Infectious Disease: Meaner MRSAs

Most methicillin-resistant Staphylococcus aureus (MRSA) infections are contracted in hospitals and other health care facilities. Antibiotic use, patients’ weakened immune systems, close contact among people, and open wounds all make hospitals prime breeding grounds for these bugs. But community-acquired MRSA strains, which attack healthy individuals with seemingly normal immune systems, are becoming more prevalent. A recent comparison of representative strains of hospital- and community-acquired MRSAs now suggests that the latter are more virulent and that they excel at escaping destruction by white blood cells. 
 
Infectious disease experts suspected that community-acquired strains can overcome a healthy immune system because they operate differently than those acquired from hospital or health care settings. Microbiologist Frank DeLeo of the National Institute of Allergy and Infectious Diseases’ Rocky Mountain Laboratories led a multi-institutional team of researchers in comparing the two types. In studies described in the 15 September 2005 issue of The Journal of Immunology, they evaluated the potency of three community-acquired MRSA strains (MW2, LAC, and MnCop) and two hospital-acquired strains (MRSA252 and COL). 
 
Healthy adult mice were injected with each strain. All the mice infected with community-acquired strains became ill, and several died. None of the mice infected with the hospital-acquired strains died, and only one mouse became ill. Then the MRSA strains were mixed with human neutrophils (white blood cells), the body’s first line of defense against bacterial invasion, which kill bacteria by producing hydrogen peroxide and other toxic oxygen metabolites. After half an hour, the community-acquired strains survived neutrophil destruction better than the hospital-acquired ones. After six hours, the community-acquired strains had begun rupturing the neutrophils and were actually growing. 
 
Next the researchers used micro-arrays to uncover genes that differed during interaction with neutrophils. Not surprisingly, genes that encode virulence factors, toxin production, and stress responses were induced in all the MRSA strains. However, about two dozen genes that encode surface or secreted proteins of unknown function were upregulated only in the community-acquired strains. Gene knockout experiments are under way to identify whether these genes contribute to neutrophil killing. The researchers are also exploring how the community-acquired strains withstand neutrophils’ toxic compounds. 
 
The findings suggest that community- and hospital-acquired MRSA strains differ broadly in their biology and genetics. Will this new information help physicians on the front lines who are fighting MRSA infections? “[The findings] do not have immediate therapeutic implications, but maybe down the line therapies will be developed based on such findings,” says Henry Chambers, an infectious disease physician at the University of California, San Francisco, School of Medicine.


Background
In the human genome, alleles at two loci on the same chromosome often show stochastic dependence. This nonrandom pair-wise allelic association, or linkage disequilibrium (LD), is both interesting evolutionarily in its own right and a powerful tool in the mapping of genes for complex genetic traits. The development of the technology to genotype large numbers of single-nucleotide polymorphisms (SNPs) provides the means to explore the extent and patterns of linkage disequilibrium in the genome. The two pseudoautosomal regions on chromosomes X and Y (PAR1 and PAR2) are approximately 2.6 and 0.4 Mb respectively, and are located at the tips of the sex chromosomes. Within these regions pairing and recombination may occur between X and Y as it naturally does in the autosomes or between two X chromosomes in females. Dense genetic linkage maps have revealed a high rate of recombination in PAR1 [1], prompting comparisons between LD patterns in PAR1 and the heterosomal region on chromosome X. The purpose of this paper is to explore the rate of decay in LD as a function of distance between males and females both across and within autosomal and sex chromosomes.

Methods
Data used for our analyses were obtained from the Collaborative Study on the Genetics of Alcoholism (COGA). COGA is a large-scale family study designed to identify genes that contribute to the risk for alcoholism. Our analyses used data genotyped by both Affymetrix and Illumina. Four hundred and thirty-four genetic markers on chromosome X were analyzed (310 SNPs by Affymetrix and 124 SNPs by Illumina). SNPs genotyped and analyzed on the 22 autosomes by Affymetrix and Illumina totaled 15,371. Given that the proportion of self-reported non-Caucasian individuals in the COGA sample was small, our results are based on 102 Caucasian pedigrees. These 102 pedigrees were composed of 430 genotyped male and 446 genotyped female participants; 9 genotyped participants whose sex was not reported were not included in these analyses.

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A sample of 172 genotyped unrelated participants was derived from the COGA pedigrees in order to compute LD statistics. To form the sample of unrelateds, a hierarchical approach was used. In pedigrees in which founders were genotyped, all available founders were chosen for analysis. Where founders were not genotyped, the proband (index case) was used if a proband was declared. Otherwise, the individual with the smallest COGA identification number within each family who had been genotyped was chosen. SNPs with minor alleles <0.05 were removed from analysis. The statistic r 2 = D 2 /(p 1 p 2 q 1 q 2 ) was calculated for each adjacent pair of markers across the genome.
Here, D is defined as D = x 11 -p 1 q 1 (x 11 is the frequency of haplotype A 1 B 1 , and p 1 and q 1 are the frequencies of alleles A 1 and B 1 at loci A and B, respectively) [2]. In males phase is known on the heterosomal region of chromosome X. Thus, in males estimates were computed directly from the genotype data using maximum likelihood estimation. For females, we used the software Dprime (available from authors) which employs the EM (expectation maximization) algorithm, a maximum likelihood method, for haplotype frequency estimation [3]. For adjacent SNPs on all chromosomes, we explored the fit of the r 2 results to the exponential, Weibull, Gamma and log- ββ ββ ββ ββ ββ normal distributions, adjusting for distance (cM). Among these distributions, the Weibull distribution provided the best fit and will be reported. Inferences based on the other distributions are comparable. Note that these models do not account for the fact that each individual contributes multiple observations to the analysis (i.e., once for each marker pair.) Chi-squared tests were used to compare the sex-specific Weibull distributions of r 2 1) within the X chromosome, 2) between the pter pseudoautosomal region (PAR1) on X and the heterosomal region on X, 3) between PAR1 and the autosomes, and 4) between chromosome X and the autosomes.

Autosomes
Sex-specific LD analyses across the genome revealed the anticipated rapid decay of LD with increased distance (cM) for both males and females ( -coefficients for distance are = -0.1005, = -0.6457, respectively). Very few adjacent SNP markers exhibited significant LD (r 2 > 0.7), generally supporting the use of these markers in linkage analyses. r 2 measures for adjacent SNPs showed that the LD distribution for males and females on chromosome X were comparable (males were coded as 0 and females coded as 1, p = 0.9573) (Table 1A).

Chromosome X
Only females were used to compare the pseudoautosomal region PAR1 to the heterosomal region of chromosome X (chromosome Y data not available). The first 7 SNPs lie in the PAR1 region, while 265 SNPs lie in the heterosomal region of X (46-130 cM). PAR2 was not analyzed due to lack of SNPs in that region. The 7 PAR1 SNPs exhibited very low LD (r 2 < 0.1). The chi-squared test showed a significant difference in LD for the two regions on X (heterosomal markers were coded as 0 and PAR1 coded as 1, p = 0.0045) (Table 1B and Figure 1A).
Although the autosomes and chromosome X both showed a similar decline in sex-specific LD as intermarker distance increased, the rate of decay proved to be quite different. LD between adjacent SNPs on chromosome X tended to be concentrated more toward the extremes (either very strong LD (r 2 > 0.9) or very weak LD (r 2 < 0.1)), while LD in the autosomes decreased gradually with distance (Tables 1C, and Table 1D, and Figure 1B and Figure 1C). Chi-squared tests confirmed this difference in both males and females (autosomes were coded as 0 and chromosome X coded as 1, p < 0.0001 for both comparisons). The chi-squared test also revealed a difference in LD between PAR1 and the autosomes (p = 0.0019) for females (Table 1E, Figure 1D).

Discussion
The primary results from this study are the observed variations in recombination rates between the autosomes and chromosome X and between PAR1 and the heterosomal regions of chromosome X. These results have obvious analysis implications. Thorough fine mapping of regions with high recombination rates requires significantly more SNPs to delineate fully the LD block structure and capture the haplotypic variation. Recombination of chromosomes tends to increase genetic diversity. More specifically, recombination is not the source of new genes but the source of new combinations of genes. The evolutionary significance as to why PAR1 and the heterosomal region on chromosome X vary in the extent of LD compared to each other and to the autosomes is not immediately obvious. That is, it is not clear whether the observed patterns in these genomic regions are the results of current or past selective pressure on specific genes, or represent an interesting characteristic of the genome void of meaningful evolutionary cause or significance. The resolution of this question likely must wait until the functions of the individual genes in these regions are known and placed in the context of modern evolutionary theory.
A significant limitation of this investigation is that the SNPs are not dense enough to appropriately explore differences in block structures, particularly in the PAR1 region. A more dense set of markers across the genome would provide additional information about LD structures as well as potential "recombinational hot-spots". Furthermore, if sufficient data from pseudoautosomal region 2 were available, a comparison of LD in PAR1 and PAR2 would be of interest. With the advent of genomewide association analysis using 500 K chips, these questions can be explored in detail.

Conclusion
A comparison of sex-specific LD across chromosome X revealed a comparable rate of decay in LD with increased distance for males and females. When compared with the autosomes, however, the rate decay of LD on chromosome X was significantly more rapid. In addition, we observed much lower LD on pseudoautosomal region 1 (PAR1) of chromosome X compared with the heterosomal region of chromosome X. This observation of low LD across PAR1 is consistent with previous results that suggest higher recombination rates on PAR1 when compared with the heterosomal region [4].

Abbreviations
COGA: Collaborative Study on the Genetics of Alcoholism EM: Expectation maximization LD: Linkage disequilibrium SNP: Single-nucleotide polymorphism