Biology subjects

Menden, K.

Publications and source records attributed to Menden, K..

4 recordsLinked to original sources

bioRxiv2025

Cellular and Extracellular microRNA Dysregulation in LRRK2-Linked Parkinson's Disease

Background and objectiveThe discovery of cell-free micro-RNAs in body fluids has made them a promising biomarker target in the field of neurodegenerative diseases. Although they have been reported to be differentially expressed in biofluids and tissues from sporadic Parkinsons disease patients, it remains unclear whether similar observations can be made in patients with genetic forms of the disease and if miRNA profiles reflect mutation-specific pathogenic pathways. Since induced pluripotent stem cell-derived neurons represent a widely used research model for both sporadic and familial Parkinsons disease, we sought to assess the usability of this model for the identification of differentially expressed cell-free micro-RNAs in the context of the Parkinsons disease-related LRRK2 G2019S mutation in a proof-of-concept study. Materials and methodsWe isolated extracellular vesicles carrying cell-free RNA from patient-derived induced pluripotent stem cells carrying the LRRK2 G2019S mutation and their gene-corrected isogenic controls. After the generation of small-RNA libraries and differential expression analysis, we quantified expression levels of fourteen micro-RNAs in an independent batch of cell-free and cellular RNA via RT-qPCR. Finally, we quantified pRab10 levels as a proxy of LRRK2 activity and correlated observable changes to the miRNA expression levels. ResultsWe successfully isolated extracellular vesicles from induced pluripotent stem cell-derived human dopaminergic neurons. We detected over 2000 different micro-RNAs of which 56 were differentially expressed. Dysregulation of four micro-RNAs was confirmed in an independent batch of cell-free RNA. We discovered a high correlation between changes in the cell-free and cellular micro-RNAomes. Finally, we showed poor correlation between LRRK2 expression or activity and miRNA expression levels. ConclusionsOur results suggest that patients carrying the LRRK2 G2019S mutation display alterations in cellular and cell-free micro-RNA expression levels. Notably, the miRNA changes observed in this study did not follow a linear relationship with LRRK2 expression levels or kinase activity. Validation in larger cohorts will be necessary.

neuroscience↗

bioRxiv2022

Pathogen Detection in RNA-Seq Data with Pathonoia

MotivationRecent evidence suggests that bacterial and viral infections may cause or exacerbate many human diseases. One method of choice to detect microbes in tissue is RNA sequencing. While the detection of specific microbes using RNA sequencing offers good sensitivity and specificity, untargeted approaches suffer from very high false positive rates and a lack of sensitivity for lowly abundant organisms. ResultsWe introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host cell interactions by correlating the microbial to host gene expression. Pathonoia outperforms competing algorithms in microbial detection specificity, both on in silico and real datasets. Lastly, we present two case studies in human liver and brain in which microbial infection might exacerbate disease. AvailabilityA Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub https://github.com/kepsi/Pathonoia. Contactaliebho1@jhu.edu and s.bonn@uke.de Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/476681v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@16775f2org.highwire.dtl.DTLVardef@17099f4org.highwire.dtl.DTLVardef@18b8dfeorg.highwire.dtl.DTLVardef@117498b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Integrated multi-omics analysis reveals common and distinct dysregulated pathways for genetic subtypes of Frontotemporal Dementia

Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, and small RNA-seq, and supplemented this date with CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease.

neuroscience↗

bioRxiv2019

Deep-learning-based cell composition analysis from tissue expression profiles.

We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple data sets. Due to this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scadens comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

bioinformatics↗