The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population.

Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO = 0.65) was higher in the recipient population than predicted had no augmentation occurred (HO = 0.58) but less than what was predicted by our model (HO = 0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype-environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.

Influencia potencial de la divergencia adaptativa a nivel genoma sobre el resultado de la reubicación para conservación en una población aislada de urogallo mayor Resumen Las reubicaciones para conservación son una herramienta importante que se usa con frecuencia para aumentar las poblaciones en declinación o reestablecer las poblaciones erradicadas. Una de las metas de este aumento es incrementar la diversidad genética y reducir el riesgo de depresión endogámica (es decir, rescate genético). Sin embargo, la introducción de individuos de una población con divergencia significativa puede perturbar los rasgos coadaptados y reducir la aptitud local (es decir, depresión exogámica). La información genética es cada vez más accesible para las especies silvestres y puede proporcionar conocimiento único con respecto a la presencia y retención de la variación genética introducida a partir del aumento como un indicador de eficiencia y las similitudes adaptativas como un indicador de la idoneidad de la población de origen y la receptora. Usamos dos conjuntos de datos genéticos para evaluar el aumento de las poblaciones aisladas del urogallo mayor (Centrocercus urophasianus) en la región noroeste de la distribución de la especie (Washington, EUA) y para evaluar de forma retrospectiva la divergencia adaptativa entre la población de origen y la receptora. Desarrollamos dos modelos estadísticos para los datos microsatelitales para así evaluar los resultados del aumento. Usamos un modelo para predecir la diversidad genética después del aumento y comparamos estas predicciones con observaciones del cambio genético. Usamos el segundo modelo para cuantificar el aumento de la reproducción observada atribuida a las reubicaciones (evidencia de la integración poblacional). También caracterizamos la divergencia adaptativa a nivel genoma entre la población de origen y la población receptora. La diversidad genética observada (HO = 0.65) fue mayor de lo que se predijo en la población receptora de no haber ocurrido el aumento (HO = 0.58) pero menor de lo que se predijo en nuestro modelo (HO = 0.75). El aumento de la variación genética compartida entre las dos poblaciones residentes geográficamente aisladas incrementó, lo cual es evidencia de un flujo génico periódico que antes se supuso casi no ocurría. Entre los genes adaptativos candidatos asociados a una FST elevada (143 genes) o a variables ambientales locales (97 y 157 genes para cada método de asociación entre el ambiente y el genotipo, respectivamente) encontramos grupos de genes con funciones relacionadas que pueden influir sobre la habilidad de cada reubicación para usar recursos locales y navegar ambientes desconocidos y su potencial reproductivo, todas posibles razones para la baja retención genética en el aumento.

Persistence of pesticide residues in weathered avian droppings.

Avian droppings (combination of fecal matter and urates) provide a non-lethal and non-invasive matrix for measuring pesticide exposures. In the field, droppings may be collected days or weeks after excretion and the persistence of pesticide residues in weathered droppings is not known. Thus, we studied the effects of weathering on pesticide residues in droppings. Domestic chicken (Gallus gallus domesticus) hens were used as a representative species for Order Galliformes. We collected droppings from hens before they were exposed to the pesticides (reference or pre-dose droppings ). Thereafter, the hens were orally administered encapsulated wheat seeds coated with Raxil® PRO Shield (containing the active ingredients imidacloprid, prothioconazole, metalaxyl, and tebuconazole) for consecutive 7 days. During this time, their droppings were collected on days 3, 5, and 8 from the start of the exposure period (post-dose droppings ). The pre-dose and post-dose droppings were weathered for up to 30 days in autumn and spring in shrubsteppe habitat. Droppings were analyzed using HPLC coupled to triple quad LC/MS for parent compound and metabolite residues. No pesticide or its metabolite residues were detected in the weathered reference droppings. No parent pesticide compounds were detected in weathered post-dose droppings but imidacloprid metabolites, imidacloprid-5-hydroxy and imidacloprid-olefin, and the prothioconazole metabolite, desthio-prothioconazole, were detected in all post-dose weathered samples from both seasons. The active ingredients metalaxyl and tebuconazole and their metabolites were not detected in any of the samples. Our results suggest that, depending on the pesticide, its concentration, and the environmental conditions, residues of some pesticides can be detected in droppings weathered for at least 30 days. Knowledge of pesticide persistence in weathered droppings can help refine the quality and quantity of fecal samples that are collected for monitoring pesticide exposures to birds.

Netrank: network-based approach for biomarker discovery.

BACKGROUND: Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA). RESULTS: The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank's biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures. CONCLUSION: In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper.

Diatom adhesive trail proteins acquired by horizontal gene transfer from bacteria serve as primers for marine biofilm formation.

Biofilm-forming benthic diatoms are key primary producers in coastal habitats, where they frequently dominate sunlit intertidal substrata. The development of gliding motility in raphid diatoms was a key molecular adaptation that contributed to their evolutionary success. However, the structure-function correlation between diatom adhesives utilized for gliding and their relationship to the extracellular matrix that constitutes the diatom biofilm is unknown. Here, we have used proteomics, immunolocalization, comparative genomics, phylogenetics and structural homology analysis to investigate the evolutionary history and function of diatom adhesive proteins. Our study identified eight proteins from the adhesive trails of Craspedostauros australis, of which four form a new protein family called Trailins that contain an enigmatic Choice-of-Anchor A (CAA) domain, which was acquired through horizontal gene transfer from bacteria. Notably, the CAA-domain shares a striking structural similarity with one of the most widespread domains found in ice-binding proteins (IPR021884). Our work offers new insights into the molecular basis for diatom biofilm formation, shedding light on the function and evolution of diatom adhesive proteins. This discovery suggests that there is a transition in the composition of biomolecules required for initial surface colonization and those utilized for 3D biofilm matrix formation.

PLIP 2021: expanding the scope of the protein-ligand interaction profiler to DNA and RNA.

With the growth of protein structure data, the analysis of molecular interactions between ligands and their target molecules is gaining importance. PLIP, the protein-ligand interaction profiler, detects and visualises these interactions and provides data in formats suitable for further processing. PLIP has proven very successful in applications ranging from the characterisation of docking experiments to the assessment of novel ligand-protein complexes. Besides ligand-protein interactions, interactions with DNA and RNA play a vital role in many applications, such as drugs targeting DNA or RNA-binding proteins. To date, over 7% of all 3D structures in the Protein Data Bank include DNA or RNA. Therefore, we extended PLIP to encompass these important molecules. We demonstrate the power of this extension with examples of a cancer drug binding to a DNA target, and an RNA-protein complex central to a neurological disease. PLIP is available online at https://plip-tool.biotec.tu-dresden.de and as open source code. So far, the engine has served over a million queries and the source code has been downloaded several thousand times.

Structure-based drug repositioning: Potential and limits.

Drug repositioning, the assignment of new therapeutic purposes to known drugs, is an established strategy with many repurposed drugs on the market and many more at experimental stage. We review three use cases, a herpes drug with benefits in cancer, a cancer drug with potential in autoimmune disease, and a selective and an unspecific drug binding the same target (GPCR). We explore these use cases from a structural point of view focusing on a deep understanding of the underlying drug-target interactions. We review tools and data needed for such a drug-centric structural repositioning approach. Finally, we show that the availability of data on targets is an important limiting factor to realize the full potential of structural drug-repositioning.

Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes.

PURPOSE: Deep convolutional neural networks (CNN) provide high accuracy for automatic classification of dopamine transporter (DAT) SPECT images. However, CNN are inherently black-box in nature lacking any kind of explanation for their decisions. This limits their acceptance for clinical use. This study tested layer-wise relevance propagation (LRP) to explain CNN-based classification of DAT-SPECT in patients with clinically uncertain parkinsonian syndromes. METHODS: The study retrospectively included 1296 clinical DAT-SPECT with visual binary interpretation as "normal" or "reduced" by two experienced readers as standard-of-truth. A custom-made CNN was trained with 1008 randomly selected DAT-SPECT. The remaining 288 DAT-SPECT were used to assess classification performance of the CNN and to test LRP for explanation of the CNN-based classification. RESULTS: Overall accuracy, sensitivity, and specificity of the CNN were 95.8%, 92.8%, and 98.7%, respectively. LRP provided relevance maps that were easy to interpret in each individual DAT-SPECT. In particular, the putamen in the hemisphere most affected by nigrostriatal degeneration was the most relevant brain region for CNN-based classification in all reduced DAT-SPECT. Some misclassified DAT-SPECT showed an "inconsistent" relevance map more typical for the true class label. CONCLUSION: LRP is useful to provide explanation of CNN-based decisions in individual DAT-SPECT and, therefore, can be recommended to support CNN-based classification of DAT-SPECT in clinical routine. Total computation time of 3 s is compatible with busy clinical workflow. The utility of "inconsistent" relevance maps to identify misclassified cases requires further investigation.

Osmolyte enhanced aqueous two-phase system for virus purification.

Due to the high variation in viral surface properties, a platform method for virus purification is still lacking. A potential alternative to the high-cost conventional methods is aqueous two-phase systems (ATPSs). However, optimizing virus purification in ATPS requires a large experimental design space, and the optimized systems are generally found to operate at high ATPS component concentrations. The high concentrations capitalize on hydrophobic and electrostatic interactions to obtain high viral particle yields. This study investigated using osmolytes as driving force enhancers to reduce the high concentration of ATPS components while maintaining high yields. The partitioning behavior of porcine parvovirus (PPV), a nonenveloped mammalian virus, and human immunodeficiency virus-like particle (HIV-VLP), a yeast-expressed enveloped VLP, were studied in a polyethylene glycol (PEG) 12 kDa-citrate system. The partitioning of the virus modalities was enhanced by osmoprotectants glycine and betaine, while trimethylamine N-oxide was ineffective for PPV. The increased partitioning to the PEG-rich phase pertained only to viruses, resulting in high virus purification. Recoveries were 100% for infectious PPV and 92% for the HIV-VLP, with high removal of the contaminant proteins and more than 60% DNA removal when glycine was added. The osmolyte-induced ATPS demonstrated a versatile method for virus purification, irrespective of the expression system.

Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes.

Pan-assay interference compounds (PAINS) are promiscuous compound classes that produce false positive hits in high-throughput screenings. Yet, the mechanisms of PAINS activity are poorly understood. Although PAINS are often associated with protein reactivity, several recent studies have shown that they also mediate noncovalent interactions. Aiming at a deep understanding of PAINS promiscuity, we performed an analysis of the Protein Data Bank to characterize the binding modes of PAINS. We explored the binding mode conservation of 34 PAINS classes present in 871 ligands and among 517 protein targets. The two major findings of this work are the following: First, different PAINS classes exhibit different levels of binding mode conservation. Our novel classification of PAINS based on binding mode similarity enables a rational assessment of PAINS from a structural perspective. Second, PAINS classes with variable binding modes can bind with high affinity. The evaluation of noncovalent binding modes of PAINS-like compounds sheds light on the mechanisms of promiscuous binding. Our findings could facilitate the decisions on how to deal with PAINS and help scientists to understand why PAINS produce hits in their screenings.

UniLectin3D, a database of carbohydrate binding proteins with curated information on 3D structures and interacting ligands.

Lectins, and related receptors such as adhesins and toxins, are glycan-binding proteins from all origins that decipher the glycocode, i.e. the structural information encoded in the conformation of complex carbohydrates present on the surface of all cells. Lectins are still poorly classified and annotated, but since their functions are based on ligand recognition, their 3D-structures provide a solid foundation for characterization. UniLectin3D is a curated database that classifies lectins on origin and fold, with cross-links to literature, other databases in glycosciences and functional data such as known specificity. The database provides detailed information on lectins, their bound glycan ligands, and features their interactions using the Protein-Ligand Interaction Profiler (PLIP) server. Special care was devoted to the description of the bound glycan ligands with the use of simple graphical representation and numerical format for cross-linking to other databases in glycoscience. We conceived the design of the database architecture and the navigation tools to account for all organisms, as well as to search for oligosaccharide epitopes complexed within specified binding sites. UniLectin3D is accessible at https://www.unilectin.eu/unilectin3D.

Mutations in bdcA and valS Correlate with Quinolone Resistance in Wastewater Escherichia coli.

Single mutations can confer resistance to antibiotics. Identifying such mutations can help to develop and improve drugs. Here, we systematically screen for candidate quinolone resistance-conferring mutations. We sequenced highly diverse wastewater E. coli and performed a genome-wide association study (GWAS) to determine associations between over 200,000 mutations and quinolone resistance phenotypes. We uncovered 13 statistically significant mutations including 1 located at the active site of the biofilm dispersal gene bdcA and 6 silent mutations in the aminoacyl-tRNA synthetase valS. The study also recovered the known mutations in the topoisomerases gyrase (gyrA) and topoisomerase IV (parC). In summary, we demonstrate that GWAS effectively and comprehensively identifies resistance mutations without a priori knowledge of targets and mode of action. The results suggest that mutations in the bdcA and valS genes, which are involved in biofilm dispersal and translation, may lead to novel resistance mechanisms.

Neonatal Abstinence Syndrome: Prevention, Recognition, Treatment, and Follow-up.

Neonatal abstinence syndrome (NAS), or neonatal opioid withdrawal syndrome (NOWS) results from acute discontinuation of transplacental opioid exposure following delivery in the setting of maternal opioid use. A rise in the incidence of NAS coincides with the nationwide opioid epidemic. Addressing NAS requires a team approach. First, all pregnant women should be screened for substance use using validated questionnaires. Mothers who screen positive for opioid abuse should be referred to a provider experienced in opioid maintenance therapy. In addition to medical treatment emphasizing stability rather than detoxification, mental and situational health should be addressed. Next, mothers with opioid dependence should be educated regarding NAS. Topics for education include increased length of hospital stay following delivery, neonatal withdrawal symptoms, importance of the mother-baby dyad to treatment, and criteria for pharmacologic intervention. Following delivery, at-risk infants should be evaluated with standardized assessment tool such as Finnegan scoring or the eat-sleep-console tool while simultaneously maximizing nonpharmacologic interventions. Breast-feeding is encouraged in the absence of ongoing illicit or polysubstance use or infectious concerns. Pharmacologic treatment options most commonly include morphine or methadone. Infants without symptoms should be monitored for four to seven days prior to discharge, dependent on type of opioid exposure. Finally, infants with NAS are at risk for long-term mental and physical health problems. Therefore, infants will benefit from connection prior to hospital discharge with a primary care provider as well as entities designed for early childhood intervention and developmental assistance. The importance of well-child exams should be stressed to the family.

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.

Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery.

Cancer cells can switch between signaling pathways to regulate growth under different conditions. In the tumor microenvironment, this likely helps them evade therapies that target specific pathways. We must identify all possible states and utilize them in drug screening programs. One such state is characterized by expression of the transcription factor Hairy and Enhancer of Split 3 (HES3) and sensitivity to HES3 knockdown, and it can be modeled in vitro. Here, we cultured 3 primary human brain cancer cell lines under 3 different culture conditions that maintain low, medium, and high HES3 expression and characterized gene regulation and mechanical phenotype in these states. We assessed gene expression regulation following HES3 knockdown in the HES3-high conditions. We then employed a commonly used human brain tumor cell line to screen Food and Drug Administration (FDA)-approved compounds that specifically target the HES3-high state. We report that cells from multiple patients behave similarly when placed under distinct culture conditions. We identified 37 FDA-approved compounds that specifically kill cancer cells in the high-HES3-expression conditions. Our work reveals a novel signaling state in cancer, biomarkers, a strategy to identify treatments against it, and a set of putative drugs for potential repurposing.-Poser, S. W., Otto, O., Arps-Forker, C., Ge, Y., Herbig, M., Andree, C., Gruetzmann, K., Adasme, M. F., Stodolak, S., Nikolakopoulou, P., Park, D. M., Mcintyre, A., Lesche, M., Dahl, A., Lennig, P., Bornstein, S. R., Schroeck, E., Klink, B., Leker, R. R., Bickle, M., Chrousos, G. P., Schroeder, M., Cannistraci, C. V., Guck, J., Androutsellis-Theotokis, A. Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery.

Repositioned Drugs for Chagas Disease Unveiled via Structure-Based Drug Repositioning.

Chagas disease, caused by the parasite Trypanosoma cruzi, affects millions of people in South America. The current treatments are limited, have severe side effects, and are only partially effective. Drug repositioning, defined as finding new indications for already approved drugs, has the potential to provide new therapeutic options for Chagas. In this work, we conducted a structure-based drug repositioning approach with over 130,000 3D protein structures to identify drugs that bind therapeutic Chagas targets and thus represent potential new Chagas treatments. The screening yielded over 500 molecules as hits, out of which 38 drugs were prioritized following a rigorous filtering process. About half of the latter were already known to have trypanocidal activity, while the others are novel to Chagas disease. Three of the new drug candidates-ciprofloxacin, naproxen, and folic acid-showed a growth inhibitory activity in the micromolar range when tested ex vivo on T. cruzi trypomastigotes, validating the prediction. We show that our drug repositioning approach is able to pinpoint relevant drug candidates at a fraction of the time and cost of a conventional screening. Furthermore, our results demonstrate the power and potential of structure-based drug repositioning in the context of neglected tropical diseases where the pharmaceutical industry has little financial interest in the development of new drugs.

Computational Drug Repositioning for Chagas Disease Using Protein-Ligand Interaction Profiling.

Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas treatment. Bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) has emerged as a promising pharmacological target. Moreover, some human dihydrofolate reductase (HsDHFR) inhibitors such as trimetrexate also inhibit T. cruzi DHFR-TS (TcDHFR-TS). These compounds serve as a starting point and a reference in a screening campaign to search for new TcDHFR-TS inhibitors. In this paper, a novel virtual screening approach was developed that combines classical docking with protein-ligand interaction profiling to identify drug repositioning opportunities against T. cruzi infection. In this approach, some food and drug administration (FDA)-approved drugs that were predicted to bind with high affinity to TcDHFR-TS and whose predicted molecular interactions are conserved among known inhibitors were selected. Overall, ten putative TcDHFR-TS inhibitors were identified. These exhibited a similar interaction profile and a higher computed binding affinity, compared to trimetrexate. Nilotinib, glipizide, glyburide and gliquidone were tested on T. cruzi epimastigotes and showed growth inhibitory activity in the micromolar range. Therefore, these compounds could lead to the development of new treatment options for Chagas disease.

Genome-wide association analysis of diverticular disease points towards neuromuscular, connective tissue and epithelial pathomechanisms.

OBJECTIVE: Diverticular disease is a common complex disorder characterised by mucosal outpouchings of the colonic wall that manifests through complications such as diverticulitis, perforation and bleeding. We report the to date largest genome-wide association study (GWAS) to identify genetic risk factors for diverticular disease. DESIGN: Discovery GWAS analysis was performed on UK Biobank imputed genotypes using 31 964 cases and 419 135 controls of European descent. Associations were replicated in a European sample of 3893 cases and 2829 diverticula-free controls and evaluated for risk contribution to diverticulitis and uncomplicated diverticulosis. Transcripts at top 20 replicating loci were analysed by real-time quatitative PCR in preparations of the mucosal, submucosal and muscular layer of colon. The localisation of expressed protein at selected loci was investigated by immunohistochemistry. RESULTS: We discovered 48 risk loci, of which 12 are novel, with genome-wide significance and consistent OR in the replication sample. Nominal replication (p<0.05) was observed for 27 loci, and additional 8 in meta-analysis with a population-based cohort. The most significant novel risk variant rs9960286 is located near CTAGE1 with a p value of 2.3×10-10 and 0.002 (ORallelic=1.14 (95% CI 1.05 to 1.24)) in the replication analysis. Four loci showed stronger effects for diverticulitis, PHGR1 (OR 1.32, 95% CI 1.12 to 1.56), FAM155A-2 (OR 1.21, 95% CI 1.04 to 1.42), CALCB (OR 1.17, 95% CI 1.03 to 1.33) and S100A10 (OR 1.17, 95% CI 1.03 to 1.33). CONCLUSION: In silico analyses point to diverticulosis primarily as a disorder of intestinal neuromuscular function and of impaired connective fibre support, while an additional diverticulitis risk might be conferred by epithelial dysfunction.

Aneuploidy in children with relapsed B-cell precursor acute lymphoblastic leukaemia: clinical importance of detecting a hypodiploid origin of relapse.

Aneuploidy is common in paediatric B-cell precursor acute lymphoblastic leukaemia (ALL). Specific subgroups, such as high hyperdiploidy (>50 chromosomes or DNA Index ≥1·16) and hypodiploidy (<45 chromosomes), predict outcome of patients after primary treatment. Whether aneuploidy has a prognostic value for relapsed disease is yet to be determined. Using DNA index and centromere screening by multiplex ligation-dependent probe amplification, we investigated aneuploidy in 413 children treated for first relapse of B-cell precursor ALL according to the ALL-REZ BFM 2002 protocol. Ten-year event-free survival of patients with high hyperdiploid relapses approached 70%, whereas it was only 40% in low hyperdiploid relapses. Three patients with apparent hyperdiploid relapse had TP53 mutations. In these cases, array-based allelotyping revealed a hypodiploid origin with absence of the hypodiploid founder clone (masked hypodiploidy). Collectively, patients with evident or masked hypodiploid relapses showed an extremely low event-free survival rate of 9%. Importantly, the current relapse risk stratification did not identify cases with masked hypodiploidy as high-risk patients, due to their favourable clinical presentation. In multivariate analysis, hypodiploidy proved to be an independent prognostic factor. This finding supports stratification of relapses with hypodiploid origin into high-risk arms in future trials or allocation of patients to alternative treatment approaches.

Backbone Brackets and Arginine Tweezers delineate Class I and Class II aminoacyl tRNA synthetases.

The origin of the machinery that realizes protein biosynthesis in all organisms is still unclear. One key component of this machinery are aminoacyl tRNA synthetases (aaRS), which ligate tRNAs to amino acids while consuming ATP. Sequence analyses revealed that these enzymes can be divided into two complementary classes. Both classes differ significantly on a sequence and structural level, feature different reaction mechanisms, and occur in diverse oligomerization states. The one unifying aspect of both classes is their function of binding ATP. We identified Backbone Brackets and Arginine Tweezers as most compact ATP binding motifs characteristic for each Class. Geometric analysis shows a structural rearrangement of the Backbone Brackets upon ATP binding, indicating a general mechanism of all Class I structures. Regarding the origin of aaRS, the Rodin-Ohno hypothesis states that the peculiar nature of the two aaRS classes is the result of their primordial forms, called Protozymes, being encoded on opposite strands of the same gene. Backbone Brackets and Arginine Tweezers were traced back to the proposed Protozymes and their more efficient successors, the Urzymes. Both structural motifs can be observed as pairs of residues in contemporary structures and it seems that the time of their addition, indicated by their placement in the ancient aaRS, coincides with the evolutionary trace of Proto- and Urzymes.

MAGPIE: Simplifying access and execution of computational models in the life sciences.

Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.