bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intratumoral and extratumoral synapses are required for glioblastoma progression in Drosophila
Authors: Losada-Pérez María , Hernández García-Moreno Mamen , Casas-Tintó Sergio 
Published: October 2021

AbstractGlioblastoma (GB) is the most aggressive, lethal and frequent primary brain tumor. It originates from glial cells and is characterized by rapid expansion through infiltration. GB cells interact with the microenvironment and healthy surrounding tissues, mostly neurons and vessels. GB cells project tumor microtubes (TMs) that contact with neurons and exchange signaling molecules related to Wingless/WNT, JNK, Insulin or Neuroligin-3 pathways. This cell to cell communication promotes GB expansion and neurodegeneration. Moreover, healthy neurons form glutamatergic functional synapses with GB cells which facilitate GB expansion and premature death in mouse GB xerograph models. Targeting signaling and synaptic components of GB progression may become a suitable strategy against glioblastoma. In a Drosophila GB model, we have determined the post-synaptic nature of GB cells with respect to neurons, and the contribution of post-synaptic genes expressed in GB cells to tumor progression. In addition, we document the presence of intratumoral synapses between GB cells, and the functional contribution of pre-synaptic genes to GB calcium dependent activity and expansion. Finally, we explore the relevance of synaptic genes in GB cells to the lifespan reduction caused by GB advance. Our results indicate that both presynaptic and postsynaptic proteins play a role in GB progression and lethality.
Human enteric viruses shape disease phenotype through divergent innate immunomodulation
Authors: Adiliaghdam Fatemeh , Amatullah Hajera , Digumarthi Sreehaas , Saunders Tahnee L. , Rahman Raza-Ur , Wong Lai Ping , Sadreyev Ruslan , Droit Lindsay , Paquette Jean , Goyette Philippe , Rioux John , Hodin Richard , Mihindukulasuriya Kathie A. , Handley Scot A. , Jeffrey Kate L. 
Published: October 2021

SummaryAltered enteric microorganisms in concert with host genetics shape inflammatory bowel disease (IBD) phenotypes. However, insight is limited to bacteria and fungi. We found virus like particles (VLPs) enriched from normal human colon resections, containing eukaryotic viruses and bacteriophages (collectively, the virome), actively elicited atypical anti-inflammatory innate immune programs. Conversely, IBD patient VLPs provoked inflammation, which was successfully dampened by healthy VLPs. The IBD colon tissue virome was perturbed, including enriched PicornovirusEnterovirus B,not previously observed in fecal virome studies. Mice with humanized healthy colon tissue viromes had attenuated intestinal inflammation while those with IBD-derived viromes exhibited exacerbated inflammation in a nucleic acid sensing-dependent fashion. Furthermore, there were detrimental consequences for IBD-associated MDA5 loss-of-function on patient intestinal epithelial cells exposed to healthy or IBD viromes. Our results demonstrate that innate recognition of either healthy or IBD human viromes autonomously influences disease phenotypes in IBD. Harnessing the virome may offer therapeutic and biomarker potential.One Sentence SummaryHuman viromes divergently shape host immunity and disease
Genomic and common garden approaches yield complementary results for quantifying environmental drivers of local adaptation in rubber rabbitbrush, a foundational Great Basin shrub
Authors: Faske Trevor M. , Agneray Alison C. , Jahner Joshua P. , Sheta Lana M. , Leger Elizabeth A. , Parchman Thomas L. 
Published: October 2021

AbstractThe spatial structure of genomic and phenotypic variation across populations reflects historical and demographic processes as well as evolution via natural selection. Characterizing such variation can provide an important perspective for understanding the evolutionary consequences of changing climate and for guiding ecological restoration. While evidence for local adaptation has been traditionally evaluated using phenotypic data, modern methods for generating and analyzing landscape genomic data can directly quantify local adaptation by associating allelic variation with environmental variation. Here, we analyze both genomic and phenotypic variation of rubber rabbitbrush (Ericameria nauseosa), a foundational shrub species of western North America. To quantify landscape genomic structure and provide perspective on patterns of local adaptation, we generated reduced representation sequencing data for 17 wild populations (222 individuals; 38,615 loci) spanning a range of environmental conditions. Population genetic analyses illustrated pronounced landscape genomic structure jointly shaped by geography and environment. Genetic-environment association (GEA) analyses using both redundancy analysis (RDA) and a machine-learning approach (Gradient Forest) indicated environmental variables (precipitation seasonality, slope, aspect, elevation, and annual precipitation) influenced spatial genomic structure, and were correlated with allele frequency shifts indicative of local adaptation at a consistent set of genomic regions. We compared our GEA based inference of local adaptation with phenotypic data collected by growing seeds from each population in a greenhouse common garden. Population differentiation in seed weight, emergence, and seedling traits was associated with environmental variables (e.g., precipitation seasonality) that were also implicated in GEA analyses, suggesting complementary conclusions about the drivers of local adaptation across different methods and data sources. Our results provide a baseline understanding of spatial genomic structure for E. nauseosa across the western Great Basin and illustrate the utility of GEA analyses for detecting the environmental causes and genetic signatures of local adaptation in a widely distributed plant species of restoration significance.
The 4D Nucleome Data Portal: a resource for searching and visualizing curated nucleomics data
Authors: Reiff Sarah B. , Schroeder Andrew J. , Kirli Koray , Cosolo Andrea , Bakker Clara , Mercado Luisa , Lee Soohyun , Veit Alexander D. , Balashov Alexander K. , Vitzthum Carl , Ronchetti William , Pitman Kent M. , Johnson Jeremy , Ehmsen Shannon R. , Kerpedjiev Peter , Abdennur Nezar , Imakaev Maxim , Öztürk Serkan Utku , Çamoğlu Uğur , Mirny Leonid A. , Gehlenborg Nils , Alver Burak H. , Park Peter J. 
Published: October 2021

AbstractThe 4D Nucleome (4DN) Network aims to elucidate the complex structure and organization of chromosomes in the nucleus and the impact of their disruption in disease biology. We present the 4DN Data Portal (https://data.4dnucleome.org/), a repository for datasets generated in the 4DN network and relevant external datasets. Datasets were generated with a wide range of experiments, including chromosome conformation capture assays such as Hi-C and other innovative sequencing and microscopy-based assays probing chromosome architecture. All together, the 4DN data portal hosts more than 1800 experiment sets and 34000 files. Results of sequencing-based assays from different laboratories are uniformly processed and quality-controlled. The portal interface allows easy browsing, filtering, and bulk downloads, and the integrated HiGlass genome browser allows interactive visualization and comparison of multiple datasets. The 4DN data portal represents a primary resource for chromosome contact and other nuclear architecture data for the scientific community.
Determining salmon provenance with automated otolith reading
Authors: Kemp Chandler E , Doherty Susan K 
Published: October 2021

AbstractSynthetic otolith marks are used at hundreds of hatcheries throughout the Pacific Rim to record the release location of salmon. Each year, human readers examine tens of thousands of otolith samples to identify the marks in salmon that are caught. The data inform dynamic management practices that maximize allowable catch while preserving populations, and guide hatchery investments. However, the method is limited by the time required to process otoliths, the inability to distinguish between wild and un-marked hatchery fish, and in some cases classification processes are limited by the subjective decisions of human readers. Automated otolith reading using computer vision has the potential to improve on all three of these limitations. Our work advances the field of automated otolith reading through a novel otolith classification algorithm that uses two neural networks trained with an adversarial algorithm to achieve 93% classification accuracy between four hatchery marks and unmarked otoliths. The algorithm relies on hemisection images of the otolith exclusively: no additional biological data are needed. Our work demonstrates a novel technique with modest training requirements that achieves unprecedented accuracy. The method can be easily adopted in existing otolith labs, scaled to accommodate additional marks, and does not require tracking additional information about the fish that the otolith was retrieved from.Author summaryMany fish harvested in commercial fisheries have a bone-like structure in their inner ear called an otolith. Otoliths are useful because as they grow changes in the fish’s environment create unique patterns that can be interpreted years later—similar to the way tree rings can be used to determine a tree’s age and rate of growth in different seasons. Hatcheries use this phenomenon to create unique patterns in the otoliths of fish they release that identify their origin. Trained otolith “readers” examine tens of thousands of otolith samples as part of commercial fishery management every year. The data help resource managers protect returns to wild streams and help hatcheries track the survival rates of their releases. However, human reading is time consuming, limited in the types of patterns that can be identified and can be subjective. Applying modern machine learning techniques to otolith reading has the potential to improve on all of these challenges. We developed a computational method that identifies hatchery marks in digital images of salmon otoliths without human input. In a test with otoliths containing one of four hatchery marks and unmarked otoliths, the method accurately classified 93% of otoliths. The method can be adopted in otolith labs and can be further improved by expanding the sample size used to train and test the method.
Golgi structural defect impairs glycosaminoglycan synthesis, sulfation, and secretion
Authors: Ahat Erpan , Song Yuefan , Xia Ke , Reid Whitney , Li Jie , Bui Sarah , Zhang Fuming , Linhardt Robert J , Wang Yanzhuang 
Published: October 2021

AbstractSynthesis of glycosaminoglycans such as heparan sulfate (HS) and chondroitin sulfate (CS) occurs in the lumen of the Golgi but the relationship between Golgi structural integrity and glycosaminoglycan synthesis is not clear. In this study, we disrupted the Golgi structure by knocking out GRASP55 and GRASP65 and determined its effect on the synthesis, sulfation, and secretion of HS and CS. We found that GRASP depletion increased HS synthesis while decreasing CS synthesis in cells, altered HS and CS sulfation, and reduced both HS and CS secretion. Using proteomics, RNA-seq and biochemical approaches, we identified EXTL3, a key enzyme in the HS synthesis pathway, whose level is upregulated in GRASP knockout cells; while GalNacT1, an essential CS synthesis enzyme, is robustly reduced. In addition, we found that GRASP depletion decreased HS sulfation via the reduction of PAPSS2, a bifunctional enzyme in HS sulfation. Our study provides the first evidence that Golgi structural defect may significantly alter the synthesis and secretion of glycosaminoglycans.
Interferon-gamma Induces Melanogenesis Via Post-Translational Modification of Tyrosinase
Authors: Mo Xuan , Kazmi Hasan Raza , Preston-Alp Sarah , Zhou Bo , Zaidi M. Raza 
Published: October 2021

ABSTRACTMelanogenesis (melanin pigment production) in melanocytes is canonically stimulated by the alpha-melanocyte stimulating hormone (αMSH), which activates the cyclic-AMP-mediated expression of the melanocyte inducing transcription factor (MITF) and its downstream melanogenic genes, including the principal rate-limiting melanogenic enzyme tyrosinase (Tyr). Here we report that interferon-gamma (IFNG; type II interferon), but not IFN-alpha (a type I interferon), induces a noncanonical melanogenic pathway. Inhibition of IFNG pathway by the JAK inhibitor ruxolitinib or knocking out Stat1 abrogated the IFNG-induced melanogenesis. Interestingly, IFNG-induced melanogenesis was independent of MITF. IFNG markedly increased the Tyr protein expression but did not affect the mRNA expression, suggesting a post-translational regulatory mechanism. In contrast, IFNG had no effect on the expression of other melanogenesis-related proteins, e.g. tyrosinase-related protein 1 (Tyrp1) and dopachrome tautomerase (Dct). Glycosidase digestion assays revealed that IFNG treatment increased the mature glycosylated form of Tyr, but not its de novo synthesis. Moreover, cycloheximide chase assay showed that degradation of Tyr was decreased in IFNG-treated cells. These results suggest that the IFNG-STAT1 pathway regulates melanogenesis via modulation of the post-translational processing and protein stability of Tyr.SIGNIFICANCEThe canonical pathway that controls melanogenesis in melanocytes is activated by the alpha melanocyte stimulating hormone (αMSH) via its receptor MC1R, which activates the cyclic-AMP-mediated expression of the melanocyte master regulator MITF and its downstream target genes involved in the melanogenic process. Here we report a novel non-canonical melanogenic pathway that is mediated via the Interferon-gamma cytokine signaling. We show that this non-canonical pathway is independent of MITF-mediated gene expression, but rather functions via post-translational modification of the principal melanogenic enzyme Tyrosinase.
Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells
Authors: Simoni Ariella , Huber Holly , Georgia Senta K. , Finley Stacey D. 
Published: October 2021

AbstractPatients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood-glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin. Leveraging this proliferative response to prolactin may be a strategy to restore endogenous insulin production for patients with diabetes. To investigate this potential treatment, we previously developed a computational model to represent the prolactin-mediated JAK-STAT signaling pathway in pancreatic beta cells. However, this model does not account for variability in protein expression that naturally occurs between cells. Here, we applied the model to understand how heterogeneity affects the dynamics of JAK-STAT signaling. We simulated a sample of 10,000 heterogeneous cells with varying initial protein concentrations responding to prolactin stimulation. We used partial least squares regression to analyze the significance and role of each of the varied protein concentrations in producing the response of the cell. Our regression models predict that the concentrations of the cytosolic and nuclear phosphatases strongly influence the response of the cell. The model also predicts that increasing prolactin receptor strengthens negative feedback mediated by the inhibitor SOCS. These findings reveal biological targets that can potentially be used to modulate the proliferation of pancreatic beta cells to enhance insulin secretion and beta cell regeneration in the context of diabetes.
Assessing HD-EEG functional connectivity states using a human brain computational model
Authors: Tabbal Judie , Kabbara Aya , Yochum Maxime , Khalil Mohamad , Hassan Mahmoud , Benquet Pascal 
Published: October 2021

AbstractObjectiveElectro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool for tracking fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of pipeline steps is challenging due to the lack of realistic ‘controlled’ data. Here, our aim is two-folded: 1) provide a quantitative assessment of the advantages and limitations of the analyzed techniques and 2) introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity.ApproachWe used a human brain computational model containing both physiologically based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging, to generate high-density EEG recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas to emulate a virtual picture-naming task. We identified fast time-varying network states and quantified the performance of the key steps involved in the pipeline: 1) inverse models to reconstruct cortical-level sources, 2) functional connectivity measures to compute statistical interdependency between regional signals, and 3) dimensionality reduction methods to derive dominant brain network states (BNS).Main ResultsUsing a systematic evaluation of the different decomposition techniques, results show significant variability among tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, and the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states.SignificanceWe suggest using such brain models to go further in the evaluation of the different steps and parameters involved in the EEG/MEG source-space network analysis. This can reduce the empirical selection of inverse model, connectivity measure, and dimensionality reduction method as some of the methods can have a considerable impact on the results and interpretation.
Clustered PHD domains in KMT2/MLL proteins are attracted by H3K4me3 and H3 acetylation-rich active promoters and enhancers
Authors: Stroynowska-Czerwinska Anna Maria , Klimczak Magdalena , Pastor Michal , Kazrani Asgar Abbas , Misztal Katarzyna , Bochtler Matthias 
Published: October 2021

ABSTRACTHistone lysine-specfic methyltransferase 2 (KMT2A-D) proteins, alternatively called mixed lineage leukaemia (MLL1-4) proteins, mediate positive transcriptional memory. As the catalytic subunits of human COMPASS-like complexes, they methylate H3K4 at promoters and enhancers. KMT2A-D contain understudied highly conserved triplets and a quartet of plant homeodomains (PHDs). Here, we show that all clustered PHDs localise to the well-defined loci of H3K4me3 and H3 acetylation-rich active promoters and enhancers. Surprisingly, we observe little difference in binding pattern between PHDs from promoter-specific KMT2A-B and enhancer-specific KMT2C-D. Fusion of the KMT2A CXXC domain to the PHDs drastically enhances their preference for promoters over enhancers. Hence, the presence of CXXC domains in KMT2A-B, but not KMT2C-D, may explain the promoter/enhancer preferences of the full-length proteins. Importantly, targets of PHDs overlap with KMT2A targets and are enriched in genes involved in the cancer pathways. We also observe that PHDs of KMT2A-D are mutated in cancer, especially within conserved folding motifs (Cys4HisCys2Cys/His), which cause a domain loss-of-function. Taken together, our data suggests that PHDs of KMT2A-D guide the full-length proteins to active promoters and enhancers, and thus play a role in positive transcriptional memory.GRAPHICAL ABSTRACT
Identifying tumor cells at the single cell level
Authors: Dohmen Jan , Baranovskii Artem , Ronen Jonathan , Uyar Bora , Franke Vedran , Akalin Altuna 
Published: October 2021

AbstractTumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. The single cell sequencing technology enables an effective molecular characterization of single cells within the tumor. This technology can help deconvolute heterogeneous tumor samples and thus revolutionize personalized medicine. However, a governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. One of the critical cell type annotation challenges is identification of tumor cells within single cell or spatial sequencing experiments.This is a critical limiting step for a multitude of research, clinical, and commercial applications. A reliable method addressing that challenge is a prerequisite for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. Here, we propose Ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single cell level. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.
Competition for water and species coexistence in phenologically structured annual plant communities
Authors: Levine Jacob I. , Levine Jonathan M. , Gibbs Theo , Pacala Stephen W. 
Published: October 2021

AbstractBoth competition for water and phenological variation are important determinants of plant community structure, but ecologists lack a synthetic theory for how they affect coexistence outcomes. We developed an analytically tractable model of water competition for Mediterranean annual communities and demonstrate that variation in phenology alone can maintain high diversity in spatially homogenous assemblages of water-limited plants. We modeled a system where all water arrives early in the season and species vary in their ability to grow under drying conditions. As a consequence, species differ in growing season length, and compete by shortening the growing season of their competitors. This model replicates and offers mechanistic explanations for qualitative patterns observed in prior empirical studies of how phenology influences coexistence among Mediterranean annuals. Additionally, we found that a decreasing, concave-up tradeoff between growth rate and access to water can theoretically maintain infinite diversity under simple but realistic assumptions. High diversity is possible because: 1) later plants escape competition after their earlier-season competitors have gone to seed and 2) early-season species are more than compensated for their shortened growing season by a growth-rate advantage. Together, these mechanisms provide an explanation for how annual plant species might coexist when competing only for water.