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.
Project description:Persisters are cells which evade stresses like antibiotics and which are characterized by reduced metabolism and a lack of genetic alterations required to achieve this state. We showed previously that MqsR and MqsA of Escherichia coli are a toxin-antitoxin pair that influence cell physiology (e.g., biofilm formation and motility) via RNase activity as well as through regulation of toxin CspD. Here, we show that deletion of the mqsRA locus decreases persister cell formation and, consistent with this result, overexpression of MqsR increases persister cell formation. Furthermore, toxins Hha, CspD, and HokA increase persister cell formation. In addition, by overproducing MqsR in a series of isogenic mutants, we show that Hha and CspD are necessary for persister cell formation via MqsR overexpression. Surprisingly, Hfq, a small RNA chaperone, decreases persistence. A whole-transcriptome study shows that Hfq induces transport-related genes (oppA, oppB, oppC, oppD, oppF, and dppA), outer membrane protein-related genes (ybfM and ybfN), toxins (hha), and proteases (clpX, clpP, and lon). Taken together, these results indicate that toxins CspD and Hha influence persister cell formation via MqsR and that Hfq plays an important role in the regulation of persister cell formation via regulation of transport or outer membrane proteins.
Project description:<p>We have used a "chemistry first" approach to discover druggable acquired vulnerabilities that arised in the pathogenesis of non-small cell lung cancer (NSCLC). We screened chemical libraries (~200,000 compounds) for chemical toxins that killed subsets of NSCLC but not normal human lung epithelial cells (HBECs). We first screened a panel of 12 NSCLC lines that represented a variety of known oncogenotypes and identified chemicals with large Z scores and appropriate properties including re-supply, chemistry, and reproducible drug response phenotypes. This was then narrowed down to a list of 202 chemicals and 18 drugs with known targeting (henceforth called "Precision Oncology Probe Set", or POPS). These, and a panel of 30 clinically available drugs, targeted therapies, and drug combinations, already in use or in trials for NSCLC treatment, were then tested on a panel of 96 NSCLC lines for their drug response phenotypes in 12-point dose response curves. This information was analyzed using scanning ranked KS (Kolmogorov-Smirnov) and elastic net biostatistics approaches to identify molecular biomarkers (mutations, mRNA expression, copy number variation, protein expression, and metabolomics) which could predict for sensitivity or resistance to a particular chemical toxin or treatment regimen. From this we have discovered that: our approach identifies already known molecular biomarker of drug sensitivities (e.g. EGFR mutations and EGFR TK inhibitors); many clinically available chemotherapy agents have molecular biomarkers predicting preclinical model drug responses; the POP set of chemical toxins provides novel drug response phenotype patterns in the large NSCLC panel different from those found with clinically available agents including a therapeutic window; many of the POP toxins only hit a small percentage (~5%) of the NSCLC panel but the POP set as a whole provides "coverage" of the entire NSCLC panel; there are simple, one or 2 component molecular biomarkers (mutations, mRNA expression) that predict responses to the different chemical toxins in the NSCLC panel; and that the molecular biomarkers provide some information on the targets and pathways involved in response to the chemical toxins. Thus, we have identified a group of chemical toxins with selectivity for subsets of NSCLC and associated tumor molecular biomarkers to facilitate their development for precision medicine, and also, in some cases, information on the targets and pathways interdicted by these chemical compounds. In addition, we have discovered NSCLC predictive biomarkers for clinically available agents.</p>
Project description:A topsoil sample obtained from a highly industrialized area was tested on CALUX® cell line and exposed cells processed with proteomic and bioinformatics analyses. GC-MS/MS analysis of topsoil extracts was also performed to confirm PCDD, PCDF and dl-PCBs presence. CALUX® and GC-MS/MS data confirmed the presence of PCDD/Fs and dl-PCBs in the topsoil extract. Proteomic analysis of the cells exposed to the soil extracts identified 43 differential proteins. Enrichment analysis highlights biological processes such as cellular response to chemical stimulus, to stress, response to inorganic substance, regulation of translation, regulation of apoptotic process as well as response to organonitrogen compounds in light of particular drugs and compounds, extrapolated by bioinformatics and linked to the identified proteins modifications. These results confirm the complex epidemiological situation occurring among Taranto inhabitants and underline the need to further investigate the presence and sources of inferred chemicals in Taranto soils. The combined analysis reveals a more complex scenarios of chemicals able to affect cellular pathways and leading to toxicities rather than those identified by the bioassay. The approach to couple proteomic to CALUX® is a promising tool and deserves further investigation to be applied in soil pollution monitoring and risk assessment.
Project description:The role of six toxin-antitoxin (TA) systems on biofilm development was investigated (MazEF, RelBEF, ChpB, YefM-YoeB, DinJ-YafQ, and TomB-Hha). Although these TA systems were reported previously to not impact bacterial fitness, we found that biofilm formation is decreased by toxins and increased by anti-toxins, in part, through YjgK. Hence, one role of TA systems is to regulate biofilm formation.
Project description:The role of six toxin-antitoxin (TA) systems on biofilm development was investigated (MazEF, RelBEF, ChpB, YefM-YoeB, DinJ-YafQ, and TomB-Hha). Although these TA systems were reported previously to not impact bacterial fitness, we found that biofilm formation is decreased by toxins and increased by anti-toxins, in part, through YjgK. Hence, one role of TA systems is to regulate biofilm formation.
Project description:Persisters are cells which evade stresses like antibiotics and which are characterized by reduced metabolism and a lack of genetic alterations required to achieve this state. We showed previously that MqsR and MqsA of Escherichia coli are a toxin-antitoxin pair that influence cell physiology (e.g., biofilm formation and motility) via RNase activity as well as through regulation of toxin CspD. Here, we show that deletion of the mqsRA locus decreases persister cell formation and, consistent with this result, overexpression of MqsR increases persister cell formation. Furthermore, toxins Hha, CspD, and HokA increase persister cell formation. In addition, by overproducing MqsR in a series of isogenic mutants, we show that Hha and CspD are necessary for persister cell formation via MqsR overexpression. Surprisingly, Hfq, a small RNA chaperone, decreases persistence. A whole-transcriptome study shows that Hfq induces transport-related genes (oppA, oppB, oppC, oppD, oppF, and dppA), outer membrane protein-related genes (ybfM and ybfN), toxins (hha), and proteases (clpX, clpP, and lon). Taken together, these results indicate that toxins CspD and Hha influence persister cell formation via MqsR and that Hfq plays an important role in the regulation of persister cell formation via regulation of transport or outer membrane proteins. Strains: E. coli BW25113 K-12 hfq deleted mutant vs. wild-type Medium: LB Culture: Planktonic cell grown OD=0.5, adjusted OD=1.0, and then exposed to 100 ug/mL ampicillin for 2 h.
Project description:Many toxins and stressors found in hydrolysates inhibit microbial metabolism and product formation, requiring mitigation strategies including strain engineering. To identify mechanisms of toxicity and targets for genetic engineering, we used a chemical genomics approach with a library of S. cerevisiae deletion mutants cultured anaerobically in dozens of individual compounds found in different types of hydrolysates, to explore shared and divergent gene requirements across inhibitors. Relationships in chemical-genomic profiles identified classes of toxins that provoked similar cellular responses, spanning inhibitor relationships that were not expected from chemical classification. Our results also revealed widespread antagonistic effects across inhibitors, such that the same gene deletions are beneficial for surviving some toxins but detrimental for others. As a proof of principle, we used the gene-deletion responses to single inhibitors to successfully predict strains whose fitness was improved in complex inhibitor mixes found in synthetic hydrolysates. We discuss the implications for strain engineering and the potential of this rich dataset for identifying engineering targets.
Project description:Natural toxins, such as isothiocyanate (ITC), are harmful secondary metabolites produced by plants. Many natural toxins occur in commercial crops, yet their possible negative repercussions on especially non-target soil organisms are largely unknown. This study examined molecular effects of 2-phenylethyl ITC on the soil arthropod species: Folsomia candida. Complementing ecotoxicological experiments with gene expression profiling allows the study of the underlying modes of action of toxins. For this the standardized ISO guideline for ecotoxicological experiments and a microarray for F. candida (GPL7150) were used. Toxic effects on reproduction were shown for F. candida, with EC50 values of around 11.5 nmol/g soil, illustrating the toxic character of this compound. A total of 130 genes were significantly differentially expressed between C and AC. The compounds were spiked in with acetone and due to the substantial difference between the two controls, only AC was chosen for further contrast analysis, comparing AC to EC10 and EC50 gene expressions. At EC10 level, 75 genes and at the EC50 level 107 genes were differentially expressed compared to AC. Approximately 36% of all significant genes could be annotated using Blast2GO. Gene expression profiles revealed the importance of lipid metabolism at low exposure concentrations (EC10), which is associated with the lipophilic nature of 2-phenylethyl ITC. At higher dosages (EC50) a wider array of genes was over-expressed, which included genes related to stress and sugar metabolism. Our study provides new insights in the molecular mode of action of a natural toxin soil animals. This study is the first step towards an understanding of these potential risks by studying the effects of a natural toxin at a life-history trait and molecular level.
Project description:Natural toxins, such as isothiocyanate (ITC), are harmful secondary metabolites produced by plants. Many natural toxins occur in commercial crops, yet their possible negative repercussions on especially non-target soil organisms are largely unknown. This study examined molecular effects of 2-phenylethyl ITC on the soil arthropod species: Folsomia candida. Complementing ecotoxicological experiments with gene expression profiling allows the study of the underlying modes of action of toxins. For this the standardized ISO guideline for ecotoxicological experiments and a microarray for F. candida (GPL7150) were used. Toxic effects on reproduction were shown for F. candida, with EC50 values of around 11.5 nmol/g soil, illustrating the toxic character of this compound. A total of 130 genes were significantly differentially expressed between C and AC. The compounds were spiked in with acetone and due to the substantial difference between the two controls, only AC was chosen for further contrast analysis, comparing AC to EC10 and EC50 gene expressions. At EC10 level, 75 genes and at the EC50 level 107 genes were differentially expressed compared to AC. Approximately 36% of all significant genes could be annotated using Blast2GO. Gene expression profiles revealed the importance of lipid metabolism at low exposure concentrations (EC10), which is associated with the lipophilic nature of 2-phenylethyl ITC. At higher dosages (EC50) a wider array of genes was over-expressed, which included genes related to stress and sugar metabolism. Our study provides new insights in the molecular mode of action of a natural toxin soil animals. This study is the first step towards an understanding of these potential risks by studying the effects of a natural toxin at a life-history trait and molecular level.