Biology subjects

Bade, J.

Publications and source records attributed to Bade, J..

3 recordsLinked to original sources

bioRxiv2026

A comprehensive reference database to support untargeted metabolomics in Pseuudomonas putida

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

A rapid platform for 3D patient-derived cutaneous neurofibroma organoid establishment and screening

Localized cutaneous neurofibromas (cNFs) are benign tumors that arise in the dermis of patients affected by Neurofibromatosis Type 1 syndrome (NF1). cNFs are fundamentally benign lesions: they do not undergo malignant transformation or metastasize. Nevertheless, in NF1 patients, they can cover a significant proportion of the body, with some individuals developing hundreds to thousands of lesions. cNFs can cause pain, itching, and disfigurement with substantial socio-emotional repercussions. To date, surgical removal or laser desiccation are the only treatment options, but can result in scarring and the leave a potential for regrowth. To support drug discovery efforts focused on identifying effective systemic therapies for cNF, we introduce an approach to routinely establish and screen cNF tumor organoids. We optimized conditions to support ex vivo growth of genomically-diverse cNFs. Patient-derived cNF organoids closely recapitulate the molecular and cellular heterogeneity of these tumors as measured by immunohistopathology, DNA methylation, RNA-seq and flow cytometry. Our tractable patient-derived cNF organoid platform enables rapid screening of hundreds of compounds in a patient- and tumor-specific manner.

systems biology↗

bioRxiv2022

Ex vivo to in vivo model of malignant peripheral nerve sheath tumors for precision oncology

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1-MPNST), but can occur sporadically. Through a multi-institution collaboration, we have developed 13 NF1-associated MPNST patient-derived xenografts (PDX). Genomic analysis of the PDX-tumor pairs identified somatic mutations in NF1 (61%), SUZ12 (61%), EED (15%), and TP53 (15%), and chromosome 8 (Chr8) gain (77%), consistent with published data. Pre-clinical models that capture this molecular heterogeneity are needed to identify and prioritize effective drug candidates for clinical translation. Here, we describe the successful development of a medium-throughput ex vivo 3D microtissue model with several advantages over 2D cell line growth, which can be utilized to predict drug response in vivo. Herein, we present proof-of-principle of this PDX-to-microtissue system, using four genomically representative MPNST and three drugs. This work highlights the development of a novel ex vivo to in vivo preclinical platform in MPNST that successfully captures the genomic diversity observed in patients and represents a resource to identify future therapeutic strategies.

cancer biology↗