High resolution LC-MS untargeted lipidomics using data independent acquisition (DIA) has the potential to increase lipidome coverage, as it enables the continuous and unbiased acquisition of all eluting ions. However, the loss of the link between the precursor and the product ions combined with the high dimensionality of DIA data sets hinder accurate feature annotation. Here, we present LipidMS, an R package aimed to confidently identify lipid species in untargeted LC-DIA-MS. To this end, LipidMS combines a coelution score, which links precursor and fragment ions with fragmentation and intensity rules. Depending on the MS evidence reached by the identification function survey, LipidMS provides three levels of structural annotations: (i) “subclass level”, e.g., PG(34:1); (ii) “fatty acyl level”, e.g., PG(16:0_18:1); and (iii) “fatty acyl position level”, e.g., PG­(16:0/18:1). The comparison of LipidMS with freely available data dependent acquisition (DDA) and DIA identification tools showed that LipidMS provides significantly more accurate and structural informative lipid identifications. Finally, to exemplify the utility of LipidMS, we investigated the lipidomic serum profile of patients diagnosed with nonalcoholic steatohepatitis (NASH), which is the progressive form of nonalcoholic fatty liver disease, a disorder underlying a strong lipid dysregulation. As previously published, a significant decrease in lysophosphatidylcholines, phosphatidylcholines and cholesterol esters and an increase in phosphatidylethanolamines were observed in NASH patients. Remarkably, LipidMS allowed the identification of a new set of lipids that may be used for NASH diagnosis. Altogether, LipidMS has been validated as a tool to assist lipid identification in the LC-DIA-MS untargeted analysis of complex biological samples.

◥Progression on therapy in non-small cell lung carcinoma (NSCLC) is often evaluated radiographically, however, imagebased evaluation of said therapies may not distinguish disease progression due to intrinsic tumor drug resistance or inefficient tumor penetration of the drugs. Here we report that the inhibition of mutated EGFR promotes the secretion of a potent vasoconstrictor, endothelin-1 (EDN1), which continues to increase as the cells become resistant with a mesenchymal phenotype. As EDN1 and its receptor (EDNR) is linked to cancer progression, EDNRantagonists have been evaluated in several clinical trials with disappointing results. These trials were based on a hypothesis that the EDN1-EDNR axis activates the MAPK-ERK signaling pathway that is vital to the cancer cell survival; the trials were not designed to evaluate the impact of tumor-derived EDN1 in modifying tumor microenvironment or contributing to drug resistance. Ectopic overexpression of EDN1 in cells with mutated EGFR resulted in poor drug delivery and retarded growth in vivo but not in vitro. Intratumoral injection of recombinant EDN significantly reduced blood flow and subsequent gefitinib accumulation in xenografted EGFRmutant tumors. Furthermore, depletion of EDN1 or the use of endothelin receptor inhibitors bosentan and ambrisentan improved drug penetration into tumors and restored blood flow in tumorassociated vasculature. Correlatively, these results describe a simplistic endogenous yet previously unrealized resistance mechanism inherent to a subset of EGFR-mutant NSCLC to attenuate tyrosine kinase inhibitor delivery to the tumors by limiting drug-carrying blood flow and the drug concentration in tumors.Significance: EDNR antagonists can be repurposed to improve drug delivery in VEGFA-secreting tumors, which normally respond to TKI treatment by secreting EDN1, promoting vasoconstriction, and limiting blood and drug delivery.

Epigenetics has emerged as a new and promising field in recent years. Because there exists a need to find new biomarkers and improve diagnosis, prognosis, and drug response for inflammatory bowel diseases, the research on epigenetic biomarkers for molecular diagnostics encourages the translation of this field from the bench to the clinical practice. In this review, we present an overview of the current knowledge and its potential applicability of this emerging field in inflammatory bowel diseases.

BackgroundTranscranial magnetic stimulation (TMS) can modulate neural activity by evoking action potentials in subpopulations of cortical neurons. The TMS-induced electric field (E-field) can be simulated in subject-specific head models derived from MR images, but the spatial distribution of the E-field alone does not predict the physiological response. Coupling E-field models to populations of biophysically realistic neuron models yields insights into the activation mechanisms of TMS, but the significant computational cost associated with these models limits their use and eventual translation to clinically relevant applications.ObjectiveThe objective was to develop computationally efficient estimators of the activation thresholds of multi-compartmental cortical neuron models in response to TMS-induced E-field distributions.MethodsMulti-scale models combining anatomically accurate finite element method (FEM) simulations of the TMS E-field with layer-specific representations of cortical neurons were used to generate a large dataset of activation thresholds. 3D convolutional neural networks (CNNs) were trained on these data to predict the activation threshold of specific model neurons given the local E-field distribution. Using training and test data from different head models, the CNN estimator was compared to an approach using the uniform E-field approximation to estimate thresholds in the non-uniform TMS-induced E-field.ResultsThe 3D CNNs were more accurate than the uniform E-field approach, with mean absolute percent error (MAPE) on the test dataset below 2.5% compared to 5.9 – 9.8% with the uniform E-field approach. Further, there was a strong correlation between the CNN predicted and actual thresholds for all cell types (R2 > 0.96) compared to the uniform E-field approach (R2 = 0.62 – 0.91). The CNNs estimate thresholds with a 2 – 4 orders of magnitude reduction in the computational cost of the multi-compartmental neuron models.Conclusion3D CNNs can estimate rapidly and accurately the TMS activation thresholds of biophysically realistic neuron models using sparse samples of the local E-field, enabling simulating responses of large neuron populations or parameter space exploration on a personal computer.