Small Molecule Drug Screening Research Articles

Screening of potential targets and small-molecule drugs related to lipid metabolism in ovarian cancer based on bioinformatics

Backgroundabout 70 % of ovarian cancer (OC) patients with postoperative chemotherapy relapse within 2–3 years due to drug resistance and metastasis, and the 5-year survival rate is only about 30 %. Lipid metabolism plays an important role in OC. We try to explore the potential targets and drugs related to lipid metabolism to provide clues for the treatment of OC. Methodsthe gene expression profiles of OC and normal ovarian tissue samples were obtained from the cancer genome atlas (TCGA) and genotype-tissue expression databases (GTEx). The differentially expressed genes (DEGs) were analyzed. Lipid metabolism related genes (LMRGs) were downloaded from MSigDB database. The DEGs related to lipid metabolism in OC was obtained by intersection. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses were performed. The protein-protein interaction (PPI) network of lipid metabolism related DEGs was constructed, and seven algorithms were used to screen core potential target genes. Its expression in OC and prognostic ability were analyzed by Univariate Cox. Cmap database mining OC lipid metabolism related potential small-molecular drugs and docking. CCK8, scratch assay, transwell test and free fatty acid (FFA) assay, fluorescence detection of cellular fatty acid uptake, and the reactivity assay of CPT1A were used to detect the biological effects of drugs on OC cell.Rreverse transcription PCR(RT-qPCR) and WesternBlot were performed to measure the expression of core targets. Results437 DEGs related to lipid metabolism of OC were screened. GO and KEGG analysis showed that these DEGs were lipid metabolism, fatty acid metabolism, sphingolipid metabolism, PPAR signal pathway and so on. The PPI network based on lipid metabolism DEGs consists of 301 nodes and 1107 interaction pairs, and 6 core target genes were screened. ROC curve analysis showed that all of the 6 genes could predict the prognosis of OC. Three small molecular drugs Cephaeline, AZD8055 and GSK-1059615 were found by cmap and molecular docking showed that they all had good binding ability to target gene. Cephaeline has the strongest inhibitory effect on SKOV3 cells of OC, and could significantly inhibit cell migration and invasion regulate the mRNA and protein expression of some targets, and inhibit lipid metabolism process in ovarian cancer cells. Conclusionsix OC potential genes related to lipid metabolism were identified and verified, which can be used as potential biomarkers and therapeutic targets to evaluate the prognostic risk of OC patients. In addition, three small-molecular drugs that may be effective in the treatment of OC were unearthed, among which Cephaeline has the most potential. We speculate that Cephaeline may target six genes to inhibit progression of OC by affecting lipid metabolism.

The stateoftheart in secondary pharmacology and its impact on the safety of new medicines.

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

A facile fluorescence-coupling approach to visualizing leonurine uptake and distribution in living cells and Caenorhabditis elegans

BackgroundCaenorhabditis elegans (C. elegans) has been recognized for being a useful model organism in small-molecule drug screens and drug efficacy investigation. However, there remain bottlenecks in evaluating such processes as drug uptake and distribution due to a lack of appropriate chemical tools. PurposeThis study aims to prepare fluorescence-labeled leonurine as an example to monitor drug uptake and distribution of small molecule in C. elegans and living cells. MethodsFITC-conjugated leonurine (leonurine-P) was synthesized and characterized by LC/MS, NMR, UV absorption and fluorescence intensity. Leonurine-P was used to stain C. elegans and various mammalian cell lines. Different concentrations of leonurine were tested in conjunction with a competing parent molecule to determine whether leonurine-P and leonurine shared the same biological targets. Drug distribution was analyzed by imaging. Fluorometry in microplates and flow cytometry were performed for quantitative measurements of drug uptake. ResultsThe UV absorption peak of leonurine-P was 490∼495 nm and emission peak was 520 nm. Leonurine-P specifically bound to endogenous protein targets in C. elegans and mammalian cells, which was competitively blocked by leonurine. The highest enrichment levels of leonurine-P were observed around 72 h following exposure in C. elegans. Leonurine-P can be used in a variety of cells to observe drug distribution dynamics. Flow cytometry of stained cells can be facilely carried out to quantitatively detect probe signals. ConclusionsThe strategy of fluorescein-labeled drugs reported herein allows quantification of drug enrichment and visualization of drug distribution, thus illustrates a convenient approach to study phytodrugs in pharmacological contexts.

Abstract 1121: Targeting the DNA damage response in high-risk medulloblastoma

Abstract Medulloblastoma (MB) is the most common malignant pediatric brain tumour and is currently treated with surgery, external beam radiation and chemotherapy however, current treatments are woefully inadequate, with survival of only 50% but morbidity approaching 100%. A very-high risk group of SHH-activated MB harboring TP53 mutations (TP53mut-SHH) account for a substantial proportion of treatment failures after radiotherapy with a uniformly fatal outcome. Through optimizing radiation as a selection pressure and employing a functional genetic approach through whole-genome CrisprCas-9 dropout screening, the present study aims to identify drivers of radiation resistance and uncover unique vulnerabilities to sensitize these tumours to therapy. The dropout screen was conducted across a panel of established in vitro models of sporadic murine TP53mut-SHH, which resemble the human disease with profound radio-resistance. Initial results reveal that targeting the DNA damage response confer radiation sensitization. However, consistent hits across all models demonstrate non-homologous end-joining (NHEJ) as being synthetically lethal with radiation specifically in TP53mut-SHH. Genetic knockout experiments confirm genes encoding main proteins involved in NHEJ are synthetically lethal in combination with radiation. A small molecule drug screen targeting NHEJ revealed such vulnerabilities may be therapeutically exploited in TP53mut-SHH and are able sensitize these tumours to radiation. Using our unique approach of leveraging newly established models of TP53mut-SHH with a combination of functional genetic screening and small-molecule drug screening, this study highlights the genetic mechanisms of treatment resistance and reveals novel approaches for radiation sensitization. Here, we develop a novel approach to identify targets that are synthetically lethal with radiotherapy and identify multiple synthetically lethal targets at the genetic level which can be therapeutically targeted, allowing the development of new treatments to overcome current barriers. Citation Format: Alexandria DeCarlo, Carolina Fernandes da Silva, Lily Shen, Vijay Ramaswamy, Uri Tabori. Targeting the DNA damage response in high-risk medulloblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1121.

A Microfluidic Platform for Evaluating the Internalization of Liposome Drug Carriers in Tumor Spheroids.

The development of in vitro models recapitulating nanoparticle transport under physiological flow conditions is of great importance for predicting the efficacy of nanoparticle drug carriers. Liposomes are extensively used for drug delivery owing to their biocompatibility and biodegradability and the ability to carry both hydrophilic and hydrophobic compounds. Here, we used a library of liposomes with various dimensions and a microfluidic platform comprising a large array of uniformly sized breast cancer spheroids to explore size-dependent liposome internalization and retention in the spheroids under close-to-physiological interstitial conditions. Such a platform showed promising applications in the preclinical screening of small molecule drugs; however, the capability to deliver nanoparticles in the spheroid interior under close-to-physiological flow conditions was not explored. For the liposomes with diameters in the range of 45-200 nm, we show experimentally and by simulations that in comparison with liposome delivery solely by diffusion, flow significantly enhances liposome internalization in the microgels and mitigates the size-dependent spheroid penetration by the liposomes. The utility of the microfluidic platform was validated by evaluating the efficacy of clinically approved doxorubicin-loaded liposomes (Doxil), which exhibited superior retention in the spheroids under flow conditions, in comparison with free doxorubicin. This MF platform can serve as an in vitro model for screening the efficacy of drugs encapsulated in liposomes and find applications for screening other types of nanoparticle carriers for vaccine delivery, diagnostics, and skincare.

Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks.

Bladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and treatment outcomes. Immunotherapy has changed the clinical treatment landscape for many advanced malignancies, opening new avenues for the precise treatment of malignancies. However, effective predictors and models to guide clinical treatment and predict immunotherapeutic outcomes are still lacking. We downloaded BCa sample data from The Cancer Genome Atlas to identify anti-PD-L1 immunotherapy-related genes through an immunotherapy dataset and used machine learning algorithms to build a new PD-L1 multidimensional regulatory index (PMRI) based on these genes. PMRI-related column-line graphs were constructed to provide quantitative tools for clinical practice. We analyzed the clinical characteristics, tumor immune microenvironment, chemotherapy response, and immunotherapy response of patients based on PMRI system. Further, we performed function validation of classical PMRI genes and their correlation with PD-L1 in BCa cells and screening of potential small-molecule drugs targeting PMRI core target proteins through molecular docking. PMRI, which consists of four anti-PD-L1 immunotherapy-associated genes (IGF2BP3, P4HB, RAC3, and CLK2), is a reliable predictor of survival in patients with BCa and has been validated using multiple external datasets. We found higher levels of immune cell infiltration and better responses to immunotherapy and cisplatin chemotherapy in the high PMRI group than in the low PMRI group, which can also be used to predict immune efficacy in a variety of solid tumors other than BCa. Knockdown of IGF2BP3 inhibited BCa cell proliferation and migration, and IGF2BP3 was positively correlated with PD-L1 expression. We performed molecular docking prediction for each of the core proteins comprising PMRI and identified 16 small-molecule drugs with the highest affinity to the target proteins. Our PD-L1 multidimensional expression regulation model based on anti-PD-L1 immunotherapy-related genes can accurately assess the prognosis of patients with BCa and identify patient populations that will benefit from immunotherapy, providing a new tool for the clinical management of intermediate and advanced BCa.

In situ visualization of the cellular uptake and sub-cellular distribution of mussel oligosaccharides

Unlike chemosynthetic drugs designed for specific molecular and disease targets, active small-molecule natural products typically have a wide range of bioactivities and multiple targets, necessitating extensive screening and development. To address this issue, we propose a strategy for the direct in situ microdynamic examination of potential drug candidates to rapidly identify their effects and mechanisms of action. As a proof-of-concept, we investigated the behavior of mussel oligosaccharide (MOS-1) by tracking the subcellular dynamics of fluorescently labeled MOS-1 in cultured cells. We recorded the entire dynamic process of the localization of fluorescein isothiocyanate (FITC)-MOS-1 to the lysosomes and visualized the distribution of the drug within the cell. Remarkably, lysosomes containing FITC-MOS-1 actively recruited lipid droplets, leading to fusion events and increased cellular lipid consumption. These drug behaviors confirmed MOS-1 is a candidate for the treatment of lipid-related diseases. Furthermore, in a high-fat HepG2 cell model and in high-fat diet-fed apolipoprotein E (ApoE)−/− mice, MOS-1 significantly promoted triglyceride degradation, reduced lipid droplet accumulation, lowered serum triglyceride levels, and mitigated liver damage and steatosis. Overall, our work supports the prioritization of in situ visual monitoring of drug location and distribution in subcellular compartments during the drug development phase, as this methodology contributes to the rapid identification of drug indications. Collectively, this methodology is significant for the screening and development of selective small-molecule drugs, and is expected to expedite the identification of candidate molecules with medicinal effects.

Bioinformatics analysis of CMTM family in pan-cancer and preliminary exploration of CMTM6 in bladder cancer

BackgroundThe chemokine-like factor (CKLF)-like Marvel transmembrane structural domain (CMTM) family is widely expressed in the tumor and immune systems and is essential in human cancer progression. However, the multi-omic profile of CMTM family genes and their role in tumor patient prognosis and immune microenvironment have not been explored. MethodsWe collected data from 33 cancers and 33 non-cancers and then comprehensively analyzed the basal expression levels of CMTM family genes in normal human tissues as well as abnormal expression in diseases, genomic alterations, diagnostic and prognostic roles, subcellular localization, pathway enrichment, the immune microenvironment, associations with immune checkpoints, and drug sensitivities as well as to predict the immunotherapeutic response of patients to ICIs and targeting of small molecule drugs, the above results were validated by immunohistochemical staining, pathology sections and experiments. We also performed protein docking of immune checkpoints binding to CMTM6 and screening of small molecule drugs targeting CMTM6 based on mass spectrometry results and molecular docking techniques. Finally, we experimentally confirmed the role of CMTM6 in bladder cancer. ResultsWe found differential expression and diagnostic biomarker value of the CMTMs family in diseases (cancer and non-cancer). CMTMs were also found to play a key role in pan-cancer with the tumor microenvironment. CMTMs were closely associated with common immune checkpoints, TMB and MSI, so we scored CMTMs based on CMTMs expression in patients undergoing ICI, and patients with lower scores had better survival and showed higher immunotherapy response after immunotherapy. Finally, molecular docking was used to identify small molecule inhibitors that could target CMTM6 and binding poses of CMTM6 to other immune checkpoint genes. Finally, it was determined experimentally that knockdown of CMTM6 gene expression inhibited the proliferation and invasion of bladder cancer cells. ConclusionsOur findings provide a valuable strategy to guide the diagnostic and therapeutic direction of CMTM family genes in disease.

Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.

Recurrent pregnancy loss defined as the occurrence of two or more pregnancy losses before 20-24 weeks of gestation, is a prevalent and significant pathological condition that impacts human reproductive health. However, the underlying mechanism of RPL remains unclear. This study aimed to investigate the biomarkers and molecular mechanisms associated with RPL and explore novel treatment strategies for clinical applications. The GEO database was utilized to retrieve the RPL gene expression profile GSE165004. This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. ANN model were constructed to assess the performance of hub genes in the dataset. The expression of hub genes in both the RPL and control group samples was validated using RT-qPCR. The immune cell infiltration level of RPL was assessed using CIBERSORT. Additionally, pan-cancer analysis was conducted using Sangerbox, and small-molecule drug screening was performed using CMap. A total of 352 DEGs were identified, including 198 up-regulated genes and 154 down-regulated genes. Enrichment analysis indicated that the DEGs were primarily associated with Fc gamma R-mediated phagocytosis, the Fc epsilon RI signaling pathway, and various metabolism-related pathways. The turquoise module, which showed the highest relevance to clinical symptoms based on WGCNA results, contained 104 DEGs. Three hub genes, WBP11, ACTR2, and NCSTN, were identified using machine learning algorithms. ROC curves demonstrated a strong diagnostic value when the three hub genes were combined. RT-qPCR confirmed the low expression of WBP11 and ACTR2 in RPL, whereas NCSTN exhibited high expression. The immune cell infiltration analysis results indicated an imbalance of macrophages in RPL. Meanwhile, these three hub genes exhibited aberrant expression in multiple malignancies and were associated with a poor prognosis. Furthermore, we identified several small-molecule drugs. This study identifies and validates hub genes in RPL, which may lead to significant advancements in understanding the molecular mechanisms and treatment strategies for this condition.

Identification of key genes and small molecule drugs in osteoarthritis by integrated bioinformatics analysis

BackgroundOsteoarthritis (OA) is a common joint degenerative disease that can affect multiple joints. Genetic events may play an important regulatory role in the early stages of the disease, but the specific mechanisms have not yet been fully elucidated. The main purpose of this study was to screen for disease-causing hub genes and effective small molecule drugs to reveal the pathogenesis of OA and to develop novel drugs for treatment. MethodsTwo gene expression profile datasets, GSE55235 and GSE55457, were integrated and further analyzed. The consistently differentially expressed genes (DEGs) were identified, and functional annotation and pathway analysis of these genes were performed with GO and KEGG. A protein–protein interaction network (PPI) of the DEGs was generated using STRING, and potential small molecule drug screening was performed on the connectivity map (CMap). ResultsA total of 158 consistently differentially expressed genes were identified from the two profile datasets. The functions of these DEGs are mainly related to the TNF signaling pathway, osteoclast differentiation, MAPK signaling pathway and so on. The PPI network contains 127 nodes and 1802 edges, and the ten hub genes were interleukin 6 (IL6), vascular endothelial growth factor A (VEGFA)and so on. 7 small molecule drugs were identified as potential interactors with these hubs. ConclusionsThis study explains the disorder of expression in the pathological process of OA at transcriptome, which will help to understand the pathogenesis of OA.

Ensemble optimizer: Interpretable scoring functions for virtual screening.

Virtual screening (VS) of potential small-molecule drugs is a key part of many computational drug discovery pipelines. This step uses molecular docking to generate a score representing a small molecule's affinity for the target (e.g., protein) pocket, which can be used to prioritize small-molecule compounds for experimental testing. Though VS commonly considers a single target protein conformation, many targets can adopt a number of conformations. Previous work in the field has demonstrated that ensemble docking, which considers these multiple conformations, improves prediction of active ligands, especially when coupled with machine learning methods. Ranking compounds for further testing requires a single score per compound, so ensemble docking scores are commonly averaged across conformations to yield a single numerical assessment of each compound. However, other methods, such as using ensembles as features in predictive models, may be more effective. We present a tool that makes machine learning and heuristic methods for ensemble docking analysis more accessible, allowing users to identify likely ligands and conformations that contribute to binding. Our tool incorporates several methods for combining and comparing ensemble docking scores, and can use raw scores or compound rankings to allow for consensus scoring using methods with incompatible scales. We also demonstrate that random-forest ensemble feature selection improves identification of known ligands over single-conformation VS, even without including known ligands in training data. With utility across a wide range of datasets, our tool streamlines virtual screening, facilitating further investigation of ensemble selection and protein-ligand binding.

The p300/CBP Inhibitor A485 Normalizes Psoriatic Fibroblast Gene Expression In Vitro and Reduces Psoriasis-Like Skin Inflammation In Vivo

Psoriasis is a chronic inflammatory skin disease that often recurs at the same locations, indicating potential epigenetic changes in lesional skin cells. In this study, we discovered that fibroblasts isolated from psoriatic skin lesions retain an abnormal phenotype even after several passages in culture. Transcriptomic profiling revealed the upregulation of several genes, including the extra domain A splice variant of fibronectin and ITGA4 in psoriatic fibroblasts. A phenotypic library screening of small-molecule epigenetic modifier drugs revealed that selective CBP/p300 inhibitors were able to rescue the psoriatic fibroblast phenotype, reducing the expression levels of extra domain A splice variant of fibronectin and ITGA4. In the imiquimod-induced mouse model of psoriasis-like skin inflammation, systemic treatment with A485, a potent CBP/p300 blocker, significantly reduced skin inflammation, immune cell recruitment, and inflammatory cytokine production. Our findings indicate that epigenetic reprogramming might represent a new approach for the treatment and/or prevention of relapses of psoriasis.

Construction of immunotherapy-related prognostic gene signature and small molecule drug prediction for cutaneous melanoma.

BackgroundCutaneous melanoma (CM), a kind of skin cancer with a high rate of advanced mortality, exhibits a wide variety of driver and transmitter gene alterations in the immunological tumor microenvironment (TME) associated with tumor cell survival and proliferation.MethodsWe analyzed the immunological infiltration of TME cells in normal and malignant tissues using 469 CM and 556 normal skin samples. We used a single sample gene set enrichment assay (ssGSEA) to quantify the relative abundance of 28 cells, then used the LASSO COX regression model to develop a riskScore prognostic model, followed by a small molecule drug screening and molecular docking validation, which was then validated using qRT-PCR and IHC.ResultsWe developed a prognosis model around seven essential protective genes for the first time, dramatically elevated in tumor tissues, as did immune cell infiltration. Multivariate Cox regression results indicated that riskScore is an independent and robust prognostic indicator, and its predictive value in immunotherapy was verified. Additionally, we identified Gabapentin as a possible small molecule therapeutic for CM.ConclusionsA riskScore model was developed in this work to analyze patient prognosis, TME cell infiltration features, and treatment responsiveness. The development of this model not only aids in predicting patient response to immunotherapy but also has significant implications for the development of novel immunotherapeutic agents and the promotion of tailored treatment regimens.

Zebrafish Xenograft Model to Study Human Cancer.

The zebrafish, Danio rerio, has been an important animal model for cancer research over the last decade. The capability of a high-throughput screen in zebrafish and a wide range of pharmacologically active compounds elicit physiological responses in zebrafish embryos comparable to those in mammalian systems, making zebrafish ideal for identifying clinically relevant drug targets and compounds that regulate tumor progression. The zebrafish model is suitable for patient-derived xenograft (pdx) and large-scale screening of lead compounds against specific malignancies. This established vertebrate model has many advantages, including fast response time, cost efficiency for drug testing, efficient manipulation of the host microenvironment by genetic tools, suitable for small molecule drug screening in high-throughput setting, easy maintenance, transparency for easy observation, high fecundity, and rapid generation time. The zebrafish model is a good alternative in vivo model to mammals for robust testing of drug candidates for cancer therapy.

A graphene oxide-based fluorescent nanosensor to identify antiviral agents via a drug repurposing screen

Currently, there are no approved therapeutics for Dengue virus (DENV) infection, even though it can cause fatal complications. Understanding DENV infection and its propagation process in host cells is necessary to develop specific antiviral therapeutics. Here, we developed a graphene oxide-based fluorescent system (Graphene Oxide-based Viral RNA Analysis system, GOViRA) that enables sensitive and quantitative real-time monitoring of the intracellular viral RNA level in living cells. The GOViRA system consists of a fluorescent dye-labeled peptide nucleic acid (PNA) with a complementary sequence to the DENV genome and a dextran-coated reduced graphene oxide nanocolloid (DRGON). When the dye labeled PNA is adsorbed onto DRGON, the fluorescence of the dye is effectively quenched. The quenched fluorescence signal is recovered when the dye labeled PNA forms interaction with intracellular viral RNA in DENV infected host cells. We demonstrated the successful use of the GOViRA platform for high-throughput screening to discover novel antiviral compounds. Through a cell-based high-throughput screening of FDA-approved small-molecule drugs, we identified ulipristal, a selective progesterone receptor modulator (SPRM), as a potent inhibitor against DENV infection. The anti-DENV activity of ulipristal was confirmed both in vitro and in vivo. Moreover, we suggest that the mode of action of ulipristal is mediated by inhibiting viral entry into the host cells.

CRTC1/MAML2 directs a PGC-1α-IGF-1 circuit that confers vulnerability to PPARγ inhibition

SUMMARYMucoepidermoid carcinoma (MEC) is a life-threatening salivary gland cancer that is driven primarily by a transcriptional coactivator fusion composed of cyclic AMP-regulated transcriptional coactivator 1 (CRTC1) and mastermind-like 2 (MAML2). The mechanisms by which the chimeric CRTC1/MAML2 (C1/M2) oncoprotein rewires gene expression programs that promote tumorigenesis remain poorly understood. Here, we show that C1/M2 induces transcriptional activation of the non-canonical peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) splice variant PGC-1α4, which regulates peroxisome proliferator-activated receptor gamma (PPARγ)-mediated insulin-like growth factor 1 (IGF-1) expression. This mitogenic transcriptional circuitry is consistent across cell lines and primary tumors. C1/M2-positive tumors exhibit IGF-1 pathway activation, and small-molecule drug screens reveal that tumor cells harboring the fusion gene are selectively sensitive to IGF-1 receptor (IGF-1R) inhibition. Furthermore, this dependence on autocrine regulation of IGF-1 transcription renders MEC cells susceptible to PPARγ inhibition with inverse agonists. These results yield insights into the aberrant coregulatory functions of C1/M2 and identify a specific vulnerability that can be exploited for precision therapy.

Rapid lipid bilayer membrane formation on Parylene coated apertures to perform ion channel analyses

We present a chip design allowing rapid and robust lipid bilayer (LBL) membrane formation using a Parylene coated thin silicon nitride aperture. After bilayer formation, single membrane channels can be reconstituted and characterized by electrophysiology. The ability for robust reconstitution will allow parallelization and enhanced screening of small molecule drugs acting on or permeating across the membrane channel. The aperture was realized on a microfabricated silicon nitride membrane by using standard clean-room fabrication processes. To ensure the lipid bilayer formation, the nitride membrane was coated with a hydrophobic and biocompatible Parylene layer. We tested both Parylene-C and Parylene-AF4. The contact angle measurements on both Parylene types showed very good hydrophobic properties and affinity to lipids. No precoating of the Parylene with an organic solvent is needed to make the aperture lipophilic, in contradiction to Teflon membranes. The chips can be easily placed in an array utilizing a 3D printed platform. Experiments show repetitive LBL formation and destruction (more than 6 times) within a very short time (few seconds). Through measurements we have established that the LBL layers are very thin. This allows the investigation of the fusion process of membrane proteins i.e. outer membrane protein (OmpF) in the LBL within a few minutes.

Hyperactivity, Memory Defects, and Craniofacial Abnormalities in Zebrafish fmr1 Mutant Larvae.

Fragile X syndrome (FXS) is a heritable mental retardation disease caused by unstable trinucleotide repeat sequences in FMR1. FXS is characterized by delayed development, hyperactivity, and autism behavior. Zebrafish is an excellent model to study FXS and the underlying function of fmr1. However, at present, fmr1 function is mainly studied via morpholinos or generated mutants using targeting induced local lesions in genomes. However, both of these methods generate off-target effects, making them suboptimal techniques for studying FXS. In this study, CRISPR/Cas9 technology was used to generate two zebrafish fmr1 mutant lines. High-throughput behavior analysis, qRT-PCR, and alcian blue staining experiments were employed to investigate fmr1 function. The fmr1 mutant line showed abnormal behavior, learning memory defects, and impaired craniofacial cartilage development. These features are similar to the human FXS phenotype, indicating that the fmr1 mutant generated in this study can be used as a new model for studying the molecular pathology of FXS. It also provides a suitable model for high-throughput screening of small molecule drugs for FXS therapeutics.

Generation of a human induced pluripotent stem cell line from an epilepsy patient carrying mutations in the PIK3R2 gene.

A 48-year-old female epilepsy patient of Han Chinese genetic descent carrying heterozygous mutations in PIK3R2 donated her peripheral blood mononuclear cells (PBMCs). The PBMCs were reprogrammed by the nonintegrating vector system with human OKSM transcription factors. The generated iPSCs contained congenital mutations in an exon of PIK3R2 and expressed endogenous pluripotency markers. They had a normal karyotype and were able to differentiate into all three germ layers. This new iPSC line will contribute to a better understanding of the pathology of epilepsy, enabling the identification of early biomarkers and the screening of small molecule drugs.

Screening and Discovery of New Potential Biomarkers and Small Molecule Drugs for Cervical Cancer: A Bioinformatics Analysis.

Background:Cervical cancer (CC) is the second most common type of malignant tumor survival rate is low in advanced stage, metastatic, and recurrent CC patients. This study aimed at identifying potential genes and drugs for CC diagnosis and targeting therapies.Methods:Three GEO mRNA microarray datasets of CC tissues and non-cancerous tissues were analyzed for differentially expressed genes (DEGs) by limma package. GO (Gene Ontologies) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used to explore the relationships between the DEGs. Protein-protein interaction (PPI) of these genes was established by the STRING database. MCODE was used for screening significant modules in the PPI networks to select hub genes. Biochemical mechanisms of the hub genes were investigated with Metascape. GEPIA database was used for validating the core genes. According to these DEGs, molecular candidates for CC were recognized from the CMAP database.Results:We identified 309 overlapping DEGs in the 2 tissue-types. Pathway analysis revealed that the DEGs were involved in cell cycle, DNA replication, and p53 signaling. PPI networks between overlapping DEGs showed 68 high-connectivity DEGs that were chosen as hub genes. The GEPIA database showed that the expression levels of RRM2, CDC45, GINS2, HELLS, KNTC1, MCM2, MYBL2, PCNA, RAD54 L, RFC4, RFC5, TK1, TOP2A, and TYMS in CC tissues were significantly different from those in the healthy tissues and were significantly relevant to the OS of CC. We found 10 small molecules from the CMAP database that could change the trend of gene expression in CC tissues, including piperlongumine and chrysin.Conclusions:The 14 DEGs identified in this study could serve as novel prognosis biomarkers for the detection and forecasting of CC. Small molecule drugs like piperlongumine and chrysin could be potential therapeutic drugs for CC treatment.