Biology subjects

Fischer, L.

Publications and source records attributed to Fischer, L..

45 records · Page 3Linked to original sources

bioRxiv2019

Representation of Visual Landmarks in Retrosplenial Cortex

The process by which visual information is incorporated into the brains spatial framework to represent landmarks is poorly understood. Studies in humans and rodents suggest that retrosplenial cortex (RSC) plays a key role in these computations. We developed an RSC-dependent behavioral task in which head-fixed mice learned the spatial relationship between visual landmark cues and hidden reward locations. Two-photon imaging revealed that these cues served as dominant reference points for most task-active neurons and anchored the spatial code in RSC. Presenting the same environment but decoupled from mouse behavior degraded encoding fidelity. Analyzing visual and motor responses showed that landmark codes were the result of supralinear integration. Surprisingly, V1 axons recorded in RSC showed similar receptive fields. However, they were less modulated by task engagement, indicating that landmark representations in RSC are the result of local computations. Our data provide cellular- and network-level insight into how RSC represents landmarks.

neuroscience↗

bioRxiv2019

In vitro and in silico modeling of cellular and matrix-related changes during the early phase of osteoarthritis

ObjectiveUnderstanding the pathophysiological processes of osteoarthritis (OA) require adequate model systems. Although different in vitro or in vivo models have been described, further comprehensive approaches are needed to study specific parts of the disease. This study aimed to combine in vitro and in silico modeling to describe cellular and matrix-related changes during the early phase of OA. We developed an in vitro OA model based on scaffold-free cartilage-like constructs (SFCCs), which was mathematically modeled using a partial differential equation (PDE) system to resemble the processes during the onset of OA.\n\nDesignSFCCs were produced from mesenchymal stromal cells and analyzed weekly by histology and qPCR to characterize the cellular and matrix-related composition. To simulate the early phase of OA, SFCCs were treated with interleukin-1{beta} (IL-1{beta}), tumor necrosis factor (TNF) and examined after 3 weeks or cultivated another 3 weeks without inflammatory cytokines to validate the regeneration potential. Mathematical modeling was performed in parallel to the in vitro experiments.\n\nResultsSFCCs expressed cartilage-specific markers, and after stimulation an increased expression of inflammatory markers, matrix degrading enzymes, a loss of collagen II (Col-2) and a reduced cell density was observed which could be partially reversed by retraction of stimulation. Based on the PDEs, the distribution processes within the SFCCs, including those of IL-1{beta}, Col-2 degradation and cell number reduction was simulated.\n\nConclusionsBy combining in vitro and in silico methods, we aimed to develop a valid, efficient alternative approach to examine and predict disease progression and new therapeutic strategies.

bioengineering↗

bioRxiv2018

Non-covalently-associated peptides are observed during liquid chromatography-mass spectrometry and affect crosslink analyses

Crosslinking mass spectrometry draws structural information from covalently-linked peptide pairs. When these links do not match to previous structural models, they may indicate changes in protein conformation. Unfortunately, such links can also be the result of experimental error or artefacts. Here, we describe the observation of non-covalently-associated peptides during liquid chromatography-mass spectrometry analysis, which can easily be misidentified as crosslinked. Strikingly, they often mismatch to the protein structure. Non-covalently-associated peptides presumably form during ionization and can be distinguished from crosslinked peptides by observing co-elution of the corresponding linear peptides in MS1, as well as the presence of the individual (intact) peptide fragments in MS2 spectra. To suppress non-covalent peptide formations increasingly disruptive ionization settings can be used, such as in-source fragmentation.

bioinformatics↗

bioRxiv2018

Identification of RNA-associated peptides, iRAP, defines precise sites of protein-RNA interaction

ABSRACTThe identification of ever-increasing numbers of RNA species has underlined the importance of robust characterization of bona fide sites of protein-RNA interaction. UV crosslinking can be used to identify precise RNA targets for individual proteins, transcriptome-wide. Here we sought to generate reciprocal data, identifying precise sites of RNA-binding proteome-wide. The resulting technique, identification of RNA-associated peptides (iRAP), was used to locate 1331 unique RNA-interaction sites at single amino acid residue resolution in 324 S. cerevisiae proteins. Our identified RNA-interaction sites in characterized RNA-protein complex agree well with available high-resolution structures. In known RNA-interacting protiens, 317 sites fall into known and suspected RNA-interaction domains while only 21 sites fall into other annotated sequence features. Strikingly, 993 of the sites identified fall into protein regions that lack any recognizable protein domain structure or annotated sequence features. This suggests that, despite binding RNA in vivo, many of these proteins will not have defined functions in RNA metabolism.\n\nHighlights Method identifies precise RNA-binding sites across proteome\n1331 sites identified that contact RNA in 324 proteins in S. cerevisiae\nRNA binding takes place predominantly outside known protein domains

molecular biology↗

bioRxiv2018

Defining the RNA Interactome by Total RNA-Associated Protein Purification

UV crosslinking can be used to identify precise RNA targets for individual proteins, transcriptome-wide. We sought to develop a technique to generate reciprocal data, identifying precise sites of RNA-binding proteome-wide. The resulting technique, total RNA-associated protein purification (TRAPP), was applied to yeast (S. cerevisiae) and bacteria (E. coli). In all analyses, SILAC labelling was used to quantify protein recovery in the presence and absence of irradiation. For S. cerevisiae, we also compared crosslinking using 254 nm (UVC) irradiation (TRAPP) with 4-thiouracil (4tU) labelling combined with ~350 nm (UVA) irradiation (PAR-TRAPP). Recovery of proteins not anticipated to show RNA-binding activity was substantially higher in TRAPP compared to PAR-TRAPP. As an example of preferential TRAPP-crosslinking, we tested enolase (Eno1) and demonstrated its binding to tRNA loops in vivo. We speculate that many protein-RNA interactions have biophysical effects on localization and/or accessibility, by opposing or promoting phase separation for highly abundant protein. Homologous metabolic enzymes showed RNA crosslinking in S. cerevisiae and E. coli, indicating conservation of this property. TRAPP allows alterations in RNA interactions to be followed and we initially analyzed the effects of weak acid stress. This revealed specific alterations in RNA-protein interactions; for example, during late 60S ribosome subunit maturation. Precise sites of crosslinking at the level of individual amino acids (iTRAPP) were identified in 395 peptides from 155 unique proteins, following phospho-peptide enrichment combined with a bioinformatics pipeline (Xi). TRAPP is quick, simple and scalable, allowing rapid characterization of the RNA-bound proteome in many systems.

cell biology↗

bioRxiv2018

An integrated workflow for cross-linking/mass spectrometry

We present a concise workflow to enhance the mass spectrometric detection of cross-linked peptides by introducing sequential digestion and the cross-link identification software Xi. Sequential digestion enhances peptide detection by shortening long tryptic peptides while avoiding over-digestion. We demonstrate our simple 12-fraction protocol for cross-linked multi-protein complexes and cell lysates, quantitative analysis, and high-density cross-linking, without requiring specific cross-linker features. This overall approach reveals dynamic protein-protein interaction sites, which are accessible, have fundamental functional relevance and are therefore ideally suited for the development of small molecule inhibitors.

biochemistry↗

bioRxiv2018

Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data

Diagnosis and risk stratification of cancer and many other diseases require the detection of genomic breakpoints as a prerequisite of calling copy number alterations (CNA). This, however, is still challenging and requires time-consuming manual curation. As deep-learning methods outperformed classical state-of-the-art algorithms in various domains and have also been successfully applied to life science problems including medicine and biology, we here propose Deep SNP, a novel Deep Neural Network to learn from genomic data. Specifically, we used a manually curated dataset from 12 genomic single nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at predicting the presence or absence of genomic breakpoints, an indicator of structural chromosomal variations, in windows of 40,000 probes. We compare our results with well-known neural network models as well as Rawcopy though this tool is designed to predict breakpoints and in addition genomic segments with high sensitivity. We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models. Qualitative examples suggest that integration of a localization unit may enable breakpoint detection and prediction of genomic segments, even if the breakpoint coordinates were not provided for network training. These results warrant further evaluation of DeepSNP for breakpoint localization and subsequent calling of genomic segments.

genomics↗

bioRxiv2018

In-Search Selection of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

Cross-linking/mass spectrometry (CLMS) has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A seldom-used search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by in-search assignment of the monoisotopic peak in Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available datasets. Xi-MPA always delivered the highest number of identifications with ~2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.

bioinformatics↗

bioRxiv2017

False discovery rate estimation and heterobifunctional cross-linkers

False discovery rate (FDR) estimation is a cornerstone of proteomics that has recently been adapted to cross-linking/mass spectrometry. Here we demonstrate that heterobifunctional cross-linkers, while theoretically different from homobifunctional cross-linkers, need not be considered separately in practice. We develop and then evaluate the impact of applying a correct FDR formula for use of heterobifunctional cross-linkers and conclude that there are minimal practical advantages. Hence a single formula can be applied to data generated from the many different non-cleavable cross-linkers.

bioinformatics↗