RBM10 is an RNA binding protein and alternative splicing regulator frequently mutated in lung adenocarcinomas. Recent results indicate that RBM10 inhibits proliferation of lung cancer cells by promoting skipping of exon 9 of the gene NUMB, a frequent alternative splicing change in lung cancer generating a negative regulator of Notch signaling. Complementing these observations, we show that knock down of RBM10 in human cancer cells enhances growth of mouse tumor xenografts, confirming that RBM10 acts as a tumor suppressor, while knock down of an oncogenic mutant version of RBM10 reduces xenograft tumor growth. A RBM10 mutation found in lung cancer cells, V354E, disrupts RBM10-mediated regulation of NUMB alternative splicing, inducing the cell proliferation-promoting isoform. We now show that 2 natural RBM10 isoforms that differ by the presence or absence of V354 in the second RNA Recognition Motif (RRM2), display similar regulatory effects on NUMB alternative splicing, suggesting that V354E actively disrupts RBM10 activity. Structural modeling localizes V354 in the outside surface of one α-helix opposite to the RNA binding surface of RBM10, and we show that the mutation does not compromise binding of the RRM2 domain to NUMB RNA regulatory sequences. We further show that other RBM10 mutations found in lung adenocarcinomas also compromise regulation of NUMB exon 9. Collectively, our previous and current results reveal that RBM10 is a tumor suppressor that represses Notch signaling and cell proliferation through the regulation of NUMB alternative splicing.

The human respiratory tract pathogen M. pneumoniae is one of the best characterized minimal bacterium. Until now, two main groups of clinical isolates of this bacterium have been described (types 1 and 2), differing in the sequence of the P1 adhesin gene. Here, we have sequenced the genomes of 23 clinical isolates of M. pneumoniae. Studying SNPs, non-synonymous mutations, indels and genome rearrangements of these 23 strains and 4 previously sequenced ones, has revealed new subclasses in the two main groups, some of them being associated with the country of isolation. Integrative analysis of in vitro gene essentiality and mutation rates enabled the identification of several putative virulence factors and antigenic proteins; revealing recombination machinery, glycerol metabolism and peroxide production as possible factors in the genetics and physiology of these pathogenic strains. Additionally, the transcriptomes and proteomes of two representative strains, one from each of the two main groups, have been characterized to evaluate the impact of mutations on RNA and proteins levels. This study has revealed that type 2 strains show higher expression levels of CARDS toxin, a protein recently shown to be one of the major factors of inflammation. Thus, we propose that type 2 strains could be more toxigenic than type 1 strains of M. pneumoniae.

Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus), or a long time ago (Neurospora crassa and Schizosaccharomyces pombe), contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

Here, we determined the relative importance of different transcriptional mechanisms in the genome-reduced bacterium Mycoplasma pneumoniae, by employing an array of experimental techniques under multiple genetic and environmental perturbations. Of the 143 genes tested (21% of the bacterium's annotated proteins), only 55% showed an altered phenotype, highlighting the robustness of biological systems. We identified nine transcription factors (TFs) and their targets, representing 43% of the genome, and 16 regulators that indirectly affect transcription. Only 20% of transcriptional regulation is mediated by canonical TFs when responding to perturbations. Using a Random Forest, we quantified the non-redundant contribution of different mechanisms such as supercoiling, metabolic control, RNA degradation, and chromosome topology to transcriptional changes. Model-predicted gene changes correlate well with experimental data in 95% of the tested perturbations, explaining up to 70% of the total variance when also considering noise. This analysis highlights the importance of considering non-TF-mediated regulation when engineering bacteria.

Different tissues express genes with particular codon usage and anticodon tRNA repertoires. However, the codon-anticodon co-adaptation in humans is not completely understood, nor is its effect on tissue-specific protein levels. Here, we first validated the accuracy of small RNA-seq for tRNA quantification across five human cell lines. We then analyzed the tRNA abundance of more than 8,000 tumor samples from TCGA, together with their paired mRNA-seq and proteomics data, to determine the Supply-to-Demand Adaptation. We thereby elucidate that the dynamic adaptation of the tRNA pool is largely related to the proliferative state across tissues. The distribution of such tRNA pools over the whole cellular translatome affects the subsequent translational efficiency, which functionally determines a condition-specific expression program both in healthy and tumor states. Furthermore, the aberrant translational efficiency of some codons in cancer, exemplified by ProCCA and GlyGGT, is associated with poor patient survival. The regulation of these tRNA profiles is partly explained by the tRNA gene copy numbers and their promoter DNA methylation.

Cells respond to their environment by sensing signals and translating them into changes in gene expression. In recent years, synthetic networks have been designed in both prokaryotic and eukaryotic systems to create new functionalities and for specific applications. In this review, we discuss the challenges associated with engineering signal transduction pathways. Furthermore, we address advantages and disadvantages of engineering signaling pathways in prokaryotic and eukaryotic cells, highlighting recent examples, and discuss how progress in synthetic biology might impact biotechnology and biomedicine.

Despite progress in the treatment of acute myelogenous leukaemia (AML) the outcome often remains poor. Tumour necrosis factor related apoptosis-inducing ligand (TRAIL) is a promising therapeutic agent in many different types of tumours, but AML cells are relatively insensitive to TRAIL-induced apoptosis. Here we show that TRAIL-induced apoptosis in AML cells is predominantly mediated by death receptor 4 (DR4) and not DR5. Therefore, we constructed a variant of TRAIL (rhTRAIL-C3) that is a strong inducer of DR4-mediated apoptosis. TRAIL-C3 demonstrated much stronger pro-apoptotic activity than wild-type (WT) TRAIL in a panel of AML cell lines as well as in primary AML blasts. The higher pro-apoptotic potential was further enhanced when the TRAIL mutant was used in combination with BMS-345541, a selective inhibitor of inhibitor-κB kinases. It illustrates that combination of this TRAIL variant with chemotherapeutics or other targeted agents can kill AML with high efficacy. This may represent a major advantage over the currently used therapies that have serious toxic side effects. The high efficacy of rhTRAIL-C3 containing therapies may enable the use of lower drug doses to reduce the toxic side effects and improve patient outcome. Our findings suggest that the rational design of TRAIL variants that target DR4 potentiate the death-inducing activity of TRAIL and offer a novel therapeutic strategy for the treatment of AML.

Knowing which proteins and RNAs directly interact is essential for understanding cellular mechanisms. Unfortunately, discovering such interactions is costly and often unreliable. To overcome these limitations, we developed rec-YnH, a new yeast two and three-hybrid-based screening pipeline capable of detecting interactions within protein libraries or between protein libraries and RNA fragment pools. rec-YnH combines batch cloning and transformation with intracellular homologous recombination to generate bait-prey fusion libraries. By developing interaction selection in liquid-gels and using an ORF sequence-based readout of interactions via next-generation sequencing, we eliminate laborious plating and barcoding steps required by existing methods. We use rec-Y2H to simultaneously map interactions of protein domains and reveal novel putative interactors of PAR proteins. We further employ rec-Y2H to predict the architecture of published coprecipitated complexes. Finally, we use rec-Y3H to map interactions between multiple RNA-binding proteins and RNAs-the first time interactions between protein and RNA pools are simultaneously detected.

Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict essential components validated with in vitro serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach allowed us to optimize medium composition, enabled growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive production.

Understanding the functional effect of Single Amino acid Substitutions (SAS), derived from the occurrence of single nucleotide variants (SNVs), and their relation to disease development is a major issue in clinical genomics. Despite the existence of several bioinformatic algorithms and servers that predict if a SAS is pathogenic or not, they give little or no information at all on the reasons for pathogenicity prediction and on the actual predicted effect of the SAS on the protein function. Moreover, few actual methods take into account structural information when available for automated analysis. Moreover, many of these algorithms are able to predict an effect that no necessarily translates directly into pathogenicity. VarQ is a bioinformatic pipeline that incorporates structural information for the detailed analysis and prediction of SAS effect on protein function. It is an online tool which uses UniProt id and automatically analyzes known and user provided SAS for their effect on protein activity, folding, aggregation and protein interactions, among others. We show that structural information, when available, can improve the SAS pathogenicity diagnosis and more important explain its causes. We show that VarQ is able to correctly reproduce previous analysis of RASopathies related mutations, saving extensive and time consuming manual curation. VarQ assessment was performed over a set of previously manually curated RASopathies (diseases that affects the RAS/MAPK signaling pathway) related variants, showing its ability to correctly predict the phenotypic outcome and its underlying cause. This resource is available online at http://varq.qb.fcen.uba.ar/. Supporting Information & Tutorials may be found in the webpage of the tool.

The Cre-Lox system is a highly versatile and powerful DNA recombinase mechanism, mainly used in genetic engineering to insert or remove desired DNA sequences. It is widely utilized across multiple fields of biology, with applications ranging from plants, to mammals, to microbes. A key feature of this system is its ability to allow recombination between mutant lox sites. Two of the most commonly used mutant sites are named lox66 and lox71, which recombine to create a functionally inactive double mutant lox72 site. However, a large portion of the published literature has incorrectly annotated these mutant lox sites, which in turn can lead to difficulties in replication of methods, design of proper vectors and confusion over the proper nomenclature. Here, we demonstrate common errors in annotations, the impacts they can have on experimental viability, and a standardized naming convention. We also show an example of how this incorrect annotation can induce toxic effects in bacteria that lack optimal DNA repair systems, exemplified by Mycoplasma pneumoniae.

In the present work, an abundant and unused residue (wheat straw) has been employed to synthesize a polyol as a substituent of castor oil in polyurethane foams. The liquefied product showed excellent properties for the proposed application. Castor oil was substituted with up to 50% wheat straw polyol in the formulation of polyurethane foams, which were prepared using two different isocyanates (methylene diphenyl diisocyanate (MDI) and toluene-2,4-diisocyanate (TDI)). The evaluation of physical, mechanical, and thermal properties of the foams revealed that these materials can successfully be formed with up to 40% wheat straw polyols since all the results were improved. Moreover, at this polyol concentration, the morphology of the foams was presented as a compact and ordered structure. Following this trend, the foams showed excellent biodegradability at 30 days (5.60 and 7.31% for TDI and MDI foams, respectively) and 60 days (8.49 and 9.88% for TDI and MDI foams, respectively) in the soil media tests carried out. Thus, the materials prepared in this work can be proposed for agricultural applications such as use in plant nurseries.

Codon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive.

Aggregates of Pseudomonas aeruginosa form a protective barrier against antibiotics and the immune system. These barriers, known as biofilms, are associated with several infectious diseases. One of the main components of these biofilms is alginate, a homo- and hetero-polysaccharide that consists of β-D-mannuronate (M) and α-L-guluronate (G) units. Alginate lyases degrade this sugar and have been proposed as biotherapeutic agents to dissolve P. aeruginosa biofilms. However, there are contradictory reports in the literature regarding the efficacy of alginate lyases against biofilms and their synergistic effect with antibiotics. We found that most positive reports used a commercial crude extract from Flavobacterium multivorum as the alginate lyase source. By using anion exchange chromatography coupled to nano LC MS/MS, we identified two distinct enzymes in this extract, one has both polyM and polyG (polyM/G) degradation activities and it is similar in sequence to a broad-spectrum alginate lyase from Flavobacterium sp. S20 (Alg2A). The other enzyme has only polyG activity and it is similar in sequence to AlyA1 from Zobellia galactanivorans. By characterizing both of these enzymes together with three recombinant alginate lyases (a polyM, a polyG and a polyM/G), we showed that only enzymes with polyM/G activity such as Alg2A and A1-II' (alginate lyase from Sphingomonas sp.) are effective in dissolving biofilms. Furthermore, both activities are required to have a synergistic effect with antibiotics.

Human skin copes with harmful environmental factors that are circadian in nature, yet how circadian rhythms modulate the function of human epidermal stem cells is mostly unknown. Here we show that in human epidermal stem cells and their differentiated counterparts, core clock genes peak in a successive and phased manner, establishing distinct temporal intervals during the 24 hr day period. Each of these successive clock waves is associated with a peak in the expression of subsets of transcripts that temporally segregate the predisposition of epidermal stem cells to respond to cues that regulate their proliferation or differentiation, such as TGFβ and calcium. Accordingly, circadian arrhythmia profoundly affects stem cell function in culture and in vivo. We hypothesize that this intricate mechanism ensures homeostasis by providing epidermal stem cells with environmentally relevant temporal functional cues during the course of the day and that its perturbation may contribute to aging and carcinogenesis.

The term "proteomics" encompasses the large-scale detection and analysis of proteins and their post-translational modifications. Driven by major improvements in mass spectrometric instrumentation, methodology, and data analysis, the proteomics field has burgeoned in recent years. It now provides a range of sensitive and quantitative approaches for measuring protein structures and dynamics that promise to revolutionize our understanding of cell biology and molecular mechanisms in both human cells and model organisms. The Proteomics Specification in Time and Space (PROSPECTS) Network is a unique EU-funded project that brings together leading European research groups, spanning from instrumentation to biomedicine, in a collaborative five year initiative to develop new methods and applications for the functional analysis of cellular proteins. This special issue of Molecular and Cellular Proteomics presents 16 research papers reporting major recent progress by the PROSPECTS groups, including improvements to the resolution and sensitivity of the Orbitrap family of mass spectrometers, systematic detection of proteins using highly characterized antibody collections, and new methods for absolute as well as relative quantification of protein levels. Manuscripts in this issue exemplify approaches for performing quantitative measurements of cell proteomes and for studying their dynamic responses to perturbation, both during normal cellular responses and in disease mechanisms. Here we present a perspective on how the proteomics field is moving beyond simply identifying proteins with high sensitivity toward providing a powerful and versatile set of assay systems for characterizing proteome dynamics and thereby creating a new "third generation" proteomics strategy that offers an indispensible tool for cell biology and molecular medicine.

Here we perform a large-scale study of the structural properties and the expression of proteins that constitute the human Centrosome. Centrosomal proteins tend to be larger than generic human proteins (control set), since their genes contain in average more exons (20.3 versus 14.6). They are rich in predicted disordered regions, which cover 57% of their length, compared to 39% in the general human proteome. They also contain several regions that are dually predicted to be disordered and coiled-coil at the same time: 55 proteins (15%) contain disordered and coiled-coil fragments that cover more than 20% of their length. Helices prevail over strands in regions homologous to known structures (47% predicted helical residues against 17% predicted as strands), and even more in the whole centrosomal proteome (52% against 7%), while for control human proteins 34.5% of the residues are predicted as helical and 12.8% are predicted as strands. This difference is mainly due to residues predicted as disordered and helical (30% in centrosomal and 9.4% in control proteins), which may correspond to alpha-helix forming molecular recognition features (α-MoRFs). We performed expression assays for 120 full-length centrosomal proteins and 72 domain constructs that we have predicted to be globular. These full-length proteins are often insoluble: Only 39 out of 120 expressed proteins (32%) and 19 out of 72 domains (26%) were soluble. We built or retrieved structural models for 277 out of 361 human proteins whose centrosomal localization has been experimentally verified. We could not find any suitable structural template with more than 20% sequence identity for 84 centrosomal proteins (23%), for which around 74% of the residues are predicted to be disordered or coiled-coils. The three-dimensional models that we built are available at http://ub.cbm.uam.es/centrosome/models/index.php.

A great challenge in the proteomics and structural genomics era is to discover protein structure and function, including the identification of biological partners. Experimental investigation is costly and time-consuming, making computational methods very attractive for predicting protein function. In this work, we used the existing structural information in the SH3 family to first extract all SH3 structural features important for binding and then used this information to select the right templates to homology model most of the Saccharomyces cerevisiae SH3 domains. Second, we classified, based on ligand orientation with respect to the SH3 domain, all SH3 peptide ligands into 29 conformations, of which 18 correspond to variants of canonical type I and type II conformations and 11 correspond to non-canonical conformations. Available SH3 templates were expanded by chimera construction to cover some sequence variability and loop conformations. Using the 29 ligand conformations and the homology models, we modelled all possible complexes. Using these complexes and in silico mutagenesis scanning, we constructed position-specific ligand binding matrices. Using these matrices, we determined which sequences will be favorable for every SH3 domain and then validated them with available experimental data. Our work also allowed us to identify key residues that determine loop conformation in SH3 domains, which could be used to model human SH3 domains and do target prediction. The success of this methodology opens the way for sequence-based, genome-wide prediction of protein-protein interactions given enough structural coverage.

Many proteins and signaling pathways are present in most cell types and tissues and yet perform specialized functions. To elucidate mechanisms by which these ubiquitous pathways are modulated, we overlaid information about cross-cell line protein abundance and variability, and evolutionary conservation onto functional pathway components and topological layers in the pathway hierarchy. We found that the input (receptors) and the output (transcription factors) layers evolve more rapidly than proteins in the intermediary transmission layer. In contrast, protein expression variability decreases from the input to the output layer. We observed that the differences in protein variability between the input and transmission layer can be attributed to both the network position and the tendency of variable proteins to physically interact with constitutively expressed proteins. Differences in protein expression variability and conservation are also accompanied by the tendency of conserved and constitutively expressed proteins to acquire somatic mutations, while germline mutations tend to occur in cell type-specific proteins. Thus, conserved core proteins in the transmission layer could perform a fundamental role in most cell types and are therefore less tolerant to germline mutations. In summary, we propose that the core signal transmission machinery is largely modulated by a variable input layer through physical protein interactions. We hypothesize that the bow-tie organization of cellular signaling on the level of protein abundance variability contributes to the specificity of the signal response in different cell types.

Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression.