Computational challenges, tools, and resources for analyzing co- and post-transcriptional events in high throughput

Co- and post-transcriptional regulation of gene expression is complex and multifaceted, spanning the complete RNA lifecycle from genesis to decay. High-throughput profiling of the constituent events and processes is achieved through a range of technologies that continue to expand and evolve. Fully leveraging the resulting data is nontrivial, and requires the use of computational methods and tools carefully crafted for specific data sources and often intended to probe particular biological processes. Drawing upon databases of information pre-compiled by other researchers can further elevate analyses. Within this review, we describe the major co- and post-transcriptional events in the RNA lifecycle that are amenable to high-throughput profiling. We place specific emphasis on the analysis of the resulting data, in particular the computational tools and resources available, as well as looking toward future challenges that remain to be addressed.

K63 polyubiquitination is a new modulator of the oxidative stress response

Ubiquitination is a post-translational modification that signals multiple processes, including protein degradation, trafficking and DNA repair. Polyubiquitin accumulates globally during the oxidative stress response, and this has been mainly attributed to increased ubiquitin conjugation and perturbations in protein degradation. Here we show that the unconventional Lys63 (K63)-linked polyubiquitin accumulates in the yeast Saccharomyces cerevisiae in a highly sensitive and regulated manner as a result of exposure to peroxides. We demonstrate that hydrogen peroxide inhibits the deubiquitinating enzyme Ubp2, leading to accumulation of K63 conjugates assembled by the Rad6 ubiquitin conjugase and the Bre1 ubiquitin ligase. Using linkage-specific isolation methods and stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics, we identified >100 new K63-polyubiquitinated targets, which were substantially enriched in ribosomal proteins. Finally, we demonstrate that impairment of K63 ubiquitination during oxidative stress affects polysome stability and protein expression, rendering cells more sensitive to stress, and thereby reveal a new redox-regulatory role for this modification.

Color Doppler sonographic dynamic tissue perfusion measurement demonstrates significantly reduced cortical perfusion in children with diabetes mellitus type 1 without microalbuminuria and apparently healthy kidneys

MOTIVATION

With respect to the devastating consequences of the increasing prevalence of diabetes mellitus, the main reason for end stage renal disease and dialysis in industrialized countries, and the very limited diagnostic and therapeutic possibilities to predict, monitor and prevent diabetic nephropathy (DN), new concepts for early recognition and quantification of the prevailing microvascular changes in DN are urgently needed.

MATERIALS AND METHODS

We present the first study of renal cortical tissue perfusion measurement by means of standardized color Doppler sonographic videos evaluated with the PixelFlux software 1 for Dynamic Tissue Perfusion Measurement (DTPM) in 92 patients with DM1 without MA compared to 71 healthy probands.

RESULTS

DTPM reveals a highly significant diminution of cortical perfusion in patients with DM1 compared to healthy probands by 31 %, most pronounced in the distal hemicortex (reduction by 50 %) compared to 21 % within the proximal hemicortex.

CONCLUSION

Thus, DTPM offers a novel means of numerically describing the state of the renal microvasculature in DM in a patient-friendly, non-invasive, non-ionizing manner.

RNA binding protein-mediated post-transcriptional gene regulation in medulloblastoma

Medulloblastoma, the most common malignant brain tumor in children, is a disease whose mechanisms are now beginning to be uncovered by high-throughput studies of somatic mutations, mRNA expression patterns, and epigenetic profiles of patient tumors. One emerging theme from studies that sequenced the tumor genomes of large cohorts of medulloblastoma patients is frequent mutation of RNA binding proteins. Proteins which bind multiple RNA targets can act as master regulators of gene expression at the post-transcriptional level to co-ordinate cellular processes and alter the phenotype of the cell. Identification of the target genes of RNA binding proteins may highlight essential pathways of medulloblastomagenesis that cannot be detected by study of transcriptomics alone. Furthermore, a subset of RNA binding proteins are attractive drug targets. For example, compounds that are under development as anti-viral targets due to their ability to inhibit RNA helicases could also be tested in novel approaches to medulloblastoma therapy by targeting key RNA binding proteins. In this review, we discuss a number of RNA binding proteins, including Musashi1 (MSI1), DEAD (Asp-Glu-Ala-Asp) box helicase 3 X-linked (DDX3X), DDX31, and cell division cycle and apoptosis regulator 1 (CCAR1), which play potentially critical roles in the growth and/or maintenance of medulloblastoma.

Protein-to-mRNA ratios are conserved between Pseudomonas aeruginosa strains

Recent studies have shown that the concentrations of proteins expressed from orthologous genes are often conserved across organisms and to a greater extent than the abundances of the corresponding mRNAs. However, such studies have not distinguished between evolutionary (e.g., sequence divergence) and environmental (e.g., growth condition) effects on the regulation of steady-state protein and mRNA abundances. Here, we systematically investigated the transcriptome and proteome of two closely related Pseudomonas aeruginosa strains, PAO1 and PA14, under identical experimental conditions, thus controlling for environmental effects. For 703 genes observed by both shotgun proteomics and microarray experiments, we found that the protein-to-mRNA ratios are highly correlated between orthologous genes in the two strains to an extent comparable to protein and mRNA abundances. In spite of this high molecular similarity between PAO1 and PA14, we found that several metabolic, virulence, and antibiotic resistance genes are differentially expressed between the two strains, mostly at the protein but not at the mRNA level. Our data demonstrate that the magnitude and direction of the effect of protein abundance regulation occurring after the setting of mRNA levels is conserved between bacterial strains and is important for explaining the discordance between mRNA and protein abundances.

Statistical approach to protein quantification

A major goal in proteomics is the comprehensive and accurate description of a proteome. This task includes not only the identification of proteins in a sample, but also the accurate quantification of their abundance. Although mass spectrometry typically provides information on peptide identity and abundance in a sample, it does not directly measure the concentration of the corresponding proteins. Specifically, most mass-spectrometry-based approaches (e.g. shotgun proteomics or selected reaction monitoring) allow one to quantify peptides using chromatographic peak intensities or spectral counting information. Ultimately, based on these measurements, one wants to infer the concentrations of the corresponding proteins. Inferring properties of the proteins based on experimental peptide evidence is often a complex problem because of the ambiguity of peptide assignments and different chemical properties of the peptides that affect the observed concentrations. We present SCAMPI, a novel generic and statistically sound framework for computing protein abundance scores based on quantified peptides. In contrast to most previous approaches, our model explicitly includes information from shared peptides to improve protein quantitation, especially in eukaryotes with many homologous sequences. The model accounts for uncertainty in the input data, leading to statistical prediction intervals for the protein scores. Furthermore, peptides with extreme abundances can be reassessed and classified as either regular data points or actual outliers. We used the proposed model with several datasets and compared its performance to that of other, previously used approaches for protein quantification in bottom-up mass spectrometry.

[Unequal social participation in later life]

The concept of active ageing comprises the maintenance of societal participation throughout the life span into old age. "Good" ageing in line with this activity paradigm develops into a starting point of social inequality rather than being its result. Based on the German Ageing Survey (DEAS) we investigated access to volunteering and to educational activities depending on social and spatial aspects of inequality. Societal participation is socially and spatially structured. Individuals from a lower social class are less often involved in educational activities or in volunteering. Moreover, individuals living in economically disadvantaged regions are less likely to participate than in economically strong regions. Disadvantages cumulate if low individual resources overlap with poor economic conditions in the living area. Measures to facilitate participation should be taken on the local level to enhance opportunities for volunteering and educational activities. This should help to sustainably increase the participation of individuals from lower social classes.

Translation regulation gets its 'omics' moment

The fate of cellular RNA is largely determined by complex networks of protein-RNA interactions through ribonucleoprotein (RNP) complexes. Despite their relatively short half-life, transcripts associate with many different proteins that process, modify, translate, and degrade the RNA. Following biogenesis some mRNPs are immediately directed to translation and produce proteins, but many are diverted and regulated by processes including miRNA-mediated mechanisms, transport and localization, as well as turnover. Because of this complex interplay estimates of steady-state expression by methods such as RNAseq alone cannot capture critical aspects of cellular fate, environmental response, tumorigenesis, or gene expression regulation. More selective and integrative tools are needed to measure protein-RNA complexes and the regulatory processes involved. One focus area is measurements of the transcriptome associated with ribosomes and translation. These so-called polysome or ribosome profiling techniques can evaluate translation efficiency as well as the interplay between translation initiation, elongation, and termination-subject areas not well understood at a systems biology level. Ribosome profiling is a highly promising technique that provides mRNA positional information of ribosome occupancy, potentially bridging the gap between gene expression (i.e., RNAseq and microarray analysis) and protein quantification (i.e., mass spectrometry). In combination with methods such as RNA immunoprecipitation, miRNA profiling, or proteomics, we obtain a fresh view of global post-transcriptional and translational gene regulation. In addition, these techniques also provide new insight into new regulatory elements, such as alternative open reading frames, and translation regulation under different conditions.

The proteomic response to mutants of the Escherichia coli RNA degradosome

The Escherichia coli RNA degradosome recognizes and degrades RNA through the coordination of four main protein components, the endonuclease RNase E, the exonuclease PNPase, the RhlB helicase and the metabolic enzyme enolase. To help our understanding of the functions of the RNA degradosome, we quantified expression changes of >2300 proteins using mass spectrometry based shotgun proteomics in E. coli strains deficient in rhlB, eno, pnp (which displays temperature sensitive growth), or rne(1-602) which encodes a C-terminal truncation mutant of RNase E and is deficient in degradosome assembly. Global protein expression changes are most similar between the pnp and rhlB mutants, confirming the functional relationship between the genes. We observe down-regulation of protein chaperones including GroEL and DnaK (which associate with the degradosome), a decrease in translation related proteins in Δpnp, ΔrhlB and rne(1-602) cells, and a significant increase in the abundance of aminoacyl-tRNA synthetases. Analysis of the observed proteomic changes points to a shared motif, CGCTGG, that may be associated with RNA degradosome targets. Further, our data provide information on the expression modulation of known degradosome-associated proteins, such as DeaD and RNase G, as well as other RNA helicases and RNases - suggesting or confirming functional complementarity in some cases. Taken together, our results emphasize the role of the RNA degradosome in the modulation of the bacterial proteome and provide the first large-scale proteomic description of the response to perturbation of this major pathway of RNA degradation.

Abstract OT2-3-11: Tivozanib in combination with paclitaxel vs placebo with paclitaxel in patients with locally advanced or metastatic triple-negative breast cancer

Background: Triple-negative breast cancer (TNBC) is an aggressive cancer with inferior survival outcomes. Although weekly paclitaxel (WP) is effective in the treatment (tx) of metastatic breast cancer (MBC), optimization of therapies for patients (pts) with TNBC is essential. Angiogenesis is a hallmark of advanced cancer, with subset analyses suggesting activity of angiogenesis inhibitors in TNBC. Tivozanib (TIVO) is a potent and selective inhibitor of vascular endothelial growth factor receptors (VEGFR) 1, 2, and 3 with a promising role in metastatic renal cell carcinoma, and established safety in Phase I combination with WP in MBC.

Purpose: This Phase II trial will assess the efficacy and safety of TIVO + WP in the first-line setting for pts with advanced or metastatic TNBC and evaluate the performance of candidate angiogenesis biomarkers.

Objectives: The primary objective of this study is to compare progression-free survival (PFS) of pts treated with TIVO + WP vs pts treated with placebo (PB) + WP. Secondary objectives include objective response rate (ORR), overall survival (OS), safety and tolerability, quality of life, and correlative candidate biomarker endpoints. The pharmacokinetics of TIVO + WP also will be characterized.

Study Design and Methods: This multicenter, randomized, PB-controlled, two-arm study will enroll pts with metastatic or unresectable TNBC (evaluable per RECIST) and no prior systemic therapy. Pts must have confirmed available archival tumor tissue. Pts will be stratified by ECOG performance score and number of metastatic sites, then randomized to receive either oral TIVO 1.5 mg once daily for 3 weeks (wks) on/1 wk off and intravenous WP 90 mg/m2 for 3 wks on/1 wk off, or PB + WP. One cycle will be 4 wks; tx will continue until disease progression or unacceptable toxicity. Archival tumor tissue and blood samples will be evaluated for response biomarkers, including a hypoxia sensitivity gene signature, a myeloid resistance gene signature, and angiogenic ligands. All pts will be followed for survival until death. Adverse events will be monitored throughout the study. Pharmacokinetic samples will be collected during cycles 1 and 2. PAM-50–defined intrinsic molecular subtype populations also will be evaluated retrospectively.

Recruitment of 130 patients is planned, with an interim analysis after 80 pts to measure ORR (130 pts with a total of 82 investigator-assessed PFS events provides 80% power to detect statistically significant PFS differences between tx arms). Endpoint analyses will use the intent-to-treat population. The primary efficacy analysis will use investigator assessments of response and a two-sided 95% confidence interval for the hazard ratio produced using Cox proportional hazards regression models. OS will be compared using the log-rank test. Analyses of candidate biomarkers and determination of an optimal predictive cutoff for response also are planned. Trial enrollment will commence in fall 2012.

Conclusion: This study will determine whether TIVO, a selective and potent VEGFR inhibitor, combined with WP improves clinical outcomes in pts with TNBC, and whether clinical activity is associated with candidate angiogenesis biomarkers.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr OT2-3-11.

The RNA-binding protein Musashi1 affects medulloblastoma growth via a network of cancer-related genes and is an indicator of poor prognosis

Musashi1 (Msi1) is a highly conserved RNA-binding protein that is required during the development of the nervous system. Msi1 has been characterized as a stem cell marker, controlling the balance between self-renewal and differentiation, and has also been implicated in tumorigenesis, being highly expressed in multiple tumor types. We analyzed Msi1 expression in a large cohort of medulloblastoma samples and found that Msi1 is highly expressed in tumor tissue compared with normal cerebellum. Notably, high Msi1 expression levels proved to be a sign of poor prognosis. Msi1 expression was determined to be particularly high in molecular subgroups 3 and 4 of medulloblastoma. We determined that Msi1 is required for tumorigenesis because inhibition of Msi1 expression by small-interfering RNAs reduced the growth of Daoy medulloblastoma cells in xenografts. To characterize the participation of Msi1 in medulloblastoma, we conducted different high-throughput analyses. Ribonucleoprotein immunoprecipitation followed by microarray analysis (RIP-chip) was used to identify mRNA species preferentially associated with Msi1 protein in Daoy cells. We also used cluster analysis to identify genes with similar or opposite expression patterns to Msi1 in our medulloblastoma cohort. A network study identified RAC1, CTGF, SDCBP, SRC, PRL, and SHC1 as major nodes of an Msi1-associated network. Our results suggest that Msi1 functions as a regulator of multiple processes in medulloblastoma formation and could become an important therapeutic target.

Forward genetic in planta screen for identification of plant-protective traits of Sphingomonas sp. strain Fr1 against Pseudomonas syringae DC3000

Sphingomonas sp. strain Fr1 has recently been shown to protect Arabidopsis thaliana against the bacterial leaf pathogen Pseudomonas syringae DC3000. Here, we describe a forward genetic in planta screen to identify genes in Sphingomonas sp. Fr1 necessary for this effect. About 5,000 Sphingomonas sp. Fr1 mini-Tn5 mutants were assayed for a defect in plant protection against a luxCDABE-tagged P. syringae DC3000 derivative in a space-saving 24-well plate system. The bioluminescence of the pathogen was used as the indicator of pathogen proliferation and allowed for the identification of Sphingomonas sp. Fr1 mutants that had lost the ability to restrict pathogen growth before disease symptoms were visible. Potential candidates were validated using the same miniaturized experimental system. Of these mutants, 10 were confirmed as plant protection defective yet colonization competent. The mutants were subsequently evaluated in a previously described standard microbox system, and plants showed enhanced disease phenotypes after pathogen infection relative to those inoculated with the parental strain as a control. However, the disease severities were lower than those observed for control plants that were grown axenically prior to pathogen challenge, which suggests that several traits may contribute to plant protection. Transposon insertion sites of validated mutants with defects in plant protection were determined and mapped to 7 distinct genomic regions. In conclusion, the established screening protocol allowed us to identify mutations that affect plant protection, and it opens the possibility to uncover traits important for in planta microbe-microbe interactions.

Insights into the regulation of protein abundance from proteomic and transcriptomic analyses

Recent advances in next-generation DNA sequencing and proteomics provide an unprecedented ability to survey mRNA and protein abundances. Such proteome-wide surveys are illuminating the extent to which different aspects of gene expression help to regulate cellular protein abundances. Current data demonstrate a substantial role for regulatory processes occurring after mRNA is made - that is, post-transcriptional, translational and protein degradation regulation - in controlling steady-state protein abundances. Intriguing observations are also emerging in relation to cells following perturbation, single-cell studies and the apparent evolutionary conservation of protein and mRNA abundances. Here, we summarize current understanding of the major factors regulating protein expression.

Treatment of diabetic ketoacidosis (DKA) with 2 different regimens regarding fluid substitution and insulin dosage (0.025 vs. 0.1 units/kg/h)

Diabetic ketoacidosis (DKA) is still the most dangerous acute complication in type 1 diabetes. The aim of this study was to compare treatment of DKA with a regimen of a low insulin dose (0.025 units/kg/h) vs. a standard insulin dose (0.1 units/kg/h).We retrospectively analysed all cases of children and adolescents (age 0-18 years) with type 1 diabetes and DKA who needed treatment in the ICU in the time period of 1998-2005 in 2 pediatric diabetes centers. In a chart review of the first 48 h after onset of DKA the following parameters where evaluated: pH, blood glucose, sodium, potassium, and ketones in urine. Consciousness, neurological status and complications such as cerebral edema, hypoglycaemia or hypokalemia were also recorded.23 children were treated in center A (low insulin dose) whereas 41 where treated in center B (standard insulin dose). Mean age of the patients was 8.9 (range 1.58-17.7) and 13.5 years (1.25-17.7) respectively (p=0.134). Mean pH was 7.1 and HCO3 was 9.05 and 7.79 respectively (p=0.122). Initial blood glucose was 26 mmol/l (no difference between the 2 centres). Treatment with the standard insulin dose resulted in a slightly shorter duration of acidosis (8 h in center A, 6.5 h in center B) and a significantly faster normalization of blood glucose (18 h in A, vs. 10.5 h in B) (p<0.005). During the first day we found similar and very low rates of hypoglycaemia. In center B one case of suspected cerebral edema and cerebral infarction occurred.Low dose insulin substitution is as safe as the recommended standard dose in respect to the occurrence of acute complications. Acidosis is broken slightly earlier with the standard dose. Implications of this earlier normalisation of pH remain unclear.

Label-free protein quantitation using weighted spectral counting

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein's probability of correct identification.The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (O (i)) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned O (i)-values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.

Protein expression regulation under oxidative stress

Oxidative stress is known to affect both translation and protein turnover, but very few large scale studies describe protein expression under stress. We measure protein concentrations in Saccharomyces cerevisiae over the course of 2 h in response to a mild oxidative stress induced by diamide, providing detailed time-resolved information for 815 proteins, with additional data for another ~1,100 proteins. For the majority of proteins, we discover major differences between the global transcript and protein response. Although mRNA levels often return to baseline 1 h after treatment, protein concentrations continue to change. Integrating our data with features of translation and protein degradation, we are able to predict expression patterns for 41% of the proteins in the core data set. Predictive features include, among others, targeting by RNA-binding proteins (Lhp1 and Khd1), RNA secondary structures, RNA half-life, and translation efficiency under unperturbed conditions and in response to oxidative reagents, but not chaperone binding. We are able to both describe general dynamics of protein concentration changes and suggest possible regulatory mechanisms for individual proteins.