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"Bakteriosin adalah peptida hasil produksi bakteri yang saat ini banyak diteliti karena aktivitasnya yang dapat menghambat pertumbuhan bakteri. Resistensi terhadap antibiotik yang semakin nyata menyebabkan bakteriosin dianggap cocok menjadi salah satu kandidat API (active pharmaceutical ingredients) yang dapat dimanfaatkan sebagai komplemen antibiotik. Salah satu bakteri yang memproduksi peptida bakteriosin adalah S. macedonicus MBF10-2 yang menghasilkan senyawa bakteriosin lantibiotik dan non-lantibiotik. Tujuan penelitian ini adalah memperoleh cara fraksinasi dan fraksi peptida bakteriosin dari S. macedonicus MBF10-2 yang aktivitasnya optimal dengan metode centrifugal filtration dan presipitasi amonium sulfat, serta profil metabolit umumnya. Konfirmasi cara fraksinasi dengan aktivitas optimal dilakukan dengan uji hambat. Profil komposisi metabolit ekstrak kasar dianalisis dengan LC-ESI-QTOF-MS/MS. Cara fraksinasi yang optimal adalah dengan centrifugal filtration. Hasilnya menunjukkan bahwa fraksi ≥ 30kDa dan ≥ 3kDa mengandung peptida bakteriosin yang memberikan penghambatan paling kuat terhadap Leuconostoc mesenteroides TISTR120, namun tidak memberikan penghambatan yang kuat terhadap Micrococcus luteus T18. Analisis metabolit ekstraseluler tak tertarget dari ekstrak kasar memberikan hasil profil kombinasi fragmen asam amino, serta adanya kandungan asam laktat dan malat yang sesuai dengan prediksi hasil metabolit bakteri asam laktat pada umumnya. Analisis metabolit ekstraseluler perlu dilakukan konfirmasi dengan analisis metabolit ekstraseluler tertarget untuk menghasilkan profil yang lebih komprehensif terhadap bakteri S. macedonicus MBF10-2.

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Bacteriocins are peptides produced by bacteria that are being developed because of their activities that can inhibit the growth of bacteria. Resistance to antibiotics that is increasingly real causes bacteriocins to be considered suitable to be one of the API (active pharmaceutical ingredients) candidates that can be used as an antibiotic complement. One of the bacteria that produces bacteriocin peptides is S. macedonicus MBF10-2 which produces lantibiotic and non-lantibiotic bacteriocin compounds. The purpose of this study is to obtain optimal fractionation and fractionation methods as well as metabolite profiles generally by centrifugal filtration and ammonium sulfate precipitation methods. Confirmation of the method of fractionation and fractionation is carried out by inhibition test. The metabolite composition profile of the crude extract was analyzed with LC-ESI-QTOF-MS/MS. Results showed that the ≥ fractions of 30kDa and ≥ 3kDa contained bacteriocin peptides that inhibit Leuconostoc mesenteroides TISTR120 significantly whereas against Micrococcus luteus T18 does not give strong inhibition results. The results of the analysis of untargeted extracellular metabolites of crude extracts provided the results of a combination profile of amino acid fragments and detected lactic and malic acid content in accordance with the prediction of the results of metabolites of lactic acid bacteria in general. Analysis of extracellular metabolites needs to be confirmed by analysis of displaced extracellular metabolites to produce a more comprehensive profile against S. macedonicus MBF10-2 bacteria."

"Indonesia memiliki keanekaragaman lebah terbesar di seluruh Asia, dimana hingga tahun 2018 tercatat terdapat 46 spesies lebah tanpa sengat (stingless bee) yang ditemukan di Indonesia. Profil kimia dari propolis sangat bervariasi tergantung pada sumber vegetasi dan asal geografisnya, hal ini menimbulkan kesulitan untuk standarisasi. Oleh karena itu tujuan dari penelitian ini adalah untuk menemukan gambaran standarisasi yang dilihat dari rentang kandungan fenolik dan flavonoid totalnya, profil kimia, klasterisasi, dan senyawa yang berpotensi sebagai marker pada propolis Indonesia menggunakan pendekatan metabolomik dengan instrumen KCKUT-SM/SM. Penelitian dilakukan terhadap 19 sampel dari wilayah Sumatera, Jawa, Kalimantan, Nusa Tenggara Barat, dan Sulawesi. Hasil analisis KCKUT-SM/SM kemudian dikombinasikan dengan analisis statistik multivariat Principal Component Analysis (PCA). Hasil dari penelitian ini menunjukkan  terdapat perbedaan profil kimia dari sampel propolis Indonesia, dimana jumlah dan jenis senyawa yang terdeteksi bervariasi antar sampel, tidak terdapat pengelompokkan tertentu pada sampel propolis Indonesia dikarenakan banyaknya variasi kandungan kimia dari seluruh sampel propolis yang digunakan, dan ditemukan 3 senyawa yang berpotensi sebagai penciri, yaitu Choline, DL-Stachydrine, dan Betaine. Standar kandungan fenolik dan flavonoid propolis Indonesia berada pada rentang 7,06+ 2,77 mg GAE/g hingga 120,32+ 13,61 mg GAE/g dan 1,34 + 0,01 mg QE/g hingga 36,45 + 3,55 mg QE/g.

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Indonesia has the largest distribution of bees in all of Asia, until 2018 there were 46 species of stingless bees found in Indonesia. The chemical profile of propolis varies greatly depending on its vegetative source and geographic origin, making the standardization difficult. Therefore the aim of this study was to find a standardization seen from the range of total phenolic and flavonoid contents, chemical profiles, clusterization, and compounds that have the potential as markers in Indonesian propolis using a metabolomics approach with the UHPLC-MS/MS instrument. The study was conducted on 19 samples from Sumatra, Java, Kalimantan, West Nusa Tenggara and Sulawesi. The results of the UHPLC-MS/MS analysis were then combined with the Principal Component Analysis (PCA) multivariate statistical analysis. The results of this study indicated that there were differences in the chemical profile of the Indonesian propolis samples, where the number and types of compounds detected varied between samples, there were no specific groupings in the Indonesian propolis samples due to the large variation in chemical content of all the propolis samples used, and found 3 different compounds potential as markers, namely Choline, DL-Stachydrine, and Betaine. The standards for Indonesian propolis phenolic and flavonoid content ranges from 7,06 + 2,77 mg GAE/g to 120,32 + 13,61 mg GAE/g and 1,34 + 0,01 mg QE/g up to 36,45 + 3,55 mg QE/g."

"Keterbatasan glomerulus filtration rate (eGFR) dan urine albumin creatinine ratio (uACR) sebagai acuan menyebabkan keterlambatan diagnosis dan prognosis penyakit ginjal diabetes. Perkembangan diabetes mengarah pada kerusakan ginjal dicerminkan oleh penanda (biomarker) yang ditemukan dalam spesimen biologis. Penelitian ini bertujuan mencari metabolit potensial sebagai biomarker pada populasi Indonesia dengan membandingkan metabolit dalam urin pasien diabetes dengan risiko ginjal rendah (n=16) dan tinggi (n=16) menurut klasifikasi KDIGO2022. Analisis metabolomik dilakukan menggunakan liquid chromatography/mass spectrometry quadrupole time-of-flight (LC/MS-QTOF) dengan analisis statistik data menggunakan software Metaboanalyst5,0. Metabolit diidentifikasi menggunakan database Human Metabolome Database (HMDB), Metlin, dan Pubchem. Diskriminasi antar 2 kelompok divisualisasikan dengan Principal Component Analysis (PCA) dan Partial Least Squares-Discriminant Analysis (PLS-DA). Signifikansi metabolit antar 2 kelompok ditentukan dengan T-test (p<0,05), variable importance for projection (VIP>1), dan fold change (log2(FC)>1,2). Metabolit yang dipilih hanya metabolit endogen yang diketahui jalur metabolismenya. Dari berbagai parameter tersebut, metabolit yang potensial sebagai biomarker harus memenuhi nilai area under curve (AUC)>0,65. Berdasarkan karakteristik dasar dan klinis, tidak terdapat perbedaan bermakna karakteristik dasar (usia, jenis kelamin, indeks massa tubuh, durasi menderita DMT2, frekuensi olahraga, kebiasaan merokok, penyakit lain, kepatuhan minum obat, regimen terapi metformin-glimepirid) dan pemeriksaan klinis (HbA1c, tekanan darah sistol, dan diastol) antara kedua kelompok (p>0,05). Ditemukan 23 metabolit yang memenuhi parameter VIP, p-value, dan fold change. Disimpulkan, tiga metabolit teratas dengan AUC>0,65 merupakan biomarker potensial yang membedakan kedua kelompok, yaitu indoksil glukuronida, N-asetilserotonin glukuronida, dan gliserofosfokolin. Indoksil glukuronida dan N-asetilserotonin glukuronida terlibat dalam metabolisme triptofan dan glukuronat, sedangkan gliserofosfokolin terlibat dalam jalur metabolisme gliserofosfolipid dan eter lipid.

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The limited utility of glomerulus filtration rate (eGFR) dan urine albumin creatinine ratio (uACR) as the gold standard lead to late diagnosing and prognosing of diabetic kidney disease. Diabetes progression contributes to kidney damage and is reflected by biomarkers in patients' biological samples. This study aims to identify potential endogenous metabolite biomarkers for improved diagnosis and prognosis by comparing metabolites in the urine of diabetic patients with low (n=16) and high (n=16) kidney disease risk in the Indonesian population according to the KDIGO2022 classification. Metabolomic analysis was conducted using liquid chromatography/mass spectrometry quadrupole time-of-flight (LC/MS-QTOF) with Metaboanalyst5.0 software. Metabolites were identified using the Human Metabolome Database, Metlin, and PubChem. Discrimination between the two groups was visualized using principal component analysis (PCA) and Partial Least squares discriminant analysis (PLS-DA). Based on patients' characteristics, no significant differences were observed in baseline characteristics (age, gender, body mass index, duration of type 2 diabetes mellitus, exercise frequency, smoking habits, comorbidities, medication adherence, metformin-glimepiride therapy regimen) and clinical characteristics (HbA1c, systolic and diastolic blood pressure) between two groups (p>0.05). According to the findings of the T-test (p<0.05), fold change (log2(FC)>1.2), and variables important for the projection (VIP>1), there were 23 metabolites substantially different between the two groups. In conclusion, the top 3 metabolites with the area under curve (AUC) value>0.65 demonstrated potential biomarker differentiating among two groups; these are indoxyl glucuronide, N-acetylserotonin glucuronide, and glycerophosphocholine. Indoxyl glucuronide and N-acetylserotonin glucuronide involved in tryptophan metabolism and glucuronate interconversion. Glycerophsophocholine involved in glycerophospholipid and ether lipid metabolism."

"This text explores the intersection of genetics and metabolomics, and points the way to more comprehensive studies of inborn variation of metabolism. All chapters refer to one or more published experimental datasets."

"Penggunaan lisat mikroba untuk perawatan kulit telah tumbuh secara substansial di pasaran. Lisat merupakan sel yang membran luarnya telah rusak karena bahan kimia atau proses fisik yang dikategorikan sebagai postbiotik. Posbiotik adalah produk inaktif atau metabolit dari mikroorganisme yang memiliki aktivitas biologis. Eksplorasi mikroba komensal kulit dalam pengembangan bahan aktif perawatan kulit telah dilakukan oleh peneliti terdahulu, empat galur bakteri dari sampel kulit suku Jawa, Staphylococcus hominis MBF12–19J, Staphylococcus warneri MBF02–19J, Bacillus subtilis MBF10–19J, Micrococcus luteus MBF05–19J. Penggabungan ke-empat galur bakteri dalam bentuk koktail telah dilakukan pada penelitian ini dengan tujuan untuk mendapatkan metode perolehan rendemen (Yield) lisat koktail bakteri yang optimum dengan mengoptimalkan pertumbuhan masing-masing bakteri dalam media produksi dilanjutkan dengan proses koktail skala Batch fermentation menggunakan biofermentor 2L. Perolehan lisat optimum berupa debris sel dan fraksi lisat diproses melalui enkapsulasi dengan inulin dan maltodekstrin metode spray drying kemudian diuji aktivitas penangkalan radikal bebas dan analisis metabolomik untuk mengetahui profil metabolitnya. Hasil menunjukkan waktu inkubasi pertumbuhan optimum kultur bakteri individu skala biofermentor 2L untuk Micrococcus luteus MBF05-19J, Bacillus subtilis MBF10-19J, Staphylococcus warneri MBF02-19J, Staphylococcus hominis MBF12-19 adalah 21, 17, 7, dan 15 jam. Waktu inkubasi fermentasi 3 jam pada suhu 37 ℃, agitasi 50 RPM, aerasi 5% oksigen terlarut. Rendemen lisat koktail kering semprot sebesar 16,5325% dengan karakteristik serbuk putih, halus, homogen, higroskopis, beraroma khas lisat. Kandungan lembab 8,93%, ukuran partikel 1150-1470 nm, aktivitas antiradikal bebas (IC50) 755,258 μg/mL. Profil metabolit lisat koktail dan serbuk lisat kering semprot menunjukkan kandungan metabolit yang masih sama.

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The use of microbial lysates for skincare has substantially grown in the market. Lysates are cells with damaged outer membrane due to chemical or physical processes and categorized as postbiotics. Postbiotics are inactive product or metabolites of microorganisms with biological activities. Exploration of skin commensal microbes in the development of skincare active ingredients was carried out by previous study, four bacterial strains from Javanese skin samples, Staphylococcus hominis MBF12–19J, Staphylococcus warneri MBF02–19J, Bacillus subtilis MBF10–19J, Micrococcus luteus MBF05–19J. The combination of bacterial strains in cocktail form was completed in this study to obtain the optimum bacterial cocktail lysate yield method by optimizing each bacterium’s growth in production medium followed by Batch fermentation cocktail process using 2L biofermentor. Optimum lysate recovery of cell debris and lysate fraction was processed through encapsulation with inulin and maltodextrin by spray drying method, followed by radical scavenging assay and metabolomic analysis to determine the metabolite profile. The result showed the optimum growth culture incubation time of 2L biofermentor scale for Micrococcus luteus MBF05-19J, Bacillus subtilis MBF10-19J, Staphylococcus warneri MBF02-19J, Staphylococcus hominis MBF12-19 were 21, 17, 7, 15 hours respectively. Fermentation incubation time was 3 hours at 37 ℃, agitation 50 RPM, aeration 5% dissolved oxygen. The yield of spray dried cocktail lysate was 16,5325% with the characteristic of white, smooth, homogenous, hygroscopic, distinctive lysate aroma. Moisture content was 8,93%, particle size 1150-1470 nm, radical scavenging activity (IC50) 755,258 μg/mL. Metabolite profile of cocktail lysate and spray dried cocktail lysate remained the same."

""While most of the focus in "omics" science over the past decade has been on sequencing the human genome [1] or annotating the human proteome [2], there is another equally important component of the human body that has, until recently, been largely overlooked: the human metabolome. The human metabolome can be thought of as the complete collection of small molecule metabolites found in our bodies. These small molecules include such chemical entities as peptides, amino acids, nucleic acids, carbohydrates, organic acids, vitamins, minerals, food additives, drugs and just about any other chemical (with a molecular weight 1500 Da) that can be used, ingested or synthesized by humans. Metabolites act as the bricks and mortar of our cells. They serve as the building blocks for all of our macromolecules including proteins, RNA, DNA, carbohydrates, membranes and all other biopolymers that give our cells their structure and integrity. Metabolites also act as the fuel for all cellular processes, the buffers to help tolerate environmental insults and the messengers for most intra- and intercellular events. Together with the genome and the proteome, the human metabolome essentially defines who and what we are."-- Provided by publisher"

"An update to the popular guide to proteomics technology applications in biomedical research Building on the strength of the original edition, this book presents the state of the art in the field of proteomics and offers students and scientists new tools and techniques to advance their own research. Written by leading experts in the field, it provides readers with an understanding of new and emerging directions for proteomics research and applications. Proteomics for Biological Discovery begins by discussing the emergence of proteomics technologies and summarizing the potential insights to be gained from proteome-level research. The tools of proteomics, from conventional to novel techniques, are thoroughly covered, from underlying concepts to limitations and future directions. Later chapters provide an overview of the current developments in post-translational modification studies, structural proteomics, biochemical proteomics, applied proteomics, and bioinformatics relevant to proteomics. Chapters cover: Quantitative Proteomics for Differential Protein Expression Profiling; Protein Microarrays; Protein Biomarker Discovery; Biomarker Discovery using Mass Spectrometry Imaging; Protein-Protein Interactions; Mass Spectrometry Of Intact Protein Complexes; Crosslinking Applications in Structural Proteomics; Functional Proteomics; High Resolution Interrogation of Biological Systems via Mass Cytometry; Characterization of Drug-Protein Interactions by Chemoproteomics; Phosphorylation; Large-Scale Phosphoproteomics; and Probing Glycoforms of Individual Proteins Using Antibody-Lectin Sandwich Arrays. Presents a comprehensive and coherent review of the major issues in proteomic technology development, bioinformatics, strategic approaches, and applications Chapters offer a rigorous overview with summary of limitations, emerging approaches, questions, and realistic future industry and basic science applications Features new coverage of mass spectrometry for high throughput proteomic measurements, and novel quantitation strategies such as spectral counting and stable isotope labeling Discusses higher level integrative aspects, including technical challenges and applications for drug discovery Offers new chapters on biomarker discovery, global phosphorylation analysis, proteomic profiling using antibodies, and single cell mass spectrometry Proteomics for Biological Discovery is an excellent advanced resource for graduate students, postdoctoral fellows, and scientists across all the major fields of biomedical science"

"Diabetes mellitus tipe II adalah penyakit gangguan metabolik yang gejalanya adalah hiperglikemia atau tingkat glukosa darah yang meningkat, yang disebabkan oleh rusaknya fungsi insulin baik dalam sekresi, kerja, atau keduanya. Penyakit ini adalah penyakit menahun yang bisa menyebabkan berbagai komplikasi. Salah satu pendekatan untuk mengobati penyakit diabetes mellitus tipe II adalah dengan memperlambat penyerapan glukosa melalui penghambatan α-glukosidase. Enzim α-glukosidase akan mengkatalisasi hidrolisis gula kompleks, termasuk karbohidrat, menjadi glukosa yang bisa diserap dalam usus. Dengan demikian penghambatan α-glukosidase akan menghambat pembentukan glukosa yang bisa diserap usus, yang kemudian akan memperlambat kenaikan tingkat glukosa darah. Banyaknya penderita diabetes mellitus tipe II mendorong usaha untuk mencari senyawa aktif penghambat α-glukosidase baru dari bahan alam, yang diharapkan mempunyai efikasi yang lebih baik atau lebih mudah diekstrak atau disintesis daripada yang ada sekarang. Penelitian ini bertujuan untuk mencari senyawa aktif penghambat α-glukosidase dari bahan alam yang ada di Indonesia, khususnya dari tanaman Artabotrys sp. Ada 4 jenis tanaman Artabotrys sp. yang diteliti keaktifan ekstrak etanolnya terhadap penghambatan α-glukosidase: Artabotrys hexapetalus (daun dan kulit batang), Artabotrys suaveolens (daun dan kulit batang), Artabotrys blumei (batang dan ranting), dan Artabotrys sumatranus (daun dan ranting). Hasil pengetesan menggunakan bioassay menunjukkan bahwa ekstrak etanol yang memiliki aktivitas penghambatan α-glukosidase terkuat adalah dari daun A. sumatranus, disusul berturut-turut oleh ranting A. sumatranus, daun A. suaveolens, kulit batang A. hexapetalus, kulit batang A. suaveolens, batang A. blumei, ranting A. blumei, dan terakhir daun A. hexapetalus. Skrining juga dilakukan dengan pengetesan bioassay DPPH (2,2-diphenyl-1-picrylhydrazyl) dan FRAP (ferric ion reducing antioxidant power) untuk mengetahui aktivitas antioksidan ekstrak etanol Artabotrys sp. yang ada, dan didapatkan bahwa secara umum aktivitas penghambatan α-glukosidase mempunyai relasi positif dengan aktivitas antioksidan. Analisis tes total fenolik, total flavonoid, tes aktivitas antioksidan (DPPH dan FRAP), dan tes aktivitas penghambatan α-glukosidase; serta relasi antara tes-tes tersebut, menghasilkan prediksi golongan senyawa aktif penghambat α-glukosidase pada ekstrak Artabotrys sp. dan relasinya terhadap aktivitas antioksidan. Prediksi tahap skrining ini secara umum dikonfirmasi oleh hasil penambatan molekuler pada senyawa-senyawa yang terindentifikasi dari analisis LC-MS/MS (liquid chromatography – tandem mass spectrometry) pada ekstrak yang ada. Penelitian lalu difokuskan pada ekstrak daun A. sumatranus yang memiliki potensi yang paling tinggi dalam penghambatan α-glukosidase dan memiliki aktivitas antioksidan yang terkuat dibandingkan ekstrak Artabotrys sp. yang lain. Perbandingan antara hasil tes aktivitas antioksidan dan penghambatan α-glukosidase, serta tes total fenolik dan total flavonoid, memberikan indikasi bahwa kebanyakan senyawa penghambat α-glukosidase pada daun A. sumatranus memiliki aktivitas antioksidan, dan berasal dari golongan fenolik dan flavonoid. Teknik metabolomik tak bertarget berbasis LC-MSn dengan menggunakan analisis statistik multivariat dan machine learning lalu dipakai untuk memprediksi senyawa aktif penghambat α-glukosidase yang juga memiliki aktivitas antioksidan pada daun A. sumatranus. Data input adalah data dari 30 sampel ekstrak daun A. sumatranus dengan berbagai kombinasi pelarut etanol dan air, serta pengulangannya, dengan 80 variabel independen (fitur) berupa nilai m/z dari senyawa yang terdeteksi pada ekstrak tersebut. Variabel output (target) adalah aktivitas penghambatan α-glukosidase (target utama) dan aktivitas antioksidan (dalam bentuk penghambatan DPPH, target sampingan) yang dinyatakan dengan nilai IC50 dan 1/IC50. Pendekatan analisis statistik multivariat dilakukan dengan berbagai metode yaitu PCA (Principal Component Analysis), PLS (Partial Least Square), OPLS (Orthogonal Partial Least Square), PLS-DA (Partial Least Square – Discriminant Analysis), dan OPLS – DA (Orthogonal Partial Least Square – Discriminant Analysis). Pendekatan machine learning dilakukan secara bertingkat, dengan yang pertama membuat model prediksi keaktifan ekstrak terhadap penghambatan α-glukosidase (dilakukan dengan metode random forest yang memiliki performansi paling baik dibandingkan metode lain), yang lalu dilanjutkan dengan analisis fitur (senyawa) yang paling berpengaruh pada model prediksi keaktifan ekstrak tersebut dengan metode permutasi acak dan SHAP (Shapley additive explanations). Penggabungan hasil metode statistik multivariat dan machine learning menghasilkan prediksi sembilan senyawa aktif penghambat α-glukosidase dari daun A. sumatranus. Diantara kesembilan senyawa aktif tersebut hanya enam yang dapat teridentifikasi yaitu mangiferin, neomangiferin, norisocorydine, lirioferin, apigenin-7-O-galaktopyranosida, dan 15,16-dihydrotanshinone. Hasil penambatan molekuler menggunakan α-glukosidase dari Saccharomyces cerevisiae menunjukkan hanya mangiferin, apigenin-7-O-galaktopyranosida, dan lirioferin yang memiliki energi bebas pengikatan yang lebih negatif (lebih aktif) dibanding acarbose yang digunakan sebagai pembanding. Sedangkan penambatan molekuler menggunakan sebagian dari α-glukosidase pada usus manusia mendapatkan hanya norisocorydine saja yang lebih lemah keaktifannya dibandingkan acarbose. Analisis korelasi menunjukkan bahwa senyawa yang diprediksi aktif, termasuk yang belum teridentifikasi, mempunyai aktivitas antioksidan dan terindikasi berasal dari beberapa biosynthesis pathway. Selanjutnya isolasi senyawa aktif penghambat α-glukosidase dilakukan dengan metode fraksinasi yang dipandu dengan bioassay, diikuti dengan elusidasi strukturnya. Senyawa aktif yang didapat adalah mangiferin, yang merupakan salah satu senyawa yang diprediksi aktif dari analisis metabolomik, dengan IC50 83,72 µg/ml terhadap α-glukosidase dari Saccharomyces cerevisiae. Kontribusi dari penelitian ini adalah memperkaya wawasan kegunaan dan literatur akan A. sumatranus yang belum pernah ada publikasinya. Selain itu, penelitian ini berhasil menemukan beberapa senyawa potensial penghambat α-glukosidase yang belum pernah dipublikasikan sebelumnya, seperti apigenin-7-O-galactopyranoside, lirioferine, and norisocorydine; selain yang belum teridentifikasi. Hasil penelitian ini dapat dikembangkan selanjutnya menjadi sediaan herbal.

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Diabetes mellitus type II is a metabolic disease whose symptom is hyperglycemia or elevated blood glucose level, caused by insulin malfunctions, either in its secretion, action, or both. This disease is a chronic disease that can cause many complications. One of the diabetes mellitus treatments is delaying glucose absorption using α-glucosidase inhibition. The α-glucosidase enzyme will catalyze the hydrolysis of complex sugar, including carbohydrate, into glucose that can be absorbed in the intestine. Therefore, inhibiting α-glucosidase will inhibit the production of glucose that can be absorbed in the intestine, which will then slow down the increase of blood glucose. The large number of diabetes mellitus type II patients drives the efforts to find new active α-glucosidase inhibitor compounds from natural products, which are hoped to have better efficacies or can be more easily extracted or synthesized compared to the existing ones. This research goal was to find active α-glucosidase inhibitor compounds from natural compounds which are available in Indonesia, especially from Artabotrys sp. plants. There are 4 kinds of Artabotrys sp. plants whose ethanol extract’s activity in α-glucosidase inhibition was investigated: Artabotrys hexapetalus (leaf and stem bark), Artabotrys suaveolens (leaf and stem bark), Artabotrys blumei (stem and twig), and Artabotrys sumatranus (leaf and twig). Bioassay test results showed that ethanol extract that had the strongest α-glucosidase inhibition activity was the leaf of A. sumatranus, followed in succession by twig of A. sumatranus, leaf of A. suaveolens, stem bark of A. hexapetalus, stem bark of A. suaveolens, stem of A. blumei, twig of A. blumei, and lastly leaf of A. hexapetalus. Screening was also done by doing bioassay tests of DPPH (2,2-diphenyl-1-picrylhydrazyl) and FRAP (ferric ion reducing antioxidant power) to know about the antioxidant activities of the ethanol extracts of the available Artabotrys sp., and it was found out that in general α-glucosidase inhibition activity had positive relation to antioxidant activity. Analyses of total phenolic test, total flavonoid test, antioxidant activity tests (DPPH and FRAP), and α-glucosidase inhibition activity test; as well as the relation between those tests, lead to predictions of the groups of the active α-glucosidase inhibitor compounds in the extract of Artabotrys sp. and their relations to antioxidant activity. These predictions in the screening stage were in general confirmed by the results of molecular docking of the identified compounds from LC-MS/MS (liquid chromatography – tandem mass spectrometry) analyses on the available extracts. The research was then focused on the leaf extract of A. sumatranus which had the highest potency in α-glucosidase inhibition and had the strongest antioxidant activity compared to other extracts of Artabotrys sp. Comparisons between test results of antioxidant and α-glucosidase activities, as well as total phenolic and total flavonoid, indicated that most of the α-glucosidase inhibitor compounds in the leaf of A. sumatranus had antioxidant activities, and belonged to phenolic and flavonoid groups. Untargeted metabolomic techniques based on LC-MSn by using multivariate statistical analysis and machine learning were then used to predict the active α-glucosidase inhibitor compounds which also had antioxidant activities. The input data was data from 30 samples of leaf extracts of A. sumatranus with various solvent combinations of ethanol and water, and their duplications, with 80 independent variables (features) consisting of m/z values of detected compounds in those extracts. The output variables (targets) were α-glucosidase inhibition activity (main target) and antioxidant activity (in the form DPPH inhibition, secondary target), which were represented by the value of nilai IC50 dan 1/IC50. The multivariate statiscal analysis approach was done with several methods, which were PCA (Principal Component Analysis), PLS (Partial Least Square), OPLS (Orthogonal Partial Least Square), PLS-DA (Partial Least Square – Discriminant Analysis), and OPLS – DA (Orthogonal Partial Least Square – Discriminant Analysis). The machine learning approach was done in stages, with the first one was making a prediction model of the α-glucosidase inhibition activity of the extracts (done with random forest method which had the best performance compared to other methods), and then followed by analysis of the most important features (compounds) in the extract’s activity prediction model using random permutation and SHAP (Shapley additive explanations) methods. Combining the results of multivariate statistical methods and machine learning produced a prediction of nine active α-glucosidase inhibitor compounds from the leaf of A. sumatranus. From these nine active compounds, only six could be identified which were mangiferin, neomangiferin, norisocorydine, lirioferin, apigenin-7-O-galaktopyranosida, and 15,16-dihydrotanshinone. Molecular docking using α-glucosidase from Saccharomyces cerevisiae showed only mangiferin, apigenin-7-O-galactopyranoside, and lirioferine had more negative free energy binding (more active) than acarbose, which was used as comparison. On the other hand, molecular docking using a part of α-glucosidase from humans showed only norisocorydine had weaker activity than acarbose. The correlation analyses showed that the predicted active compounds, including the unidentified ones, had antioxidant activities and were indicated to come from several biosynthesis pathways. Next, the isolation active compound as α-glucosidase inhibitor was done by using bioassay-guided fractionation, continued by structure elucidation. The isolated active compound turned out to be mangiferin, which was one the predicted compound from metabolomic analysis, with IC50 83,72 µg/ml with respect to α-glucosidase from Saccharomyces cerevisiae. The contribution of this research is to enrich and broaden the knowledge about the usefulness and literature on A. sumatranus, which had no publications before. Moreover, this research succeeded in discovering potential α-glucosidase inhibitors which were not yet published before, such as apigenin-7-O-galactopyranoside, lirioferine, and norisocorydine; besides the unidentified ones. The results of this research can be developed later on to become herbal medicine."