Computational Target Prediction Research Articles

1,3-Disubstituted-1,2,4-triazin-6-ones with potent activity against androgen receptor-dependent prostate cancer cells

Synthesis and biological evaluation of a small, focused library of 1,3-disubstituted-1,2,4-triazin-6-ones for in vitro inhibitory activity against androgen-receptor-dependent (22Rv1) and androgen-receptor independent (PC3) castration-resistant prostate cancer (CRPC) cells led to highly active compounds with in vitro IC50 values against 22Rv1 cells of <200 nM, and with apparent selectivity for this cell type over PC3 cells. From metabolic/PK evaluations of these compounds, a 3–benzyl-1–(2,4-dichlorobenzyl) derivative had superior properties and showed considerably stronger activity, by nearly an order of magnitude, against AR–dependent LNCaP and C4–2B cells compared to AR–independent DU145 cells. This lead compound decreased AR expression in a dose and time dependent manner and displayed promising therapeutic effects in a 22Rv1 CRPC xenograft mouse model. Computational target prediction and subsequent docking studies suggested three potential known prostate cancer targets: p38a MAPK, TGF-β1, and HGFR/c–Met, with the latter case of c–Met appearing stronger, owing to close structural similarity of the lead compound to known pyridazin-3-one derivatives with potent c-Met inhibitory activity. RNA-seq analysis showed dramatic reduction of AR signalling pathway and/or target genes by the lead compound, subsequently confirmed by quantitative PCR analysis. The lead compound was highly inhibitory against HGF, the c–Met ligand, which fitted well with the computational target prediction and docking studies. These results suggest that this compound could be a promising starting point for the development of an effective therapy for the treatment of CRPC.

Identification of Antagonistic Action of Pyrrolizidine Alkaloids in Muscarinic Acetylcholine Receptor M1 by Computational Target Prediction Analysis.

Pyrrolizidine alkaloids (PAs) are one of the largest distributed classes of toxins in nature. They have a wide range of toxicity, such as hepatotoxicity, pulmonary toxicity, neuronal toxicity, and carcinogenesis. Yet, biological targets responsible for these effects are not well addressed. Using methods of computational biology for target identification, we tested more than 200 PAs. We used a machine-learning approach that applies structural similarity for target identification, ChemMapper, and SwissTargetPrediction. The predicted targets with high probabilities were muscarinic acetylcholine receptor M1. The predicted interactions between these two targets and PAs were further studied by molecular docking-based binding energies using AutoDock and VinaLC, which revealed good binding affinities. The PAs are bound to the same binding pocket as pirenzepine, a known M1 antagonist. These results were confirmed by in vitro assays showing that PAs increased the levels of intracellular calcium. We conclude that PAs are potential acetylcholine receptor M1 antagonists. This elucidates for the first time the serious neuro-oncological toxicities exerted by PA consumption.

Inhibition of miR-199a-3p in a murine hypertrophic cardiomyopathy (HCM) model attenuates fibrotic remodeling

BackgroundHypertrophic cardiomyopathy (HCM) is an autosomal dominant genetic disorder, characterized by cardiomyocyte hypertrophy, cardiomyocyte disarray and fibrosis, which has a prevalence of ∼1: 200–500 and predisposes individuals to heart failure and sudden death. The mechanisms through which diverse HCM-causing mutations cause cardiac dysfunction remain mostly unknown and their identification may reveal new therapeutic avenues. MicroRNAs (miRNAs) have emerged as critical regulators of gene expression and disease phenotype in various pathologies. We explored whether miRNAs could play a role in HCM pathogenesis and offer potential therapeutic targets. Methods and resultsUsing high-throughput miRNA expression profiling and qPCR analysis in two distinct mouse models of HCM, we found that miR-199a-3p expression levels are upregulated in mutant mice compared to age- and treatment-matched wild-type mice. We also found that miR-199a-3p expression is enriched in cardiac non-myocytes compared to cardiomyocytes. When we expressed miR-199a-3p mimic in cultured murine primary cardiac fibroblasts and analyzed the conditioned media by proteomics, we found that several extracellular matrix (ECM) proteins (e.g., TSP2, FBLN3, COL11A1, LYOX) were differentially secreted (data are available via ProteomeXchange with identifier PXD042904). We confirmed our proteomics findings by qPCR analysis of selected mRNAs and demonstrated that miR-199a-3p mimic expression in cardiac fibroblasts drives upregulation of ECM gene expression, including Tsp2, Fbln3, Pcoc1, Col1a1 and Col3a1. To examine the role of miR-199a-3p in vivo, we inhibited its function using lock-nucleic acid (LNA)-based inhibitors (antimiR-199a-3p) in an HCM mouse model. Our results revealed that progression of cardiac fibrosis is attenuated when miR-199a-3p function is inhibited in mild-to-moderate HCM. Finally, guided by computational target prediction algorithms, we identified mRNAs Cd151 and Itga3 as direct targets of miR-199a-3p and have shown that miR-199a-3p mimic expression negatively regulates AKT activation in cardiac fibroblasts. ConclusionsAltogether, our results suggest that miR-199a-3p may contribute to cardiac fibrosis in HCM through its actions in cardiac fibroblasts. Thus, inhibition of miR-199a-3p in mild-to-moderate HCM may offer therapeutic benefit in combination with complementary approaches that target the primary defect in cardiac myocytes.

Discovery of thiazolo[5,4-c]isoquinoline based compounds as acetylcholinesterase inhibitors through computational target prediction, molecular docking and bioassay

A computer-aided drug design (CADD) approach was developed for a focused chemical library comprising a series of sixteen thiazolo[5,4-c]isoquinoline derivatives. Little is known about this group of heteroaromatic compounds, both from the point of view of their synthesis and their biological properties. First, our CADD approach included target prediction by Mondrian conformal prediction with the ChEMBL database. The acetylcholinesterase (AChE) was identified as having a high probability of thiazolo[5,4-c]isoquinolines being active against it. Secondly, the molecular docking predictions revealed four promising thiazoloisoquinolines (2, 7, 13 and 14) according to their prominent ligand-protein energy scores and relevant binding affinities with the AChE pocket residues. The subsequent in vitro evaluation of promising hits and related ones revealed a set of novel AChE inhibitors. Therefore, the findings reported herein may provide a new strategy for discovering novel AChE inhibitors.

A preliminary integrated analysis of miRNA-mRNA expression profiles reveals a role of miR-146a-3p/TRAF6 in plasma from gestational diabetes mellitus patients.

To utilize an integrative strategy to construct functional miRNA-mRNA regulatory networks by combining the reverse expression relationships between miRNAs and targets and computational predictions for gestational diabetes mellitus (GDM). A total of three microarray or RNA-seq datasets (GSE98043, GSE19649 and GSE92772) of plasma samples comparing GDM pregnant women and healthy control pregnant women were downloaded from the GEO database. The differentially expressed genes (DEmRNAs) and the differentially expressed miRNAs (DEmiRNAs) was performed. The target genes of DEmiRNAs were identified using two independent and complementary types of information: computational target predictions and expression relationships. An interaction network was constructed to identify hub genes of GDM. Another dataset (GSE92772) was used to externally verify the predictive ability of the hub genes. A total of 264 DEmiRNAs and 1217 DEmRNAs were identified with Hsa-miR-146a-3p ranked first of DEmiRNAs. Functions of GDM-related miRNAs were mainly enriched in the glypican pathway, proteoglycan syndecan-mediated signaling events, and syndecan-1-mediated signaling events. A total of 47 target genes, including TRAF6, were shared between the computational target predictions and DEmRNAs and were identified as target genes of hsa-miR-146a-3p. The interaction network analysis identified TRAF6, CASP8, PTPN6, and CHD3 as hub genes involved in the pathophysiological process of GDM. Next, independent external validation was performed using the GSE19649 dataset. The expression of TRAF6, CASP8 and CHD3 in eight pairs of GDM blood samples was confirmed to be higher than that in healthy pregnant women blood samples with a AUC of 0.813, 0.813, and 0.703, respectively. Our preliminary analysis revealed that miR-146a-3p/TRAF6 might play a central role in the pathogenesis of GDM. Three hub genes, TRAF6, CASP8, and CHD3, were identified and independently externally validated as potential GDM noninvasive serum markers for future biomarkers research.

Abstract P2-25-03: β-arrestin1, a potential tumour suppressor in breast cancer is downregulated in PERK/ATF4-dependent manner

Abstract Introduction: β-Arrestin1 (ARRB1) belongs to the arrestin family originally identified as a multifunctional adaptor protein that negatively regulates the desensitization and internalization of G-protein-coupled receptors. Recent studies have indicated that ARRB1 can form multiprotein complexes with transcription regulators, thereby indirectly inhibiting the function of some transcription factors, e.g., NF-kB, and promoting the activity of others, e.g., p53. Further, arrestins function as multifunctional adaptors in many signalling pathways, such as the Hedgehog (Hh), Wingless, Notch, and transforming growth factor-β (TGF-β) pathways. Expression of ARRB1 is downregulated in TNBC patients and ARRB1 expression level is inversely correlated with the histological grade of the breast cancer and positively associated with TNBC patient survival, suggestive of a tumour-suppressive function of ARRB1 in breast cancer. Loss of ARRB1 expression is associated with a poor prognosis for non-small cell lung cancer patients. However, the mechanisms regulating the expression of ARRB1 in human cancers remain unclear. We hypothesized that metabolic endoplasmic reticulum (EnR) stressors including low glucose and hypoxia that activate an unfolded protein response (UPR) in tumour cells might regulate the expression of ARRB1 in human cancers. Methods: Expression of ARRB1 was determined by a combination of real-time RT-PCR, western blotting along with promoter reporter assays. Computational target prediction analysis was done to identify miRNAs targeting ARRB1 as well as transcription factors regulating ARRB1 expression. ARRB1overexpressing breast cancer clones were generated by lentiviral transduction to investigate its functional role in tumour progression and drug sensitivity. Results: We observed that ARRB1 expression was significantly reduced in several cancers including breast tumours as compared to normal tissue and decreased expression of ARRB1 was associated with poor prognosis in Luminal A subtype. We found significant downregulation of ARRB1 mRNA and protein during conditions of EnR stress in multiple ER-positive breast cancer cell lines. Using PERK, ATF6 and XBP1 knockdown sub-clones of MCF7 we observed that PERK signalling was required for the downregulation of ARRB1. ATF4 and miR-204/211 downstream of PERK were identified as key regulators of ARRB1 expression. Conclusion: Our results show that ATF4 and miR-204/211 downstream of the PERK signalling pathway repress ARRB1 expression in breast cancer cells. Our results suggest that the downregulation of ARRB1 by EnR stressors in the tumour microenvironment may contribute to breast cancer progression. Citation Format: Afrin Sultana, Ananya Gupta, Sanjeev Gupta. β-arrestin1, a potential tumour suppressor in breast cancer is downregulated in PERK/ATF4-dependent manner [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-25-03.

Recently Emerged Genome Wide Computational Enhancer Target Prediction Tools: A Brief Survey

Enhancers are non-coding genomic regulatory elements capable of elevating gene transcription in various biological as well as developmental stages in the host organism. Discovered since 1981, the enhancers play major roles in genetic disease onset and development, orchestrating gene regulation patterns even across the same species via the sequence variations. To date, predicting enhancers and their targets remain a daunting task as universal enhancer markers are yet to be discovered. Computational enhancer target prediction involves three major approaches: supervised, unsupervised and semi-supervised machine learning methods which work on enhancer target features such as enhancer-promoter distance, closest promoter, co-conservation and correlation of molecular signals. In this review, we introduced some recently emerged enhancer target prediction tools as well as their modus operandi, in hope that we can provide future directions towards the development of a more robust tool to aid in the advancement of enhancer targeted treatment researches.

Recently Emerged Genome Wide Computational Enhancer Target Prediction Tools: A Brief Survey

Recently Emerged Genome Wide Computational Enhancer Target Prediction Tools: A Brief Survey

MicroRNA-Target Identification: A Combinatorial In Silico Approach.

Contemporary computational target prediction tools with their distinctive properties and stringency have been playing a vital role in pursuing putative targets for a solitary miRNA or a subcategory of miRNAs. These tools utilize a defined set of probabilistic algorithms, machine learning techniques, and information of experimentally validated miRNA targets to provide the best selection. However, there are numerous false-positive predictions, and a method to choose an archetypal approach and put the data provided by the prediction tools into context is still lacking. Moreover, sensitivity, specificity, and overall efficiency of a single tool have not yet been achieved. Therefore, a systematic combination of selective online tools combining elementary and advanced factors of miRNA target identification might reinforce the current target prediction regime. The focus of this study was to build a comprehensive workflow by combining six available online tools to facilitate the current understanding of miRNA-target prediction.

An expedient synthesis spectral characteristic computational studies and target prediction through insilco studies of 24α-ethylcholest-5, 22E-dien-3-yl-2- mercapto-benzoate and 11, 17 di-hydroxy preg-4-ene-3, 20-dione-21-yl-2-mercapto-benzoate

In the current work (24α-ethylcholest-5, 22E-dien-3-yl-2- mercapto-benzoate) (3) and (11, 17 di-hydroxy-preg-4-ene-3, 20-dione-21-yl-2-mercapto-benzoate) (4) were synthesized from Stigmasterol (1) and Hydrocortisone (2), respectively isolated as a major components from chloroform extract of stem part of the plant Allamandaviolacea. The synthesized compound (3) and (4) have been characterized with the aid of 1H, 13C, UV, FT-IR spectroscopy. Density functional theory (DFT) is used to reckonthe molecular geometry of synthesized compounds with B3LYP functional and 631 G (d,p) basis set in ground state.1H and 13C NMR chemical shifts were premeditated in ground state by means of Gauge-Including Atomic Orbital (GIAO) method and these values were correlated with experimental interpretations. Time dependent Density Functional Theory (TD-DFT) is employed in computing electronic properties such as HOMO and LUMO. AIM (Atom in molecule) analysis is used in evaluation of strength of H-bonding and its intra molecular interactions. Global reactivity descriptors were calculated to study the reactive sites within molecules. Dipole moment, polarizability and first hyperpolarizability values have been determined for interpretation of nonlinear optical (NLO) property of the synthesized compounds. Molecular electrostatic potential (MEP) has also been performed.The potential of both the compounds were evaluated against quorum sensing protein of Gram negative bacteria LsrB (1TM2), LsrK (5YA2), LasR (2UV0) and LuxS (5E68). Binding energy ranging from -8.40133 to -12.49721 kcal/mol and virtual score from -9.67971 to -13.81495 suggest that the compounds can play important role in controlling the unwanted growth of Gram negative bacteria by inhibiting their quorum sensing system.

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308–338 K, and pressure 120–400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E−04, 7.40E−05, and 9.73E−06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E−01, DTR shows an error rate equal to 1.63E−01, and GPR as the best mode illustrates 5.06E−02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E−03).

Anti-prostate cancer metabolites from the soil-derived Aspergillus neoniveus.

Prostate cancer (PCa) ranks as one of the most commonly diagnosed malignancies worldwide. Toxicity, lack of clinical efficacy, and development of resistance phenotypes are the main challenges in the control of prostate malignancies. Notably, castration-resistance prostate cancer (CRPCa) is a highly aggressive and metastatic phenotype of the disease with a poor prognosis and very limited therapeutic options. Herein, we report the isolation and genotypic identification of a soil-derived fungus Aspergillus neoniveus using the PCR-based internal transcribed spacer (ITS) region amplification approach. HPLC/MS investigation of the metabolic profile of the ethyl acetate extract from the fungal biomass revealed tentative identification of forty-five compounds belonging to various chemical classes including γ-butyrolactones, alkaloids, phenolics, and quinoids. Furthermore, the chromatographic purification of microbial extract enabled the identification of nervonic acid methyl ester (1) for the first time from endophytic fungi, as well as acetyl aszonalenin (2), and butyrolactone II (3) for the first time from A. neoniveus. The chemical frameworks of the isolated compounds were identified via extensive spectral analysis including 1 and 2D NMR and MS. The X-ray crystal structure and absolute configuration of acetyl aszonalenin (2) were also determined. Additionally, screening of in vitro anticancer activity of the fungal extract revealed its potential antiproliferative and anti-migratory activities against five different prostate cancer cells (PC3, PC-3M, DU-145, CWR-R1ca, and 22Rv1), including different cells with the castration-resistance phenotype. Moreover, the isolated metabolites significantly inhibited the proliferation, migration, and colonization of human prostate cancer cells at low micromolar levels, thus providing credence for future investigation of these metabolites in relevant anti-prostate cancer animal models. Furthermore, computational target prediction tools identified the cannabinoid G-protein coupled receptors type 1 (CB1) as a potential biological target mediating, at least in part, the anticancer effects of acetylaszonalenin (2). Moreover, molecular modeling and docking studies revealed a favorable binding pose at the CB1 receptor orthosteric ligand pocket aided by multiple polar and hydrophobic interactions with critical amino acids. In conclusion, the Aspergillus neoniveus-derived prenylated indole alkaloid acetylaszonalenin has promising anticancer activity and is amenable to further hit-to-lead optimization for the control of prostate malignancies via modulating CB1 receptors

Neuroprotective effects of dimethyl fumarate against depression-like behaviors via astrocytes and microglia modulation in mice: possible involvement of the HCAR2/Nrf2 signaling pathway.

We postulated that dimethyl fumarate (DMF) exerts neuroprotective effects against depression-like behaviors through astrocytes and microglia modulation. To ascertain our hypothesis and define the mechanistic pathways involved in effect of DMF on neuroinflammation, we used the depression model induced by chronic unpredictable mild stress (CUMS), in which, the mice were exposed to stressful events for 28 days and from the 14th day they received DMF in the doses of 50 and 100 mg/kg or fluoxetine 10 mg/kg or saline. On the 29th day, the animals were subjected to behavioral tests. Microglia (Iba1) and astrocyte (GFAP) marker expressions were evaluated by immunofluorescence analyzes and the cytokines TNF-α and IL-Iβ by immunoenzymatic assay. In addition, computational target prediction, 3D protein structure prediction, and docking calculations were performed with monomethyl fumarate (DMF active metabolite) and the Keap1 and HCAR2 proteins, which suggested that these could be the probable targets related protective effects. CUMS induced anxiety- and depressive-like behaviors, cognitive deficit, decreased GFAP, and increased Iba1, TNF-α, and IL-Iβ expression in the hippocampus. These alterations were reversed by DMF. Thus, it is suggested that one of the mechanisms involved in the antidepressant effect of DMF is neuroinflammatory suppression, through the signaling pathway HCAR2/Nrf2. However, more studies must be performed to better understand the molecular mechanisms of this drug.

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation.

As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.

Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer.

Ovarian cancer (OC), the eighth-leading cause of cancer-related death among females worldwide, is mainly represented by epithelial OC (EOC) that can be further subdivided into four subtypes: serous (75%), endometrioid (10%), clear cell (10%), and mucinous (3%). Major reasons for high mortality are the poor biological understanding of the OC mechanisms and a lack of reliable markers defining each EOC subtype. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression primarily by targeting messenger RNA (mRNA) transcripts. Their aberrant expression patterns have been associated with cancer development, including OC. However, the role of miRNAs in tumorigenesis is still to be determined, mainly due to the lack of consensus regarding optimal methodologies for identification and validation of miRNAs and their targets. Several tools for computational target prediction exist, but false interpretations remain a problem. The experimental validation of every potential miRNA-mRNA pair is not feasible, as it is laborious and expensive. In this study, we analyzed the correlation between global miRNA and mRNA expression patterns derived from microarray profiling of 197 EOC patients to identify the signatures of miRNA-mRNA interactions associated with overall survival (OS). The aim was to investigate whether these miRNA-mRNA signatures might have a prognostic value for OS in different subtypes of EOC. The content of our cohort (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas) reflects a real-world scenario of EOC. Several interaction pairs between 6 miRNAs (hsa-miR-126-3p, hsa-miR-223-3p, hsa-miR-23a-5p, hsa-miR-27a-5p, hsa-miR-486-5p, and hsa-miR-506-3p) and 8 mRNAs (ATF3, CH25H, EMP1, HBB, HBEGF, NAMPT, POSTN, and PROCR) were identified and the findings appear to be well supported by the literature. This indicates that our study has a potential to reveal miRNA-mRNA signatures relevant for EOC. Thus, the evaluation on independent cohorts will further evaluate the performance of such findings.

Angucyclines containing β-ᴅ-glucuronic acid from Streptomyces sp. KCB15JA151

Two angucyclines, pseudonocardones D (1) and E (2), were isolated from Streptomyces sp. KCB15JA151. The planar structure was elucidated by comprehensive spectroscopic analysis. The absolute configuration of the sugar unit was determined based on the basis of coupling constants, ROESY, chemical derivatization and HPLC analysis. The biological activities of compounds 1 and 2 were examined by performing a computational target prediction, which led to tests of the antiestrogenic activity. The result suggested that compound 1 might be an ERα antagonist.

MiR-548d-3p Alters Parasite Growth and Inflammation in Leishmania (Viannia) braziliensis Infection.

American Tegumentary Leishmaniasis (ATL) is an endemic disease in Latin America, mainly caused in Brazil by Leishmania (Viannia) braziliensis. Clinical manifestations vary from mild, localized cutaneous leishmaniasis (CL) to aggressive mucosal disease. The host immune response strongly determines the outcome of infection and pattern of disease. However, the pathogenesis of ATL is not well understood, and host microRNAs (miRNAs) may have a role in this context. In the present study, miRNAs were quantified using qPCR arrays in human monocytic THP-1 cells infected in vitro with L. (V.) braziliensis promastigotes and in plasma from patients with ATL, focusing on inflammatory response-specific miRNAs. Patients with active or self-healed cutaneous leishmaniasis patients, with confirmed parasitological or immunological diagnosis, were compared with healthy controls. Computational target prediction of significantly-altered miRNAs from in vitro L. (V.) braziliensis-infected THP-1 cells revealed predicted targets involved in diverse pathways, including chemokine signaling, inflammatory, cellular proliferation, and tissue repair processes. In plasma, we observed distinct miRNA expression in patients with self-healed and active lesions compared with healthy controls. Some miRNAs dysregulated during THP-1 in vitro infection were also found in plasma from self-healed patients, including miR-548d-3p, which was upregulated in infected THP-1 cells and in plasma from self-healed patients. As miR-548d-3p was predicted to target the chemokine pathway and inflammation is a central to the pathogenesis of ATL, we evaluated the effect of transient transfection of a miR-548d-3p inhibitor on L. (V.) braziliensis infected-THP-1 cells. Inhibition of miR-548d-3p reduced parasite growth early after infection and increased production of MCP1/CCL2, RANTES/CCL5, and IP10/CXCL10. In plasma of self-healed patients, MCP1/CCL2, RANTES/CCL5, and IL-8/CXCL8 concentrations were significantly decreased and MIG/CXCL9 and IP-10/CXCL10 increased compared to patients with active disease. These data suggest that by modulating miRNAs, L. (V.) braziliensis may interfere with chemokine production and hence the inflammatory processes underpinning lesion resolution. Our data suggest miR-548d-3p could be further evaluated as a prognostic marker for ATL and/or as a host-directed therapeutic target.

Bufotenine and its derivatives: synthesis, analgesic effects identification and computational target prediction.

Natural product bufotenine (5) which could be isolated from Venenum Bufonis, has been widely used as a tool in central nervous system (CNS) studies. We present here its quaternary ammonium salt (6) which was synthesized with high yields using 5-benzyloxyindole as raw materials, and we firstly discover its analgesic effects in vivo. The analgesic evaluation showed that compounds 5 and 6 had stronger effects on the behavior of formalin induced pain in mice. Moreover, the combination of compound 6 and morphine has a synergistic effect. We intended to explain the molecular mechanism of this effect. Therefore, 36 analgesic-related targets (including 15 G protein-coupled receptors, 6 enzymes, 13 ion channels, and 2 others) were systemically evaluated using reverse docking. The results indicate that bufotenine and its derivatives are closely related to acetyl cholinesterase (AChE) or α4β2 nicotinic acetylcholine receptor (nAChR). This study provides practitioners a new insight of analgesic effects.

272-OR: Impact of Maternal Diabetes on MicroRNA Expression in Fetal Tissue

In utero diabetes (DM) exposure alters offspring metabolic memory and increases future cardiovascular risk (CVD) risk. MicroRNAs (miR), small noncoding RNAs that repress mRNA translation, have been proposed as an epigenetic mechanism that alters metabolic programming. Using an unbiased sequencing approach, the impact of in utero DM exposure on miR abundance in human umbilical vein endothelial cells (HUVEC) from 6 DM-exposed infants and 6 infants born to mothers with normal glycemia matched for infant sex, ethnicity, and gestational age was examined. Computational target prediction algorithms were used to identify genes targeted by the most abundant miRNAs. The gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the most abundant miRNAs and their mRNA targets were annotated. MiRNA expression was quantified using RT-PCR. GO target enrichment analysis suggested the targets of the most abundant miRNAs were involved in transport, protein binding, mitochondria, DNA transcription, and cell cycle. The KEGG analysis found the most significantly enriched pathways were PI3K-Akt, AGE-RAGE signaling in diabetic complications, apoptosis, FoxO signaling, MAPK signaling, and cell senescence. In HUVEC from infants exposed to DM in utero, 31 miRNAs were more abundant including miR-210, miR-130b-3p, miR-491-5p while 6 miRs were less abundant including miR-146b-5p, mIR-192-3p, and miR-30e-5p (p&amp;lt;0.05) compared to matched controls. Analysis with RT-PCR revealed a 25% decrease in miR-146b (p 0.03) from the HUVEC of DM-exposed infants (N = 23) compared to controls (N = 39). MiRNA-146b is an anti-inflammatory molecule that targets IRAK1 and TRAF6 both of which modulate the NF-kB inflammatory pathway and affect the progression of CVD. MiRNA-146b is reduced in hypercholesterolemia and is protective to cardiomyocytes under chronic hypoxia. Sortilin, a target of miR-146b, is also associated with CVD. Thus, miR-146b may play a role in the development of excess CVD risk noted after in utero DM exposure. Disclosure A. Rughani: None. J. B. Tryggestad: None.

MiRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm.

microRNAs (miRNAs) are small non-coding RNAs which modulate the stability of gene targets and their rates of translation into proteins at transcriptional level and post-transcriptional level. miRNA dysfunctions can lead to human diseases because of dysregulation of their targets. Correct miRNA target prediction will lead to better understanding of the mechanisms of human diseases and provide hints on curing them. In recent years, computational miRNA target prediction methods have been proposed according to the interaction rules between miRNAs and targets. However, these methods suffer from high false positive rates due to the complicated relationship between miRNAs and their targets. The rapidly growing number of experimentally validated miRNA targets enables predicting miRNA targets with high precision via accurate data analysis. Taking advantage of these known miRNA targets, a novel recommendation system model (miRTMC) for miRNA target prediction is established using a new matrix completion algorithm. In miRTMC, a heterogeneous network is constructed by integrating the miRNA similarity network, the gene similarity network, and the miRNA-gene interaction network. Our assumption is that the latent factors determining whether a gene is the target of miRNA or not are highly correlated, i.e., the adjacency matrix of the heterogeneous network is low-rank, which is then completed by using a nuclear norm regularized linear least squares model under non-negative constraints. Alternating direction method of multipliers (ADMM) is adopted to numerically solve the matrix completion problem. Our results show that miRTMC outperforms the competing methods in terms of various evaluation metrics. Our software package is available at https://github.com/hjiangcsu/miRTMC.