Project description:DDA non-targeted LC-MS/MS, PPL-SPE extracted marine organic matter. Positive Mode. Samples taken from during the Scripps Pier Diel Project 2022.
Project description:The outbreak-causing monkeypox virus of 2022 (2022 MPXV) is classified as a clade IIb strain and phylogenetically distinct from prior endemic MPXV strains (clades I or IIa), suggesting that its virological properties may also differ. Here, we used human keratinocytes and induced pluripotent stem cell-derived colon organoids to examine the efficiency of viral growth in these cells and the MPXV infection-mediated host responses. MPXV replication was much more productive in keratinocytes than in colon organoids. We observed that MPXV infections, regardless of strain, caused cellular dysfunction and mitochondrial damage in keratinocytes. Notably, a significant increase in the expression of hypoxia-related genes was observed specifically in 2022 MPXV-infected keratinocytes. Our comparison of virological features between 2022 MPXV and prior endemic MPXV strains revealed signaling pathways potentially involved with the cellular damages caused by MPXV infections and highlights host vulnerabilities that could be utilized as protective therapeutic strategies against human mpox in the future.
Project description:The World Health Organization Classification of Hematolymphoid Tumors (WHO) and the International Consensus Classification (ICC) of 2022 introduced major changes to the definition of CMML. To assess qualitative and quantitative implications for patient care, we started with 3,311 established CMML cases (according to WHO 2017 criteria) and included also 2,130 oligomonocytosis cases fulfilling the new CMML diagnostic criteria. Applying both classification systems from 2022, 356 and 241 of oligomonocytosis cases were newly classified as myelodysplastic (MD)-CMML (WHO and ICC 2022, respectively), most of which were diagnosed as MDS according to WHO 2017. Importantly, 1.5 times more oligomonocytosis cases were classified as CMML according to WHO 2022 than based on ICC, due to different diagnostic criteria. Genetic analyses of the newly classified CMML cases showed a distinct mutational profile with strong enrichment of MDS-typical alterations, resulting in a transcriptional subgroup separated from established MD- and myeloproliferative (MP)-CMML. Despite a different cytogenetic, molecular, immunophenotypic, and transcriptional landscape, no differences in overall survival were found between newly classified and established MD-CMML cases. To the best of our knowledge, this study represents the most comprehensive analysis of routine CMML cases to date, both in terms of clinical characterization and transcriptomic analysis, placing newly classified CMML cases on a disease continuum between MDS and previously established CMML.
Project description:A major pharmacological strategy toward HIV cure aims to reverse latency in infected cells as a first step leading to their elimination. While the unbiased identification of molecular targets physically associated with the latent HIV-1 provirus would be highly valuable to unravel the molecular determinants of HIV-1 transcriptional repression and latency reversal, due to technical limitations, this has been challenging. Here we use a dCas9 targeted chromatin and histone enrichment strategy coupled to mass spectrometry (Catchet-MS) to probe the differential protein composition of the latent and activated HIV-1 5'LTR. Catchet-MS identified known and novel latent 5’LTR-associated host factors. Among these, IKZF1 is a novel HIV-1 transcriptional repressor, required for Polycomb Repressive Complex 2 recruitment to the LTR. We find the clinically advanced thalidomide analogue iberdomide, and the FDA approved analogues lenalidomide and pomalidomide, to be novel LRAs that, by targeting IKZF1 for degradation, reverse HIV-1 latency in CD4+T-cells isolated from virally suppressed people living with HIV-1.
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.