bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aberrant miR-29 is a predictive feature of severe phenotypes in pediatric Crohn’s disease
Authors: Shumway Alexandria J. , Shanahan Michael T. , Hollville Emilie , Chen Kevin , Beasley Caroline , Villanueva Jonathan W. , Albert Sara , Lian Grace , Cure Moises R. , Schaner Matthew , Zhu Lee-Ching , Bantumilli Surekha , Deshmukh Mohanish , Furey Terrence S. , Sheikh Shehzad Z. , Sethupathy Praveen 
Published: December 2022

AbstractCrohn’s disease (CD) is a chronic inflammatory gut disorder. Molecular mechanisms underlying the clinical heterogeneity of CD remain poorly understood. MicroRNAs (miRNAs) are important regulators of gut physiology and several have been implicated in the pathogenesis of adult CD. However, there is a dearth of large-scale miRNA studies for pediatric CD. We hypothesized that specific miRNAs uniquely mark pediatric CD. We performed small RNA-sequencing of patient-matched non-inflamed colon and ileum biopsies from treatment-naïve pediatric patients with CD (n=169) and a control cohort (n=108). Comprehensive miRNA analysis revealed 58 miRNAs altered in pediatric CD. Notably, multinomial logistic regression analysis revealed that index levels of ileal miR-29 are strongly predictive of severe inflammation and stricturing. Transcriptomic analyses of transgenic mice overexpressing miR-29 show a significant reduction of the tight junction protein genePmp22and classic Paneth cell markers. The dramatic loss of Paneth cells was confirmed by histologic assays. Moreover, we also found that pediatric CD patients with elevated miR-29 exhibit significantly lower Paneth cell counts, increased inflammation scores, and reduced levels ofPMP22. These findings strongly indicate that miR-29 up-regulation is a distinguishing feature of pediatric CD, highly predictive of severe phenotypes, and associated with inflammation and Paneth cell loss.
Synthetic whole-slide image tile generation with gene expression profiles infused deep generative models
Authors: Carrillo-Perez Francisco , Pizurica Marija , Ozawa Michael G. , Vogel Hannes , West Robert B. , Kong Christina S. , Herrera Luis Javier , Shen Jeanne , Gevaert Olivier 
Published: December 2022

AbstractThe acquisition of multi-modal biological data for the same sample, such as RNA sequencing and whole slide imaging (WSI), has increased in recent years, enabling studying human biology from multiple angles. However, despite these emerging multi-modal efforts, for the majority of studies only one modality is typically available, mostly due to financial or logistical constraints. Given these difficulties, multi-modal data imputation and multi-modal synthetic data generation are appealing as a solution for the multi-modal data scarcity problem. Currently, most studies focus on generating a single modality (e.g. WSI), without leveraging the information provided by additional data modalities (e.g. gene expression profiles). In this work, we propose an approach to generate WSI tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GAN) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists in comparison to tiles generated using traditional GANs and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles:https://rna-gan.stanford.edu/and the code for RNA-GAN is available here:https://github.com/gevaertlab/RNA-GAN.
Evolution of seasonal plasticity in response to climate change differs between life-stages of a butterfly
Authors: Nielsen Matthew E. , Nylin Sören , Wiklund Christer , Gotthard Karl 
Published: December 2022

AbstractClimate change alters seasonal environments without altering photoperiod, creating a cue-environment mismatch for organisms that rely on photoperiod as a cue for seasonal plasticity and phenology. Evolution can potentially correct for this mismatch by altering the photoperiodic reaction norm, but often phenology depends on multiple plastic decisions made at different life stages and times of year. We tested whether seasonal plasticity in different life stages evolves independently or in concert under climate change usingPararge aegeria(Speckled wood butterfly). This butterfly uses day length as a cue for life history plasticity in two different life stages: larval development time and pupal diapause. Photoperiodic reaction norms for plasticity in these traits were first measured over 30 years ago for two different Swedish populations. In this study, we replicated historic experiments that measured these reaction norms using the contemporary populations. We found evidence for evolution of the reaction norm for larval development time, but in opposite directions in the two populations. In contrast, we found no evidence for evolution of the reaction norm for pupal diapause. These results show that different life stages can evolve differently in response to climate change and only studying one part of the life cycle will not always be enough to fully understand how climate change impacts phenotypic plasticity and phenology.
Machine learning prediction of cancer cell metabolism from autofluorescence lifetime images
Authors: Hu Linghao , Wang Nianchao , Bryant Joshua D , Liu Lin , Xie Linglin , West A Phillip , Walsh Alex J. 
Published: December 2022

AbstractMetabolic reprogramming at a cellular level contributes to many diseases including cancer, yet few assays are capable of measuring metabolic pathway usage by individual cells within living samples. Here, we combine autofluorescence lifetime imaging with single-cell segmentation and machine-learning models to predict the metabolic pathway usage of cancer cells. The metabolic activities of MCF7 breast cancer cells and HepG2 liver cancer cells were controlled by growing the cells in culture media with specific substrates and metabolic inhibitors. Fluorescence lifetime images of two endogenous metabolic coenzymes, reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), were acquired by a multi-photon fluorescence lifetime microscope and analyzed at the cellular level. Quantitative changes of NADH and FAD lifetime components were observed for cells using glycolysis, oxidative phosphorylation, and glutaminolysis. Conventional machine learning models trained with the autofluorescence features classified cells as dependent on glycolytic or oxidative metabolism with 90 – 92% accuracy. Furthermore, adapting convolutional neural networks to predict cancer cell metabolic perturbations from the autofluorescence lifetime images provided improved performance, 95% accuracy, over traditional models trained via extracted features. In summary, autofluorescence lifetime imaging combined with machine learning models can detect metabolic perturbations between glycolysis and oxidative metabolism of living samples at a cellular level, providing a label-free technology to study cellular metabolism and metabolic heterogeneity.
Parallels and divergences in landscape genetic and metacommunity patterns in zooplankton inhabiting soda pans
Authors: Horváth Zsófia , Haileselasie Tsegazeabe Hadush , Vad Csaba F. , Ptacnik Robert , Meester Luc De 
Published: December 2022

AbstractEcological processes maintaining landscape genetic variation and metacommunity structure in natural landscapes have traditionally been studied in isolation. Their integrated study may hold important information as to what extent the effect of major ecological processes are species-or landscape-specific, resulting in a more coherent picture on the spatial organization of biodiversity. Here, we explicitly compared the relative importance of spatial and environmental drivers of both cladoceran metacommunity structure as well as landscape genetic structure of its most widespread member, the water fleaDaphnia magna, in soda pans of the Seewinkel region in Austria. This landscape of soda pans is characterized by strong environmental gradients and unidirectional wind acting as a key dispersal agent among these temporary habitats. Our study shows both parallels and divergences in the relative importance of local environmental sorting and spatial connectivity in determining landscape genetic versus metacommunity structure. The metacommunity is structured primarily by the environment, while in theD. magnametapopulation, the spatial signal is predominant. The much weaker environmental signal inDaphniacan be explained by the fact that the microsatellite markers are presumably neutral and was confirmed by a per-allele analysis. An important parallel between metacommunity and landscape genetic structure is the strong signal of the prevailing wind direction in determining the spatial pattern. This suggests that for both community assembly in cladocerans and population assembly inD. magna, wind plays an important role in determining connectivity among soda pans, thereby affecting dispersal and colonization rates, influencing both local species and genetic composition.
Genetic basis and selection of glyceollin induction in wild soybean
Authors: Yasmin Farida , Zhang Hengyou , Leamy Larry , Wang Baosheng , Winnike Jason , Reid Robert W. , Brouwer Cory R. , Song Bao-Hua 
Published: December 2022

SummaryGlyceollins, a family of phytoalexin induced in legume species, play essential roles in responding to environmental stresses and in human health. However, little is known about the genetic basis and selection of glyceollin induction.We employed a metabolite-based genome-wide association (mGWA) approach to identify candidate genes involved in glyceollin induction from genetically diverse and understudied wild soybeans subjected to soybean cyst nematode stress.Eight SNPs on chromosomes 3, 9, 13, 15, and 20 showed significant association with glyceollin induction. Six genes close to one of the significant SNPs (ss715603454) on chromosome 9 fell into two clusters, and they encode enzymes in the glycosyltransferase class within the phenylpropanoid pathway. Transcription factors (TFs) genes, such asMYBandWRKYwere also found within the linkage disequilibrium of the significant SNPs on chromosome 9. Epistasis and a strong selection signal were detected on the four significant SNPs on chromosome 9.Gene clusters and transcription factors may play important roles in regulating glyceollin induction in wild soybeans. Additionally, as major evolutionary factors, epistatic interactions and selection may influence glyceollin variation in natural populations.
The pan-genome and local adaptation ofArabidopsis thaliana
Authors: Kang Minghui , Wu Haolin , Liu Wenyu , Zhu Mingjia , Han Yu , Liu Wei , Chen Chunlin , Yin Kangqun , Zhao Yusen , Yan Zhen , Liu Huanhuan , Lou Shangling , Zan Yanjun , Liu Jianquan 
Published: December 2022

AbstractArabidopsis thalianahas been used as a model species for research in a diverse collection of plant species. However, previous studies based on single reference genomes and short-read sequencing data are restricted to detecting variable genes and large structural variation (SV) underlying local adaptation. Here wede novoassemble high-quality chromosomal genomes of 38A. thalianaecotypes (with 6 relict ones) using PacBio-HiFi long-read sequencing. From these newly assembled genomes, we annotate several thousand new genes through pan-genomic analysis in comparison to the previous reference genome. The identified variable genes are mainly enriched in and associated with ecological adaptation and this species substantially expands its gene repertoire for local adaptation. We construct a graph-based pan-genome and identify 62,525 SVs which overlap with 14,243 genes. These genes are enriched in multiple ecological adaptation functions, including secondary metabolic processes, enzyme regulation, and biotic/abiotic stimulus. For example, a 566 bp insertion in the promoter of the light-adaptationKNAT3gene was specific to the high-altitude relict Tibet-0 ecotype. This SV reduces the expression level ofKNAT3and promotesA. thalianaadaptation to habitats high in light radiation. In addition, compared with the SNPs, the SVs identified in this study captured the missing heritability and we detected novel SV associations with environmental variables in their native range, highlighting the value of SVs in environmental adaptation. The genome resources presented here will help pinpoint genetic changes that include both SVs and the ecotype-specific genes for local adaptation ofA. thalianaand increase our understanding of the molecular mechanisms in this model species to respond to varied habitats.
Regulatory changes associated with the head to trunk developmental transition
Authors: Duarte Patrícia , Correia Rion Brattig , Nóvoa Ana , Mallo Moisés 
Published: December 2022

AbstractDevelopment of vertebrate embryos is characterized by early formation of the anterior tissues followed by the sequential extension of the axis at their posterior end to build the trunk and tail structures, first by the activity of the primitive streak and then of the tail bud. Embryological, molecular and genetic data demonstrate that head and trunk development are significantly different, indicating that the transition into the trunk formation stage involves changes in regulatory gene networks, that might include the acquisition of cell competence to respond to key regulatory factors. Herein, we explored the regulatory changes involved in this developmental transition by assessing the transcriptome and chromatin accessibility profiles from the posterior epiblast region of mouse embryos at embryonic day (E)7.5 and E8.5. We observed changes in various cell processes, including signaling pathways, ubiquitination, ion dynamics and metabolic processes involving lipids that could contribute to the functional switch in the progenitor region of the embryo. Our data also led to the identification of novel mechanisms controlling the differential Wnt functional requirements during head and trunk development. Moreover, we found substantial changes in chromatin accessibility mostly mapping to intergenic regions, indicating a significant switch in the regulatory elements controlling either head or trunk development. In addition, we tested the functional relevance of potential enhancers ofWnt5aandNr2f2, identified in the accessibility assays, that reproduced the expression profiles of the target genes. Deletion of these regions by genome editing had limited effect on the expression of those genes, suggesting the existence of redundant enhancers that guarantee robust expression patterns. Overall, this study provides new insights on the regulatory mechanisms that change during the transition from head to trunk development.Author SummaryVertebrate main body axis is generated sequentially from head to tail. The developmental processes building head and trunk structures are significantly different, and the transition between these two stages requires substantial changes in functional gene regulatory networks. Herein, we explored such changes through genome wide analyses in developing mouse embryos. We observed significant differences in several signaling pathways and in the basic cell machinery, which may interact promoting a functional switch in the differentiating progenitor cells. We also found substantial changes in the accessibility of regulatory elements controlling either head or trunk formation, which conditioned the binding activity of key developmental transcription factors. Overall, our study gives relevant insights into the mechanisms regulating the head to trunk transition that, if disrupted, can lead to embryonic developmental arrest.
Polygenic Parkinson’s disease genetic risk score as risk modifier of parkinsonism in Gaucher disease
Authors: Blauwendraat Cornelis , Tayebi Nahid , Woo Elizabeth Geena , Lopez Grisel , Fierro Luca , Toffoli Marco , Limbachiya Naomi , Hughes Derralynn , Pitz Vanessa , Patel Dhairya , Vitale Dan , Koretsky Mathew J. , Hernandez Dena , Real Raquel , Alcalay Roy N. , Nalls Mike A , Morris Huw R , Schapira Anthony H. V. , Balwani Manisha , Sidransky Ellen 
Published: December 2022

AbstractBackgroundBi-allelic pathogenic variants inGBA1are the cause of Gaucher disease (GD1), a lysosomal storage disorder resulting from deficient glucocerebrosidase. HeterozygousGBA1variants are also a common genetic risk factor for Parkinson’s disease (PD). GD manifests with considerable clinical heterogeneity and is also associated with an increased risk of PD.ObjectiveTo investigate the contribution of PD risk variants to risk of PD in patients with GD1.MethodsWe studied 225 patients with GD1, including 199 without PD and 26 with PD. All cases were genotyped and the genetic data was imputed using common pipelines.ResultsOn average, patients with GD1 with PD have a significantly higher PD genetic risk score than those without PD (P=0.021).ConclusionsOur results indicate that variants included in the PD genetic risk score were more frequent in patients with GD1 who developed PD, suggesting that common risk variants may affect underlying biological pathways.Supplemental datahere
Assessing the clinical application of deep-learning-derived CT volumetric measures in neurodegenerative disease diagnostics
Authors: Srikrishna Meera , Ashton Nicholas J. , Moscoso Alexis , Pereira Joana B. , Heckemann Rolf A. , van Westen Danielle , Volpe Giovanni , Simrén Joel , Zettergren Anna , Kern Silke , Wahlund Lars-Olof , Gyanwali Bibek , Hilal Saima , Ruifen Joyce Chong , Zetterberg Henrik , Blennow Kaj , Westman Eric , Chen Christopher , Skoog Ingmar , Schöll Michael 
Published: December 2022

AbstractBackgroundExisting bio fluid and imaging biomarkers used in research and clinical diagnostics of neurodegenerative diseases are often expensive or invasive and are mainly available in specialised care centres. CT is an affordable and widely available imaging modality predominantly used to evaluate structural abnormalities, but not for the volumetric quantification of neurodegeneration. Previously, we developed a deep learning model trained on MRI segmentations from individuals with paired CT and MR scans, which achieved high accuracy and robust tissue classification based on brain CT images.PurposeTo explore the diagnostic utility of deep-learning-derived CT-based atrophy measures and study their association with relevant cognitive, biochemical and other imaging markers of neurodegenerative diseases.Materials and methodsIn this retrospective study, we analysed 917 CT and 744 MR scans from cognitively healthy participants of the Gothenburg H70 Birth Cohort (70.4 ± 2.6 years) and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore (73 Alzheimer’s disease, 20 vascular dementia, 22 cognitively normal; 74.0 ± 8.2 years). We tested associations between six CT-derived volumetric measures with clinical diagnosis, fluid and imaging biomarkers and cognition.ResultsIn the Memory Clinic Cohort, deep-learning-derived CT-based atrophy measures differentiated cognitively healthy individuals from Alzheimer’s disease (AUC 0.88; 95% CI: 0.79-0.96) and vascular dementia (AUC 0.91; 95% CI: 0.81-1.00) patients with high accuracy levels comparable to MR-derived measures. Additionally, CT-based measures distinguished early, prodromal Alzheimer’s disease (AUC= 0.73, 95% CI: 0.62, 0.85) and prodromal vascular dementia patients from healthy individuals (CT-GM: AUC= 0.7, 95% CI: 0.51, 0.81). CT-derived volumes were significantly associated with measures of cognition and biochemical markers of neurodegeneration, notably plasma-derived neurofilament light (ρ=-0.43, p<0.001, in the Memory Clinic Cohort).ConclusionOur findings provide strong evidence for the potential of deep-learning-derived CT-based atrophy measures in aiding neurodegenerative disease diagnostics in primary care settings.
The effect of the foreign language on cognitive reappraisal during exposure to a phobic stimulus
Authors: Ortigosa-Beltrán Isabel , Jaén Irene , Costumero Víctor , García-Palacios Azucena 
Published: December 2022

AbstractThe foreign language effect shows that emotional reactivity is reduced when we use a second language instead of our native one to address different situations. The present study aims to test whether the use of cognitive reappraisal could be influenced by the participant’s language (native/foreign). For this purpose, a sample of 60 participants with subclinical phobia to cockroaches was exposed to several phobic and neutral pictures while they used cognitive reappraisal in their native or in a foreign language. Physiological measures (pupil dilation and electrodermal activity) and self-reports of affective valence and arousal were collected. Results show an overall effectiveness of the strategy of reappraisal in both languages. Additionally, the use of a foreign language depicts a decrease in the affective negativity of the threat stimuli in terms of valence. The ratings of arousal also show a general higher arousal as an effect of the use of a foreign language. The present results suggest that using a foreign language could be advantageous to reduce negative emotionality by reappraisal. However, it could be a barrier for reappraising non-unpleasant pictures. Further studies should explore the foreign language effect in psychotherapy to open new ways of approaching different disorders.
Young people’s data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom
Authors: Sieberts Solveig K. , Marten Carly , Bampton Emily , Björling Elin A. , Burn Anne-Marie , Carey Emma Grace , Carlson Sonia , Fernandes Blossom , Kalha Jasmine , Lindani Simthembile , Masomera Hedwick , Neelakantan Lakshmi , Pasquale Lisa , Ranganathan Swetha , Scanlan Jo , Shah Himani , Sibisi Refiloe , Sumant Sushmita , Suver Christine , Thungana Yanga , Tummalacherla Meghasyam , Velloza Jennifer , Collins Pamela Y. , Fazel Mina , Ford Tamsin , Freeman Melvyn , Pathare Soumitra , Zingela Zukiswa ,   The MindKind Consortium, Doerr Megan 
Published: December 2022

AbstractMobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them. To answer this question, we developed the MindKind Study, a multinational, mixed methods study that solicits young people’s preferences for how their data are governed and quantifies potential participants’ willingness to join under different conditions. We employed a community-based participatory approach, involving young people as stakeholders and co-researchers. At sites in India, South Africa, and the UK, we enrolled 3575 participants ages 16-24 in the mobile app-mediated quantitative study and 143 participants in the public deliberation-based qualitative study. We found that while youth participants have strong preferences for data governance, these preferences did not translate into (un)willingness to join the smartphone-based study. Participants grappled with the risks and benefits of participation as well as their desire that the “right people” access their data. Throughout the study, we recognized young people’s commitment to finding solutions and co-producing research architectures to allow for more open sharing of mental health data to accelerate and derive maximal benefit from research.