Biology subjects

Schneider, R.

Publications and source records attributed to Schneider, R..

53 records · Page 3Linked to original sources

bioRxiv2021

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProteins infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. AvailabilityFreely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlab

bioinformatics↗

bioRxiv2021

CrossTalkeR: Analysis and Visualisation of Ligand Receptor Networks

MotivationLigand-receptor (LR) network analysis allows the characterization of cellular crosstalk based on single cell RNA-seq data. However, current methods typically provide a list of inferred LR interactions and do not allow the researcher to focus on specific cell types, ligands or receptors. In addition, most of these methods cannot quantify changes in crosstalk between two biological phenotypes. ResultsCrossTalkeR is a framework for network analysis and visualisation of LR interactions. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological phenotypes, i.e. disease vs. homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterisation of changes in cellular crosstalk in disease. Availability and ImplementationCrosstalkeR is an R package available at https://github.com/CostaLab/CrossTalkeR.

systems biology↗

bioRxiv2020

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually-built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

systems biology↗

bioRxiv2020

Arena3Dweb: Interactive 3D visualization of multilayered networks.

Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3Dweb using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3Dweb is available at http://bib.fleming.gr:3838/Arena3D.

bioinformatics↗

bioRxiv2020

BioKC: a collaborative platform for systems biology model curation and annotation

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).

systems biology↗

bioRxiv2020

GigaSOM.jl: High-perfomance clustering and visualization of huge cytometry datasets

BackgroundThe amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow to easily generate data with hundreds of millions of single-cell data points with more than 40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to down-sample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. ResultsWe present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality-reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community, and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. ConclusionsGigaSOM.jl facilitates utilization of the commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from an massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. Key pointsO_LIGigaSOM.jl improves the applicability of FlowSOM-style single-cell cytometry data analysis by increasing the acceptable dataset size to billions of single cells. C_LIO_LISignificant speedup over current methods is achieved by distributed processing and utilization of efficient algorithms. C_LIO_LIGigaSOM.jl package includes support for fast visualization of multidimensional data. C_LI

bioinformatics↗

bioRxiv2020

Oral administration of a single anti-CfaE nanobody provides broadly cross-protective immunity against major pathogenic Enterotoxigenic Escherichia coli strains

Enterotoxigenic Escherichia coli (ETEC) is estimated to cause approximately 380,000 deaths annually during sporadic or epidemic outbreaks worldwide. There is currently no vaccine licensed to prevent ETEC. Development of prophylaxis against ETEC is challenging due to the vast heterogeneity of the ETEC strains. The discovery of nanobodies has emerged as a successful new biologics in treating mucosal infectious disease as nanobodies can recognize conserved epitopes on hypervariable pathogens. In this study, we performed large screens using immunized llamas and a naive nanobody yeast display library against adhesins of colonization factors. Cross-protective nanobodies were selected with in vitro activities inhibiting mannose-resistant hemagglutination (MRHA) against all eleven major pathogenic ETEC strains. Oral administration of nanobodies led to significant reduction of bacterial colonization in animals challenged with multiple ETEC strains. Structural analysis revealed novel conserved epitopes as critical structural features for pan-ETEC vaccine design. Two of the lead nanobodies, 2R215 and 1D7, were further engineered as trimer or fused with human IgA Fc-fragments as fusionbodies. Oral administration of the trimers or fusionbodies protected mice from infection at a much lower dose compared to the monomeric format. Importantly, fusionbodies prevented infection as a pre-treatment when administrated 2 hours before ETEC challenge to the animals. Together, our study provides the first proof of concept that oral administration of a single nanobody could confer broad protection against major pathogenic ETEC strains. Technological advances in large-scale manufacturing of biological proteins in plants and microorganisms will make nanobody-based immunotherapy a potent and cost-effective prophylaxis or treatment for ETEC.

microbiology↗

bioRxiv2020

Whi5 is diluted and protein synthesis does not dramatically increase in pre-Start G1

In their manuscript, Litsios et al.1 report a new model for how cell growth and biosynthetic activity control the G1/S transition in budding yeast. In essence, Litsios et al. claim that Start is driven by an increasing concentration of the G1 cyclin Cln3 due to a dramatic acceleration of protein synthesis in pre-Start G1 and not by the dilution of the cell cycle inhibitor Whi5. While we previously reported that Start was in part driven by cell growth during G1 diluting out the Start inhibitor Whi52, Litsios et al. report that Whi5 remains at constant concentration during G1, and changes in Whi5 concentration therefore do not contribute to Start. Since Litsios et al. directly contradict several key points of our own model of how cell growth triggers Start, we decided to investigate their claims and data. More specifically, we decided to investigate Litsios et al.s three major claims: O_LIWhi5 concentration remains constant during G1 C_LIO_LICln3 concentration strongly increases prior to Start C_LIO_LIGlobal protein synthesis rates increase by 2-3 fold prior to Start C_LI We investigated each of these three claims and found that the evidence presented by Litsios et al. does not support their claims due to inadequate analysis methods and flaws in their experiments.

molecular biology↗

bioRxiv2020

ITPK1 is an InsP6/ADP phosphotransferase that controls systemic phosphate homeostasis in Arabidopsis

In plants, phosphate (Pi) homeostasis is regulated by the interaction of Pi starvation response transcription factors (PHRs) with stand-alone SPX proteins, which act as sensors for inositol pyrophosphates (PP-InsPs). Recently, ITPK1 was shown to generate the PP-InsP InsP7 from InsP6 in vitro, but the importance of this activity in Pi signaling remained unknown. Here, we show that uncontrolled Pi accumulation in ITPK1-deficient plants is accompanied by impaired Pi-dependent InsP7 and InsP8 synthesis. Reciprocal grafting demonstrates that Pi starvation responses are mainly controlled by ITPK1 activity in shoots. Nuclear magnetic resonance assays and PAGE analyses with recombinant protein reveal that besides InsP6 phosphorylation, ITPK1 is also able to catalyze ATP synthesis using 5-InsP7 but not any other InsP7 isomer as a P-donor when ATP is low. Additionally, we show that the dynamic changes in InsP7 and InsP8 to cellular Pi are conserved from land plant species to human cells, suggesting that Pi-dependent PP-InsP synthesis is a common component of Pi signaling across kingdoms. Together, our study demonstrates how Pi-dependent changes in nutritional and energetic states modulate ITPK1 activities to fine-tune the synthesis of PP-InsPs.

plant biology↗

bioRxiv2020

IgA MAb blocks SARS-CoV-2 Spike-ACE2 interaction providing mucosal immunity

COVID-19 caused by SARS-CoV-2 has become a global pandemic requiring the development of interventions for the prevention or treatment to curtail mortality and morbidity. No vaccine to boost mucosal immunity or as a therapeutic has yet been developed to SARS-CoV-2. In this study we discover and characterize a cross-reactive human IgA monoclonal antibody, MAb362. MAb362 binds to both SARS-CoV and SARS-CoV-2 spike proteins and competitively blocks hACE2 receptor binding, by completely overlapping the hACE2 structural binding epitope. Furthermore, MAb362 IgA neutralizes both pseudotyped SARS-CoV and SARS-CoV-2 in human epithelial cells expressing hACE2. SARS-CoV-2 specific IgA antibodies, such as MAb362, may provide effective immunity against SARS-CoV-2 by inducing mucosal immunity within the respiratory system, a potentially critical feature of an effective vaccine.

microbiology↗

bioRxiv2020

Long-term single-cell imaging and simulations of microtubules reveal driving forces for wall pattering during proto-xylem development

Plants are the tallest organisms on Earth; a feature sustained by solute-transporting xylem vessels in the plant vasculature. The xylem vessels are supported by strong cell walls that are assembled in intricate patterns. Cortical microtubules direct wall deposition and need to rapidly re-organize during xylem cell development. We established long-term live-cell imaging of single Arabidopsis cells undergoing proto-xylem trans-differentiation, resulting in spiral wall patterns, to investigate the microtubule re-organization. The initial disperse microtubule array rapidly readjusted into well-defined microtubule bands, which required local de-stabilization of individual microtubules in band-interspersing gap regions. Using extensive microtubule simulations, we could recapitulate the process in silico and found that local recruitment of microtubule-bound nucleation is critical for pattern formation, which we confirmed in vivo. Our simulations further indicated that the initial microtubule alignment impact microtubule band patterning. We confirmed this prediction using katanin mutants, which have microtubule organization defects, and uncovered active KATANIN recruitment to the forming microtubule bands. Our combination of quantitative microscopy and modelling outlines a framework towards a comprehensive understanding of microtubule re-organization during wall pattern formation.

plant biology↗

bioRxiv2019

Melanoma-secreted Amyloid Beta Supresses Neuroinflammation and Promotes Brain Metastasis

Brain metastasis is a significant cause of morbidity and mortality in multiple cancer types and represents an unmet clinical need. The mechanisms that mediate metastatic cancer growth in the brain parenchyma are largely unknown. Melanoma, which has the highest rate of brain metastasis among common cancer types, is an ideal model to study how cancer cells adapt to the brain parenchyma. We performed unbiased proteomics analysis of melanoma short-term cultures, a novel model for the study of brain metastasis. Intriguingly, we found that proteins implicated in neurodegenerative pathologies are differentially expressed in melanoma cells explanted from brain metastases compared to those derived from extracranial metastases. This raised the exciting hypothesis that molecular pathways implicated in neurodegenerative disorders are critical for metastatic adaptation to the brain. Here, we show that melanoma cells require amyloid beta (A{beta}), a polypeptide heavily implicated in Alzheimers disease, for growth and survival in the brain parenchyma. Melanoma cells produce and secrete A{beta}, which activates surrounding astrocytes to a pro-metastatic, anti-inflammatory phenotype. Furthermore, we show that pharmacological inhibition of A{beta} decreases brain metastatic burden. Our results reveal a mechanistic connection between brain metastasis and Alzheimers disease - two previously unrelated pathologies, establish A{beta} as a promising therapeutic target for brain metastasis, and demonstrate suppression of neuroinflammation as a critical feature of metastatic adaptation to the brain parenchyma.

cancer biology↗

bioRxiv2019

Longitudinal single cell transcriptomics reveals Krt8+ alveolar epithelial progenitors in lung regeneration

Lung injury activates quiescent stem and progenitor cells to regenerate alveolar structures. The sequence and coordination of transcriptional programs during this process has largely remained elusive. Using single cell RNA-seq, we first generated a whole-organ birds-eye view on cellular dynamics and cell-cell communication networks during mouse lung regeneration from [~]30,000 cells at six timepoints. We discovered an injury-specific progenitor cell state characterized by Krt8 in flat epithelial cells covering alveolar surfaces. The number of these cells peaked during fibrogenesis in independent mouse models, as well as in human acute lung injury and fibrosis. Krt8+ progenitors featured a highly distinct connectome of receptor-ligand pairs with endothelial cells, fibroblasts, and macrophages. To sky dive into epithelial differentiation dynamics, we sequenced >30,000 sorted epithelial cells at 18 timepoints and computationally derived cell state trajectories that were validated by lineage tracing genetic reporter mice. Airway stem cells within the club cell lineage and alveolar type-2 cells underwent transcriptional convergence onto the same Krt8+ progenitor cell state, which later resolved by terminal differentiation into alveolar type-1 cells. We derived distinct transcriptional regulators as key switch points in this process and show that induction of TNF-alpha/NFkappaB, p53, and hypoxia driven gene expression programs precede a Sox4, Ctnnb1, and Wwtr1 driven switch towards alveolar type-1 cell fate. We show that epithelial cell plasticity can induce non-gradual transdifferentiation, involving intermediate progenitor cell states that may persist and promote disease if checkpoint signals for terminal differentiation are perturbed.

systems biology↗

bioRxiv2019

A microtubule-mediated mechanical feedback controls leaf blade development in three dimensions

Many plant species have thin leaf blades, which is an important adaptation that optimizes the exchanges with the environment. Here, we provide evidence that their three-dimensional geometry is governed by microtubule alignment along mechanical stress patterns in internal walls. Depending on the primary shape of the primordium, this process has the potential to amplify an initial degree of flatness, or promote the formation of nearly axisymmetric, mostly elongating organs, such as stems and roots. This mechanism may explain leaf evolution from branches, which is alternative to Zimmermanns influential, but widely questioned, telome theory.\n\nOne Sentence SummaryMechanical feedback controls leaf development in three dimensions

developmental biology↗

bioRxiv2018

Pathogenic Budding Yeasts Isolated outside of Clinical Settings

Budding yeasts are distributed across a wide range of habitats, including as human commensals. However, under some conditions, these commensals can cause superficial, invasive, and even lethal infections. Despite their importance to human health, little is known about the ecology of these opportunistic pathogens, aside from their associations with mammals and clinical environments. During a survey of approximately 1000 non-clinical samples across the United States of America, we isolated 54 strains of budding yeast species considered opportunistic pathogens, including Candida albicans and Candida (Nakaseomyces) glabrata. We found that, as a group, pathogenic yeasts were positively associated with fruits and soil environments, while the species Pichia kudriavzevii (syn. Candida krusei syn. Issatchenkia orientalis) had a significant association with plants. These results suggest that pathogenic yeast ecology is more complex and diverse than is currently appreciated and raises the possibility that these additional environments could be a point of contact for human infections.\n\nImportanceWe isolated several opportunistic pathogenic species of yeasts from the subphylum Saccharomycotina from multiple non-clinical environments across the United States of America. Among those strains isolated, over 50% represent the most common opportunistic pathogens. These species, C. albicans, Candida tropicalis, Candida parapsilosis, and C. glabrata, have been rarely isolated from non-clinical settings and have been usually interpreted as contamination. Our extensive isolations from natural settings challenge this assumption, suggesting that opportunistic pathogens can persist in alternative niches and that their ecology may be more complicated than is currently assumed. Non-clinical environments could be a short-term habitat as these yeasts are passed between their predominant hosts, endothermic animals.

microbiology↗

bioRxiv2018

A molecular mechanism for salt stress-induced microtubule array formation in Arabidopsis

Microtubules are filamentous structures necessary for cell division, motility and morphology, with dynamics critically regulated by microtubule-associated proteins (MAPs). We outline the molecular mechanism by which the MAP, COMPANION OF CELLULOSE SYNTHASE1 (CC1), controls microtubule bundling and dynamics to sustain plant growth under salt stress. CC1 contains an intrinsically disordered N-terminus that links microtubules at evenly distributed distances through four conserved hydrophobic regions. NMR analyses revealed that two neighboring residues in the first hydrophobic binding motif are crucial for the microtubule interaction, which we confirmed through live cell analyses. The microtubule-binding mechanism of CC1 is remarkably similar to that of the prominent neuropathology-related protein Tau, indicating evolutionary convergence of MAP functions across animal and plant cells.

cell biology↗

bioRxiv2018

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

A multitude of factors contribute to complex diseases and can be measured with \"omics\" methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, http://vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources \"Human metabolism\", \"Gut microbiome\", \"Disease\", \"Nutrition\", and \"ReconMaps\". The VMH captures 5,180 unique metabolites, 17,730 unique reactions, 3,288 human genes, 255 Mendelian diseases, 818 microbes, 632,685 microbial genes, and 8,790 food items. The VMHs unique features are i) the hosting the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; ii) seven human metabolic maps for data visualization; iii) a nutrition designer; iv) a user-friendly webpage and application-programming interface to access its content; and v) user feedback option for community engagement. We demonstrate with four examples the VMHs utility. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

systems biology↗