bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transposable elements contribute to the spatiotemporal microRNA landscape in human brain development
Authors: Playfoot Christopher J. , Sheppard Shaoline , Planet Evarist , Trono Didier 
Published: January 2022

AbstractTransposable elements (TEs) contribute to the evolution of gene regulatory networks and are dynamically expressed throughout human brain development and disease. One gene regulatory mechanism influenced by TEs is the miRNA system of post-transcriptional control. miRNA sequences frequently overlap TE loci and this miRNA expression landscape is crucial for control of gene expression in adult brain and different cellular contexts. Despite this, a thorough investigation of the spatiotemporal expression of TE-embedded miRNAs in human brain development is lacking. Here, we identify a spatiotemporally dynamic TE-embedded miRNA expression landscape between childhood and adolescent stages of human brain development. These miRNAs sometimes arise from two apposed TEs of the same subfamily, such as for L2 or MIR elements, but in the majority of cases stem from solo TEs. They give rise to in silico predicted high-confidence pre-miRNA hairpin structures, likely represent functional miRNAs and have predicted genic targets associated with neurogenesis. TE-embedded miRNA expression is distinct in the cerebellum when compared to other brain regions, as has previously been described for gene and TE expression. Furthermore, we detect expression of previously non-annotated TE-embedded miRNAs throughout human brain development, suggestive of a previously undetected miRNA control network. Together, as with non-TE-embedded miRNAs, TE-embedded sequences give rise to spatiotemporally dynamic miRNA expression networks, the implications of which for human brain development constitute extensive avenues of future experimental research. To facilitate interactive exploration of these spatiotemporal miRNA expression dynamics, we provide the “Brain miRTExplorer” web application freely accessible for the community.
Region Selective Cortical Control Of The Thalamic Reticular Nucleus
Authors: Hádinger Nóra , Bősz Emília , Tóth Boglárka , Vantomme Gil , Lüthi Anita , Acsády László 
Published: January 2022

SummaryCorticothalamic pathways, responsible for the top-down control of the thalamus display a classical, canonical organization in that every cortical region sends dual, layer 6 (L6) and layer 5 (L5) output to the thalamus. Here we demonstrate a qualitative, region-specific difference in the organization of corticothalamic pathways. We show that L5 pyramidal cells of the frontal, but not other cortical regions establish monosynaptic connection with the inhibitory thalamic reticular nucleus (TRN). The frontal L5-TRN pathway paralleled the L6-TRN projection but displayed distinct morphological and physiological features. The exact spike output of the L5 contacted TRN cells correlated with the level of cortical synchrony. Optogenetic perturbation of the L5-TRN connection disrupted the tight link between cortical and TRN activity. L5-driven TRN cells innervated all thalamic nuclei involved in the control of frontal cortical activity. Our data show that frontal cortical functions require a highly specialized cortical control over intrathalamic inhibitory processes.
Scale Bar of Aging Trajectories for Screening Personal Rejuvenation Treatments
Authors: Shen Xilin , Wu Bingbing , Jiang Wei , Li Yu , Zhang Yuping , Zhao Kun , Nie Nanfang , Gong Lin , Liu Yixiao , Zou Xiaohui , Liu Jian , Jin Jingfen , Ouyang HongWei 
Published: January 2022

AbstractAlthough aging is an increasingly severe healthy, economic, and social global problem, it is far from well-modeling aging due to the aging process’s complexity. To promote the aging modeling, here we did the quantitative measurement based on aging blood transcriptome. Specifically, the aging blood transcriptome landscape was constructed through ensemble modeling in a cohort of 505 people, and 1138 age-related genes were identified. To assess the aging rate in the linear dimension of aging, we constructed a simplified linear aging clock, which distinguished fast-aging and slow-aging populations and showed the differences in the composition of immune cells. Meanwhile, the non-linear dimension of aging revealed the transcriptome fluctuations with a crest around the age of 40 and showed that this crest came earlier and was more vigorous in the fast-aging population. Moreover, the aging clock was applied to evaluate the rejuvenation effect of molecules in vitro, such as Nicotinamide Mononucleotide (NMN) and Metformin. In sum, this study developed a de novo aging clock to evaluate agedependent precise medicine by revealing its fluctuation nature based on comprehensively mining the aging blood transcriptome, promoting the development of personal aging monitoring and anti-aging therapies.
Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data
Authors: Köhn-Luque Alvaro , Myklebust Even Moa , Tadele Dagim Shiferaw , Giliberto Mariaserena , Schmiester Leonard , Noory Jasmine , Harivel Elise , Arsenteva Polina , Mumenthaler Shannon M. , Schjesvold Fredrik , Taskén Kjetil , Enserink Jorrit M. , Leder Kevin , Frigessi Arnoldo , Foo Jasmine 
Published: January 2022

SummaryTumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses, and estimates their drug-sensitivities and frequencies within the bulk. We apply PhenoPop to synthetically-generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples, and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.MotivationTumors are typically comprised of heterogeneous cell populations exhibiting diverse phenotypes. This heterogeneity, which is correlated with tumor aggressiveness and treatment failure, confounds current drug screening efforts aimed at informing therapy selection. In order to optimally select treatments, understanding the frequency and drug-response profile of individual subpopulations within a tumor is critical. Furthermore, quantitative profiles of tumor drug-response heterogeneity, in combination with predictive mathematical modeling of tumor dynamics, can be used to design effective temporal drug-sequencing strategies for tumor reduction.Here, we present a method that enables the deconvolution of tumor samples into individual sub-components exhibiting differential drug-response. This method relies on standard bulk drug-screen measurements and outputs the frequencies and drug-sensitivities of tumor subpopulations. This framework can also be applied more broadly to deconvolve cellular populations with heterogeneous responses to a variety of external stimuli and environmental factors.
The formal demography of kinship IV: Two-sex models
Authors: Caswell Hal 
Published: January 2022

AbstractBackgroundPrevious kinship models analyze female kin through female lines of descent, neglecting male kin and male lines of descent. Because males and females differ in mortality and fertility, including both sexes in kinship models is an important unsolved problem.ObjectivesThe objectives are to develop a kinship model including female and male kin through all lines of descent, to explore approximations when full sex-specific rates are unavailable, and to apply the model to several populations as an example.MethodsThe kin of a focal individual form an age×sex-classified population and are projected as Focal ages using matrix methods, providing expected age-sex structures for every type of kin at every age of Focal. Initial conditions are based on the distribution of ages at maternity and paternity.ResultsThe equations for two-sex kinship dynamics are presented. As an example, the model is applied to populations with large (Senegal), medium (Haiti), and small (France) differences between female and male fertility. Results include numbers and sex ratios of kin as Focal ages. An approximation treating female and male rates as identical provides some insight into kin numbers, even when male and female rates are very different.ContributionMany demographic and sociological parameters (e.g., aspects of health, bereavement, labor force participation) differ markedly between the sexes. This model permits analysis of such parameters in the context of kinship networks. The matrix formulation makes it possible to extend the two-sex analysis to include kin loss, multistate kin demography, and time varying rates.
Impact of ionizing radiation on the environmental microbiomes of Chernobyl wetlands
Authors: Videvall Elin , Burraco Pablo , Orizaola Germán 
Published: January 2022

AbstractRadioactive contamination in the form of ionizing radiation can be a devastating pollutant because it has the potential to cause damage to DNA and other biomolecules. Anthropogenic sources of ionizing radiation include accidents in nuclear power plants, such as the one in Chernobyl 1986, which caused long-term radioactive pollution. Studies on animals within radioactive zones have provided us with a greater understanding of how wildlife can persevere despite chronic radiation exposure, however, we still know very little about the effects of radiation on the microbial communities in the environment. Here, we examined the impact of ionizing radiation and other environmental factors on the diversity and composition of environmental microbiomes in the wetlands of Chernobyl. We combined extensive field sampling along a gradient of radiation together with 16S rRNA high-throughput metabarcoding (Illumina NovaSeq). While radiation did not affect the alpha diversity of the microbiomes in sediment, soil, or water, it had a strong effect on the beta diversity, indicating that the microbial composition was affected by ionizing radiation. Specifically, we detected several microbial taxa that were more abundant in areas with high radiation levels within the Chernobyl Exclusion Zone, including bacteria and archaea known to be radioresistant. Overall, our results reveal the existence of rich and diverse microbiomes in Chernobyl wetlands, with multiple taxonomic groups that are able to thrive despite the radioactive contamination. Further field and laboratory-based approaches will help to forecast the functionality and re-naturalization dynamics of radiocontaminated environments.
Integration of heterogeneous biological data in multiscale mechanistic model calibration: application to lung adenocarcinoma
Authors: Palgen Jean-Louis , Perrillat-Mercerot Angélique , Ceres Nicoletta , Peyronnet Emmanuel , Coudron Matthieu , Tixier Eliott , Illigens Ben M.W. , Bosley Jim , L’Hostis Adèle , Monteiro Claudio 
Published: January 2022

AbstractMechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.
A deep CNN framework for neural drive estimation from HD-EMG across contraction intensities and joint angles
Authors: Wen Yue , Kim Sangjoon J. , Avrillon Simon , Levine Jackson T. , Hug François , Pons José L. 
Published: January 2022

AbstractObjectivePrevious studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units.MethodsWe recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario.ResultsWith the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario.ConclusionThe proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks.SignificanceWith the proposed deep CNN, we could potentially build a neuraldrive-based human-machine interface that is generalizable to different contraction tasks without retraining.
Differences in environmental stability among SARS-CoV-2 variants of concern: Omicron has higher stability
Authors: Hirose Ryohei , Itoh Yoshito , Ikegaya Hiroshi , Miyazaki Hajime , Watanabe Naoto , Yoshida Takuma , Bandou Risa , Daidoji Tomo , Nakaya Takaaki 
Published: January 2022

AbstractSARS-CoV-2 variants of concern (VOCs) could cause significant human and economic damage owing to increased infectivity and transmissibility, and understanding their characteristics is crucial for infection control. Here, we analyzed differences in viral stability and disinfection efficacy between the Wuhan strain and all VOCs. On plastic and skin surfaces, Alpha, Beta, Delta, and Omicron variants exhibited more than two-fold longer survival than the Wuhan strain, and the Omicron variant had the longest survival time. Specifically, survival times of the Wuhan strain, Alpha variant, Beta variant, Gamma variant, Delta variant, and Omicron variant on skin surfaces were 8.6 h (95% CI, 6.5–10.9 h), 19.6 h (95% CI, 14.8–25.3 h), 19.1 h (95% CI, 13.9– 25.3 h), 11.0 h (95% CI, 8.1–14.7 h), 16.8 h (95% CI, 13.1–21.1 h), and 21.1 h (95% CI, 15.8– 27.6 h), respectively. In vitro, disinfectant effectiveness evaluations showed that Alpha, Beta, Delta, and Omicron were slightly more resistant to ethanol than the Wuhan strain. However, ex vivo evaluation showed that on human skin, all viruses were completely inactivated by exposure to 35 w/w % ethanol for 15 s. The high environmental stability of these VOCs could increase transmission risk and contribute to spread. Additionally, the Omicron variant might have been replaced by the Delta variant due to its increased environmental stability and rapid spread. To prevent VOC spread, it is highly recommended that current infection control practices use disinfectants with appropriate ethanol concentrations.
Reconstructing the spatiotemporal patterns of admixture during the European Holocene using a novel genomic dating method
Authors: Chintalapati Manjusha , Patterson Nick , Moorjani Priya 
Published: January 2022

AbstractRecent studies have shown that gene flow or admixture has been pervasive throughout human history. While several methods exist for dating admixture in contemporary populations, they are not suitable for sparse, low coverage data available from ancient specimens. To overcome this limitation, we developed DATES that leverages ancestry covariance patterns across the genome of a single individual to infer the timing of admixture. By performing simulations, we show that DATES provides reliable results under a range of demographic scenarios and outperforms available methods for ancient DNA applications. We apply DATES to ~1,100 ancient genomes to reconstruct gene flow events during the European Holocene. Present-day Europeans derive ancestry from three distinct groups, local Mesolithic hunter-gatherers, Anatolian farmers, and Yamnaya Steppe pastoralists. These ancestral groups were themselves admixed. By studying the formation of Anatolian farmers, we infer that the gene flow related to Iranian Neolithic farmers occurred no later than 9,600 BCE, predating agriculture in Anatolia. We estimate the early Steppe pastoralist groups genetically formed more than a millennium before the start of steppe pastoralism, providing new insights about the history of proto-Yamnaya cultures and the origin of Indo-European languages. Using ancient genomes across sixteen regions in Europe, we provide a detailed chronology of the Neolithization across Europe that occurred from ~6,400–4,300 BCE. This movement was followed by a rapid spread of steppe ancestry from ~3,200–2,500 BCE. Our analyses highlight the power of genomic dating methods to elucidate the legacy of human migrations, providing insights complementary to archaeological and linguistic evidence.SignificanceThe European continent was subject to two major migrations during the Holocene: the movement of Near Eastern farmers during the Neolithic and the migration of Steppe pastoralists during the Bronze Age. To understand the timing and dynamics of these movements, we developed DATES that leverages ancestry covariance patterns across the genome of a single individual to infer the timing of admixture. Using ~1,100 ancient genomes spanning ~8,000–350 BCE, we reconstruct the chronology of the formation of the ancestral populations and the fine-scale details of the spread of Neolithic farming and Steppe pastoralist-related ancestry to Europe. Our analysis demonstrates the power of genomic dating methods to provide an independent and complementary timeline of population origins and movements using genetic data.
Evolutionary PTEN gene divergence underpins the remodeling of plant vacuolar compartments
Authors: Gujas Bojan , Champeyroux Chloe , Hunkeler Anna , Robinson Emilija , Blanco-Touriñán Noel , Dias Cruz Tiago Miguel , Durme Matthias Van , Nowack Moritz K. , Rodriguez-Villalon Antia 
Published: January 2022

SummaryMembrane fusion and fission are fundamental processes in sustaining cellular compartmentalization. Fission of a lipid bilayer requires a furrow formation that brings membranes in close proximity prior to a contiguous membrane cleavage. Although plant ancestors abandoned cleavage furrow-mediated cytokinesis more than 500 million years ago, here we show that plants still employ this mechanical principle to divide embryonic vacuoles. The evolutionary divergence in PHOSPHATASE AND TENSIN HOMOLOG DELETED ON CHROMOSOME TEN (PTEN) enzymes was required to coordinate this process, as Arabidopsis loss-of-function pten2a pten2b mutants contain hyper compartmentalized embryonic vacuoles. In contrast, PTEN2 overexpression hinders lytic and secretion cellular pathways downstream of TGN in xylem cells. These processes are critical for the formation of secondary cell walls in xylem cells and depend on a poorly characterized and evolutionarily novel N-terminal domain in PTEN2s. The PTEN2 subfamily appeared with the emergence of the Phragmoplastophyta clade, when vacuolar compartments enlarged and cleavage furrow-mediated cytokinesis became extinct. Together, our work suggests that the evolutionary innovation of the PTEN family is conserved across terrestrial plants and central to vacuolar remodelling.
Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets
Authors: Figueiredo Rebeca Queiroz , del Ser Sara Díaz , Raschka Tamara , Hofmann-Apitius Martin , Kodamullil Alpha Tom , Mubeen Sarah , Domingo-Fernández Daniel 
Published: January 2022

AbstractDistinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein-protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts athttps://zenodo.org/record/5831786andhttps://github.com/ContNeXt/, respectively and developed ContNeXt (https://contnext.scai.fraunhofer.de/), a web application to explore the networks generated in this work.