KLASIFIKASITujuan klasifikasi:- Alat penyampaian informasi- Sebagai dasar pengembangan sistem identifikasi- Mengetahui sejarah evolusi mahluk hidup (mikrobia)

1. Alat penyampaian informasi Klasifikasi: summarizing & cataloging information about microorganisms (database) Information retrieval system (large amount) Its position in the classification system is denoted by the use of “a name”. e.g. Bacillus: gram + bacteria, forms endospore under aerobic conditions B. subtilis: secret extracellular enzymes, amylase & protease, use nitrate, competent to perform transformation e

2. Dasar penyusunan sistem identifikasi Microorganisms must be classified into groups before identification

system ca be devised For recognition of new isolates Without prior classification of strain into groups -> impossible to

assign new isolates to a taxon

3. Mengetahui sejarah evolusi mikroba: Indicate the phylogenetic relationships For some, phylogeny and classification are identical

Kriteria klasifikasi yang efektifTo serve the purposes effectively, a classification system should: have high information content be stable be empirical be scientifically based

Kelemahan Klasifikasi Tradisional Tidak prediktif Tidak stabil Tidak objektif (subjektif)

Alasan kelemahan: Apriory choice of characters (pengaruh Linnaeus) Subjective -> disagreement between scientists Lengthy discourse concerning the relative important of

characters e.g. misguided assumption: morphological -> genera physiological -> species serological -> sub-species

Tipe-tipe Klasifikasi

1. Klasifikasi artifisial: tujuan khusus:

Useful for the specialist Little value to microbiology most bacteria are excluded artificial seldom display the natural relationship, e.g. Escherichia

coli & Shigella dysenteriae: strain of the taxa share great DNA sequence relatedness, phenotypically are very similar, from every view point they are “single species”.

E.g. Bacillus cereus & Bacillus thuringiensis: plasmid coding for -endotoxin

monothetic ( single character) S. dysenteriae must cause dysentry in human non-pathogenic strain can not be included in this taxa

Klasifikasi Artfisial

Based on restricted information: e.g. pathogenicity Tend to be unstable: Erwinia herbicola (plant pathologist) and

Enterobacter agglomerans (clinical microbiologist) Erwinia agglomerans.

Identification system derived from monothetic classification missclassification !

Non-pathogenic isolates of S. dysenteriae genus Escherichia. Non-toxic, plasmid deficient strain of B. thuringiensis

identified as B. cereus. Conclussion: although artificial classification have their use, as

a general system of value to all microbiologists, their limitations are severe !!

2. Klasifikasi alamia. Fenetikb. Filogenetik

a. Klasifikasi Alami Fenetik General purpose classification A system that is of value to all microbiologists Encompass all bacteria and all aspects of them Natural based on overall similarity (affinity) containing

all aspects (molecular physiological habitat relationship)

Phenetic: refer to similarities based on the complete organism (phenotype & genotype) as it exists at present with no reference to the evolutionary pathways or ancestry of the organism.

Contrast with the term natural used in evolutionary context Polythetic: good predictivity

b. Klasifikasi Alami Filogenetik Natural: a unique history of decent with modification Based on phylogenetic relationship This will be congruent with phenetic if there is no

parallel and convergent evolution and the rate of changes proceed constantly in all lineages

Cladistic: the branching pattern that describes the pathway of ancestry of a group of organism monophyletic group (posses a homologous characters: primitive or derived characters

Traditional evolutionist: classification is practised with reference to the phylogeny but without the requirement that all groups be monophyletic

Keunggulan klasifikasi fenetik vs filogenetik

1. Goodness of the classification: Phylogenetic classification: reflect the evolutionary

pathway of the organisms it is impossible to compare with the true cladogeny

Phenetic classification: less well defined, but represent the similarities between and every organism. Various statistical methods have been developed. The accuracy of the classification cannot be evaluated difficult to define the ultimate phenetic classification

Keunggulan Klasifikasi

2. Keterujian (Veriviability) Phylogenetic approach : difficult to verify Phenetic classification: more accesible to

verification, objective and can be repeated

3. Kepraktisan (Practicalities):Phylogenetic approach: rely on gene sequences

data, hybridization technology offering simple identification procedures molecular systematics

Phenetic approach: can be analised to select the most diagnostic characters for delineation of groups and provide reliable identification system

Pilihan antara Klasifikasi fenetik dan FilogenetikJensen (1983) suggested that the classification what

is needed are: Classification that reflect what is known about the

taxa Procedures for generating hypothesis about

evolutionary relationships. Many systematists now agree that the two

systems (phenetic & phylogenetic) should be combined as far as possible

KLASIFIKASI NUMERIK – FENETIK(Taksonomi Adansonian)

Taksonomi Numerik: pengelompokan unit takson dengan metode kuantitatif berdasarkan keeadaan sifat-sifat

Perintis Aplikasi Sistematik Numerik : Peter H.A. Sneath (1957)

Lima Prinsip Taksonomi Adansonian:1. Taksonomi alami ideal: taksonomi yang mengandung informasi terbesar

yaitu yang didasarkan atas sebanyak-banyaknya sifat.2. Masing-masing sifat diberi “nilai” yang setara dalam mengkonstruksi

taksa alami.3. Similaritas keseluruhan (afinitas) merupakan fungsi proporsi sifat yang

dimiliki bersama.4. Taksa yang berbeda didasarkan atas sifat yang dimiliki.5. Similaritas tidak besifat filogenetis.

Taksonomi Tradisional: monotetik karakter tunggal dipilih secara subyektif tidak dapat mengakomodasi variasi (mutan)

Taksonomi Numerik: mengandung banyak informasi sebanyak-banyak karakter (politetik) dapat mengakomodasi variasi sistem simpanan informasi yang berharga sistem retrieval bagi para ilmuwan

Prosedur Taksonomi Numerik:1. Pemilihan strain dan uji karakter

Pemilihan strain (OTU)Pemilihan karakterAkuisisi data secara tepatPengkodean data (data coding)

2. Evaluasi Eror Estimasi test error Komputasi resemblance Konstruksi dendrogram (pengklasteran) Evaluasi dendrogram (co-phenetic-correlation test)

3. Pendefinisian tingkat takson

Contoh: Tabel n x tKarakter Strain Mikroba (Operational Taxonomical Unit)

A B C D E

1 + + - - -

2 + - + - -

3 + - - - -

4 - - + - +

5 + + + + +

6 - - + + +

7 + + - - +

8 + + - + +

9 - - + - +

10 - - - + +

Komputasi nilai resemblance (similaritas):

Hasil Uji Strain BHasil uji Strain A

+ -

+ a b

- c d

Indeks similaritas:

Simple matching coefficient a + d

(SSM) = -------------------- x 100% a + b + c + d

Jaccard coefficient

a(SJ) = ----------------- x 100%

a + b + c

Contoh kalkulasi SSM

SSM (A-B) : a = 4; b = 2; c = 0; d = 4: SSM = 80% SSM (A-C) : a = 2; b = 4; c = 3; d = 1: SSM = 30% SSM (A-D) : a = 2; b = 4; c = 2; d = 2: SSM = 40% SSM (A-E) : a = 3; b = 3; c = 4; d = 0: SSM = 30% …………dan selanjutnya !!!

Matriks Similaritas A B C D E

A 100

B 80 100

C 30 30 100

D 40 60 50 100

E 30 50 60 70 100

Clustring analysis (Analisis Kluster)Sim (%) Strain Mikrobia (OTU)

100 A B C D E

90 A B C D E

80 (A, B) C D E

70 (A, B) C (D,E)

60 (A, B) C (D,E)

55 (A, B) (C)(D,E)}

50 (A, B) (C)(D,E)}

40 (A, B) } (C)(D,E)}]

30 (A, B) } (C)(D,E)}]

20 (A, B) } (C)(D,E)}]

10 (A, B) } (C)(D,E)}]

Algoritme Pengklasteran (Clustering Algoritm)1. Single linkage: fusi klaster dengan nilai similaritas tertinggi2. Average linkage: fusi klaster dengan nilai similaritas rerata (UPGMA)3. Complete linkage: fusi klaster dengan nilai similaritas terkecilUPGMA: Unweighted Paired Group Method with Arithmetic Averages

Konstruksi dendrogramHasil klasifikasi:

A

B

D

E

C

10090807060504030

Evaluasi dendrogram: Analisis korelasi ko-fenetik

A B C D E

A 100

B 80 100

C 30 30 100

D 40 60 50 100

E 30 50 60 70 100

Matriks Similaritas original (X)

Matriks similaritas derived from Dendrogram (Y)

A B C D E

A 100

B 80 100

C 40 40 100

D 40 40 55 100

E 40 40 55 70 100

Analisis Kofenetik-korelasiSSM X Y X2 Y2 XYA-B 80 80A-C 30 40A-D 40 40A-E 30 40B-C 30 40B-D 60 40B-E 50 40C-D 50 55C-E 60 55D-E 70 70

ΣX ΣY ΣX2 ΣY2 ΣXY

Koefisien Korelasi (r)

(nXY –XY) r = ------------------------------------------- (nX2 – (X)2) (n (Y2) – (Y)2)

r ≥ 60% (nilai r yang dapat diterima)