Biology subjects

Witte, J.

Publications and source records attributed to Witte, J..

5 recordsLinked to original sources

bioRxiv2024

Spatial Proteomics by Trapped Ion Mobility supported MALDI MS/MS Imaging: A First Glance into Multiplexed and Spatial Peptide Identification

RATIONALEIn spatial proteomics, matrix-assisted laser desorption/ionization (MALDI) imaging enables rapid and cost-effective peptide measurements. Yet, in situ peptide identification remains challenging. Therefore, this study aims to integrate the trapped ion mobility spectrometry (TIMS)-based parallel accumulation-serial fragmentation (PASEF) into MALDI imaging of tryptic peptides to enable multiplexed MS/MS imaging. METHODSAn initial MALDI TIMS MS1 survey measurement was performed, followed by a manual generation of a precursor list containing mass over charge values and ion mobility windows. Inside the dual TIMS system, submitted precursors were trapped, separately eluted by their ion mobility and analyzed in a quadrupole time-of-flight device, thereby enabling multiplexed MALDI MS/MS imaging. Finally, precursors were identified by peptide to spectrum matching. RESULTSThis study presents the first multiplexed MALDI TIMS MS/MS imaging (iprm-PASEF) of tryptic peptides. Its applicability was showcased on two histomorphologically distinct tissue specimens in a 4-plex and 5-plex setup. Precursors were successfully identified by the search engine MASCOT in one single MALDI imaging experiment for each respective tissue. Peptide identifications were corroborated by liquid-chromatography tandem mass spectrometry experiments and fragment co-localization analyses. CONCLUSIONSIn this study, we present a novel pipeline, based on iprm-PASEF that allows the multiplexed and spatial identification of tryptic peptides in MALDI imaging. Hence, it marks a first step towards the integration of MALDI imaging into the emerging field of spatial proteomics.

biochemistry↗

bioRxiv2024

Broad diversity of human gut bacteria accessible via a traceable strain deposition system

Numerous bacteria in the human gut microbiome remain unknown and/or have yet to be cultured. While collections of human gut bacteria have been published, few strains have been made publicly available. A major hurdle in making strains publicly available is their deposition to public culture collections. We propose a framework for the bulk-deposition of strains to culture collections, which removes many of the barriers previously identified (www.dsmz.de/bulk-deposit). Using this bulk-deposition system we have created a publicly available collection of human gut isolates. The Human intestinal Bacteria Collection (HiBC) (www.hibc.rwth-aachen.de) contains 340 strains representing 198 species within 29 families and 7 phyla, of which 29 previously unknown species are taxonomically described and named. These included two butyrate-producing species of Faecalibacterium and new dominant species associated with health and inflammatory bowel disease, Ruminococcoides intestinale and Blautia intestinihominis, respectively. Plasmids were prolific within the HiBC isolates, with almost half (46%) of strains containing plasmids, with a maximum of six within a strain. This included a broadly occurring plasmid (pBAC) that exists in three diverse forms across Bacteroidales species. Megaplasmids were identified within two strains, the pMMCAT megaplasmid is globally present within multiple Bacteroidales species. This collection of easily searchable and publicly available gut bacterial isolates will facilitate functional studies of the gut microbiome.

microbiology↗

bioRxiv2023

Increased beta2-adrenergic signaling is a targetable stimulus essential for bone healing by promoting callus neovascularization

Traumatic brain injury (TBI) is associated with a hyperadrenergic state and paradoxically causes systemic bone loss while accelerating fracture healing. Here, we identify the beta2-adrenergic receptor (Adrb2) as a central mediator of these skeletal manifestations. While the negative effects of TBI on the unfractured skeleton can be explained by the established impact of Adrb2 signaling on bone formation, Adrb2 promotes neovascularization of the fracture callus under conditions of high sympathetic tone, including TBI and advanced age. Mechanistically, norepinephrine stimulates the expression of Vegfa and Cgrp primarily in periosteal cells via Adrb2, both of which synergistically promote the formation of osteogenic type-H vessels in the fracture callus. Accordingly, the beneficial effect of TBI on bone repair is abolished in mice lacking Adrb2 or Cgrp, and aged Adrb2-deficient mice without TBI develop fracture nonunions despite high bone formation in uninjured bone. Pharmacologically, the Adrb2 antagonist propranolol impairs, and the agonist formoterol promotes fracture healing in aged mice by regulating callus neovascularization. Clinically, intravenous beta-adrenergic sympathomimetics are associated with improved callus formation in trauma patients with long bone fractures. Thus, Adrb2 is a novel target for promoting bone healing, and widely used beta-blockers may cause fracture nonunion under conditions of increased sympathetic tone. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/548550v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@cea57corg.highwire.dtl.DTLVardef@d29642org.highwire.dtl.DTLVardef@186ea5aorg.highwire.dtl.DTLVardef@83eaae_HPS_FORMAT_FIGEXP M_FIG Artwork was created in BioRender. C_FIG

cell biology↗

bioRxiv2019

Efficient estimation and applications of cross-validated genetic predictions

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRS). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work we present a novel reference-free method to produce PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial over-fitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues, and can produce informative in-sample PRS over any existing cohort without over-fitting. We then demonstrate several novel applications of reference-free PRS including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

genetics↗

bioRxiv2017

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes produces a substantially better accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected nine independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis, and Peripheral Vascular Disease. The GERA cohort analysis suggests that CPBayes is more powerful than ASSET with respect to detecting independent pleiotropic variants. We provide an R-package CPBayes implementing the proposed method.\n\nAuthor SummaryGenome-wide association studies (GWASs) have highlighted shared genetic susceptibility to various human diseases (pleiotropy). We propose a Bayesian meta-analysis method CPBayes that simultaneously evaluates the evidence of aggregate-level pleiotropic association and selects an optimal subset of associated traits underlying a pleiotropic signal. CPBayes analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate GWASs with or without shared subjects, cohort study for multiple traits. It performs a fully Bayesian analysis and offers various flexibilities in the inference. In addition to parameters of primary interest (e.g., the measures of overall pleiotropic association, the optimal subset of associated traits), it provides additional interesting insights into a pleiotropic signal (e.g., the trait-specific posterior probability of association, the credible interval of unknown true genetic effects). Using computer simulations and a real data application to the large Kaiser GERA cohort, we demonstrate that CPBayes offers substantially better accuracy while selecting the non-null traits compared to a well known subset-based meta analysis ASSET. In the GERA cohort analysis, CPBayes detected a larger number of independent pleiotropic variants than ASSET. We provide a user-friendly R-package CPBayes for general use.

genetics↗