Hsab Classification Essay

HSAB concept is an initialism for "hard and soft (Lewis) acids and bases". Also known as the Pearson acid base concept, HSAB is widely used in chemistry for explaining stability of compounds, reaction mechanisms and pathways. It assigns the terms 'hard' or 'soft', and 'acid' or 'base' to chemical species. 'Hard' applies to species which are small, have high charge states (the charge criterion applies mainly to acids, to a lesser extent to bases), and are weakly polarizable. 'Soft' applies to species which are big, have low charge states and are strongly polarizable.[1] The concept is a way of applying the notion of orbital overlap to specific chemical cases.[citation needed]

The theory is used in contexts where a qualitative, rather than quantitative, description would help in understanding the predominant factors which drive chemical properties and reactions. This is especially so in transition metalchemistry, where numerous experiments have been done to determine the relative ordering of ligands and transition metal ions in terms of their hardness and softness.

HSAB theory is also useful in predicting the products of metathesis reactions. In 2005 it was shown that even the sensitivity and performance of explosive materials can be explained on basis of HSAB theory.[2]

Ralph Pearson introduced the HSAB principle in the early 1960s[3][4][5] as an attempt to unify inorganic and organic reaction chemistry.[6]

Theory[edit]

Hard-soft trends for acids and bases

Acids 

Bases 

Essentially, the theory states that soft acids react faster and form stronger bonds with soft bases, whereas hard acids react faster and form stronger bonds with hard bases, all other factors being equal.[7] The classification in the original work was mostly based on equilibrium constants for reaction of two Lewis bases competing for a Lewis acid.

Borderline cases are also identified: borderline acids are trimethylborane, sulfur dioxide and ferrous Fe2+, cobalt Co2+caesium Cs+ and lead Pb2+ cations. Borderline bases are: aniline, pyridine, nitrogen N2 and the azide, chloride, bromide, nitrate and sulfate anions.

Generally speaking, acids and bases interact and the most stable interactions are hard-hard (ionogenic character) and soft-soft (covalent character).

An attempt to quantify the 'softness' of a base consists in determining the equilibrium constant for the following equilibrium:

BH + CH3Hg+ ⇌ H+ + CH3HgB

Where CH3Hg+ (methylmercury ion) is a very soft acid and H+ (proton) is a hard acid, which compete for B (the base to be classified).

Some examples illustrating the effectiveness of the theory:

Chemical hardness[edit]

In 1983 Pearson together with Robert Parr extended the qualitative HSAB theory with a quantitative definition of the chemical hardness (η) as being proportional to the second derivative of the total energy of a chemical system with respect to changes in the number of electrons at a fixed nuclear environment:[9]

.

The factor of one-half is arbitrary and often dropped as Pearson has noted.[10]

An operational definition for the chemical hardness is obtained by applying a three-point finite difference approximation to the second derivative:[11]

where I is the ionization potential and A the electron affinity. This expression implies that the chemical hardness is proportional to the band gap of a chemical system, when a gap exists.

The first derivative of the energy with respect to the number of electrons is equal to the chemical potential, μ, of the system,

,

from which an operational definition for the chemical potential is obtained from a finite difference approximation to the first order derivative as

which is equal to the negative of the electronegativity (χ) definition on the Mulliken scale: μ = −χ.

The hardness and Mulliken electronegativity are related as

,

and in this sense hardness is a measure for resistance to deformation or change. Likewise a value of zero denotes maximum softness, where softness is defined as the reciprocal of hardness.

In a compilation of hardness values only that of the hydride anion deviates. Another discrepancy noted in the original 1983 article are the apparent higher hardness of Tl3+ compared to Tl+.

Modifications[edit]

If the interaction between acid and base in solution results in an equilibrium mixture the strength of the interaction can be quantified in terms of an equilibrium constant. An alternative quantitative measure is the heat (enthalpy) of formation of the Lewis acid-base adduct in a non-coordinating solvent. The ECW Model is quantitative model that describes and predicts the strength of Lewis acid base interactions, -ΔH . The model assigned E and C parameters to many Lewis acids and bases. Each acid is characterized by an EA and a CA. Each base is likewise characterized by its own EB and CB. The E and C parameters refer, respectively, to the electrostatic and covalent contributions to the strength of the bonds that the acid and base will form. The equation is

-ΔH = EAEB + CACB + W

The W term represents a constant energy contribution for acid–base reaction such as the cleavage of a dimeric acid or base. The equation predicts reversal of acids and base strengths. The graphical presentations of the equation show that there is no single order of Lewis base strengths or Lewis acid strengths.[12] The ECW model accommodates the failure of single parameter descriptions of acid-base interactions.

A related method adopting the E and C formalism of Drago and co-workers quantitatively predicts the formation constants for complexes of many metal ions plus the proton with a wide range of unidentate Lewis acids in aqueous solution, and also offered insights into factors governing HSAB behavior in solution.[13]

Another quantitative system has been proposed, in which Lewis acid strength toward Lewis base fluoride is based on gas-phase affinity for fluoride.[14] Additional one-parameter base strength scales have been presented.[15]

Kornblum's rule[edit]

An application of HSAB theory is the so-called Kornblum's rule (after Nathan Kornblum) which states that in reactions with ambident nucleophiles (nucleophiles that can attack from two or more places), the more electronegative atom reacts when the reaction mechanism is SN1 and the less electronegative one in a SN2 reaction. This rule (established in 1954)[16] predates HSAB theory but in HSAB terms its explanation is that in a SN1 reaction the carbocation (a hard acid) reacts with a hard base (high electronegativity) and that in a SN2 reaction tetravalent carbon (a soft acid) reacts with soft bases.

According to findings, electrophilic alkylations at free CN occur preferentially at carbon, regardless of whether the SN1 or SN2 mechanism is involved and whether hard or soft electrophiles are employed. Preferred N attack, as postulated for hard electrophiles by the HSAB principle, could not be observed with any alkylating agent. Isocyano compounds are only formed with highly reactive electrophiles that react without an activation barrier because the diffusion limit is approached. It is claimed that the knowledge of absolute rate constants and not of the hardness of the reaction partners is needed to predict the outcome of alkylations of the cyanide ion.[17]

Criticism[edit]

In 2011, Herbert Mayr et al. from Ludwig Maximilian University of Munich (LMU) published a critical review in Angewandte Chemie.[18] Consecutive analysis of various types of ambident organic system reveals that an older approach based on thermodynamic/kinetic control describes reactivity of organic compounds perfectly, while the HSAB principle actually fails and should be abandoned in the rationalization of ambident reactivity of organic compounds.

See also[edit]

References[edit]

  1. ^Jolly, W. L. (1984). Modern Inorganic Chemistry. New York: McGraw-Hill. ISBN 0-07-032760-2. 
  2. ^[1] E.-C. Koch, Acid-Base Interactions in Energetic Materials: I. The Hard and Soft Acids and Bases (HSAB) Principle-Insights to Reactivity and Sensitivity of Energetic Materials, Prop.,Expl.,Pyrotech. 302005, 5
  3. ^Pearson, Ralph G. (1963). "Hard and Soft Acids and Bases". J. Am. Chem. Soc.85 (22): 3533–3539. doi:10.1021/ja00905a001. 
  4. ^Pearson, Ralph G. (1968). "Hard and soft acids and bases, HSAB, part 1: Fundamental principles". J. Chem. Educ.1968 (45): 581–586. Bibcode:1968JChEd..45..581P. doi:10.1021/ed045p581. (Subscription required (help)). 
  5. ^Pearson, Ralph G. (1968). "Hard and soft acids and bases, HSAB, part II: Underlying theories". J. Chem. Educ.1968 (45): 643–648. Bibcode:1968JChEd..45..643P. doi:10.1021/ed045p643. (Subscription required (help)). 
  6. ^[2] R. G. Pearson, Chemical Hardness - Applications From Molecules to Solids, Wiley-VCH, Weinheim, 1997, 198 pp
  7. ^ abcIUPAC, Glossary of terms used in theoretical organic chemistry, accessed 16 Dec 2006.
  8. ^ abMiessler G.L. and Tarr D.A. "Inorganic Chemistry" 2nd ed. Prentice-Hall 1999, p.181-5
  9. ^ abRobert G. Parr & Ralph G. Pearson (1983). "Absolute hardness: companion parameter to absolute electronegativity". J. Am. Chem. Soc.105 (26): 7512–7516. doi:10.1021/ja00364a005. 
  10. ^Ralph G. Pearson (2005). "Chemical hardness and density functional theory"(PDF). J. Chem. Sci. 117 (5): 369–377. doi:10.1007/BF02708340. 
  11. ^Delchev, Ya. I.; A. I. Kuleff; J. Maruani; Tz. Mineva; F. Zahariev (2006). Jean-Pierre Julien; Jean Maruani; Didier Mayou, eds. Strutinsky's shell-correction method in the extended Kohn-Sham scheme: application to the ionization potential, electron affinity, electronegativity and chemical hardness of atoms in Recent Advances in the Theory of Chemical and Physical Systems. New York: Springer-Verlag. pp. 159–177. ISBN 978-1-4020-4527-1. 
  12. ^Vogel G. C.;Drago, R. S. (1996). "The ECW Modeldoi=10.1021/ed073p701". Journal of Chemical Education. 73: 701–707. Bibcode:1996JChEd..73..701V. doi:10.1021/ed073p701. 
  13. ^Hancock, R. D.; Martell, A. E. (1989). "Ligand design for the selective complexation of metal ions in aqueous solution". Chemical Reviews. 89: 1875–1914. doi:10.1021/cr00098a011. 
  14. ^Christe, K.O.; Dixon, D.A.; McLemore, D.; Wilson, W.W.; Sheehy, J.A.; Boatz, J.A. (2000). "On a quantitative scale for Lewis acidity and recent progress in polynitrogen chemistry". Journal of Fluorine Chemistry. 101 (2): 151–153. doi:10.1016/S0022-1139(99)00151-7. ISSN 0022-1139. 
  15. ^Laurence, C. and Gal, J-F. Lewis Basicity and Affinity Scales, Data and Measurement, (Wiley 2010) p 51 IBSN 978-0-470-74957-9
  16. ^The Mechanism of the Reaction of Silver Nitrite with Alkyl Halides. The Contrasting Reactions of Silver and Alkali Metal Salts with Alkyl Halides. The Alkylation of Ambident Anions Nathan Kornblum, Robert A. Smiley, Robert K. Blackwood, Don C. Iffland J. Am. Chem. Soc.; 1955; 77(23); 6269-6280. doi:10.1021/ja01628a064
  17. ^Tishkov, Alexander A.; Mayr, Herbert (2004). "Ambident Reactivity of the Cyanide Ion: A Failure of the HSAB Principle". Angewandte Chemie International Edition. 44 (1): 142–145. doi:10.1002/anie.200461640. PMID 15599920. 
  18. ^Mayr, Herbert. "Farewell to the HSAB Treatment of Ambident Reactivity". Angewandte Chemie International Edition. 50: 6470–6505. doi:10.1002/anie.201007100. 

1. Introduction

Cholesterol is a terpenoid lipid formed by a carbon skeleton and four fused alicyclic rings. It plays an essential role as a structural component of animal cell membranes [1]. Cholesterol is frequently found in the biosphere with great relevance in biology, medicine and chemistry, not only because of its natural abundance, but also due to its high resistance to microbial degradation. Therefore, the catabolism of cholesterol by specific bacteria has attracted considerable attention, in part as a potential means of producing bioactive derivatives from this steroid [2,3].

As the leading cause of tuberculosis (TB), Mycobacterium tuberculosis can enter monolayers of HeLa, monkey kidney, human amnion cells and lung epithelial cells [4,5]. It uses different parts of the cholesterol molecule as the sole carbon source for energy and also for the biosynthesis of phthiocerol dimycocerosate (PDIM), a virulence-associated lipid [6]. Other mycobacteria and members of the Nocardia, Rhodococcus, and Streptomyces genera show the same ability to degrade cholesterol and have the potential to synthesize new steroid derivatives [7,8]. Biochemical and genetic studies have revealed that a suite of genes involved in cholesterol degradation are critical for the survival of M. tuberculosis in macrophages [9,10]. The MCE4 operon encodes a specific cholesterol uptake system in cholesterol degrading bacteria [6,11,12,13]. The subsequent pathway responsible for the aerobic degradation of cholesterol can be divided into four major steps [3]. The first step involves the oxidation of cholesterol to cholest-5-en-3-one followed by the isomerization to cholestenone [14,15]. The degradation of the alkyl side-chain can be initiated by the cytochromes Cyp125 [16,17] or Cyp142 [18,19], followed by a β-oxidation-like process initiated by an ATP-dependent sterol/steroid CoA ligase [16,20,21]. The third step is central catabolism. Once the aliphatic chain has been oxidized, androstenedione (AD) is converted to androsta-1,4-diene-3,17-dione (ADD) [22,23,24], followed by a sequential transformation yielding 2-hydroxyhexa-2,4-dienoic and 9,17-dioxo-1,2,3,4,10,19-hexanorandrostan-5-oic (DOHNAA) acids [3,10]. The last step involves the degradation of 2-hydroxyhexa-2,4-dienoic DOHNAA acids leading the metabolites to enter central metabolic pathways, however, the corresponding process remains largely uncharacterized [3].

Unlike M. tuberculosis, M. smegmatis is a non-pathogenic member of the genus Mycobacterium. It is not dependent on animal cells for growth, but is frequently found in soil, water and plants. Although it shares more than 2000 gene homologs with M. tuberculosis and shares the same unusual cell wall structure of M. tuberculosis and other mycobacterial species [25], M. smegmatis grows much more rapidly than other species. M. smegmatis strain MC2 155 is a mutant of M. smegmatis [26]. It can be much more efficiently transformed with plasmid vectors using electroporation (10 to 100 thousand times) than other strains of this species. Therefore, M. smegmatis MC2 155 has been developed as a very attractive model organism for research on M. tuberculosis and other mycobacterial pathogens in the laboratory, including genetic analyses and investigations of cholesterol catabolism.

Uhía et al. found that 89 M. smegmatis genes were up-regulated at least three fold when cells were grown on cholesterol compared with that growing on glycerol by using microarray [27], and 39 of the catabolic genes were organized into three specific clusters. However, there is only one study done in M. smegmatis and microarrays show some limitations on throughput and noise ratios [28]. A more analysis is urgently needed to investigate the genome-wide cholesterol response in this model bacterium. The inception of RNA-sequencing technology (RNA-Seq) in 2005 provides a new method to uncover transcriptomic data with a much higher throughput than previous technology [29]. It shows major advantages in robustness, resolution and inter-lab portability over several microarray platforms [30]. In the current study, M. smegmatis strain MC2 155 was cultured in chemically defined media supplemented with androstenedione, cholesterol or glycerol. Total RNAs were extracted from three corresponding samples and submitted to next-generation sequencing (NGS) platform for RNA-Seq study. The global expression patterns were carefully analyzed to characterize the genome-wide cholesterol response, and also whether androstenedione, a derivative of cholesterol, can stimulate the same response.

2. Results

2.1. RNA Sequencing and Global Expression Patterns

To analyze the response to different supplemental carbon sources, M.smegmatis MC2 155 cultures grown in liquid minimal media supplemented with androstenedione (AD), cholesterol (CHOL) or glycerol (GL) were collected for RNA-Seq. A total of 1.41 giga base pairs (Gb) of paired-end (PE) 90 bp clean reads were obtained from the three sequenced samples (Table 1). The clean reads data are deposited in NCBI’s Sequence Read Archive (SRA) under the accession numbers of SRR1976255, SRR1976286 and SRR1976288. All reads were aligned to the M.smegmatis MC2 155 genome sequence with Bowtie2 (v2.2.3) [31,32]. Based on the mapping results, the number of mapped reads was calculated and then normalized for genome-wide gene expression profiling. Gene annotations and expression level data for each gene were combined and used for the following analyses. The results showed that 3, 237 and 8 genes were specifically expressed in AD, CHOL and GL (Figure 1, Table S1). The 3 AD-specific genes were all annotated as hypothetical protein encoding genes, whereas the 8 GL-specific genes included seven hypothetical proteins and a rubredoxin encoding gene. GO (Gene Ontology, http://geneontology.org/) classification results showed that 21 genes specifically expressed in CHOL were involved in primary metabolic processes and 15 were involved in nitrogen compound metabolic process. These results indicated that almost all of the genes expressed in androstenedione or glycerol supplemented media were also expressed in cholesterol supplemented media, and that many more genes were induced in cholesterol supplemented media than the other two media.

2.2. Differentially Expressed Genes and Functional Enrichment Analyses

To compare the expression patterns between each pairs of conditions, differentially expressed genes (DEGs) with log2 fold-change (log2FC) ≥1 and False Discovery Rate (FDR) ≤0.05 were identified pair-wisely by edgeR [34]. Results showed that 2019, 489 and 201 DEGs were identified in three comparisons, respectively (Figure 1, Table S2–S4). Compared with AD and GL, a total of 1852 and 454 DEGs were significantly up-regulated in CHOL. Moreover, regardless of the FDR value, a total of 2102 and 1695 genes were up-regulated at least two fold in CHOL when compared with AD and GL, respectively. Among the 1852 significantly up-regulated DEGs, 270 genes were involved in nitrogen compound metabolic processes, 170, 224, 203, 145 and 144 DEGs had oxidoreductase, transferase, hydrolase, nucleic acid binding and ion binding activity, respectively. When compared together, cholesterol appeared to up regulate many more genes than its derivative, androstenedione (Figure 1). A total of 420 genes were up-regulated by cholesterol specifically (CHOL-Up). Most of the CHOL-Up genes were up-regulated by CHOL specifically, including several cholesterol metabolism related genes and some mammalian cell entry related genes (explained further below).

To identify the enriched GO terms, a hypergeometric test was performed and the p-value was adjusted using the Bonferroni method [35]. DEGs identified between AD and CHOL were enriched in the GO term “regulation of response to stimulus”, whereas those identified between CHOL and GL were enriched in “tetrapyrrole binding and oxidoreductase activity”.

To verify the reliability of the RNA-Seq data, we repeated the bacterial cultivation and treatment, re-sampled three biological replicates and confirmed the data by qRT-PCR. qRT-PCR were performed using Ssofast Evagreen supermix (BIO-RAD, Hercules, CA, USA) on a CFX Connect Real-time PCR detection system (BIO-RAD) according to the user manual. A total of 52 genes were selected for qRT-PCR analyses, and 50 fragments were successfully amplified (Table S5, Figure S1). The results demonstrated that most of these genes showed changes consistent with the RNA-Seq data, confirming that the NGS based expression profiling gave reliable expression data in this study.

2.3. Nitrogen and Carbon Metabolism Related Genes

To detect whether the supplemented carbon source changed basic nitrogen and carbon metabolism, key genes involved in nitrogen and carbon metabolism were carefully analyzed. The assimilation and metabolism of inorganic nitrogen is a complex process involving a series of enzymes, including nitrate reductase (NR, EC:1.7.99.4), nitrite reductase (NiR, EC:1.7.1.4), glutamine synthetase (GS, EC:6.3.1.2), glutamate synthase (GOGAT, EC:1.4.1.13/1.4.1.14/1.4.7.1), glutamate dehydrogenase (GDH, EC:1.4.1.2/1.4.1.3/1.4.1.4), carbamoyl phosphate synthase (CPS, EC:6.3.4.16) et al. [36,37]. In this study, M. smegmatis strain MC2 155 was grown in chemically defined medium with ammonia-nitrogen as the sole nitrogen source, therefore, the assimilation of ammonia-nitrogen is necessary for maintaining basic metabolism. Three ammonium transporter (AMT) encoding genes were analyzed carefully (Table 2). MSMEG_4635 was the most highly expressed AMT, and its expression abundance under different culture conditions was not obviously different between the samples (22.86, 28.85 and 28.80). The absorbed ammonium can be then converted into nitrite by ammonia monooxygenase (EC:1.14.99.39) and hydroxylamine dehydrogenase (EC:1.7.2.6), and then converted into nitrate by NR. However, we could not identify ammonia monooxygenase and hydroxylamine dehydrogenase encoding genes in our RNA-Seq data. Five NR encoding genes were detected and were all highly expressed (Table 2 and Table S1). Among these genes, MSMEG_2837 was most highly expressed in AD, followed by GL and CHOL, while MSMEG_5140 and MSMEG_5139 were most highly expressed in CHOL. The absorbed ammonium can be also catalyzed by CPS, GS and GOGAT to produce carbamoyl phosphate and l-Glutamine. Three CPS encoding genes were all highly expressed in CHOL. Meanwhile, the most highly expressed GS (MSMEG_4294) had expression levels of 3744.30, 990.23 and 1818.46 under the three conditions, whereas the other two highly expressed GS genes (MSMEG_3561 and MSMEG_4290) were both most highly expressed in GL. In contrast to the expression pattern of MSMEG_4294, the two most highly expressed GOGATs, MSMEG_3225 and MSMEG_3226, were both up-regulated by GL, while the other four were all more highly expressed in CHOL. GDH catalyzes the reversible reaction between ammonium and l-Glutamine. However, no obvious difference was observed under different conditions for the most highly expressed GDH encoding gene (MSMEG_4699). Furthermore, key enzymes encoding genes involved in glycolysis and the citrate cycle were carefully analyzed, including the hexokinase (HXK, EC:2.7.1.1), 6-phosphofructokinase (PFK, EC:2.7.1.11), pyruvate kinase (PK, EC:2.7.1.40), citrate synthase (CS, EC:2.3.3.1), isocitrate dehydrogenase (IDH, EC:1.1.1.41), alpha-ketoglutarate decarboxylase (KGD, EC:1.2.4.2) and dihydrolipoamide dehydrogenase (DLD, EC:1.8.1.4). Results demonstrated that there were no apparent tendency was observed among three different conditions (Table S1). These results indicated that although different supplements resulted in some changes in gene expression, primary metabolism was not obviously affected. To verify these results, we measured the optical density (OD) value at different time points for three cultivation conditions. It was found that M.smegmatis MC2 155 showed an S-shaped growth curve in the three different supplemented media (Figure 2).

2.4. Glycerol and Androstenedione Metabolism

Glycerol kinase (EC:2.7.1.30) plays a critical role in glycerol metabolism by converting glycerol to glycerol 3-phosphate in an ATP dependent reaction [38]. In this study, we found that three glycerol kinase genes were expressed (Table S1). MSMEG_6229 was highly expressed under the three different culture conditions, whereas MSMEG_6756 was expressed at a lower level. MSMEG_6759 had low expression levels in AD (25.14) and CHOL (8.36), but was significantly up-regulated in GL (456.85). A total of five glycerol-3-phosphate dehydrogenase genes were expressed in this study, and three were highly expressed in all conditions (MSMEG_1736, MSMEG_1140 and MSMEG_2393), while MSMEG_6761 was up-regulated in GL when compared with the other conditions.

Glycerol dehydratase (EC:4.2.1.30) is the rate-limiting enzyme involved in 3-hydroxypropionic acid biosynthesis and glycerol can suppress the activity of glycerol dehydratase [39,40]. Three glycerol dehydratase large subunit encoding genes were identified in our data set (Table S1). MSMEG_6321 and MSMEG_0497 had the highest expression level in CHOL, whereas MSMEG_1547 was highly expressed in all three conditions. However, no glycerol dehydratase medium subunit gene was detected in this study. Four 3-ketosteroid-δ-1-dehydrogenase (EC:1.3.99.4) encoding genes, which are involved in the degradation of androstenedione, were all most highly expressed in CHOL.

2.5. Cholesterol Metabolism Related Genes

In the classic cholesterol catabolism pathway, degradation is initiated by 3-beta hydroxysteroid dehydrogenase/isomerase (3β-HSD) and cholest-4-en-3-one 26-monooxygenase (CYP125, EC:1.14.13.141). In this study, one 3β-HSD encoding gene was found to be expressed (MSMEG_5228). When M. smegmatis was grown in cholesterol supplemented media, 3β-HSD was significantly up-regulated, and its expression level was 6.86, 26.34 and 5.96 in AD, CHOL and GL, respectively (Table S6). Three CYP125 encoding genes were detected and all had the highest expression levels in CHOL (MSMEG_3524, MSMEG_5853 and MSMEG_5995) (Figure 3, Table S6). The 3-ketosteroid-δ-1-dehydrogenase (KSTD, EC:1.3.99.4) can catalyze the conversion of androstenedione into androsta-1,4-diene-3,17-dione. When growth media were supplemented with androstenedione or cholesterol M. smegmatis strain MC2 155 increased the expression of several KSTD genes (MSMEG_2867, MSMEG_2869, MSMEG_4864 and MSMEG_5941) (Figure 3, Table S6). For example, the expression level of MSMEG_5941 was 20.86 in GL, but in androstenedione and cholesterol supplemented media, it was 41.15 and 115.42, respectively. MSMEG_2867 is another KSTD encoding gene that had an expression level of 25.14, 35.54 and 17.88 in AD, CHOL and GL, respectively. 3-Ketosteroid 9-α-hydroxylase (KshAB, EC:1.14.13.142), 3-hydroxy-9,10-secoandrosta-1,3,5(10)-triene-9,17-dione monooxygenase (HsaAB, EC:1.14.14.12), 3,4-dihydroxy-9,10-secoandrosta-1,3,5(10)-triene-9,17-dione 4,5-dioxygenase (HsaC, EC:1.13.11.25) and 4,5:9,10-diseco-3-hydroxy-5,9,17-trioxoandrosta-1(10), 2-diene-4-oate hydrolase (HsaD, EC:3.7.1.17) are the downstream enzymes involved in steroid degradation. However, all of these enzyme encoding genes were not detected based on the annotation information in the genome.

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