bluuebunny/crossref_metadata_2025 huggingface dataset:
Seizures, behavioral deficits and adverse drug responses in two new genetic mouse models of HCN1 epileptic encephalopathy
Authors: Merseburg Andrea , Kasemir Jacquelin , Buss Eric W. , Leroy Felix , Bock Tobias , Porro Alessandro , Barnett Anastasia , Tröder Simon E. , Engeland Birgit , Stockebrand Malte , Moroni Anna , Siegelbaum Steven A , Isbrandt Dirk , Santoro Bina
Published: August 2021

De novo mutations in voltage- and ligand-gated channels have been associated with an increasing number of cases of developmental and epileptic encephalopathies, which often fail to respond to classic antiseizure medications. Here, we examine two knock-in mouse models replicating de novo mutations in the HCN1 voltage-gated channel gene, p.G391D and p.M153I (Hcn1G380D/+ and Hcn1M142I/+ in mouse), associated with severe drug-resistant neonatal- and childhood-onset epilepsy, respectively. Heterozygous mice from both lines displayed spontaneous generalized tonic-clonic seizures. Hcn1G380D/+ animals had an overall more severe phenotype, with pronounced alterations in the levels and distribution of HCN1 protein, including disrupted targeting to the axon terminals of basket cell interneurons. In line with clinical reports from HCN1 patients, administration of the antiepileptic Na+ channel antagonists lamotrigine and phenytoin resulted in the paradoxical induction of seizures in both lines, consistent with an effect to further impair inhibitory neuron function. We also show that these variants can render HCN1 channels unresponsive to classic antagonists, indicating the need to screen mutated channels to identify novel compounds with diverse mechanism of action. Our results underscore the need to tailor effective therapies for specific channel gene variants, and how strongly validated animal models may provide an invaluable tool towards reaching this objective.
Adolescent development of multiscale structural wiring and functional interactions in the human connectome
Authors: Park Bo-yong , Paquola Casey , Bethlehem Richard A. I. , Benkarim Oualid ,   Neuroscience in Psychiatry Network (NSPN) Consortium, Mišić Bratislav , Smallwood Jonathan , Bullmore Edward T. , Bernhardt Boris C.
Published: August 2021

Abstract

Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14–25 years (n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of cortico-cortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple cortico-cortical structural networks in youth. Studying resting-state functional MRI measures in the same participants at baseline, we found that regions with more similar structural wiring were more likely to be functionally coupled. Moreover, longitudinal structural wiring changes, particularly between sensory/unimodal and default mode networks, were reflected in tendencies for increased differentiation in brain function. These longitudinal findings provide new insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.
Natural selection does not affect the estimates of effective population size based on linkage disequilibrium
Authors: Novo Irene , Santiago Enrique , Caballero Armando
Published: August 2021

The effective population size (Ne) is a key parameter to quantify the magnitude of genetic drift and inbreeding, with important implications in human evolution. The increasing availability of high-density genetic markers allows the estimation of historical changes in Ne across time using measures of genome diversity or linkage disequilibrium between markers. Selection is expected to reduce diversity and Ne, and this reduction is modulated by the heterogeneity of the genome in terms of recombination rate. Here we investigate by computer simulations the consequences of selection (both positive and negative) and of recombination rate heterogeneity in the estimation of historical Ne. We also investigate the relationship between diversity parameters and Ne across the different regions of the genome using human marker data. We show that the estimates of historical Ne obtained from linkage disequilibrium between markers (NeLD) are virtually unaffected by selection. In contrast, those estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, what could have important consequences for the estimation of human demography. The simulation results are supported by the analysis of human data. The estimates of NeLD obtained for particular genomic regions do not correlate with recombination rate, nucleotide diversity, polymorphism, background selection statistic, minor allele frequency of SNPs, loss of function and missense variants and gene density. This suggests that NeLD measures are merely indicative of demographic changes in population size across generations.

Author summary

The inference of the demographic history of populations is of great relevance in evolutionary biology. This inference can be made from genomic data using coalescence methods or linkage disequilibrium methods. However, the assessment of these methods is usually made assuming neutrality (absence of selection). Here we show by computer simulations and analyses of human data that the estimates of historical effective population size obtained from linkage disequilibrium between markers are unaffected by natural selection, either positive or negative. In contrast, estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, which could have important consequences for recent estimations of human demography. Thus, only linkage disequilibrium methods appear to provide unbiased estimates of the population census size.
Unique structure and positive selection promote the rapid divergence of Drosophila Y chromosomes
Authors: Chang Ching-Ho , Gregory Lauren E. , Gordon Kathleen E. , Meiklejohn Colin D. , Larracuente Amanda M.
Published: August 2021

Y chromosomes across diverse species convergently evolve a gene-poor, heterochromatic organization enriched for duplicated genes, LTR retrotransposable elements, and satellite DNA. Sexual antagonism and a loss of recombination play major roles in the degeneration of young Y chromosomes. However, the processes shaping the evolution of mature, already degenerated Y chromosomes are less well-understood. Because Y chromosomes evolve rapidly, comparisons between closely related species are particularly useful. We generated de novo long read assemblies complemented with cytological validation to reveal Y chromosome organization in three closely related species of the Drosophila simulans complex, which diverged only 250,000 years ago and share >98% sequence identity. We find these Y chromosomes are divergent in their organization and repetitive DNA composition and discover new Y-linked gene families whose evolution is driven by both positive selection and gene conversion. These Y chromosomes are also enriched for large deletions, suggesting that the repair of double-strand breaks on Y chromosomes may be biased toward microhomology-mediated end joining over canonical non-homologous end-joining. We propose that this repair mechanism generally contributes to the convergent evolution of Y chromosome organization.
On the nature of the earliest known lifeforms
Authors: Kanaparthi Dheeraj , Westall Frances , Lampe Marko , Zhu Baoli , Boesen Thomas , Scheu Bettina , Klingl Andreas , Schwille Petra , Lueders Tillmann
Published: August 2021

Microfossils from the Paleoarchean Eon are the oldest known evidence of life. Despite their significance in understanding the history of life on Earth, any interpretation of the nature of these microfossils has been a point of contention among researchers. Decades of back-and-forth arguments led to the consensus that reconstructing the lifecycles of Archaean Eon organisms is the most promising way of understanding the nature of these microfossils. Here, we transformed a Gram-positive bacterium into a primitive lipid vesicle-like state and studied it under environmental conditions prevalent on early Earth. Using this approach, we successfully reconstructed morphologies and life cycles of Archaean microfossils. In addition to reproducing microfossil morphologies, we conducted experiments that spanned years to understand the process of cell degradation and how Archaean cells could have undergone encrustation minerals (in this case, salt), leading to their preservation as fossilized organic carbon in the rock record. These degradation products strongly resemble fossiliferous features from Archaean rock formations. Our observations suggest that microfossils aged between 3.8 to 2.5Ga most likely were liposome-like protocells that have evolved physiological pathways of energy conservation but not the mechanisms to regulate their morphology. Based on these observations, we propose that morphology is not a reliable indicator of taxonomy in these microfossils.
Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments
Authors: Patrick Ellis , Canete Nicolas P. , Iyengar Sourish S. , Harman Andrew N. , Sutherland Greg T. , Yang Pengyi
Published: August 2021

Highly multiplexedin situimaging cytometry assays have made it possible to study the spatial organisation of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarising the information-rich data generated from these technologies.
Particle Uptake Driven Phagocytosis in Macrophages and Neutrophils Enhances Bacterial Clearance
Authors: Sharma Preeti , Vijaykumar Anjali , Raghavan Jayashree Vijaya , Rananaware Supriya Rajendra , Alakesh Alakesh , Bodele Janhavi , Rehman Junaid Ur , Shukla Shivani , Wagde Virta , Nadig Savitha , Chakrabarti Sveta , Visweswariah Sandhya S. , Nandi Dipankar , Gopal Balasubramanian , Jhunjhunwala Siddharth
Published: August 2021

Humans are exposed to numerous synthetic foreign particulates in the form of environmental pollutants and diagnostic or therapeutic agents. Specialized immune cells (phagocytes) clear these particulates by phagocytosing and attempting to degrade them. The process of recognition and internalization of the particulates may trigger changes in the function of phagocytes. Some of these changes, especially the ability of a particle-loaded phagocyte to take up and neutralize pathogens, remains poorly studied. Herein, we demonstrate that the uptake of non-stimulatory cargo-free particles enhances the phagocytic ability of monocytes, macrophages and neutrophils. The enhancement in phagocytic ability was independent of particle properties, such as size or the base material constituting the particle. Additionally, we show that the increased phagocytosis was not a result of cellular activation or cellular heterogeneity but was driven by changes in cell membrane fluidity and cellular compliance. A consequence of the enhanced phagocytic activity was that particulate-laden immune cells neutralize E. coli faster in culture. Moreover, when administered in mice as a prophylactic, particulates enable faster clearance of E. coli and S. epidermidis. Together, we demonstrate that the process of uptake induces cellular changes that favor additional phagocytic events. This study provides insights into using non-stimulatory cargo-free particles to engineer immune cell functions for applications involving faster clearance of phagocytosable particulates.
Endothelial cell polarity and extracellular matrix production rely on functional ATP6AP2 during developmental and pathological angiogenesis
Authors: Patel NR , Blanks A , Li Y , Prieto MC , Meadows SM
Published: August 2021

The (Pro)renin receptor ((P)RR), also known as ATP6AP2, is a single-transmembrane protein that is implicated in a multitude of biological processes. However, the exact role of ATP6AP2 during blood vessel development remains largely undefined. Here, we use an inducible endothelial cell (EC)-specificAtp6ap2knockout mouse model to investigate the role of ATP6AP2 during both physiological and pathological angiogenesisin vivo. We observed that postnatal deletion ofAtp6ap2in ECs results in cell migration defects, loss of tip cell polarity and subsequent impairment of retinal angiogenesis.In vitro, Atp6ap2deficient ECs similarly displayed reduced cell migration, impaired sprouting, and defective cell polarity. Transcriptional profiling of ECs isolated fromAtp6ap2mutant mice further indicated regulatory roles in angiogenesis, cell migration and extracellular matrix composition. Mechanistically, we showed that expression of various extracellular matrix components is controlled by ATP6AP2 via the extracellular-signal-regulated kinase (ERK) pathway. Furthermore,Atp6ap2deficient retinas exhibited reduced revascularization in an oxygen induced retinopathy model. Collectively, our results demonstrated a critical role of ATP6AP2 as a regulator of developmental and pathological angiogenesis.
A Novel Model for Encephalomyosynangiosis Surgery after Middle Cerebral Artery Occlusion-Induced Stroke in Mice
Authors: Paro Mitch , Turro Daylin Gamiotea , Blumenfeld Leslie , Bulsara Ketan R. , Verma Rajkumar
Published: August 2021

Background and Purpose

No effective treatment is available for most patients who suffer ischemic stroke. Development of novel treatment options is imperative. The brain attempts to self-heal after ischemic stroke via various mechanism mediated by restored blood circulation in affected region of brain but this process is limited by inadequate angiogenesis or neoangiogenesis. Encephalomyosynangiosis (EMS) is a neurosurgical procedure that achieves angiogenesis with low morbidity in patients with moyamoya disease, reducing risk of stroke. However, EMS, surgery has never been studied as an therapeutic option after ischemic stroke. Here we described a novel procedure and feasibility data for EMS after ischemic stroke in mice.

Methods

A 60 mins of middle cerebral artery occlusion (MCAo) was used to induce ischemic stroke in mice. After 3-4 hours of MCAo onset/sham, EMS was performed. Mortality of EMS, MCAo and. MCAo+EMS mice was recorded up to 21 days after surgery. Graft tissue viability was measured using a nicotinamide adenine dinucleotide reduced tetrazolium reductase assay.

Results

EMS surgery after ischemic stroke does not increase mortality compared to stroke alone. Graft muscle tissue remained viable 21 days after surgery.

Conclusions

This novel protocol is effective and well-tolerated, may serve as novel platform for new angiogenesis and thus recovery after ischemic stroke. If successful in mice, EMS can a very feasible and novel treatment option for ischemic stroke in humans.
Understanding the microbial biogeography of ancient human dentitions to guide study design and interpretation
Authors: Fagernäs Zandra , Salazar-García Domingo C. , Avilés Azucena , Haber María , Henry Amanda , Maurandi Joaquín Lomba , Ozga Andrew , Velsko Irina M , Warinner Christina
Published: August 2021

The oral cavity is a heterogeneous environment, varying in factors such as pH, oxygen levels, and salivary flow. These factors affect the microbial community composition and distribution of species in dental plaque, but it is not known how well these patterns are reflected in archaeological dental calculus. In most archaeological studies, a single sample of dental calculus is studied per individual and is assumed to represent the entire oral cavity. However, it is not known if this sampling strategy introduces biases into studies of the ancient oral microbiome. Here, we present the results of a shotgun metagenomic study of a dense sampling of dental calculus from four Chalcolithic individuals from the southeast Iberian peninsula (ca. 4500-5000 BP). Inter-individual differences in microbial composition are found to be much larger than intra-individual differences, indicating that a single sample can indeed represent an individual in most cases. However, there are minor spatial patterns in species distribution within the oral cavity that should be taken into account when designing a study or interpreting results. Finally, we show that plant DNA identified in the samples may be of environmental origin, showing the importance of including environmental controls or several lines of biomolecular evidence.
Generation of nanobodies targeting the human, transcobalamin-mediated vitamin B12 uptake route
Authors: Bloch Joël S. , Sequeira Jeffrey M. , Ramírez Ana S. , Quadros Edward V. , Locher Kaspar P.
Published: August 2021

Cellular uptake of vitamin B12 in humans is mediated by the endocytosis of the B12 carrier protein transcobalamin (TC) via its cognate cell surface receptor TCblR (or CD320), encoded by the CD320 gene(1). Because CD320 expression is associated with the cell cycle and upregulated in highly proliferating cells such as cancer cells(2–4), this uptake route is a potential target for cancer therapy(5). We developed and characterized four camelid nanobodies that bind TC or the interface of the TC:TCblR complex with nanomolar affinities. We determined X-ray crystal structures of all four nanobodies in complex with TC:TCblR, which enabled us to map their binding sites. When conjugated to a toxin, three of these nanobodies are capable of inhibiting the growth of HEK293T cells and therefore have the potential to inhibit the growth of human cancer cells. We visualized the cellular binding and endocytic uptake of the most potent nanobody (TCNB4) using fluorescent light microscopy. The co-crystal structures of TC:TCblR with another nanobody (TCNB34) revealed novel features of the interface of TC and the LDLR-A1 domain of TCblR. Our findings rationalize the structural basis for a decrease in affinity of TC-B12 binding caused by the TCblR-Glu88 deletion mutant.
A deep learning-based approach to model anomalous diffusion of membrane proteins: The case of the nicotinic acetylcholine receptor
Authors: Buena Maizón Héctor , Barrantes Francisco J.
Published: August 2021

We present a concatenated deep-learning multiple neural network system for the analysis of single-molecule trajectories. We apply this machine learning-based analysis to characterize the translational diffusion of the nicotinic acetylcholine receptor at the plasma membrane, experimentally interrogated using superresolution optical microscopy. The receptor protein displays a heterogeneous diffusion behavior that goes beyond the ensemble level, with individual trajectories exhibiting more than one diffusive state, requiring the optimization of the neural networks through a hyperparameter analysis for different numbers of steps and durations, especially for short trajectories (<50 steps) where the accuracy of the models is most sensitive to localization errors. We next use the statistical models to test for Brownian, continuous-time random walk, and fractional Brownian motion, and introduce and implement an additional, two-state model combining Brownian walks and obstructed diffusion mechanisms, enabling us to partition the two-state trajectories into segments, each of which is independently subjected to multiple analysis. The concatenated multi-network system evaluates and selects those physical models that most accurately describe the receptor’s translational diffusion. We show that the two-state Brownian-obstructed diffusion model can account for the experimentally observed anomalous diffusion (mostly subdiffusive) of the population and the heterogeneous single-molecule behavior, accurately describing the majority (72.5% to 88.7% for α-bungarotoxin-labeled receptor and between 73.5% and 90.3% for antibody-labeled molecules) of the experimentally observed trajectories, with only ∼15% of the trajectories fitting to the fractional Brownian motion model.
2020 BioImage Analysis Survey: Community experiences and needs for the future
Authors: Jamali Nasim , Dobson Ellen TA , Eliceiri Kevin W. , Carpenter Anne E. , Cimini Beth A.
Published: August 2021

In this paper, we summarize a global survey of 484 participants of the imaging community, conducted in 2020 through the NIH Center for Open BioImage Analysis (COBA). This 23-question survey covered experience with image analysis, scientific background and demographics, and views and requests from different members of the imaging community. Through open-ended questions we asked the community to provide feedback for the open-source tool developers and tool user groups. The community’s requests for tool developers include general improvement of tool documentation and easy-to-follow tutorials. Respondents encourage tool users to follow the best practices guidelines for imaging and ask their image analysis questions on the Scientific Community Image forum (forum.image.sc). We analyzed the community’s preferred method of learning, based on level of computational proficiency and work description. In general, written step-by-step and video tutorials are preferred methods of learning by the community, followed by interactive webinars and office hours with an expert. There is also enthusiasm for a centralized location online for existing educational resources. The survey results will help the community, especially developers, trainers, and organizations like COBA, decide how to structure and prioritize their efforts.

Impact statement

The Bioimage analysis community consists of software developers, imaging experts, and users, all with different expertise, scientific background, and computational skill levels. The NIH funded Center for Open Bioimage Analysis (COBA) was launched in 2020 to serve the cell biology community’s growing need for sophisticated open-source software and workflows for light microscopy image analysis. This paper shares the result of a COBA survey to assess the most urgent ongoing needs for software and training in the community and provide a helpful resource for software developers working in this domain. Here, we describe the state of open-source bioimage analysis, developers’ and users’ requests from the community, and our resulting view of common goals that would serve and strengthen the community to advance imaging science.
SeqDistK: a Novel Tool for Alignment-free Phylogenetic Analysis
Authors: Liu Xuemei , Li Wen , Huang Guanda , Huang Tianlai , Xiong Qingang , Chen Wen , Xia Li C.
Published: August 2021

Algorithms for constructing phylogenetic trees are fundamental to study the evolution of viruses, bacteria, and other microbes. Established multiple alignment-based algorithms are inefficient for large scale metagenomic sequence data because of their high requirement of inter-sequence correlation and high computational complexity. In this paper, we present SeqDistK, a novel tool for alignment-free phylogenetic analysis. SeqDistK computes the dissimilarity matrix for phylogenetic analysis, incorporating seven k-mer based dissimilarity measures, namely d2, d2S, d2star, Euclidean, Manhattan, CVTree, and Chebyshev. Based on these dissimilarities, SeqDistK constructs phylogenetic tree using the Unweighted Pair Group Method with Arithmetic Mean algorithm. Using a golden standard dataset of 16S rRNA and its associated phylogenetic tree, we compared SeqDistK to Muscle – a multi sequence aligner. We found SeqDistK was not only 38 times faster than Muscle in computational efficiency but also more accurate. SeqDistK achieved the smallest symmetric difference between the inferred and ground truth trees with a range between 13 to 18, while that of Muscle was 62. When measures d2, d2star, d2S, Euclidean, and k-mer size k=5 were used, SeqDistK consistently inferred phylogenetic tree almost identical to the ground truth tree. We also performed clustering of 16S rRNA sequences using SeqDistK and found the clustering was highly consistent with known biological taxonomy. Among all the measures, d2S (k=5, M=2) showed the best accuracy as it correctly clustered and classified all sample sequences. In summary, SeqDistK is a novel, fast and accurate alignment-free tool for large-scale phylogenetic analysis. SeqDistK software is freely available athttps://github.com/htczero/SeqDistK.
Revisiting a classic hybrid zone: rapid movement of the northern flicker hybrid zone in contemporary times
Authors: Aguillon Stepfanie M. , Rohwer Vanya G.
Published: August 2021

Natural hybrid zones have provided important insights into the evolutionary process, and their geographic stability/instability over time can help to disentangle the underlying biological processes that maintain them. Here, we leverage replicated sampling of an identical transect across the hybrid zone between yellow-shafted and red-shafted flickers in the Great Plains to assess its stability over ∼60 years (1955-1957 to 2016-2018). We identify a ∼73 km westward shift in the hybrid zone center towards the range of the red-shafted flicker, but find no associated changes in width over our sampling period. In fact, the hybrid zone remains remarkably narrow, suggesting some kind of selective pressure maintains the zone. By comparing to previous work in the same geographic region, it appears likely that the movement in the hybrid zone has occurred rapidly in the years since the early 1980s. This recent, rapid movement may be related to changes in climate or land management practices that have allowed asymmetric westward movement of yellow-shafted flickers into the Great Plains.
The Molecular Basis of FimT-mediated DNA Uptake during Bacterial Natural Transformation
Authors: Braus Sebastian A.G. , Short Francesca L. , Holz Stefanie , Stedman Matthew J.M. , Gossert Alvar D. , Hospenthal Manuela K.
Published: August 2021

Naturally competent bacteria encode sophisticated protein machineries for the uptake and translocation of exogenous DNA into the cell. If this DNA is integrated into the bacterial genome, the bacterium is said to be naturally transformed. Most competent bacterial species utilise type IV pili for the initial DNA uptake step. These proteinaceous cell-surface structures are composed of thousands of pilus subunits (pilins), designated as major or minor according to their relative abundance in the pilus. In this study, we show that the minor pilin FimT plays an important role in the natural transformation ofLegionella pneumophila. We used NMR spectroscopy,in vitroDNA binding assays andin vivotransformation assays to understand the molecular basis of FimT’s role in this process. FimT directly interacts with DNAviaan electropositive patch, rich in arginines, several of which are well-conserved and located in FimT’s conformationally flexible C-terminal tail. We also show that FimT orthologues from other γ-Proteobacteria share the ability to bind to DNA. Our functional characterisation and comprehensive bioinformatic analysis of FimT, suggest that it plays an important role for DNA uptake in a wide range of competent species.
Deep Learning Features Encode Interpretable Morphologies within Histological Images
Authors: Pour Ali Foroughi , White Brian , Park Jonghanne , Sheridan Todd B. , Chuang Jeffrey H.
Published: August 2021

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture), which we show can be construed as abstract morphological genes (“mones”) with strong independent associations to biological phenotypes. We observe that many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC=97.1% ± 2.8% for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC=99.2% ± 0.12%). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values.
RNA is required for the maintenance of multiple cytoplasmic and nuclear membrane-less organelles
Authors: Decker Carolyn J. , Burke James M. , Mulvaney Patrick K. , Parker Roy
Published: August 2021

Numerous membrane-less organelles composed of a combination of RNA and proteins, referred to as RNP granules, are observed in the nucleus and cytoplasm of eukaryotic cells, including stress granules, processing bodies, Cajal bodies, and nuclear speckles. An unresolved issue is how frequently RNA molecules are required for the maintenance of RNP granules in either the nucleus or cytosol. To address this issue, we degraded intracellular RNA in either the cytosol or the nucleus by the activation of RNase L and examined the impact of RNA loss on several RNP granules. Strikingly, we find the majority of RNP granules, including stress granules, processing bodies, Cajal bodies, nuclear speckles and the nucleolus are altered by the degradation of their RNA components. In contrast, super-enhancer complexes and TIS granules were largely unaffected by widespread intracellular RNA degradation. This highlights a critical and widespread role of RNA in the organization of many, but not all, RNP granules.

Decker et al. examine the role of RNA in maintaining membrane-less organelles composed of RNA and protein by activating RNase L in the cytoplasm or nucleus. Degradation of RNA alters the majority of membrane-less organelles indicating that RNA plays a widespread role in their organization.
A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology
Authors: Lutnick Brendon , Manthey David , Becker Jan U. , Ginley Brandon , Moos Katharina , Zuckerman Jonathan E. , Rodrigues Luis , Gallan Alexander J. , Barisoni Laura , Alpers Charles E. , Wang Xiaoxin X. , Myakala Komuraiah , Jones Bryce A. , Levi Moshe , Kopp Jeffrey B. , Yoshida Teruhiko , Han Seung Seok , Jain Sanjay , Rosenberg Avi Z. , Jen Kuang Yu. , Sarder Pinaki ,   the Kidney Precision Medicine Project
Published: August 2021

Background

Image-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power.

Methods

We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis.

Results

By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy.

Conclusion

The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.
Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches
Authors: Francisco Felipe Roberto , Aono Alexandre Hild , da Silva Carla Cristina , Gonçalves Paulo de Souza , Scaloppi Erivaldo José , Guen Vincent Le , Neto Roberto Fritsche , Souza Livia Moura , de Souza Anete Pereira
Published: August 2021

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.
A toolbox of engineered mosquito lines to study salivary gland biology and malaria transmission
Authors: Klug Dennis , Arnold Katharina , Mela-Lopez Raquel , Marois Eric , Blandin Stéphanie A.
Published: August 2021

Mosquito saliva is a vehicle for the transmission of vector borne pathogens such as Plasmodium parasites and different arboviruses. Despite the key role of the salivary glands in the process of disease transmission, knowledge of host-pathogen interactions taking place within this organ is very limited. To improve the experimental tractability of the salivary glands, we have generated fluorescent reporter lines in the African malaria mosquito Anopheles coluzzii using the salivary gland-specific promoters of the anopheline antiplatelet protein (AAPP), the triple functional domain protein (TRIO) and saglin (SAG). Promoter activity was specifically observed in the distal-lateral lobes or in the median lobe of the salivary gland. Besides a comparison of the expression patterns of the selected promoters, the fluorescent probes allowed us to evaluate the inducibility of the selected promoters upon blood feeding and to measure intracellular redox changes. We also combined the aapp-DsRed fluorescent reporter line with a pigmentation-deficient yellow(-) mosquito mutant to assess the feasibility of in vivo microscopy of parasitized salivary glands. This combination allowed locating the salivary gland through the cuticle and imaging of individual sporozoites in vivo, which facilitates live imaging studies of salivary gland colonization by Plasmodium sporozoites.
A point mutation in the kinase domain of CRK10 leads to xylem vessel collapse and activates defence responses
Authors: Piovesana Maiara , Wood Ana K. M. , Smith Daniel P. , Deery Michael J. , Bayliss Richard , Carrera Esther , Napier Johnathan A. , Kurup Smita , Matthes Michaela C.
Published: August 2021

Cysteine-rich receptor-like kinases (CRKs) are a large family of plasma membrane-bound receptors ubiquitous in higher plants. They are transcriptionally regulated by a wide variety of environmental cues and stresses, however their precise biological roles remain largely unknown. Here we report a novel mutant isolated for the CYSTEINE-RICH RECEPTOR-LIKE KINASE 10 (CRK10) ofArabidopsis thalianawhich harbours the substitution of alanine 397 by a threonine in the αC-helix of its kinase domain and which we registered ascrk10-A397Tin the community database.In situphosphorylation assays with the His-tagged wild type (WT) andcrk10-A397Tversions of the CRK10 kinase domain revealed that both alleles are active kinases capable of auto-phosphorylation with the newly introduced threonine acting as an additional phosphorylation site incrk10-A397T. Phenotypically the mutant is a dwarf and the analysis of thin cross sections with light and transmission electron microscopy revealed that collapsed xylem vessels in roots and hypocotyls are very likely the cause for this reduction in stature. Transcriptomic analysis of WT and mutant hypocotyls revealed that predominantly biotic and abiotic stress-responsive genes are constitutively up-regulated in the mutant. Root-infection assays with the vascular pathogenFusarium oxysporumdemonstrated that thecrk10-A397Tmutant has enhanced resistance to this pathogen compared to WT plants. Taken together our results suggest thatcrk10-A397Tis a gain-of-function allele ofCRK10and open up new avenues for the investigation of this elusive receptor-like kinase family.
Application of components analysis in dynamic thermal breast imaging to identify pathophysiologic mechanisms of heat transfer
Authors: Gershenson Meir , Gershenson Jonathan
Published: August 2021

Significance

Of the most compelling unsolved issues in the paradigm to create success in the field of breast cancer infrared imaging is localization of direct internal heat of the tumor. The contribution of differential heat production related to metabolism versus perfusion is not understood. Previous work until now has not shown progress beyond identifying veins which are fed by the ‘hot’ cancer. Employing signal analysis techniques, we probe important questions which may lead to further understanding pathophysiology of heat transfer occurring in the setting of malignancy.

Aim

When using thermal imaging to detect breast cancer, the dominant heat signature is that of indirect heat transported in gradient away from the tumor location. Unprocessed images strikingly display vasculature which acts to direct excess heat superficially towards the skin surface before dissipating. In current clinical use, interpretation of thermogram images considers abnormal vascular patterns and overall temperature as indicators of disease. The goal of this work is to present a processing method for dynamic external stimulus thermogram images to isolate and separate the indirect vascular heat while revealing the desired direct heat from the tumor.

Approach

In dynamic thermal imaging of the breast, a timed series of images are taken following application of external temperature stimulus (most often cooled air). While the tumor heat response is thought to be independent of the external stimulus, the secondary heat of the veins is known to be affected by vasomodulation. The recorded data is analyzed using independent component analysis (ICA) and principal component analysis (PCA) methods. ICA separates the image sequence into new independent images having a common characteristic time behavior. Resulting individual components are analyzed for correspondence to the presence or lack of vasomodulation.

Results

Using the Brazilian visual lab mastology data set containing dynamic thermograms, applying components analysis resulted in three corresponding images: 1. Minimum change as a function of applied temperature or time (suggests correlation with the cancer generated heat), 2. Moderate temperature dependence (suggests correlation with veins affected by vasomodulation) and 3. Complex time behavior (suggests correlation with heat absorption due to high tumor perfusion). All components appear clear and distinct.

Conclusions

Applying signal processing methods to the dynamic infrared data, we found three distinct components with correspondence to understood physiologic processes. The two cases shown are self-evident of the capability of the method but are lacking supporting ground truth that is unavailable with such a limited data set. Validation of this proposed paradigm and studying furthering clinical applications has potential to create significant achievement for IR modality in diagnostic imaging.
Network analysis of ten thousand genomes shed light on Pseudomonas diversity and classification
Authors: Passarelli-Araujo Hemanoel , Franco Glória Regina , Venancio Thiago M.
Published: August 2021

The growth of sequenced bacterial genomes has revolutionized the assessment of microbial diversity. Pseudomonas is a widely diverse genus, containing more than 254 species. Although type strains have been employed to estimate Pseudomonas diversity, they represent a small fraction of the genomic diversity at a genus level. We used 10,035 available Pseudomonas genomes, including 210 type strains, to build a genomic distance network to estimate the number of species through community identification. We identified taxonomic inconsistencies with several type strains and found that 25.65% of the Pseudomonas genomes deposited on Genbank are misclassified. The phylogenetic tree using single-copy genes from representative genomes in each species cluster in the distance network revealed at least 14 Pseudomonas groups, including P. alcaligenes group proposed here. We show that Pseudomonas is likely an admixture of different genera and should be further divided. This study provides an overview of Pseudomonas diversity from a network and phylogenomic perspective that may help reduce the propagation of mislabeled Pseudomonas genomes.
Learning predictive cognitive maps with spiking neurons during behaviour and replays
Authors: Bono Jacopo , Zannone Sara , Pedrosa Victor , Clopath Claudia
Published: August 2021

We describe a framework where a biologically plausible spiking neural network mimicking hippocampal layers learns a cognitive map known as the successor representation. We show analytically how, on the algorithmic level, the learning follows the TD(λ) algorithm, which emerges from the underlying spike-timing dependent plasticity rule. We then analyze the implications of this framework, uncovering how behavioural activity and experience replays can play complementary roles when learning the representation of the environment, how we can learn relations over behavioural timescales with synaptic plasticity acting on the range of milliseconds, and how the learned representation can be flexibly encoded by allowing state-dependent delay discounting through neuromodulation and altered firing rates.
Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces
Authors: Dehghanpoor Ramin , Afrasiabi Fatemeh , Haspel Nurit
Published: August 2021

An essential step to understanding how different functionalities of proteins work is to explore their conformational space. However, because of the fleeting nature of conformational changes in proteins, investigating protein conformational spaces is a challenging task to do experimentally. Nonetheless, computational methods have shown to be practical to explore these conformational pathways. In this work, we use Topological Data Analysis (TDA) methods to evaluate our previously introduced algorithm called RRTMC, that uses a combination of Rapidly-exploring Random Trees algorithm and Monte Carlo criteria to explore these pathways. TDA is used to identify the intermediate conformations that are generated the most by RRTMC and examine how close they are to existing known intermediate conformations. We concluded that the intermediate conformations generated by RRTMC are close to existing experimental data and that TDA can be a helpful tool to analyze protein conformation sampling methods.
Precision engineering of an anti-HLA-A2 chimeric antigen receptor in regulatory T cells for transplant immune tolerance
Authors: Muller Yannick D. , Ferreira Leonardo M.R. , Ronin Emilie , Ho Patrick , Nguyen Vinh , Faleo Gaetano , Zhou Yu , Lee Karim , Leung Kevin K. , Skartsis Nikolaos , Kaul Anupurna M. , Mulder Arend , Claas Frans H.J. , Wells James A. , Bluestone Jeffrey A. , Tang Qizhi
Published: August 2021

Infusion of regulatory T cells (Tregs) engineered with a chimeric antigen receptor (CAR) targeting donor-derived human leukocyte antigen (HLA) is a promising strategy to promote transplant tolerance. Here, we describe an anti-HLA-A2 CAR (A2-CAR) generated by grafting the complementarity-determining regions (CDRs) of a human monoclonal anti-HLA-A2 antibody into the framework regions of the Herceptin 4D5 single-chain variable fragment and fusing it with a CD28-ζ signaling domain. The CDR-grafted A2-CAR maintained the specificity of the original antibody. We then generated HLA-A2 mono-specific human CAR Tregs either by deleting the endogenous T-cell receptor (TCR) via CRISPR/Cas9 and introducing the A2-CAR using lentiviral transduction or by directly integrating the CAR construct into the TCR alpha constant locus using homology-directed repair. These A2-CAR+TCRdeficient human Tregs maintained both Treg phenotype and function in vitro. Moreover, they selectively accumulated in HLA-A2-expressing islets transplanted from either HLA-A2 transgenic mice or deceased human donors. A2-CAR+TCRdeficient Tregs did not impair the function of these HLA-A2+ islets, whereas similarly engineered A2-CAR+TCRdeficientCD4+ conventional T cells rejected the islets in less than 2 weeks. A2-CAR+TCRdeficient Tregs delayed graft-versus-host disease only in the presence of HLA-A2, expressed either by co-transferred peripheral blood mononuclear cells or by the recipient mice. Altogether, we demonstrate that genome-engineered mono-antigen-specific A2-CAR Tregs localize to HLA-A2-expressing grafts and exhibit antigen-dependent in vivo suppression, independent of TCR expression. These approaches may be applied towards developing precision Treg cell therapies for transplant tolerance.