Mycobacterium tuberculosis Utilizes a Unique Heterotetrameric Structure for Dehydrogenation of the Cholesterol Side Chain

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• 作者：Suzanne T. Thomas ; Nicole S. Sampson
• 刊名：Biochemistry
• 出版年：2013
• 出版时间：April 30, 2013
• 年：2013
• 卷：52
• 期：17
• 页码：2895-2904
• 全文大小：487K
• 年卷期：v.52,no.17(April 30, 2013)
• ISSN：1520-4995

文摘

Compounding evidence supports the important role in pathogenesis that the metabolism of cholesterol by Mycobacterium tuberculosis plays. Elucidating the pathway by which cholesterol is catabolized is necessary to understand the molecular mechanism by which this pathway contributes to infection. On the basis of early metabolite identification studies in multiple actinomycetes, it has been proposed that cholesterol side chain metabolism requires one or more acyl-CoA dehydrogenases (ACADs). There are 35 genes annotated as encoding ACADs in the M. tuberculosis genome. Here we characterize a heteromeric ACAD encoded by Rv3544c and Rv3543c, formerly named fadE28 and fadE29, respectively. We now refer to genes Rv3544c and Rv3543c as chsE1 and chsE2, respectively, in recognition of their validated activity in cholesterol side chain dehydrogenation. Analytical ultracentrifugation and liquid chromatography鈥搖ltraviolet experiments establish that ChsE1鈥揅hsE2 forms an 伪₂尾₂ heterotetramer, a new architecture for an ACAD. Our bioinformatic analysis and mutagenesis studies reveal that heterotetrameric ChsE1鈥揅hsE2 has only two active sites. E241 in ChsE2 is required for catalysis of dehydrogenation by ChsE1鈥揅hsE2. Steady state kinetic analysis establishes the enzyme is specific for an intact steroid ring system versus hexahydroindanone substrates with specificity constants (k_cat/K_M) of (2.5 卤 0.5) 脳 10⁵ s^鈥? M^鈥? versus 9.8 脳 10² s^鈥? M^鈥?, respectively, at pH 8.5. The characterization of a unique ACAD quaternary structure involved in sterol metabolism that is encoded by two distinct cistronic ACAD genes opens the way to identification of additional sterol-metabolizing ACADs in M. tuberculosis and other actinomycetes through bioinformatic analysis.