Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.     

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction .     

Reconstitution of the Anti-Apoptotic Bcl-2 Protein into Lipid Membranes and Biophysical Evidence for Its Detergent-Driven Association with the Pro-Apoptotic Bax Protein

The anti-apoptotic B-cell CLL/lymphoma-2 (Bcl-2) protein and its counterpart, the pro-apoptotic Bcl-2-associated X protein (Bax), are key players in the regulation of the mitochondrial pathway of apoptosis. However, how they interact at the mitochondrial outer membrane (MOM) and there determine whether the cell will live or be sentenced to death remains unknown. Competing models have been presented that describe how Bcl-2 inhibits the cell-killing activity of Bax, which is common in treatment-resistant tumors where Bcl-2 is overexpressed. Some studies suggest that Bcl-2 binds directly to and sequesters Bax, while others suggest an indirect process whereby Bcl-2 blocks BH3-only proteins and prevents them from activating Bax. Here we present the results of a biophysical study in which we investigated the putative interaction of solubilized full-length human Bcl-2 with Bax and the scope for incorporating the former into a native-like lipid environment. Far-UV circular dichroism (CD) spectroscopy was used to detect direct Bcl-2-Bax-interactions in the presence of polyoxyethylene-(23)-lauryl-ether (Brij-35) detergent at a level below its critical micelle concentration (CMC). Additional surface plasmon resonance (SPR) measurements confirmed this observation and revealed a high affinity between the Bax and Bcl-2 proteins. Upon formation of this protein-protein complex, Bax also prevented the binding of antimycin A₂ (a known inhibitory ligand of Bcl-2) to the Bcl-2 protein, as fluorescence spectroscopy experiments showed. In addition, Bcl-2 was able to form mixed micelles with Triton X-100 solubilized neutral phospholipids in the presence of high concentrations of Brij-35 (above its CMC). Following detergent removal, the integral membrane protein was found to have been fully reconstituted into a native-like membrane environment, as confirmed by ultracentrifugation and subsequent SDS-PAGE experiments.     

Climate limitation at the cold edge: contrasting perspectives from species distribution modelling and a transplant experiment

The role of climate in determining range margins is often studied using species distribution models (SDMs), which are easily applied but have well-known limitations, e.g. due to their correlative nature and colonization and extinction time lags. Transplant experiments can give more direct information on environmental effects, but often cover small spatial and temporal scales. We simultaneously applied a SDM using high-resolution spatial predictors and an integral projection (demographic) model based on a transplant experiment at 58 sites to examine the effects of microclimate, light and soil conditions on the distribution and performance of a forest herb, Lathyrus vernus, at its cold range margin in central Sweden. In the SDM, occurrences were strongly associated with warmer climates. In contrast, only weak effects of climate were detected in the transplant experiment, whereas effects of soil conditions and light dominated. The higher contribution of climate in the SDM is likely a result from its correlation with soil quality, forest type and potentially historic land use, which were unaccounted for in the model. Predicted habitat suitability and population growth rate, yielded by the two approaches, were not correlated across the transplant sites. We argue that the ranking of site habitat suitability is probably more reliable in the transplant experiment than in the SDM because predictors in the former better describe understory conditions, but that ranking might vary among years, e.g. due to differences in climate. Our results suggest that L. vernus is limited by soil and light rather than directly by climate at its northern range edge, where conifers dominate forests and create suboptimal conditions of soil and canopy-penetrating light. A general implication of our study is that to better understand how climate change influences range dynamics, we should not only strive to improve existing approaches but also to use multiple approaches in concert.     

Biggest challenges in bioinformatics

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to discuss the ‘Biggest Challenges in Bioinformatics’ in a ‘World Café’ style event.     

Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe

Recent studies from mountainous areas of small spatial extent (<2500 km²) suggest that fine‐grained thermal variability over tens or hundreds of metres exceeds much of the climate warming expected for the coming decades. Such variability in temperature provides buffering to mitigate climate‐change impacts. Is this local spatial buffering restricted to topographically complex terrains? To answer this, we here study fine‐grained thermal variability across a 2500‐km wide latitudinal gradient in Northern Europe encompassing a large array of topographic complexities. We first combined plant community data, Ellenberg temperature indicator values, locally measured temperatures (LmT) and globally interpolated temperatures (GiT) in a modelling framework to infer biologically relevant temperature conditions from plant assemblages within <1000‐m² units (community‐inferred temperatures: CiT). We then assessed: (1) CiT range (thermal variability) within 1‐km² units; (2) the relationship between CiT range and topographically and geographically derived predictors at 1‐km resolution; and (3) whether spatial turnover in CiT is greater than spatial turnover in GiT within 100‐km² units. Ellenberg temperature indicator values in combination with plant assemblages explained 46–72% of variation in LmT and 92–96% of variation in GiT during the growing season (June, July, August). Growing‐season CiT range within 1‐km² units peaked at 60–65°N and increased with terrain roughness, averaging 1.97 °C (SD = 0.84 °C) and 2.68 °C (SD = 1.26 °C) within the flattest and roughest units respectively. Complex interactions between topography‐related variables and latitude explained 35% of variation in growing‐season CiT range when accounting for sampling effort and residual spatial autocorrelation. Spatial turnover in growing‐season CiT within 100‐km² units was, on average, 1.8 times greater (0.32 °C km^?1) than spatial turnover in growing‐season GiT (0.18 °C km^?1). We conclude that thermal variability within 1‐km² units strongly increases local spatial buffering of future climate warming across Northern Europe, even in the flattest terrains.     

snoRNPs Regulate Telomerase Activity in Neuroblastoma and Are Associated with Poor Prognosis

Amplification of the MYCN oncogene is strongly associated with poor prognosis in neuroblastoma (NB). In addition to MYCN amplification, many studies have focused on identifying patients with a poor prognosis based on gene expression profiling. The majority of prognostic signatures today are comprised of large gene lists limiting their clinical application. In addition, although of prognostic significance,most of these signatures fail to identify cellular processes that can explain their relation to prognosis. Here, we determined prognostically predictive genes in a data set containing 251 NBs. Gene Ontology analysis was performed on significant genes with a positive hazard ratio to search for cellular processes associated with poor prognosis. An enrichment in ribonucleoproteins (RNPs) was found. Genes involved in the stabilization and formation of the central small nucleolar RNP (snoRNP) complex were scrutinized using a backward conditional Cox regression resulting in an snoRNP signature consisting of three genes: DKC1, NHP2, and GAR1. The snoRNP signature significantly and independently predicted prognosis when compared to the established clinical risk factors. Association of snoRNP protein expression and prognosis was confirmed using tissue microarrays. Knockdown of snoRNP expression in NB cell lines resulted in reduced telomerase activity and an increase in anaphase bridge frequency. In addition, in patient material, expression of the snoRNP complex was significantly associated with telomerase activity, occurrence of segmental aberrations, and expression-based measurements of chromosomal instability. Together, these results underscore the prognostic value of snoRNP complex expression in NB and suggest a role for snoRNPs in telomere maintenance and genomic stability.