PENGANTAR ANALISIS METABOLOMIK

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DIVISI KIMIA ANALITIK 2015

M Rafi, R Heryanto

PENGANTAR ANALISIS

METABOLOMIK

Metabolomik

• Metabolomik adalah teknologi yang berkembang pesat

dekade terakhir, sebagai bagian dari keluarga "omics"

yang melengkapi analisis transkrip gen (transkriptomik)

skala besar dan sidikjari protein (proteomik)

• Menjelaskan dan mengidentifikasi perbedaan antara

organisme (misalnya perbedaan genotipe dan fenotipe

dan klasifikasinya yang disebut kemotaksonomi) dan

menjelaskan faktor-faktor lingkungan yang

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Metabolomik

(b) Our ‘‘traditional’’ linear view of a metabolic pathway and ‘‘scale free’’ connections in a metabolite neighbourhood.

R Goodacre. 2005. Metabolomics 1: 2

(a) Skema umum organisasi Omics.

The general flow of information is from genes to transcripts, to proteins, to metabolites, to function (or phenotype); whilst blue vertical arrows indicate interactions regulating

respective omic expression.

Metabolomik

The three cornerstones of metabolomics and the three main challenges related to metabolomic data analysis.

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Metabolomik

A Krastanov. 2010. Biotechnol. & Biotechnol. Eq. 24:1

Metabolomik

Term

Deskripsi

Metabolomics

Identifikasi non-bias dan kuantifikasi semua metabolit

dalam suatu sistem biologi. Persiapan sampel tidak

harus mengecualikan suatu kelompok metabolit, dan

selektivitas dan sensitivitas teknik analitis harus tinggi

Metabolite

profiling

Identifikasi dan kuantifikasi sejumlah tertentu metabolit

yang telah ada, umumnya terkait dengan jalur metabolit

tertentu. Persiapan sampel dan instrumentasi yang

digunakan dapat mengisolasi senyawa-senyawa target

tersebut dari matriks lainnya sebelum deteksi, biasanya

menggunakan teknik pemisahan kromatografi lalu

dideteksi dengan MS. Dalam industri farmasi, cara ini

secara luas digunakan untuk studi penemuan kandidat

obat baru, produk metabolisme obat dan efek

perawatan terapi

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Metabolomik

Term

Deskripsi

Metabolic

fingerprinting

High-throughput, rapid, global analysis of samples to

provide sample classification. Quantification and

metabolic identification are generally not employed. A

screening tool to discriminate between samples of

different biological status or origin. Sample preparation

is simple and, as chromatographic separation is absent,

rapid analysis times are small (normally 1 min or less)

Metabolite

target analysis

Qualitative and quantitative analysis of one or a few

metabolites related to a specific metabolic reaction.

Extensive sample preparation and separation from other

metabolites is required and this approach is especially

employed when low limits of detection are required.

Generally, chromatographic separation is used followed

by sensitive MS or UV detection

Metabolomik

Term

Deskripsi

Metabonomics Evaluation of tissues and biological fluids for changes in

endogenous metabolite levels that result from disease

or therapeutic treatments

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Metabolomik

WB Dunn, DI Ellis. 2005. Trends in Analytical Chemistry 24: 285

Alur Analisis Metabolomik

Flowchart of the metabolomic study in plants. Sample preparation steps can be changed depending on the analytical methods; however, in general many steps are common. *This step can be omitted

in a certain analysis.)

HK Kim, R Verpoorte. 2010. Phytochemical Analysis 21: 4

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Teknik Analitik Dalam Analisis Metabolomik

R Goodacre et al. 2004. Trends in Biotechnology 22: 245

Teknik Analitik Dalam Analisis Metabolomik

Some standard techniques used in metabolomic analysis. In general, one technology is not sufficient for the analysis of all compounds, but any form of separation will inherently introduce a bias towards the analytes being detected.

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Alur Analisis Metabolomik

http://www.cial.uam-csic.es/metabolomics/workflow.html

Pipa Saluran Metabolomik

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Metabolomik Dalam Riset Obat Herbal

LF Shyur, NS Yang. 2008. Current Opinion in Chemical Biology 12: 66

Key features of the technologies used in metabolomics for herbal medicine research

Analisis Data Dalam Metabolomik

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Kemometrika Dalam Metabolomik

Chemometrics

A science of relating measurements made on a chemical system or

process to the state of the system via application of mathematical or

statistical method (International Chemometrics Society)

H.A. Gad et al. 2014. Phytochemical Analysis 24: 1

Chemometrics

Pattern recognition (qualitative) Principal component analysis Cluster

analysis Discriminant analysis

Multivariate calibration (quantitative) Multiple linear regression Partial least square

Kemometrik Dalam Metabolomik

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Kebutuhan Dalam Studi Metabolomik

1. How can we

extract

all metabolites?

2. How can we

separate or detect

the

metabolites extracted?

3. How can we

reduce

the huge data set

obtained from analytical detection?

4. How can we

identify

the metabolites?

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Multikomponen Multikomponen

Obat Herbal

Sinergi Lingkungan tumbuh Lingkungan tumbuh Genetik Genetik Budidaya Budidaya Panen dan pascapanen Panen dan pascapanen

Karakteristik Obat Herbal

Produksi senyawa bioaktif

Kadar gingerol and shogaol pada tiga varietas jahe Indonesia mg/g

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Karakteristik Obat Herbal

Metode Kendali Mutu Berbasis Metabolomik

Kendali Mutu dengan Senyawa Kimia Pattern-oriented (Fingerprint analysis) Compound-oriented (Marker analysis)

Z. Zeng et al., Chin. Med., 3 (2008) 9

S. Govindaraghavan et al., Fitoterapia, 83 (2012) 979

Kemometrika

Identifikasi

Diskriminasi

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Sidikjari KLT

Pola KLT sidikjari dengan visualisasi sinar tampak (a), UV 254 nm (b), dan UV 366 nm (c)

Keterangan:

CCM = kurkumin DMC = demetoksikurkumin BDC = bisdemetoksikurkumin TMK = Temulawak KNY = Kunyit BNGL = Bangle

Diskriminasi Kunyit, Temulawak, dan Bangle

a b c

Sidikjari KLT

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Pola KLT sidikjari bangle (a), kunyit (b), dan temulawak (c) dengan visualisasi sinar tampak

Keterangan:

STD = senyawa standar kurkuminoid NGD = Ngadirejo, Wonogiri TMB = Tembalang, Semarang TWN = Tawangmangu, Karanganyar SMN = Semen, Kediri SLH = Slahung, Ponorogo NGR = Ngrayun, Ponorogo TJK = Tanjung Kerta, Sumedang RCK = Rancakalong, Sumedang CKR = Cikembar, Sukabumi DMG = Dramaga, Bogor a b c

Sidikjari KLT

Pola KLT sidikjari bangle (a), kunyit (b), dan temulawak (c) dengan visualisasi UV 254 nm

Keterangan:

STD = senyawa standar kurkuminoid NGD = Ngadirejo, Wonogiri TMB = Tembalang, Semarang TWN = Tawangmangu, Karanganyar SMN = Semen, Kediri SLH = Slahung, Ponorogo NGR = Ngrayun, Ponorogo TJK = Tanjung Kerta, Sumedang RCK = Rancakalong, Sumedang CKR = Cikembar, Sukabumi DMG = Dramaga, Bogor a b c

Sidikjari KLT

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Pola KLT sidikjari bangle (a), kunyit (b), dan temulawak (c) dengan visualisasi UV 366 nm

Keterangan:

STD = senyawa standar kurkuminoid NGD = Ngadirejo, Wonogiri TMB = Tembalang, Semarang TWN = Tawangmangu, Karanganyar SMN = Semen, Kediri SLH = Slahung, Ponorogo NGR = Ngrayun, Ponorogo TJK = Tanjung Kerta, Sumedang RCK = Rancakalong, Sumedang CKR = Cikembar, Sukabumi DMG = Dramaga, Bogor

a

b

c

Sidikjari KLT

Preparasi Sampel Koleksi sinyal dan prapemrosesan

Analisis kemometrik

Validasi

Langkah-langkah dalam mengembangkan metode kendali

mutu obat herbal menggunakan kemometrik

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Metode Kendali Mutu --- Kemometrik

The pretreatments and the

logical flow of different

calibration, validation, and

prediction sets

I. C. Yang et al., J. Food Drug Anal. 21 (2013) 268 Data spektrum

original

Prapemrosesan sinyal, data pretreatment

Set kalibrasi Set validasi Pemilihan model

Spektrum original dari sampel X

Model IDA

Set prediksi

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Diskriminasi Tiga Varietas Jahe

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Diskriminasi Tiga Varietas Jahe

mg/g ZOA-1 ZOA-2 ZOA-3 ZOA-4 ZOA-5 ZOA-6 ZOA-7 ZOA-8 ZOA-9 ZOA-10 ZOA-11 ZOA-12 ZOA-13 ZOO-1 ZOO-2 ZOO-3 ZOO-4 ZOO-5 ZOO-6 ZOO-7 ZOO-8 ZOO-9 ZOO-10 ZOO-11 ZOO-12 ZOR-1 ZOR-2 ZOR-3 ZOR-4 ZOR-5 ZOR-6 ZOR-7 ZOR-8 ZOR-9 ZOR-10 ZOR-11 ZOR-12 -4 -2 0 2 4 -4 -2 0 2 4 DF -2 (2 7. 1 7 % ) DF-1 (72.83 %)

• Instrumentasi: KCKT

• Prapemrosesan

sinyal: koreksi garis

dasar

• Metode kemometrik:

analisis

diskriminant/discrimin

ant analysis (DA)

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Lempuyang Gajah

Diskriminasi Bangle, Lempuyang Emprit, dan

Lempuyang Gajah

Z. montanum

Identifikasi & Diskriminasi

Analisis sidikjari

+

Kemometrik

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Lempuyang Gajah

Kromatogram sidikjari KCKT

Z. montanum

(a),

Z. americans

(b) dan

Z. zerumbet

(c)

min v

Reference peak

Diskriminasi Bangle, Lempuyang Emprit, dan

Lempuyang Gajah

Analisis Komponen Utama

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Identifikasi Kunyit, Temulawak dan Bangle

Turmeric (C. longa) Turmeric (C.

longa) ((C. xanthorrhizaC. xanthorrhizaJava turmericJava turmeric )) Cassumunar ginger Cassumunar ginger (Z. cassumunar) (Z. cassumunar)

Turmeric?? Java turmeric?? Cassumunar ginger??

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Spektra FTIR representatif dari C. longa (A), C. xanthorrhiza (B), and

Z. cassumunar (C)

Identifikasi Kunyit, Temulawak dan Bangle

• Instrumentasi:

Spektroskopi FTIR

• Prapemrosesan

sinyal: standar normal

variate

• Metode kemometrik:

analisis variat

kanonik/canonical

variate analysis (CVA)

Plot CVA ZC

CX CL

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Autentikasi Temulawak --- Sidikjari KCKT

Kadar kurkuminoid

mg/g 0 5 10 15 20 25 30 CUR DMC BDMC

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Autentikasi Temulawak --- Sidikjari KCKT

0 10 20 30 40 0 0.5 1 1.5 [105]

Kromatogram CX (a), 5% CL dalam CX (b), 25% CL dalam CX (c), 50% CL dalam CX (d) and CL (e) (UV 254 nm)

a b c d e min v

Autentikasi Temulawak --- Sidikjari KCKT

Plot CVA CX (◆), 5% CL dalam CX (▲), 25% CL dalam CX (), 50% CL dalam CX () and CL (■) CV1 (98.9%) CV 2 (1 .1 % )

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GC-MS Based Metabolomics

ABSTRACT

Introduction – Metabonomic analysis is an important molecular phenotyping

method for characterising plant ecotypic variations; hence, it may become a powerful tool for quality control and discrimination of traditional Chinese medicine (TCM).

Objective – To discriminate and assess the quality of Curcuma phaeocaulis,

C. kwangsiensis and C. wenyujin from different ecotypes. The identification of the compositions of essential oils from the three Curcuma species was included in this study.

Methodology – Metabolomics analysis was carried out on all samples by gas

chromatography–mass spectrometry (GC‐MS) coupled with multivariate statistical analysis. Characterisation of phytochemicals in essential oils was performed by automated matching to the MS library and comparison of their mass spectra (NIST05 database).

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GC-MS Based Metabolomics

Results – Principal component analysis (PCA) effectively distinguished the

samples from different species and ecotypes. Partial least squares

discrimination analysis (PLS‐DA) was successfully employed in classifying the GC‐MS data of authentic, commercial and introduction cultivation samples. Furthermore, the components contributing significantly to the discrimination, namely curzerenone, germacrone, curdione and

epicurzerenone, were screened by PCA and PLS‐DA loading plots and further can be used as chemical markers for discrimination and quality control among different groups of samples.

GC-MS Based Metabolomics

Representative GC‐MS chromatograms of the essential oil from (a) C. wenyujin, (b) C. kwangsiensis and (c) C. phaeocaulis.

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GC-MS Based Metabolomics

Score plots of (a) PCA and (b) PLS‐DA, and loading plots of (c) PCA and (d) PLS‐DA for the 62 samples, using common components as input data.

(a) PCA and (b) PLS‐DA projection plots for the 62 samples, using peak areas of four chemical markers as input data.

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NMR Based Metabolomics

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