bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards a Comprehensive Variation Benchmark for Challenging Medically-Relevant Autosomal Genes
Authors: Wagner Justin , Olson Nathan D , Harris Lindsay , McDaniel Jennifer , Cheng Haoyu , Fungtammasan Arkarachai , Hwang Yih-Chii , Gupta Richa , Wenger Aaron M , Rowell William J , Khan Ziad M , Farek Jesse , Zhu Yiming , Pisupati Aishwarya , Mahmoud Medhat , Xiao Chunlin , Yoo Byunggil , Sahraeian Sayed Mohammad Ebrahim , Miller Danny E. , Jáspez David , Lorenzo-Salazar José M. , Muñoz-Barrera Adrián , Rubio-Rodríguez Luis A. , Flores Carlos , Narzisi Giuseppe , Evani Uday Shanker , Clarke Wayne E. , Lee Joyce , Mason Christopher E. , Lincoln Stephen E. , Miga Karen H. , Ebbert Mark T. W. , Shumate Alaina , Li Heng , Chin Chen-Shan , Zook Justin M , Sedlazeck Fritz J 
Published: June 2021

AbstractThe repetitive nature and complexity of multiple medically important genes make them intractable to accurate analysis, despite the maturity of short-read sequencing, resulting in a gap in clinical applications of genome sequencing. The Genome in a Bottle Consortium has provided benchmark variant sets, but these excluded some medically relevant genes due to their repetitiveness or polymorphic complexity. In this study, we characterize 273 of these 395 challenging autosomal genes that have multiple implications for medical sequencing. This extended, curated benchmark reports over 17,000 SNVs, 3,600 INDELs, and 200 SVs each for GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically important genes including CBS, CRYAA, and KCNE1. Our proposed solution improves variant recall in these genes from 8% to 100%. This benchmark will significantly improve the comprehensive characterization of these medically relevant genes and guide new method development.
PALB2 maintains redox and mitochondrial homeostasis in the brain and cooperates with ATG7 to suppress p53 dependent neurodegeneration
Authors: Huo Yanying , Sawant Akshada , Tan Yongmei , Mahdi Amar H , Li Tao , Ma Hui , Bhatt Vrushank , Yan Run , Coleman Jake , Dreyfus Cheryl F. , Guo Jessie Yanxiang , Mouradian M. Maral , White Eileen , Xia Bing 
Published: June 2021

SummaryThe PALB2 tumor suppressor plays key roles in DNA repair and has been implicated in redox homeostasis. Autophagy maintains mitochondrial quality, mitigates oxidative stress and suppresses neurodegeneration. Here we show that Palb2 deletion in the mouse brain leads to motor deficits and that co-deletion of Palb2 with the essential autophagy gene Atg7 accelerates and exacerbates neurodegeneration induced by ATG7 loss. Palb2 deletion leads to elevated DNA damage, oxidative stress and mitochondrial markers, especially in Purkinje cells, and co-deletion of Palb2 and Atg7 results in accelerated Purkinje cell loss. Further analyses suggest that the accelerated Purkinje cell loss and severe neurodegeneration in the double deletion mice are due to oxidative stress and mitochondrial dysfunction, rather than DNA damage, and partially dependent on p53 activity. Our studies uncover a role of PALB2 in mitochondrial regulation and a cooperation between PALB2 and ATG7/autophagy in maintaining redox and mitochondrial homeostasis essential for neuronal survival.
Variation in Human Herpesvirus 6B telomeric integration, excision and transmission between tissues and individuals
Authors: Wood M.L. , Veal C. , Neumann R. , Suárez N.M. , Nichols J. , Parker A.J. , Martin D. , Romaine S.P.R , Codd V. , Samani N.J. , Voors A.A. , Tomaszewski M. , Flamand L. , Davison A.J. , Royle N.J. 
Published: June 2021

AbstractHuman herpesviruses 6A and 6B (HHV-6A/6B) are ubiquitous pathogens that persist lifelong in latent form and can cause severe conditions upon reactivation. They are spread by community-acquired infection of free virus (acqHHV6A/6B) and by germline transmission of inherited chromosomally- integrated HHV-6A/6B (iciHHV-6A/6B) in telomeres. We exploited a hypervariable region of the HHV- 6B genome to investigate the relationship between acquired and inherited virus and revealed predominantly maternal transmission of acqHHV-6B in families. Remarkably, we demonstrate that some copies of acqHHV-6B in saliva from healthy adults gained a telomere, indicative of integration and latency, and that the frequency of viral genome excision from telomeres in iciHHV-6B carriers is surprisingly high and varies between tissues. In addition, newly formed short telomeres generated by partial viral genome release are frequently lengthened, particularly in telomerase-expressing pluripotent cells. Consequently, iciHHV-6B carriers are mosaic for different iciHHV-6B structures, including circular extra-chromosomal forms that have the potential to reactivate. Finally, we show transmission of an HHV-6B strain from an iciHHV-6B mother to her non-iciHHV-6B son. Altogether we demonstrate that iciHHV-6B can readily transition between telomere-integrated and free virus forms.
The local anaesthetic procaine prodrugs ProcCluster® and Procaine-hydrochloride impair SARS-CoV-2 replication in vitro
Authors: Häring Clio , Schroeder Josefine , Löffler Bettina , Engert Beatrice , Ehrhardt Christina 
Published: June 2021

AbstractThe SARS-CoV-2 pandemic has had the world in suspense for more than a year. Even if more and more vaccines are approved there is still an urgent need for efficient antiviral treatment strategies. Here, we present data on the inhibitory effect of the local anaesthetic procaine, especially the prodrugs ProcCluster® and Procaine-hydrochloride on SARS-CoV-2 infection in vitro. Remarkably, similar effects could be shown on the replication of influenza A viruses in cell culture systems. Since the active ingredient procaine is well-tolerated and already used in the clinics for anaesthetic purposes, the further investigation of this substance could enable its reuse in antiviral therapy, including SARS-CoV-2.
Natural variation in theirldgene family affects starvation resistance inC. elegans
Authors: Webster Amy K. , Chitrakar Rojin , Powell Maya , Chen Jingxian , Fisher Kinsey , Tanny Robyn , Stevens Lewis , Evans Kathryn , Wei Angela , Antoshechkin Igor , Andersen Erik C. , Baugh L. Ryan 
Published: June 2021

ABSTRACTStarvation resistance is important to disease and fitness, but the genetic basis of its natural variation is unknown. We developed a synthetic-population (re)sequencing approach using molecular inversion probes (MIP-seq) to measure relative fitness during and after larval starvation inC. elegans. We applied this competitive assay to 100 genetically diverse, sequenced, wild strains, revealing natural variation in starvation resistance. We confirmed that the most starvation-resistant strains survive and recover from starvation better than the most starvation-sensitive strain, MY2147, using standard assays. We performed genome-wide association with the MIP-seq trait data and identified three quantitative trait loci (QTL) for starvation resistance. These QTL contain several members of the Insulin/EGF Receptor-L Domain (irld) family with sequence variation associated with variation in starvation resistance. We used genome editing to show that individual modification of fourirldgenes increases starvation resistance of MY2147. Modification ofirld-39andirld-52together increases starvation resistance of the laboratory-reference strain N2. Increased starvation resistance of theirld-39; irld-52double mutant depends ondaf-16/FoxO, and these worms also show increased nuclear localization of DAF-16 during starvation. DAF-16/FoxO is a widely conserved transcriptional effector of insulin/IGF signaling (IIS), and these results suggest that IRLD proteins modify IIS. This work demonstrates efficacy of using MIP-seq to dissect a complex trait, identifiesirldgenes as natural modifiers of starvation resistance inC. elegans, and suggests that an expanded gene family affects a deeply conserved signaling pathway to alter a fitness-proximal trait.
RBPSpot: Learning on Appropriate Contextual Information for RBP Binding Sites Discovery
Authors: Sharma Nitesh Kumar , Gupta Sagar , Kumar Prakash , Kumar Ashwani , Pradhan Upendra Kumar , Shankar Ravi 
Published: June 2021

AbstractIdentifying RBP binding sites and mechanistic factors determining the interactions remain a big challenge. Besides the sparse binding motifs across the RNAs, it also requires a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites while using an ultra-fast BWT/FM-indexing coupled inexact k-mer spectrum search for statistically significant seeds. The seed works as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network (DNN). Contextual features based on pentamers/dinucloetides which also capture shape and structure properties appeared critical. Contextual CG distribution pattern appeared important. The developed models also got support from MD-simulation studies and the implemented software, RBPSpot, scored consistently high for the considered performance metrics including average accuracy of ∼90% across a large number of validated datasets while maintaining consistency. It clearly outperformed some recently developed tools, including some with much complex deep-learning models, during a highly comprehensive bench-marking process involving three different data-sets and more than 50 RBPs. RBPSpot, has been made freely available, covering most of the human RBPs for which sufficient CLIP-seq data is available (131 RBPs). Besides identifying RBP binding spots across RNAs in human system, it can also be used to build new models by user provided data for any species and any RBP, making it a valuable resource in the area of regulatory system studies.
Acceleration of Evolutionary Processes by Learning and Extended Fisher’s Fundamental Theorem
Authors: Nakashima So , Kobayashi Tetsuya J. 
Published: June 2021

AbstractNatural selection is general and powerful concept not only to explain evolutionary processes of biological organisms but also to design engineering systems such as genetic algorithms and particle filters. There is a surge of interest, both from biology and engineering, in considering natural selection of intellectual agents that can learn individually. Learning by individual agents of better behaviors for survival may accelerate the evolutionary processes by natural selection. We have accumulating pieces of evidence that organisms can transmit its information to the next generation via epigenetic states or memes. Also, such idea is important for engineering applications to improve the genetic algorithms and the particle filter. To accelerate the evolutionary process, an agent should change their strategy so that the population fitness increases the most. Equivalently, an agent should update the strategy towards a gradient (derivative) of the population fitness with respect to the strategy. However, it has not yet been clarified whether and how an agent can estimate the gradient and accelerate the evolutionary process. We also lack methodology to quantify the acceleration to understand and predict the impact of learning. In this paper, we address these problems. We show that an learning agent can accelerate the evolutionary process by proposing ancestral learning, which uses the information transmitted from the ancestor (ancestral information) via epigenetic states or memes. Numerical experiments show that ancestral learning actually accelerates the evolutionary process. We next show that the ancestral information is sufficient to estimate the gradient. In particular, learning can accelerate the evolutionary process without communications between agents. Finally, to quantify the acceleration, we extend the Fisher’s fundamental theorem (FF-thm) for natural selection to ancestral learning. The conventional FF-thm relates the speed of evolution by natural selection to the variety of the individual fitness in the population. Our extended FF-thm relates the acceleration of the evolutionary process to the variety of individual fitness of the agent. By the theorem, we can quantitatively understand when and why learning is beneficial.
A particle size threshold governs diffusion and segregation of PAR-3 during cell polarization
Authors: Chang Yiran , Dickinson Daniel J. 
Published: June 2021

SUMMARYRegulation of subcellular components’ localization and motion is a critical theme in cell biology. Cells use the actomyosin cortex to regulate protein distribution on the plasma membrane, but the interplay between membrane binding, cortical movements and protein distribution remains poorly understood. In a polarizing one-cell stage Caenorhabditis elegans embryo, actomyosin flows transport PAR protein complexes into an anterior cortical domain to establish the anterior-posterior axis of the animal. Oligomerization of a key scaffold protein, PAR-3, is required for aPAR cortical localization and segregation. Although PAR-3 oligomerization is essential for polarization, it remains unclear how oligomer size contributes to aPAR segregation because PAR-3 oligomers are a heterogeneous population of many different sizes. To address this question, we engineered PAR-3 to defined sizes. We report that PAR-3 trimers are necessary and sufficient for PAR-3 function during polarization and later embryo development, while larger PAR-3 clusters are dispensable. Quantitative analysis of PAR-3 diffusion showed that PAR-3 clusters with three or more subunits are transported by frictional drag and experience extensive collisions with the actomyosin cortex. Our study provides a quantitative model for size-dependent protein transportation of membrane proteins by cortical flow.
Alpha and beta rhythms differentially support the effect of symbols on visual object recognition
Authors: Morucci Piermatteo , Giannelli Francesco , Richter Craig , Molinaro Nicola 
Published: June 2021

AbstractHearing spoken words can enhance visual object recognition, detection and discrimination. Yet, the mechanisms that underpin this facilitation are incompletely understood. On one account, words do not bias early visual processing, but rather affect later semantic or decision-making stages. However, recent proposals suggest that words can alter early visual processes by activating category-specific priors in sensory regions. A prediction of this account is that top-down priors evoke changes in occipital areas in anticipation of visual stimuli. Here, we tested the hypothesis that neural oscillations serve as a mechanism to activate language-generated visual priors. Participants performed a cue-picture matching task where cues were either spoken words, in their native or second language, or natural sounds, while their EEG and reaction times were recorded. Behaviorally, we replicated the previously reported label-advantage effect, with images cued by words being recognized faster than those cued by natural sounds. A time-frequency analysis of cue-target intervals revealed that this behavioral label-advantage was associated with enhanced power in posterior alpha (9-11 Hz) and beta oscillations (17-19 Hz), both of which were larger when the image was preceded by a word compared to a natural sound. Importantly, object recognition performance improved with high alpha power but slowed down with enhancement of beta synchronization. These results suggest that alpha and beta rhythms play distinct functional roles to support language-mediated visual object recognition: alpha might function to amplify sensory priors in posterior regions, while beta may (re)activate the network states elicited by the auditory cue.
Evolutionary velocity with protein language models
Authors: Hie Brian L. , Yang Kevin K. , Kim Peter S. 
Published: June 2021

AbstractPredicting the order of biological homologs is a fundamental task in evolutionary biology. For protein evolution, this order is often determined by first arranging sequences into a phylogenetic tree, which has limiting assumptions and can suffer from substantial ambiguity. Here, we demonstrate how machine learning algorithms called language models can learn mutational likelihoods that predict the directionality of evolution, thereby enabling phylogenetic analysis that addresses key limitations of existing methods. Our main conceptual advance is to construct a “vector field” of protein evolution through local evolutionary predictions that we refer to as evolutionary velocity (evo-velocity). We show that evo-velocity can successfully predict evolutionary order at vastly different timescales, from viral proteins evolving over years to eukaryotic proteins evolving over geologic eons. Evo-velocity also yields new evolutionary insights, predicting strategies of viral-host immune escape, resolving conflicting theories on the evolution of serpins, and revealing a key role of horizontal gene transfer in the evolution of eukaryotic glycolysis. In doing so, our work suggests that language models can learn sufficient rules of natural protein evolution to enable evolutionary predictability.
Host phenology regulates ecological and eco-evolutionary feedbacks underlying parasite-host demographic cycles
Authors: MacDonald Hannelore , Brisson Dustin 
Published: June 2021

AbstractParasite-host interactions can result in periodic population dynamics when parasites over-exploit host populations. The timing of host seasonal activity, or host phenology, determines the frequency and demographic impact of parasite-host interactions which may govern if the parasite can sufficiently over-exploit their hosts to drive population cycles. We describe a mathematical model of a monocyclic, obligate-killer parasite system with seasonal host activity to investigate the consequences of host phenology on host-parasite dynamics. The results suggest that parasites can reach the densities necessary to destabilize host dynamics and drive cycling in only some phenological scenarios, such as environments with short seasons and synchronous host emergence. Further, only parasite lineages that are sufficiently adapted to phenological scenarios with short seasons and synchronous host emergence can achieve the densities necessary to over-exploit hosts and produce population cycles. Host-parasite cycles can also generate an eco-evolutionary feedback that slows parasite adaptation to the phenological environment as rare advantageous phenotypes are driven to extinction when introduced in phases of the cycle where host populations are small and parasite populations are large. The results demonstrate that seasonal environments can drive population cycling in a restricted set of phenological patterns and provides further evidence that the rate of adaptive evolution depends on underlying ecological dynamics.
DNA damage models phenotypes of β cell senescence in Type 1 Diabetes
Authors: Brawerman Gabriel , Pipella Jasmine , Thompson Peter J. 
Published: June 2021

ObjectiveType 1 Diabetes (T1D) is characterized by progressive loss of insulin-producing pancreatic β cells as a result of autoimmune destruction. In addition to β cell death, recent work has shown that subpopulations of β cells acquire dysfunction during T1D. We previously reported that some β cells adopt a senescent fate involving a DNA damage response (DDR) during the pathogenesis of T1D, however, the question of how senescence develops in β cells has not been investigated.MethodsHere, we tested the hypothesis that unrepaired DNA damage triggers β cell senescence using culture models including the mouse NIT1 β cell line derived from the T1D-susceptible nonobese diabetic (NOD) strain, human donor islets and EndoC β cells. DNA damage was chemically induced using etoposide or bleomycin and cells or islets were analyzed by a combination of molecular assays for senescence phenotypes including Western blotting, qRT-PCR, Luminex assays, flow cytometry and histochemical staining. RNA-seq was carried out to profile global transcriptomic changes in human islets undergoing DDR and senescence. Insulin ELISAs were used to quantify glucose stimulated insulin secretion from chemically-induced senescent islets and cells in culture.ResultsSub-lethal DNA damage in NIT1 cells led to several classical hallmarks of senescence including sustained DDR activation, growth arrest, enlarged flattened morphology and a senescence-associated secretory phenotype (SASP) resembling what occurs in primary β cells during T1D in NOD mice. Some of these phenotypes differed between NIT1 cells and the MIN6 β cell line derived from a non-T1D susceptible mouse strain. RNA-seq analysis of human islets undergoing DDR and early senescence revealed a coordinated p53-p21 transcriptional program and upregulation of prosurvival signaling and SASP genes, which were confirmed in independent islet preparations at the protein level. Importantly, chemically induced DNA damage also led to DDR activation and senescence phenotypes in the EndoC-βH5 human β cell line, confirming that this response can occur directly in human β cells. Finally, DNA damage and senescence in both mouse β cell lines and human islets led to decreased insulin content.ConclusionsTaken together, these findings suggest that some of the phenotypes of β cell senescence during T1D can be modeled by chemically induced DNA damage in mouse β cell lines and human islets and β cells in culture. These culture models will be useful tools to understand some of the mechanisms of β cell senescence in T1D.HighlightsDNA damage induces senescent phenotypes in mouse β cell linesDNA damage induces a p53-p21 transcriptional program and senescent phenotypes in human islets and EndoC cellsDNA damage and senescence leads to decreased insulin contentDNA damage models some aspects of β cell senescence in Type 1 Diabetes
Metacells untangle large and complex single-cell transcriptome networks
Authors: Bilous Mariia , Tran Loc , Cianciaruso Chiara , Gabriel Aurélie , Michel Hugo , Carmona Santiago J. , Pittet Mikael J. , Gfeller David 
Published: June 2021

AbstractThe exponential scaling of scRNA-seq data represents an important hurdle for downstream analyses. Here we develop a coarse-graining framework where highly similar cells are merged into metacells. We demonstrate that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.