Prediction of the Signaling Pathway in Polycystic Ovary Syndrome Using an Integrated Bioinformatics Approach.

OBJECTIVES: The purpose of this study was to define the underlying biological mechanisms of polycystic ovarian syndrome (PCOS) utilizing the protein-protein interaction networks (PPINs) that were constructed based on the putative disease-causing genes for PCOS. DESIGN: No animals were used in this research because this is an in silico study that mainly uses software and online analysis tools. Participants/Materials, Settings: Gene datasets related to PCOS were obtained from Genecards. METHODS: The PPINs of PCOS were created using the String Database after genes related to PCOS were obtained from Genecards. After that, we performed an analysis of the hub-gene clusters extracted from the PPIN using the ShinyGO algorithm. In the final step of this research project, functional enrichment analysis was used to investigate the primary biological activities and signaling pathways that were associated with the hub clusters. RESULTS: The Genecards database provided the source for the identification of a total of 1,072 potential genes related to PCOS. The PPIN that was generated by using the genes that we collected above contained a total of 82 genes and three different types of cluster interaction interactions. In addition, after conducting research on the PPIN with the shinyGO plug-in, 19 of the most important gene clusters were discovered. The primary biological functions that were enriched in the key clusters that were developed were ovarian steroidogenesis, the breast cancer pathway, regulation of lipid and glucose metabolism by the AMPK pathway, and ovarian steroidogenesis. The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS. LIMITATIONS: Several of the significant genes that were identified in this study, such as ACVR1, SMAD5, BMP6, SMAD3, SMAD4, and anti-mullerian hormone. It is necessary to do additional research using large samples, several centers, and multiple ethnicities in order to verify these findings. CONCLUSIONS: The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS. This information may possibly bring unique insights for the treatment of PCOS as well as the investigation of its underlying pathogenic mechanism.

Mycobacterium tuberculosis Lineage Distribution Using Whole-Genome Sequencing and Bedaquiline, Clofazimine, and Linezolid Phenotypic Profiles among Rifampicin-Resistant Isolates from West Java, Indonesia.

Tuberculosis (TB) is caused by Mycobacterium tuberculosis infection. Indonesia is ranked second in the world for TB cases. New anti-TB drugs from groups A and B, such as bedaquiline, clofazimine, and linezolid, have been shown to be effective in curing drug resistance in TB patients, and Indonesia is already using these drugs to treat patients. However, studies comparing the TB strain types with anti-TB resistance profiles are still relevant to understanding the prevalent strains in the country and their phenotypic characteristics. This study aimed to determine the association between the TB lineage distribution using whole-genome sequencing and bedaquiline, clofazimine, and linezolid phenotypic profile resistance among M. tuberculosisrifampicin-resistant isolates from West Java. M. tuberculosis isolates stock of the Department of Microbiology, Faculty of Medicine, Universitas Indonesia, was tested against bedaquiline, clofazimine, and linezolid using a mycobacteria growth indicator tube liquid culture. All isolates were tested for M. tuberculosis and rifampicin resistance using Xpert MTB/RIF. The DNA genome of M. tuberculosis was freshly extracted from a Löwenstein-Jensen medium culture and then sequenced. The isolates showed phenotypically resistance to bedaquiline, clofazimine, and linezolid at 5%, 0%, and 0%, respectively. We identified gene mutations on phenotypically bedaquiline-resistant strains (2/3), and other mutations also found in phenotypically drug-sensitive strains. Mykrobe analysis showed that most (88.33%) of the isolates could be classified as rifampicin-resistant TB. Using Mykrobe and TB-Profiler to determine the lineage distribution, the isolates were found to belong to lineage 4 (Euro-American; 48.33%), lineage 2 (East Asian/Beijing; 46.67%), and lineage 1 (Indo-Oceanic; 5%). This work underlines the requirement to increase the representation of genotype-phenotype TB data while also highlighting the importance and efficacy of WGS in predicting medication resistance and inferring disease transmission.

Forecasting the Effects of Platelet-Rich Plasma on Follicle Development Following Premature Ovarian Insufficiency by Network Interaction Analysis.

<b>Background and Objective:</b> Platelet-Rich Plasma (PRP) is proposed to have a potential regenerative effect on the ovaries following premature ovarian insufficiency (POI), however, the main impact on the process of follicle development (folliculogenesis) remains unclear. Protein-protein interaction (PPI) or network interaction analysis enables us to predict the targets of PRP's biomolecules using biological data through public databases and platforms. Therefore, this method is more efficient in time and cost than traditional laboratory procedures. The purpose of this study was to predict the targets of PRP's biomolecules on folliculogenesis following POI using PPI analysis and to clarify if those predictive targets integrate into PI3K/Akt signaling pathway which is an important pathway in folliculogenesis. <b>Materials and Methods:</b> Mining data targets of POI, PRP and folliculogenesis was done by GeneCards. Only genes with "protein-coding" category were analyzed further. Network analysis was performed using Cytoscape and STRING. Finally, STRING, Enricher and ShinyGO platforms were conducted to analyze gene ontology, including biological processes, molecular function and cellular components, as well as pathways. <b>Results:</b> Network analysis with Cytoscape and STRING discovered 107 gene hubs for POI, PRP and folliculogenesis. Analysis of KEGG pathway using STRING, Enricher and ShinyGO identified 43 genes integrated into PI3K/Akt signaling pathway. From the KEGG pathway, PI3K and Akt were revealed as two main targets following PRP treatment for POI patients. <b>Conclusion:</b> Biomolecules in PRP may recover ovarian follicle development following POI through the PI3K/Akt signaling pathway.

Exploration of the main active metabolites from Tinospora crispa (L.) Hook. f. & Thomson stem as insulin sensitizer in L6.C11 skeletal muscle cell by integrating in vitro, metabolomics, and molecular docking.

ETHNOPHARMACOLOGICAL RELEVANCE: Tinospora crispa (L.) Hook. f. & Thomson stem (TCS) has long been used as folk medicine for the treatment of diabetes mellitus. Previous study revealed that TCS possesses multi-ingredients and multi-targets characteristic potential as insulin sensitizer activity. However, its mechanisms of action and molecular targets are still obscure. AIM OF THE STUDY: In the present study, we investigated the effects of TCS against insulin resistance in muscle cells through integrating in vitro experiment and identifying its active biomarker using metabolomics and in molecular docking validation. MATERIALS AND METHODS: We used centrifugal partition chromatography (CPC) to isolate 33 fractions from methanolic extract of TCS, and then used UHPLC-Orbitrap-HRMS to identify the detectable metabolites in each fraction. We assessed the insulin sensitization activity of each fraction using enzyme-linked immunosorbent assay (ELISA), and then used confocal immunocytochemistry microscopy to measure the translocation of glucose transporter 4 (GLUT4) to the cell membrane. The identified active metabolites were further simulated for its molecular docking interaction using Autodock Tools. RESULTS: The polar fractions of TCS significantly increased insulin sensitivity, as measured by the inhibition of phosphorylated insulin receptor substrate-1 (pIRS1) at serine-312 residue (ser312) also the increasing number of translocated GLUT4 and glycogen content. We identified 58 metabolites of TCS, including glycosides, flavonoids, alkaloids, coumarins, and nucleotides groups. The metabolomics and molecular docking simulations showed the presence of minor metabolites consisting of tinoscorside D, higenamine, and tinoscorside A as the active compounds. CONCLUSIONS: Our findings suggest that TCS is a promising new treatment for insulin resistance and the identification of the active metabolites in TCS could lead to the development of new drugs therapies for diabetes that target these pathways.

In silico docking analysis of beta-defensin 20 against cation channel sperm-associated protein 1-4 to predict its role in the sperm maturation.

Beta-defensin 20 (DEFB20) is widely expressed in the epididymis with gene features involved in epididymal sperm maturation. However, the action mechanism and function of DEFB20 in sperm maturation are still unclear. One of the important roles of beta-defensin is the ion channel activity. The cation channel sperm-associated protein (CatSper) alpha is an ion channel protein found on the sperm surface. This study aimed to investigate the interaction between DEFB20 and CatSper1-4 protein in relation to the sperm maturation process. Protein sequences were obtained from the National Center for Biotechnology Information (NCBI). Protein modeling and validation were carried out by using the Robetta modeling server and the Ramachandran plot method. Rosetta web server was used for the docking analysis. The results revealed a natural interaction between DEFB20 and CatSper1-4. The interaction occurred at the cation channel (close to the casein kinase II), ion transport protein, and kinase c phosphorylation of the CatSper1-4 active site. The DEFB20 region interacting with CatSper2-4 was the beta-defensin domain, while with CatSper1 was the non-beta-defensin domain. Based on the analysis, DEFB20 may interact with CatSper α subunits, particularly CatsSper1, to affect ion channel activity during sperm maturation.

Synthesis and in vitro Activity of Eugenyl Benzoate Derivatives as BCL-2 Inhibitor in Colorectal Cancer with QSAR and Molecular Docking Approach.

OBJECTIVE: This study is aimed to acquiring new compounds of Eugenyl benzoate (2-methoxy-4-(prop-2-en-1-yl)phenyl benzoate) derivatives that can inhibit HT29 colorectal cancer cells. METHODS: In this research, we used several chemical reactions to synthesize novel compounds, such as Esterification, Demethylation, Halohydrin, and Sharpless reaction. Cytotoxicity assays were performed to test the inhibitory activity of compounds against HT29 colon cancer cells. QSAR analysis were carried out to analyse the relationship of chemical structure of the novel compounds with their cytotoxic activity. RESULT: Ten novel compounds were successfully synthesized and tested in vitro against the HT29 cell. The IC50 of the novel compounds were between 26.56 µmol/ml - 286.81 µmol/ml which compound 4-[(2S)-2,3-dihydroxypropyl]-2-methoxyphenyl 2-hydroxybenzoate (9) showed as best active compound as BCL-2 inhibitors better than other synthesized compounds and Eugenol as well. QSAR analysis of the in vitro results gave a Log equation: 1/IC50 = -0.865-0.210 (LogP)2 + 1,264 (logP)-0.994 CMR (n = 10; r = 0.706; SE: 0.21; F = 0.497, sig = 7.86). The equation shows the log variable P and CMR affect IC50. The properties of hydrophobicity (log P) are more instrumental than the ones of steric (CMR). CONCLUSION: The active compound (9) given best activities as BCL-2 inhibitors than other eugenol derivatives. QSAR indicates the logP and CMR have effect on its colorectal cytotoxic activity which the hydrophobicity parameter (logP) plays more role than the steric parameter (CMR).

Molecular docking, ADMET profiling of gallic acid and its derivatives (N-alkyl gallamide) as apoptosis agent of breast cancer MCF-7 Cells.

Background: In 2020, breast cancer has become the most common cancer in the world and in Indonesia. Searching for anticancer drugs using computational methods is considered more effective and selective than other methods. Gallic acid and its derivatives (esters and amides) are compounds that have biological activities such as anticancer effects. The purpose of this study was to analyse the molecular modelling and ADMET (Adsorption, Distribution, Metabolism, Excretion and Toxicity) profile of gallic acid derivative compounds (N-alkyl gallamides) as anticancer agents. Methods: Target proteins were selected by analysis of protein-protein and drug-protein interactions. Molecular modelling was done by molecular docking and molecular dynamic simulation. Predictive analysis of the ADMET profile of gallic acid and its derivatives (N-alkyl gallamide) was conducted using Marvin Sketch, Swissadme, protox II, and pkCSM pharmacokinetics. The selected target proteins were JUN, AKT1, CASP3, and CASP7. Results: Compounds N-octyl gallamide, N-ters-butyl gallamide, and N-isoamil gallamide were the three best gallic acid derivatives based on molecular modelling analysis of target proteins associated with breast cancer. The ADMET profile of the N-alkyl gallamide compound is predictable and shows a good profile as a candidate for anticancer drugs. Conclusion: N-octyl gallamide, N-ters-butyl gallamide, and N-isoamil gallamide have potential as anti-breast cancer agents.

Genomic landscape of pathogenic mutation of APC, KRAS, TP53, PIK3CA, and MLH1 in Indonesian colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) needs several mutations to occur in various genes, and can vary widely in different individuals; hence it is essential to be discovered in a specific population. Until recently, there has been no known study describing APC, TP53, PIK3CA, KRAS, and MLH1 of CRC in Indonesian population. This study describes the nature and location of mutation in CRC patients treated at three different hospitals in Jakarta. METHODS: This descriptive study was conducted on CRC patients who underwent neoadjuvant, surgical, and adjuvant therapy at RSCM, RSKJ, and MRCCC in 2017-2018. DNA analysis was performed using next-generation sequencing and aligned against GRCh38. The pathogenic variant was identified using ACMG classification and FATHMM score. Data related to behavior and survival were collected from medical records. RESULTS: Twenty-two subjects in which APC, TP53, and PIKCA were mutated. KRAS mutation occurred in 64%, while MLH1 in 45%. There were five mutation types: nonsense, missense, frameshift, splice-site, and silent mutation. There are four groups of co-occurring mutations: APC, TP53, PIK3CA (triple mutation/TM) alone; TM+KRAS; TM+MLH1; and TM+KRAS+MLH1, presenting different nature and survival. CONCLUSION: Indonesia has a distinct profile of pathogenic mutation, mainly presenting with locally-advanced stage with various outcomes and survival rate.

Whole genome sequencing data of Candida krusei, the pathogen causing Candidaemia, from Department of Parasitology Culture Collection, Faculty of Medicine Universitas Indonesia.

Candida krusei is a Candida non-albicans species with a high prevalence, which causes candidaemia. Current treatment guidelines include fluconazole as a primary therapeutic option for the treatment of these infections; however, it is only a fungistatic against Candida spp., and both inherent and acquired resistance to fluconazole have been reported. C. krusei species is also reported as the only Candida sp. which has an intrinsic resistance factor to fluconazole. Therefore, in dealing with antifungal resistance, it is necessary to develop new antifungal agents that are efficient in the treatment of fungal infections, especially those caused by C. krusei. The purpose of this study was to investigate the genome of clinical C. krusei isolates and correlate the resistant phenotypes with mutations in resistance genes. A total of 16 samples of C. krusei from clinical samples from hospitals in Jakarta were used in the experiment. All colonies were extracted using the QIAamp DNA Mini Kit. The library was prepared using the Illumina DNA Prep Kit. The sequencing process was carried out on the Illumina MiSeq Platform using a 2x301 paired-end configuration. FASTQ raw files are available under the BioProject Accession Number PRJNA819536 and Sequence Read Archive Accession Numbers SRR18739949 and SRR18739964.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches.

BACKGROUND: The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. METHODS: We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. RESULTS: From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. CONCLUSIONS: Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Xanthine Oxidase-Induced Inflammatory Responses in Respiratory Epithelial Cells: A Review in Immunopathology of COVID-19.

Xanthine oxidase (XO) is an enzyme that catalyzes the production of uric acid and superoxide radicals from purine bases: hypoxanthine and xanthine and is also expressed in respiratory epithelial cells. Uric acid, which is also considered a danger associated molecule pattern (DAMP), could trigger a series of inflammatory responses by activating the inflammasome complex path and NF-κB within the endothelial cells and by inducing proinflammatory cytokine release. Concurrently, XO also converts the superoxide radicals into hydroxyl radicals that further induce inflammatory responses. These conditions will ultimately sum up a hyperinflammation condition commonly dubbed as cytokine storm syndrome (CSS). The expression of proinflammatory cytokines and neutrophil chemokines may be reduced by XO inhibitor, as observed in human respiratory syncytial virus (HRSV)-infected A549 cells. Our review emphasizes that XO may have an essential role as an anti-inflammation therapy for respiratory viral infection, including coronavirus disease 2019 (COVID-19).

Genome-based characterization of Escherichia coli causing bloodstream infection through next-generation sequencing.

Escherichia coli are one of the commonest bacteria causing bloodstream infection (BSI). The aim of the research was to identify the serotypes, MLST (Multi Locus Sequence Type), virulence genes, and antimicrobial resistance of E. coli isolated from bloodstream infection hospitalized patients in Cipto Mangunkusumo National Hospital Jakarta. We used whole genome sequencing methods rather than the conventional one, to characterized the serotypes, MLST (Multi Locus Sequence Type), virulence genes, and antimicrobial resistance (AMR) of E. coli. The composition of E. coli sequence types (ST) was as follows: ST131 (n = 5), ST38 (n = 3), ST405 (n = 3), ST69 (n = 3), and other STs (ST1057, ST127, ST167, ST3033, ST349, ST40, ST58, ST6630). Enteroaggregative E. coli (EAEC) and Extra-intestinal pathogenic E. coli (ExPEC) groups were found dominant in our samples. Twenty isolates carried virulence genes for host cells adherence and 15 for genes that encourage E. coli immune evasion by enhancing survival in serum. ESBL-genes were present in 17 E. coli isolates. Other AMR genes also encoded resistance against aminoglycosides, quinolones, chloramphenicol, macrolides and trimethoprim. The phylogeny analysis showed that phylogroup D is dominated and followed by phylogroup B2. The E. coli isolated from 22 patients in Cipto Mangunkusumo National Hospital Jakarta showed high diversity in serotypes, sequence types, virulence genes, and AMR genes. Based on this finding, routinely screening all bacterial isolates in health care facilities can improve clinical significance. By using Whole Genome Sequencing for laboratory-based surveillance can be a valuable early warning system for emerging pathogens and resistance mechanisms.

Targeted genome sequencing data of young women breast cancer patients in Cipto Mangunkusumo national hospital, Jakarta.

Breast cancer is the most common cancer in women, accounting for approximately 25% of all cancer cases worldwide. Some breast cancer patients are genetically predisposed to genes involved in genomic stability. We report the targeted genome sequencing data of 24 young women (aged below 45 years) breast cancer patients admitted to Cipto Mangunkusumo National Hospital, Jakarta, Indonesia. These data will be useful in detecting the genome markers of breast cancer and in deciding the diagnostics and therapies. DNA sequences were obtained using the Illumina NextSeq 500 platform. FASTQ raw files are available under BioProject accession number PRJNA606794 and Sequence Read Archive accession numbers SRR11774092-SRR11774115.

Whole genome sequencing data of Escherichia coli isolated from bloodstream infection patients in Cipto Mangunkusumo National Hospital, Jakarta, Indonesia.

Bloodstream infections (BSIs) are some of the most devastating preventable complications in critical care units. Of the bacterial causes of BSIs, Escherichia coli is the most common among Enterobacteriaceae. Bacteria resistant to therapeutic antibiotics represent a significant global health challenge. In this study, we present whole genome sequence data of 22 E. coli isolates that were obtained from bloodstream infection patients admitted to Cipto Mangunkusumo National Hospital, Jakarta, Indonesia. These data will be useful for analysing the serotypes, virulence genes, and antimicrobial resistance genes of E. coli. DNA sequences of E. coli were obtained using the Illumina MiSeq platform. The FASTQ raw files of these sequences are available under BioProject accession number PRJNA596854 and Sequence Read Archive accession numbers SRR10761126-SRR10761147.

In silico and in vitro identification of candidate SIRT1 activators from Indonesian medicinal plants compounds database.

Sirtuin 1 (SIRT1) is a class III family of protein histone deacetylases involved in NAD+-dependent deacetylation reactions. It has been suggested that SIRT1 activators may have a protective role against type 2 diabetes, the aging process, and inflammation. This study aimed to explore and identify medicinal plant compounds from Indonesian Herbal Database (HerbalDB) that might potentially become a candidate for SIRT1 activators through a combination of in silico and in vitro methods. Two pharmacophore models were developed using co-crystalized ligands that allosterically bind with SIRT1 similar to the putative ligands used by SIRT1 activators. Then, these were used for the virtual screening of HerbalDB. The identified compounds were subjected to molecular docking and 50 ns molecular dynamics simulation. Molecular dynamics simulation was analyzed using MM-GB(PB)SA methods. The compounds identified by these methods were tested in an in vitro study using a SIRT-Glo™ luminescence assay. Virtual screening using structure-based pharmacophores predicted that mulberrin as the best candidate SIRT1 activator. Virtual screening using ligand-based pharmacophores predicted that gartanin, quinidine, and quinine to be the best candidates as SIRT1 activators. The molecular docking studies showed the important residues involved were Ile223 and Ile227 at the allosteric region. The MM-GB(PB)SA calculations confirmed that mulberrin, gartanin, quinidine, quinine showed activity at allosteric region and their EC50 in vitro values are 2.10; 1.79; 1.71; 1.14 µM, respectively. Based on in silico and in vitro study results, mulberin, gartanin, quinidine, and quinine had good activity as SIRT1 activators.

In Silico Approach to Finding New Active Compounds from Histone Deacetylase (HDAC) Family.

BACKGROUND: Histone Deacetylase (HDAC) enzymes in the human body play an important role in the transcriptional regulation of gene expression. In the last decade, HDAC inhibitors and activators have been explored and have become known as therapeutic agents for many diseases such as osteodystrophy, neurogenerative disorders, cardiomyopathy, cancer, and diabetes. In recent years, the development of HDAC inhibitors or activators to obtain new potent lead compounds has been conducted using in vitro, in vivo, and in silico methods. Some HDAC family inhibitors and activators have been discovered. But some compounds have limitations such as not selectively binding to one of the HDAC variants. METHODS: At present, through bioinformation, HDAC family sequences have been revealed, and some in silico methods such as molecular modelling (homology modelling and pharmacophore modelling), virtual screening, and molecular dynamics are widely used to find and develop new potent and selective compounds. RESULTS: The main utilization of molecular modelling in this work is intended to complete the HDAC structure that partially lacks data regarding its amino acid monomer. Virtual screening methods are helpful in finding the best binding affinity of the test compounds. By molecular dynamic simulation, the temperature, time, and pressure can be adjusted to analyze the hydrogen bond. CONCLUSION: Combining these in silico approaches will be a more effective and efficient solution in finding new lead compounds for HDAC drug discovery research in the future.