bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differences in disease burdens across human populations are governed more by neutral evolution than by natural selection
Authors: Hazra Ujani , Lachance Joseph 
Published: December 2021

AbstractThe prevalence of most complex diseases varies across human populations, and a combination of socioeconomic and biological factors drives these differences. Likewise, divergent evolutionary histories can lead to different genetic architectures of disease, where allele frequencies and linkage disequilibrium patterns at disease-associated loci differ across global populations. However, it is presently unknown how much natural selection contributes to the health inequities of complex polygenic diseases. Here, we focus on ten hereditary diseases with the largest global disease burden in terms of mortality rates (e.g., coronary artery disease, stroke, type 2 diabetes, and lung cancer). Leveraging multiple GWAS and polygenic risk scores for each disease, we examine signatures of selection acting on sets of disease-associated variants. First, on a species level, we find that genomic regions associated with complex diseases are enriched for signatures of background selection. Second, tests of polygenic adaptation incorporating demographic histories of continental super-populations indicate that most complex diseases are primarily governed by neutral evolution. Third, we focus on a finer scale, testing for recent positive selection on a population level. We find that even though some disease-associated loci have undergone recent selection (extreme values of integrated haplotype scores), sets of disease-associated loci are not enriched for selection when compared to baseline distributions of control SNPs. Collectively, we find that recent natural selection has had a negligible role in driving differences in the genetic risk of complex diseases between human populations. These patterns are consistent with the late age of onset of many complex diseases.
Chloride-dependent mechanisms of multimodal sensory discrimination and neuropathic sensitization in Drosophila
Authors: Himmel Nathaniel J. , Sakurai Akira , Letcher Jamin M. , Patel Atit A. , Bhattacharjee Shatabdi , Benson Maggie N. , Gray Thomas R. , Cymbalyuk Gennady S. , Cox Daniel N. 
Published: December 2021

AbstractIndividual sensory neurons can be tuned to many stimuli, each driving unique, stimulus-relevant behaviors, and the ability of multimodal nociceptor neurons to discriminate between potentially harmful and innocuous stimuli is broadly important for organismal survival. Moreover, disruptions in the capacity to differentiate between noxious and innocuous stimuli can result in neuropathic pain. Drosophila larval Class III (CIII) neurons are peripheral noxious cold nociceptors and gentle touch mechanosensors; high levels of activation drive cold-evoked contraction (CT) behavior, while low levels of activation result in a suite of touch-associated behaviors. However, it is unknown what molecular factors underlie CIII multimodality. Here, we show that the TMEM16/anoctamins subdued and white walker (wwk; CG15270) are required for cold-evoked CT, but not for touch-associated behavior, indicating a conserved role for anoctamins in nociception. We also evidence that CIII neurons make use of atypical depolarizing chloride currents to encode cold, and that overexpression of ncc69—a fly homologue of NKCC1—results in phenotypes consistent with neuropathic sensitization, including behavioral hypersensitization and spontaneous nociceptor activity, making Drosophila CIII neurons a candidate system for future studies of the basic mechanisms underlying neuropathic pain.
Single-fiber nucleosome density shapes the regulatory output of a mammalian chromatin remodeling enzyme
Authors: Abdulhay Nour J , Hsieh Laura J , McNally Colin P , Ketavarapu Mythili , Kasinathan Sivakanthan , Nanda Arjun S , Ostrowski Megan S , Wu Ke , Moore Camille M , Goodarzi Hani , Narlikar Geeta J , Ramani Vijay 
Published: December 2021

ABSTRACTATP-dependent chromatin remodelers regulate the DNA accessibility required of virtually all nuclear processes. Biochemical studies have provided insight into remodeler action at the nucleosome level, but how these findings translate to activity on chromatin fibers in vitro and in vivo remains poorly understood. Here, we present a massively multiplex single-molecule platform allowing high-resolution mapping of nucleosomes on fibers assembled on mammalian genomic sequences. We apply this method to distinguish between competing models for chromatin remodeling by the essential ISWI ATPase SNF2h: linker-length-dependent dynamic positioning versus fixed-linker-length static clamping. Our single-fiber data demonstrate that SNF2h operates as a density-dependent, length-sensing chromatin remodeler whose ability to decrease or increase DNA accessibility depends on single-fiber nucleosome density. In vivo, this activity manifests as different regulatory modes across epigenomic domains: at canonically-defined heterochromatin, SNF2h generates evenly-spaced nucleosome arrays of multiple nucleosome repeat lengths; at SNF2h-dependent accessible sites, SNF2h slides nucleosomes to increase accessibility of motifs for the essential transcription factor CTCF. Overall, our generalizable approach provides molecularly-precise views of the processes that shape nuclear physiology. Concurrently, our data illustrate how a mammalian chromatin remodeling enzyme can effectively sense nucleosome density to induce diametrically-opposed regulatory effects within the nucleus.
Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review
Authors: Borchert R , Azevedo T , Badhwar A , Bernal J , Betts M , Bruffaerts R , Burkhart MC , Dewachter I , Gellersen HM , Low A , Machado L , Madan CR , Malpetti M , Mejia J , Michopoulou S , Muñoz-Neira C , Peres M , Phillips V , Ramanan S , Tamburin S , Tantiangco H , Thakur L , Tomassini A , Vipin A , Tang E , Newby D , Ranson J , Llewellyn D.J. , Veldsman M , Rittman T 
Published: December 2021

AbstractIntroductionRecent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review.MethodsWe systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results.ResultsWe found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other individual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimer’s disease dementias.ConclusionThe use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.
Genomics-informed outbreak investigations of SARS-CoV-2 using civet
Authors: O’Toole Áine , Hill Verity , Jackson Ben , Dewar Rebecca , Sahadeo Nikita , Colquhoun Rachel , Rooke Stefan , McCrone JT , McHugh Martin P , Nicholls Sam , Poplawski Radoslaw ,   The COVID-19 Genomics UK (COG-UK) Consortium,   COVID-19 Impact Project (Trinidad & Tobago Group), Aanensen David , Holden Matt , Connor Tom , Loman Nick , Goodfellow Ian , Carrington Christine V. F. , Templeton Kate , Rambaut Andrew 
Published: December 2021

AbstractThe scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 5 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different ’catchments’ and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health.
Relationship Between Alcohol Consumption and Telecommuting Preference-Practice Mismatch During the COVID-19 Pandemic
Authors: Watanabe Chihiro , Konno Yusuke , Yoshimura Reiji , Tateishi Seiichiro , Hino Ayako , Tsuji Mayumi , Ogami Akira , Nagata Masako , Muramatsu Keiji , Fujino Yoshihisa ,   for the CORoNaWork project
Published: December 2021

AbstractIntroductionThis study examined the association between increased alcohol consumption and telecommuting, comparing employees who expressed a preference for telecommuting and those who did not.MethodsWe conducted an internet monitor survey. Responses from 20,395 of the 33,302 participants were included in the final sample. Participants were asked about their desire for and frequency of telecommuting, and about changes in alcohol consumption under the COVID-19 pandemic. Data were analyzed by logistic regression analysis.ResultsParticipants who telecommuted despite preferring not to do so reported significantly increased alcohol consumption, as revealed by a multivariate analysis (OR=1.62, 95% CI 1.25-2.12). Participants who expressed a preference for telecommuting showed no such increase.ConclusionsUnder the COVID-19 pandemic, telecommuting that involves a mismatch with employee preference for way of working may be a new risk factor for problematic drinking.
AutoMicroED: A semi-automated MicroED processing pipeline
Authors: Powell Samantha M. , Novikova Irina V. , Kim Doo Nam , Evans James E. 
Published: December 2021

AbstractDespite rapid adaptation of micro-electron diffraction (MicroED) for protein and small molecule structure determination to sub-angstrom resolution, the lack of automation tools for easy MicroED data processing remains a challenge for expanding to the broader scientific community. In particular, automation tools, which are novice user friendly, compatible with heterogenous datasets and can be run in unison with data collection to judge the quality of incoming data (similar to cryosparc LIVE for single particle cryoEM) do not exist. Here, we present AutoMicroED, a cohesive and semi-automatic MicroED data processing pipeline that runs through image conversion, indexing, integration and scaling of data, followed by merging of successful datasets that are pushed through phasing and final structure determination. AutoMicroED is compatible with both small molecule and protein datasets and creates a straightforward and reproducible method to solve single structures from pure samples, or multiple structures from mixed populations. The immediate feedback on data quality, data completeness and more parameters, aids users to identify whether they have collected enough data for their needs. Overall, AutoMicroED permits efficient structure elucidation for both novice and experienced users with comparable results to more laborious manual processing.
Spike initiation properties in the axon support high-fidelity signal transmission
Authors: Kamaleddin Mohammad Amin , Abdollahi Nooshin , Ratté Stéphanie , Prescott Steven A 
Published: December 2021

ABSTRACTThe axon initial segment (AIS) converts graded depolarization into all-or-none spikes that are transmitted by the axon to downstream neurons. Analog-to-digital transduction and digital signal transmission call for distinct spike initiation properties (filters) and those filters should, therefore, differ between the AIS and distal axon. Here we show that unlike the AIS, which spikes repetitively during sustained depolarization, the axon spikes transiently and only if depolarization reaches threshold before KV1 channels activate. Rate of depolarization is critical. This was shown by optogenetically evoking spikes in the distal axon of CA1 pyramidal neurons using different photostimulus waveforms and pharmacological conditions while recording antidromically propagated spikes at the soma, thus circumventing the prohibitive difficulty of patching intact axons. Computational modeling shows that KV1 channels in the axon implement a high-pass filter that is matched to the axial current waveform associated with spike propagation, thus maximizing the signal-to-noise ratio to ensure high-fidelity transmission of spike-based signals.
Transcriptomics reveal stretched human pluripotent stem cell-derived cardiomyocytes as an advantageous hypertrophy model
Authors: Pohjolainen Lotta , Ruskoaho Heikki , Talman Virpi 
Published: December 2021

AbstractLeft ventricular hypertrophy, characterized by hypertrophy of individual cardiomyocytes, is an adaptive response to an increased cardiac workload that eventually leads to heart failure. Previous studies using neonatal rat ventricular myocytes (NRVMs) and animal models have revealed several genes and signaling pathways associated with hypertrophy and mechanical load. However, these models are not directly applicable to humans. Here, we studied the effect of cyclic mechanical stretch on gene expression of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) using RNA sequencing. hiPSC-CMs showed distinct hypertrophic changes in gene expression at the level of individual genes and in biological processes. We also identified several differentially expressed genes that have not been previously associated with cardiomyocyte hypertrophy and thus serve as attractive targets for future studies. When compared to previously published data attained from stretched NRVMs and human embryonic stem cell-derived cardiomyocytes, hiPSC-CMs displayed a smaller number of changes in gene expression, but the differentially expressed genes revealed more pronounced enrichment of hypertrophy-related biological processes and pathways. Overall, these results establish hiPSC-CMs as a valuable in vitro model for studying human cardiomyocyte hypertrophy.Non-standard Abbreviations and AcronymsET-1, endothelin-1; GO, gene ontology; hESC-CM, human embryonic stem cell-derived cardiomyocyte; hiPSC, human induced pluripotent stem cell; hiPSC-CM, human induced pluripotent stem cell-derived cardiomyocyte; MAPK, mitogen-activated protein kinase; MEK1/2, mitogen-activated protein kinase kinase 1/2; NRVM, neonatal rat ventricular myocyte; PKC, protein kinase C; PBS, phosphate-buffered saline; RB+, RPMI 1640 supplemented with B-27; RB-, RPMI 1640 medium supplemented with B-27 without insulin; RT, room temperature; TF, transcription factor
Identifying Protein Features and Pathways Responsible for Toxicity using Machine learning, CANDO, and Tox21 datasets: Implications for Predictive Toxicology
Authors: Moukheiber Lama , Mangione William , Maleki Saeed , Falls Zackary , Gao Mingchen , Samudrala Ram 
Published: December 2021

AbstractHumans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning methods have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational model using machine learning for selecting the most important proteins representing features in predicting the toxicity of the compounds in the Tox21 dataset using the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) platform for therapeutic discovery. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For our computational model, we employed a random forest (RF) with the combination of Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) method, aka SMOTE+ENN, which is resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR), toxicity mediating transcription factor, and mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were passed into enrichment analysis to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with NR-AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong, with twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.
Prolactin maintains parental responses and alters reproductive axis gene expression, but not courtship behaviors, in both sexes of a biparental bird
Authors: Farrar Victoria S. , Flores Laura , Viernes Rechelle C. , Pereira Laura Ornelas , Mushtari Susan , Calisi Rebecca M. 
Published: December 2021

AbstractProlactin, a hormone involved in vertebrate parental care, is hypothesized to inhibit reproductive hypothalamic-pituitary-gonadal (HPG) axis activity during parenting, thus maintaining investment in the current brood as opposed to new reproductive efforts. While prolactin underlies many parental behaviors in birds, its effects on other reproductive behaviors, such as courtship, remain unstudied. How prolactin affects neuropeptide and hormone receptor expression across the avian HPG axis also remains unknown. To address these questions, we administered ovine prolactin (oPRL) or a vehicle control to both sexes in experienced pairs of the biparental rock dove (Columba livia), after nest removal at the end of incubation. We found that oPRL promoted parental responses to novel chicks and stimulated crop growth compared to controls, consistent with other studies. However, we found that neither courtship behaviors, copulation rates nor pair maintenance differed with oPRL treatment. Across the HPG, we found oPRL had little effect on gene expression in hypothalamic nuclei, but increased expression ofFSHBand hypothalamic hormone receptor genes in the pituitary. In the gonads, oPRL increased testes size and gonadotropin receptor expression, but did not affect ovarian state or small white follicle gene expression. However, the oviducts of oPRL-treated females were smaller and had lower estrogen receptor expression compared with controls. Our results highlight that some species, especially those that show multiple brooding, may be able to maintain mating behavior despite elevated prolactin. Thus, mechanisms may exist for prolactin to promote investment in parental care without concurrent inhibition of reproductive function or HPG axis activity.
Native SAD Phasing at Room Temperature
Authors: Greisman Jack B. , Dalton Kevin M. , Sheehan Candice J. , Klureza Margaret A. , Hekstra Doeke R. 
Published: December 2021

AbstractSingle-wavelength anomalous diffraction (SAD) is a routine method for overcoming the phase problem when solving a new macromolecular structure. This technique requires the accurate measurement of intensities to sensitively determine differences across Bijvoet pairs, making it a stringent test for the reliability of a data collection method. Although SAD experiments are commonly conducted at cryogenic temperatures to mitigate the effects of radiation damage, such temperatures can alter the conformational ensemble of the protein crystal and may impede the merging of data from multiple crystals due to non-uniform freezing. Here, we propose a data collection strategy to obtain high-quality data from room temperature samples. To illustrate the strengths of this approach we use native SAD phasing at 6.5 keV to solve four structures of three model systems at 295 K. The resulting datasets allow for automatic phasing and model building, and exhibit alternate conformations that are well-supported by the electron density. The high-redundancy data collection method demonstrated here enables the routine collection of high-quality, room-temperature diffraction to improve the study of protein conformational ensembles.
A Multiscale computational model of YAP signaling in epithelial fingering behaviour
Authors: Mukhtar Naba , Cytrynbaum Eric N , Edelstein-Keshet Leah 
Published: December 2021

ABSTRACTIn epithelial-mesenchymal transition (EMT), cells organized into sheets break away and become motile mesenchymal cells. EMT plays a crucial role in wound healing, embryonic development, and cancer metastasis. Intracellular signaling in response to mechanical, topographic, or chemical stimuli can promote EMT. We present a multiscale model for EMT downstream of the protein YAP, which suppresses the cell-cell adhesion protein E-cadherin and activates the GTPase Rac1 that enhances cell migration. We first propose an ODE model for YAP/Rac1/E-cadherin interactions. The model dynamics are bistable, accounting for motile loose cells as for adherent slower cells. We implement this model in a cellular Potts model simulation of 2D wound-healing using the open source platform Morpheus. We show that, under suitable stimuli (depicting topographic cues) the sheet exhibits finger-like projections and EMT. Morphological, as well as quantitative differences in YAP levels as well as cell speed across the sheet are consistent with preexisting experimental observations of epithelial sheets grown on topographic features in vitro. The simulation is also consistent with experiments that knockdown or over-express YAP, inhibit Rac1, or block E-cadherin.SIGNIFICANCEIn normal wound-healing, cell in an epithelium divide, grow, and migrate so as to seal a gap. In some pathological states, epithelial-mesenchymal transition (EMT) can lead to abnormal morphology, including fingering, breakage of single cells or multicellular clusters from the sheet edge. The mechanochemical control of this behaviour by cell signaling circuits (YAP, Rac1, and E-cadherin) reveals how the competition between cell adhesion and cell migration contributes to the process. We use the open-source computational platform Morpheus to investigate a multiscale model for the interactions of the proteins inside cells and the resulting morphology of the cell sheet. Results are consistent with experimental results in the literature.