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BACKGROUND: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives. OBJECTIVE: This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways. RESULTS: The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The models effectiveness was further validated using independent datasets, confirming its potential clinical utility. DISCUSSION: Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives. CONCLUSIONS: This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers.

Life-threatening thrombotic events and neurological symptoms are prevalent in COVID-19 and are persistent in patients with long COVID experiencing post-acute sequelae of SARS-CoV-2 infection1-4. Despite the clinical evidence1,5-7, the underlying mechanisms of coagulopathy in COVID-19 and its consequences in inflammation and neuropathology remain poorly understood and treatment options are insufficient. Fibrinogen, the central structural component of blood clots, is abundantly deposited in the lungs and brains of patients with COVID-19, correlates with disease severity and is a predictive biomarker for post-COVID-19 cognitive deficits1,5,8-10. Here we show that fibrin binds to the SARS-CoV-2 spike protein, forming proinflammatory blood clots that drive systemic thromboinflammation and neuropathology in COVID-19. Fibrin, acting through its inflammatory domain, is required for oxidative stress and macrophage activation in the lungs, whereas it suppresses natural killer cells, after SARS-CoV-2 infection. Fibrin promotes neuroinflammation and neuronal loss after infection, as well as innate immune activation in the brain and lungs independently of active infection. A monoclonal antibody targeting the inflammatory fibrin domain provides protection from microglial activation and neuronal injury, as well as from thromboinflammation in the lung after infection. Thus, fibrin drives inflammation and neuropathology in SARS-CoV-2 infection, and fibrin-targeting immunotherapy may represent a therapeutic intervention for patients with acute COVID-19 and long COVID.

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs-piwi-RNAs (piRNAs)-and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC). Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers. With this data, we developed a machine learning (ML) algorithm and created 1020 different piRNA sequence descriptors. With the Naïve Bayes Multinomial classifier, we were able to isolate the 27 most influential sequence descriptors and achieve an accuracy of 96.4%. To test the validity of our model, we used data from piRBase with known associations with CRC that we did not use to train the ML model. We were able to achieve an accuracy of 85.7% with these new independent data. To further validate our model, we also tested data from unrelated diseases, including piRNAs with a correlation to breast cancer and no proven correlation to CRC. The model scored 44.4% on these piRNAs, showing that it can identify a difference between biomarkers of CRC and biomarkers of other diseases. The final results show that our model is an effective tool for diagnosing colorectal cancer. We believe that in the future, this model will prove useful for colorectal cancer and other diseases diagnostics.

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patients body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the models accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.

We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a divide and conquer strategy gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step.

Several lines of evidence suggest that a characteristic of the neuropathology of Alzheimer's disease (AD) is the aggregation of the amyloid beta peptides (Aβ), fragments of the human amyloid precursor protein (hAPP). The dominating species are the Aβ40 and Aβ42 fragments with 40 and 42 amino acids, respectively. Aβ initially forms soluble oligomers that continue to expand to protofibrils, suggestively the neurotoxic intermediates, and thereafter turn into insoluble fibrils that are markers of the disease. Using the powerful tool of pharmacophore simulation, we selected small molecules not known to possess central nervous system (CNS) activity but that might interact with Aβ aggregation, from the NCI Chemotherapeutic Agents Repository, Bethesda, MD. We assessed the activity of these compounds on Aβ aggregation using the thioflavin T fluorescence correlation spectroscopy (ThT-FCS) assay. Förster resonance energy transfer-based fluorescence correlation spectroscopy (FRET-FCS) was used to characterize the dose-dependent activity of selected compounds at an early stage of Aβ aggregation. Transmission electron microscopy (TEM) confirmed that the interfering substances block fibril formation and identified the macrostructures of Aβ aggregates formed in their presence. We first found three compounds generating protofibrils with branching and budding never observed in the control. One compound generated a two-dimensional sheet structure and another generated a double-stranded filament. Importantly, these compounds generating protofibrils with altered macrostructure protected against Aβ-induced toxicity in a cell model while showing no toxicity in a model of cognition in normal mice. The data suggest that the active compounds act as decoys turning the aggregation into nontoxic trajectories and pointing toward novel approaches to therapy.

Severe acute respiratory syndrome coronavirus 2 (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. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 induced cytokine storm pathway. We developed machine-learning (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. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. 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. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.

PURPOSE: Patients with diabetes mellitus have an elevated chance of developing cataracts, a degenerative vision-impairing condition often needing surgery. The process of the reduction of glucose to sorbitol in the lens of the human eye that causes cataracts is managed by the Aldose Reductase Enzyme (AR), and it is been found that AR inhibitors may mitigate the onset of diabetic cataracts. There exists a large pool of natural and synthetic AR inhibitors that can prevent diabetic complications, and the development of a machine-learning (ML) prediction model may bring new AR inhibitors with better characteristics into clinical use. METHODS: Using known AR inhibitors and their chemical-physical descriptors we created the ML model for prediction of new AR inhibitors. The predicted inhibitors were tested by computational docking to the binding site of AR. RESULTS: Using cross-validation in order to find the most accurate ML model, we ended with final cross-validation accuracy of 90%. Computational docking testing of the predicted inhibitors gave a high level of correlation between the ML prediction score and binding free energy. CONCLUSIONS: Currently known AR inhibitors are not used yet for patients for several reasons. We think that new predicted AR inhibitors have the potential to possess more favorable characteristics to be successfully implemented after clinical testing. Exploring new inhibitors can improve patient well-being and lower surgical complications all while decreasing long-term medical expenses.

Bcl2-associated athanogene 3 (BAG3) is a multifunctional cochaperone responsible for protein quality control within cells. BAG3 interacts with chaperones HSPB8 and Hsp70 to transport misfolded proteins to the Microtubule Organizing Center (MTOC) and degrade them in autophagosomes in a process known as Chaperone Assisted Selective Autophagy (CASA). Mutations in the second conserved IPV motif of BAG3 are known to cause Dilated Cardiomyopathy (DCM) by inhibiting adequate removal of non-native proteins. The proline 209 to leucine (P209L) BAG3 mutant in particular causes the aggregation of BAG3 and misfolded proteins as well as the sequestration of essential chaperones. The exact mechanisms of protein aggregation in DCM are unknown. However, the similar presence of insoluble protein aggregates in Charcot-Marie-Tooth disease type 2 (CMT2) induced by the proline 182 to leucine (P182L) HSPB1 mutant points to a possible avenue for future research: IPV motif. In this review, we summarize the molecular mechanisms of CASA and the currently known pathological effects of mutated BAG3 in DCM. Additionally, we will provide insight on the importance of the IPV motif in protein aggregation by analyzing a potential association between DCM and CMT2.

Successful control of the COVID-19 pandemic depends on vaccines that prevent transmission. The full-length Spike protein is highly immunogenic but the majority of antibodies do not target the virus: ACE2 interface. In an effort to affect the quality of the antibody response focusing it to the receptor-binding motif (RBM) we generated a series of conformationally-constrained immunogens by inserting solvent-exposed RBM amino acid residues into hypervariable loops of an immunoglobulin molecule. Priming C57BL/6 mice with plasmid (p)DNA encoding these constructs yielded a rapid memory response to booster immunization with recombinant Spike protein. Immune sera antibodies bound strongly to the purified receptor-binding domain (RBD) and Spike proteins. pDNA primed for a consistent response with antibodies efficient at neutralizing authentic WA1 virus and three variants of concern (VOC), B.1.351, B.1.617.2, and BA.1. We demonstrate that immunogens built on structure selection can be used to influence the quality of the antibody response by focusing it to a conserved site of vulnerability shared between wildtype virus and VOCs, resulting in neutralizing antibodies across variants.

Bovine leukemia virus (BLV) is a virus that infects cattle around the world and is very similar to the human T-cell leukemia virus (HTLV), which causes adult T-cell leukemia/lymphoma (ATL). Recently, presence of BLV DNA and protein was demonstrated in commercial bovine products and in humans. BLV DNA is generally found at higher rates in humans who have or will develop breast cancer, according to research done with subjects from several countries. These findings have led to a hypothesis that BLV transmission plays a role in breast cancer oncogenesis in humans. Here we summarize the current knowledge in the field.

Predicting oncologic outcome is challenging due to the diversity of cancer histologies and the complex network of underlying biological factors. In this study, we determine whether machine learning (ML) can extract meaningful associations between oncologic outcome and clinical trial, drug-related biomarker and molecular profile information. We analyzed therapeutic clinical trials corresponding to 1102 oncologic outcomes from 104 758 cancer patients with advanced colorectal adenocarcinoma, pancreatic adenocarcinoma, melanoma and nonsmall-cell lung cancer. For each intervention arm, a dataset with the following attributes was curated: line of treatment, the number of cytotoxic chemotherapies, small-molecule inhibitors, or monoclonal antibody agents, drug class, molecular alteration status of the clinical arm's population, cancer type, probability of drug sensitivity (PDS) (integrating the status of genomic, transcriptomic and proteomic biomarkers in the population of interest) and outcome. A total of 467 progression-free survival (PFS) and 369 overall survival (OS) data points were used as training sets to build our ML (random forest) model. Cross-validation sets were used for PFS and OS, obtaining correlation coefficients (r) of 0.82 and 0.70, respectively (outcome vs model's parameters). A total of 156 PFS and 110 OS data points were used as test sets. The Spearman correlation (rs ) between predicted and actual outcomes was statistically significant (PFS: rs = 0.879, OS: rs = 0.878, P

A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing compounds (PTCSC) have been developed in order to restore protein translation by allowing the incorporation of an amino acid in place of a stop codon. However, limitations exist in terms of efficacy and toxicity. To identify new compounds that have PTC-suppressing ability, we selected and clustered existing PTCSC, allowing for the construction of a common pharmacophore model. Machine learning (ML) and deep learning (DL) models were developed for prediction of new PTCSC based on known compounds. We conducted a search of the NCI compounds database using the pharmacophore-based model and a search of the DrugBank database using pharmacophore-based, ML and DL models. Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, and DL searches. Our results suggest notable correspondence of the pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing compounds.

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.

BACKGROUND:Because imaging has a high sensitivity to diagnose hepatocellular carcinoma (HCC) and tissue biopsies carry risks such as bleeding, the latter are often not performed in HCC. Blood-derived circulating tumor DNA (ctDNA) analysis can identify somatic alterations, but its utility has not been characterized in HCC. MATERIALS AND METHODS:We evaluated 14 patients with advanced HCC (digital ctDNA sequencing [68 genes]). Mutant relative to wild-type allele fraction was calculated. RESULTS:All patients (100%) had somatic alterations (median = 3 alterations/patient [range, 1-8]); median mutant allele fraction, 0.29% (range, 0.1%-37.77%). Mutations were identified in several genes: TP53 (57% of patients), CTNNB1 (29%), PTEN (7%), CDKN2A (7%), ARID1A (7%), and MET (7%); amplifications, in CDK6 (14%), EGFR (14%), MYC (14%), BRAF (7%), RAF1 (7%), FGFR1 (7%), CCNE1 (7%), PIK3CA (7%), and ERBB2/HER2 (7%). Eleven patients (79%) had ≥1 theoretically actionable alteration. No two patients had identical genomic portfolios, suggesting the need for customized treatment. A patient with a CDKN2A-inactivating and a CTNNB1-activating mutation received matched treatment: palbociclib (CDK4/6 inhibitor) and celecoxib (COX-2/Wnt inhibitor); des-gamma-carboxy prothrombin level decreased by 84% at 2 months (1,410 to 242 ng/mL [normal: ≤7.4 ng/mL]; alpha fetoprotein [AFP] low at baseline). A patient with a PTEN-inactivating and a MET-activating mutation (an effect suggested by in silico molecular dynamic simulations) received sirolimus (mechanistic target of rapamycin inhibitor) and cabozantinib (MET inhibitor); AFP declined by 63% (8,320 to 3,045 ng/mL [normal: 0-15 ng/mL]). CONCLUSION:ctDNA derived from noninvasive blood tests can provide exploitable genomic profiles in patients with HCC. IMPLICATIONS FOR PRACTICE:This study reports that blood-derived circulating tumor DNA can provide therapeutically exploitable genomic profiles in hepatocellular cancer, a malignancy that is known to be difficult to biopsy.

"Liquid biopsy" approaches analyzing cell-free DNA (cfDNA) from the blood of patients with cancer are increasingly utilized in clinical practice. However, it is not yet known whether cfDNA sequencing from large cohorts of patients with cancer can detect genomic alterations at frequencies similar to those observed by direct tumor sequencing, and whether this approach can generate novel insights. Here, we report next-generation sequencing data from cfDNA of 1,397 patients with colorectal cancer. Overall, frequencies of genomic alterations detected in cfDNA were comparable to those observed in three independent tissue-based colorectal cancer sequencing compendia. Our analysis also identified a novel cluster of extracellular domain (ECD) mutations in EGFR, mediating resistance by blocking binding of anti-EGFR antibodies. Patients with EGFR ECD mutations displayed striking tumor heterogeneity, with 91% harboring multiple distinct resistance alterations (range, 1-13; median, 4). These results suggest that cfDNA profiling can effectively define the genomic landscape of cancer and yield important biological insights.Significance: This study provides one of the first examples of how large-scale genomic profiling of cfDNA from patients with colorectal cancer can detect genomic alterations at frequencies comparable to those observed by direct tumor sequencing. Sequencing of cfDNA also generated insights into tumor heterogeneity and therapeutic resistance and identified novel EGFR ectodomain mutations. Cancer Discov; 8(2); 164-73. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.

Eosinophilic cystitis is a rare manifestation of hypereosinophilia and a cause of morbidity, including dysuria and hematuria. Although some cases can be attributed to infection or allergy, most cases are assessed to be idiopathic and treated with corticosteroids. However, hypereosinophilia can also be due to actionable clonal molecular alterations in the haematopoietic cells, similar to other myeloproliferative neoplasms. Common mutations associated with eosonophilic syndromes are of platelet-derived growth factor receptor α or β or c-kit, though other pathogenic mutations have been found by next generation sequencing. Determination of a specific mutation may therefore identify clonality and refine treatment of some cases. Here we review the molecular features of eosinophilic disorders. We also describe the use of a liquid biopsy of circulating cell-free DNA in the workup of a case of eosinophilic cystitis in which next generation sequencing of cell-free DNA showed a BRAF I463T mutation. In silico modeling supports the functional impact and potential clinical relevance of BRAF I463T.

Immunotherapy with checkpoint inhibitors, such as antibodies blocking the programmed cell-death receptor-1 (PD-1), has resulted in remarkable responses in patients having traditionally refractory cancers. Although response to PD-1 inhibitors correlates with PD-1 ligand (PD-L1 or PD-L2) expression, PD-1 ligand positivity represents only a part of the predictive model necessary for selecting patients predisposed to respond to immunotherapy. We used all genomic, transcriptomic, proteomic and phenotypic data related to 8,475 pan-cancer samples available in The Cancer Genome Atlas (TCGA) and conducted a logistic regression analysis based on a large set of variables, such as microsatellite instability (MSI-H), mismatch repair (MMR) alterations, polymerase δ (POLD1) and polymerase ε (POLE) mutations, activation-induced/apolipoprotein-B editing cytidine deaminases (AID/APOBEC) alterations, lymphocyte markers and mutation burden estimates to determine independent factors that associate with PD-1 ligand overexpression. PD-1 ligand overexpression was independently and significantly correlated with overexpression of and mutations in APOBEC3 paralogs. Additionally, while high tumor mutation burden and overexpression of PD-L1 have been previously correlated with each other, we demonstrate that the specific mutation pattern caused by APOBEC enzymes and called kataegis-rather than overall mutation burden, MSI-H or MMR alterations-correlates independently with PD-L1/PD-L2 expression. These observations suggest that APOBEC3 alterations, APOBEC3 overexpression and kataegis play an important role in the regulation of PD-1 ligand overexpression, and thus, their relationship with immune checkpoint inhibitor response warrants exploration.

Background: Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. The diagnosis through these methods is sometimes delayed and unreliable, leading to increased chances of mortality. Objectives: The objective of this study was to identify the biomarkers, including blood metabolites and genes, related to feline mammary carcinoma, study their relationships, and develop a machine learning (ML) model for the early diagnosis of the disease. Methods: We analyzed the blood metabolites of felines with mammary carcinoma using the pathway analysis feature in MetaboAnalyst software, v. 5.0. We utilized machine-learning (ML) methods to recognize FMC using the blood metabolites of sick patients. Results: The metabolic pathways that were elucidated to be associated with this disease include alanine, aspartate and glutamate metabolism, Glutamine and glutamate metabolism, Arginine biosynthesis, and Glycerophospholipid metabolism. Furthermore, we also elucidated several genes that play a significant role in the development of FMC, such as ERBB2, PDGFA, EGFR, FLT4, ERBB3, FIGF, PDGFC, PDGFB through STRINGdb, a database of known and predicted protein-protein interactions, and MetaboAnalyst 5.0. The best-performing ML model was able to predict metabolite class with an accuracy of 85.11%. Conclusion: Our findings demonstrate that the identification of the biomarkers associated with FMC and the affected metabolic pathways can aid in the early diagnosis of feline mammary carcinoma. [ABSTRACT FROM AUTHOR]

Abnormal accumulation and propagation of the neuronal protein α-synuclein has been hypothesized to underlie the pathogenesis of Parkinson's disease, dementia with Lewy bodies and multiple system atrophy. Here we report a de novo-developed compound (NPT100-18A) that reduces α-synuclein toxicity through a novel mechanism that involves displacing α-synuclein from the membrane. This compound interacts with a domain in the C-terminus of α-synuclein. The E83R mutation reduces the compound interaction with the 80-90 amino acid region of α-synuclein and prevents the effects of NPT100-18A. In vitro studies showed that NPT100-18A reduced the formation of wild-type α-synuclein oligomers in membranes, reduced the neuronal accumulation of α-synuclein, and decreased markers of cell toxicity. In vivo studies were conducted in three different α-synuclein transgenic rodent models. Treatment with NPT100-18A ameliorated motor deficits in mThy1 wild-type α-synuclein transgenic mice in a dose-dependent manner at two independent institutions. Neuropathological examination showed that NPT100-18A decreased the accumulation of proteinase K-resistant α-synuclein aggregates in the CNS and was accompanied by the normalization of neuronal and inflammatory markers. These results were confirmed in a mutant line of α-synuclein transgenic mice that is prone to generate oligomers. In vivo imaging studies of α-synuclein-GFP transgenic mice using two-photon microscopy showed that NPT100-18A reduced the cortical synaptic accumulation of α-synuclein within 1 h post-administration. Taken together, these studies support the notion that altering the interaction of α-synuclein with the membrane might be a feasible therapeutic approach for developing new disease-modifying treatments of Parkinson's disease and other synucleinopathies.

Mammalian cells have two influx Cu transporters that form trimers in membranes. CTR1 is the high affinity transporter that resides largely in the plasma membrane, and CTR2 is the low affinity transporter that is primarily associated with vesicular structures inside the cell. The major differences between CTR1 and CTR2 are that CTR1 contains a HIS/MET-rich domain N-terminal of the METS that participate in the first two stacked rings that form the pore, and a longer C-terminal tail that includes a Cu binding HIS-CYS-HIS (HCH) motif right at the end. It has been reported that CTR1 and CTR2 are physically associated with each other in the cell. We used the CRISPR-Cas9 technology to knock out either CTR1 or CTR2 in fully malignant HEK293T and OVCAR8 human ovarian cancer cells to investigate the interaction of CTR1 and CTR2. We report here that the level of CTR2 protein is markedly decreased in CTR1 knockout clones while the CTR2 transcript level remains unchanged. CTR2 was found to be highly ubiquitinated in the CTR1 knock out cells, and inhibition of the proteasome prevented the degradation of CTR2 when CTR1 was not present while inhibition of autophagy had no effect. Re-expression of CTR1 rescued CTR2 from degradation in the CTR1 knockout cells. We conclude that CTR1 is essential to maintain the stability of CTR2 and that in the absence of CTR1 CTR2 is degraded by the proteasome. This reinforces the concept that the functions of CTR1 and CTR2 are inter-dependent within the Cu homeostasis system.

Cisplatin (cDDP) is known to bind to the CXXC motif of proteins containing a ferrodoxin-like fold but little is known about its ability to interact with other Cu-binding proteins. MEK1/2 has recently been identified as a Cu-dependent enzyme that does not contain a CXXC motif. We found that cDDP bound to and inhibited the activity of recombinant MEK1 with an IC50 of 0.28 μM and MEK1/2 in whole cells with an IC50 of 37.4 μM. The inhibition of MEK1/2 was relieved by both Cu+1 and Cu+2 in a concentration-dependent manner. cDDP did not inhibit the upstream pathways responsible for activating MEK1/2, and did not cause an acute depletion of cellular Cu that could account for the reduction in MEK1/2 activity. cDDP was found to bind MEK1/2 in whole cells and the extent of binding was augmented by supplementary Cu and reduced by Cu chelation. Molecular modeling predicts 3 Cu and cDDP binding sites and quantum chemistry calculations indicate that cDDP would be expected to displace Cu from each of these sites. We conclude that, at clinically relevant concentrations, cDDP binds to and inhibits MEK1/2 and that both the binding and inhibitory activity are related to its interaction with Cu bound to MEK1/2. This may provide the basis for useful interactions of cDDP with other drugs that inhibit MAPK pathway signaling.

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.

The Toll-like receptors (TLRs) are critical components of the innate immune system that regulate immune recognition in part through NF-κB activation. A human cell-based high throughput screen (HTS) revealed substituted 4-aminoquinazolines to be small molecular weight activators of NF-κB. The most potent hit compound predominantly stimulated through the human TLR4/MD2 complex, and had less activity with the mouse TLR4/MD2. There was no activity with other TLRs and the TLR4 activation was MD-2 dependent and CD14 independent. Synthetic modifications of the quinazoline scaffold at the 2 and 4 positions revealed trends in structure-activity relationships with respect to TLR dependent production of the NF-κB associated cytokine IL-8 in human peripheral blood mononuclear cells, as well as IL-6 in mouse antigen presenting cells. Furthermore, the hit compound in this series also activated the interferon signaling pathway resulting in type I interferon production. Substitution at the O-phenyl moiety with groups such as bromine, chlorine and methyl resulted in enhanced immunological activity. Computational studies indicated that the 4-aminoquinazoline compounds bind primarily to human MD-2 in the TLR4/MD-2 complex. These small molecules, which preferentially stimulate human rather than mouse innate immune cells, may be useful as adjuvants or immunotherapeutic agents.

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.

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.

Clearance of fibrin through proteolytic degradation is a critical step of matrix remodeling that contributes to tissue repair in a variety of pathological conditions, such as stroke, atherosclerosis, and pulmonary disease. However, the molecular mechanisms that regulate fibrin deposition are not known. Here, we report that the p75 neurotrophin receptor (p75(NTR)), a TNF receptor superfamily member up-regulated after tissue injury, blocks fibrinolysis by down-regulating the serine protease, tissue plasminogen activator (tPA), and up-regulating plasminogen activator inhibitor-1 (PAI-1). We have discovered a new mechanism in which phosphodiesterase PDE4A4/5 interacts with p75(NTR) to enhance cAMP degradation. The p75(NTR)-dependent down-regulation of cAMP results in a decrease in extracellular proteolytic activity. This mechanism is supported in vivo in p75(NTR)-deficient mice, which show increased proteolysis after sciatic nerve injury and lung fibrosis. Our results reveal a novel pathogenic mechanism by which p75(NTR) regulates degradation of cAMP and perpetuates scar formation after injury.