bluuebunny/crossref_metadata_2025_split huggingface dataset:
CluStrat: a structure informed clustering strategy for population stratification
Authors: Bose Aritra , Burch Myson C. , Chowdhury Agniva , Paschou Peristera , Drineas Petros 
Published: January 2020

AbstractGenome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. Recent independent studies failed to replicate the strong evidence of selection for height across Europe implying the shortcomings of standard population stratification correction approaches. Here, we present CluStrat, a stratification correction algorithm for complex population structure that leverages the linkage disequilibrium (LD)-induced distances between individuals. CluStrat performs agglomerative hierarchical clustering using the Mahalanobis distance and then applies sketching-based randomized ridge regression on the genotype data to obtain the association statistics. With the growing size of data, computing and storing the genome wide covariance matrix is a non-trivial task. We get around this overhead by computing the GRM directly using a connection between statistical leverage scores and the Mahalanobis distance. We test CluStrat on a large simulation study of discrete and admixed, arbitrarily-structured sub-populations identifying two to three-fold more true causal variants when compared to Principal Component (PC) based stratification correction methods while trading off for a slightly higher spurious associations. Applying CluStrat on WTCCC2 Parkinson’s disease (PD) data, we identified loci mapped to a host of genes associated with PD such as BACH2, MAP2, NR4A2, SLC11A1, UNC5C to name a few.Availability and ImplementationCluStrat source code and user manual is available at:https://github.com/aritra90/CluStrat
Positive selection and gene duplications in tumour suppressor genes reveal clues about how cetaceans resist cancer
Authors: Tejada-Martinez Daniela , de Magalhães João Pedro , Opazo Juan C. 
Published: January 2020

AbstractCetaceans are the longest-living species of mammals and the largest in the history of the planet. They have developed mechanisms against diseases such cancer, although the underlying molecular bases of these remain unknown. The goal of this study was to investigate the role of natural selection in the evolution of 1077 tumour suppressor genes (TSGs) in cetaceans. We used a comparative genomic approach to analyse two sources of molecular variation in the form of dN/dS rates and gene copy number variation. We found a signal of positive selection in the ancestor of cetaceans within theCXCR2gene, an important regulator of DNA-damage, tumour dissemination, and immune system. Further, in the ancestor of baleen whales, we found six genes exhibiting positive selection relating to such diseases as breast carcinoma, lung neoplasm (ADAMTS8) and leukaemia (ANXA1). The TSG turnover rate (gene gain and loss) was almost 2.4-fold higher in cetaceans as compared to other mammals, and noticeably even faster in baleen whales. The molecular variants in TSGs found in baleen whales, combined with the faster gene turnover rate, could have favoured the evolution of their particular traits of anti-cancer resistance, gigantism and longevity. Additionally, we report 71 genes with duplications, of which 11 genes are linked to longevity (e.g.NOTCH3andSIK1) and are important regulators of senescence, cell proliferation and metabolism. Overall, these results provide evolutionary evidence that natural selection in tumour suppressor genes could act on species with large body sizes and extended life span, providing novel insights into the genetic basis of disease resistance.
WNT and inflammatory signaling distinguish human Fallopian tube epithelial cell populations
Authors: Rose Ian , Bidarimath Mallikarjun , Webster Alex , Godwin Andrew K. , Flesken-Nikitin Andrea , Nikitin Alexander Yu. 
Published: January 2020

AbstractA substantial fraction of ovarian/extra-uterine high-grade serous carcinomas (HGSCs) likely originate in the distal region of the Fallopian tube’s epithelium (TE) before implanting/metastasizing to the ovary. Unfortunately, molecular and cellular mechanisms rendering preferential cancer susceptibility of the human distal TE remain insufficiently elucidated, largely due to limited primary human TE gene expression data. Here we report an in depth bioinformatic characterization of 34 primary TE cell mRNA-seq samples. These samples were prepared from the proximal and distal TE regions of 12 normal Fallopian tubes. TE cells were segregated based on their aldehyde dehydrogenase (ALDH) activity. As compared to the proximal TE, cells from the distal region form organoids with higher frequency and larger size during serial organoid formation assays. Consistent with enrichment for organoid-forming stem/progenitor cells, ALDH+ cells have greater WNT signaling activity. Comparative evaluation of proximal and distal TE cell population’s shows heightened inflammatory signaling in distal differentiated (ALDH-) TE. Furthermore, comparisons of proximal and distal TE cell populations finds that the distal TE express gene sets characteristic of four HGSC molecular sub-types, and that distal ALDH+ cell populations exhibit greater enrichment than their ALDH-counterparts. Taken together, our study shows that increased organoid forming capacity, WNT and inflammatory signaling, and HGSC signatures underlie the differences between distal and proximal regions of the human TE. These findings provide the basis for further mechanistic studies of distal TE susceptibility to the malignant transformation.
Improvement of phycocyanobilin synthesis for genetically encoded phytochrome-based optogenetics
Authors: Uda Youichi , Miura Haruko , Goto Yuhei , Aoki Kazuhiro 
Published: January 2020

AbstractOptogenetics is a powerful technique using photoresponsive proteins, and light-inducible dimerization (LID) systems, an optogenetic tool, allow to manipulate intracellular signaling pathways. One of the red/far-red responsive LID system, phytochrome B (PhyB)-phytochrome interacting factor (PIF), has a unique property of controlling both association and dissociation by light on the second time scale, but PhyB requires a linear tetrapyrrole chromophore such as phytochromobilin or phycocyanobilin (PCB), and such chromophores are present only in higher plants and cyanobacteria. Here, we report that we further improved our previously developed PCB synthesis system (synPCB), and successfully established a stable cell line containing a genetically encoded PhyB-PIF LID system. First, four genes responsible for PCB synthesis, namely,PcyA,HO1,Fd, andFnr, were replaced with their counterparts derived from thermophilic cyanobacteria. Second, Fnr was truncated, followed by fusion with Fd to generate a chimeric protein, tFnr-Fd. Third, these genes were concatenated with P2A peptide cDNAs for polycistronic expression, resulting in an approximately 4-fold increase in PCB synthesis compared with the previous version. Finally, we incorporated PhyB-PIF and synPCB into drug inducible lentiviral and transposon vectors, which enabled us to induce PCB synthesis and PhyB-PIF LID system by doxycycline treatment. These tools provide a new opportunity to advance our understanding of the causal relationship between intracellular signaling and cellular functions.
Deep Learning Modelling of Androgen Receptor Responses to Prostate Cancer Therapies
Authors: Snow Oliver , Lallous Nada , Ester Martin , Cherkasov Artem 
Published: January 2020

AbstractGain-of-function mutations in human Androgen Receptor (AR) are amongst major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly, and therefore predictive models are needed to anticipate resistant mutations and to guide drug discovery process. In this work, we leverage experimental data collected on 69 clinically observed and/or literature described AR mutants to train a deep neural network (DNN) to predict their responses to currently used and experimental AR anti-androgens. We demonstrate that the use of DNN provides more accurate prediction of the biological outcome (inhibition, activation, no-response) in AR mutant-drug pairs compared to other machine learning approaches and also allows the use of more general 2D descriptors. Finally, the developed approach was used to predict the effect of the latest AR inhibitor darolutamide on all reported AR mutants.
Distinguishing the neural correlates of perceptual awareness and post-perceptual processing
Authors: Cohen Michael A. , Ortego Kevin , Kyroudis Andrew , Pitts Michael 
Published: January 2020

AbstractTo identify the neural correlates of perceptual awareness, researchers often compare the differences in neural activation between conditions in which an observer is or is not aware of a stimulus. While intuitive, this approach often contains a critical limitation: In order to link brain activity with perceptual awareness, observers traditionally report the contents of their perceptual experience. However, relying on observers’ reports is problematic because it is difficult to know if the neural responses being measured are associated with conscious perception or with post-perceptual processes involved in the reporting task (i.e., working memory, decision-making, etc.). To address this issue, we combined a standard visual masking paradigm with a recently developed “no-report” paradigm in male/female human participants. In the visual masking paradigm, observers saw images of animals and objects that were visible or invisible depending on their proximity to masks. Meanwhile, on half of the trials, observers reported the contents of their perceptual experience (i.e., report condition), while on the other half of trials they refrained from reporting about their experiences (i.e., no-report condition). We used electroencephalography (EEG) to examine how visibility interacts with reporting by measuring the P3b event related potential (ERP), one of the proposed canonical “signatures” of conscious processing. Overall, we found a robust P3b in the report condition, but no P3b whatsoever in the no-report condition. This finding suggests that the P3b itself is not a neural signature of conscious processing and highlights the importance of carefully distinguishing the neural correlates of perceptual awareness from post-perceptual processing.Significance statementWhat are the neural signatures that differentiate conscious and unconscious processing in the brain? Perhaps the most well-established candidate signature is the P3b event-related potential (ERP), a late slow wave that appears when observers are aware of a stimulus, but disappears when a stimulus fails to reach awareness. Here, however, we found that the P3b does not track what observers areperceivingbut instead tracks what observers arereporting. When observers are aware of simple visual stimuli, the P3b is nowhere to be found unless observers are reporting the contents of their experience. These results challenge the well-established notion of the P3b as a neural marker of awareness and highlight the need for new approaches to the neuroscience of consciousness.
Cryo-EM structures of cardiac thin filaments reveal the 3D architecture of troponin
Authors: Oda Toshiyuki , Yanagisawa Haruaki , Wakabayashi Takeyuki 
Published: January 2020

AbstractTroponin is an essential component of striated muscle and it regulates the sliding of actomyosin system in a calcium-dependent manner. Despite its importance, the structure of troponin has been elusive due to its high structural heterogeneity. In this study, we analyzed the 3D structures of murine cardiac thin filaments using a cryo-electron microscope equipped with a Volta phase plate (VPP). Contrast enhancement by a VPP enabled us to reconstruct the entire repeat of the thin filament. We determined the orientation of troponin relative to F-actin and tropomyosin, and characterized the interactions between troponin and tropomyosin. This study provides a structural basis for understanding the molecular mechanism of actomyosin system.
Modular and Distinct Plexin-A4/FARP2/Rac1 Signaling Controls Dendrite Morphogenesis
Authors: Danelon Victor , Goldner Ron , Martinez Edward , Gokhman Irena , Wang Kimberly , Yaron Avraham , Tran Tracy S. 
Published: January 2020

Diverse neuronal populations with distinct cellular morphologies coordinate the complex function of the nervous system. Establishment of distinct neuronal morphologies critically depends on signaling pathways that control axonal and dendritic development. The Sema3A-Nrp1/PlxnA4 signaling pathway promotes cortical neuron basal dendrite arborization but also repels axons. However, the downstream signaling components underlying these disparate functions of Sema3A signaling are unclear. Using the novelPlxnA4KRK-AAAknock-in male and female mice, generated by CRISPR/cas9, we show here that the KRK motif in the PlxnA4 cytoplasmic domain is required for Sema3A-mediated cortical neuron dendritic elaboration but is dispensable for inhibitory axon guidance. The RhoGEF FARP2, which binds to the KRK motif, shows identical functional specificity as the KRK motif in the PlxnA4 receptor. We find that Sema3A activates the small GTPase Rac1, and that Rac1 activity is required for dendrite elaboration but not axon growth cone collapse. This work identifies a novel Sema3A-Nrp1/PlxnA4/FARP2/Rac1 signaling pathway that specifically controls dendritic morphogenesis but is dispensable for repulsive guidance events. Overall, our results demonstrate that the divergent signaling output from multifunctional receptor complexes critically depends on distinct signaling motifs, highlighting the modular nature of guidance cue receptors and its potential to regulate diverse cellular responses.Significance StatementThe proper formation of axonal and dendritic morphologies is crucial for the precise wiring of the nervous system that ultimately leads to the generation of complex functions in an organism. The Semaphorin3A-Neuropilin1/Plexin-A4 signaling pathway has been shown to have multiple key roles in neurodevelopment, from axon repulsion to dendrite elaboration. This study demonstrates that three specific amino acids, the KRK motif within the Plexin-A4 receptor cytoplasmic domain, are required to coordinate the downstream signaling molecules to promote Sema3A-mediated cortical neuron dendritic elaboration, but not inhibitory axon guidance. Our results unravel a novel Semaphorin3A-Plexin-A4 downstream signaling pathway and shed light on how the disparate functions of axon guidance and dendritic morphogenesis are accomplished by the same extracellular ligandin vivo.
EWS-FLI1 regulates and cooperates with core regulatory circuitry in Ewing sarcoma
Authors: Shi Xianping , Zheng Yueyuan , Jiang Liling , Zhou Bo , Yang Wei , Li Liyan , Ding Lingwen , Huang Moli , Gery Sigal , Lin De-Chen , Koeffler H. Phillip 
Published: January 2020

AbstractCore regulatory circuitry (CRC)-dependent transcriptional network is critical for developmental tumors in children and young adults carrying few gene mutations. However, whether and how CRC contributes to transcription regulation in Ewing sarcoma is unknown. Here, we identify and functionally validate a CRC “trio” constituted by three transcription factors (TFs): KLF15, TCF4 and NKX2-2, in Ewing sarcoma cells. Epigenomic analyses demonstrate that EWS-FLI1, the primary fusion driver for this cancer, directly establishes super-enhancers of each of these three TFs to activate their transcription. In turn, KLF15, TCF4 and NKX2-2 co-bind to their own and each other’s super-enhancers and promoters, forming an inter-connected auto-regulatory loop. Functionally, CRC factors contribute significantly to cell proliferation of Ewing sarcoma bothin vitroandin vivo, and are all overexpressed in this cancer. Mechanistically, CRC factors exhibit prominent capacity of co-regulating the epigenome in cooperation with EWS-FLI1, occupying 77.2% of promoters and 55.6% of enhancers genome-wide. Downstream, CRC TFs coordinately regulate gene expression networks in Ewing sarcoma, directly controlling important signaling pathways for cancer, such as lipid metabolism pathway, PI3K/AKT and MAPK signaling pathways. Together, molecular characterization of the oncogenic CRC model advances our understanding of the biology of Ewing sarcoma. Moreover, this study identifies CRC-downstream genes and signaling pathways, which may contain potential targets for therapeutic intervention for this malignancy.
Identifying Causal Variants by Fine Mapping Across Multiple Studies
Authors: LaPierre Nathan , Taraszka Kodi , Huang Helen , He Rosemary , Hormozdiari Farhad , Eskin Eleazar 
Published: January 2020

AbstractIncreasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. In a trans-ethnic, trans-biobank Type 2 Diabetes analysis, we show that MsCAVIAR returns causal set sizes that are over 20% smaller than those given by current state of the art methods for trans-ethnic fine-mapping.
A comparative ‘omics approach to candidate pathogenicity factor discovery in the brain-eating amoebaNaegleria fowleri
Authors: Herman Emily K. , Greninger Alex , van der Giezen Mark , Ginger Michael L. , Ramirez-Macias Inmaculada , Miller Haylea C. , Morgan Matthew J. , Tsaousis Anastasios D. , Velle Katrina , Vargová Romana , Rodrigo Najle Sebastian , MacIntyre Georgina , Muller Norbert , Wittwer Mattias , Zysset-Burri Denise C. , Elias Marek , Slamovits Claudio , Weirauch Matthew , Fritz-Laylin Lillian , Marciano-Cabral Francine , Puzon Geoffrey J. , Walsh Tom , Chiu Charles , Dacks Joel B. 
Published: January 2020

AbstractOf the 40 describedNaegleriaspecies, onlyN. fowlerican establish infection in humans, killing almost invariably within two weeks. In the brain, the amoeba performs piece-meal ingestion, or trogocytosis, of brain material causing massive inflammation. Conversely, its close relativeNaegleria gruberi, which is used as a laboratory model organism, is non-pathogenic. The exact pathogenicity factors distinguishingN. fowlerifrom its harmless relatives are unclear. We have here taken an -omics approach to understandingN. fowleribiology and infection at the system level. We provide the first analysis of genomic diversity between strains, finding little conservation in synteny but high conservation in protein complement. We also demonstrate that theN. fowlerigenome encodes a similarly complete cellular repertoire to that found inN. gruberi. Our comparative genomic analysis, together with a transcriptomic analysis of low versus high pathogenicityN. fowlericultured in a mouse infection model, allowed us to construct a model of cellular systems involved in pathogenicity and furthermore provides ~500 novel candidate pathogenicity factors in this currently rare but highly fatal pathogen.
C subunit of the ATP synthase is an amyloidogenic channel-forming peptide: possible implications in mitochondrial pathogenesis
Authors: Amodeo Giuseppe Federico , Lee Brenda Yasie , Krilyuk Natalya , Filice Carina Teresa , Valyuk Denis , Otzen Daniel , Noskov Sergey , Leonenko Zoya , Pavlov Evgeny V. 
Published: January 2020

AbstractThe c subunit is an inner mitochondrial membrane (IMM) protein and is an integral part of the F0complex of the ATP synthase. Under physiological conditions, this short 75 residue-long peptide folds into an α-helical hairpin and forms oligomers spanning the lipid bilayer. In addition to its physiological role, the c subunit has been proposed as a key participant in stress-induced IMM permeabilization by the mechanism of calcium-induced permeability transition. However, the molecular mechanism of the c subunit participation in IMM permeabilization is not completely understood. Here we used fluorescence spectroscopy, atomic force microscopy and black lipid membrane methods to gain insights into the structural and functional properties of c subunit protein that make it relevant to mitochondrial toxicity. We discovered that c subunit is an amyloidogenic peptide that can spontaneously fold into β-sheets and self-assemble into fibrils and oligomers. C subunit oligomers exhibited ion channel activity in lipid membranes. We propose that the toxic effects of c subunit might be linked to its amyloidogenic properties and are driven by mechanisms similar to those of neurodegenerative polypeptides such as Aβ and α-synuclein.
Evolutionary context-integrated deep sequence modeling for protein engineering
Authors: Luo Yunan , Vo Lam , Ding Hantian , Su Yufeng , Liu Yang , Qian Wesley Wei , Zhao Huimin , Peng Jian 
Published: January 2020

AbstractProtein engineering seeks to design proteins with improved or novel functions. Compared to rational design and directed evolution approaches, machine learning-guided approaches traverse the fitness landscape more effectively and hold the promise for accelerating engineering and reducing the experimental cost and effort. A critical challenge here is whether we are capable of predicting the function or fitness of unseen protein variants. By learning from the sequence and large-scale screening data of characterized variants, machine learning models predict functional fitness of sequences and prioritize new variants that are very likely to demonstrate enhanced functional properties, thereby guiding and accelerating rational design and directed evolution. While existing generative models and language models have been developed to predict the effects of mutation and assist protein engineering, the accuracy of these models is limited due to their unsupervised nature of the general sequence contexts they captured that is not specific to the protein being engineered. In this work, we propose ECNet, a deep-learning algorithm to exploit evolutionary contexts to predict functional fitness for protein engineering. Our method integrated local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest, as well as the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. This biologically motivated sequence modeling approach enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-orders. Through extensive benchmark experiments, we showed that our method outperforms existing methods on ∼50 deep mutagenesis scanning and random mutagenesis datasets, demonstrating its potential of guiding and expediting protein engineering.