bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lamin-related congenital muscular dystrophy alters mechanical signaling and skeletal muscle growth
Authors: Owens Daniel J. , Messéant Julien , Moog Sophie , Viggars Mark , Ferry Arnaud , Mamchaoui Kamel , Lacène Emmanuelle , Roméro Norma , Brull Astrid , Bonne Gisèle , Butler-Browne Gillian , Coirault Catherine 
Published: August 2020

AbstractBackgroundLaminopathies are a clinically heterogeneous group of disorders caused by mutations in theLMNAgene, which encodes the nuclear envelope proteins lamins A and C. The most frequent diseases associated withLMNAmutations are characterized by skeletal and cardiac involvement, and include autosomal dominant Emery-Dreifuss muscular dystrophy (EDMD), limb-girdle muscular dystrophy type 1B, andLMNA-related congenital muscular dystrophy (LMNA-CMD). Although the exact pathophysiological mechanisms responsible forLMNA-CMD are not yet understood, severe contracture and muscle atrophy suggest that impair skeletal muscle growth may contribute to the disease severity.MethodsWe used human muscle stem cells (MuSCs) carrying 4 differentLMNAmutations and two mouse models of muscle laminopathies, representing a spectrum of disease severity, to investigate the ability of skeletal muscle to differentiate and to hypertrophy in response to mechanical challenges. We extended these finding to individuals withLMNA-related muscular dystrophy using muscle biopsies.ResultsIn vitro, we observe impaired myogenic differentiation with disorganized cadherin/β catenin adhesion complexes in MuSCs carryingLMNA-CMD. We show that skeletal muscle fromLmna-CMD mice is unable to hypertrophy in response to functional overload, due to defective accretion of activated MuSCs, defective protein synthesis and defective remodeling of the neuro-muscular junction. Moreover, stretched myotubes and overloaded muscle fibers withLMNA-CMD mutations display aberrant mechanical regulation of the Yes-Associated Protein (YAP), a key sensor and mediator of mechanical cues. We also observe defects in MuSC activation and YAP signaling in muscle biopsies fromLMNA-CMD patients. These phenotypes are not recapitulated in closely-related EDMD models.ConclusionsCombining studiesin vitro,in vivoand patient samples, we find thatLMNA-CMD mutations interfere with mechano-signaling pathways in skeletal muscle, implicating defective skeletal muscle growth as a pathogenic contributor for the severity ofLMNA-related muscular dystrophy.
Effect of pancreatic lipase inhibitor and sorbent of lipids on cholesterolaemia and faecal output of fat in rats
Authors: Marounek Milan , Volek Zdeněk , Taubner Tomáš , Dušková Dagmar , Czauderna Marian 
Published: August 2020

AbstractObesity and high cholesterolaemia are major health problems in industrialized countries. The effects of the antiobesity drug orlistat at 0.3 g kg-1and amidated alginate at 40 g kg-1on serum and hepatic cholesterol and the faecal output of fat and sterols were compared in female rats. Rats were fed diets containing cholesterol and palm fat at 10 and 70 g kg-1, respectively. Palm fat was provided by coconut meal. Amidated alginate (the octadecylamide of alginic acid) is a sorbent of lipids, and orlistat (tetrahydrolipstatin) is an inhibitor of pancreatic lipase. Both agents significantly increased the faecal loss of fat, orlistat, however, did not significantly decrease serum total cholesterol and its effect on hepatic cholesterol was less pronounced. Amidated alginate at 40 g kg-1significantly decreased serum total cholesterol, LDL cholesterol, hepatic cholesterol, and hepatic lipids, and increased the faecal output of fat and coprostanol (a metabolite of cholesterol). Both orlistat and amidated alginate modified the fatty acid profile in excreted lipids. The concentration of saturated fatty acids decreased and the concentration of unsaturated fatty acids increased. Despite different modes of action, orlistat and amidated alginate were equally efficient in the removing dietary fat from the body. Amidated alginate, however, was more active in the control of serum and hepatic lipid metabolism.
Expression and distribution of synaptotagmin isoforms in the zebrafish retina
Authors: Henry Diane , Joselevitch Christina , Matthews Gary G. , Wollmuth Lonnie P. 
Published: August 2020

ABSTRACTSynaptotagmins belong to a large family of proteins. While various synaptotagmins have been implicated as Ca2+sensors for vesicle replenishment and release at conventional synapses, their roles at retinal ribbon synapses remain incompletely understood. Zebrafish is a widely used experimental model for retinal research. We therefore investigated the homology between human, rat, mouse, and zebrafish synaptotagmins 1 to 10 using a bioinformatics approach. We also characterized the expression and distribution of various synaptotagmin (syt) genes in the zebrafish retina using RT-PCR andin situhybridization, focusing on the family members whose products likely underlie Ca2+-dependent exocytosis in the central nervous system (synaptotagmins 1, 2, 5 and 7). We find that most zebrafish synaptotagmins are well conserved and can be grouped in the same classes as mammalian synaptotagmins, based on crucial amino acid residues needed for coordinating Ca2+binding and determining phospholipid binding affinity. The only exception is synaptotagmin 1b, which lacks 34 amino acid residues in the C2B domain and is therefore unlikely to bind Ca2+there. Additionally, the products of zebrafishsyt5aandsyt5bgenes share identity with mammalian class 1 and 5 synaptotagmins. Zebrafishsyt1,syt2,syt5andsyt7paralogues are found in the zebrafish brain, eye, and retina, exceptingsyt1b, which is only present in the brain. The complementary expression pattern of the remaining paralogues in the retina suggests thatsyt1aandsyt5amay underlie synchronous release andsyt7aandsyt7bmay mediate asynchronous release or other Ca2+dependent processes in different types of retinal neurons.
3D quantification of zebrafish cerebrovascular architecture by automated image analysis of light sheet fluorescence microscopy datasets
Authors: Kugler E. C. , Frost J. , Silva V. , Plant K. , Chhabria K. , Chico T. J.A. , Armitage P. A. 
Published: August 2020

AbstractZebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into vascular developmentin vivoand 3D. However, robust quantification of the zebrafish cerebral vasculature in 3D remains a challenge, and would be essential to describe the vascular architecture. Here, we report an image analysis pipeline that allows 3D quantification of the total or regional zebrafish brain vasculature. This is achieved by landmark- or object-based inter-sample registration and extraction of quantitative parameters including vascular volume, surface area, density, branching points, length, radius, and complexity. Application of our analysis pipeline to a range of sixteen genetic or pharmacological manipulations shows that our quantification approach is robust, allows extraction of biologically relevant information, and provides novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface, and workflow documentation are provided. Together, we present the first 3D quantification approach to assess the whole 3D cerebrovascular architecture in zebrafish.
Particulate Matter emission sources and meteorological parameters combine to shape the airborne microbiome communities in the Ligurian coast, Italy
Authors: Palladino Giorgia , Morozzi Pietro , Biagi Elena , Brattich Erika , Turroni Silvia , Rampelli Simone , Tositti Laura , Candela Marco 
Published: August 2020

AbstractHere we explore how the chemical composition of particulate matter (PM) and meteorological conditions combine in shaping the air microbiome in a heavily inhabited industrial urban settlement. During the observation time, the air microbiome was highly dynamic, fluctuating between different compositional states, likely resulting from the aerosolization of different microbiomes emission sources. This dynamic process depends on the combination of local meteorological parameters and particle emission sources, which may affect the prevalent aerosolized microbiomes. In particular, we showed that, in the investigated area, industrial emissions and winds blowing from the inlands combine with an airborne microbiome that includes faecal microbiomes components, suggesting multiple citizens’ exposure to both chemicals and microorganisms of faecal origin, as related to landscape exploitation and population density. In conclusion, our findings support the need to include monitoring of the air microbiome compositional structure as a relevant factor for the final assessment of local air quality.
Predicting Residues Involved in Anti-DNA Autoantibodies with Limited Neural Networks
Authors: St.Clair Rachel , Teti Michael , Pavlovic Mirjana , Hahn William , Barenholtz Elan 
Published: August 2020

AbstractComputer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for functional prediction. A potentially strong target for such a computational approach is autoimmune antibodies which are the result of broken tolerance in the immune system where it cannot distinguish “self” from “non-self” resulting in attack of its own structures (proteins and DNA, mainly). The information on structure, function and pathogenicity of autoantibodies may assist in engineering RVD against autoimmune diseases. Current computational approaches exploit large datasets curated with extensive domain knowledge, most of which include the need for many computational resources and have been applied indirectly to problems of interest for DNA, RNA, and monomer protein binding. Here, we present a novel method for discovering potential binding sites. We employed long short-term memory (LSTM) models trained on FASTA primary sequences directly to predict protein binding in DNA-binding hydrolytic antibodies (abzymes). We also employed CNN models applied to the same dataset. While the CNN model outperformed the LSTM on the primary task of binding prediction, analysis of internal model representations of both models showed that the LSTM models highlighted sub-sequences that were more strongly correlated with sites known to be involved in binding. These results demonstrate that analysis of internal processes of recurrent neural network models may serve as a powerful tool for primary sequence analysis.
SNPC-1.3 is a sex-specific transcription factor that drives male piRNA expression inC. elegans
Authors: Choi Charlotte P. , Tay Rebecca J. , Starostik Margaret R. , Feng Suhua , Moresco James J. , Montgomery Brooke E. , Xu Emily , Hammonds Maya A. , Schatz Michael C. , Montgomery Taiowa A. , Yates John R. , Jacobsen Steven E. , Kim John K. 
Published: August 2020

ABSTRACTPiwi-interacting RNAs (piRNAs) play essential roles in silencing repetitive elements to promote fertility in metazoans. Studies in worms, flies, and mammals reveal that piRNAs are expressed in a sex-specific manner. However, the mechanisms underlying this sex-specific regulation are unknown. Here we identify SNPC-1.3, a variant of a conserved subunit of the snRNA activating protein complex, as a male-specific piRNA transcription factor inC. elegans. Binding of SNPC-1.3 at male piRNA loci drives spermatogenic piRNA transcription and requires the core piRNA transcription factor SNPC-4. Loss ofsnpc-1.3leads to depletion of male piRNAs and defects in male-dependent fertility. Furthermore, TRA-1, a master regulator of sex determination, binds to thesnpc-1.3promoter and represses its expression during oogenesis. Loss of TRA-1 targeting causes ectopic expression ofsnpc-1.3and male piRNAs during oogenesis. Thus, sexual dimorphic regulation ofsnpc-1.3coordinates male and female piRNA expression during germline development.
SVCollector: Optimized sample selection for cost-efficient long-read population sequencing
Authors: Ranallo-Benavidez T. Rhyker , Lemmon Zachary , Soyk Sebastian , Aganezov Sergey , Salerno William J. , McCoy Rajiv C. , Lippman Zachary B. , Schatz Michael C. , Sedlazeck Fritz J. 
Published: August 2020

AbstractAn increasingly important scenario in population genetics is when a large cohort has been genotyped using a low-resolution approach (e.g. microarrays, exome capture, short-read WGS), from which a few individuals are selected for resequencing using a more comprehensive approach, especially long-read sequencing. The subset of individuals selected should ensure that the captured genetic diversity is fully representative and includes variants across all subpopulations. For example, human variation has historically been focused on individuals with European ancestry, but this represents a small fraction of the overall diversity.To address this goal, SVCollector (https://github.com/fritzsedlazeck/SVCollector) identifies the optimal subset of individuals for resequencing. SVCollector analyzes a population-level VCF file from a low resolution genotyping study. It then computes a ranked list of samples that maximizes the total number of variants present from a subset of a given size. To solve this optimization problem, SVCollector implements a fast greedy heuristic and an exact algorithm using integer linear programming. We apply SVCollector on simulated data, 2504 human genomes from the 1000 Genomes Project, and 3024 genomes from the 3K Rice Genomes Project and show the rankings it computes are more representative than widely used naive strategies. Notably, we show that when selecting an optimal subset of 100 samples in these two cohorts, SV-Collector identifies individuals from every subpopulation while naive methods yield an unbalanced selection. Finally, we show the number of variants present in cohorts of different sizes selected using this approach follows a power-law distribution that is naturally related to the population genetic concept of the allele frequency spectrum, allowing us to estimate the diversity present with increasing numbers of samples.
Behavioral correlates of cortical semantic representations modeled by word vectors
Authors: Nishida Satoshi , Blanc Antione , Maeda Naoya , Kado Masataka , Nishimoto Shinji 
Published: August 2020

AbstractThe quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings.Author summeryWord vectors, which have been originally developed in the field of engineering (natural language processing), have been extensively leveraged in neuroscience studies to model semantic representations in the human brain. These studies have attempted to model brain semantic representations by associating them with the meanings of thousands of words via a word vector space. However, there has been no study explicitly examining whether the modeled semantic representations actually capture our perception of semantic information. To address this issue, we compared the semantic representational structure of words estimated from word vector-based brain models with that evaluated from behavioral data in psychological experiments. The results revealed a significant correlation between these model- and behavior-derived semantic representational structures of words. This indicates that the brain semantic representations modeled using word vectors actually reflect the human perception of word meanings. Our findings contribute to the establishment of word vector-based brain modeling as a useful tool in studying human semantic processing.
Gluconeogenic Enzyme PCK1 Deficiency Is Critical for CHK2 O-GlcNAcylation and Hepatocellular Carcinoma Growth upon Glucose Deprivation
Authors: Xiang Jin , Chen Chang , Liu Rui , Gou Dongmei , Chang Lei , Deng Haijun , Gao Qingzhu , Zhang Wanjun , Tuo Lin , Pan Xuanming , Liang Li , Xia Jie , Huang Luyi , Huang Ailong , Wang Kai , Tang Ni 
Published: August 2020

ABSTRACTElevated hexosamine-biosynthesis pathway (HBP) activity and O-GlcNAcylation are emerging hallmarks of hepatocellular carcinoma (HCC). Inhibiting O-GlcNAcylation could be a promising anti-cancer strategy. Here, we investigate this possibility and demonstrate that deficiency of phosphoenolpyruvate carboxykinase 1 (PCK1), a rate-limiting enzyme in gluconeogenesis, promotes O-GlcNAcylation and hepatoma cell proliferation under low-glucose conditions. PCK1 loss results in oxaloacetate accumulation and AMPK inactivation, promoting uridine diphosphate-N-acetylglucosamine (UDP-GlcNAc) synthesis and CHK2 threonine 378 O-GlcNAcylation and counteracting its ubiquitination and degradation. O-GlcNAcylation also promotes CHK2-dependent Rb phosphorylation and HCC cell proliferation. Therefore, blocking HBP-mediated O-GlcNAcylation suppresses tumor progression in liver-specificPck1-knockout mice. We reveal a link between PCK1 depletion and hyper-O-GlcNAcylation that underlies HCC oncogenesis and suggest therapeutic targets for HCC that act by inhibiting O-GlcNAcylation.
Wrong person, place and time: viral load and contact network structure predict SARS-CoV-2 transmission and super-spreading events
Authors: Goyal Ashish , Reeves Daniel B. , Cardozo-Ojeda E. Fabian , Schiffer Joshua T. , Mayer Bryan T. 
Published: August 2020

AbstractSARS-CoV-2 is difficult to contain because many transmissions occur during the pre-symptomatic phase of infection. Moreover, in contrast to influenza, while most SARS-CoV-2 infected people do not transmit the virus to anybody, a small percentage secondarily infect large numbers of people. We designed mathematical models of SARS-CoV-2 and influenza which link observed viral shedding patterns with key epidemiologic features of each virus, including distributions of the number of secondary cases attributed to each infected person (individual R0) and the duration between symptom onset in the transmitter and secondarily infected person (serial interval). We identify that people with SARS-CoV-2 or influenza infections are usually contagious for fewer than one day congruent with peak viral load several days after infection, and that transmission is unlikely below a certain viral load. SARS-CoV-2 super-spreader events with over 10 secondary infections occur when an infected person is briefly shedding at a very high viral load and has a high concurrent number of exposed contacts. The higher predisposition of SARS-CoV-2 towards super-spreading events is not due to its 1-2 additional weeks of viral shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks than a person infected with influenza, likely due to aerosolization of virus. Our results support policies that limit crowd size in indoor spaces and provide viral load benchmarks for infection control and therapeutic interventions intended to prevent secondary transmission.One Sentence SummaryWe developed a coupled within-host and between-host mathematical model to identify viral shedding levels required for transmission of SARS-CoV-2 and influenza, and to explain why super-spreading events occur more commonly during SARS-CoV-2 infection.
Mapping access to basic hygiene services in low- and middle-income countries: A cross-sectional case study of geospatial disparities
Authors: Yu Weiyu , Bain Robert E. S. , Yu Jie , Alegana Victor , Dotse-Gborgbortsi Winfred , Lin Yi , Wright Jim A. 
Published: August 2020

AbstractHandwashing with water and soap, is among the most a cost-effective interventions to improve public health. Yet billions of people globally lacking handwashing facilities with water and soap on premises, with gaps particularly found in low- and middle-income countries. Targeted efforts to expand access to basic hygiene services require data at geospatially explicit scales. Drawing on country-specific cross-sectional Demographic and Health Surveys with georeferenced hygiene data, we developed an ensemble model to predict the prevalence of basic hygiene facilities in Malawi, Nepal, Nigeria, Pakistan and Uganda. The ensemble model was based on a multiple-level stacking structure, where five predictive modelling algorithms were used to produce sub-models, and a random forest model was used to generalise the final predictions. An inverse distance weighted interpolation was incorporated in the random forest model to account for spatial autocorrelation. Local coverage and a local dissimilarity index were calculated to examine the geographic disparities in access. Our methodology produced robust outputs, as evidenced by performance evaluations (all R2were above 0.8 with the exception of Malawi where R2= 0.6). Among the five study countries, Pakistan had the highest overall coverage, whilst Malawi had the poorest coverage. Apparent disparities in basic hygiene services were found across geographic locations and between urban and rural settings. Nigeria had the highest level of inequalities in basic hygiene services, whilst Malawi showed the least segregation between populations with and without basic hygiene services. Both educational attainment and wealth were important predictors of the geospatial distribution of basic hygiene services. By producing geospatially explicit estimates of the prevalence of handwashing facilities with water and soap, this study provides a means of identifying geographical disparities in basic hygiene services. The method and outputs can be useful tools to identify areas of low coverage and to support efficient and precise targeting of efforts to scale up access to handwashing facilities and shift social and cultural norms on handwashing.