bluuebunny/crossref_metadata_2025_split huggingface dataset:
PRDM1 DNA-binding zinc finger domain is required for normal limb development and is disrupted in Split Hand/Foot Malformation
Authors: Truong Brittany T. , Shull Lomeli C. , Lencer Ezra , Bend Eric G. , Field Michael ,   University of Washington Center for Mendelian Genomics (UW-CMG), Everman David , Schwartz Charles E. , Flanagan-Steet Heather , Artinger Kristin B. 
Published: November 2022

ABSTRACTSplit Hand/Foot Malformation (SHFM) is a rare limb abnormality with clefting of the fingers and/or toes. For many patients, the genetic etiology is unknown. Through whole exome and targeted sequencing, we detected three novel variants in a transcription factor,PRDM1that arosede novoin families with SHFM or segregated with the phenotype. PRDM1 is required for limb development; however, its role is not well understood, and it is unclear how thePRDM1variants affect protein function. Using transient and stable overexpression rescue experiments in zebrafish, we show that the variants, which disrupt the proline/serine-rich and DNA-binding zinc finger domains have reduced function compared to wildtypePRDM1. Through gene expression assays, RNA-seq, and CUT&RUN in isolated pectoral fin cells, we demonstrate that Prdm1a directly binds to and regulates genes required for limb induction, outgrowth, and anterior/posterior patterning, such asfgfr1a, dlx5a, dlx6a, andsmo. Together, these results improve our understanding of the role of PRDM1 in the limb gene regulatory network and demonstrate the pathogenicity ofPRDM1variants in humans.SUMMARY STATEMENTPRDM1 proline/serine and zinc finger domains are required to regulate limb induction, outgrowth, and anterior/posterior patterning. Variants in PRDM1 are shown to cause Split Hand/Foot Malformation in humans.
Effect of COVID-19 pandemic on essential health care services and mitigation strategies implemented in Benishangul gumuz: An emerging region of Ethiopia
Authors: Amaya Alemayehu Assefa 
Published: November 2022

AbstractHealth systems of low-to-middle-income countries may have fewer buffering resources and capacity against shocks from a pandemic. This study assessed the effects of COVID-19 on essential health care services and its mitigation strategies employed in Benishangul Gumuz Region in the Assosa district of western Ethiopia. Institutional based cross-sectional study design with multilevel triangulated mixed approach was used. Purposively selected health facilities and key informant or in-depth interviewee from different facilities were involved. Document review from health facilities reports and case registration logs were used to access the service utilizations per- and -post the pandemic. The study showed that there was a significant decrement in antenatal care (ANC) visits, family planning new acceptors, family planning repeated acceptors, skilled delivery, and in postnatal care within 7 days of delivery during the COVID-19 pandemic. There was an increase in home delivery, teenage pregnancy and still births. There was also a significant reduction in the vaccination coverage for almost all vaccines. During the pandemic, children treated for diarrheal disease and the number of people screened for human immunodeficiency virus (HIV), new HIV-positive individuals, and new HIV-positive individuals receiving antiretroviral therapy have decreased significantly. There has been a statistically significant increase in the number of people with elevated blood sugar during the pandemic. The COVID-19 pandemic has had a significant negative impact on essential health services, most notably in Benishangul Gumuz Region. Groups of individuals considered to be at higher risks of morbidity and mortality are the most significantly affected. Mitigation strategies have been implemented to address the indirect effects of the COVID-19 pandemic and improve health care delivery in the region’s highly affected areas. Urgent and comprehensive action is needed to mitigate the future (long-term) impact of Covid-19 on the health of community in the region.
Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: a resting-state hdEEG study
Authors: Duma Gian Marco , Danieli Alberto , Mento Giovanni , Vitale Valerio , Opipari Raffaella Scotto , Jirsa Viktor , Bonanni Paolo , Sorrentino Pierpaolo 
Published: November 2022

ABSTRACTObjectiveLarge aperiodic bursts of activations named neuronal avalanches have been used to characterize whole-brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations of large-scale brain network dynamics. Here, we exploited neuronal avalanches to characterize differences in the electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls.MethodWe defined neuronal avalanches as starting when the z-scored source-reconstructed EEG signals crossed a specific threshold in any region and ending when all regions went back to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale-free components of the signals. We computed individual avalanche transition matrices, to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients.ResultsWe observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long-term memory performance (delay recall Rey figure test).SignificanceOur results show that the propagation patterns of large-scale neuronal avalanches are altered in TLE during resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance supports the neurophysiological relevance of neuronal avalanches.Key PointsInvestigation of the brain dynamics during resting-state activity in patients with TLE using neuronal avalanches (i.e., large scale patterns of activation)We found higher transition probabilities in patients with TLE in the entorhinal cortex, inferior temporal and fusiform gyri, and anterior cingulate cortexWe found higher eigenvector centrality of the left entorhinal cortex in the avalanche transition matrix, which was related to reduced long term memory performanceDiscussion of the potential application of the avalanche transition matrix as diagnostic tool in presurgical evaluations and epilepsy types differentiation
Development of prediction models for antenatal care attendance in Amhara region, Ethiopia
Authors: Wilder Bryan , Pons-Duran Clara , Goddard Frederick G. B. , Hunegnaw Bezawit Mesfin , Haneuse Sebastien , Bekele Delayehu , Chan Grace J. 
Published: November 2022

ABSTRACTBackgroundIn low-resource settings, coverage of at least four antenatal care (ANC) visits remains low. As a first step towards enhancing ANC attendance, this study aims to develop a series of predictive models to identify women who are at high risk of failing to attend ANC in a rural setting in Ethiopia.MethodsThis is a cohort study conducted in the Birhan field site, Amhara region. Using data of a surveillance system and a pregnancy cohort, we developed and internally validated a series of logistic regressions with regularization (LASSO), and ensembles of decision trees.Discrimination was estimated using the area under the receiving operator characteristic curve (AUC). Three prediction time points were considered: conception, and gestational weeks 13 and All models were internally validated using 5-fold cross validation to avoid overfitting.ResultsThe study sample size was 2195. Mean age of participants was 26.8 years (Standard Deviation (SD) 6.1) and mean gestational age at enrolment was 25.5 weeks (SD 8.8). A total of 582 women (26.5%) failed to attend ANC during cohort follow-up. We observed AUC in the range of 0.61-0.70, with higher values for models predicting at weeks 13 and 24. All AUC values were similar with slightly higher performance for the ensembles of decision trees.ConclusionThis study presents a series of prediction models for ANC attendance with modest performance. The developed models may be useful to identify women at high risk of missing their ANC visits to target interventions to improve attendance rates. This study opens the possibility to develop and validate easy-to-use tools to predict health-related behaviors in settings with scarce resources.SUMMARY BOXNo published studies to date have developed risk prediction models for ANC attendance.The presented models show modest performance, but may be useful to identify pregnancies at a high risk of not initiating ANC.This type of models could be used by countries with strong community health programs to identify high-risk women to target specific interventions aiming to improve ANC attendance rates, increasing feasibility and cost-effectiveness of those interventions.Our models were internally validated using cross-validation to avoid overfitting, and despite not being tested in other populations, they are useful to inform local and regional health authorities.This study demonstrates that it is possible to develop predictive models for behavioral outcomes using data from surveillance systems and pregnancy cohorts in settings with scarcity of resources.
High Throughput Transcriptomics to Understand Chemical Drivers of Aggressive Breast Cancer Subtypes
Authors: Sala-Hamrick Kimberley E. , Tapaswi Anagha , Polemi Katelyn M. , Nguyen Vy. K , Colacino Justin A. 
Published: November 2022

AbstractBackgroundThe impact of chemical exposures on breast cancer progression is poorly characterized and may influence the development of more severe and aggressive subtypes.ObjectivesThere is a suite of toxicants, including metals, pesticides, and personal care product compounds, which are commonly detected at high levels in US Center for Disease Control’s National Health and Nutrition Examination Survey (NHANES) chemical biomarker screens. To characterize the impact of these toxicants on breast cancer pathways, we performed high throughput dose-response transcriptomic analysis of toxicant exposed breast cells.MethodsWe treated non-tumorigenic mammary epithelial cells, MCF10A, with 21 chemicals at four doses (25nM, 250nM, 2.5µM, 25µM) for 48 hours. We conducted RNA-sequencing for these 408 samples, adapting the PlexWell plate-based RNA-sequencing method to analyze changes in gene expression resulting from these exposures. For each chemical, we calculated gene and biological pathway specific benchmark doses using BMDExpress2, identifying differentially expressed genes and generating the best fit benchmark dose models for each gene. We employed enrichment testing to test whether each chemical’s upregulated or downregulated genes were over-represented in a biological process or pathway. We contextualized benchmark doses relative to human population biomarker concentrations in NHANES.ResultsOverall, significant changes in gene expression varied across doses of each chemical and benchmark dose modeling revealed dose-responsive alterations of thousands of different genes. Comparison of benchmark data to NHANES chemical biomarker concentrations indicated an overlap between actual exposure levels and levels sufficient to cause a gene expression response. Enrichment and cell deconvolution analyses showed benchmark dose responses correlated with changes in cancer and breast cancer related pathways, including induction of basal-like characteristics for some chemicals, including p,p’-DDE, lead, copper, and methyl paraben.DiscussionThese analyses revealed that these 21 chemicals induce significant changes in pathways involved in breast cancer initiation and progression at human exposure relevant doses.
A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia
Authors: Kazemivash Behnam , VanErp Theo GM , Kochunov Peter , Calhoun Vince D. 
Published: November 2022

AbstractSchizophrenia is a severe brain disorder with serious symptoms including delusions, disorganized speech, and hallucinations that can have a long-term detrimental impact on different aspects of a patient’s life. It is still unclear what the main cause of schizophrenia is, but a combination of altered brain connectivity and structure may play a role. Neuroimaging data has been useful in characterizing schizophrenia, but there has been very little work focused on voxel-wise changes in multiple brain networks over time, despite evidence that functional networks exhibit complex spatiotemporal changes over time within individual subjects. Recent studies have primarily focused on static (average) features of functional data or on temporal variations between fixed networks; however, such approaches are not able to capture multiple overlapping networks which change at the voxel level.In this work, we employ a deep residual convolutional neural network (CNN) model to extract 53 different spatiotemporal networks each of which captures dynamism within various domains including subcortical, cerebellar, visual, sensori-motor, auditory, cognitive control, and default mode. We apply this approach to study spatiotemporal brain dynamism at the voxel level within multiple functional networks extracted from a large functional magnetic resonance imaging (fMRI) dataset of individuals with schizophrenia (N=708) and controls (N=510). Our analysis reveals widespread group level differences across multiple networks and spatiotemporal features including voxel-wise variability, magnitude, and temporal functional network connectivity in widespread regions expected to be impacted by the disorder. We compare with typical average spatial amplitude and show highly structured and neuroanatomically relevant results are missed if one does not consider the voxel-wise spatial dynamics. Importantly, our approach can summarize static, temporal dynamic, spatial dynamic, and spatiotemporal dynamics features, thus proving a powerful approach to unify and compare these various perspectives.In sum, we show the proposed approach highlights the importance of accounting for both temporal and spatial dynamism in whole brain neuroimaging data generally, shows a high-level of sensitivity to schizophrenia highlighting global but spatially unique dynamics showing group differences, and may be especially important in studies focused on the development of brain-based biomarkers.
TimiGP: inferring inter-cell functional interactions and clinical values in the tumor immune microenvironment through gene pairs
Authors: Li Chenyang , Zhang Baoyi , Schaafsma Evelien , Reuben Alexandre , Zhang Jianjun , Cheng Chao 
Published: November 2022

AbstractDetermining how immune cells functionally interact in the tumor microenvironment and identifying their biological roles and clinical values are critical for understanding cancer progression and developing new therapeutic strategies. Here we introduce TimiGP, a computational method to infer inter-cell functional interaction networks and annotate the corresponding prognostic effect from bulk gene expression and survival statistics data. When applied to metastatic melanoma, TimiGP overcomes the prognostic bias caused by immune co-infiltration and identifies the prognostic value of immune cells consistent with their anti- or pro-tumor roles. It reveals the functional interaction network in which the interaction X→Y indicates a more positive impact of cell X than Y on survival. This network provides immunological insights to facilitate the development of prognostic models, as evidenced by our computational-friendly, biologically interpretable, independently validated models. By leveraging single-cell RNA-seq data for specific immune cell subsets, TimiGP has the flexibility to delineate the tumor microenvironment at different resolutions and is readily applicable to a wide range of cancer types.
Dietary fiber controls blood pressure and cardiovascular risk by lowering large intestinal pH and activating the proton-sensing receptor GPR65
Authors: Xie Liang , Muralitharan Rikeish R. , Dinakis Evany , Antonacci Simona , Leung Kwan Charmaine , McArdle Zoe , Mirabito Colafella Katrina , Nakai Michael , Paterson Madeleine , Peh Alex , Jama Hamdi , Salimova Ekaterina , Anderson Dovile , Ang Caroline , Alam Md Jahangir , Yap Yu-Anne , Creek Darren , Robert Remy , O’Donnell Joanne A. , Mackay Charles R. , Marques Francine Z. 
Published: November 2022

AbstractHigh blood pressure (BP) is the most common cause of death globally, due to increasing the risk of cardiovascular diseases. Dietary fiber regulates BP through gut microbial production of acidic metabolites known as short-chain fatty acids (SCFAs). The specific mechanisms of how SCFAs regulate BP are still emerging. In a phenome-wide association study, we identified that the proton-sensing G-protein-coupled receptorGPR65gene is associated with hypertension and its associated end-organ damage phenotypes. We hypothesized that acidic metabolites produced from the gut microbiota may activate GPR65, thus conferring BP regulating effects. We found that dietary fiber levels determined the luminal and interstitial tissue pH in the large intestine through production of SCFAs by the gut microbiota. We identified that low pH produced by high fiber intake, acting via GPR65 signaling, increased cAMP production and phosphorylation of CREB, and restricted the production of hypertension-promoting inflammatory cytokines by CD8+T cells.Gpr65−/−mice spontaneously developed higher BP, cardiac and renal hypertrophy and fibrosis. We showed that the benefits of a diet high in fiber, which prevented hypertension and associated end-organ damage, were decreased inGpr65−/−mice. Finally, adoptive transfers revealed that GPR65 deficiency in CD8+T cells causally explained this phenotype. In conclusion, we showed that pH sensing by GPR65 in CD8+ T cells mediates much of the cardiovascular benefits of dietary fiber. pH sensing represents a novel gene-by-environment interaction of gut microbiota-to-host biological effects and may form the basis for new therapeutic strategies for hypertension.
An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic Discovery
Authors: Yuan Bo , Hormozdiari Farhad , McLean Cory Y. , Cosentino Justin 
Published: November 2022

AbstractGenome-wide association studies (GWAS) are used to identify genetic variants significantly correlated with a target disease or phenotype as a first step to detect potentiallycausalgenes. The availability of high-dimensional biomedical data in population-scale biobanks has enabled novel machine-learning-based phenotyping approaches in which machine learning (ML) algorithms rapidly and accurately phenotype large cohorts with both genomic and clinical data, increasing the statistical power to detect variants associated with a given phenotype. While recent work has demonstrated that these methods can be extended to diseases for which only low quality medical-record-based labels are available, it is not possible to quantify changes in statistical power since the underlying ground-truth liability scores for the complex, polygenic diseases represented by these medical-record-based phenotypes is unknown. In this work, we aim to empirically study the robustness of ML-based phenotyping procedures to label noise by applying varying levels of random noise to vertical cup-to-disc ratio (VCDR), a quantitative feature of the optic nerve that is predictable from color fundus imagery and strongly influences glaucoma referral risk. We show that the ML-based phenotyping procedure recovers the underlying liability score across noise levels, significantly improving genetic discovery and PRS predictive power relative to noisy equivalents. Furthermore, initial denoising experiments show promising preliminary results, suggesting that improving such methods will yield additional gains.
Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
Authors: Villemin Jean-Philippe , Bassaganyas Laia , Pourquier Didier , Boissiere Florence , Cabello-Aguilar Simon , Crapez Evelyne , Tanos Rita , Cornillot Emmanuel , Turtoi Andrei , Colinge Jacques 
Published: November 2022

ABSTRACTThe study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different bulk datasets, including new Visium liver metastasis ST data, with experimental validation of selected interactions. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.
DNAJB6 Isoform Specific Knockdown: Therapeutic Potential for LGMDD1
Authors: Findlay Andrew R. , Paing May M. , Daw Jil A. , Bengoechea Rocio , Pittman Sara K. , Li Shan , Wang Feng , Miller Timothy M. , True Heather L. , Chou Tsui-Fen , Weihl Conrad C. 
Published: November 2022

AbstractDominant missense mutations in DNAJB6, an HSP40 co-chaperone, cause limb girdle muscular dystrophy (LGMD) D1. No treatments are currently available. Two isoforms exist, DNAJB6a and DNAJB6b, each with distinct localizations in muscle. Mutations reside in both isoforms, yet evidence suggests only DNAJB6b is responsible for disease pathogenesis. Mechanistic data supports either a toxic gain of function, a dominant negative mechanism, or a combination of both. Knockdown treatment strategies involving both isoforms carry risk as DNAJB6 knockout is embryonic lethal. We therefore developed an isoform specific knockdown approach using morpholinos. Selective reduction of each isoform was achievedin-vitroin primary mouse myotubes and human myoblasts, as well asin-vivoin mouse skeletal muscle. To assess isoform specific knockdown in LGMDD1, we created primary myotube cultures from aknock-inLGMDD1 mouse model. Using mass spectrometry, we identified an LGMDD1 protein signature related to protein homeostasis and myofibril structure. Selective reduction of DNAJB6b levels in LGMDD1 myotubes corrected much of the proteomic disease signature towards wild type levels. While additionalin-vivofunctional data is required, these findings suggest selective reduction of DNAJB6b may be a viable therapeutic target for LGMDD1.