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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,279 records · Page 71Linked to original sources

bioRxiv2020

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Image-based particle tracking is an essential tool to answer research questions in cell biology and beyond. A major challenge of particle tracking in living systems is that low light exposure is required to avoid phototoxicity and photobleaching. In addition, high-speed imaging used to fully capture particle motion dictates fast image acquisition rates. Short exposure times come at the expense of tracking accuracy. This is generally true for quantitative microscopy approaches and particularly relevant to single molecule tracking where the number of photons emitted from a single chromophore is limited. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic datasets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved the number of trackable particles and tracking accuracy, supervised learning generally outperformed the unsupervised approach, as expected. We also highlight that with extremely noisy image sequences, deep learning algorithms produce deceiving artifacts, which underscores the need to carefully evaluate the results. Finally, we address the challenge of selecting hyper-parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optional particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of the approaches presented here to critically evaluate artificial intelligence solutions for quantitative microscopy.

biophysics↗

bioRxiv2020

Antagonistic fungal enterotoxins intersect at multiple levels with host innate immune defences.

Animals and plants need to defend themselves from pathogen attack. Their defences drive innovation in virulence mechanisms, leading to never-ending cycles of co-evolution in both hosts and pathogens. A full understanding of host immunity therefore requires examination of pathogen virulence strategies. Here, we take advantage of the well-studied innate immune system of Caenorhabditis elegans to dissect the action of two virulence factors from its natural fungal pathogen Drechmeria coniospora. We show that these two enterotoxins have strikingly different effects when expressed individually in the nematode epidermis. One is able to interfere with diverse aspects of host cell biology, altering vesicle trafficking and preventing the key STAT-like transcription factor STA-2 from activating defensive antimicrobial peptide gene expression. The second, potentially as a consequence of a host surveillance mechanism, increases STA-2 levels in the nucleus, modifies the nucleolus, and causes increased defence gene expression. Our results highlight the remarkably complex and potentially antagonistic mechanisms that come into play in the interaction between co-evolved hosts and pathogens.

microbiology↗

bioRxiv2020

Establishment of a highly efficient gene disruption strategy to analyze and manipulate lipid co-regulatory networks

Gene disruption has been dramatically facilitated by genome editing tools. Despite improvements in gene disruption rates in cultured cells, clone isolation remains routinely performed to obtain mutants, potentially leading to artifacts due to clonal variation in cellular phenotypes. Here we report GENF, a highly efficient strategy to disrupt genes without isolating clones, which can be multiplexed. Using it, we obtained reliable lipidomics datasets from mutant cells without being affected by variances related to clone isolation. Through this, we found that an enzyme involved in congenital generalized lipodystrophy regulates glycerophospholipids with specific acyl-chains. We also demonstrate the possibility to dissect complex lipid co-regulatory mechanisms, explaining cell adaptations to altered lipid metabolism. With its simplicity and the avoidance of cloning-related artifacts, GENF is likely to contribute to many cell biology studies, especially those involving -omics approaches.

genetics↗

bioRxiv2020

The tumor suppressor protein PTEN undergoes amyloid-like aggregation in tumor cells.

Protein aggregation is an underappreciated mechanism that may contribute to the loss- and oncogenic-gain-of-function of mutant tumor suppressors such as p53 and axin. In the present study, we describe amyloid-like aggregation behaviour of the second most frequently mutated tumor suppressor in human cancer, PTEN. In silico analysis revealed a particularly high aggregation vulnerability for this protein, which was corroborated by in vitro aggregation assays. In cultured tumor cells, we found that under stress conditions, PTEN readily undergoes amyloid-like aggregation as a result of mutation. However, we also show that severe dysregulation of protein homeostasis may lead to aggregation of wild-type PTEN. These observations were supported by a small survey of patient-derived uterine tumor tissues, which found that more than 25% of tumors analyzed displayed wild-type PTEN aggregation. Finally, in an exploratory clinical study we found that PTEN aggregation status was correlated with a decline in clinical outcome. Our findings establish that the tumor suppressor PTEN is highly aggregation-prone and our work suggests that protein aggregation might be an underestimated but prevalent component of cancer cell biology.

cancer biology↗

bioRxiv2020

Design of smart antibody mimetics with photosensitive switches

As two prominent examples of intracellular single-domain antibodies or antibody mimetics derived from synthetic protein scaffolds, monobodies and nanobodies are gaining wide applications in cell biology, structural biology, synthetic immunology, and theranostics. We introduce herein a generally-applicable method to engineer light-controllable monobodies and nanobodies, designated as moonbody and sunbody, respectively. These engineered antibody-like modular domains enable rapid and reversible antibody-antigen recognition by utilizing light. By paralleled insertion of two LOV2 modules into a single sunbody and the use of bivalent sunbodies, we substantially enhance the range of dynamic changes of photo-switchable sunbodies. Furthermore, we demonstrate the use of moonbodies or sunbodies to precisely control protein degradation, gene transcription, and base editing by harnessing the power of light.

synthetic biology↗

bioRxiv2020

Population genomics of the maize pathogen Ustilago maydis: demographic history and role of virulence clusters in adaptation

The tight interaction between pathogens and their hosts results in reciprocal selective forces that impact the genetic diversity of the interacting species. The footprints of this selection differ between pathosystems because of distinct life-history traits, demographic histories, or genome architectures. Here, we studied the genome-wide patterns of genetic diversity of 22 isolates of the causative agent of the corn smut disease, Ustilago maydis, originating from five locations in Mexico, the presumed center of origin of this species. In this species, many genes encoding secreted effector proteins reside in so-called virulence clusters in the genome, an arrangement that is so far not found in other filamentous plant pathogens. Using a combination of population genomic statistical analyses, we assessed the geographical, historical and genome-wide variation of genetic diversity in this fungal pathogen. We report evidence of two partially admixed subpopulations that are only loosely associated with geographic origin. Using the multiple sequentially Markov coalescent model, we inferred the demographic history of the two pathogen subpopulations over the last 0.5 million years. We show that both populations experienced a recent strong bottleneck starting around 10,000 years ago, coinciding with the assumed time of maize domestication. While the genome average genetic diversity is low compared to other fungal pathogens, we estimated that the rate of non-synonymous adaptive substitutions is three times higher in genes located within virulence clusters compared to non-clustered genes, including non-clustered effector genes. These results highlight the role that these singular genomic regions play in the evolution of this pathogen. Significance statementThe maize pathogen Ustilago maydis is a model species to study fungal cell biology and biotrophic host-pathogen interactions. Population genetic studies of this species, however, were so far restricted to using a few molecular markers, and genome-wide comparisons involved species that diverged more than 20 million years ago. Here, we sequenced the genomes of 22 Mexican U. maydis isolates to study the recent evolutionary history of this species. We identified two co-existing populations that went through a recent bottleneck and whose divergence date overlaps with the time of maize domestication. Contrasting the patterns of genetic diversity in different categories of genes, we further showed that effector genes in virulence clusters display a high rate of adaptive mutations, highlighting the importance of these effector arrangements for the adaptation of U. maydis to its host.

genomics↗

bioRxiv2020

Label2label: Using deep learning and dual-labelling to retrieve cellular structures in fluorescence images

Fluorescence microscopy is an essential tool in cell biology to visualise the spatial distribution of proteins that dictates their role in cellular homeostasis, dynamic cellular processes, and dysfunction during disease. However, unspecific binding of the antibodies that are used to label a cellular target often leads to high background signals in the images, decreasing the contrast of a cellular structure of interest. Recently, convolutional neural networks (CNNs) have been successfully employed for denoising and upsampling in fluorescence microscopy, but current image restoration methods cannot correct for background signals originating from the label. Here, we report a new method to train a CNN as content filter for non-specific signals in fluorescence images that does not require a clean benchmark, using dual-labelling to generate the training data. We name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure of interest. We show that after L2L training a network restores images not only with reduced image noise but also label-induced unspecific fluorescence signal in images of a variety of cellular structures, resulting in images with enhanced structural contrast. By implementing a multi-scale structural similarity loss function, the performance of the CNN as a content filter is further enhanced, for example, in STED images of caveolae. We show evidence that, for this loss function, sample differences in the training data significantly decrease so-called hallucination effects in the restorations that we otherwise observe when training the CNN with images of the same label. We also assess the performance of a cycle generative adversarial network as a content filter after L2L training with unpaired image data. Lastly, we show that a CNN can be trained to separate structures in superposed fluorescence images of two different cellular targets, allowing multiplex imaging with microscopy setups where the number of excitation sources or detectors is limited.

bioinformatics↗

bioRxiv2021

Convergent extension requires adhesion-dependent biomechanical integration of cell crawling and junction contraction

Convergent extension is an evolutionarily conserved collective cell movement that elongates the body axis of almost all animals and is required for the morphogenesis of several organ systems. Decades of study have revealed two distinct mechanisms of cell movement during CE, one based on cell crawling and the other on junction contraction. How these two behaviors collaborate during CE is not understood. Here, using quantitative live cell imaging we show that these two modes act both independently and in concert during CE, but that cell movement is more effective when the two modes are integrated via mechano-reciprocity. Based on these findings, we developed a novel computational model that for the first time treats crawling and contraction independently. This model not only confirmed the biomechanical efficacy of integrating the two modes, but also revealed for the first time how the two modes -and their integration- are influenced by cell adhesion. Finally, we use these new insights to further understand the complex CE phenotype resulting from loss of the C-cadherin interacting catenin Arvcf. These data are significant for providing new biomechanical and cell biological insights into a fundamental morphogenetic process that is implicated in human neural tube defects and skeletal dysplasias.

developmental biology↗

bioRxiv2021

Female gametophyte expressed Arabidopsis thaliana lipid transfer proteins AtLtpI.4 and AtLtpI.8 provide a link between callose homeostasis, pollen tube guidance, and fertilization success

Non-specific lipid transfer proteins (LTPs) represent a sub-class among the large family of Cysteine-rich proteins (CRPs) specific to land plants. LTPs possess a hydrophobic cavity, enabling them to bind and stabilize a variety of lipid molecules outside membranes. In line with the existence of an N-terminal signal peptide, secreted LTPs represent a well-suited mobile signal carrier in the plants extracellular matrix. Thus, LTPs are currently considered as key players to mediate the bulk flow of lipids between membranes/compartments as well as the buildup of lipid barrier polymers including cutin and suberin. Here, we show that floral expressed Arabidopsis thaliana AtLtpI.4 (AtLTP2) and AtLtpI.8 (AtLTP5), mutually control cell-cell communication between growing pollen tubes and ovules during fertilization. Arabidopsis mutants lacking functional AtLtpI.4 and AtLtpI.8 exhibit significantly reduced fertilization success. Cross-pollination and cell biological analyses revealed that AtLtpI.4/I.8 double mutants are impaired in pollen tube guidance towards ovules. Our finding that the AtLtpI.4/I.8 phenotype correlates with aberrant callose depositions in the micropylar region during ovule development suggests that both LTPs represent novel players of a joint signaling pathway that controls callose homeostasis in the female gametophyte.

plant biology↗

bioRxiv2021

Glycoproteomics of Haloferax volcanii reveals an extensive glycoproteome and concurrence of different N-glycosylation pathways

Glycosylation is one of the most complex post-translational protein modifications. Its importance has been established not only for eukaryotes but also for a variety of prokaryotic cellular processes, such as biofilm formation, motility and mating. However, comprehensive glycoproteomic analyses are largely missing in prokaryotes. Here we extend the phenotypic characterisation of N-glycosylation pathway mutants in Haloferax volcanii and provide a detailed glycoproteome for this model archaeon through the mass spectrometric analysis of intact glycopeptides. Using in-depth glycoproteomic datasets generated for the wild-type and mutant strains as well as a reanalysis of datasets within the Archaeal Proteome Project, we identify the largest archaeal glycoproteome described so far. We further show that different N-glycosylation pathways can modify the same glycosites under the same culture conditions. The extent and complexity of the Hfx. volcanii N-glycoproteome revealed here provides new insights into the roles of N-glycosylation in archaeal cell biology.

biochemistry↗

bioRxiv2021

Protein domain patterns reveal a functional diversity of the archaeal Cdv system and give insights into the origin of the eukaryotic ESCRT system

The Endosomal Sorting Complex Required for Transport (ESCRT) system is a multi-protein machinery that is involved in cell division of both Eukaryotes and Archaea. This spread across super-phyla suggests that a precursor ESCRT machinery existed already at an evolutionary early stage of life, making it a promising candidate for the (re)construction of a minimal cell division machinery. There are, however, only few experimental data about ESCRT machineries in Archaea, due to high technical challenges in cultivation and microscopy. Here, we analyse the proteins of archaeal ESCRT machineries bioinformatically on a domain level, to enable mechanistical comparison without such challenging experiments. First, we infer that there are at least three different cell division mechanisms utilizing ESCRT proteins in archaea, probably similar in their constriction mechanisms but different in membrane tethering. Second, we show that ESCRT proteins in Asgard archaea are highly similar to eukaryotic ESCRT proteins, strengthening the recently developed idea that all Eukaryotes descended from archaea. Third, we reconstruct a plausible evolutionary development of ESCRT machineries and suggest that a simple ESCRT-based constriction machinery existed in the last archaeal common ancestor. These findings not only give very interesting insights into the evolution of cell division in Archaea and Eukaryotes, but also reveal new research avenues by suggesting hypothesis-driven experiments for both, cell biology and bottom-up synthetic biology.

molecular biology↗

bioRxiv2021

The neural G protein Gαo tagged with GFP at an internal loop is functional in C. elegans

Go is the alpha subunit of the major heterotrimeric G protein in neurons and mediates signaling by every known neurotransmitter, yet the signaling mechanisms activated by Go remain to be fully elucidated. Genetic analysis in Caenorhabditis elegans has shown that Go signaling inhibits neuronal activity and neurotransmitter release, but studies of the molecular mechanisms underlying these effects have been limited by lack of tools to complement genetic studies with other experimental approaches. Here we demonstrate that inserting the green fluorescent protein (GFP) into an internal loop of the Go protein results in a tagged protein that is functional in vivo and that facilitates cell biological and biochemical studies of Go. Transgenic expression of Go-GFP rescues the defects caused by loss of endogenous Go in assays of egg laying and locomotion behaviors. Defects in body morphology caused by loss of Go are also rescued by Go-GFP. The Go-GFP protein is localized to the plasma membrane of neurons, mimicking localization of endogenous Go. Using GFP as an epitope tag, Go-GFP can be immunoprecipitated from C. elegans lysates to purify Go protein complexes. The Go-GFP transgene reported in this study enables studies involving in vivo localization and biochemical purification of Go to complement the already well-developed genetic analysis of Go signaling.

neuroscience↗

bioRxiv2021

Scaling concepts in 'omics: nuclear lamin-B scales with tumor growth and predicts poor prognosis, whereas fibrosis can be pro-survival

Spatiotemporal relationships between genes expressed in tissues likely reflect physicochemical principles that range from stoichiometric interactions to co-organized fractals with characteristic scaling. For key structural factors within the nucleus and extracellular matrix (ECM), gene-gene power laws are found to be characteristic across several tumor types in The Cancer Genome Atlas (TCGA) and across single-cell RNA-seq data. The nuclear filament LMNB1 scales with many tumor-elevated proliferation genes that predict poor survival in liver cancer, and cell line experiments show LMNB1 regulates cancer cell cycle. Also high in the liver, lung, and breast tumors studied here are the main fibrosis-associated collagens, COL1A1 and COL1A2, that scale stoichiometrically with each other and super-stoichiometrically with a pan-cancer fibrosis gene set. However, high fibrosis predicts prolonged survival of patients undergoing therapy and does not correlate with LMNB1. Single-cell RNA-seq data also reveal scaling consistent with the pan-cancer power laws obtained from bulk tissue, allowing new power law relations to be predicted. Lastly, although noisy data frustrate weak scaling, concepts such as stoichiometric scaling highlight a simple, internal consistency check to qualify expression data. ClassificationApplied Physical Sciences (major) and Cell Biology (minor) Significance StatementNon-linear scaling analyses pervade polymer physics and chemistry and conceivably provide new insight into polymeric assemblies of genes expressed in tissues as well as co-regulated gene sets. Fractal scaling and stoichiometric scaling are among the gene-gene power law results identified here for key structural polymers in nuclei or extracellular matrix in human cancer data. Among nuclear envelope factors that might scale with DNA mass, only one nuclear filament scales with tumor proliferation and predicts poor survival in some cancer types. Collagen-1 scales with fibrosis and also tends to increase in multiple tumor types, but patients in therapy surprisingly survive longest with the highest levels of fibrosis, consistent with a therapeutic response.

cancer biology↗

bioRxiv2021

Laboratory Yeast Strains Rely On Oxidative Phosphorylation For Efficient ATP Production

Even though it is a well-accepted fact that the energy metabolism of yeast is likely to impact all cellular activities, surprising little is known about the ATP homeostasis of particular yeast strains that are commonly used in cell biological studies. Therefore, we determined key parameters such as oxygen consumption and fermentation rates of the lab strain SEY6210. Our data indicated that even at high glucose concentrations, SEY6210 produces 30-50% of cellular ATP from oxidative phosphorylation. Loss of respiration, either by disrupting ATP synthase function or by growth in anaerobic conditions, was not fully compensated by fermentation and as a result affected energy intensive processes such as the maintenance of the plasma membrane proton gradient and the associated import of nutrients.

microbiology↗

bioRxiv2021

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Background and ObjectiveA variety of genetic mutations are known to affect cell proliferation and apoptosis during organism development, leading to structural birth defects such as facial clefting. Yet, the mechanisms how these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied in high detail using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points. However, the large number of LSM images captured at cellular resolution precludes manual analysis. Thus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way. MethodsWe developed, trained, and evaluated convolutional neural networks (CNNs) for segmenting tissues, cells, and specifically proliferating cells in LSM datasets. We compared the automatically extracted tissue and cell annotations to corresponding manual segmentations for three specific applications: (i) tissue segmentation (neural ectoderm and mesenchyme) in nuclear-stained LSM images, (ii) cell segmentation in nuclear-stained LSM images, and (iii) segmentation of proliferating cells in Phospho-Histone H3 (PHH3)-stained LSM images. ResultsThe automatic CNN-based tissue segmentation method achieved a macro-average F-score of 0.84 compared to a macro-average F-score of 0.89 comparing corresponding manual segmentations from two observers. The automatic cell segmentation method in nuclear-stained LSM images achieved an F-score of 0.57, while comparing the manual segmentations resulted in an F-score of 0.39. Finally, the automatic segmentation method of proliferating cells in the PHH3-stained LSM datasets achieved an F-score of 0.56 for the automated method, while comparing the manual segmentations resulted in an F-score of 0.45. ConclusionsThe proposed automatic CNN-based framework for tissue and cell segmentation leads to results comparable to the inter-observer agreement, accelerating the LSM image analysis. The trained CNN models can also be applied for shape or morphological analysis of embryos, and more generally in other areas of cell biology.

bioinformatics↗

bioRxiv2021

Two subsets of human marginal zone B cells resolved by global analysis of lymphoid tissues and blood

B cells generate antibodies that are essential for immune protection. Major events driving B cell responses occur in lymphoid tissues, which guide antigen acquisition and support cellular interactions, yet complexities of B cell subsets in human lymphoid tissues are poorly understood. Here we perform undirected, global profiling of B cells in matched human lymphoid tissues from deceased transplant organ donors and tracked dissemination of B cell clones. In addition to identifying unanticipated features of tissue-based B cell differentiation, we resolve two clonally independent subsets of marginal zone B cells that differ in cell surface and transcriptomic profiles, tendency to disseminate, distribution bias within splenic marginal zone microenvironment and immunoglobulin repertoire diversity and hypermutation frequency. Each subset is represented in spleen, gut-associated lymphoid tissue, mesenteric lymph node, and also blood. Thus, we provide clarity and diffuse controversy surrounding human MZB - the elephant in the room of human B cell biology.

immunology↗

bioRxiv2021

Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes

In order to advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increase exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits. Significance StatementMolecular simulations of proteins are often interpreted using Markov state models (MSMs), in which each protein configuration is assigned to a global state. As we explore larger and more complex biological systems, the size of this global state space will face a combinatorial explosion, rendering it impossible to gather sufficient sampling data. In this work, we introduce an approach to decompose a system of interest into separable subsystems. We show that MSMs built for each subsystem can be later coupled to reproduce the behaviors of the global system. To aid in the choice of decomposition we also describe a score to quantify its goodness. This decomposition strategy has the promise to enable robust modeling of complex biomolecular systems.

biophysics↗

bioRxiv2021

Curvature-driven feedback on aggregation-diffusion of proteins in lipid bilayers

AO_SCPLOWBSTRACTC_SCPLOWMembrane bending is an extensively studied problem from both modeling and experimental perspectives because of the wide implications of curvature generation in cell biology. Many of the curvature generating aspects in membranes can be attributed to interactions between proteins and membranes. These interactions include protein diffusion and formation of aggregates due to protein-protein interactions in the plane of the membrane. Recently, we developed a model that couples the in-plane flow of lipids and diffusion of proteins with the out-of-plane bending of the membrane. Building on this work, here, we focus on the role of explicit aggregation of proteins on the surface of the membrane in the presence of membrane bending and diffusion. We develop a comprehensive framework that includes lipid flow, membrane bending energy, the entropy of protein distribution, and an explicit aggregation potential and derive the governing equations. We compare this framework to the Cahn-Hillard formalism to predict the regimes in which the proteins form patterns on the membrane. We demonstrate the utility of this model using numerical simulations to predict how aggregation and diffusion, coupled with curvature generation, can alter the landscape of membrane-protein interactions.

biophysics↗