Molecular determinants of drug-specific sensitivity for epidermal growth factor receptor (EGFR) exon 19 and 20 mutants in non-small cell lung cancer.

We hypothesized that aberrations activating epidermal growth factor receptor (EGFR) via dimerization would be more sensitive to anti-dimerization agents (e.g., cetuximab). EGFR exon 19 abnormalities (L747_A750del; deletes amino acids LREA) respond to reversible EGFR kinase inhibitors (TKIs). Exon 20 in-frame insertions and/or duplications (codons 767 to 774) and T790M mutations are clinically resistant to reversible/some irreversible TKIs. Their impact on protein function/therapeutic actionability are not fully elucidated.In our study, the index patient with non-small cell lung cancer (NSCLC) harbored EGFR D770_P772del_insKG (exon 20). A twenty patient trial (NSCLC cohort) (cetuximab-based regimen) included two participants with EGFR TKI-resistant mutations ((i) exon 20 D770>GY; and (ii) exon 19 LREA plus exon 20 T790M mutations). Structural modeling predicted that EGFR exon 20 anomalies (D770_P772del_insKG and D770>GY), but not T790M mutations, stabilize the active dimer configuration by increasing the interaction between the kinase domains, hence sensitizing to an agent preventing dimerization. Consistent with predictions, the two patients harboring D770_P772del_insKG and D770>GY, respectively, responded to an EGFR antibody (cetuximab)-based regimen; the T790M-bearing patient showed no response to cetuximab combined with erlotinib. In silico modeling merits investigation of its ability to optimize therapeutic selection based on structural/functional implications of different aberrations within the same gene

p75 neurotrophin receptor is a clock gene that regulates oscillatory components of circadian and metabolic networks.

The p75 neurotrophin receptor (p75(NTR)) is a member of the tumor necrosis factor receptor superfamily with a widespread pattern of expression in tissues such as the brain, liver, lung, and muscle. The mechanisms that regulate p75(NTR) transcription in the nervous system and its expression in other tissues remain largely unknown. Here we show that p75(NTR) is an oscillating gene regulated by the helix-loop-helix transcription factors CLOCK and BMAL1. The p75(NTR) promoter contains evolutionarily conserved noncanonical E-box enhancers. Deletion mutagenesis of the p75(NTR)-luciferase reporter identified the -1039 conserved E-box necessary for the regulation of p75(NTR) by CLOCK and BMAL1. Accordingly, gel-shift assays confirmed the binding of CLOCK and BMAL1 to the p75(NTR-)1039 E-box. Studies in mice revealed that p75(NTR) transcription oscillates during dark and light cycles not only in the suprachiasmatic nucleus (SCN), but also in peripheral tissues including the liver. Oscillation of p75(NTR) is disrupted in Clock-deficient and mutant mice, is E-box dependent, and is in phase with clock genes, such as Per1 and Per2. Intriguingly, p75(NTR) is required for circadian clock oscillation, since loss of p75(NTR) alters the circadian oscillation of clock genes in the SCN, liver, and fibroblasts. Consistent with this, Per2::Luc/p75(NTR-/-) liver explants showed reduced circadian oscillation amplitude compared with those of Per2::Luc/p75(NTR+/+). Moreover, deletion of p75(NTR) also alters the circadian oscillation of glucose and lipid homeostasis genes. Overall, our findings reveal that the transcriptional activation of p75(NTR) is under circadian regulation in the nervous system and peripheral tissues, and plays an important role in the maintenance of clock and metabolic gene oscillation

APOBEC-related mutagenesis and neo-peptide hydrophobicity: implications for response to immunotherapy.

Tumor-associated neo-antigens are mutated peptides that allow the immune system to recognize the affected cell as foreign. Cells carrying excessive mutation load often develop mechanisms of tolerance. PD-L1/PD-1 checkpoint immunotherapy is a highly promising approach to overcome these protective signals and induce tumor shrinkage. Yet, the nature of the neo-antigens driving those beneficial responses remains unclear. Here, we show that APOBEC-related mutagenesis - a mechanism at the crossroads between anti-viral immunity and endogenous nucleic acid editing - increases neo-peptide hydrophobicity (a feature of immunogenicity), as demonstrated by in silico computation and in the TCGA pan-cancer cohort, where APOBEC-related mutagenesis was also strongly associated with immune marker expression. Moreover, APOBEC-related mutagenesis correlated with immunotherapy response in a cohort of 99 patients with diverse cancers, and this correlation was independent of the tumor mutation burden (TMB). Combining APOBEC-related mutagenesis estimate and TMB resulted in greater predictive ability than either parameter alone. Based on these results, further investigation of APOBEC-related mutagenesis as a marker of response to anti-cancer checkpoint blockade is warranted

Role of Synucleins in Alzheimer’s Disease

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common causes of dementia and movement disorders in the elderly. While progressive accumulation of oligomeric amyloid-β protein (Aβ) has been identified as one of the central toxic events in AD leading to synaptic dysfunction, accumulation of α-synuclein (α-syn) resulting in the formation of oligomers has been linked to PD. Most of the studies in AD have been focused on investigating the role of Aβ and Tau; however, recent studies suggest that α-syn might also play a role in the pathogenesis of AD. For example, fragments of α-syn can associate with amyloid plaques and Aβ promotes the aggregation of α-syn in vivo and worsens the deficits in α-syn tg mice. Moreover, α-syn has also been shown to accumulate in limbic regions in AD, Down’s syndrome, and familial AD cases. Aβ and α-syn might directly interact under pathological conditions leading to the formation of toxic oligomers and nanopores that increase intracellular calcium. The interactions between Aβ and α-syn might also result in oxidative stress, lysosomal leakage, and mitochondrial dysfunction. Thus, better understanding the steps involved in the process of Aβ and α-syn aggregation is important in order to develop intervention strategies that might prevent or reverse the accumulation of toxic proteins in AD

Mechanisms of Hybrid Oligomer Formation in the Pathogenesis of Combined Alzheimer's and Parkinson's Diseases

Background: Misfolding and pathological aggregation of neuronal proteins has been proposed to play a critical role in the pathogenesis of neurodegenerative disorders. Alzheimer’s disease (AD) and Parkinson’s disease (PD) are frequent neurodegenerative diseases of the aging population. While progressive accumulation of amyloid b protein (Ab) oligomers has been identified as one of the central toxic events in AD, accumulation of a-synuclein (a-syn) resulting in the formation of oligomers and protofibrils has been linked to PD and Lewy body Disease (LBD). We have recently shown that Ab promotes a-syn aggregation and toxic conversion in vivo, suggesting that abnormal interactions between misfolded proteins might contribute to disease pathogenesis. However the molecular characteristics and consequences of these interactions are not completely clear. Methodology/Principal Findings: In order to understand the molecular mechanisms involved in potential Ab/a-syn interactions, immunoblot, molecular modeling, and in vitro studies with a-syn and Ab were performed. We showed in vivo in the brains of patients with AD/PD and in transgenic mice, Ab and a-synuclein co-immunoprecipitate and form complexes. Molecular modeling and simulations showed that Ab binds a-syn monomers, homodimers, and trimers, forming hybrid ringlike pentamers. Interactions occurred between the N-terminus of Ab and the N-terminus and C-terminus of a-syn. Interacting a-syn and Ab dimers that dock on the membrane incorporated additional a-syn molecules, leading to th

New inhibitors of p38 mitogen-activated protein kinase: Repurposing of existing drugs with deep learning

P38-alpha (MAPK14) is a protein kinase that is implicated in the pathological mechanisms of BAG3 P209L myofibrillar myopathy, cancers, Alzheimer’s disease and other diseases like rheumatoid arthritis. Inhibition of p38 has shown promise as treatment for these diseases. Traditional drug discovery methods were unable to create both effective and safe small molecule inhibitors, so we used machine learning to elucidate potential p38 blockers from existing FDA-approved drugs. Using available bioactivity data, we determined the best existing p38 inhibitors and applied fingerprint clustering to isolate the compounds with similar structures. Descriptors were calculated for these clustered compounds and the most important of these descriptors were determined through a machine-learning based feature selection algorithm. This data served as the training set for a deep neural network that was fine-tuned to a 92% validation accuracy. The neural network model was applied to a database of FDA-approved drugs, revealing 149 potential p38 inhibitors, whose efficacy were confirmed by docking simulations to be statistically significantly higher than random FDA drugs and slightly higher than known inhibitors. Our study not only reveals potential treatments for p38-mediated diseases but also demonstrates the capability of integrating various machine-learning techniques and computational algorithms to predict novel functions of existing pharmaceuticals

Repurposing of drugs for combined treatment of COVID 19 cytokine storm using machine learning

Context: SARS CoV 2 induced cytokine storm is the major cause of COVID 19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. Objective: To elucidate using machine learning (ML) the set of drugs targeting a group of proteins involved in the mechanism of cytokine storm. Methods: We selected for targeting five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor Kappa B (NF B), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3) that are involved in the SARS CoV 2 induced cytokine storm pathway. We developed ML models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID 19. Results: We identified twenty drugs that are active for four proteins and eight drugs active for five proteins. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein–ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. Conclusions: It is possible to elucidate the drugs, targeting simultaneously several proteins related to cytokine production to treat the cytokine storm in COVID 19 patients

Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model

Purpose Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the expensiveness and inaccessible nature of current diagnosis methods. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers. Methods In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a calculated miRNAs’ attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs. Results Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree (HT) classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, HT-based model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in HT-based model to understand their relationship with cancer proliferation or suppression pathways. Conclusion Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as a cost-effective and accessible method for diagnosing laryngeal cancer