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 GAAGACAACAACAAGCCTGGT AAA GAAGATGGCAACAAGCCTGGT AAA GAAGACAACAAAAAACCTGGC AAA GAAGATGGCAACAAACCTGGT AAA GAAGACGGCAACAAGCCTGGT AAA GAAGATGGCAACAAGCCTGGT X1 K1 A1 O1 M1 Q1 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 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 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 * * D Distance = 8 Similarity = 4 U1 J1 G1 F1 B1 B1 B1 B1 P1 B1Affine T1 J1 * * * * B1 B1 B1 D 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: S 1 = A, T, R, S 2 = AT, S 3 = TR, S 4 = ATR Gap weighted scores: Choose a weight 0< ג<=1 S 1 = 1 ¸ 0 = 1, S 2 = 2 ¸, S 3 = 2 ¸ 3, S 4 = 3 ¸ 4

If ג =1, then S4 is the optimal choice. If ג =0.9, the scores are 1, 1.8, 1.46 and 1.97 respectively If ג =0.8, the scores are 1, 1.6, 1.02 and 1.23 respectively S 1 = A, T, R, S 2 = AT, S 3 = TR, S 4 = ATR S 1 = 1 ¸ 0 = 1, S 2 = 2 ¸, S 3 = 2 ¸ 3, S 4 = 3 ¸ 4

Normalizing the similarity scores The similarity scores M are normalized as follows: where n 1 and n 2 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 ( s 1 ; s 2 ) = 1 ¡ s i m ( s 1 ; s 2 )

Hierarchical Clustering Uses a distance matrix It iteratively ‘merges’ the two nearest items/clusters 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 measured pairwise between the spa clusterings obtained at each iteration Spa clustering 1 Spa clustering 3 Spa clustering 2 J1J1 J2J2 J3J3 Stability = mean(J 1,J 2,J 3 ) 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’

MDS with our distances

MDS – a closer look

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 Macrovariation Larry 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 Strains B. 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 Biology Joao Setubal and Joao Meidanis Kernel Methods for Pattern Analysis John Shawe-Taylor and Nello Cristianini Framework for kernel regularization with application to protein clustering Fan Lu, Sunduz Keles, Stephen J. Wright and Grace Wahba

This work is published in IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 4, Issue 4, Oct.-Dec Page(s): Thanks! Questions?