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Biology subjects

Jönsson, M.

Publications and source records attributed to Jönsson, M..

3 recordsLinked to original sources

A synthetic platform for multiplex screening of cell-cell communication between soil fungi

Fungi are essential members across ecosystems, yet phytopathogenic fungi pose an increasing risk to crop yields. Despite their ecologic importance, cell-cell communication in fungi is underexplored, partly due to the lack of high-throughput techniques. Here we developed a Yeast Mating Platform (YeMaP) to investigate the interaction between fungal G protein-coupled receptors (GPCRs) and pheromone peptides. We used YeMaP for high- throughput screening of 8,000 pheromone sequences and identified new peptides with improved agonism or antagonism action. We found that these peptides can be applied in a native fungal system such as the plant pathogen Fusarium oxysporum, to control hyphal chemotropism and reduce plant root penetration. Additionally, we utilized YeMaP in a one- pot assay to investigate how abiotic factors influence the communication of multiple pheromone-GPCR combinations and found that the cell-cell communication mediated by the GPCR Ste2 from F. oxysporum signalled robustly across different abiotic factors, while other fungal GPCR-pheromone interactions were more sensitive to changes. Taken together, YeMaP accelerates the identification of fungal GPCR-peptide interactions by enabling one- pot assays, and serves as model system for studying fungal cell-cell communication.

synthetic biology↗

Multi-omic profiling of squamous cell lung cancer identifies metabolites and related genes associated with squamous cell carcinoma

BackgroundSquamous cell lung carcinoma (SqCC) is the second most common histological subtype of lung cancer. Besides -tumor-initiating and promoting DNA, RNA, and epigenetic alterations, aberrant tumor cell metabolism has been identified as one of the hallmarks of carcinogenesis. The aim of the current study was to identify SqCC-specific metabolites and key gene regulators that could eventually be used as new anticancer targets. MethodsTranscriptional (n=156), proteomics (n=118), and mass spectrometry-based metabolomic data (n=73) were gathered for a cohort of resected early-stage lung cancers representing all major histological subgroups. SqCC-specific differentially expressed genes were integrated with proteogenomic and metabolic data using genome scale metabolic models (GEMs). Findings were validated in cohorts of tumors, normal specimens, and cancer cell lines. In situ protein expression of SLC6A8 was investigated in 213 tumors. ResultsDifferential gene expression analysis identified 280 SqCC-specific genes, of which 57 were connected to metabolites through GEMs. Metabolic profiling identified 7 SqCC-specific metabolites, of which increased creatine and decreased phosphocholine levels matched to SqCC-specific elevated expression of SLC6A8 and decreased expression of CHKA, part of respective GEMs. Expression of both genes appeared tumor cell-associated, and in particular the elevated expression of SLC6A8 identified SqCC also in stage IV disease. ConclusionElevated creatine levels and the overexpression of its transporter protein SLC6A8 appear as a distinct metabolic feature of SqCC. Considering ongoing clinical trials focused on SLC6A8 inhibition in other malignancies, exploring SLC6A8 inhibition in SqCC appears motivated based on a metabolic addiction hypothesis.

cancer biology↗

Machine Learning Uncovers the Transcriptional Regulatory Network for the Production Host Streptomyces albidoflavus J1074

Streptomyces albidoflavus is a popular and genetically tractable platform strain used for natural product discovery and production via the expression of heterologous biosynthetic gene clusters (BGCs). However, its transcriptional regulatory network (TRN) and its impact on secondary metabolism is poorly understood. Here we characterized its TRN by applying an independent component analysis to a compendium of 218 high quality RNA-seq transcriptomes from both in-house and public sources spanning 88 unique growth conditions. We obtained 78 independently modulated sets of genes (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through analyses of condition-dependent TRN activity states, we (i) describe how the TRN adapts to different growth conditions, (ii) conduct a cross-species iModulon comparison, uncovering shared features and unique characteristics of the TRN across lineages, (iii) detail the transcriptional activation of several endogenous BGCs, including surugamide, minimycin and paulomycin, and (iv) infer potential functions of 40% of the uncharacterized genes in the S. albidoflavus genome. Our findings provide a comprehensive and quantitative understanding of the TRN of S. albidoflavus, providing a knowledge base for further exploration and experimental validation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/574332v3_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1a7d0aorg.highwire.dtl.DTLVardef@10750eborg.highwire.dtl.DTLVardef@1518644org.highwire.dtl.DTLVardef@145f5b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗