bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comethyl: A network-based methylome approach to investigate the multivariate nature of health and disease
Authors: Mordaunt Charles E. , Mouat Julia S. , Schmidt Rebecca J. , LaSalle Janine M. 
Published: July 2021

AbstractHealth outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social, and environmental factors. DNA methylation patterns reflect such multi-variate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing (WGBS) enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis (WGCNA) of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth, and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing gene regions with brain glial functions. Comethyl is expected to be useful in uncovering the multi-variate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl.Description of the AuthorsCharles E. Mordaunt, Ph.D. developed Comethyl while a postdoctoral fellow in the department of Medical Microbiology and Immunology at UC Davis. He is currently a Computational Biologist at GSK.Julia S. Mouat is a doctoral student in the Integrative Genetics and Genomics graduate group at UC Davis with interests in health disparities and intergenerational epigenetic risk factors for autism spectrum disorders.Rebecca J. Schmidt, Ph.D. is an Associate Professor of Public Health Sciences at UC Davis, with expertise in the use of epigenetics in epidemiology and neurodevelopmental disorders.Janine M. LaSalle, Ph.D. is a Professor of Medical Microbiology and Immunology, Co-Director of the Perinatal Origins of Disparities Center, and Deputy Director of the Environmental Health Sciences Center at UC Davis, with expertise in epigenomics and neurodevelopmental disorders.
Social risk factors for SARS-CoV-2 acquisition in University students: cross sectional survey
Authors: Blakey Eleanor , Reeve Lucy , Verlander Neville Q , Edwards David , Wyllie David , Reacher Mark 
Published: July 2021

AbstractObjectivesTo define risk factors for SARS-CoV-2 infection in University of Cambridge students during a period of increased incidence in October and November 2020.Study designSurveyMethodsRoutine public health surveillance identified a marked increase in the numbers of University of Cambridge students with respiratory illness and SARS-CoV-2 positivity in the 10 days after a national lockdown was announced in the UK on 5 November 2020. Cases were identified both through symptom-triggered testing and a universal asymptomatic testing program. An online questionnaire was sent to all University of Cambridge students on 25 November to investigate risk factors for testing positive in the period after 30thOctober 2020. This asked about symptoms, SARS-CoV-2 test results, in-person teaching settings, other aspects of University life, and attendance at social events in the period just prior to lockdown, from 30thOctober and 4thNovember 2020. Univariate and multivariable analyses were undertaken evaluating potential risk factors for SARS-CoV-2 positivity.ResultsAmong 3,980 students responding to the questionnaire, 99 (2.5%) reported testing SARS-CoV-2 positive in the period studied; 28 (28%) were asymptomatic. We found strong independent associations with SARS-CoV-2 positivity were attendance at two social settings in the City of Cambridge (adjusted odds ratio favouring disease 13.0 (95% CI 6.2,26.9) and 14.2 (95% CI 2.9,70)), with weaker evidence of association with three further social settings. By contrast, we did not observe strong independent associations between disease risk and type of accommodation or attendance at, or participation in, a range of activities associated with the University curriculum.ConclusionsAttendance at social settings can facilitate widespread SARS-CoV-2 transmission in University students. Constraint of transmission in higher education settings needs to emphasise risks outside University premises, as well as a COVID-safe environment within University premises.HighlightsIn a population of University students, a large increase in individuals testing positive for SARS-CoV-2 occurred in the days following a national lockdown.Attendance at particular social gatherings was strongly linked to the development of disease, independent of other risk factors.By contrast, a range of risk factors including age, gender, ethnicity, accommodation type, shared kitchen facilities, attendance at supermarkets, and attending teaching sessions were not significantly associated with SARS-CoV-2 risk.These data emphasise the increased risk associated with University students attending social settings with large numbers of others, even when other risks associated with university attendance are well controlled.
Measuring biodiversity from DNA in the air
Authors: Clare Elizabeth L. , Economou Chloe K. , Bennett Frances J. , Dyer Caitlin E. , Adams Katherine , McRobie Benjamin , Drinkwater Rosie , Littlefair Joanne E. 
Published: July 2021

AbstractImpacts of the biodiversity crisis far exceed our ability to monitor changes in terrestrial ecosystems. Environmental DNA has revolutionized aquatic biomonitoring, permitting remote population and diversity assessments. Here we demonstrate that DNA from terrestrial animals can now be collected from the air under natural conditions, a ground-breaking advance for terrestrial biomonitoring. Using air samples from a zoological park, where species are spatially confined and unique compared to native fauna, we show that DNA in air can be used to identify the captive species and their potential interactions with local taxa. Air samples contained DNA from 25 species of mammal and bird including 17 known (and distinct) terrestrial zoo species. We also identified food items from air sampled in enclosures and detected four taxa native to the local area, including the Eurasian hedgehog, endangered in the UK, and the muntjac deer, a locally established invasive species. Our data provide evidence that airDNA is concentrated around recently inhabited areas (e.g., indoor enclosures) but that there is dispersal away from the source suggesting an ecology to airDNA movement which highlights the potential for airDNA sampling at distance. Our data clearly demonstrate the profound potential of air as a source of DNA for global terrestrial biomonitoring and ecological analysis.Significance StatementThe global decline in biodiversity requires rapid non-invasive biomonitoring tools applicable at a global scale. In this study we collect environmental DNA from mammals and birds from air samples collected in a natural setting. Using only air, we identified 25 species of mammal and bird known to be in the area. Our dataset detected species at risk of local extinction and several confirmed predator-prey interactions. This approach will revolutionize terrestrial biodiversity surveys.
Single-Molecule Manipulation of Macromolecules on Membranes Using High-Resolution Optical Tweezers
Authors: Wang Yukun , Jin Huaizhou , Zhang Yongli 
Published: July 2021

AbstractDespite their wide applications into soluble macromolecules, optical tweezers have rarely been used to characterize dynamics of membrane proteins, mainly due to lack of model membranes compatible with optical trapping. Here, we found that optical tweezers can stably trap giant unilamellar vesicles (GUVs) containing iodixanol with controlled membrane tension, which can potentially serve as a model membrane to study dynamics of membranes, membrane proteins, or their interactions. We also observed that small unilamellar vesicles (SUVs) are rigid enough to resist large pulling force and offer potential advantages to pull membrane proteins. To demonstrate the use of both model membranes, we pulled membrane tethers from the trapped GUVs and measured the folding or binding dynamics of a single DNA hairpin or synaptotagmin-1 C2 domain attached to the GUV or SUV with high spatiotemporal resolution. Our methodologies facilitate single-molecule manipulation studies of membranes or membrane proteins using optical tweezers.
Decellularized Normal and Tumor Extracellular Matrix as Scaffold for Cancer Organoid Cultures of Colorectal Peritoneal Metastases
Authors: Varinelli Luca , Guaglio Marcello , Brich Silvia , Zanutto Susanna , Belfiore Antonino , Zanardi Federica , Iannelli Fabio , Oldani Amanda , Costa Elisa , Chighizola Matteo , Lorenc Ewelina , Minardi Simone P. , Fortuzzi Stefano , Filugelli Martina , Garzone Giovanna , Pisati Federica , Vecchi Manuela , Pruneri Giancarlo , Shigeki Kusamura , Baratti Dario , Cattaneo Laura , Parazzoli Dario , Podestà Alessandro , Milione Massimo , Deraco Marcello , Pierotti Marco A. , Gariboldi Manuela 
Published: July 2021

AbstractPeritoneal metastases (PM) from colorectal cancer (CRC) are associated with poor survival. The extracellular matrix (ECM) plays a fundamental role in modulating the homing of CRC metastases to the peritoneum. The mechanisms underlying the interactions between metastatic cells and the ECM, however, remain poorly understood and the number ofin vitromodels available for the study of the peritoneal metastatic process is limited. Here, we show that decellularized ECM of the peritoneal cavity allows the growth of organoids obtained from PM, favoring the development of three-dimensional nodules that maintain the characteristics ofin vivoPM. Organoids preferentially grow on scaffolds obtained from neoplastic peritoneum, which are characterized by greater stiffness than normal scaffolds. A gene expression analysis of organoids grown on different substrates reflected faithfully the clinical and biological characteristics of the organoids. An impact of the ECM on the response to standard chemotherapy treatment for PM was also observed.SignificanceEvidence of the value of ex vivo 3D models obtained by combining patient-derived extracellular matrices depleted of cellular components and organoids to mimic the metastatic niche, to be used as a tool to develop new therapeutic strategies in a biologically relevant context, to personalize treatments and increase their efficacy.
Variant calling tool evaluation for variable size indel calling from next generation whole genome and targeted sequencing data
Authors: Wang Ning , Lysenkov Vladislav , Orte Katri , Kairisto Veli , Aakko Juhani , Khan Sofia , Elo Laura L. 
Published: July 2021

AbstractInsertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools on indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage, coupled with specific variant calling tools.Author summaryThe development of next generation sequencing (NGS) technologies and computational algorithms enabled large scale, simultaneous detection of wide range of genetic variants, such as single nucleotide variants as well as insertions and deletions (indels), which may confer potential clinical significance. Recently, many studies have been conducted to evaluate variant calling tools on indel calling. However, the optimal indel size range for different variant calling tools remain unclear. A good benchmarking dataset for indel calling evaluation should contain biologically representative high-confident indels with a wide size range and preferably come from various sequencing settings. In this article, we created a semi-simulated whole genome sequencing dataset where the sequencing data was computationally generated. The indels in the semi-simulated genome were incorporated from a real human sample to represent biologically realistic indels and to avoid inclusion of variants due to potential technical sequencing errors. Furthermore, we used three real-world NGS datasets generated by whole genome or targeted sequencing to further evaluate our candidate tools. Our results demonstrated that variant calling tools varies greatly in calling different sizes of indels. Deletion calling and insertion calling also showed differences among the tools. The sequencing settings in coverage and read length also had a great impact on indel calling. Our results suggest that the accurate indel calling was dependent on the combination of a variant calling tool, indel size range and sequencing settings.
How Axon and Dendrite Branching Are Governed by Time, Energy, and Spatial Constraints
Authors: Desai-Chowdhry Paheli , Brummer Alexander , Savage Van 
Published: July 2021

AbstractNeurons are connected by complex branching processes - axons and dendrites - that collectively process information for organisms to respond to their environment. Classifying neurons according to differences in structure or function is a fundamental part of neuroscience. Here, by constructing new biophysical theory and testing against our empirical measures of branching structure, we establish a correspondence between neuron structure and function as mediated by principles such as time or power minimization for information processing as well as spatial constraints for forming connections. Specifically, based on these principles, we use undetermined Lagrange multipliers to predict scaling ratios for axon and dendrite sizes across branching levels. We test our predictions for radius and length scale factors against those extracted from neuronal images, measured for cell types and species that range from insects to whales. Notably, our findings reveal that the branching of axons and peripheral nervous system neurons is mainly determined by time minimization, while dendritic branching is mainly determined by power minimization. Further comparison of different dendritic cell types reveals that Purkinje cell dendrite branching is constrained by material costs while motoneuron dendrite branching is constrained by conduction time delay over a range of species. Our model also predicts a quarter-power scaling relationship between conduction time delay and species body size, which is supported by experimental data and may help explain the emergence of hemispheric specialization in larger animals as a means to offset longer time delays.Author summaryNeurons are the basic building blocks of the nervous system, responsible for information processing and communication in animals. They consist of a centralized cell body and two types of processes - axons and dendrites - that connect to one another. Previous studies of the differences among neuron cell types have focused on comparisons of either structure or function separately, without considering combined effects. Based on theory for structure of and flow through biological resource distribution networks, we develop a new model that relates neuron structure to function. We find that differences in structure between axons and dendrites as well as between dendrites of different cell types can be related to differences in function and associated evolutionary pressures. Moreover, using our mathematical model, we find that the conduction time delay of electrical signals systematically varies with species body size - neurons in larger species have longer delays - providing a possible explanation for hemispheric specialization in larger animals.
MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes
Authors: Zhang Xiaoxiao , Kschischo Maik 
Published: July 2021

AbstractTranslating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F1 score > 90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology.Author summaryCancer researchers perform experiments with cell lines to better understand the biology of cancer and to develop new anti-cancer treatments. A prerequisite to translate promising results from these in vitro experiments to clinical applications is to use the most appropriate cell line for a given tumour or cancer subtype. We present MFmap (model fidelity map), a deep learning technique to integrate cancer genomic data from patients with cell line data. The MFmap neural network compresses complex genomic features from thousands of genes into a small set of features called latent representations. This makes cell line and tumour data comparable and allows cancer researchers to select the best cell line which closely resembles a specific type of tumours or even an individual tumour. By classifying cancer cell lines into subtypes, MFmap offers a new possibility to predict the effect of therapeutic compounds in a particular tumour subtype. For the example of an aggressive brain tumour we demonstrate that MFmap can be used to study cell-state transformations during the disease course. In addition, MFmap is a promising machine learning method with potential applications in many other areas of biology and medicine.
Heterogeneity in the transcriptional response of the human pathogen Aspergillus fumigatus to the antifungal agent caspofungin
Authors: Colabardini Ana Cristina , Wang Fang , Dong Zhiqiang , Pardeshi Lakhansing , Rocha Marina Campos , Costa Jonas Henrique , dos Reis Thaila Fernanda , Brown Alec , Jaber Qais Z. , Fridman Micha , Fill Taicia , Rokas Antonis , Malavazi Iran , Wong Koon Ho , Goldman Gustavo Henrique 
Published: July 2021

AbstractAspergillus fumigatus is the main causative agent of invasive pulmonary aspergillosis (IPA), a severe disease that affects immunosuppressed patients worldwide. The fungistatic drug caspofungin is the second line therapy against IPA but has increasingly been used against clinical strains that are resistant to azoles, the first line antifungal therapy. In high concentrations, caspofungin induces a tolerance phenotype with partial reestablishment of fungal growth called caspofungin paradoxical effect (CPE), resulting from a change in the composition of the cell wall. An increasing number of studies has shown that different isolates of A. fumigatus exhibit phenotypic heterogeneity, including heterogeneity in their CPE response. To gain insights into the underlying molecular mechanisms of CPE response heterogeneity, we analyzed the transcriptomes of two A. fumigatus reference strains, Af293 and CEA17, exposed to low and high caspofungin concentrations. We found that there is a core transcriptional response that involves genes related to cell wall remodeling processes, mitochondrial function, transmembrane transport, and amino acid and ergosterol metabolism, and a variable response related to secondary metabolite (SM) biosynthesis and iron homeostasis. Specifically, we show here that the overexpression of a SM pathway that works as an iron chelator extinguishes the CPE in both backgrounds, whereas iron depletion is detrimental for the CPE in Af293 but not in CEA17. We next investigated the function of the transcription factor CrzA, whose deletion was previously shown to result in heterogeneity in the CPE response of the Af293 and CEA17 strains. We found that CrzA constitutively binds to and modulates the expression of several genes related to processes involved in caspofungin tolerance, and that crzA deletion differentially impacts the SM production and growth of Af293 and CEA17. As opposed to the ΔcrzACEA17 mutant, the ΔcrzAAf293 mutant fails to activate cell wall remodeling genes upon caspofungin exposure, which most likely severely affects its macrostructure and extinguishes its CPE. This work describes how heterogeneity in the response to an antifungal agent between A. fumigatus strains stems from heterogeneity in the function of a transcription factor and its downstream target genes.
Supervised generative design of regulatory DNA for gene expression control
Authors: Zrimec Jan , Fu Xiaozhi , Muhammad Azam Sheikh , Skrekas Christos , Jauniskis Vykintas , Speicher Nora K. , Börlin Christoph S. , Verendel Vilhelm , Chehreghani Morteza Haghir , Dubhashi Devdatt , Siewers Verena , David Florian , Nielsen Jens , Zelezniak Aleksej 
Published: July 2021

AbstractIn order to control gene expression, regulatory DNA variants are commonly designed using random synthetic approaches with mutagenesis and screening. This however limits the size of the designed DNA to span merely a part of a single regulatory region, whereas the whole gene regulatory structure including the coding and adjacent non-coding regions is involved in controlling gene expression. Here, we prototype a deep neural network strategy that models whole gene regulatory structures and generatesde novofunctional regulatory DNA with prespecified expression levels. By learning directly from natural genomic data, without the need for large synthetic DNA libraries, our ExpressionGAN can traverse the whole sequence-expression landscape to produce sequence variants with target mRNA levels as well as natural-like properties, including over 30% dissimilarity to any natural sequence. We experimentally demonstrate that this generative strategy is more efficient than a mutational one when using purely natural genomic data, as 57% of the newly-generated highly-expressed sequences surpass the expression levels of natural controls. We foresee this as a lucrative strategy to expand our knowledge of gene expression regulation as well as increase expression control in any desired organism for synthetic biology and metabolic engineering applications.