Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns.

Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition.

The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies.Communicated by Ramaswamy H. Sarma.

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein-ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.

Investigation of anti-Alzheimer's activity of aqueous extract of areca nuts (Areca catechu L): in vitro and in vivo studies / Investigación de la actividad anti-Alzheimer del extracto acuoso de nueces de areca (Areca catechu L): estudios in vitro e in vivo

Alzheimer's disease (AD) is an age-related neurodegenerative disorder. Sever cognitive and memory impairments, huge increase in the prevalence of the disease, and lacking definite cure have absorbed worldwide efforts to develop therapeutic approaches. Since many drugs have failed in the clinical trials due to multifactorial nature of AD, symptomatic treatments are still in the center attention and now, nootropic medicinal plants have been found as versatile ameliorators to reverse memory disorders. In this work, anti-Alzheimer's activity of aqueous extract of areca nuts (Areca catechu L.) was investigated via in vitro and in vivo studies. It depicted good amyloid ß (Aß) aggregation inhibitory activity, 82% at 100 µg/mL. In addition, it inhibited beta-secretase 1 (BACE1) with IC50 value of 19.03 µg/mL. Evaluation of neuroprotectivity of the aqueous extract of the plant against H2O2-induced cell death in PC12 neurons revealed 84.5% protection at 1 µg/mL. It should be noted that according to our results obtained from Morris Water Maze (MWM) test, the extract reversed scopolamine-induced memory deficit in rats at concentrations of 1.5 and 3 mg/kg.

La enfermedad de Alzheimer (EA) es un trastorno neurodegenerativo relacionado con la edad. Los severos deterioros cognitivos y de la memoria, el enorme aumento de la prevalencia de la enfermedad y la falta de una cura definitiva han absorbido los esfuerzos mundiales para desarrollar enfoques terapéuticos. Dado que muchos fármacos han fallado en los ensayos clínicos debido a la naturaleza multifactorial de la EA, los tratamientos sintomáticos siguen siendo el centro de atención y ahora, las plantas medicinales nootrópicas se han encontrado como mejoradores versátiles para revertir los trastornos de la memoria. En este trabajo, se investigó la actividad anti-Alzheimer del extracto acuoso de nueces de areca (Areca catechu L.) mediante estudios in vitro e in vivo. Representaba una buena actividad inhibidora de la agregación de amiloide ß (Aß), 82% a 100 µg/mL. Además, inhibió la beta-secretasa 1 (BACE1) con un valor de CI50 de 19,03 µg/mL. La evaluación de la neuroprotección del extracto acuoso de la planta contra la muerte celular inducida por H2O2 en neuronas PC12 reveló una protección del 84,5% a 1 µg/mL. Cabe señalar que, de acuerdo con nuestros resultados obtenidos de la prueba Morris Water Maze (MWM), el extracto revirtió el déficit de memoria inducido por escopolamina en ratas a concentraciones de 1,5 y 3 mg/kg.

PROTECTIVE EFFECT OF IRIS GERMANICA L. IN Β-AMYLOID-INDUCED ANIMAL MODEL OF ALZHEIMER'S DISEASE.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia that is an irretrievable chronic neurodegenerative disease. In the current study, we have examined the therapeutic effects of Iris germanica extract on Amyloid ß (Aß) induced memory impairment. MATERIALS AND METHODS: Wistar rats were divided into five groups of 8 per each. Groups were as followed: control group which were normal rats without induction of AD, Aß group which received Aß (50 ng/side), iris 100 group which received Aß + Iris (100 mg/kg), iris 200 group which received Aß + Iris (200 mg/kg), and iris 400 group which received Aß + Iris (400 mg/kg). AD was established by intrahippocampal injection of 50 ng/µl/side Aß1-42. The day after surgery, animals in treatment groups received different doses of the aqueous extract of Iris by gavage for 30 days. Morris water maze test (MWM) was performed to assess the effects of I. germanica on learning and memory of rats with Aß induced AD. RESULTS: Data from MWM tests, including escape latency and traveled distance, demonstrated that I. germanica extract could markedly improve spatial memory in comparison to control. Moreover, the plant had a significantly better effect on the performance of AD rats in the probe test. CONCLUSION: I. germanica extract can successfully reverse spatial learning dysfunction in an experimental model of AD. Further neuro psyco-pharmacological studies are mandatory to reveal the mechanism of action of this natural remedy in the management of AD symptoms.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Genome-scale co-expression network comparison across Escherichia coli and Salmonella enterica serovar Typhimurium reveals significant conservation at the regulon level of local regulators despite their dissimilar lifestyles.

Availability of genome-wide gene expression datasets provides the opportunity to study gene expression across different organisms under a plethora of experimental conditions. In our previous work, we developed an algorithm called COMODO (COnserved MODules across Organisms) that identifies conserved expression modules between two species. In the present study, we expanded COMODO to detect the co-expression conservation across three organisms by adapting the statistics behind it. We applied COMODO to study expression conservation/divergence between Escherichia coli, Salmonella enterica, and Bacillus subtilis. We observed that some parts of the regulatory interaction networks were conserved between E. coli and S. enterica especially in the regulon of local regulators. However, such conservation was not observed between the regulatory interaction networks of B. subtilis and the two other species. We found co-expression conservation on a number of genes involved in quorum sensing, but almost no conservation for genes involved in pathogenicity across E. coli and S. enterica which could partially explain their different lifestyles. We concluded that despite their different lifestyles, no significant rewiring have occurred at the level of local regulons involved for instance, and notable conservation can be detected in signaling pathways and stress sensing in the phylogenetically close species S. enterica and E. coli. Moreover, conservation of local regulons seems to depend on the evolutionary time of divergence across species disappearing at larger distances as shown by the comparison with B. subtilis. Global regulons follow a different trend and show major rewiring even at the limited evolutionary distance that separates E. coli and S. enterica.

Reconstruction of an integrated genome-scale co-expression network reveals key modules involved in lung adenocarcinoma.

Our goal of this study was to reconstruct a "genome-scale co-expression network" and find important modules in lung adenocarcinoma so that we could identify the genes involved in lung adenocarcinoma. We integrated gene mutation, GWAS, CGH, array-CGH and SNP array data in order to identify important genes and loci in genome-scale. Afterwards, on the basis of the identified genes a co-expression network was reconstructed from the co-expression data. The reconstructed network was named "genome-scale co-expression network". As the next step, 23 key modules were disclosed through clustering. In this study a number of genes have been identified for the first time to be implicated in lung adenocarcinoma by analyzing the modules. The genes EGFR, PIK3CA, TAF15, XIAP, VAPB, Appl1, Rab5a, ARF4, CLPTM1L, SP4, ZNF124, LPP, FOXP1, SOX18, MSX2, NFE2L2, SMARCC1, TRA2B, CBX3, PRPF6, ATP6V1C1, MYBBP1A, MACF1, GRM2, TBXA2R, PRKAR2A, PTK2, PGF and MYO10 are among the genes that belong to modules 1 and 22. All these genes, being implicated in at least one of the phenomena, namely cell survival, proliferation and metastasis, have an over-expression pattern similar to that of EGFR. In few modules, the genes such as CCNA2 (Cyclin A2), CCNB2 (Cyclin B2), CDK1, CDK5, CDC27, CDCA5, CDCA8, ASPM, BUB1, KIF15, KIF2C, NEK2, NUSAP1, PRC1, SMC4, SYCE2, TFDP1, CDC42 and ARHGEF9 are present that play a crucial role in cell cycle progression. In addition to the mentioned genes, there are some other genes (i.e. DLGAP5, BIRC5, PSMD2, Src, TTK, SENP2, PSMD2, DOK2, FUS and etc.) in the modules.