Colorectal Cancer Detection via Metabolites and Machine Learning.

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 patient's 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.

Structure-selected RBM immunogens prime polyclonal memory responses that neutralize SARS-CoV-2 variants of concern.

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.

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 inaccessible nature of current diagnosis methods in some parts of the world. 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 created series of miRNA 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 classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, our 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 our 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 an additional method for diagnosing laryngeal cancer.

Artificial Intelligence in Drug Treatment.

The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.

A strategy for designing allosteric modulators of transcription factor dimerization.

Transcription factors (TFs) are fundamental in the regulation of gene expression in the development and differentiation of cells. They may act as oncogenes and when overexpressed in tumors become plausible targets for the design of antitumor agents. Homodimerization or heterodimerization of TFs are required for DNA binding and the association interface between subunits, for the design of allosteric modulators, appears as a privileged structure for the pharmacophore-based computational strategy. Based on this strategy, a set of compounds were earlier identified as potential suppressors of OLIG2 dimerization and found to inhibit tumor growth in a mouse glioblastoma cell line and in a whole-animal study. To investigate whether the antitumor activity is due to the predicted mechanism of action, we undertook a study of OLIG2 dimerization using fluorescence cross-correlation spectroscopy (FCCS) of live HEK cells transfected with 2 spectrally different OLIG2 clones. The selected compounds showed an effect with potency, which correlated with the earlier observed antitumor activity. The OLIG2 proteins showed change in diffusion time under compound treatment in line with dissociation from DNA. The data suggest a general approach of drug discovery based on the design of allosteric modulators of protein-protein interaction.

Machine-learning-based virtual screening to repurpose drugs for treatment of Candida albicans infection.

BACKGROUND: Approximately 30% of Candida genus isolates are resistant to all currently available antifungal drugs and it is highly important to develop new treatments. Additionally, many current drugs are toxic and cause unwanted side effects. 1,3-beta-glucan synthase is an essential enzyme that builds the cell walls of Candida. OBJECTIVES: Targeting CaFKS1, a subunit of the synthase, could be used to fight Candida. METHODS: In the present study, a machine-learning model based on chemical descriptors was trained to recognise drugs that inhibit CaFKS1. The model attained 96.72% accuracy for classifying between active and inactive drug compounds. Descriptors for FDA-approved and other drugs were calculated, and the model was used to predict the potential activity of these drugs against CaFKS1. RESULTS: Several drugs, including goserelin and icatibant, were detected as active with high confidence. Many of the drugs, interestingly, were gonadotrophin-releasing hormone (GnRH) antagonists or agonists. A literature search found that five of the predicted drugs inhibit Candida experimentally. CONCLUSIONS: This study yields promising drugs to be repurposed to combat Candida albicans infection. Future steps include testing the drugs on fungal cells in vitro.

Dynamic Cellular Cartography: Mapping the Local Determinants of Oligodendrocyte Transcription Factor 2 (OLIG2) Function in Live Cells Using Massively Parallel Fluorescence Correlation Spectroscopy Integrated with Fluorescence Lifetime Imaging Microscopy (mpFCS/FLIM).

Compartmentalization and integration of molecular processes through diffusion are basic mechanisms through which cells perform biological functions. To characterize these mechanisms in live cells, quantitative and ultrasensitive analytical methods with high spatial and temporal resolution are needed. Here, we present quantitative scanning-free confocal microscopy with single-molecule sensitivity, high temporal resolution (∼10 µs/frame), and fluorescence lifetime imaging capacity, developed by integrating massively parallel fluorescence correlation spectroscopy with fluorescence lifetime imaging microscopy (mpFCS/FLIM); we validate the method, use it to map in live cell location-specific variations in the concentration, diffusion, homodimerization, DNA binding, and local environment of the oligodendrocyte transcription factor 2 fused with the enhanced Green Fluorescent Protein (OLIG2-eGFP), and characterize the effects of an allosteric inhibitor of OLIG2 dimerization on these determinants of OLIG2 function. In particular, we show that cytoplasmic OLIG2-eGFP is largely monomeric and freely diffusing, with the fraction of freely diffusing OLIG2-eGFP molecules being fD,freecyt = (0.75 ± 0.10) and the diffusion time τD,freecyt = (0.5 ± 0.3) ms. In contrast, OLIG2-eGFP homodimers are abundant in the cell nucleus, constituting ∼25% of the nuclear pool, some fD,boundnuc = (0.65 ± 0.10) of nuclear OLIG2-eGFP is bound to chromatin DNA, whereas freely moving OLIG2-eGFP molecules diffuse at the same rate as those in the cytoplasm, as evident from the lateral diffusion times τD,freenuc = τD,freecyt = (0.5 ± 0.3) ms. OLIG2-eGFP interactions with chromatin DNA, revealed through their influence on the apparent diffusion behavior of OLIG2-eGFP, τD,boundnuc (850 ± 500) ms, are characterized by an apparent dissociation constant Kd,appOLIG2-DNA = (45 ± 30) nM. The apparent dissociation constant of OLIG2-eGFP homodimers was estimated to be Kd,app(OLIG2-eGFP)2 ≈ 560 nM. The allosteric inhibitor of OLIG2 dimerization, compound NSC 50467, neither affects OLIG2-eGFP properties in the cytoplasm nor does it alter the overall cytoplasmic environment. In contrast, it significantly impedes OLIG2-eGFP homodimerization in the cell nucleus, increasing five-fold the apparent dissociation constant, Kd,app,NSC50467(OLIG2-eGFP)2 ≈ 3 µM, thus reducing homodimer levels to below 7% and effectively abolishing OLIG2-eGFP specific binding to chromatin DNA. The mpFCS/FLIM methodology has a myriad of applications in biomedical research and pharmaceutical industry. For example, it is indispensable for understanding how biological functions emerge through the dynamic integration of location-specific molecular processes and invaluable for drug development, as it allows us to quantitatively characterize the interactions of drugs with drug targets in live cells.

Artificial intelligence in drug combination therapy.

Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.

Potential SARS-CoV-2 protease Mpro inhibitors: repurposing FDA-approved drugs.

Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin-the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

Finding distinctions between oral cancer and periodontitis using saliva metabolites and machine learning.

OBJECTIVE: The aim of this research is the study of metabolic pathways related to oral cancer and periodontitis along with development of machine-learning model for elucidation of these diseases based on saliva metabolites of patients. METHODS: Data mining, metabolomic pathways analysis, study of metabolite-gene networks related to these diseases. Machine-learning and deep-learning methods for development of the model for recognition of oral cancer versus periodontitis, using patients' saliva. RESULTS: The most accurate classifications between oral cancer and periodontitis were performed using neural networks, logistic regression and stochastic gradient descent confirmed by the separate 10-fold cross-validations. The best results were achieved by the deep-learning neural network with the TensorFlow program. Accuracy of the resulting model was 79.54%. The other methods, which did not rely on deep learning, were able to achieve comparable, although slightly worse results with respect to accuracy. CONCLUSION: Our results demonstrate a possibility to distinguish oral cancer from periodontal disease by analysis the saliva metabolites of a patient, using machine-learning methods. These findings may be useful in the development of a non-invasive method to aid care providers in determining between oral cancer and periodontitis quickly and effectively.

ANKRD9 is a metabolically-controlled regulator of IMPDH2 abundance and macro-assembly.

Members of a large family of Ankyrin Repeat Domain (ANKRD) proteins regulate numerous cellular processes by binding to specific protein targets and modulating their activity, stability, and other properties. The same ANKRD protein may interact with different targets and regulate distinct cellular pathways. The mechanisms responsible for switches in the ANKRDs' behavior are often unknown. We show that cells' metabolic state can markedly alter interactions of an ANKRD protein with its target and the functional outcomes of this interaction. ANKRD9 facilitates degradation of inosine monophosphate dehydrogenase 2 (IMPDH2), the rate-limiting enzyme in GTP biosynthesis. Under basal conditions ANKRD9 is largely segregated from the cytosolic IMPDH2 in vesicle-like structures. Upon nutrient limitation, ANKRD9 loses its vesicular pattern and assembles with IMPDH2 into rodlike filaments, in which IMPDH2 is stable. Inhibition of IMPDH2 activity with ribavirin favors ANKRD9 binding to IMPDH2 rods. The formation of ANKRD9/IMPDH2 rods is reversed by guanosine, which restores ANKRD9 associations with the vesicle-like structures. The conserved Cys109Cys110 motif in ANKRD9 is required for the vesicle-to-rods transition as well as binding and regulation of IMPDH2. Oppositely to overexpression, ANKRD9 knockdown increases IMPDH2 levels and prevents formation of IMPDH2 rods upon nutrient limitation. Taken together, the results suggest that a guanosine-dependent metabolic switch determines the mode of ANKRD9 action toward IMPDH2.

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials.

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 < .0001). The better outcome arm was predicted in 81% (PFS: N = 59/73, z = 5.24, P < .0001) and 71% (OS: N = 37/52, z = 2.91, P = .004) of randomized trials. The success of our algorithm to predict clinical outcome may be exploitable as a model to optimize clinical trial design with pharmaceutical agents.

Deep-learning- and pharmacophore-based prediction of RAGE inhibitors.

The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzheimer's disease. RAGE is a target with no crystallographic data on inhibitors in complex with RAGE, multiple hypothesized binding modes, and small amounts of activity data. The main objective of this study was to demonstrate the efficacy of deep-learning (DL) techniques on small bioactivity datasets, and to identify candidate inhibitors of RAGE. We applied transfer learning in the form of a semi-supervised molecular representation in order to address small dataset problems. To validate the candidate inhibitors, we examined them using more computationally expensive pharmacophore-modeling and docking techniques. We created a strong classifier of RAGE activity, producing 79 candidate inhibitors. These candidates agreed with docking models and were shown to have no significant statistical difference from pharmacophore-based results. The transfer-learning techniques used allow DL to generalize chemical features from small bioactivity datasets to a broader library of compounds with high accuracy. Furthermore, the DL model is able to handle multiple binding modes without explicit instructions. Our results demonstrate the potential of a broad family of DL techniques on bioactivity predictions.

Bovine leukemia virus relation to human breast cancer: Meta-analysis.

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.

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning.

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.

Recognition of early and late stages of bladder cancer using metabolites and machine learning.

INTRODUCTION: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function. OBJECTIVES: The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa. METHODS: Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity® Pathway Analysis (IPA®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function. RESULTS: We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set. CONCLUSION: By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

Next-Generation Sequencing of Circulating Tumor DNA Reveals Frequent Alterations in Advanced Hepatocellular Carcinoma.

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.

A de novo compound targeting α-synuclein improves deficits in models of Parkinson's disease.

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.

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.

Discovery of substituted 4-aminoquinazolines as selective Toll-like receptor 4 ligands.

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.