bluuebunny/crossref_metadata_2025_split huggingface dataset:
DNABERT-based explainable lncRNA identification in plant genome assemblies
Authors: Danilevicz Monica F. , Gill Mitchell , Tay Fernandez Cassandria G. , Petereit Jakob , Upadhyaya Shriprabha R. , Batley Jacqueline , Bennamoun Mohammed , Edwards David , Bayer Philipp E. 
Published: February 2022

AbstractLong non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays,Arabidopsis thaliana,Brassica napus,Brassica oleracea,Brassica rapa,Glycine maxandOryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% forZ. maysand the lowest accuracy of 57.9% forB. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence.HighlightsWe demonstrate for the first time the identification of lncRNAs from genomic sequences, instead of transcriptome sequences, allowing the identification of lowly expressed lncRNAs.A deep learning model (natural language processing) was employed to predict lncRNAs in two monocot and five dicot plant species.We used explainable machine learning to extract the genomic motifs associated with lncRNA identification and highlighted potentially conserved structures.
Single-cell analysis reveals a pathogenic cellular module associated with early allograft dysfunction after liver transplantation
Authors: Wang Zheng , Shao Xin , Wang Kai , Lu Xiaoyan , Zhuang Li , Yang Xinyu , Zhang Ping , Yang Penghui , Zheng Shusen , Xu Xiao , Fan Xiaohui 
Published: February 2022

ABSTRACTLiver transplantation (LT) is the standard therapy for patients with end-stage liver disease. Although LT technology has markedly progressed in recent decades, early allograft dysfunction (EAD) exacerbates the current organ shortage and impacts the prognosis of recipients. However, understanding of cellular characteristics and molecular events contributing to EAD is limited. Here, a large single-cell transcriptomic atlas of transplanted livers collected from four patients is constructed, including 58,243 cells, which are classified into 14 cell types and 29 corresponding subtypes with known markers, including liver parenchymal cells and non-parenchymal cells with different cell states. Compared to the pre-LT livers, graft remodeling is noted in the post-LT livers, with marked changes in several immune cells in either cell ratios or cell states. More importantly, an EAD-associated pathogenic cellular module is identified, consisting of mucosal-associated invariant T (MAIT) cells, granzyme B (GZMB)+granzyme K (GZMK)+natural killer (NK) cells, and S100A12+neutrophils, all of which are elevated in EAD patient after LT. This cellular module is also verified in two independent datasets. Collectively, these results reveal the cellular characteristics of transplanted livers and the EAD-associated pathogenic cellular module at the single-cell level, offering new insights into the EAD occurrence after LT.
Auto-Immunoproteomics Analysis of COVID-19 ICU Patients Revealed Increased Levels of Autoantibodies Related to Male Reproductive System
Authors: Schmidt Frank , Abdesselem Houari B. , Suhre Karsten , Sohail Muhammad U. , Al-Nesf Maryam , Bensmail Ilham , Mashod Fathima , Sarwath Hina , Bernhardt Joerg , Tan Ti-Myen , Morris Priscilla E , Schenck Edward J. , Price David , Vaikath Nishant N. , Mohamed-Ali Vidya , Al-Maadheed Mohammed , Arredouani Abdelilah , Decock Julie , Blackburn Jonathan M. , Choi Augustine M.K. , El-Agnaf Omar M. 
Published: February 2022

AbstractThe role of autoantibodies in coronavirus disease (COVID-19) complications is not yet fully understood. The current investigation screened two independent cohorts of 97 COVID-19 patients (Discovery (Disc) cohort from Qatar (n = 49) and Replication (Rep) cohort from New York (n = 48)) utilizing high-throughput KoRectly Expressed (KREX) immunome protein-array technology. Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (P ≤ 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients that served as controls. Both cohorts showed substantial similarities (r2= 0.73) and exhibited higher autoantibodies responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. KREX analysis of the specific IgG autoantibody responses indicates that the targeted host proteins are supposedly increased in COVID-19 patients. The autoantigen-autoantibody response was cross-validated for SPANXN4 and STK25 proteins using Uniprot BLASTP and sequence alignment tools. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19 associated male reproductive tract complications and warrants further research.Significance StatementCoronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, has emerged as a global pandemic with a high morbidity rate and multiorgan complications. It is observed that the host immune system contributes to the varied responses to COVID-19 pathogenesis. Autoantibodies, immune system proteins that mistakenly target the body’s own tissue, may underlie some of this variation. We screened total IgG autoantibody responses against 1,318 human proteins in two COVID-19 patient cohorts. We observed several novel markers in COVID-19 patients that are associated with male fertility, such as sperm protein SPANXN4, STK25, and the apoptotic factor ATF4. Particularly, elevated levels of autoantibodies against the testicular tissue-specific protein SPANXN4 offer significant evidence of anticipating the protein role in COVID-19 associated male reproductive complications.
Resolving single-cell copy number profiling for large datasets
Authors: Wang Ruohan , Zhang Yuwei , Wang Mengbo , Feng Xikang , Wang Jianping , Li Shuai Cheng 
Published: February 2022

ABSTRACTThe advances of single-cell DNA sequencing (scDNA-seq) enable us to characterize the genetic heterogeneity of cancer cells. However, the high noise and low coverage of scDNA-seq impede the estimation of copy number variations (CNVs). In addition, existing tools suffer from intensive execution time and often fail on large datasets. Here, we propose SeCNV, a novel method that leverages structural entropy, to profile the copy numbers. SeCNV adopts a local Gaussian kernel to construct a matrix, depth congruent map, capturing the similarities between any two bins along the genome. Then SeCNV partitions the genome into segments by minimizing the structural entropy from the depth congruent map. With the partition, SeCNV estimates the copy numbers within each segment for cells. We simulate nine datasets with various breakpoint distributions and amplitudes of noise to benchmark SeCNV. SeCNV achieves a robust performance, i.e., the F1-scores are higher than 0.95 for breakpoint detections, significantly outperforming state-of-the-art methods. SeCNV successfully processes large datasets (>50,000 cells) within four minutes while other tools failed to finish within the time limit, i.e., 120 hours. We apply SeCNV to single-nucleus sequencing (SNS) datasets from two breast cancer patients and acoustic cell tagmentation (ACT) sequencing datasets from eight breast cancer patients. SeCNV successfully reproduces the distinct subclones and infers tumor heterogeneity. SeCNV is available at https://github.com/deepomicslab/SeCNV.
Rheological transition driven by matrix makes cancer spheroids resilient under confinement
Authors: Dutt Tavishi , Langthasa Jimpi , Monica U , Mishra Satyarthi , Bothra Siddharth , Vadaparty Annapurna , Sen Prosenjit , Bhat Ramray 
Published: February 2022

AbstractCancer metastasis through a confining peritoneal fluid microenvironment is mediated by spheroids: clusters of disseminated transformed cells. Ovarian cancer spheroids are frequently cavitated and their ‘blastuloid’ morphology is correlated with an extracellular matrix (ECM) coat. Here, we investigate the effects of such morphology on the mechanical integrity of confined cancer spheroids. Atomic force microscopy showed higher elastic modulus for blastuloid spheroids relative to their prefiguring non-lumen ‘moruloid’ counterparts. Subsequently, spheroids were flowed through microfluidic conditions mimicking peritoneal confinement. Traversing moruloids exhibited asymmetric cell flows during entry, often deformed and disintegrated through travel, and showed an incomplete- and slow shape recovery upon exit. In contrast, blastuloids traveled faster, exhibited rapid and efficient shape recovery upon exit, symmetric vector flows, and lesser disintegration. A multiscale computational model predicted higher intercellular adhesion and a dynamical lumen make blastuloids resilient. Although, E-cadherin overexpression in moruloids did not affect their resilience, blastuloid ECM-debridement decreased E-cadherin membrane localization, obliterated the lumen, and reversed the rheological properties of blastuloids to those typifying moruloids. The ECM-induced lumen therefore drives spheroidal transition from a labile viscoplastic to a resilient elastic state allowing them to survive spatially-constrained peritoneal flows.
Seasonal dynamics of the wild rodent faecal virome
Authors: Raghwani Jayna , Faust Christina L. , François Sarah , Nguyen Dung , Marsh Kirsty , Raulo Aura , Hill Sarah C. , Parag Kris V. , Simmonds Peter , Knowles Sarah C. L. , Pybus Oliver G. 
Published: February 2022

ABSTRACTViral discovery studies in wild animals often rely on cross-sectional surveys at a single time point. As a result, our understanding of the temporal stability of wild animal viromes remains poorly resolved. While studies of single host-virus systems indicate that host and environmental factors influence seasonal virus transmission dynamics, comparable insights for whole viral communities in multiple hosts are lacking. Leveraging non-invasive faecal samples from a long-term wild rodent study, we characterised viral communities of three common European rodent species (Apodemus sylvaticus, A. flavicollis, andM. glareolus) living in temperate woodland over a single year. Our findings indicate that a substantial fraction of the rodent virome is seasonally transient and associated with vertebrate or bacteria hosts. Further analyses of one of the most abundant virus families, Picornaviruses, show pronounced temporal changes in viral richness and diversity, which were associated with concurrent and up to ∼3-month lags in host density, ambient temperature, rainfall and humidity, suggesting complex feedbacks from the host and environmental factors on virus transmission and shedding in seasonal habitats. Overall, this study emphasizes the importance of understanding the seasonal dynamics of wild animal viromes in order to better predict and mitigate zoonotic risks.
Pathogen prevalence modulates medication behavior in ant Formica fusca
Authors: Rissanen Jason , Helanterä Heikki , Freitak Dalial 
Published: February 2022

AbstractAnts face unique challenges regarding pathogens, as the sociality which has allowed them to form large and complex colonies also raises the potential for transmission of disease within these colonies. To cope with the threat of pathogens, ants have developed a variety of behavioral and physiological strategies. One of these strategies is self-medication, in which animals use biologically active compounds to combat pathogens in a way which would be harmful in the absence of them. Formica fusca ants are to date the only species of ants proven to successfully self-medicate against an active infection caused by a fungal pathogen by supplementing their diet with food containing hydrogen peroxide. Here, we build on that research by investigating how the prevalence of disease in colonies of F. fusca affects the strength of the self-medication response. We exposed either half of the workers of each colony or all of them to a fungal pathogen and offered them different combinations of diets. We see that workers of F. fusca engage in self-medication behavior even if exposed to a low lethal dose of a pathogen, and that the strength of that response is affected by the prevalence of the disease in the colonies. We also saw that the infection status of the individual foragers did not significantly affect their decision to forage on either control food or medicinal food as uninfected workers were also foraging on hydrogen peroxide food, which opens up the possibility of kin medication in partially infected colonies. Our results further affirm the ability of ants to self-medicate against fungal pathogens, shed new light on plasticity of self-medication and raise new questions to be investigated on the role self-medication has in social immunity.
Shoulder muscle recruitment of small amplitude motor units during the delay period encodes a reach movement plan which is sensitive to task context
Authors: Rungta Satya , Murthy Aditya 
Published: February 2022

AbstractUnderstanding how motor plans are transformed into appropriate patterns of muscle activity is a central question in motor control. While muscle activity during the delay period has not been reported using conventional EMG approaches, we isolated motor unit activity using a high-density surface EMG signal from the anterior deltoid muscle to test whether heterogeneity in motor units could reveal early preparatory activity. Consistent with our previous work (Rungta et al., 2021), we observed early selective recruitment of small-amplitude size motor units during the delay period for hand movements like early recruitment of small-amplitude motor units in neck muscles of non-human primates performing delayed saccade tasks. This early activity was spatially specific and increased with time and resembled an accumulation to threshold model that correlated with movement onset time. Such early recruitment of ramping motor units was also observed at the single trial level as well. In contrast, no such recruitment of large amplitude size motor units, called non-rampers, was observed during the delay period. Instead, non-rampers became spatially specific and predicted movement onset time after the delay period. Interestingly, spatially specific delay period activity was only observed for hand movements but was absent for isometric force-driven cursor movements. Nonetheless, muscle activity was correlated with the time it took to initiate movements in both task conditions for non-rampers. Overall, our results reveal a novel heterogeneity in the EMG activity which allows the expression of early motor preparation via small amplitude size motor units which are differentially activated during initiation of movements.Significance statementWe studied the spatial and temporal aspects of response preparation in the anterior deltoid muscle using high-density surface EMG. Our results show that early spatially specific ramping activity that predicted reaction times could be accessed from muscle activity but was absent during isometric force-driven cursor movements. Such ramping activity could be quantified using an accumulator framework across trials, as well as within single trials but was not observed in isometric reach tasks involving cursor movements.
A single dose of the catecholamine precursor Tyrosine reduces physiological arousal and decreases decision thresholds in reinforcement learning and temporal discounting
Authors: Mathar David , Abdoust Mani Erfanian , Tuszus Deniz , Marrenbach Tobias , Peters Jan 
Published: February 2022

AbstractSupplementation with the catecholamine precursor L-Tyrosine might enhance cognitive performance, but overall findings are mixed. Here, we investigate the effect of a single dose of tyrosine (2g) vs. placebo on two key aspects of catecholamine-dependent decision-making: model-based reinforcement learning (2-step task) and temporal discounting, using a double-blind, placebo-controlled, within-subject design (n=28 healthy male participants). We leveraged drift diffusion models in a hierarchical Bayesian framework to jointly model participants’ choices and response times in both tasks. Furthermore, comprehensive autonomic monitoring (heart rate, heart rate variability, pupillometry, spontaneous eye-blink rate) was performed both pre- and post-supplementation, to explore potential physiological effects of supplementation. Across tasks, tyrosine consistently reduced participants’ RTs without deteriorating task-performance. Diffusion modeling linked this effect to attenuated decision-thresholds in both tasks and further revealed increased model-based control (2-step task) and (if anything) attenuated temporal discounting. On the physiological level, participants’ pupil dilation was predictive of the individual degree of temporal discounting. Tyrosine supplementation reduced physiological arousal as revealed by increases in pupil dilation variability and reductions in hear rate. Supplementation-related changes in physiological arousal predicted individual changes in temporal discounting. Our findings provide first evidence that tyrosine supplementation might impact psychophysiological parameters, and suggest that modeling approaches based on sequential sampling models can yield novel insights into latent cognitive processes modulated by amino-acid supplementation.
Sen1 is a key regulator of transcription-driven conflicts
Authors: Aiello Umberto , Challal Drice , Wentzinger Griselda , Lengronne Armelle , Appanah Rowin , Pasero Philippe , Palancade Benoit , Libri Domenico 
Published: February 2022

ABSTRACTCellular homeostasis requires the coordination of several machineries concurrently engaged on the DNA. Wide-spread transcription can interfere with other processes and transcription-replication conflicts (TRCs) threaten genome stability. The conserved Sen1 helicase terminates non-coding transcription, but also interacts with the replisome and reportedly resolves genotoxic R-loops. Sen1 prevents genomic instability but how this relates to its molecular functions remains unclear. We generated high-resolution, genome-wide maps of transcription-dependent conflicts and R-loops using a Sen1 mutant that has lost interaction with the replisome but is termination proficient. We show that Sen1 removes RNA polymerase II at TRCs within genes and the rDNA, but also at sites of transcription-transcription conflicts under physiological conditions, thus qualifying as a “key regulator of conflicts”. We demonstrate that genomic stability is only affected by Sen1 mutation when, in addition to its role at the replisome, termination of non-coding transcription or R-loop removal are additionally compromised.
An evaluation of RNA-seq differential analysis methods
Authors: Li Dongmei , Zand Martin , Dye Timothy , Goniewicz Maciej , Rahman Irfan , Xie Zidian 
Published: February 2022

AbstractRNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay.To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method.For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.
The LINC complex inhibits excessive chromatin repression
Authors: Amiad Pavlov Daria , Unnikannan CP , Lorber Dana , Bajpai Gaurav , Olender Tsviya , Stoops Elizabeth , Reuveny Adriana , Safran Samuel , Volk Talila 
Published: February 2022

AbstractWe show here that the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex is needed to minimize chromatin repression. The genomic binding profile of Polycomb, Heterochromatin Protein1 (HP1a) repressors, and of RNA-Pol II were studied inDrosophilalarval muscles lacking functional LINC complex. A significant increase in the binding of Polycomb, and parallel reduction of RNA-Pol-II binding to a set of muscle genes was observed. Consistently, enhanced tri-methylated H3K9 and H3K27 repressive modifications, and reduced chromatin activation by H3K9 acetylation were found. Furthermore, larger tri-methylated H3K27me3 repressive clusters, and chromatin redistribution from the nuclear periphery towards nuclear center, were detected in live LINC mutant larval muscles. Computer simulation indicated that the observed dissociation of the chromatin from the nuclear envelope promotes growth of tri-methylated H3K27 repressive clusters. Thus, we suggest that by promoting chromatin-nuclear envelope binding, the LINC complex restricts the size of repressive H3K27 tri-methylated clusters, thereby limiting the binding of Polycomb transcription repressor, directing robust transcription in muscle fibers.