Biology subjects

Schlapbach, R.

Publications and source records attributed to Schlapbach, R..

14 recordsLinked to original sources

bioRxiv2026

qg: Configuration-Driven, Multi-Vendor Acquisition Queue Generation with Reproducible Run-Order and QC Control for Mass Spectrometry

Mass spectrometry laboratories must turn lists of submitted samples into acquisition queues. The run order and the placement of quality-control (QC) injections determine whether a design controls batch effects and signal drift, and whether those effects stay correctable afterward. Yet operators usually set them by hand in vendor worklist editors that neither randomize run order nor offer configurable, pattern-driven QC. We present qg, an open-source tool that builds acquisition queues with systematic run-order handling: four run-order modes (none, simple, blocked/randomized-complete-block, and group-uniform blocked), pattern-driven QC and standard injections, and sampler- and plate-aware positioning. Unlike plate-design tools that stop at a generic sample sheet, qg writes the native vendor acquisition file directly, for three instrument ecosystems (Thermo Fisher XCalibur, Axel Semrau Chronos, Bruker HyStar) across proteomics, metabolomics, and lipidomics. It separates a small, stateless generation pipeline from a declarative configuration layer, so a laboratory adapts instruments, QC patterns, layouts, and naming by editing version-controlled configuration through a validating editor rather than changing code. qg runs from a reactive web interface or a scripted command-line interface, integrated with a LIMS (B-Fabric) or standalone from uploaded tables; randomized runs record their seed and reproduce from exported parameters. On an unbalanced design, group-uniform blocked randomization spreads biological groups evenly across acquisition time, whereas textbook block randomization leaves a tail of the largest group and can track acquisition time worse than a plain shuffle. qg is released under the Apache-2.0 license.

bioinformatics↗

bioRxiv2025

A combination of phenotypic responses and genetic adaptations enables Staphylococcus aureus to withstand inhibitory molecules secreted by Pseudomonas aeruginosa

Staphylococcus aureus and Pseudomonas aeruginosa frequently co-occur in infections, and there is evidence that their interactions can negatively affect disease outcomes. P. aeruginosa is known to be dominant, often compromising S. aureus through the secretion of inhibitory compounds. We previously demonstrated that S. aureus can become resistant to growth-inhibitory compounds during experimental evolution. While resistance arose rapidly, the underlying mechanisms were not obvious as only a few genetic mutations were associated with resistance, while ample phenotypic changes occurred. We thus hypothesize that resistance may result from a combination of phenotypic responses and genetic adaptation. Here, we tested this hypothesis using proteomics. We first focused on an evolved strain that acquired a single mutation in tcyA (encoding a transmembrane transporter unit) upon exposure to P. aeruginosa supernatant. We show that this mutation leads to a complete abolishment of transporter synthesis, which confers moderate protection against PQS and selenocystine, two toxic compounds produced by P. aeruginosa. However, this genetic effect was minor compared to the fundamental phenotypic changes observed at the proteome level when both ancestral and evolved S. aureus strains were exposed to P. aeruginosa supernatant. Major changes involved the downregulation of virulence factors, metabolic pathways, membrane transporters, and the upregulation of ROS scavengers and an efflux pump. Our results suggest that the observed multi-variate phenotypic response is a powerful adaptive strategy, offering instant protection against competitors in fluctuating environments and reducing the need for hard-wired genetic adaptations. ImportanceDifferent bacterial pathogens can co-occur in infections, where they interact with one another and influence disease severity. Previous research showed that pathogens can evolve and adapt to co-infecting species. Here, we show that evolution through genetic mutations and selection are not necessarily required to change pathogen behavior. Instead, we found that the human pathogen Staphylococcus aureus is able to plastically respond to the presence of Pseudomonas aeruginosa, a competing pathogen. Through proteomics and metabolomics, we demonstrate that S. aureus undergoes substantial proteomic alterations in response to P. aeruginosa by down-regulating virulence factor expression, changing metabolism, and mounting protective measures against toxic compounds. Our work highlights that pathogens possess sophisticated mechanisms to respond to competitors to secure growth and survival in polymicrobial infections. We predict such plastic responses to have significant impacts on infection outcomes.

microbiology↗

bioRxiv2024

prolfquapp - A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics

AbstractMass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, traditional interactive data analysis methods become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to non-programmers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements and multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software. By leveraging advanced statistical models from the prolfqua R package, prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics studies, combining efficient data processing with insightful, publication-ready outputs. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/617391v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@10930a2org.highwire.dtl.DTLVardef@57f5dborg.highwire.dtl.DTLVardef@ce0d73org.highwire.dtl.DTLVardef@1d531da_HPS_FORMAT_FIGEXP M_FIG C_FIG This visual table of contents illustrates the workflow and key features of the prolfquapp tool for differential expression analysis in proteomics. On the left are the inputs, like the CSV for annotation and quantification results, YAML for parameters, and FASTA files for protein information. In the center are the prolfquapp and prolfqua R packages and supporting tools like crosstalk and knitr, representing the core processing components. On the right side, the figure highlights the various outputs generated by prolfquapp O_LIXLSX files containing protein abundances, group summaries, and differential expression results. C_LIO_LIHTML reports with text, graphs, interactive volcano plots, and dynamic tables for data exploration. C_LIO_LIPDF documents with detailed protein boxplots and peptide-level matrix plots. C_LIO_LIIntegration with exploreDE for interactive data visualization. C_LI This diagram concisely summarizes the flow from data input to the creation of analysis-ready outputs, offering a clear overview of the prolfquapp toolset.

bioinformatics↗

bioRxiv2024

Onco-fetal protein Nogo-A restricts human and mouse glioma vascularization and growth via VEGF-Notch-hippo-metabolic signaling

Glioblastoma is one of the most deadly human cancers characterized by high degrees of vascularization, but targeting its vasculature has resulted in very limited success so far. Angiogenesis, the growth of new blood vessels, is highly dynamic during brain development, enters a mostly quiescent state in the adult homeostatic brain, and is reactivated in vascular-dependent CNS diseases including brain tumors. In consequence, a better understanding of the relevance of the onco-fetal axis - describing the reactivation of fetal signaling programs in tumors - in endothelial- and perivascular cells of the human brain tumor vasculature harbors great translational potential, yet remains poorly defined. In development, neurovascular link (NVL) molecules guide both neuronal growth cones as well as capillary endothelial tip cells. Nogo-A is an NVL molecule known to inhibit axonal growth in the developing and adult CNS and to restrict angiogenesis during brain development, but its role in the mouse and human brain tumor vasculature along the onco-fetal axis remains unknown. Here, we characterize Nogo-A as an onco-fetal protein expressed in the neurovascular unit (NVU) in human fetal brains and human gliomas in vivo that negatively regulates sprouting angiogenesis and endothelial tip cells in glioma vascularization. The Nogo-A-specific Delta 20 domain restricts angiogenic sprouting and branching and promotes vascular normalization while inhibiting glioma growth in experimental gliomas. Moreover, Nogo-A expression in tumor cells negatively correlate/s with glioma malignancy in vivo. In vitro, Nogo-A Delta 20 reduced human brain- and brain tumor endothelial cell (HBMVEC, HBTMVEC) and human umbilical vein endothelial cell (HUVEC) spreading, migration, and sprouting, in a dose-dependent manner and inhibited filopodia extension glucose metabolism. Mechanistically, RNA sequencing of Nogo-A Delta 20-treated HBMVECs and HBTMVECs revealed Nogo-A Delta 20-induced positive regulation of the angiogenesis-inhibiting Dll4-Notch-pathway and inhibition of the angiogenesis-promoting VEGF-VEGFR and Hippo-YAP-TAZ pathways, whereas metabolomics and functional metabolic assays revealed Nogo-A Delta 20-induced negative regulation of endothelial glycolysis in HBMVECs and HBTMVECs. These findings characterize Nogo-A as an onco-fetal protein in the human glial brain tumor vasculature and identify Nogo-A Delta 20 signaling as an important negative regulator of human glioma vascularization and growth. Enhancing Nogo-A signaling may be an attractive alternative or combinatorial anti-angiogenic therapy to restrict human glioma/glioblastoma vascularization and growth.

neuroscience↗

bioRxiv2023

Multiomic ALS signatures highlight sex differences and molecular subclusters and identify the MAPK pathway as therapeutic target

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease and lacks effective disease-modifying treatments. Here, we performed a multiomic analysis of the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, as well as four transgenic mouse models of C9orf72-, SOD1-, TDP-43-, and FUS-ALS to characterize early and sex-specific disease mechanisms in ALS. Integrated analyses of transcriptomes, (phospho)proteomes, and miRNAomes revealed more pronounced changes in males. We identified transcriptome-based human ALS subclusters driven by the immune response, ECM, mitochondrial respiration, and RNA metabolism. The molecular signatures of human subclusters were reflected in specific mouse models. Individual and integrative multiomics analysis highlighted the mitogen-activated protein kinase pathway as an early disease-relevant mechanism. Its modulation by trametinib in vitro and in vivo validated that mitogen-activated protein kinase kinase 2 is a promising therapeutic target with beneficial effects in females.

neuroscience↗

bioRxiv2022

prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. Nevertheless, there is a need for a flexible and easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures. The prolfqua package can model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. It integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The application programmer interface strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis easy and exciting. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard dataset. Finally, we show that the implemented methods allow sensitive and specific differential expression analysis. The prolfqua R package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT licence, and runs on all platforms supported by the R free software environment for statistical computing and graphics.

bioinformatics↗

bioRxiv2021

The haplotype-resolved chromosome pairs and transcriptome of a heterozygous diploid African cassava cultivar

BackgroundCassava (Manihot esculenta) is an important clonally propagated food crop in tropical and sub-tropical regions worldwide. Genetic gain by molecular breeding is limited because cassava has a highly heterozygous, repetitive and difficult to assemble genome. FindingsHere we demonstrate that Pacific Biosciences high-fidelity (HiFi) sequencing reads, in combination with the assembler hifiasm, produced genome assemblies at near complete haplotype resolution with higher continuity and accuracy compared to conventional long sequencing reads. We present two chromosome scale haploid genomes phased with Hi-C technology for the diploid African cassava variety TME204. Genome comparisons revealed extensive chromosome re-arrangements and abundant intra-genomic and inter-genomic divergent sequences despite high gene synteny, with most large structural variations being LTR-retrotransposon related. Allele-specific expression analysis of different tissues based on the haplotype-resolved transcriptome identified both stable and inconsistent alleles with imbalanced expression patterns, while most alleles expressed coordinately. Among tissue-specific differentially expressed transcripts, coordinately and biasedly regulated transcripts were functionally enriched for different biological processes. We use the reference-quality assemblies to build a cassava pan-genome and demonstrate its importance in representing the genetic diversity of cassava for downstream reference-guided omics analysis and breeding. ConclusionsThe haplotype-resolved genome allows the first systematic view of the heterozygous diploid genome organization in cassava. The completely phased and annotated chromosome pairs will be a valuable resource for cassava breeding and research. Our study may also provide insights into developing cost-effective and efficient strategies for resolving complex genomes with high resolution, accuracy and continuity.

genomics↗

bioRxiv2021

Molecular atlas of the human brain vasculature at the single-cell level

A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains poorly understood. Here, we performed single-cell RNA sequencing of 599,215 freshly isolated endothelial, perivascular and other tissue-derived cells from 47 fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We uncover extensive molecular heterogeneity of healthy fetal and adult human brains and across eight vascular-dependent CNS pathologies including brain tumors and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict numerous endothelial-to-perivascular cell ligand-receptor crosstalk including immune-related and angiogenic pathways, thereby unraveling a central role for the endothelium within brain neurovascular unit signaling networks. Our single-cell brain atlas provides insight into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.

neuroscience↗

bioRxiv2020

Nucleolin is expressed in human fetal brain development and reactivated in human glial brain tumors regulating angiogenesis and vascular metabolism

Glioblastoma (GBM) is amongst the deadliest human cancers and is characterized by high levels of vascularisation. Angiogenesis is highly dynamic during brain development and almost quiescent in the adult brain, but is reactivated in vascular-dependent CNS pathologies such as brain tumors. Nucleolin (NCL) is a known regulator of cell proliferation and angiogenesis, but its roles on physiological and pathological brain vasculature remain unknown. Here, we studied the expression of Nucleolin in the neurovascular unit (NVU) in human fetal brains and human gliomas in vivo as well as its effects on sprouting angiogenesis and endothelial metabolism in vitro. Nucleolin is highly expressed in endothelial- and perivascular cells during brain development, downregulated in the adult brain, and upregulated in glioma. Moreover, Nucleolin expression in tumor- and blood vessel cells correlated with glioma malignancy in vivo. In culture, siRNA-mediated NCL knockdown reduced human umbilical vein endothelial cell (HUVEC) sprouting angiogenesis, proliferation and filopodia extension, and reduced glucose metabolism. Mechanistically, RNA sequencing of Nucleolin knockdown in HUVECs revealed a putative p53-TIGAR-HK2 regulation of endothelial glycolysis. These findings identify Nucleolin as a neurodevelopmental factor reactivated in glioma that positively regulates sprouting angiogenesis and endothelial metabolism. Our findings have important implications in therapeutic targeting of glioma.

neuroscience↗

bioRxiv2020

HiDRA-seq: High-Throughput SARS-CoV-2 Detection by RNA Barcoding and Amplicon Sequencing

The recent outbreak of a new coronavirus that causes a Severe Acute Respiratory Syndrome in humans (SARS-CoV-2) has developed into a global pandemic with over 6 million reported cases and more than 375,000 deaths worldwide. Many countries have faced a shortage of diagnostic kits as well as a lack of infrastructure to perform necessary testing. Due to these limiting factors, only patients showing symptoms indicating infection were subjected to testing, whilst asymptomatic individuals, who are widely believed to be responsible for the fast dispersion of the virus, were largely omitted from the testing regimes. The inability to implement high throughput diagnostic and contact tracing strategies has forced many countries to institute lockdowns with severe economic and social consequences. The World Health Organization (WHO) has encouraged affected countries to increase testing capabilities to identify new cases, allow for a well-controlled lifting of lockdown measures, and prepare for future outbreaks. Here, we propose HiDRA-seq, a rapidly implementable, high throughput, and scalable solution that uses NGS lab infrastructure and reagents for population-scale SARS-CoV-2 testing. This method is based on the use of indexed oligo-dT primers to generate barcoded cDNA from a large number of patient samples. From this, highly multiplexed NGS libraries are prepared targeting SARS-CoV-2 specific regions and sequenced. The low amount of sequencing data required for diagnosis allows the combination of thousands of samples in a sequencing run, while reducing the cost to approximately 2 CHF/EUR/USD per RNA sample. Here, we describe in detail the first version of the protocol, which can be further improved in the future to increase its sensitivity and to identify other respiratory viruses or analyze individual genetic features associated with disease progression.

genomics↗

bioRxiv2018

Long fragments achieve lower base quality in Illumina paired-end sequencing

Illuminas technology provides high quality reads of DNA fragments with error rates below 1/1000 per base. Runs typically generate a millions of reads where the vast majority of the reads has also an average error rate below 1/1000. However, some paired-end sequencing data show the presence of a subpopulation of reads where the second read has lower average qualities. We show that the fragment length is a major driver of increased error rates in the R2 reads. Fragments above 500 nt tend to yield lower base qualities and higher error rates than shorter fragments. We demonstrate the fragment length dependency of the R2 read qualities using publicly available Illumina data generated by various library protocols, in different labs and using different sequencer models. Our finding extends the understanding of the Illumina read quality and has implications on error models for Illumina reads. It also sheds a light on the importance of the fragmentation during library preparation and the resulting fragment length distribution.

bioinformatics↗

bioRxiv2018

rawDiag - an R package supporting rational LC-MS method optimization for bottom-up proteomics

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a non-trivial task. Here we present rawDiag, a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan level metadata. rawDiag is implemented as R package and can be executed on the command line, or through a graphical user interface (GUI) for less experienced users. The code runs platform independent and can process a hundred raw files in less than three minutes on current consumer hardware as we show by our benchmark. In order to demonstrate the functionality of our package, we included a real-world example taken from our daily core facility business.

bioinformatics↗

bioRxiv2018

Pushing the limits of de novo genome assembly for complex prokaryotic genomes harboring very long, near identical repeats

Generating a complete, de novo genome assembly for prokaryotes is often considered a solved problem. However, we here show that Pseudomonas koreensis P19E3 harbors multiple, near identical repeat pairs up to 70 kilobase pairs in length. Beyond long repeats, the P19E3 assembly was further complicated by a shufflon region. Its complex genome could not be de novo assembled with long reads produced by Pacific Biosciences technology, but required very long reads from the Oxford Nanopore Technology. Another important factor for a full genomic resolution was the choice of assembly algorithm.\n\nImportantly, a repeat analysis indicated that very complex bacterial genomes represent a general phenomenon beyond Pseudomonas. Roughly 10% of 9331 complete bacterial and a handful of 293 complete archaeal genomes represented this dark matter for de novo genome assembly of prokaryotes. Several of these dark matter genome assemblies contained repeats far beyond the resolution of the sequencing technology employed and likely contain errors, other genomes were closed employing labor-intense steps like cosmid libraries, primer walking or optical mapping. Using very long sequencing reads in combination with assemblers capable of resolving long, near identical repeats will bring most prokaryotic genomes within reach of fast and complete de novo genome assembly.

genomics↗

bioRxiv2016

Cell Cycle Constraints and Environmental Control of Local DNA Hypomethylation in Alpha-Proteobacteria

Heritable DNA methylation imprints are ubiquitous and underlie genetic variability from bacteria to humans. In microbial genomes, DNA methylation has been implicated in gene transcription, DNA replication and repair, nucleoid segregation, transposition and virulence of pathogenic strains. Despite the importance of local (hypo)methylation at specific loci, how and when these patterns are established during the cell cycle remains poorly characterized. Taking advantage of the small genomes and the synchronizability of -proteobacteria, we discovered that conserved determinants of the cell cycle transcriptional circuitry establish specific hypomethylation patterns in the cell cycle model system Caulobacter crescentus. We used genome-wide methyl-N6-adenine (m6A-) analyses by restriction-enzyme-cleavage sequencing (REC-Seq) and single-molecule real-time (SMRT) sequencing to show that MucR, a transcriptional regulator that represses virulence and cell cycle genes in S-phase but no longer in G1-phase, occludes 5-GANTC-3 sequence motifs that are methylated by he DNA adenine methyltransferase CcrM. Constitutive expression of CcrM or heterologous methylases in at least two different -proteobacteria homogenizes m6A patterns even when MucR is present and affects promoter activity. Environmental stress (phosphate limitation) can override and reconfigure local hypomethylation patterns imposed by the cell cycle circuitry that dictate when and where local hypomethylation is instated.\n\nAuthor SummaryDNA methylation is the post-replicative addition of a methyl group to a base by a methyltransferase that recognise a specific sequence, and represents an epigenetic regulatory mechanism in both eukaryotes and prokaryotes. In microbial genomes, DNA methylation has been implicated in gene transcription, DNA replication and repair, nucleoid segregation, transposition and virulence of pathogenic strains. CcrM is a conserved, cell cycle regulated adenine methyltransferase that methylates GANTC sites in -proteobacteria. N6-methyl-adenine (m6A) patterns generated by CcrM can change the affinity of a given DNA-binding protein for its target sequence, and therefore affect gene expression. Here, we combine restriction enzyme cleavage-deep sequencing (REC-Seq) with SMRT sequencing to identify hypomethylated 5-GANTC-3 (GANTCs) in -proteobacterial genomes instated by conserved cell cycle factors. By comparing SMRT and REC-Seq data with chromatin immunoprecipitation-deep sequencing data (ChIP-Seq) we show that a conserved transcriptional regulator, MucR, induces local hypomethylation patterns by occluding GANTCs to the CcrM methylase and we provide evidence that this competition occurs during S-phase, but not in G1-phase cells. Furthermore, we find that environmental signals (such as phosphate depletion) are superimposed to the cell cycle control mechanism and can override the specific hypomethylation pattern imposed by the cell cycle transcriptional circuitry.

microbiology↗