Biology subjects

Schmidt, A.

Publications and source records attributed to Schmidt, A..

105 records · Page 6Linked to original sources

bioRxiv2019

AGO1x prevents dsRNA-induced interferon signaling to promote breast cancer cell proliferation

Initially reported for viral RNA, elongation of polypeptide chains beyond the stop codon (translational readthrough (TR)) also occurs on eukaryotic transcripts. TR diversifies the proteome and can modulate protein levels 1-6. Here we report that AGO1x, a conserved TR isoform of Argonaute 1, is generated in highly proliferative breast cancer cells, where it curbs accumulation of double stranded RNAs, the induction of the interferon response and apoptosis. In contrast to other mammalian Argonaute protein family members with primarily cytoplasmic functions, AGO1x localizes to the nucleus, in the vicinity of nucleoli. We identify a novel interaction of AGO1x with the Polyribonucleotide Nucleotidyltransferase 1, depletion of either protein leading to dsRNA accumulation and impaired cell proliferation. Our study thus uncovers a novel function of an Argonaute protein outside of the miRNA effector pathway, in buffering dsRNA-induced interferon responses. As AGO1x expression is tightly linked to breast cancer cell proliferation, our study suggests a new direction for limiting tumor growth.

cell biology↗

bioRxiv2019

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

bioinformatics↗

bioRxiv2019

Gene regulatory network of human GM-CSF secreting T helper cells

GM-CSF produced by autoreactive CD4 positive T helper cells is involved in the pathogenesis of autoimmune diseases, such as Multiple Sclerosis. However, the molecular regulators that establish and maintain the features of GM-CSF positive CD4 T cells are unknown. In order to identify these regulators, we isolated human GM-CSF producing CD4 T cells from human peripheral blood by using a cytokine capture assay. We compared these cells to the corresponding GM-CSF negative fraction, and furthermore, we studied naive CD4 T cells, memory CD4 T cells and bulk CD4 T cells from the same individuals as additional control cell populations. As a result, we provide a rich resource of integrated chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) data from these primary human CD4 T cell subsets, and we show that the identified signatures are associated with human autoimmune disease, especially Multiple Sclerosis. By combining information about mRNA expression, DNA accessibility and predicted transcription factor binding, we reconstructed directed gene regulatory networks connecting transcription factors to their targets, which comprise putative key regulators of human GM-CSF positive CD4 T cells as well as memory CD4 T cells. Our results suggest potential therapeutic targets to be investigated in the future in human autoimmune disease.

bioinformatics↗

bioRxiv2019

Ketamine Affects Prediction Errors About Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities. Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the worlds statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

neuroscience↗

bioRxiv2018

Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates

Although protein synthesis dynamics has been studied both with theoretical models and by profiling ribosome footprints, the determinants of ribosome flux along open reading frames (ORFs) are not fully understood. Combining measurements of protein synthesis rate with ribosome footprinting data, we here inferred translation initiation and elongation rates for over a thousand ORFs in exponentially-growing wildtype yeast cells. We found that the amino acid composition of synthesized proteins is as important a determinant of translation elongation rate as parameters related to codon and tRNA adaptation. We did not find evidence of ribosome collisions curbing the protein output of yeast transcripts, either in high translation conditions associated with exponential growth, or in strains in which deletion of individual ribosomal protein genes leads to globally increased or decreased translation. Slow translation elongation is characteristic of RP-encoding transcripts, which have markedly lower protein output than other transcripts with equally high ribosome densities.\n\nSignificance StatementAlthough sequencing of ribosome footprints has uncovered new aspects of mRNA translation, the determinants of ribosome flux remain incompletely understood. Combining ribosome footprint data with measurements of protein synthesis rates, we here inferred translation initiation and elongation rates for over a thousand ORFs in yeast strains with varying translation capacity. We found that the translation elongation rate varies up to ~20-fold among yeast transcripts, and is significantly correlated with the rate of translation initiation. Furthermore, the amino acid composition of synthesized proteins impacts the rate of translation elongation to the same extent as measures of codon and tRNA adaptation. Transcripts encoding ribosomal proteins are translated especially slow, having markedly lower protein output than other transcripts with equally high ribosome densities.

biophysics↗

bioRxiv2018

Intricate structure of the interphase chromocenter revealed by the analysis of a factor involved in species formation

In higher eukaryotes centromeres often coalesce into a large intranuclear domain called the chromocenter. Chromocenters are important for the organization of pericentric heterochromatin and a disturbance of their formation results in an upregulation of repetitive elements and causes defects in chromosome segregation. Mutations in the gene encoding for the centromere associated Drosophila speciation factor HMR show very similar phenotypes suggesting a role of HMR in chromocenter architecture and function. We performed confocal and super resolution microscopy as well as proximity based biotinylation experiments of HMR and its associated protein HP1a to generate a molecular map of HMR and HP1a bound chromatin. Our work reveals an intricate internal structure of the centromeric chromatin region, which suggests a role of HMR in separating heterochromatin from centromeric chromatin.

biochemistry↗

bioRxiv2018

Differential gene expression indicates involvement of F-box proteins and E3 ligases in sexual versus apomictic germline specification in Boechera.

Germline specification is the first step during sexual and apomictic plant reproduction. This takes place in a specialized domain of the reproductive flower tissues, the nucellus of the ovule. In each case, a sporophytic cell is determined to initiate germline development. These cells, the megaspore mother cell (MMC) or apomictic initial cell (AIC) in sexual plants and apomicts, respectively, differ in their developmental fate. While the MMC undergoes meiosis, the AIC aborts or omits meiosis to form the female gametophyte. Although these distinct developmental processes have long been described, little is known about the gene regulatory basis involved.\n\nTo elucidate gene regulatory networks underlying sexual and apomictic germline specification, we conducted tissue-specific transcriptional profiling using laser-assisted microdissection and RNA-Seq. We compared the transcriptomes of the nucellar tissues harbouring the MMC or AIC between different accessions of Boechera. The six accessions we used represented four species and two ploidy levels, allowing us to distinguish between differences in gene expression caused by the genetic background or the reproductive mode.\n\nComparative data analysis revealed widely overlapping gene expression patterns in apomictic versus sexual Boechera accessions. Nevertheless, 45 significantly differentially expressed genes were identified, which potentially play a role for determination of sexual versus apomictic reproductive mode. Interestingly, based on annotations, these include F-box proteins and E3 ligases that might relate to genes previously described as regulators important for sexual or apomictic reproduction. Thus, our findings provide new insight into the transcriptional basis of sexual and apomictic germline specification.\n\nOne sentence summary\n\nA comprehensive tissue type-specific transcriptional analysis using laser-assisted microdissection combined with RNA-Seq identifies 45 genes consistently differentially expressed during germline specification in different sexual versus apomictic Boechera accessions, indicating roles of protein degradation related to cell cycle, transcriptional and post-transcriptional regulatory processes, and stress response for apomixis.

plant biology↗

bioRxiv2018

Not scene learning, but attentional processing is superior in team sport athletes and action video game players

We tested if high-level athletes or action video game players have superior context learning skills. Incidental context learning was tested in a spatial contextual cueing paradigm. We found comparable contextual cueing of visual search in repeated displays in high-level amateur handball players, dedicated action video game players and normal controls. In contrast, both handball players and action video game players showed faster search than controls, measured as search time per display item, independent of display repetition. Thus, our data do not indicate superior context learning skills in athletes or action video game players. Rather, both groups showed more efficient visual search in abstract displays that were not related to sport-specific situations.

neuroscience↗

bioRxiv2018

Multi-omics approach identifies novel pathogen-derived prognostic biomarkers in patients with Pseudomonas aeruginosa bloodstream infection

Pseudomonas aeruginosa is a human pathogen that causes health-care associated blood stream infections (BSI). Although P. aeruginosa BSI are associated with high mortality rates, the clinical relevance of pathogen-derived prognostic biomarker to identify patients at risk for unfavorable outcome remains largely unexplored. We found novel pathogen-derived prognostic biomarker candidates by applying a multi-omics approach on a multicenter sepsis patient cohort. Multi-level Cox regression was used to investigate the relation between patient characteristics and pathogen features (2298 accessory genes, 1078 core protein levels, 107 parsimony-informative variations in reported virulence factors) with 30-day mortality. Our analysis revealed that presence of the helP gene encoding a putative DEAD-box helicase was independently associated with a fatal outcome (hazard ratio 2.01, p = 0.05). helP is located within a region related to the pathogenicity island PAPI-1 in close proximity to a pil gene cluster, which has been associated with horizontal gene transfer. Besides helP, elevated protein levels of the bacterial flagellum protein FliL (hazard ratio 3.44, p < 0.001) and of a bacterioferritin-like protein (hazard ratio 1.74, p = 0.003) increased the risk of death, while high protein levels of a putative aminotransferase were associated with an improved outcome (hazard ratio 0.12, p < 0.001). The prognostic potential of biomarker candidates and clinical factors was confirmed with different machine learning approaches using training and hold-out datasets. The helP genotype appeared the most attractive biomarker for clinical risk stratification due to its relevant predictive power and ease of detection.

microbiology↗

bioRxiv2018

Spatial contextual cueing is not a limiting factor for expert performance in the domain of team sports or action video game playing

We investigated in two experiments if handball and action video game players show improved implicit learning of repeated spatial configurations for efficient search guidance in comparison to a control group without sport or video game proficiency. To this end, we used both a sport-specific pseudo 3-D contextual cueing task and the original contextual cueing paradigm (Chun & Jiang, 1998). Contextual cueing was present in all groups. However, handball and action video game players did not differ in the strength of contextual cueing from the control group. Action video game players had shorter search times than controls in both experiments. In contrast, the handball players searched faster than controls in the sport-specific displays of Experiment 1 but not in the symbolic search task of Experiment 2. Thus, our findings provide no evidence that contextual cueing is a limiting factor for expert performance in the domain of team sports or action video game playing.

neuroscience↗

bioRxiv2018

Dual signaling of Wamide myoinhibitory peptides through a peptide-gated channel and a GPCR in Platynereis

Neuropeptides commonly signal by metabotropic G-protein coupled receptors (GPCRs). In some mollusks and cnidarians, RFamide neuropeptides mediate fast ionotropic signaling by peptide-gated ion channels that belong to the DEG/ENaC family. Here we describe a neuropeptide system with a dual mode of signaling by both a peptide-gated channel and a GPCR. We identified and characterised a peptide-gated channel in the marine annelid Platynereis dumerilii that is specifically activated by Wamide myoinhibitory peptides derived from the same proneuropeptide. The myoinhibitory peptide-gated ion channel (MGIC) belongs to the DEG/ENaC family and is paralogous to RFamide-gated channels. Platynereis myoinhibitory peptides also activate a previously described GPCR, MAG. We measured the potency of all Wamides on both MGIC and MAG and identified peptides that preferentially activate one or the other receptor. Analysis of a single-cell transcriptome resource indicates that MGIC and MAG signal to distinct target neurons. The identification of a Wamide-gated channel suggests that peptide-gated channels are more diverse and widespread in animals than previously appreciated. The possibility of neuropeptide signaling by both ionotropic and metabotropic receptors to different target cells in the same organism highlights an additional level of complexity in peptidergic signaling networks.

neuroscience↗

bioRxiv2017

An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

We introduce a new conceptual framework and a model-based interventional calculus to steer, manipulate, and reconstruct the dynamics and generating mechanisms of non-linear dynamical systems from partial and disordered observations based on the contributions of each of the systems, by exploiting first principles from the theory of computability and algorithmic information. This calculus entails finding and applying controlled interventions to an evolving object to estimate how its algorithmic information content is affected in terms of positive or negative shifts towards and away from randomness in connection to causation. The approach is an alternative to statistical approaches for inferring causal relationships and formulating theoretical expectations from perturbation analysis. We find that the algorithmic information landscape of a system runs parallel to its dynamic attractor landscape, affording an avenue for moving systems on one plane so they can be controlled on the other plane. Based on these methods, we advance tools for reprogramming a system that do not require full knowledge or access to the systems actual kinetic equations or to probability distributions. This new approach yields a suite of universal parameter-free algorithms of wide applicability, ranging from the discovery of causality, dimension reduction, feature selection, model generation, a maximal algorithmic-randomness principle and a systems (re)programmability index. We apply these methods to static (e.coli Transcription Factor network) and to evolving genetic regulatory networks (differentiating naive from Th17 cells, and the CellNet database). We highlight their ability to pinpoint key elements (genes) related to cell function and cell development, conforming to biological knowledge from experimentally validated data and the literature, and demonstrate how the method can reshape a systems dynamics in a controlled manner through algorithmic causal mechanisms.

systems biology↗

bioRxiv2017

Multi-reference spectral library yields almost complete coverage of heterogeneous LC-MS/MS data sets

Spectral libraries play a central role in the analysis of data-independent-acquisition (DIA) proteomics experiments. A main assumption in current spectral library tools is that a single characteristic intensity pattern (CIP) suffices to describe the fragmentation of a peptide in a particular charge state (peptide charge pair). However, we find that this is often not the case. We carry out a systematic evaluation of spectral variability over public repositories and in-house datasets. We show that spectral variability is widespread and partly occurs under fixed experimental conditions. Using clustering of preprocessed spectra, we derive a limited number of Multiple Characteristic Intensity Patterns (MCIPs) for each peptide charge pair, which allow almost complete coverage of our heterogeneous dataset without affecting the false discovery rate. We show that a MCIP library derived from public repositories performs in most cases similar to a \"custom-made\" spectral library, which has been acquired under identical experimental conditions as the query spectra. We apply the MCIP approach to a DIA data set and observe a significant increase in peptide recognition. We propose the MCIP approach as an easy-to-implement addition to current spectral library search engines and as a new way to utilize the data stored in spectral repositories.

bioinformatics↗

bioRxiv2017

Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

bioinformatics↗

bioRxiv2017

Gut microbiota of the pine weevil degrades conifer diterpenes and increases insect fitness

The pine weevil (Hylobius abietis), a major pest of conifer forests throughout Europe, feeds on the bark and cambium, tissues rich in terpenoid resins that are toxic to many insect herbivores. Here we report the ability of the pine weevil gut microbiota to degrade the diterpene acids of Norway spruce. The diterpene acid levels present in ingested bark were substantially reduced on passage through the pine weevil gut. This reduction was significantly less upon antibiotic treatment, and supplementing the diet with gut suspensions from untreated insects restored the ability to degrade diterpenes. In addition, cultured bacteria isolated from pine weevil guts were shown to degrade a Norway spruce diterpene acid. In a metagenomic survey of the insects bacterial community, we were able to annotate several genes of a previously described diterpene degradation (dit) gene cluster. Antibiotic treatment disrupted the core bacterial community of H. abietis guts and eliminated nearly all dit-genes concordant with its reduction of diterpene degradation. Pine weevils reared on an artificial diet spiked with diterpenes, but without antibiotics, were found to lay more eggs with a higher hatching rate than weevils raised on diets with antibiotics or without diterpenes. These results suggest that gut symbionts contribute towards host fitness, but not by detoxification of diterpenes, since these compounds do not show toxic effects with or without antibiotics. Rather the ability to thrive in a terpene rich environment appears to allow gut microbes to benefit the weevil in other ways, such as increasing the nutritional properties of their diet.

evolutionary biology↗