proteins secreted from both classical and nonclassical secretory pathways. The work and database described in our study will enable the CHO community to rapidly identify high-abundance HCPs in their cultures and therefore help assess process and purification methods used in the production of biologic drugs.

Thyroid Hormone Receptors Predict Prognosis in BRCA1 Associated Breast Cancer in Opposing Ways

Since BRCA1 associated breast cancers are frequently classified as hormone receptor negative or even triple negative, the application of endocrine therapies is rather limited in these patients. Like hormone receptors that bind to estrogen or progesterone, thyroid hormone receptors (TRs) are members of the nuclear hormone receptor superfamily. TRs might be interesting biomarkers - especially in the absence of classical hormone receptors. The current study aimed to investigate whether TRs may be specifically expressed in BRCA1 associated cancer cases and whether they are of prognostic significance in these patients as compared to sporadic breast cancer cases. This study analyzed TRα and TRβ immunopositivity in BRCA1 associated (n = 38) and sporadic breast cancer (n = 86). Further, TRs were studied in MCF7 (BRCA1 wildtype) and HCC3153 (BRCA1 mutated) cells. TRβ positivity rate was significantly higher in BRCA1 associated as compared to sporadic breast cancers (p = 0.001). The latter observation remained to be significant when cases that had been matched for clinicopathological criteria were compared (p = 0.037). Regarding BRCA1 associated breast cancer cases TRβ positivity turned out to be a positive prognostic factor for five-year (p = 0.007) and overall survival (p = 0.026) while TRα positivity predicted reduced five-year survival (p = 0.030). Activation of TRβ resulted in down-modulation of CTNNB1 while TRα inhibition reduced cell viability in HCC3153. However, only BRCA1 wildtype MCF7 cells were capable of rapidly degrading TRα1 in response to T3 stimulation. Significantly, this study identified TRβ to be up-regulated in BRCA1 associated breast cancer and revealed TRs to be associated with patients’ prognosis. TRs were also found to be expressed in triple negative BRCA1 associated breast cancer. Further studies need to be done in order to evaluate whether TRs may become interesting targets of endocrine therapeutic approaches, especially when tumors are triple-negative.

Codon usage and protein sequence pattern dependency in different organisms: A Bioinformatics approach

Although it is known that synonymous codons are not chosen randomly, the role of the codon usage in gene regulation is not clearly understood, yet. Researchers have investigated the relation between the codon usage and various properties, such as gene regulation, translation rate, translation efficiency, mRNA stability, splicing, and protein domains. Recently, a universal codon usage based mechanism for gene regulation is proposed. We studied the role of protein sequence patterns on the codons usage by related genes. Considering a subsequence of a protein that matches to a pattern or motif, we showed that, parts of the genes, which are translated to this subsequence, use specific ratios of synonymous codons. Also, we built a multinomial logistic regression statistical model for codon usage, which considers the effect of patterns on codon usage. This model justifies the observed codon usage preference better than the classic organism dependent codon usage. Our results showed that the codon usage plays a role in controlling protein levels, for genes that participate in a specific biological function. This is the first time that this phenomenon is reported.

Multiple correlations of mRNA expression and protein abundance in human cytokine profile

With the development of genomic study, researchers found that it is insufficient to predict protein expression from quantitative mRNA data in large scale, which is contrary to the traditional opinion that mRNA expression correlates with protein abundance at the single gene level. To try to solve the apparent conflicting views, here we set up a series of research models and chose soluble cytokines as targets. First, human peripheral blood mononuclear cell (PBMC) from one health donor was treated with 16 continuously changing conditions, the protein and mRNA profile were analyzed by multiplex Luminex and genomic microarray, respectively. Among the tested genes, around half mRNA correlated well with their corresponding proteins (ρ > 0.8), however if we put all the genes together, the correlation coefficient for the 16 conditions varied from 0.29 to 0.71. Second, PBMC from 14 healthy donors were stimulated with the same condition and it was found that the correlation coefficient went down (ρ < 0.6). Third, 28 rheumatoid arthritis (RA) patients were tested for their response to the same external stimuli and it turned out different individual displayed different protein expression pattern as expect. Lastly, autoimmune disease cohorts (8 diseases including RA, 103 patients in total) were assayed on the whole view. It was observed that there was still some similarity in the protein profile among patients from the single disease type although completely different patterns were displayed across different disease categories. This study built a good bridge between single gene analysis and the whole genome study and may give a reasonable explanation for the two conflicting views in current biological science.

Correlation of mRNA and protein abundance in the developing maize leaf

To help understand regulation of maize leaf blade development, including sink-source transitions and induction of C4 photosynthesis, we compared large-scale quantitative proteome and transcriptomes collected at specific stages along the developmental maize leaf blade gradient. Proteome data were based on label-free shotgun proteomics (spectral counting) and transcript data were based on RNA-seq using the same source materials, and had been published previously (Nat Genet, 42, 2010, 1060-1067; The Plant Cell, 22, 2010, 3509-3542). Transcript and protein abundance followed near normal distributions, in contrast with several studies with other organisms. Protein observability correlated with transcript abundance following a 'lazy step function' similar to that in bacteria and yeast. mRNA and protein abundance showed significant positive correlations (up to 0.8) for log-transformed length-weighted normalized spectral abundance factor (NSAF) and reads per kilobase of exon model per million mapped reads (RPKM) and non-weighted abundances (NadjSPC and COV) in dependence of function and development. Correlations were much weaker in the leaf 'sink-source' transition zone, i.e. the zone with massive investments in leaf chloroplast biogenesis and build-up of photosynthetic capacity. Clustering analyses of gene-specific protein-mRNA ratios revealed co-ordinated shifts in control points in gene expression along the leaf blade developmental gradient. The highest protein-mRNA ratio for each gene generally corresponded to leaf developmental stages in which the protein function was most important, with the exception of the 80S ribosome. Specific examples are discussed in the context of C4 photosynthesis, leaf development and sink-source transitions. This large-scale mRNA-protein correlation analysis in plants (maize) using label-free spectral counting for protein quantification and RNA-seq for mRNA abundance will provide a template for future mRNA-protein correlation studies.

Semiquantitative analysis of clinical heat stress in Clostridium difficile strain 630 using a GeLC/MS workflow with emPAI quantitation

Clostridium difficile is considered to be the most frequent cause of infectious bacterial diarrhoea in hospitals worldwide yet its adaptive ability remains relatively uncharacterised. Here, we used GeLC/MS and the exponentially modified protein abundance index (emPAI) calculation to determine proteomic changes in response to a clinically relevant heat stress. Reproducibility between both biological and technical replicates was good, and a 37°C proteome of 224 proteins was complemented by a 41°C proteome of 202 proteins at a 1% false discovery rate. Overall, 236 C. difficile proteins were identified and functionally categorised, of which 178 were available for comparative purposes. A total of 65 proteins (37%) were modulated by 1.5-fold or more at 41°C compared to 37°C and we noted changes in the majority of proteins associated with amino acid metabolism, including upregulation of the reductive branch of the leucine fermentation pathway. Motility was reduced at 41°C as evidenced by a 2.7 fold decrease in the flagellar filament protein, FliC, and a global increase in proteins associated with detoxification and adaptation to atypical conditions was observed, concomitant with decreases in proteins mediating transcriptional elongation and the initiation of protein synthesis. Trigger factor was down regulated by almost 5-fold. We propose that under heat stress, titration of the GroESL and dnaJK/grpE chaperones by misfolded proteins will, in the absence of trigger factor, prevent nascent chains from emerging efficiently from the ribosome causing translational stalling and also an increase in secretion. The current work has thus allowed development of a heat stress model for the key cellular processes of protein folding and export.

Deciphering the rules by which dynamics of mRNA secondary structure affect translation efficiency in Saccharomyces cerevisiae

Messenger RNA (mRNA) secondary structure decreases the elongation rate, as ribosomes must unwind every structure they encounter during translation. Therefore, the strength of mRNA secondary structure is assumed to be reduced in highly translated mRNAs. However, previous studies in vitro reported a positive correlation between mRNA folding strength and protein abundance. The counterintuitive finding suggests that mRNA secondary structure affects translation efficiency in an undetermined manner. Here, we analyzed the folding behavior of mRNA during translation and its effect on translation efficiency. We simulated translation process based on a novel computational model, taking into account the interactions among ribosomes, codon usage and mRNA secondary structures. We showed that mRNA secondary structure shortens ribosomal distance through the dynamics of folding strength. Notably, when adjacent ribosomes are close, mRNA secondary structures between them disappear, and codon usage determines the elongation rate. More importantly, our results showed that the combined effect of mRNA secondary structure and codon usage in highly translated mRNAs causes a short ribosomal distance in structural regions, which in turn eliminates the structures during translation, leading to a high elongation rate. Together, these findings reveal how the dynamics of mRNA secondary structure coupling with codon usage affect translation efficiency.

Dependency of codon usage on protein sequence patterns: a statistical study

BACKGROUND

Codon degeneracy and codon usage by organisms is an interesting and challenging problem. Researchers demonstrated the relation between codon usage and various functions or properties of genes and proteins, such as gene regulation, translation rate, translation efficiency, mRNA stability, splicing, and protein domains. Researchers usually represent segments of proteins responsible for specific functions or structures in a family of proteins as sequence patterns or motifs. We asked the question if organisms use the same codons in pattern segments as compared to the rest of the sequence.

METHODS

We used the likelihood ratio test, Pearson's chi-squared test, and mutual information to compare these two codon usages.

RESULTS

We showed that codon usage, in segments of genes that code for a given pattern or motif in a group of proteins, varied from the rest of the gene. The codon usage in these segments was not random. Amino acids with larger number of codons used more specific codon ratios in these segments. We studied the number of amino acids in the pattern (pattern length). As patterns got longer, there was a slight decrease in the fraction of patterns with significant different codon usage in the pattern region as compared to codon usage in the gene region. We defined a measure of specificity of protein patterns, and studied its relation to the codon usage. The difference in the codon usage between pattern region and gene region, was less for the patterns with higher specificity.

CONCLUSIONS

We provided a hypothesis that there are segments on genes that affect the codon usage and thus influence protein translation speed, and these regions are the regions that code protein pattern regions.

Controllability in cancer metabolic networks according to drug targets as driver nodes

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

From measurement to implementation of metabolic fluxes

The intracellular reaction rates (fluxes) are the ultimate outcome of the activities of the complete inventory (from DNA to metabolite) and in their sum determine the cellular phenotype. The genotype-phenotype relationship is fundamental in such different fields as cancer research and biotechnology. Here, we summarize the developments in determining metabolic fluxes, inferring major pathways from the DNA-sequence, estimating optimal flux distributions, and how these flux distributions can be achieved in vivo. The technical advances to intervene with the many levels of the cellular architecture allow the implementation of new strategies in for example Metabolic Engineering.

Comparative transcriptional analysis of clinically relevant heat stress response in Clostridium difficile strain 630

Clostridium difficile is considered to be one of the most important causes of health care-associated infections worldwide. In order to understand more fully the adaptive response of the organism to stressful conditions, we examined transcriptional changes resulting from a clinically relevant heat stress (41 °C versus 37 °C) in C. difficile strain 630 and identified 341 differentially expressed genes encompassing multiple cellular functional categories. While the transcriptome was relatively resilient to the applied heat stress, we noted upregulation of classical heat shock genes including the groEL and dnaK operons in addition to other stress-responsive genes. Interestingly, the flagellin gene (fliC) was downregulated, yet genes encoding the cell-wall associated flagellar components were upregulated suggesting that while motility may be reduced, adherence--to mucus or epithelial cells--could be enhanced during infection. We also observed that a number of phage associated genes were downregulated, as were genes associated with the conjugative transposon Tn5397 including a group II intron, thus highlighting a potential decrease in retromobility during heat stress. These data suggest that maintenance of lysogeny and genome wide stabilisation of mobile elements could be a global response to heat stress in this pathogen.

Dynamic 13C-labeling experiments prove important differences in protein turnover rate between two Saccharomyces cerevisiae strains

We developed a method for the quantification of protein turnover rates using (13)C-labeled substrates combined with the analysis of the labeling patterns of proteinogenic amino acids. Using this method, the specific amino acid turnover rates between proteins and the pool of free amino acids were determined for eight different amino acids (alanine, valine, proline, aspartic acid, glycine, leucine, isoleucine, and threonine) in two Saccharomyces cerevisiae strains (CEN.PK 113-7D and YSBN2). Furthermore, proteasome activities were compared for both strains. Both results confirmed the hypothesis of a higher protein turnover rates in CEN.PK 113-7D, which was generated in a previous comparative systems biology study of these two yeast strains. The ATP costs associated with the observed differences in protein turnover were quantified and could explain accurately the differences in biomass yield between both strains that are observed in chemostat cultures. The percentage of maintenance ATP associated with protein polymerization (polymerization for growth and re-polymerization because of turnover) and degradation was estimated to be 72% for YSBN2 and 79% for CEN.PK 113-7D, which makes these processes the dominant nonbiosynthetic drain of ATP in living cells, and hence, it represents an energetic parameter of great relevance.

Bioinformatic study of the relationship between protein regulation and sequence properties

Although protein expression and regulation have been intensively studied, a complete picture of its mechanisms is still to be drawn. Analysis of high-throughput quantitative proteomics data provides a way to better understand protein regulation. Here, we introduce a bioinformatic analysis method to correlate protein regulation with individual amino acid patterns. We compare the amino acid composition between groups of regulated and unregulated proteins and investigate the correlation between codon usage patterns and protein regulation levels in two Sulfolobus species in "biofilm vs planktonic" experiments. The identified amino acids can then be associated with the regulation of specific gene functions. Strikingly, our analysis shows that functional categories of regulated proteins with similar composition and codon usage pattern of specific amino acids behave similarly. This finding can contribute to a better understanding of protein and gene expression regulation and could find applications in gene optimisation.

Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT

Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment.

Investigation of the Listeria monocytogenes Scott A acid tolerance response and associated physiological and phenotypic features via whole proteome analysis

The global proteomic responses of the foodborne pathogen Listeria monocytogenes strain Scott A, during active growth and transition to the stationary growth phase under progressively more acidic conditions, created by addition of lactic acid and HCl, were investigated using label-free liquid chromatography/tandem mass spectrometry. Approximately 56% of the Scott A proteome was quantitatively assessable, and the data provides insight into its acquired acid tolerance response (ATR) as well as the relation of the ATR to the growth phase transition. Alterations in protein abundance due to acid stress were focused in proteins belonging to the L. monocytogenes common genome, with few strain-dependent proteins involved. However, one of the two complete prophage genomes appeared to enter lysogeny. During progressive acidification, the growth rate and yield were reduced 55% and 98%, respectively, in comparison to nonacidified control cultures. The maintenance of the growth rate was determined to be connected to activation of cytoplasmic pH homeostatic mechanisms while cellular reproductive-related and cell component turnover proteins were markedly more abundant in acid stressed cultures. Cell biomass accumulation was impeded predominantly due to repression of phosphodonor-linked enzymes involved with sugar phosphotransfer, glycolysis, and cell wall polymer biosynthesis. Acidification caused a shift from heterofermentation to an oxidatively stressed state in which ATP appears to be generated mainly through the pyruvate dehydrogenase/pyruvate oxidase/phosphotransacetylase/acetate kinase and branched chain acid dehydrogenase pathways. Analysis of regulons indicated energy conservation occurs due to repression by the GTP/isoleucine sensor CodY and also the RelA mediated stringent response. Whole proteome analysis proved to be an effective way to highlight proteins involved with the acquisition of the ATR.

Sequence variability of Rhizobiales orthologs and relationship with physico-chemical characteristics of proteins

Background

Chromosomal orthologs can reveal the shared ancestral gene set and their evolutionary trends. Additionally, physico-chemical properties of encoded proteins could provide information about functional adaptation and ecological niche requirements.

Results

We analyzed 7080 genes (five groups of 1416 orthologs each) from Rhizobiales species (S. meliloti, R. etli, and M. loti, plant symbionts; A. tumefaciens, a plant pathogen; and B. melitensis, an animal pathogen). We evaluated their phylogenetic relationships and observed three main topologies. The first, with closer association of R. etli to A. tumefaciens; the second with R. etli closer to S. meliloti; and the third with A. tumefaciens and S. meliloti as the closest pair. This was not unusual, given the close relatedness of these three species. We calculated the synonymous (dS) and nonsynonymous (dN) substitution rates of these orthologs, and found that informational and metabolic functions showed relatively low dN rates; in contrast, genes from hypothetical functions and cellular processes showed high dN rates. An alternative measure of sequence variability, percentage of changes by species, was used to evaluate the most specific proportion of amino acid residues from alignments. When dN was compared with that measure a high correlation was obtained, revealing that much of evolutive information was extracted with the percentage of changes by species at the amino acid level. By analyzing the sequence variability of orthologs with a set of five properties (polarity, electrostatic charge, formation of secondary structures, molecular volume, and amino acid composition), we found that physico-chemical characteristics of proteins correlated with specific functional roles, and association of species did not follow their typical phylogeny, probably reflecting more adaptation to their life styles and niche preferences. In addition, orthologs with low dN rates had residues with more positive values of polarity, volume and electrostatic charge.

Conclusions

These findings revealed that even when orthologs perform the same function in each genomic background, their sequences reveal important evolutionary tendencies and differences related to adaptation.

This article was reviewed by: Dr. Purificación López-García, Prof. Jeffrey Townsend (nominated by Dr. J. Peter Gogarten), and Ms. Olga Kamneva.

Transcriptomic and proteomic profiling of maize embryos exposed to camptothecin

BACKGROUND

Camptothecin is a plant alkaloid that specifically binds topoisomerase I, inhibiting its activity and inducing double stranded breaks in DNA, activating the cell responses to DNA damage and, in response to severe treatments, triggering cell death.

RESULTS

Comparative transcriptomic and proteomic analyses of maize embryos that had been exposed to camptothecin were conducted. Under the conditions used in this study, camptothecin did not induce extensive degradation in the genomic DNA but induced the transcription of genes involved in DNA repair and repressed genes involved in cell division. Camptothecin also affected the accumulation of several proteins involved in the stress response and induced the activity of certain calcium-dependent nucleases. We also detected changes in the expression and accumulation of different genes and proteins involved in post-translational regulatory processes.

CONCLUSIONS

This study identified several genes and proteins that participate in DNA damage responses in plants. Some of them may be involved in general responses to stress, but others are candidate genes for specific involvement in DNA repair. Our results open a number of new avenues for researching and improving plant resistance to DNA injury.