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Characterization of brain dystrophins absence and impact in dystrophin-deficient Dmdmdx rat model
No full textResponse to mTOR and PI3K inhibitors in enzalutamide-resistant luminal androgen receptor triple-negative breast cancer patient-derived xenografts
No full text
Targeted intestinal tight junction hyperpermeability alters the microbiome, behavior, and visceromotor responses
No full textSymptom evolution following the emergence of maize streak virus
No full textFor pathogens infecting single host species evolutionary trade-offs have previously been demonstrated between pathogen-induced mortality rates and transmission rates. It remains unclear, however, how such trade-offs impact sub-lethal pathogen-inflicted damage, and whether these trade-offs even occur in broad host-range pathogens. Here, we examine changes over the past 110 years in symptoms induced in maize by the broad host-range pathogen, maize streak virus (MSV). Specifically, we use the quantified symptom intensities of cloned MSV isolates in differentially resistant maize genotypes to phylogenetically infer ancestral symptom intensities and check for phylogenetic signal associated with these symptom intensities. We show that whereas symptoms reflecting harm to the host have remained constant or decreased, there has been an increase in how extensively MSV colonizes the cells upon which transmission vectors feed. This demonstrates an evolutionary trade-off between amounts of pathogen-inflicted harm and how effectively viruses position themselves within plants to enable onward transmission
Calcium delivery by electroporation induces in vitro cell Ddeath through mitochondrial dysfunction without DNA damages
No full textAdolescent cancer survivors present increased risks of developing secondary malignancies due to cancer therapy. Electrochemotherapy is a promising anti-cancer approach that potentiates the cytotoxic effect of drugs by application of external electric field pulses. Clinicians proposed to associate electroporation and calcium. The current study aims to unravel the toxic mechanisms of calcium electroporation, in particular if calcium presents a genotoxic profile and if its cytotoxicity comes from the ion itself or from osmotic stress. Human dermal fibroblasts and colorectal HCT-116 cell line were treated by electrochemotherapy using bleomycin, cisplatin, calcium, or magnesium. Genotoxicity, cytotoxicity, mitochondrial membrane potential, ATP content, and caspases activities were assessed in cells grown on monolayers and tumor growth was assayed in tumor spheroids. Results in monolayers show that unlike cisplatin and bleomycin, calcium electroporation induces cell death without genotoxicity induction. Its cytotoxicity correlates with a dramatic fall in mitochondrial membrane potential and ATP depletion. Opposite of magnesium, over seven days of calcium electroporation led to spheroid tumor growth regression. As non-genotoxic, calcium has a better safety profile than conventional anticancer drugs. Calcium is already authorized by different health authorities worldwide. Therefore, calcium electroporation should be a cancer treatment of choice due to the reduced potential of secondary malignancies
Machine learning-based classification to improve Gas Chromatography-Mass spectrometry data processing.
No full textIntroduction
Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GCMS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult.
Technological and methodological innovation
We present part of the work published in [1] and implemented in our workflow for improved peak picking (WiPP),
focusing on the use of machine learning-based classification to optimize and improve different steps of the common GC-MS metabolomics data processing workflow. Our approach evaluates the quality of detected peaks using a machine learning based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis.
Results and impact
We benchmarked our workflow to standard compound mixes and a complex biological dataset, demonstrating that peak detection is improved. Furthermore, the approach can provide an impartial performance comparison of different peak picking algorithms. We also discuss the applicability of the approach to liquid chromatography-mass spectrometry data.
References
[1] Gloaguen, Y.; Borgsmüller, N. et al. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass
Spectrometry (GC-MS) Data. Metabolites 2019, 9, 171
