bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating healthcare and research genetic data empowers the discovery of 49 novel developmental disorders
Authors: Kaplanis Joanna , Samocha Kaitlin E. , Wiel Laurens , Zhang Zhancheng , Arvai Kevin J. , Eberhardt Ruth Y. , Gallone Giuseppe , Lelieveld Stefan H. , Martin Hilary C. , McRae Jeremy F. , Short Patrick J. , Torene Rebecca I. , Boer Elke de , Danecek Petr , Gardner Eugene J. , Huang Ni , Lord Jenny , Martincorena Iñigo , Pfundt Rolph , Reijnders Margot R. F. , Yeung Alison , Yntema Helger G. , Study DDD , Vissers Lisenka E. L. M. , Juusola Jane , Wright Caroline F. , Brunner Han G. , Firth Helen V. , FitzPatrick David R. , Barrett Jeffrey C. , Hurles Matthew E. , Gilissen Christian , Retterer Kyle 
Published: February 2018

SummaryDe novomutations (DNMs) in protein-coding genes are a well-established cause of developmental disorders (DD). However, known DD-associated genes only account for a minority of the observed excess of such DNMs. To identify novel DD-associated genes, we integrated healthcare and research exome sequences on 31,058 DD parent-offspring trios, and developed a simulation-based statistical test to identify gene-specific enrichments of DNMs. We identified 299 significantly DD-associated genes, including 49 not previously robustly associated with DDs. Despite detecting more DD-associated genes than in any previous study, much of the excess of DNMs of protein-coding genes remains unaccounted for. Modelling suggests that over 500 novel DD-associated genes await discovery, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of dominant DDs.
MirGeneDB 2.0: The metazoan microRNA complement
Authors: Fromm Bastian , Domanska Diana , Høye Eirik , Ovchinnikov Vladimir , Kang Wenjing , Aparicio-Puerta Ernesto , Johansen Morten , Flatmark Kjersti , Mathelier Anthony , Hovig Eivind , Hackenberg Michael , Friedländer Marc R. , Peterson Kevin J. 
Published: February 2018

ABSTRACTSmall non-coding RNAs have gained substantial attention due to their roles in animal development and human disorders. Among them, microRNAs are unique because individual gene sequences are conserved across the animal kingdom. In addition, unique and mechanistically well understood features can clearly distinguishbona fidemiRNAs from the myriad other small RNAs generated by cells. However, making this separation is not a common practice and, thus, not surprisingly, the heterogeneous quality of available miRNA complements has become a major concern in microRNA research. We addressed this by extensively expanding our curated microRNA gene database MirGeneDB to 45 organisms that represent the full taxonomic breadth of Metazoa. By consistently annotating and naming more than 10,900 microRNA genes in these organisms, we show that previous microRNA annotations contained not only many false positives, but surprisingly lacked more than 2,100bona fidemicroRNAs. Indeed, curated microRNA complements of closely related organisms are very similar and can be used to reconstruct Metazoan evolution. MirGeneDB represents a robust platform for microRNA-based research, providing deeper and more significant insights into the biology and evolution of miRNAs but also biomedical and biomarker research. MirGeneDB is publicly and freely available athttp://mirgenedb.org/.
Causal interactions from proteomic profiles: molecular data meets pathway knowledge
Authors: Babur Özgün , Luna Augustin , Korkut Anil , Durupinar Funda , Siper Metin Can , Dogrusoz Ugur , Aslan Joseph E. , Sander Chris , Demir Emek 
Published: February 2018

ABSTRACTMeasurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at http://causalpath.org.
Addressing the Most Neglected Diseases through an Open Research Model: the Discovery of Fenarimols as Novel Drug Candidates for Eumycetoma
Authors: Lim Wilson , Melse Youri , Konings Mickey , Duong Hung Phat , Eadie Kimberly , Laleu Benoît , Perry Ben , Todd Matthew H. , Ioset Jean-Robert , van de Sande Wendy W.J. 
Published: February 2018

AbstractEumycetoma is a chronic infectious disease characterized by a large subcutaneous mass, often caused by the fungusMadurella mycetomatis.A combination of surgery and prolonged medication is needed to treat this infection with a success rate of only 30%. There is, therefore, an urgent need to find more effective drugs for the treatment of this disease. In this study, we screened 800 diverse drug-like molecules and identified 215 molecules that were activein vitro.Minimal inhibitory concentrations were determined for the 13 most active compounds. One of the most potent compounds, a fenarimol analogue for which a large analogue library is available, led to the screening of an additional 35 compounds for theirin vitroactivity againstM. mycetomatishyphae, rendering four further hit compounds. To assess thein vivopotency of these hit compounds, aGalleria mellonellalarvae model infected withM. mycetomatiswas used. Several of the compounds identifiedin vitrodemonstrated promising efficacyin vivoin terms of prolonged larval survival and/or reduced fungal burden. The results presented in this paper are the starting point of anOpen Source Mycetoma (MycetOS)approach in which members of the global scientific community are invited to participate and contribute as equal partners. We hope that this initiative, coupled with the promising new hits we have reported, will lead to progress in drug discovery for this most neglected of neglected tropical diseases.Author summaryMycetoma is a poverty-associated disease that was recently recognised as a neglected tropical disease by the World Health Organisation (WHO). This disease can be caused by either bacteria (actinomycetoma) or fungi (eumycetoma). The most common causative agent of mycetoma is the fungusMadurella mycetomatis.Actinomycetoma can be easily treated, but for eumycetoma, the current and only antifungal drug used is only able to successfully treat 30% of patients. Treatment often involves prolonged medication use and amputation of the affected area. This disease is disfiguring and is a social stigma for patients in endemic countries. To improve treatment for patients, we have looked at over 800 diverse drug-like molecules and compounds in hope to develop new drugs in this study. We have identified 215 compounds with activity againstM. mycetomatisin vitro and several in vivo with ourGalleria mellonellalarvae model. We have chosen an open source approach with this study and placed our findings in an online database and made it available to the public. We invite the global scientific community to participate in our study and contribute as equal partners as long as an open source approach is held in hopes to fast track and boost drug discovery for Eumycetoma.
Mathematical Modelling of Actin treadmill in Apicomplexans
Authors: Prajapat Mahendra , Pathak Samridhi , Gauba Ricka , Kale Avinash , Saini Supreet 
Published: February 2018

AbstractPlasmodium parasite, a representative member of phylum Apicomplexa is a causative agent of malaria in human as well as other animals. To infect host cells, Plasmodium first finds receptors on the host cell surface, then binds specifically, and finally penetrates host cell membrane to acquire the host cellular resources. The motility for moving on the cell surface is equipped by the precise and tight control of actin treadmill. Several regulators are required to achieve precision and robustness in the control of actin treadmill. However, the mechanistic detail of the treadmill regulatory network and the cross-talk among regulators are not well understood. We developed a stochastic model of treadmill regulation and explored the dynamics of filament growth, nucleation time, and elongation time. Our study mainly highlighted on how and what helps cells to maintain an average size of the actin filaments within a species. This is particularly important, since, excessive growth of filament can lead to cell lysis. Moreover, we also explore how the regulators interact to fine-tune the control elements in the actin treadmill.
Determination of growth-coupling strategies and their underlying principles
Authors: Alter Tobias B. , Blank Lars M. , Ebert Birgitta E. 
Published: February 2018

AbstractMetabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. We have developed the computational tool gcOpt, which identifies knockout strategies leading to the best possible GC by maximizing the minimally guaranteed product yield. gcOpt implicitly favors solutions resulting in strict coupling of product synthesis to growth and metabolic activity while avoiding solutions inferring weak, conditional coupling.GC intervention strategies identified by gcOpt were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies. Thus, generating a dependency between supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC.AbbreviationsATPAdenosine triphosphateATPMATP maintenance requirements reactionATPcscCarbon specific ATP synthesis capabilityATPscATP synthesis capabilityCoACoenzyme AEMElementary modeEMAElementary modes analysisFBAFlux balance analysisGCGrowth-couplingGCSGrowth-coupling strengthH+ProtonhGCHolistic growth-couplingMCSMinimal cut setsMILPMixed integer linear programNAD+Nicotinamide adenine dinucleotide (oxidized)NADHNicotinamide adenine dinucleotide (reduced)NADP+Nicotinamide adenine dinucleotide phosphate (oxidized)NADPHNicotinamide adenine dinucleotide phosphate (reduced)NGAMNon-growth associated maintenancePiPhosphate moleculePPPPentose phosphate pathwaysGCStrong growth-couplingwGCWeak growth-coupling
MVP: predicting pathogenicity of missense variants by deep learning
Authors: Qi Hongjian , Chen Chen , Zhang Haicang , Long John J. , Chung Wendy K. , Guan Yongtao , Shen Yufeng 
Published: February 2018

AbstractAccurate pathogenicity prediction of missense variants is critical to improve power in genetic studies and accurate interpretation in clinical genetic testing. Here we describe a new prediction method, MVP, which uses a deep learning approach to leverage large training data sets and many correlated predictors. Using cancer mutation hotspots and de novo germline mutations from developmental disorders for benchmarking, MVP achieved better performance in prioritizing pathogenic missense variants than previous methods.
Standardized Informatics Computing Platform for Advancing Biomedical Discovery Through Data Sharing
Authors: Navale Vivek , Ji Michelle , McCreedy Evan , Gebremichael Tsega , Garcia Alison , Misquitta Leonie , Lin Ching-Heng , Fann Yang , McAuliffe Matthew 
Published: February 2018

AbstractObjectiveThe goal is to develop a standardized informatics computing system that can support end-to-end research data lifecycle management for biomedical research applications.Materials and MethodsDesign and implementation of biomedical research informatics computing system (BRICS) is demonstrated. The system architecture is modular in design with several integrated tools: global unique identifier, validation, upload, download and query tools that support user friendly informatics system capability.ResultsBRICS instances were deployed to support research for improvements in diagnosis of traumatic brain injury, biomarker discovery for Parkinson’s Disease, the National Ophthalmic Disease Genotyping and Phenotyping network, the informatics core for the Center for Neuroscience and Regenerative Medicine, the Common Data Repository for Nursing Science, Global Rare Diseases Patient Registry, and National Institute of Neurological Disorders and Stroke Clinical Informatics system for trials and research.DiscussionData deidentification is conducted by using global unique identifier methodology. No personally identifiable information exists on the BRICS supported repositories. The Data Dictionary provides defined Common Data Elements and Unique Data Elements, specific to each of the BRICS instance that enables Query Tool to search through research data. All instances are supported by the Medical Imaging Processing, statistical analysis R, and Visualization software program.ConclusionThe BRICS core modules can be easily adapted for various biomedical research needs thereby reducing cost in developing new instances for additional biomedical research needs. It provides user friendly tools for researchers to query and aggregate genetic, phenotypic, clinical and medical imaging data. Data sets are findable, accessible and reusable for researchers to foster new research on various diseases.
Convergent evolution of gene expression in two high-toothed stickleback populations
Authors: Hart James C. , Ellis Nicholas A. , Eisen Michael B. , Milller Craig T. 
Published: February 2018

AbstractChanges in developmental gene regulatory networks enable evolved changes in morphology. These changes can be incisregulatory elements that act in an allele-specific manner, or changes to the overalltransregulatory environment that interacts withcisregulatory sequences. Here we address several questions about the evolution of gene expression accompanying a convergently evolved constructive morphological trait, increases in tooth number in two independently derived freshwater populations of threespine stickleback fish (Gasterosteus aculeatus). Are convergently evolvedcisand/ortranschanges in gene expression associated with convergently evolved morphological evolution? Docisortransregulatory changes contribute more to the evolutionary gain of a morphological trait? Transcriptome data from dental tissue of ancestral low-toothed and two independently derived high-toothed stickleback populations revealed significantly shared gene expression changes that have convergently evolved in the two high-toothed populations. Comparingcisandtransregulatory changes using phased gene expression data from F1 hybrids, we found thattransregulatory changes were predominant and more likely to be shared among both high-toothed populations. In contrast, whilecisregulatory changes have evolved in both high-toothed populations, overall these changes were distinct and not shared among high-toothed populations. Together these data suggest that a convergently evolved trait can occur through genetically distinct regulatory changes that converge on similartransregulatory environments.Author SummaryConvergent evolution, where a similar trait evolves in different lineages, provides an opportunity to study the repeatability of evolution. Convergent morphological evolution has been well studied at multiple evolutionary time scales ranging from ancient, to recent, such as the gain in tooth number in freshwater stickleback fish. However, much less is known about the accompanying evolved changes in gene regulation during convergent evolution. Here we compared evolved changes in gene expression in dental tissue of ancestral low-toothed marine fish to fish from two independently derived high-toothed freshwater populations. We also partitioned gene expression changes into those affecting a gene’s regulatory elements (cis), and those affecting the overall regulatory environment (trans). Both freshwater populations have evolved similar gene expression changes, including a gain of expression of putative dental genes. These similar gene expression changes are due mainly to shared changes to thetransregulatory environment, while thecischanges are largely population specific. Thus, during convergent evolution, overall similar and perhaps predictable transcriptome changes can evolve despite largely different underlying genetic bases.
Investigating Protein-Protein Allosteric Network using Current-Flow Scheme
Authors: Botello-Smith Wesley M. , Luo Yun 
Published: February 2018

AbstractProtein dynamic network analysis provides a powerful tool for investigating protein allosteric regulation. We recently developed a current-flow betweenness scheme for protein network analysis and demonstrated that this method, i.e. using current-flow betweenness as edge weights, is more appropriate and more robust for investigating the signal transmission between two predefined protein residues or domains as compared with direct usage of correlation scores as edge weights. Here we seek to expand the current-flow scheme to study allosteric regulations involving protein-protein binding. Specifically, we investigated three gain-of-function mutations located at the binding interface of ALK2 (also known as ACVR1) kinase and its inhibitory protein FKBP12. We first searched for the optimal smoothing function for contact network construction and then calculated the subnetwork between FKBP12 protein and ALK2 ATP binding site using current-flow betweenness. By comparing the networks between the wild-type and three mutants, we have identified statistically significant changes in the protein-protein networks that are common among all three mutants that allosterically shift the kinase towards a catalytically competent configuration.
Local adaptation primes cold-edge populations for range expansion but not warming-induced range shifts
Authors: Hargreaves Anna L. , Eckert Christopher G. 
Published: February 2018

AbstractAccording to theory, edge populations may be the best suited to initiate range expansions and climate-driven range shifts if they are locally adapted to extreme edge conditions, or the worst suited to colonize beyond-range habitat if their offspring are genetically and competitively inferior. We tested these contrasting predictions by comparing fitness of low, mid, and high-elevation (edge) populations of the annualRhinanthus minor, transplanted throughout and above its elevational distribution under natural and experimentally-warmed conditions. Seed from low-quality edge habitat had inferior emergence across sites, but high-elevation seeds were also locally adapted. High-elevation plants initiated flowering earlier than plants from lower populations, required less heat accumulation to mature seed, and so achieved higher lifetime fitness at high elevations. Fitness was strongly reduced above the range, but adaptive phenology enhanced the relative fitness of high-elevation seeds. Experimental warming improved fitness above the range, confirming climate’s importance in limitingR. minor’s distribution, but eliminated the advantage of local cold-edge populations. These results provide experimental support for recent models in which cold-adapted edge populations do not always facilitate warming-induced range shifts. The highest fitness above the range was achieved by a ‘super edge phenotype’ from a neighboring mountain, suggesting key adaptations exist at the regional scale even if absent from local edge populations. Our results demonstrate that assessing the value of edge populations will not be straightforward, but suggest that a regional approach to their conservation, potentially enhancing gene flow among them, might maximize species’ ability to respond to global change.SignificanceIndividuals from range-edge populations are the most likely to disperse to habitat beyond the species current range, but are they best suited to colonize it? Our multi-year transplant experiment throughout and above the elevational range of an annual herb in the Canadian Rocky Mountains found that adaptive flowering phenology enhanced the fitness of high-edge seeds above the range, outweighing detrimental effects of poor seed quality. However, only one edge population maintained its advantage over central populations under experimental warming. While edge populations were most likely to drive range expansion, adaptation to cold climates may not help them initiate range shifts in response to climate warming, unless superior genotypes spread among isolated edge populations.
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Authors: Rakhlin Alexander , Shvets Alexey , Iglovikov Vladimir , Kalinin Alexandr A. 
Published: February 2018

AbstractBreast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available athttps://github.com/alexander-rakhlin/ICIAR2018
Endoderm Morphogenesis Reveals Integration of Distinct Processes in the Development and Evolution of Pharyngeal Arches
Authors: Okada Kazunori , Wada Hiroshi , Takada Shinji 
Published: February 2018

ABSTRACTThe vertebrate pharyngeal arches (PAs) are established by a combination of two styles of segmentation; the most anterior 2 PAs are simultaneously but the others are sequentially formed. However, the mechanism underlying their coexistence is unclear. Here, we show that the simultaneous and sequential segmentation discretely proceeded, respectively, but were finally integrated at the second PP (PP2), by dynamic morphogenesis of pharyngeal endoderm in the zebrafish. The coordination of these 2 distinct processes appears to be common in the PA development of many vertebrates, in which specific developmental defects posterior to the PP2 are caused by mutations of particular genes or perturbation of retinoic acid signaling. Surprisingly, comparative analysis of PA segmentation showed that the combinatorial styles of PA development is present in shark but not in lamprey, suggesting that PA segmentation was modified in the stem gnathostomes corresponding to the drastic pharyngeal innovations, such as PA2-derived opercular.