typing staphylococcus aureus using the protein a gene phaedra agius – january, 2008, completed at...

Typing Staphylococcus aureus using the protein A gene

Phaedra Agius – January, 2008, completed at RPI in New York

in collaboration with Barry Kreiswirth, Steve Naidich, Kristin Bennett

Introduction

• What is staph?• Typing methods and the spA gene• The data• Comparing Sequences• Similarities and differences• Hierarchical clustering• Evaluating the results• Multidimensional Scaling• Conclusion

•Staphylococcus aureus is a bacteria often living on the skin or in the nose of a healthy person.

•Staph can cause a multitude of infections, from skin infections to more deadly infections such as pneumonia and meningitis

•It can spread rapidly

•Some strains are resistant to antibiotics (MRSA)

Typing Methods

• Multi Locus Sequence Typing (MLST) is a well established typing method that looks at 7 house-keeping genes in staph. These are genes that are always turned on.

• Our method looks at just ONE gene – the spA gene.

The spA gene

• The spA gene contains information for making Protein A.

• The protein A in staph is a virulence factor. It inhibits white blood cells from ingesting and destroying the bacteria by acting as an immunological disguise.

Preprocessed DNA sequences of the spA gene

AAA GAG GAAGACAACAACAAGCCTGGTAAA GAAGATGGCAACAAGCCTGGT AAA GAAGACAACAAAAAACCTGGCAAA GAAGATGGCAACAAACCTGGT AAA GAAGACGGCAACAAGCCTGGT AAA GAAGATGGCAACAAGCCTGGT

X1K1A1O1M1Q1

The spA DNA sequences can be preprocessed into a sequence of repeats, or cassettes.

Instead of dealing with the long DNA sequences, we use these shorter preprocessed spa sequences X1-K1-A1-O1-M1-Q1

Note, first cassette has 27bp, the others have 24bp

Labeled data

• The MLST allelic profile is provided for each sequence

• 194 sequences labeled with their MLST type

DukeId SpaMotif spa MLST arcc aroe glpf gmk pta tpi yqil

1075014 X1-K1-A1-M1-B3 538 395 10 47 8 26 26 32 2

584 X1-K1-B1-B3 541 ? 10 ? 8 26 26 32 2

1771 X1-K1-B1 93 47 10 11 8 6 10 3 2

40 X1-K1-A1-K1-A1-O1-M1-Q1-Q1 468 30 2 2 2 2 6 3 2

1073088 X1-K1-A1-K1-A1-O1-M1-Q1-Q1-Q1 536 30 2 2 2 2 6 3 2

349 X1-K1-A1-O1-M1-Q1 390 30 2 2 2 2 6 3 2

Spa sequences MLST labels

Comparing spa sequences

• T1-J1-M1-G1-M1-K1

• T1-K1-B1-M1-D1-M1-G1-M1-K1• T1-M1-B1-M1-D1-M1-G1-M1-K1• T1-M1-D1-M1-G1-M1-M1-K1

• U1-J1-F1-K1-P1-E1• T1-J1-F1-K1-B1-P1-E1• U1-J1-G1-F1-M1-B1

These ‘preprocessed’ sequences are highly conserved.

How can we generate numbers from sequences that reflect the subtle differences and/or similarities between them?

Comparing spa sequences

– Global alignment– Affine alignment– BCGS - Best common gap-weighted

subsequence• Weighting the sequence ends (B and E)

Using these methods each spa sequence can be represented as a vector of similarity scores between itself and all the other sequences

Global alignment

• Costs: Gap =1, Mismatch = 1

C L O U D Y D A Y

G * O * * A W A Y

1 0 1 1 1 1 0

• Distance: d = 5 Similarity: s = 2

Affine gap alignment• Costs: Gap Initialization = 2, Gap =1, Mismatch = 1

U1 J1 G1 F1 B1 B1 B1 B1 P1 B1 Global T1 J1 * * B1 B1 B1 * * D1

0 3 1 0 0 0 3 1 Distance = 8 Similarity = 4

U1 J1 G1 F1 B1 B1 B1 B1 P1 B1 AffineT1 J1 * * * * B1 B1 B1 D1

0 3 1 1 1 0 0 1 Distance = 7 Similarity =

3

BCGS-Best Common Gap-weighted Subsequence

P A R T Y H A R D

P A N T * * * R Y

Common subsequences are:

S1=A,T,R, S2=AT, S3=TR, S4=ATR

Gap weighted scores: Choose a weight 0< 1>=ג

S1 = 1̧ 0 = 1, S2 = 2̧ ,S3 = 2̧ 3, S4 = 3̧ 4

If 1=ג , then S4 is the optimal choice.

If 0.9=ג , the scores are 1, 1.8, 1.46 and 1.97 respectively

If 0.8=ג , the scores are 1, 1.6, 1.02 and 1.23 respectively

S1=A,T,R, S2=AT, S3=TR, S4=ATR

S1 = 1̧ 0 = 1, S2 = 2̧ ,S3 = 2̧ 3, S4 = 3̧ 4

Normalizing the similarity scores

• The similarity scores M are normalized as follows:

where n1 and n2 are the sequence lengths

Example: C L O U D Y D A Y

G * O * * A W A Y

Similarity = 3, Normalized similarity = 3/√(7*4)=0.57

B and E

The cassettes at the beginning (B) and end (E) of a sequence are highly conserved within spa families

These cassettes shall be compared separately, scored as a match (1) or mismatch (0) and weighted

B

E

M=middle

Let B and E have a weight of 20% in the overall score

Sim score = 0.2*B + 0.6*M + 0.2*E

Similarities Distances

Normalized similarity scores can be transformed to distances as follows:

Spa sequence vector of distances between that sequence and every other sequence in the dataset.

The set of spa sequences is now represented by a (normalized) distance matrix.

D(s1;s2) = 1¡ sim(s1;s2)

Hierarchical Clustering

Uses a distance matrix

It iteratively ‘merges’ the two nearest items/clusters

1 2 3 4 5 6 7 8

1 0 9 4 7 8 4 5 9

2 0 6 9 6 8 5 8

3 0 6 7 1 2 9

4 0 5 4 5 3

5 0 7 5 4

6 0 2 6

7 0 5

8 0

---Cutoff c … this determines the number of clusters to be formed

Training and Testing

• Split the data into two – a TRAINING set and a TEST set• Build a model on the Training set by

choosing optimal B, E and c parameters

• Assign the Test data to the nearest clusters

• Evaluate the results• Repeat multiple times for validation

Train

Test

Assigning Test sequences to the Training clusters

•We define the distance between a point and a cluster to be the mean of the distances between that point and the members of the cluster.

IF the distance between a test point and the nearest cluster exceeds an outlier threshold t , the test point is defined to be an outlier (a novel strain of the bacteria)

ELSE the test point is assigned to the nearest cluster.

>t

Evaluation

• Compare our clusters to the groups defined by the MLST labels via the Jaccard coefficient

• Split our data into a Training and Testing set multiple times and measure the consistency of the clusters formed via a Stability score

• Measure the Accuracy of our spa groups by comparing them to the MLST groups

Jaccard coefficient

Clustering S

Clustering M

Stability

The stability is measured over the n Training and Testing iterations.

It is defined to be the mean of the Jaccard scores measuredpairwise between the spa clusterings obtained at each iteration

Spa clustering 1

Spa clustering 3

Spa clustering 2J1

J2J3

Stability = mean(J1,J2,J3)

Iterations 1, 2, 3 ….

Accuracy

Spa group

MLST group

The MLST label assigned to a spa group is the label of the MLST group with which the spa group has the largest intersection.

The accuracy for that spa group is defined to be the percentage of correctly labeled points.

The overall accuracy of a spa clustering is defined to be the percentage of correctly labeled points.Accuracy = 8/11

Results: Jaccard scores(40 iters, outlier threshold = 1.5 sd)

Results: Stability scores(40 iters, outlier threshold = 1.5 sd)

Results: Accuracy scores(40 iters, outlier threshold = 1.5 sd)

Results: Outlier detection(40 iters, outlier threshold = 1.5 sd)

Results: Varying the Outlier threshold(10 iters, test set size = 30%)

Multidimensional Scaling (MDS)

• MDS translates a distances matrix to a set of coordinates such that the distances between the points are approximately equal to the dissimilarities.

Picture taken from Forrest W. Young’s paper ‘Multidimensional Scaling’

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

MLST 1

MLST 5MLST 8

MLST 15

MLST 30

MLST 45

MLST 59MLST 109

MLST 188

MDS with our distances

MDS – a closer look

-0.22 -0.2 -0.18 -0.16 -0.14 -0.12 -0.1 -0.08 -0.06

0

0.05

0.1

0.15

0.2

0.25

0.3

MLST 20T1-G2-M1-F1-B1-B1-B1T1-G2-M1-F1-F1-B1-B1-B1U1-G2-M1-F1-B1-L1-B1U1-G2-M1-F1-B1-B1-L1-B1

MLST 59Z1-D1-M1-D1-M1-N1-K1-B1Z1-D1-M1-D1-M1-N1-K1-E1Z1-D1-M1-N1-K1-B1

Conclusion and future work• The Spa clustering method can refine groups in ways that

MLST cannot • BCGS worked best• MDS on our spa distances clearly draws out the clusters

Future research• More data, compare to other typing methods• Use BCGS on other data types• Different distance measures• Different ways of assigning test points to clusters• Better ways for finding the optimal parameters other than a

grid search

References• Spa Typing method for Discriminating among Staphylococcus

aureus Isolates: Implications for Use of a Single marker to Detect Genetic Micro and MacrovariationLarry koreen, Srinivas Ramaswamy, Edward Graviss, Steven Naidich, James Musser and Barry Kreiswirth

• Evaluation of protein A Gene Polymorphic Region DNA Sequencing for Typing of Staphylococcus aureus StrainsB. Shopsin, M. Gomes, S.O. Montgomery, D.H. Smith, M. Waddington, D.E. Dodge, D.A.Bost, M. Riehman, S. Naidich and B. Kreiswirth

• Introduction to Computational molecular BiologyJoao Setubal and Joao Meidanis

• Kernel Methods for Pattern AnalysisJohn Shawe-Taylor and Nello Cristianini

• Framework for kernel regularization with application to protein clusteringFan Lu, Sunduz Keles, Stephen J. Wright and Grace Wahba

This work is published in IEEE/ACM Transactions on Computational Biology and BioinformaticsVolume 4, Issue 4, Oct.-Dec. 2007 Page(s):693 - 704

Thanks!Questions?