bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using methylation data to improve transcription factor binding prediction
Authors: Morgan Daniel , DeMeo Dawn L. , Glass Kimberly 
Published: May 2022

AbstractScanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) is commonly used to estimate transcription factor (TF) binding “motif” locations. However, the PWM-based scores assigned to these inferred locations are associated with only modest accuracy when benchmarked against in vivo TF binding. One reason for this limited performance may be because PWMs do not incorporate information regarding the epigenetic context necessary for TF binding. To investigate this, we developed a framework to score inferred TF binding locations using CpG methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that, for most TFs, binding is better predicted using methylation-based scoring compared to standard PWM scores. In addition, our analysis shows that, while most TFs do not bind to methylated promoter regions, there are several exceptions to this rule, indicating that the role of methylation in TF binding may be cell-type and context specific. Finally, we also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context specific TF binding using methylation data, a crucial initial step for understanding the impact of methylation on gene regulatory processes.
Deep learning-based approach for the characterization and quantification of histopathology in mouse models of colitis
Authors: Kobayashi Soma , Shieh Jason , de Sabando Ainara Ruiz , Kim Julie , Liu Yang , Zee Sui Y. , Prasanna Prateek , Bialkowska Agnieszka B. , Saltz Joel H. , Yang Vincent W. 
Published: May 2022

AbstractInflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. As such, researchers have studied mouse models of colitis to further understand its pathogenesis and identify new treatment targets. Although bench methods like flow cytometry and RNA-sequencing can characterize immune responses with single-cell resolution, whole murine colon specimens are processed at once. Given the simultaneous presence of colonic regions that are involved or uninvolved with abnormal histology, processing whole colons may lead to a loss of spatial context. Detecting these regions in hematoxylin and eosin (H&E)-stained colonic tissues offers the downstream potential of quantifying immune populations in areas with and without disease involvement by immunohistochemistry on serially sectioned slides. This could provide a complementary, spatially-aware approach to further characterize populations identified by other methods. However, detection of such regions requires expert interpretation by pathologists and is a tedious process that may be difficult to perform consistently across experiments. To this end, we have trained a deep learning model to detect ‘Involved’ and ‘Uninvolved’ regions from H&E-stained colonic slides across controls and three mouse models of colitis – the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5ΔIND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. The trained classifier allows for extraction of ‘Involved’ colonic regions across mice to cluster and identify histological classes. Here, we show that quantification of ‘Involved’ and ‘Uninvolved’ image patch classes in swiss rolls of colonic specimens can be utilized to train a linear determinant analysis classifier to distinguish between mouse models. Such an approach has the potential for revealing histological links and improving synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms.
Fob1-dependent condensin recruitment and loop extrusion on yeast chromosome III
Authors: Dinda Manikarna , Fine Ryan D. , Saha Shekhar , Wang Zhenjia , Zang Chongzhi , Li Mingguang , Smith Jeffrey S. 
Published: May 2022

AbstractDespite recent advances in single-molecule and structural analysis of condensin activity in vitro, mechanisms of functional condensin loading and loop extrusion that lead to specific chromosomal organization remain unclear. In Saccharomyces cerevisiae, the most prominent condensin loading site is the rDNA locus on chromosome XII, but its repetitiveness deters rigorous analysis of individual genes. An equally prominent non-rDNA condensin site is located on chromosome III (chrIII). It lies in the promoter of a putative non-coding RNA gene called RDT1, which is in a segment of the recombination enhancer (RE) that dictates MATa-specific chrIII organization. Here, we unexpectedly find that condensin is recruited to RDT1 through interactions with Fob1, Tof2, and cohibin (Lrs4/Csm1), a set of nucleolar factors that also recruit condensin to the rDNA. Using Micro-C XL, we uncover evidence for condensin-driven loop extrusion anchored by Fob1 and cohibin at RDT1 that extends toward MATa on the right arm of chrIII, supporting donor preference during mating-type switching. S. cerevisiae chrIII therefore provides a new platform for the study of programmed condensin-mediated chromosome conformation.
SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles
Authors: Wu Zhenqin , Trevino Alexandro E. , Wu Eric , Swanson Kyle , Kim Honesty J. , D’Angio H. Blaize , Preska Ryan , Charville Gregory W. , Dalerba Piero D. , Egloff Ann Marie , Uppaluri Ravindra , Duvvuri Umamaheswar , Mayer Aaron T. , Zou James 
Published: May 2022

AbstractMultiplexed immunofluorescence imaging enables high-dimensional molecular profiling at subcellular resolution. However, learning disease-relevant cellular environments from these rich imaging data is an open challenge. We developed SPAtial CEllular Graphical Modeling (SPACE-GM), a geometric deep learning framework that flexibly models tumor microenvironments (TMEs) as cellular graphs. We applied SPACE-GM to 658 head-and-neck and colorectal human cancer samples assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with cancer recurrence and patient survival after immunotherapy. SPACE-GM is substantially more accurate in predicting patient outcomes than previous approaches for modeling spatial data using neighborhood cell-type compositions. Computational interpretation of the disease-relevant microenvironments identified by SPACE-GM generates insights into the effect of spatial dispersion of tumor cells and granulocytes on patient prognosis.
Profile of Mycobacterium tuberculosis-specific CD4 T cells at the site of disease and blood in pericardial tuberculosis
Authors: Du Bruyn Elsa , Ruzive Sheena , Howlett Patrick , Jacobs Ashley J. , Lindestam Arlehamn Cecilia S. , Sette Alessandro , Sher Alan , Mayer-Barber Katrin D. , Barber Daniel L. , Mayosi Bongani , Ntsekhe Mpiko , Wilkinson Robert J. , Riou Catherine 
Published: May 2022

ABSTRACTOur understanding of the immune response at the site of disease in extra-pulmonary tuberculosis (EPTB) is still limited. In this study, using flow cytometry, we defined the pericardial fluid (PCF) cellular composition and compared the phenotypic and functional profile of Mycobacterium tuberculosis (Mtb)-specific T cells between PCF and whole blood in 16 patients with pericardial TB (PCTB). We found that lymphocytes were the predominant cell type in PCF in PCTB, with a preferential influx of CD4 T cells. The frequencies of TNF-α producing myeloid cells and Mtb-specific T cells were significantly higher in PCF compared to blood. Mtb-specific CD4 T cells in PCF exhibited a distinct phenotype compared to those in blood, with greater GrB expression and lower CD27 and KLRG1 expression. We observed no difference in the production IFNγ, TNF or IL-2 by Mtb-specific CD4 T cells between the two compartments, but MIP-1β production was lower in the PCF T cells. Bacterial loads in the PCF did not relate to the phenotype or function of Mtb-specific CD4 T cells. Upon anti-tubercular treatment completion, HLA-DR, Ki-67 and GrB expression was significantly decreased, and relative IL-2 production was increased in peripheral Mtb-specific CD4 T cells. Overall, using a novel and rapid experimental approach to measure T cell response ex vivo at site of disease, these results provide novel insight into molecular mechanisms and immunopathology at site of TB infection of the pericardium.
Climatic niche evolution of infectious diseases driving amphibian declines
Authors: Ellepola Gajaba , Herath Jayampathi , Dan Sun , Pie Marcio R. , Murray Kris A. , Pethiyagoda Rohan , Hanken James , Meegaskumbura Madhava 
Published: May 2022

AbstractClimate change and infectious diseases continue to drive global amphibian population declines, contributing to one of the greatest vertebrate extinctions of the Anthropocene. Currently around 16% amphibian species across the world are affected by four pathogens –Batrachochytrium dendrobatidis(Bd),B. salamandrivorans(Bsal),RanavirusandPerkinsea. A climatic context behind the dispersal of some of these diseases is hypothesized. However, the interplay between niche conservatism (NC) and climatic niche evolution (CNE), essential to understand disease evolution and dispersal, has so far received little attention. Here we show that the impacts of amphibian pathogens are intensifying as their climatic niches evolve. NC-based analyses suggest that niches of these diseases overlap, especially in Europe and East/southeast Asia (ESEA), and that all four pathogens will continue to devastate amphibians through seasonality shifts and range expansions, penetrating deeper into temperate regions and global amphibian diversity hotspots.Bdwill spread over diversity-rich mountain ranges and ranaviruses will overwhelm lowlands. CNE-based analyses suggest that the earliest lineages of these diseases originated in colder regions and that some lineages subsequently evolved towards warmer climatic niches. We caution that quiescent, warm-adapted strains are likely to become widespread and novel ranaviruses adapted to local climatic conditions and new hosts are likely to emerge. These results portend the dangers of introducing pathogens into new regions given their ability to adapt to changing climate scenarios. In a climatic background conducive to most of these diseases, frequent monitoring, enhanced biosecurity measures and policy reforms are needed for disease control and mitigation.
In situ architecture and membrane fusion of SARS-CoV-2 Delta variant
Authors: Song Yutong , Yao Hangping , Wu Nanping , Xu Jialu , Zhang Zheyuan , Peng Cheng , Li Shibo , Kong Weizheng , Chen Yong , Zhu Miaojin , Wang Jiaqi , Shi Danrong , Zhao Chongchong , Lu Xiangyun , Galindo Martín Echavarría , Li Sai 
Published: May 2022

AbstractAmong the current five Variants of Concern, infections caused by the SARS-CoV-2 B.1.617.2 (Delta) variant are often associated with the greatest severity. Despite recent advances on the molecular basis of elevated pathogenicity using recombinant proteins, architecture of intact Delta virions remains veiled. Moreover, molecular evidences for the detailed mechanism of S-mediated membrane fusion are missing. Here we reported the in situ structure and distribution of S on the authentic Delta variant, and discovered invagination in the distinctive Delta architecture. We also captured fusion snapshots from the virus-virus fusion events, provided structural evidences for Delta’s attenuated dependency on cellular factors for fusion activation, and proposed a model of S-mediated membrane fusion. Site-specific glycan analysis revealed increased oligomannose-type glycosylation of native Delta S over that of the Wuhan-Hu-1 S. Together, these results disclose distinctive factors of Delta being the most virulent SARS-CoV-2 variant.In BriefCryo-ET of intact SARS-CoV-2 Delta variant revealed its distinctive architecture and captured snapshots of its membrane fusion in action.
Low spontaneous mutation rate in a complex multicellular eukaryote with a haploid-diploid life cycle
Authors: Krasovec Marc , Lipinska Agnieszka P. , Coelho Susana M. 
Published: May 2022

AbstractThe spontaneous mutation rate µ is a crucial parameter driving evolution and biodiversity. Mutation rates are highly variable across species, suggesting that µ is susceptible to selection and drift and that species life cycle and life history may impact its evolution. In particular, asexual reproduction and haploid selection are expected to affect mutation rate, but very little empirical data is available to test this expectation. Here, we sequence 30 genomes of a parent-offspring pedigree in the brown algae Ectocarpus to test the impact of its life cycle on mutation rate. Ectocarpus alternates between a haploid and a diploid stage, both multicellular and free living, and utilizes both sexual and asexual reproduction. Ectocarpus is therefore a unique model to empirically test expectations of the effect of asexual reproduction and haploid selection on mutation rate evolution. We estimate that Ectocarpus has a nucleotide mutation rate of µbs=4.07×10−10 per site per generation, a surprisingly low number for a multicellular complex eukaryote. Effective population size (Ne) and genome size could not explain this low mutation rate. We propose that the haploid-diploid life cycle, combined with extensive asexual reproduction may be key drivers of mutation rate.
Integration of single cell gene expression data in Bayesian association analysis of rare variants
Authors: Zhong Guojie , Choi Yoolim A. , Shen Yufeng 
Published: May 2022

AbstractWe present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts.
An algorithmic framework for isoform-specific functional analysis
Authors: Karlebach Guy , Carmody Leigh , Sundaramurthi Jagadish Chandrabose , Casiraghi Elena , Hansen Peter , Reese Justin , Mungall Chris J , Valentini Giorgio , Robinson Peter N. 
Published: May 2022

AbstractGene Ontology (GO) overrepresentation analysis characterizes the biological mechanisms common to sets of differentially expressed genes identified by high-throughput experiments. To date, GO overrepresentation analysis has mainly been used to evaluate differentially expressed genes, but short- and long-read RNA-seq technologies now allow increasingly accurate identification of differential alternative splicing. The function of most splice isoforms remain unknown, but if acccurate predictions could be made, overrepresentation analysis could be applied to differentially spliced isoforms to assess the functional implications of alternative splicing in RNA-seq experiments. We present isopret (Isoform Interpretation), a new paradigm for isoform function prediction based on the expectation-maximization framework. isopret leverages the relationships between sequence and functional isoform similarity to infer isoform specific functions in a highly accurate fashion. This enabled us to adapt GO overrepresentation analysis, which to date has been limited to differential gene expression, to be extended to assess overrepresentation of GO annotations in differentially spliced isoforms. An analysis of 100 RNA-seq studies including investigations of development, cancer, and common disease demonstrated that expression and splicing regulate different sets of biological functions. We make isopret predictions freely available in a desktop application that can be used to analyze differential expression and splicing in any bulk RNA-seq dataset.
Machine Learning Predicts Treatment Response in Bipolar & Major Depressive Disorders
Authors: Salman Mustafa S. , Verner Eric , Bockholt H. Jeremy , Fu Zening , Misiura Maria , Baker Bradley T. , Osuch Elizabeth , Sui Jing , Calhoun Vince D. 
Published: May 2022

AbstractDiagnosis of bipolar disorder (BD) patients with complex symptoms presents a challenge to clinicians. Patients tend to spend more time in a depressive state than a manic state. In such complex cases, the current Diagnostic and Statistical Manual (DSM), which is not based on pathophysiology, can lead to misdiagnosis as major depressive disorder (MDD) and an imperfect or even harmful medication response. A biologically-based classification algorithm is needed to improve the accuracy of diagnosis. Osuch et al. (2018) presented a kernel support vector machine (SVM) algorithm to predict the medication-class of response from new patient samples whose diagnoses were unclear. Here we also utilize the kernel support vector machine (SVM) algorithm but with a few novel contributions. We applied the robust, fully automated neuromark independent component analysis (ICA) framework to extract comparable features in a multi-dataset setting and learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework successfully replicates the prior result with 95.45% accuracy (sensitivity 90.24%, specificity 92.3%). To further evaluate the generalizability of our approach, we incorporated two additional datasets comprising bipolar disorder (BD) and major depressive disorder (MDD) patients. We validated the trained algorithm on these datasets, resulting in a testing accuracy of up to 89% (sensitivity 0.88, specificity 0.89) without using site or scanner harmonization techniques. We also translated the model to predict improvement scores of major depressive disorder (MDD) with up to 70% accuracy. This approach reveals some salient biological markers of medication-class of response within mood disorders.HighlightsWe demonstrate a DSM-free approach for predicting treatment response from resting-state functional magnetic resonance imaging (fMRI) data.We identify several replicable biomarkers using the approach.Our work has potential for clinical application by replacing trial-and-error in treating complex psychiatric disorders.
Maturation kinetics of phagosomes depends on their transport switching from actin to microtubule tracks
Authors: Yu Yanqi , Zhang Zihan , Yu Yan 
Published: May 2022

AbstractPhagosomes, where internalized pathogens are ingested, mature into a degradative unit through a sequence of dynamical and biochemical changes. The maturation process requires the active transport of phagosomes on actin and microtubules, but how phagosome maturation is kinetically dependent on their actin- and microtubule-mediated transport is poorly characterized. Here, we focused on the early-to-late phagosome maturation and investigated how the kinetic rate of this early maturation process depends on the timing of phagosome transition from actin to microtubules. We performed single-phagosome tracking of their sequential maturation activities, including recruitment of phagosome markers and acidification. Simultaneously, we measured their transport dynamics. We showed that the timing of phagosome transport from actin cortex to microtubules controls the kinetics of early phagosome assembly and transition to late phagosomes. This ultimately determines the fate of phagosome maturation. Our results reveal distinct mechanisms by which the actin- and microtubule-based transport of phagosomes temporally controls the maturation progression of phagosomes during their degradative function.
Novel epidemiological model of gastrointestinal-nematode infection to assess grazing cattle resilience by integrating host growth, parasite, grass and environmental dynamics
Authors: Filipe J.A.N. , Kyriazakis I. , McFarland C. , Morgan E.R. 
Published: May 2022

ABSTRACTGastrointestinal nematode (GIN) infections are ubiquitous and often cause morbidity and reduced performance in livestock. Emerging anthelmintic resistance and increasing change in climate patterns require evaluation of alternatives to traditional treatment and management practices. Mathematical models of parasite transmission between hosts and the environment have contributed towards the design of appropriate control strategies in ruminants, but have yet to account for relationships between climate, infection pressure, immunity, resources, and growth. Here, we develop a new epidemiological model of GIN transmission in a herd of grazing cattle, including host tolerance (body weight and feed intake), parasite burden and acquisition of immunity, together with weather-dependent development of parasite free-living stages, and the influence of grass availability on parasite transmission. Dynamic host, parasite and environmental factors drive a variable rate of transmission. Using literature sources, the model was parametrised forOstertagia ostertagi, the prevailing pathogenic GIN in grazing cattle populations in temperate climates. Model outputs were validated on published empirical studies from first season grazing cattle in Northern Europe. These results show satisfactory qualitative and quantitative performance of the model; they also indicate the model may approximate the dynamics of grazing systems under co-infection byO. ostertagiandCooperia oncophora, a second GIN species common in cattle. In addition, model behaviour was explored under illustrative anthelmintic treatment strategies, considering impacts on parasitological and performance variables. The model has potential for extension to explore altered infection dynamics as a result of management and climate change, and to optimise treatment strategies accordingly. As the first mechanistic model to combine parasitic and free-living stages of GIN with host feed-intake and growth, it is well suited to predict complex system responses under non-stationary conditions. We discuss the implications, limitations and extensions of the model, and its potential to assist in the development of sustainable parasite control strategies.HIGHLIGHTSNematode control in cattle is complicated by drug resistance and climate changeA model was developed to predict GIN epidemiology under varying conditionsThe model incorporates cattle growth, infection and immunity, grass availability, weatherPredictions were validated against empirical studies of GIN in N EuropeThe model applies toOstertagia ostertagi, and possibly to co-infectingCooperiaAbstract Figurehttps://www.lmcni.com/