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Background: Spinal cord stimulation (SCS) is a highly effective therapy for treating and management of refractory chronic pain. However, complex nature of pain and infrequent in-clinic visits, determining subject’s long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to SCS therapy. Method: Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects and the sub-study designed for collecting additional wearables data on a subset of participants for up to six months after SCS implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict SCS therapy outcome based on the subject’s response to NRS or PGIC. Results: Principal component analysis results showed that psychological aspects of pain were closely associated with heart rate variability, while movement-related measures were closely associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted both PGIC and NRS outcomes with high accuracy in the absence of subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions, reflects an overall change since the study onset and could be a better predictor of long-term therapy outcome. Conclusions: The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient’s response to therapy and their general well-being.

Background Spinal cord stimulation (SCS) is a highly effective therapy for treatment of chronic pain, however due to the complexity of pain, measuring a subject’s long-term response to the therapy remains difficult. Frequent measurement of patient-reported outcomes (PROs) to reflect multiple aspects of subjects’ pain is a crucial step in determining therapy outcomes. However, collecting full-length PROs is burdensome for both patients and clinicians. The objective of this work is to investigate the feasibility of using limited sets of outcomes from multiple PROs to accurately characterize subjects’ response to SCS therapy. Methods Validated PROs were used to capture pain, physical function and disability, as well as psychometric, satisfaction, and global health metrics. PROs were collected from 509 patients implanted with a neurostimulator enrolled in the post-market REALITY study. Combination of Pearson’s correlation and factor analysis was used to eliminate highly correlated questions and find the minimal meaningful set of questions within the predefined domains of each scale. Results The shortened versions of the questionnaires presented almost identical accuracy for classifying SCS therapy outcome as compared to the validated full-length versions. In addition, principal component analysis was performed on all the PROs and showed a robust clustering of pain intensity, psychological factors, physical function, and sleep across multiple PROs. A selected set of questions captured from multiple PROs can provide adequate information for measuring SCS therapy outcomes. Conclusions These shorter and more targeted PROs could result in better patient engagement, enhanced and more frequent data collection processes for digital health platforms, and ultimately increased patient satisfaction with SCS therapy. Trial registration Data for our analysis were extracted from the ongoing prospective, multicenter, international REALITY (Long-Term Real-World Outcomes Study on Patients Implanted with a Neurostimulator) study (NCT03876054).

Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, treatment response to SCS therapy may vary among people with chronic pain due to diverse pain perspectives amongst them. While, currently, chronic pain is evaluated by clinically validated patient reported outcomes (PROs) measures completed during a clinical assessment, monitoring improvement of the participant’s progress over time and comparison of PROs across participants remains subjective and challenging. Thus, objective measures to quantify pain are invaluable. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain and PRO measures. We were able to reach the accuracy of 0.832 ± 0.012 in pain intensity prediction. The feature importance analysis showed that digital biomarker from the smart watch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain and bring us another step closer towards objective evaluation of chronic pain.

Aberrant overexpression of endoplasmic reticulum (ER)-resident oxidoreductase protein disulfide isomerase (PDI) plays an important role in cancer progression. In this study, we demonstrate that PDI promotes glioblastoma (GBM) cell growth and describe a class of allosteric PDI inhibitors that are selective for PDI over other PDI family members. Methods: We performed a phenotypic screening triage campaign of over 20,000 diverse compounds to identify PDI inhibitors cytotoxic to cancer cells. From this screen, BAP2 emerged as a lead compound, and we assessed BAP2-PDI interactions with gel filtration, thiol-competition assays, and site-directed mutagenesis studies. To assess selectivity, we compared BAP2 activity across several PDI family members in the PDI reductase assay. Finally, we performed in vivo studies with a mouse xenograft model of GBM combining BAP2 and the standard of care (temozolomide and radiation), and identified affected gene pathways with nascent RNA sequencing (Bru-seq). Results: BAP2 and related analogs are novel PDI inhibitors that selectively inhibit PDIA1 and PDIp. Though BAP2 contains a weak Michael acceptor, interaction with PDI relies on Histidine 256 in the b' domain of PDI, suggesting allosteric binding. Furthermore, both in vitro and in vivo, BAP2 reduces cell and tumor growth. BAP2 alters the transcription of genes involved in the unfolded protein response, ER stress, apoptosis and DNA repair response. Conclusion: These results indicate that BAP2 has anti-tumor activity and the suppressive effect on DNA repair gene expression warrants combination with DNA damaging agents to treat GBM.

Protein disulfide isomerase (PDI) is the major enzyme responsible for nascent protein folding in the endoplasmic reticulum (ER) and plays a vital role in glioblastoma progression and survival. To improve the potency of our lead PDI inhibitor BAP2 ((E)-3-(3-(4-hydroxyphenyl)-3-oxoprop-1-en-1-yl)benzonitrile), we designed and synthesized 67 novel analogues. We determined that the A ring hydroxyl group of the chalcone scaffold was necessary for PDI inhibition and stepwise ClogP increase in the sulfonamide chain improved potency. Docking studies revealed that BAP2 and its close analogues bind PDI via His256 in the b’ domain, and mutation of His256 to Ala confirmed that this residue is critical for BAP2 analogue activity. While BAP2 and its potent analogue 59 have some thiol reactivity, we propose that PDI inhibition by BAP2 analogues is dependent on allosteric binding in the b’ domain. Importantly, active analogues selectively inhibit glioblastoma cell growth and migration, induce ER stress, increase expression of G2M checkpoint proteins, and reduce expression of DNA repair proteins. Cumulatively, our results support inhibition of PDI as a novel strategy to treat glioblastoma.

Protein disulfide isomerase (PDI) is overexpressed in glioblastoma, the most aggressive form of brain cancer, and folds nascent proteins responsible for the progression and spread of the disease. Herein we describe a novel nanomolar PDI inhibitor, pyrimidotriazinedione 35G8, that is toxic in a panel of human glioblastoma cell lines. We performed a medium-throughput 20 000-compound screen of a diverse subset of 1 000 000 compounds to identify cytotoxic small molecules. Cytotoxic compounds were screened for PDI inhibition, and, from the screen, 35G8 emerged as the most cytotoxic inhibitor of PDI. Bromouridine labeling and sequencing (Bru-seq) of nascent RNA revealed that 35G8 induces nuclear factor-like 2 (Nrf2) antioxidant response, endoplasmic reticulum (ER) stress response, and autophagy. Specifically, 35G8 upregulated heme oxygenase 1 and solute carrier family 7 member 11 (SLC7A11) transcription and protein expression and repressed PDI target genes such as thioredoxin-interacting protein 1 (TXNIP) and early growth response 1 (EGR1). Interestingly, 35G8-induced cell death did not proceed via apoptosis or necrosis, but by a mixture of autophagy and ferroptosis. Cumulatively, our data demonstrate a mechanism for a novel PDI inhibitor as a chemical probe to validate PDI as a target for brain cancer.

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy ((1)H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R(2)) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

A three-phase hollow fiber liquid phase microextraction (HF-LPME) method was evaluated for the extraction and preconcentration of naproxen and diclofenac using a polypropylene membrane followed by analysis using HPLC or LC/MS. In this technique, the drugs were extracted into di-n-hexyl ether immobilized in the wall pores of a porous hollow fiber from 50 mL of sludge slurry sample as a donor phase with pH 3, and then back-extracted into the acceptor phase located in the lumen of the hollow fiber. Experimental factors were studied in 16 trials using a Taguchi orthogonal array experimental design with an OA16 (45) matrix. The significance of these factors was investigated using analysis of variance. The extraction time was statistically demonstrated as the main factor for the extraction of naproxen and diclofenac, while ionic strength played the role of the second most important factor for HF-LPME extraction of diclofenac. The method permitted a detection limit of 0.2–0.7 ng g−1 with relative standar...

Parkinson’s disease (PD) is a neurodegenerative disease, which is not easily diagnosed using clinical tests and the discovery of proper methods would be a major step towards a successful diagnosis. In the present study, we employed metabolic profiling using proton nuclear magnetic resonance spectroscopy to find metabolites in serum, which are helpful for the diagnosis of PD. Classification of PD and healthy subject was done using random forest. Serum levels of selenium measured by atomic absorption spectrometry in PD group were lower than the serum selenium levels in the control group. The metabolites causing selenium changes in PD patients were identified using random forest, and a model was created for correlation between these metabolites and serum levels of selenium. The obtained classification model showed 88 % correct classification of PD and healthy samples for an external test set. The regression model of selenium and metabolites levels resulted in correlation value (R2) of 0.90 for the external test set. The findings of the present study indicate that serum metabonomics have great potential in PD detection and could be beneficial as a diagnostic method. As a novel approach, a model with good prediction capability was constructed between serum levels of selenium and nuclear magnetic resonance data.

Multiple sclerosis (MS) is a nervous system disease that affects the fatty myelin sheaths around the axons of the brain and spinal cord, leading to demyelination and a broad range of signs and symptoms. MS can be difficult to diagnose because its signs and symptoms may be similar to other medical problems. To find out which metabolites in serum are effective for the diagnosis of MS, we utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy ((1)H-NMR). Random forest (RF) was used to classify the MS patients and healthy subjects. Atomic absorption spectroscopy was used to measure the serum levels of selenium. The results showed that the levels of selenium were lower in the MS group, when compared with the control group. RF was used to identify the metabolites that caused selenium changes in people with MS by building a correlation model between these metabolites and serum levels of selenium. For the external test set, the obtained classification model showed a 93% correct classification of MS and healthy subjects. The regression model of levels of selenium and metabolites showed the correlation (R(2)) value of 0.88 for the external test set. The results indicate the suitability of NMR as a screen for identifying MS patients and healthy subjects. A novel model with good prediction outcomes was constructed between serum levels of selenium and NMR data.

Diamond D5 is the name proposed by Diudea for hyper-diamonds having their rings mostly pentagonal. Within D5, in crystallographic terms: the mtn structure, known in clathrates of type II, several substructures can be defined. In the present work, the structural stability of such intermediates/fragments appearing in the construction/destruction of D5 net was investigated using molecular dynamics simulation. Calculations were performed using an empirical many-body potential energy function for hydrocarbons. It has been found that, at normal temperature, the hexagonal hyper-rings are more stable while at higher temperature, the pentagonal ones are relatively more resistant against heat treatment.

Defining the role of calcitonin gene-related peptide in migraine pathogenesis could lead to the application of calcitonin gene-related peptide antagonists as novel migraine therapeutics. In this work, quantitative structure–activity relationship modeling of biological activities of a large range of calcitonin gene-related peptide antagonists was performed using a panel of physicochemical descriptors. The computational studies evaluated different variable selection techniques and demonstrated shuffling stepwise multiple linear regression to be superior over genetic algorithm-multiple linear regression. The linear quantitative structure–activity relationship model revealed better statistical parameters of cross-validation in comparison with the non-linear support vector regression technique. Implementing only five peptide descriptors into this linear quantitative structure–activity relationship model resulted in an extremely robust and highly predictive model with calibration, leave-one-out and leave-20-out validation R2 of 0.9194, 0.9103, and 0.9214, respectively. We performed docking of the most potent calcitonin gene-related peptide antagonists with the calcitonin gene-related peptide receptor and demonstrated that peptide antagonists act by blocking access to the peptide-binding cleft. We also demonstrated the direct contact of residues 28–37 of the calcitonin gene-related peptide antagonists with the receptor. These results are in agreement with the conclusions drawn from the quantitative structure–activity relationship model, indicating that both electrostatic and steric factors should be taken into account when designing novel calcitonin gene-related peptide antagonists.

Peptides interaction with carbon nanotubes is of potential importance in a wide range of biological and chemical applications. Coating carbon nanotubes (CNTs) with peptides solubilizes nanotubes in water. To consider the affinities of CNTs for different peptides, binding free energies of peptides to single-walled carbon nanotubes (SWCNTs) were calculated using linear interaction energy (LIE) model in conjugation with molecular dynamics simulation. This simple model represents the linear relationship between van der Waals energy as the dominant interaction in binding process and the energy of binding. The model yields an average error of 0.83 kcal/mol for the binding free energy prediction of five peptides considered as training set. The predictive power of the model was revealed by high value of Q 2 (0.97) and low value of s PRESS (1.07) from the leave-one-out cross validation. The calculated binding free energies of peptides in the training set were in good agreement with the experimental data and the ranking of binding affinities of peptides in both training and test sets was well reproduced. The findings of our study can be utilized in the estimation of ranking the binding affinities of new peptides to CNTs to open the door in exploring the novel ways for increasing the solubility of CNTs in water.

Polymerization and self-assembly of proteins into nanoaggregates of different sizes and morphologies (nanoensembles or nanofilaments) is a phenomenon that involved problems in various neurodegenerative diseases (medicine) and enzyme instability/inactivity (biotechnology). Thermal polymerization of horse liver alcohol dehydrogenase (dimeric) and yeast alcohol dehydrogenase (tetrameric), as biotechnological ADH representative enzymes, was evaluated for the development of a rational strategy to control aggregation. Constructed ADH nuclei, which grew to larger amorphous nanoaggregates, were prevented via high repulsion strain of the net charge values. Good correlation between the variation in scattering and λ −2 was related to the amorphousness of the nanoaggregated ADHs, shown by electron microscopic images. Scattering corrections revealed that ADH polymerization was related to the quaternary structural changes, including delocalization of subunits without unfolding, i.e. lacking the 3D conformational and/or secondary-ordered structural changes. The results demonstrated that electrostatic repulsion was not only responsible for disaggregation but also caused a delay in the onset of aggregation temperature, decreasing maximum values of aggregation and amounts of precipitation. Together, our results demonstrate and propose a new model of self-assembly for ADH enzymes based on the construction of nuclei, which grow to formless nanoaggregates with minimal changes in the tertiary and secondary conformations.

The purpose of this study was to develop the structure–toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis . The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison with the previous models. The results of the model are promising and descriptive. Five descriptors of octanol–water partition coefficient (log P ), bond information content (BIC0), number of R-CX-R (C-026), eigenvalue sum from Z weighted distance matrix (SEigZ) and fragment based polar surface area (PSA) selected by Shuffling-ANFIS reveal the role of hydrophobicity, electronic and steric interactions in the mechanism of toxic action. Sequential zeroing of weights (SZW) as a sensitivity analysis method revealed that the hydrophobicity and electronic interactions play a major role in toxicity of these compounds. Satisfactory results ( q 2 = 0.828 and RMSE = 0.348) in comparison with the previous works indicate that the Shuffling-ANFIS-ANN technique is able to model a diverse chemical class with more than one mechanism of toxicity by using simple and interpretable descriptors. Shuffling-ANFIS can be used as powerful feature selection technique, because its application in prediction of toxicity potency results in good statistical and interpretable physiochemical parameters.

A quantitative structure–retention relationships model has been developed to study the retention behavior of 87 aliphatic and aromatic compounds in Reversed-Phase Liquid Chromatography (RPLC) on five bonded-phase columns differing in silanol group acidity. Six numerical descriptors of Molecular Mass (M), partial charge of the most negative atom (NPCH), partial charge of the most positive hydrogen (PCHH), van der Waals volume (VOLUME), Dipole Moment (DIMO), and Highest Occupied Molecular Orbital (HOMO) have been calculated for each compound. A separate Multiple Linear Regression (MLR) model has been developed using the six descriptors for each column. Partial Least Square (PLS) combined with MLR mean effects has been used for the mechanism interpretation. The descriptors of M, HOMO, and DIMO showed the largest PLS regression coefficients. It is found in the present work that solute size, n–π donor–acceptor and dipole–dipole interactions play a major role in the RPLC retention mechanism. Also, a self-training artificial neural network was developed using the six descriptors as its inputs. The results obtained using this model are in good agreement with the experimental results of all five columns. Superiority of this model with low standard errors and high correlation coefficients for all five stationary phases reveals nonlinear characteristics of retention in RPLC. Successful application of the six descriptors in modeling of the retention behavior of different data sets using gas chromatography (previous work) and RPLC (present work) with different stationary phases reveals the robustness of the parameters.

This paper compares the Shuffling-Adaptive Neuro Fuzzy Inference System (Shuffling-ANFIS) with conventional ANFIS as the method for selecting the features of nonlinear systems. Shuffling-ANFIS technique uses the advantage of data splitting with the ANFIS as a powerful feature selection method to select the most important factors affecting nonlinear phenomena. In this technique, the features with the largest percent of frequency can be found by running the conventional ANFIS sequential forward search on the large number of training and test sets using leave-one-out validation criteria. The superiority of Shuffling-ANFIS over the conventional ANFIS was evaluated by using two synthetic and one biological dataset. For all three sets, Shuffling-ANFIS method was superior in finding effective variables compared with five different conventional ANFIS and multiple linear regression technique. As a real dataset, a series of 80 1-[(2-Hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives acting as Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) was studied. Five descriptors were selected by Shuffling-ANFIS and were used to interpret the mechanism of HIV-1 inhibition of HEPT derivatives. A 5-5-1 network was developed using the five descriptors as its inputs. The accuracy of this network was illustrated by validation techniques of Leave-One-Out (LOO) and Leave-Multiple-Out (LMO) Cross-Validations (CV) and Y-randomization test. Superiority of Shuffling-ANFIS-ANN method with generalized bell membership functions over the previous works and also the five conventional ANFIS-ANN models indicates the power of this approach in selecting of the variables for nonlinear systems.

This paper introduces the genetic algorithm-kernel partial least square (GA-KPLS), as a novel nonlinear feature selection method. This technique combines genetic algorithms (GAs) as powerful optimization methods with KPLS as a robust nonlinear statistical method for variable selection. This feature selection method is combined with artificial neural network to develop a nonlinear QSAR model for predicting activities of a series of substituted aromatic sulfonamides as carbonic anhydrase II (CA II) inhibitors. Eight simple one- and two-dimensional descriptors were selected by GA-KPLS and considered as inputs for developing artificial neural networks (ANNs). These parameters represent the role of acceptor-donor pair, hydrogen bonding, hydrosolubility and lipophilicity of the active sites and also the size of the inhibitors on inhibitor-isozyme interaction. The accuracy of 8-4-1 networks was illustrated by validation techniques of leave-one-out (LOO) and leave-multiple-out (LMO) cross-validations and Y-randomization. Superiority of this method (GA-KPLS-ANN) over the linear one (MLR) in a previous work and also the GA-PLS-ANN in which a linear feature selection method has been used indicates that the GA-KPLS approach is a powerful method for the variable selection in nonlinear systems.

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy ((1)H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R(2)) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

Telomeric DNA contains some unique secondary structures, such as G-quadruplex and I-motif. These structures may be stabilized or changed by binding to specific proteins or small molecules. Herein, we report the in vitro effect of crocin, crocetin, picrocrocin, and safranal on these structures. Circular dichroism (CD) data indicate that crocetin has higher affinity for these structures. Safranal and crocin induce little change in the I-motif and G-quadruplex, respectively. The molecular docking confirms the experimental data and indicates the minor groove binding of ligands with G-quadruplex. The possibility for application of these ligands as sequence-specific drugs should be further investigated.

Multiple sclerosis (MS) is a nervous system disease that affects the fatty myelin sheaths around the axons of the brain and spinal cord, leading to demyelination and a broad range of signs and symptoms. MS can be difficult to diagnose because its signs and symptoms may be similar to other medical problems. To find out which metabolites in serum are effective for the diagnosis of MS, we utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy ((1)H-NMR). Random forest (RF) was used to classify the MS patients and healthy subjects. Atomic absorption spectroscopy was used to measure the serum levels of selenium. The results showed that the levels of selenium were lower in the MS group, when compared with the control group. RF was used to identify the metabolites that caused selenium changes in people with MS by building a correlation model between these metabolites and serum levels of selenium. For the external test set, the obtained classification model showed a 93% correct classification of MS and healthy subjects. The regression model of levels of selenium and metabolites showed the correlation (R(2)) value of 0.88 for the external test set. The results indicate the suitability of NMR as a screen for identifying MS patients and healthy subjects. A novel model with good prediction outcomes was constructed between serum levels of selenium and NMR data.

Defining the role of calcitonin gene-related peptide in migraine pathogenesis could lead to the application of calcitonin gene-related peptide antagonists as novel migraine therapeutics. In this work, quantitative structure-activity relationship modeling of biological activities of a large range of calcitonin gene-related peptide antagonists was performed using a panel of physicochemical descriptors. The computational studies evaluated different variable selection techniques and demonstrated shuffling stepwise multiple linear regression to be superior over genetic algorithm-multiple linear regression. The linear quantitative structure-activity relationship model revealed better statistical parameters of cross-validation in comparison with the non-linear support vector regression technique. Implementing only five peptide descriptors into this linear quantitative structure-activity relationship model resulted in an extremely robust and highly predictive model with calibration, leave-one-out and leave-20-out validation R(2) of 0.9194, 0.9103, and 0.9214, respectively. We performed docking of the most potent calcitonin gene-related peptide antagonists with the calcitonin gene-related peptide receptor and demonstrated that peptide antagonists act by blocking access to the peptide-binding cleft. We also demonstrated the direct contact of residues 28-37 of the calcitonin gene-related peptide antagonists with the receptor. These results are in agreement with the conclusions drawn from the quantitative structure-activity relationship model, indicating that both electrostatic and steric factors should be taken into account when designing novel calcitonin gene-related peptide antagonists.

Intra-tumor heterogeneity represents one of the main challenges in cancer therapeutics. This heterogeneity is generated at the genetic level primarily by genomic alterations and clonal selection and at the epigenetic level by tumor cell differentiation. In addition, inter-tumor heterogeneities may also be related to differences in tumor biology in different ethnic populations. To explore the cell heterogeneity in breast cancer, we studied patient derived xenograft (PDX) models generated from African (Ghana), African-American, and Caucasian breast cancer patients. Gene expression of a selected panel of cancer associated genes was determined in single cells using C1 and BioMark HD platforms. Analyzed multiplex RT-qPCR data revealed significant heterogeneity in mRNA expression within and between studied PDX samples at single cell resolution. We observed a significantly different mRNA expression profile within each ethnic group of breast cancer PDX samples. In addition, distinct gene expression signatures were observed between the PDX samples of different ethnic origin. ALDH1a1, ALDH1a3, CD44, CD24, and CD133 were among the CSC markers that were differentially expressed in different PDX single cell samples. Vimentin, EpCAM, HER2, CDH1, CDH2, TGFb1, cytokeratins, GATA3 and MKI67 genes related to the epithelial to mesenchymal (EMT) and mesenchymal to epithelial (MET) phenotypes also demonstrated significantly variable expression in the studied PDX samples. YAP1, TM4SF1, TSPAN6, AMOTL2, STAP2 and ANXA3 genes were differentially expressed in the PDX samples. These candidate biomarkers represent a collection of genes that may warrant further investigation for their potential involvement in cancer initiation, promotion and progression to metastasis, and drug resistance. These data emphasize the importance of studying tumors at single cell resolution to unravel gene expression variation. Upon validation in clinical tumor specimens, this approach could help to identify cellular subpopulations within tumors and possibly identify novel targets for cancer therapy. Citation Format: Ebrahim Azizi, Evelyn M. Jiagge, Shamileh Fouladdel, Shukmei Wong, Michele L. Dziubinski, Mary Sehl, Anahita Kyani, Jun Li, Hui Jiang, Tahra K. Luther, Shawn G. Clouthier, Sean P. McDermott, John Carpten, Lisa A. Newman, Sofia D. Merajver, Max S. Wicha. Single cell multiplex gene expression analysis to unravel heterogeneity of PDX samples established from tumors of breast cancer patients with different ethnicity. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4834. doi:10.1158/1538-7445.AM2015-4834

Peptides having a regularly repeating pattern of L and D amino acids adopt unique structures termed beta-helix. In beta helix structure, intramolecular hydrogen bonding looks like that in parallel β-sheets. The beta helix structure has been reported for both natural peptides, like gramicidin A, and syntetic peptides such as poly (D, L-γ -benzylglutamyl). Studies have shown a short beta sheet tetraicosa-peptide (VSLGLSIAFSVAVSIAWSFARSRG, where all As are D-alanine) accept beta-helical conformation in lipid bilayer similar to gramicidin A [4], called gramicidin-like channel (GLC).In the present work, the GLC syntetic peptide has been modeled and simulated in different orientations with respect to a lipid bilayer. The aim of this study was to find the stable conformation of the peptide and understanding its key interactions with lipid bilayer. GROMOS force field implemented in GROMACS software, version 4.0 was employed for the molecular dynamics simulations. The results showed that this peptide adopted beta helical structure, in agreement with the previous studies. However, GLC was stable in the trans-membrane state during 20 ns molecular dynamics simulation. The findings of this study proved that the elctrostatic interactions between the polar residues of the peptide and the polar head group of the lipid bilayer are the most important interactions in the insertion of peptide into the membrane. Furthermore, simulation revealed that the hydrophobic interactions play a key role in the stability of GLC peptide in hydrophobic environment of lipid bilayer.