Our study resolves the apparent dichotomy between experimental and theoretical studies, and highlights the advantage of using a multipronged approach to reveal the complexities of a protein's free-energy landscape.A fundamental question in protein folding is whether proteins fold through one or multiple trajectories. While most experiments indicate a single pathway, simulations suggest proteins can fold through many parallel pathways. Here, we use a combination of chemical denaturant, mechanical force and site-directed mutations to demonstrate the presence of multiple unfolding pathways in a simple, two-state folding protein. We show that these multiple pathways have structurally different transition states, and that seemingly small changes in protein sequence and environment can strongly modulate the flux between the pathways. These results suggest that  Although most protein folding experiments can be explained by a single pathway, theoretical evidence suggests the presence of multiple pathways. Here, the authors resolve this using a combination of force, chemical denaturation and mutagenesis to modulate the flux between parallel pathways. A fundamental question in protein folding is whether proteins fold through a single pathway or many parallel pathways. Theoretical studies and molecular dynamics simulations suggest that proteins access multiple folding and unfolding trajectories, describing the native state as a kinetic hub or the bottom of a funnel12345789Parallel pathways, or flux through multiple barriers, are rarely detected in experiments. The observed rate is the sum of the individual rates for all accessible pathways, so flux through multiple barriers is kinetically indistinguishable from one lower barrier. Hence, monitoring the kinetics alone cannot reveal the entire picture; experiments must also probe the structural and physicochemical characteristics of the transition state or pathway in greater detail. In some examples of experimentally observed parallel pathways, one pathway can be distinguished from the other because it populates an intermediate1213141517One strategy to detect parallel pathways is to perturb the energy landscape in a manner that favours one trajectory over another, potentially increasing flux through previously invisible pathways. In an elegant study, Clarke and coworkers1919212324252728Force can be applied to specific locations within a protein and the response monitored at a single-molecule level using optical tweezersφ-value analysis. Our results suggest that multiple unfolding pathways are accessed even in seemingly simple two-state proteins, but they are rarely detected because the experimental observable reports only on the pathway with dominant flux. We find that small changes in environmental conditions or point mutations can shift flux between pathways, suggesting that in vivo, the heterogeneous crowded cellular environment could affect protein-folding pathways. Our study helps resolve the apparent dichotomy between experimental and theoretical studies, and highlights the advantage of using a multipronged approach to reveal the complexities of a protein's free-energy landscape.To answer these questions, we now perturb the energy landscape of the src SH3 domain using a combination of force, chemical denaturant, and site-directed mutations. The effect of denaturants on mechanical unfolding has not been studied in the near-equilibrium, low-force regime of optical tweezers, although a few studies have examined co-solute effects at higher forces using atomic force microscopy32m‡-values quantify the dependence of a protein's unfolding rate constant (kU) on urea via Chemical denaturant effects can be used to characterize all experimental trajectories because they can be measured in both bulk and force experiments. We denature with urea here because it is a commonly used non-electrolyte denaturant that probes burial of both amide and hydrocarbon surface in protein foldingkU0 M urea is kU in the absence of urea, and [urea] is the molar concentration of urea. Urea unfolding m‡-values are proportional to ΔASAN->TS, the protein surface area exposed during unfolding from the native state to the transition state; this can be used to compare the unfolding pathways of src SH3 when it is pulled across different axes and at different forces21where m‡ for R19C/N59C src SH3 (the ‘unzipping' geometry variant) from kinetic chevron plots using stopped-flow fluorescence spectroscopy (m‡ (0.49±0.06 M−1) may not be reliable. To determine m‡ under conditions where unfolding is clearly observed, we recorded three guanidinium chloride (GdmCl) dependent kinetic chevron plots in the presence of either 0, 1 or 2 M urea  that exhibits only a single unfolding pathway (pathway Z). We measured kU as a function of force in both 0 and 1 M urea. The resulting ln kU(F) versus F plots , indicating that urea lowers the unfolding free-energy barrier without altering the unfolding pathway. In addition, because the urea m‡-value at a given force is the difference between ln kU in 1 and 0 M urea . For the global analysis, the values of xU‡ and ln kU0 pN, 0 M urea  were fixed to the same value for the 0 and 1 M urea force-dependent data sets. The results of this analysis are summarized in where 0 M urea , this obm‡-value for unzipping at a single force, we also measured kU at an applied force of 15 pN over a range of urea concentrations (kU(F) versus urea concentration is linear, indicating that similar to bulk, zero-force experiments m‡ for mechanical unzipping also does not depend on the urea concentration, and hence can be determined using m‡ at 15 pN (0.40±0.05 M−1) is the same as the urea m‡ obtained from the global analysis of the force-dependent unfolding rates  versus F plots are not parallel to each other: urea increases the unfolding rate more significantly at lower forces than at higher forces, yielding different m‡-values in the two force regimes. This means that ΔASAN->TS is different for each force regime, supporting our hypothesis of two parallel pathways (S1 and S2) that have differently structured transition states.To determine how urea affects unfolding when parallel pathways are apparent, a similar approach was followed for the more complicated shearing geometry (A7C/N59C src SH3). We again measured 1 M urea ; in thism‡-values for pathways S1 and S2, we assumed that, like unzipping, the m‡ for each shearing pathway is force and urea-concentration independent. We globally fit the 0 and 1 M urea data (see Methods for details) by fixing the ln kU,10 pN, 0 M urea, ln kU,20 pN, 0 M urea, xU1‡ and xU2‡ to be identical for both data sets (the subscripts 1 and 2 represent pathway S1 or S2). The data are well captured by the parallel pathways model and the resulting fit parameters are summarized in xU‡ and m‡-values for the two trajectories are significantly different from each other, again suggesting that they have structurally different transition states. Interestingly, pathway S2 has a higher xU‡, but lower m‡ than pathway S1. This indicates that pathway S2 has a longer distance to the transition state from the native state, but that less protein surface area is exposed in unfolding to the transition state. This result demonstrates that the end-to-end distance change and the surface area change for the unfolding of a protein are not necessarily correlated.To calculate separate kU for an individual pathway to the sum of kU for all observed pathways:The parameters from the global fit can be used to calculate the flux through each pathway at any force in the range studied. The flux is simply the ratio of m‡ and ln kU0 pN, 0 M urea values measured for all trajectories observed here. Pathway B and pathway Z have very similar m‡-values, suggesting that they are unfolding through the same transition state and so are the same pathways, which we will refer to as pathway B/Z. Although it is also possible that they unfold through different transition states with similar amounts of surface area burial, the results of the mechanical φ-value analysis described below support the hypothesis that they unfold via the same pathway. The extrapolated ln kU0 pN, 0 M urea values for pathways B and Z are not the same (pathway B extrapolates to faster unfolding in the absence of force and denaturant); however, this is likely due to the presence of the optical trap, beads and DNA handles in force spectroscopy experiments, which decrease the unfolding rate of the proteinWe have characterized four unfolding pathways: the bulk (zero-force) pathway (B), the unzipping pathway (Z) and the shearing pathways (S1 and S2). Are any of these the same pathway? m‡ and ln kU0 pN, 0 M urea values for pathways S1 and S2 suggest that they are different from pathway B/Z. Although the m‡ for pathway S2 is similar to that of pathway B/Z, the ln kU0 pN, 0 M urea value is significantly lower, even after accounting for the effects of the optical trap and DNA handle attachment, suggesting that S2 is a distinct pathway. The m‡ for pathway S1 is significantly different from the m‡ for pathway B/Z or pathway S2, suggesting that S1 is a third unique pathway.The m‡-values yield ΔASAN->TS, they do not give any information about which regions of the protein are structured. Indeed, if different regions are structured in the transition states of the various pathways, simple mutations could modulate the flux or even alter the dominant pathway.The presence of at least three different pathways raises the question of the potential structural differences in the corresponding transition states. While φ-value methodologyφ-value indicates whether a residue is unstructured (φ=1) or structured (φ=0) in the transition state11φ-values for pathway Z (φZ) of 0.90 and 0.55, respectively, similar to the corresponding bulk φ-values (φB) of 0.88 and 0.45, respectively. The S47A variant, which does not exhibit hopping, yields φZ=0.30 and φB=0.20. The similarity between the mechanical and bulk φ-values further corroborate our hypothesis that the unzipping and zero-force, bulk-unfolding pathways are the same.We investigated the mechanical unfolding behaviour of several site-directed point variants to characterize the transition state structure of the different unfolding pathways via the φ-values in the shearing geometry (A7C/N59C background), we globally fit the data set for all the site-directed variants, using the two distances to the transition state (xU1‡=0.20±0.05 nm and xU2‡=1.20±0.04 nm) as shared variables. The point mutations can be characterized based on how they affect pathways S1 and S2. Some variants influence both pathways equally; for instance, the F10I and L44A mutations increase the unfolding rates across the entire measured force regime , all φ-values cluster between 0.23 and 0.57, with no extremities . At zero-force, the dominant flux is through Pathway B/Z, yielding a highly polarized transition state. With increasing shearing force, the contribution from Pathway S1 increases, resulting in φ-values ∼0.5 due to a mixture of flux contributions from pathways with different transition state structures. This heterogeneity of the transition state at low forces cannot be detected from the ln kU versus F plots, which yield a single distance to the transition state (xU1‡=0.20±0.05 nm).The calculated bulk and mechanical 1 and S2 , Table 21 and S2 . At low remities , indicatand S2 F< pN, all F>35 pN), Pathway S2 dominates, and the transition state becomes polarized again  to account for contributions of the DNA handles, beads and the optical trap.The ln kU0 pN, 0 M urea for pathway B/Z (−3.27) is higher than ln kU0 pN, 0 M urea for pathway S1 or S2  is actually lower than ln kU0 pN, 0 M urea for pathway B/Z, indicating that ln kU for pathway B/Z must decrease when shearing forces are applied, that is, a negative xU‡ value  than the end-to-end extension along the shearing reaction coordinate. Our results suggest that the unfolding energy landscape is very complicated even at low perturbant concentrations, an aspect that is rarely considered in most experiments that rely on extrapolating data from high denaturant concentrations or high forces.Our results clearly indicate that unzipping forces favour pathway B/Z but shearing forces do not. While this may not seem intuitive, Dudko and co-workersBy analysing combined effects of force, chemical denaturant and point mutations on unfolding of src SH3, we have identified and characterized three different pathways: pathway B/Z, pathway S1 and pathway S2 . Our stuFor most proteins under typical experimental conditions, it is likely that most of the flux is channelled through a dominant pathway that appears robust to sequence variation. We show that by perturbing with force, urea and point mutations, however, we can shift the flux between different pathways. This is relevant because in the crowded cellular environment, proteins are exposed to many perturbants that could strongly influence the choice of folding and unfolding pathways. For instance, Gruebele and co-workers454748φ-value analysis is commonly used to characterize protein-folding transition states by assuming that point mutations only affect the height of the barrier for a single transition state11φ-value for that mutation is relevant to a completely different transition state.Our results also add a layer of complexity to interpretation of experimental data. Most experiments interpret the effect of a perturbant on protein-folding rates in terms of a change in the height of the rate-limiting barrier, not a shift to a parallel pathway with a different barrier. For instance, Our multipronged approach using force, urea and point mutations has shed new light on the complexity of a protein's energy landscape. If even a simple protein like the src SH3 domain unfolds through multiple pathways, it is likely that many other proteins can also access parallel pathways even if a single dominant pathway is observed under experimental conditions. The quantitative nature of our studies, following two different reaction coordinates, provides a unique platform to test simulation methods, which should enhance the ability to connect experiments and theory.Site-directed cysteine mutations in the chicken src SH3 domain sequence were introduced using QuikChange mutagenesis. The variant proteins were expressed and purified as described previously−1.DNA handles were attached to the protein as described previously24Chemical denaturant melts were performed as described previously using a Horiba FloroMax-3 fluorimeterm‡-values were determined from a series of three GdmCl chevrons collected in buffer  containing three different urea concentrations—0, 1 and 2 M. Each GdmCl chevron was fit to determine kU0 M GdmCl, the unfolding rate constant in the absence of GdmCl . The urea m‡-value was determined from the slope of a plot of these kU0 M GdmCl against urea molarity.The urea m‡-values. These experiments were performed in the same buffer  as the bulk urea chevrons to ensure that m‡-values from bulk and single-molecule force experiments can be directly compared. A different buffer  was used in previous experiments and in the φ-value analysis performed here :In the unzipping pulling geometry, only one unfolding pathway is observed. Unfolding kinetics obtained in 0 and 1 M urea were fit using kU0 pN, 0 M urea is the mechanical unfolding rate constant in the absence of force and urea. Igor Pro v6.22a was used to globally fit both data sets to kU0 pN, 0 M urea, xU‡ and m‡ to be the same for the two data sets.where kU is the sum of the kU values for pathways S1 and S2. Therefore, the data obtained at 0 and 1 M urea can be described by kU values for each pathway:In the shearing pulling geometry, the observed kU,10 pN, 0 M urea, kU,20 pN, 0 M urea, xU1‡, xU2‡, m‡1 and m‡2 to be identical for both data sets.where the subscripts 1 and 2 represent pathways S1 or S2. Igor Pro v6.22a was used to globally fit the 0 and 1 M urea data to B.J., E.J.G. and S.M. designed the research; B.J. and E.J.G. performed the research; B.J., E.J.G. and S.M. analysed the data; B.J., E.J.G. and S.M. wrote the paper.How to cite this article: Guinn, E. J. et al. Single-molecule chemo-mechanical unfolding reveals multiple transition state barriers in a small single-domain protein. Nat. Commun. 6:6861 doi: 10.1038/ncomms7861 (2015).Supplementary Figures 1-4, Supplementary Tables 1-2 and Supplementary Notes 1-3


Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host.Infections caused by S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen.We took the whole proteome sequence data of 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins of the unique metabolic pathways of the pathogens and mining the proteomic data of all completely sequenced strains of the pathogen, thus improving the quality and application of the results. We believe that the sharing of the knowledge from this study would eventually lead to bring about novel and unique therapeutic regimens against the infections caused by the S. enterica.The identification of 73 enzymes common in 42 strains of  Salmonella enterica is a Gram-negative facultative anaerobic intracellular bacterium. According to the classification scheme of Kauffmann-White [S. enterica serovars infect the host through the mouth, leading to the three major symptoms: enterocolitis, bacteremia and enteric fever, or asymptomatic chronic carriage [nn-White , more thnn-White , 3. Mostnn-White . Typicalcarriage . Human pSalmonella enterica initiates with its entry in the host organism. Salmonella is usually acquired from the environment by contact with a carrier host or by oral intake of contaminated food or water. After ingestion, Salmonella survives the low pH of the stomach, eventually leading to entry of the intestine where it uses a type III secretion system to deliver effecter proteins essential for intestinal invasion [Pathogenesis of invasion . Hereaftinvasion . In Typhinvasion  and the invasion . Both tyinvasion .A genome is the set of genes in a single functional organism, whereas the pangenome of a prokaryote is the set of non-redundant genes which includes a core genome containing genes present in all strains; dispensable genes that are absent from one or more strains, but not all; and genes that are unique to each strain . RecentlS. enterica and KEGG-annotated metabolic pathway data of Homo sapiens, identified and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and identified enzymes essential to the pathogen using DEG database. We compared our results to a previous study [In the present study, we took the whole proteome sequence data of 42 strains of 19 serovars of us study  where thA schematic representation of the methodology is given in H. sapiens and 42 strains of S. enterica containing information about the genes of respective metabolic pathways were downloaded from the KEGG database [KEGG Brite Hierarchy files of database . The metThe KEGG IDs of all the genes from UMPs were converted to corresponding NCBI GIs using KEGG-API service . Amino aHomo sapiens downloaded from NCBI FTP server [S. enterica using the hierarchy files of 42 strains [To process all COPs for subtractive proteomic analyses at once, a novel strategy was applied which comprised of two approaches. In first approach, proteins of all COPs were subjected to BLASTp  against P server  and the P server  and the P server  to ident strains .VFDB (Virulence Factors Database) containinThe hypothetical proteins were identified among the enzymes to characterize their structure and/or function. All the hypothetical protein sequences were subjected to standalone BLASTp against protein sequences available in PDB (Protein Data Bank)  obtainedThe non-homologous catalytic proteins considered as putative drug targets were validated from DrugBank database  and publS. enterica was compared with the complete human metabolic pathway. On average, each strain has 117 metabolic pathways and at least 34 UMPs  was predicted to metabolize the Atrazine, thus may be resistant to it. However the dataset lacked the pathway information of β-Lactam resistance and Bisphenol degradation which were also the next most frequent absent pathways among all studied strains. The strains Heidelberg CFSAN002069 and Typhi CT18 needed to update in KEGG since the data was not updated and hence 22 and 11 NCBI GIs were appended, respectively in both strains and mentioned in Each of the metabolic pathways of 42 strains of the  34 UMPs  with allS. enterica. The CD-HIT resulted in 537 COPs and each cluster was comprised of more than 1 protein. Out of total, 241 COPs contained at least 42 proteins belonging to the 42 strains of Nitrate reductase shared more than 80% sequence similarity and hence clustered in a single COP. The enzyme Succinate Dehydrogenase Cytochrome b556 large membrane was somehow not characterized as an enzyme during KEGG analysis hence its UniProt ID was mentioned in The complete human proteome was obtained from NCBI FTP server  is a well curated open-access database consisting of essential genes from various organisms ranging from single-cell prokaryotes to multicellular eukaryotes. The bacteria harbor various virulent genes which lead to pathogenecity. Therefore, identifying virulent factors in the genome could lead us to elucidate the molecular mechanism of bacterial pathogenecity. The VFDB  is an onHypothetical proteins are those for which the sequences are available but their family and functional classification has not been established. As such they may represent unidentified drug targets , 39. TheLater on, we performed a BLASTp search using 114 hypothetical sequences as ‘query’ and sequences of PDB as ‘database’. It was performed so that if there is any homology in already well characterized PDB database then it may lead us to classify the hypothetical proteins. The BLASTp showed hits against 81 queries with the PDB database while rest (i.e. 33) queries showed no hits . The namin silico approach. We also looked in the DrugBank of the possible entry of any drug target(s) against Salmonella. The DrugBank [S. enterica. 11 out of 19 belonged to the human, while remaining 8 belonged to the bacteria. The oxygen-insensitive NADPH Nitro reductase was common in 35 strains only. Other five did not belong to UMP. Only one (i.e. Penicillin-binding protein) out of 8 genes was present in the output of current strategy. Results are summarized in S. enterica serovar Typhimurium strain LT2, and compared those with various strains of S. enterica by performing extensive experimental study. We compared our identified 73 enzymes with the results of Becker and observed that 24 enzymes were shared by the reports of Becker et. al.  enzymes are current drug targets. The remaining 49 enzymes are new potential drug targets. We have annotated the function of 114 hypothetical proteins unique to S. enterica, providing additional new potential drug targets. Finally, our organization of the available core proteomic data Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Two parallel pathways produce cholesterol: the Bloch and Kandutsch-Russell pathways. Here we used stable isotope labeling and isotopomer analysis to trace sterol flux through the two pathways in mice. Surprisingly, no tissue used the canonical K–R pathway. Rather, a hybrid pathway was identified that we call the modified K–R (MK–R) pathway. Proportional flux through the Bloch pathway varied from 8% in preputial gland to 97% in testes, and the tissue-specificity observed in vivo was retained in cultured cells. The distribution of sterol isotopomers in plasma mirrored that of liver. Sterol depletion in cultured cells increased flux through the Bloch pathway, whereas overexpression of 24-dehydrocholesterol reductase (DHCR24) enhanced usage of the MK–R pathway. Thus, relative use of the Bloch and MK–R pathways is highly variable, tissue-specific, flux dependent, and epigenetically fixed. Maintenance of two interdigitated pathways permits production of diverse bioactive sterols that can be regulated independently of cholesterol.DOI:http://dx.doi.org/10.7554/eLife.07999.001 Cholesterol is important for animals, both as an essential component of the membrane that surrounds cells and as a building block to make hormones and other biologically important molecules. However, cells limit how much cholesterol they make because an excess of this fatty molecule can cause serious health problems, including heart disease and stroke.Cholesterol is made via a complex process that involves more than 30 different steps, which can be organized into two biochemical pathways (named the Bloch pathway and the Kandutsch–Russell pathway). The enzymes that carry out the steps in these pathways have been characterized in detail. Less is known about which of the two pathways is actually used in different cells and tissues, or how much cholesterol each pathway produces. This is partly because it is difficult to distinguish between the closely related intermediate molecules that are formed in each pathway.Mitsche et al. have now used mass spectrometry and isotope labeling techniques to analyze the relative contributions of the two cholesterol-making pathways in both cells grown in the laboratory and in mice. The experiments show that many cells use the Bloch pathway. However, no cells were found to use the Kandutsch–Russell pathway as it was originally described. Rather, some of the cells used a hybrid pathway where the production of cholesterol was started using the Bloch pathway and then after a certain number of steps, the process switched to using part of the Kandutsch–Russell pathway. Mitsche et al. referred to this mixed system as the ‘modified Kandutsch–Russell pathway’.Mitsche et al. next examined the flow of molecules through these two pathways in different tissues and observed that the Bloch pathway is exclusively used in the testes and adrenal glands, which produce high levels of cholesterol. In contrast, the skin and brain use the modified Kandutsch–Russell pathway. In some tissues, a fraction of the building blocks that can be used to make cholesterol were instead diverted to make other products. This suggests that animals have maintained the two pathways over the course of evolution to enable them to generate a variety of products, which can be used to carry out different biological processes. One challenge following this work will be to use the newly developed methods to analyze other complex biochemical pathways.DOI:http://dx.doi.org/10.7554/eLife.07999.002 Cholesterol is an essential structural component of vertebrate cell membranes  and a prTwo intersecting pathways have been described for biosynthesis of cholesterol from lanosterol. The two pathways use the same catalytic steps and are distinguished by the stage at which the double bond at C24 in the side chain is reduced. The analytical challenges associated with distinguishing the multiple closely related sterol intermediates in each pathway, and interpreting the complex patterns of isotope enrichment generated by in vivo labeling have been major obstacles to in vivo studies of the Bloch and K–R pathways. 2O) labeling to analyze the turnover of a representative sterol, lanosterol, in immortalized human skin fibroblasts (SV-589 cells). The isotopomer distributions of lanosterol before and 24 hr after addition of 5% D2O to the medium are shown in 13C and a 0.015% natural abundance of deuterium  and comprised 72% of the total. Lanosterol molecules containing a single extra neutron  had an m/z peak of 410 Da (M = 1) comprised 24% of the total while those containing two extra neutrons (M = 2) comprised 4%.As a first step towards characterizing the flux of precursor sterols through the cholesterol biosynthetic pathway, we used LC-MS/MS and deuterium oxide , only 25% of the lanosterol had not incorporated any deuterium atoms (M = 0). Over 75% of the lanosterol molecules had incorporated at least one heavy atom (M1 + M2 + M3) (13C atom (M = 1). The shift in isotopomer distribution over time was used to infer the incorporation of deuterium, which was then used to measure the rate of lanosterol synthesis as described in the ‘Materials and methods’ and reviewed in Cells grown in the presence of D20 to the medium. The rate of lanosterol biosynthesis was determined by sampling cells at the indicated time points. At each time point the fraction of newly synthesized lanosterol molecules (termed ‘g’) was determined from the isotopomer spectrum.Next, we examined the effect of 25-hydroxycholesterol (25-OH Chol), a potent suppressor of cholesterol biosynthesis , on lanoThe relationship between time and g was fitted to a first-order kinetic model to determine the rate constant (k), which was multiplied by the lanosterol concentration to determine the rate of synthesis (ng/hr/μg protein). The addition of 25-OH Chol to the medium decreased the rate of lanosterol biosynthesis by 90% , bottom.2O into post-squalene cholesterol biosynthetic intermediates in cultured mouse adrenal cells (Y1-BS1 cells)   and tran1 cells)   .Deuterium was incorporated almost exclusively into Bloch pathway intermediates in Y1BS1 cells, in which the rates of incorporation were similar for lanosterol, ff-MAS, t-MAS, dehydrodesmosterol, and desmosterol  grown in cholesterol-containing medium (10% FCS) or in cholesterol-depleted serum (NCLPPS) to upregulate cholesterol synthesis. Second, cholesterol synthesis was examined in a mutant line of CHO-7 cells (SRD13A cells) that lack SREBP cleavage activating protein (SCAP), a protein required to activate the transcription of cholesterol biosynthesis genes . SRD13A In cultured human hepatoma cells (HuH-7 cells) grown in cholesterol-replete serum (FCS) , the patTo determine if the increase in Bloch pathway utilization associated with upregulation of cholesterol synthesis was coordinated at the transcriptional level, we compared levels of mRNAs encoding cholesterol biosynthetic enzymes in the HuH7 cells grown in FCS or NCLPPS . DespiteTo determine if changing DHCR24 expression altered the relative use of the Bloch and MK–R pathways, we expressed recombinant mouse DHCR24 in human embryonal kidney cells (HEK-293 cells). In HEK-293 cells transfected with vector alone, 78% of cholesterol biosynthesis proceeded through the Bloch pathway (6-lanosterol or d5-zymosterol) that have isotopic distributions not found in natural sterols. By monitoring the conversion of these synthetic isotopomers into other sterols we were able to resolve all of the stable biosynthetic intermediate sterols in their labeled forms. In this experiment we were also able to examine cholesterol biosynthesis from desmosterol and from 7-dehydrocholesterol . In cells expressing vector alone, the d5-zymosterol. In cells expressing the empty vector alone, d5 label appeared in zymosterol, dehydrolathosterol, dehydrodesmosterol, and desmosterol  via intraperitoneal injection and by enriching their drinking water to 6% (vol/vol) D2O. Rates of sterol synthesis were calculated as described in the ‘Materials and methods’ and To examine the pathways of post-squalene cholesterol biosynthesis in vivo, we assessed rates of sterol synthesis in different mouse tissues. Mice were labeled to approximately 5% DIn testes, deuterium label was only detected in Bloch pathway intermediates . The ratIn adrenal glands, cholesterol was also synthesized exclusively via the Bloch pathway  with litIn contrast to adrenal glands, the MK–R pathway predominated in skin. The synthesis of desmosterol was much less than either lanosterol or 7-dehydrocholesterol . There wIn liver, the rates of synthesis of desmosterol and 7-dehydrocholesterol were similar. Thus, approximately half of the sterol flux followed the Bloch pathway, whereas the other half used the MK–R pathway. In contrast to all other tissues examined, dihydrolanosterol was synthesized in liver at ∼5–10% the rate of lanosterol synthesis (2 ng/μg tissue/day). Essentially no synthesis of dihydro-ff-MAS was detected. This result suggests that a fraction of hepatic lanosterol was converted to dihydrolanosterol but did not proceed down the K–R pathway. Consistent with this finding, cultured human hepatocytes (HuH7 cells) also synthesized dihydolanosterol that was not further metabolized into dihydro-ff-MAS . Thus ∼5We performed similar in vivo analyses on preputial gland and brain . The K–RA similar pattern of sterol flux through the MK–R pathway was measured in the skin and brain. The skin makes Vitamin D, but since we did not measure incorporation of label into cholesterol we do not know how much of the 7-dehydrocholesterol that is made in the dermis is converted to Vitamin D vs. cholesterol. In the brain, the absolute rate of cholesterol synthesis was low, corresponding to less than 2% of that observed in liver .Thus, none of the tissues examined in this study utilized the classic K–R pathway. Instead, sterols were demethylated, at least partially, before the side chain was saturated.To compare the relative pathway utilization across tissues, the fractional utilization of the Bloch pathway was estimated for each tissue by dividing the rate of synthesis of desmosterol by the sum of the rates of synthesis of desmosterol and 7-dehydrocholesterol. The values obtained ranged from 0.97 (testes) to 0.08  . In geneIt has been suggested that relative usage of the Bloch and K–R pathway is determined by level of expression of DHCR24 . To deteIn general, DHCR24 was expressed at very low levels in tissues that predominantly use the Bloch pathway  and in those with low cholesterol biosynthetic rates (heart and muscle) .In mammals, liver is the major source of plasma cholesterol . To asse. The major finding of the study is that the architecture of the cholesterol biosynthetic pathway shows striking differences among tissues. The testes and adrenal gland utilize the canonical Bloch pathway , the expression of auxiliary proteins  or factors that affect the relative proximity of the enzymes to their substrates.Since DHCR24 activity is a major determinant of relative pathway utilization, cell-type specific control of pathway utilization may involve epigenetic control of DHCR24 expression. The pattern of DHCR24 expression in tissues was an imperfect predictor of relative pathway utilization. For example, the brain used the MK–R pathway despite low DHCR24 expression and activity  while liThe patterns of deuterium enrichment of biosynthetic precursor sterols in plasma were similar to those seen in the liver, but distinct from those observed in extrahepatic tissues. This finding indicates that the cholesterol pool in extrahepatic tissues is relatively isolated from the cholesterol in circulating lipoproteins. This hypothesis is supported by data from Dietschy and colleagues, who reported that 80% of cholesterol biosynthesis in mice takes place in extra-hepatic tissues, while 80% of plasma low density lipoprotein cholesterol is taken up by the liver . Thus, mIf extrahepatic tissues contribute little to the plasma sterol pool as shown in this study, how are sterols synthesized in peripheral tissues transported to the liver (or gut) for excretion? The route by which cholesterol is transported from the peripheral tissues to the liver, a process termed reverse cholesterol transport , remainsThe approach used in this study could potentially be confounded by the presence of two pools of an intermediate in the pathway that turn over at different rates. This problem would be particularly acute if a small pool of intermediate turns over rapidly while a second, larger pool sequestered from the biosynthesis pathway turned over slowly. Under these conditions the larger pool may significantly reduce the labeling of the total intermediate isolated from the cells resulting in falsely low estimates of flux. The observation that multiple independent intermediates showed essentially identical kinetics argues against this possibility, but we cannot formally exclude it.Metabolic pathways have traditionally been defined in two stages. First, the sequence of reactions that comprise the pathway is determined biochemically. These determinations are almost invariably qualitative in nature. Second, flux through the pathway is quantified using radioisotopes to trace the turnover of a single metabolite, typically the end product of the pathway. By combining LC-MS/MS methods with stable isotope tracing, flux can be monitored through multiple intermediates in a biosynthetic pathway simultaneously. As illustrated in the present study, this approach can reveal bifurcations in established pathways that imply novel biochemistry and physiology for molecules previously viewed simply as biosynthetic intermediates.2O, 99.8% pure) was purchased from Sigma. Cholesterol, lanosterol, dihydrolanosterol, ff-MAS, t-MAS, zymosterol, desmosterol, 7-dehydrocholesterol, d5-zymosterol, d6-lanosterol, and d6-sitosterol were obtained from Avanti Polar Lipids . 25-hydroxycholesterol (25-OHC) was obtained from Steraloids, Inc. . Methyl-cyclodextrin (MCD) was obtained from Cyclodextrin Technologies . d5-zymosterol, d6-lanosterol were bound to MCD in a 1:10 and 1:20, respectively, stoichiometric ratio for solubilization as previously described (Deuterium labeled water (Described .SCAP−/−) cells were used in this study  supplemented with either 10% (vol/vol) fetal calf serum (FCS) or newborn calf lipoprotein poor serum (NCLPPS). Y1-BS1 cells were grown in a 1:1 mixture of Ham's F-12 medium and DMEM supplemented with 15% horse serum (HS). All media contained 100 units/ml penicillin and 100 μg/ml streptomycin sulfate. In experiments using NCLPPS and/or 25-OHC, the medium was changed 16 hr before beginning D2O labeling.Immortalized cells from human fibroblasts (SV-589 cells) , human his study . Cells w2O. After 0, 0.5, 1, 2, 4, 6, 8, 12, and 24 hr, cells (triplicate dishes at each time point) were washed and then harvested in 3 ml Dulbecco's phosphate buffered saline (PBS) with 0.5% Triton X-100. A 300 μl aliquot of each cell lysate was reserved for measurement of protein concentration by bicinchoninic acid assay (BCA) analysis (Pierce BCA Protein Assay Kit). The remaining cell lysate was mixed with 3 ml methanol (MeOH) containing 20 ng of d6-sitosterol as an internal standard. The samples were sonicated for 5 min and stored at room temperature until they were prepared for LC-MS/MS analysis (see below).For measurements of sterol synthesis, cells were plated and grown to ∼60% confluence either 2 days , 3 days (HuH7), or 5 days (Y1BS1). Then the medium was exchanged with medium containing 5% Dad libitium with standard chow  for 4 weeks prior to experimentation. 3 days prior to experimentation the mice were entrained to a synchronized feeding cycle  by removing food at the beginning of the light cycle and returning food at the onset of the dark cycle. All research protocols involving mice were reviewed and approved by the Institutional Animal Care and Use Committee at University of Texas Southwestern Medical Center.Male C57BL/6J mice were obtained from the Jackson Laboratory , housed (4 per cage) in a controlled environment  and fed 2O over time. At 9 AM (3 hr into the light cycle) each mouse was injected intraperitoneally with 500 μl of D2O with 150 mM NaCl. After injection, the drinking water was supplemented with 6% D2O. Mice (3 per time point) were sacrificed at 1, 2, 3, 4, 6, 8, 12, 18, 24, 48, 72, 120, and 168 hr after injection. Six mice that did not receive D2O were also sacrificed to measure the concentrations of sterols in their tissues. Immediately after sacrifice the adrenals, blood, brain, brown adipose tissue (BAT) from the nape of the neck, heart, kidneys, liver, preputial glands, skin, spleen, posterior hind limb muscle, testes, and epididymal adipose tissue (WAT) were removed. Plasma was isolated from blood obtained in an EDTA-coated tube after centrifugation. Fur was removed from the skin by applying Veet, thoroughly washing in PBS to remove residue, and then placed at −80°C. All other tissues were snap frozen in liquid N2 and stored at −80°C.Sterol synthesis rates in mice were determined by measuring deuterium incorporation from D6-sitosterol. Tissues were thoroughly homogenized using an IKA pole rotor and bath sonication for 10 min at RT. The sample was then vortexed and centrifuged at 3000×g for 10 min. The supernatant was decanted and the pellet was resuspended in 5 ml MeOH and recentrifuged. The supernatant was pooled with the supernatant from the initial centrifugation. 1 ml of supernatant was removed and diluted with 2 ml of MeOH and 3 ml of PBS. Due to their small size, the adrenal and preputial glands were prepared by weighing the entire tissue, and then adding 3 ml MeOH and 20 ng of d6-sitosterol. The tissues were then homogenized and 3 ml PBS was added. Plasma was prepared by adding 100 μl to 6 ml of 1:1 MeOH/PBS while sonicating and adding 20 ng d6-sitosterol. From this point forward, cell culture, tissue, and plasma samples were handled identically.Larger tissues were prepared for lipid extraction by weighing 50–100 mg pieces and immediately adding 5 ml of ice cold MeOH and 200 ng of d2, resuspended in 300 μl of 9:1 MeOH/H2O, and transferred to vials for LC-MS/MS analysis.Lipids were extracted from the samples by a modified Bligh-Dyer extraction . Samples2O to 100% MeOH in 7 min. The column was washed for 5 min in 100% MeOH and then returned to the initial solvent. Sterols were detected using an ABSciex  4000 Qtrap MS/MS in positive mode with atmospheric pressure chemical ionization at a temperature of 350°C. The MS/MS detected mass to charge ratios (m/z) of 365–370, 393–400, 404, and 409–414, which spans the ion m/z plus 3 mass units for each sterol, along with the internal standard  consuming water supplemented with 10% D2O for 6 weeks. Sterol spectra in the D2O labeled mice were compared to those of unlabeled mice to identify peaks of the correct molecular mass that had an MS/MS fragmentation pattern characteristic of sterols and were endogenously synthesized (based on deuterium incorporation). Only a single chromatographic peak met these criteria for each sterol. This strategy was tested using two additional sterols, ff-MAS and zymostenol, which were correctly identified when compared to the authentic standard.Standards were commercially available for all but four of the sterols in the cholesterol biosynthetic pathway. These standards were used for quantitation of sterol concentrations relative to dm/z value of 369 Da. Cholesterol also saturated the isotopomers greater than M+1 for sterols with m/z = 367, including desmosterol, 7-dehydrocholesterol, and zymosterol.Three biosynthetic intermediates were not measured in this study. The concentrations of dehydrolathosterol were too low in all tissues and cell culture lines to reliably measure the isotopomer distribution. Lathosterol and zymostenol cannot be reliably measured in this system because they are isobaric with cholesterol, which saturates the signal at the m) was calculated by dividing the intensity of the M+0 peak by the summed intensity of all measureable isotopomers of that sterol . The fraction of each sterol that was newly synthesized (g) at each time point was determined by linearly deconvoluting M0m based on the fractional contribution of M+0 of natural  and fully labeled  sterols, expressed as:Individual peaks for each sterol in Which was solved for g:This approach, which is called ISA or mass isotopomer distribution analysis (MIDA), was developed by n was determined from the isotopic distributions of unlabeled samples  and the number of incorporation sites for label per molecule . M0t was initially determined based on the experimentally determined asymptotic spectrum of isotopomers at the last time point of labeling (2O). The value was refined using MIDA as described by 20 thus p is 0.05. The value of N was determined by regression of the observed M0t values relative to theoretical prediction. The calculated value of N was between 21 and 26 for all sterols in all cell lines, which is consistent with previously reported values  was used as an initial value. This analysis yielded a value for p of 0.048 which was used for all tissues. The value of N for all sterols was then calculated based on the asymptotic value of M0t. The value of N ranged from 20 to 28 in all tissues.For mouse experiments, the value of p was determined by iteratively regressing N and p values against the experimentally determined asymptotic value of M0∞ is the asymptotic value of g, t is time, and k is the rate constant. The value of g∞ and k were calculated using the Matlab Optimization Toolbox . The rate of synthesis of each sterol was determined by multiplying the rate constant (k) by the concentration.The rate of synthesis of each sterol was determined by assuming first-order kinetics, using the equation:Total RNA from tissues and cells was isolated using commercial reagents (RNA-STAT 60). cDNA was synthesized from 2 μg RNA using Taqman  with random hexamer primers and amplified by PCR in 2× SYBR Master Mix (Applied Biosystems). The specific oligonucleotides for each transcript are shown in 2, 10 mM KCl, 5 mM EDTA, 5 mM EGTA, 250 mM sucrose, pH 7.6) and homogenized using an Ultra-TURRAX homogenizer and then passed through a 22 G needle 35 times. Membranes were isolated from the homogenates by sequential centrifugation . The pellet from the second centrifugation step was re-suspended in lysis buffer  and shaken for 1 hr at 4°C. After an hour, an equal volume of solubilization buffer  and 5× loading buffer was added (Fermantas). Samples were size fractionated on 8% SDS-polyacrylamide gels and transferred to nitrocellulose  before incubating overnight at 4°C with a rabbit anti-mouse DHCR24 antibody  in PBST (Sigma) plus 5% (wt/vol) fat free milk. Anti-mouse calnexin  was used to detect calnexin as a loading control. After incubation, the filter was washed and HRP-conjugated anti-rabbit IgG  was used as a secondary antibody. The filter was scanned using a LiCor Odyssey and visualized using iS ImageStudio software.Samples for immublotting were placed in sample buffer . The insert sequence was verified by sequencing and the plasmid was amplified with a Maxiprep kit (Origene). The plasmid was prepared in a 1:3 ratio with Fugene 6 (Promega) (wt/vol) and diluted in Opti-Mem medium (Life Technologies). Cells were transfected by exchanging the medium for Opti-Mem medium containing 8 μg plasmid DNA at a 1:3 ratio with Fugene 6 (wt/vol). After 20 hr the medium was changed to Opti–Mem supplemented with 5% D review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and three reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for submitting your work entitled “Flux analysis of cholesterol biosynthesis in vivo reveals multiple tissue and cell-type specific pathways” for peer review at The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.The following individuals responsible for the peer review of your submission have agreed to reveal their identity: Stephen Young (Reviewing editor); Joanne Kelleher and Marc Hellerstein (peer reviewers).eLife.All three reviewers were enthusiastic about your paper and all three thought that a revised manuscript that attended to the reviewers' criticisms would be suitable for publication in In their manuscript, the authors have analyzed cholesterol synthesis through the Bloch and Kandutsch-Russell pathways in different tissues and cell types. They found that no tissue used the canonical K-R pathway, but that several used a modified K-R pathway. They found that the use of the Bloch and MK-R pathways is highly variable, tissue-specific, and flux dependent. The factors that regulated use of the different pathways was not clearly delineated, but the manuscript represents an important contribution to cholesterol biochemistry.None of the three reviewers requested additional experiments, but all three reviewers thought that it was essential for the authors to defend some of their conclusions and expand upon their interpretations of the data. Also, reviewers 2 and 3 thought that the authors needed to clarify methodological issues and in some cases qualify their conclusions. For example, the authors need to discuss how subpools of intermediates might affect their interpretations and conclusions. The authors are strongly encouraged to respond to all of the reviewers' criticisms regarding methodology and data interpretation in a revised manuscript.Reviewer #1:This is a beautifully prepared manuscript with great figures and novel data. The authors have worked define the importance of two different cholesterol biosynthetic pathways in different cell lines and in different mouse tissues. They have also examined the two biosynthetic pathways in response to perturbations that alter cholesterol synthetic rates. They have identified several important novel twists and turns in the pathway. The authors clearly have carved out a fruitful and intriguing topic for future research.I have several comments. First, the authors should defend their contention that the identification of the hybrid cholesterol synthesis pathway is novel. This reviewer noted that this hybrid pathway appears to be described in the cholesterol synthesis discussion in the Wikipedia page on Smith Lemli Opitz syndrome. Of course, Wikipedia has plenty of errors. Nevertheless, I would request that the authors defend their contention that the “pathway switchover” is novel – or whether their hybrid pathway had been conceptualized and proposed previously.Second, I would ask the authors to elaborate on the fact that plasma and liver have similar proportions of deuterium enrichment in cholesterol and precursor sterols. This is a nice finding. This reviewer believes that some sort of model would be useful to reconcile this finding with the observation that most of the cholesterol is made outside of the liver. How do the authors propose that this occurs? Please elaborate.Also, the authors showed that expression of DHCR24 affects the pathway, but that the extent to which pathway usage depends on DHCR24 expression was not 100% consistent. One possibility is that pathway utilization depends on the concentrations, or relative concentrations, of pathway intermediates, perhaps including pre-squalene intermediates. Was there evidence for this? Can one perturb pathway utilization with low doses of squalene synthase inhibitors? Please elaborate on your thoughts about what is controlling pathway utilization.It would be interesting, if possible, to tie this work to disease. For example, it might be interesting to compare pathway usage in different tissues of Smith Lemli Opitz syndrome mice. In those mice, is the utilization of different biosynthetic pathways in different tissues perturbed or maintained? Could you quantify the amount of 7-Dehydrocholesterol that is shunted away into oxidized derivatives? Does different pathway utilization in different tissues explain the susceptibility of different tissues to disease? Such studies are almost certainly too much for this paper, but the authors should at least put forward a more detailed description of what they consider to be the most important biochemical factors that govern pathway utilization of the two different pathways in different cell lines and tissues.Reviewer #2:This is an original and very well-conceived study of metabolic regulation, addressing the pathway of cholesterol biosynthesis. The results are consistent and convincing, indicating that the pathway for cholesterol synthesis in most mouse tissues – specifically, the step at which cholesterol-24,25 reduction occurs, catalyzed by DHCR24 – is late (classic Bloch pathway) or is mid-pathway, after zymosterol (modified Kandutsch-Russell [K-R] pathway). Accordingly, it is somewhat misleading to talk of an eponymous, pure K-R pathway. Variability among tissues is not explained by gene expression  and may have regulatory consequences.The methods appear solid, combining heavy water labeling and LC/MS mass isotopomer analysis with pool size measurements to measure synthesis rates and using established modifiers of cholesterol flux . The paper is well written.I believe that this is an unusually compelling contribution to the classic field of cholesterol biosynthesis.There are a few important methodologic issues to clarify:1) How much of the differences in total synthesis rates are due to the kinetic measurements (turnover rates) vs. the tissue concentrations of each metabolite? The k values are not shown . Would their findings have been discernable from simple measurements of metabolite concentrations ?6-lanosterol and d5-zymostenol, overcame the detectability problem and gave more detail on the cross-over site. The latter results support their general conclusion – i.e., cross-over seemingly around the dehydrolathosterol-lathosterol step, or maybe a step earlier. This supportive labeling result is worth noting in context of the greyed out parts of 2) The inability to detect some key biosynthetic intermediates analytically which happen to be right about where the DHCR24 crossover often occured – zymostenol, lathosterol, dehydrolathosterol – is a problem. The authors are forced to place this key step somewhere at or after zymosterol. This uncertainty, which is entirely analytic in nature, might be emphasized a bit more clearly in the text. Their one non-heavy water labeling study, with d3) The kinetic method is not accurately described as ISA, which solves for p and synthesis fraction (g) by best fit to a family of curves. The authors know p here independently (tissue water enrichment). Their equation for g (in the last paragraph of the subsection headed “Data interpretation”) is basic mass isotopomer analysis, identical for MIDA  and ISA (AJP 1992).4) A point worth mentioning about Additional data files and statistical comments:Appendix showing kinetic rate constants (k) vs. metabolite concentrations, in their calculations of absolute synthesis rates (= k x concentration).Reviewer #3:This manuscript contributes to understanding the pathway for cholesterol synthesis. Specifically it demonstrates that a kinetic labeling approach using ISA may be used to resolve the flux through the Bloch vs the K-R pathway for cholesterol synthesis. A possible difficulty with this approach would be the presence of two pools of an intermediate in the pathway. Consider the possibility of a small pool involved in the biosynthetic pathway which labeled fully and quickly and a second larger pool sequestered from the biosynthesis pathway which did not turn over. Such a pool might reduce to negligible values the labeling of the total intermediate isolated from the cells and falsely give the impression that there was no flux through this poorly labeled intermediate. Clearly this does not occur for the well labeled intermediates which label to 100% with expected kinetics. However, it might be useful if the authors commented on this possibility and any experiments that might rule this out. Reviewer #1:I have several comments. First, the authors should defend their contention that the identification of the hybrid cholesterol synthesis pathway is novel. This reviewer noted that this hybrid pathway appears to be described in the cholesterol synthesis discussion in the Wikipedia page on Smith Lemli Opitz syndrome. Of course, Wikipedia has plenty of errors. Nevertheless, I would request that the authors defend their contention that the “pathway switchover” is novel – or whether their hybrid pathway had been conceptualized and proposed previously.5-dehydrogenation and Δ7-reduction of the sterol nucleus. This proposal was not assessed in vivo and did not gain wide acceptance. Subsequently, Gaylor et al. noted the enzymes in the pathway act on sterols with both unsaturated and saturated side chains and so crossover between the Bloch and Kandutsch-Russell pathways could potentially occur at any point in the pathway . The interpretation was challenged by Brown and his colleagues  since desmosterol, not zymosterol, accumulates in DHCR24 deficiency. In response to this comment, we have revised the manuscript as follows:We are not aware of any previous demonstration of the hybrid pathway in a living cell or animal. The comment in Wikipedia appears to have its origin in a paper by Bae and Paik , who reported that zymosterol was preferred over lanosterol as a substrate for C-24 reduction in rat liver microsomes. Those authors hypothesized that the physiological pathway for conversion of lanosterol to cholesterol did not correspond to either the Bloch or the KR pathways, but rather to a reaction sequence in which C24-reduction occurred after demethylation but before the final ΔIn the first paragraph of the Discussion we have changed the statement “A previously unrecognized hybrid pathway that we have named the modified Kandutsch-Russell pathway (MK-R) exists in skin, preputial glands and brain , and we agree that it deserves comment. Since the pathway appears not to be captured by our present data, any model we propose would be entirely speculative. We have added the following to the Discussion: “If extrahepatic tissues contribute little to the plasma sterol […] plasma elements to the centripetal transport of peripheral tissue cholesterol.”Also, the authors showed that expression of DHCR24 affects the pathway, but that the extent to which pathway usage depends on DHCR24 expression was not 100% consistent. One possibility is that pathway utilization depends on the concentrations, or relative concentrations, of pathway intermediates, perhaps including pre-squalene intermediates. Was there evidence for this? Can one perturb pathway utilization with low doses of squalene synthase inhibitors? Please elaborate on your thoughts about what is controlling pathway utilization.We did not see an obvious relationship between concentrations of sterol intermediates and relative pathway utilization. We have added this data to the paper as requested by Reviewer 2 . We did not test squalene synthase inhibitors. Nonsterol intermediates are not detected in our assay, so we cannot exclude the hypothesis that the differences observed are due to concentrations of nonsterol these intermediates in the pathway.We have expanded our discussion on possible factors that control pathway utilization as follows: “The relative utilization of the Bloch and MK-R pathways in cultured cells […] route of flux through the cholesterol biosynthetic pathways”.It would be interesting, if possible, to tie this work to disease. For example, it might be interesting to compare pathway usage in different tissues of Smith Lemli Opitz syndrome mice. In those mice, is the utilization of different biosynthetic pathways in different tissues perturbed or maintained? Could you quantify the amount of 7-Dehydrocholesterol that is shunted away into oxidized derivatives? Does different pathway utilization in different tissues explain the susceptibility of different tissues to disease? Such studies are almost certainly too much for this paper, but the authors should at least put forward a more detailed description of what they consider to be the most important biochemical factors that govern pathway utilization of the two different pathways in different cell lines and tissues.We agree that a disease connection would be interesting, and we looked at pathway utilization in primary fibroblasts cultured from patients with Smith-Lemli-Opitz Syndrome. In these experiments, sterol biosynthesis was reduced by about 50%, which is consistent with previous studies by Tint and Salen, and the modified pathway was used exclusively, which is consistent with the biochemical defect (the cells cannot synthesize desmosterol). Accordingly we elected not to include these data.It is likely that the amount of 7-dehydrocholesterol that is diverted to oxidized derivatives could be determined using the methodology applied here, but it would require nontrivial method development. The assay of oxysterols requires completely different sample preparation, LC conditions (gradient), and mass spectrometry (ion source) from those used in this study.Reviewer #2:There are a few important methodologic issues to clarify:1) How much of the differences in total synthesis rates are due to the kinetic measurements (turnover rates) vs. the tissue concentrations of each metabolite? The k values are not shown . Would their findings have been discernable from simple measurements of metabolite concentrations ?We have now included the k-values and sterol concentrations for all mouse tissues in Figure 5–figure supplement 1. We could not predict the relative flux from the substrate concentrations.2) The inability to detect some key biosynthetic intermediates analytically which happen to be right about where the DHCR24 crossover often occured – zymostenol, lathosterol, dehydrolathosterol – is a problem. The authors are forced to place this key step somewhere at or after zymosterol. This uncertainty, which is entirely analytic in nature, might be emphasized a bit more clearly in the text. Their one non-heavy water labeling study, with d6-lanosterol and d5-zymostenol, overcame the detectability problem and gave more detail on the cross-over site. The latter results support their general conclusion – i.e., cross-over seemingly around the dehydrolathosterol-lathosterol step, or maybe a step earlier. This supportive labeling result is worth noting in context of the greyed out parts of2O labeling studies, experiments using deuterium-labeled lanosterol and zymosterol confirmed that zymosterol is the first sterol in the pathway to undergo appreciable side-chain reduction, at least in these cells.”To emphasize this point in the text, we have added the following statement to the Discussion: “Whereas we were not able to localize the specific DHCR24 substrate in the D3) The kinetic method is not accurately described as ISA, which solves for p and synthesis fraction (g) by best fit to a family of curves. The authors know p here independently (tissue water enrichment). Their equation for g (in the last paragraph of the subsection headed “Data interpretation”) is basic mass isotopomer analysis, identical for MIDA  and ISA (AJP 1992).To avoid mischaracterizing our analysis we have changed “ISA” to “isotopomer analysis (IA)” and cite both MIDA and ISA.4) A point worth mentioning about(and subsequent figures): it is not possible to have a higher absolute flux rate in the final product of a linear pathway, in this case 7-dehydrocholesterol, than in preceding intermediates. Like water through a hose, flow cannot be greater over one length than over an earlier length. So these figures by themselves must mean a crossover into “K-R” has occurred – i.e., that K-R is not a linear pathway.We agree.Additional data files and statistical comments:Appendix showing kinetic rate constants (k) vs. metabolite concentrations, in their calculations of absolute synthesis rates (= k x concentration).We have added the k values as requested. Figure 5–figure supplement 1.Reviewer #3:This manuscript contributes to understanding the pathway for cholesterol synthesis. Specifically it demonstrates that a kinetic labeling approach using ISA may be used to resolve the flux through the Bloch versus the K-R pathway for cholesterol synthesis. A possible difficulty with this approach would be the presence of two pools of an intermediate in the pathway. Consider the possibility of a small pool involved in the biosynthetic pathway which labeled fully and quickly and a second larger pool sequestered from the biosynthesis pathway which did not turn over. Such a pool might reduce to negligible values the labeling of the total intermediate isolated from the cells and falsely give the impression that there was no flux through this poorly labeled intermediate. Clearly this does not occur for the well labeled intermediates which label to 100% with expected kinetics. However, it might be useful if the authors commented on this possibility and any experiments that might rule this out.We agree that this is an important caveat and we thank the reviewer for bringing it to our attention. We have added the following caveat to the Discussion: “The approach used in this study could potentially be confounded by the presence of two pools of an intermediate in the pathway that turn over at different rates. This problem would be particularly acute if a small pool of intermediate turns over rapidly while a second, larger pool sequestered from the biosynthesis pathway turned over slowly. Under these conditions the larger pool may significantly reduce the labeling of the total intermediate isolated from the cells resulting in falsely low estimates of flux. The observation that multiple independent intermediates showed essentially identical kinetics argues against this possibility, but we cannot formally exclude it.”


Rhodospirillum rubrum and Bacillus subtilis, our results reveal metabolic pathways that are interconnected with methionine salvage. The rubrum-type methionine salvage pathway is interconnected with the active methyl cycle in which re-methylation, a key reaction for recycling methionine from homocysteine, is unexpectedly suppressed; instead, homocysteine is catabolized by the trans-sulfuration pathway. Notably, the non-mevalonate pathway is repressed, whereas the rubrum-type methionine salvage pathway contributes to isoprenoid biosynthesis upon 5′-methylthioadenosine feeding. In this process, glutathione functions as a coenzyme in vivo when 1-methylthio-d-xylulose 5-phosphate (MTXu 5-P) methylsulfurylase catalyzes dethiomethylation of MTXu 5-P. These results clearly show that our analytical approach enables unexpected metabolic pathways to be uncovered.While recent advances in metabolomic measurement technologies have been dramatic, extracting biological insight from complex metabolite profiles remains a challenge. We present an analytical strategy that uses data obtained from high resolution liquid chromatography–mass spectrometry and a bioinformatics toolset for detecting actively changing metabolic pathways upon external perturbation. We begin with untargeted metabolite profiling to nominate altered metabolites and identify pathway candidates, followed by validation of those pathways with transcriptomics. Using the model organisms The online version of this article (doi:10.1007/s11306-014-0713-3) contains supplementary material, which is available to authorized users. S-adenosyl-l-methionine (SAM)-dependent polyamine biosynthesis, to methionine. The classical pathway, as determined in Bacillus subtilis (B. subtilis), is an eight-step pathway in which the carbon and sulfur skeleton of methionine is completely synthesized from the methylthioribose moiety of MTA  and involves a bifurcation of the methylthioribose molecule between methionine and isoprenoid biosynthesis. In R. rubrum, the pathway intermediate, methylthioxylulose-5-phosphate, is cleaved into methanethiol and the non-mevalonate isoprenoid precursor, 1-deoxy-d-xylulose 5-phosphate (DXP), in a novel enzymatic reaction that is dependent on 1-methylthio-d-xylulose 5-phosphate (MTXu 5-P) methylsulfurylase –mass spectrometry (MS)-based metabolomics pipeline  through the coordinated regulation of several expected and unexpected metabolic pathways.Here, we integrated untargeted metabolomics with transcriptomics [quantitative real time polymerase chain reaction (qRT-PCR) and RNA sequencing (RNAseq)] to correlate specific changes in bacterial growth conditions and a genetic knockout with changes in the activities of metabolic pathways. We used the concept of seed metabolites, which we define as the metabolites showing higher abundance changes upon perturbation, unambiguous formula determination and search hits in a database. These seed metabolites and a dynamic build-up of metabolite sets were used for the detection of actively changing metabolic pathways observed in the raw LC–MS data. In this process, we used the refined mass spectral features from the LC–MS data, regardless of the metabolite annotations, as a reference metabolome for our enrichment analysis, which is based on the same procedure used in GSEA. Putative metabolite annotation of the seed metabolites was carried out with high confidence and high coverage, and with a low number of false positives. Metabolite annotation was followed by a dynamic build-up of metabolite sets based on the pathway information extracted from the seed metabolites. Detected active pathways were then validated with transcriptomics. We applied this analytical strategy to unravel metabolic pathways linked to methionine salvage in Bacillus subtilis 168  was grown aerobically in either Luria-Bertani broth  and its MTXu 5-P methylsulfurylase mutant (a gift from R. Tabita and Jaya Singh) were grown aerobically in the dark at 30 °C on 20–2,000 mL minimal medium with sulfate or MTA as the sole sulfur sources, as previously described  were incubated at optimum growth temperature (temperature = 30 °C) in minimal media without a sulfur source (control samples) or with 1 mM MTA (feeding experiments). Sampling was carried out at various time points  according to the strains. Then cells were pelleted and immediately frozen in liquid nitrogen. Metabolites were extracted from the frozen cell pellets by resuspending with 0.375 mL 10 mM ammonium bicarbonate buffer (pH 9.2) containing 90 % acetonitrile. LC–FTMS analysis was carried out using an 11T LTQ-FT Ultra mass spectrometer  equipped with an Agilent 1200 HPLC system in negative mode  . Peak lists were filtered to remove adducts and isotopic peaks. Molecular formulas were determined by comparing experimental isotopic patterns with theoretically predicted isotopic patterns modeled with Bayesian statistics. Seed metabolites were automatically detected based on raw LC–MS data. Both monoisotopic masses and the top three predicted molecular formulas were searched against the Kyoto Encyclopedia of Gene and Genomes (KEGG) ΔCp/(Effhousekeeping gene)ΔCp. The fold changes were estimated relative to 16SrRNA.qRT-PCR was performed as previously described Pfaffl . Cells wR. rubrum genome (Accession number NC_007643) and analyzed using the CLC genomics workbench software, version 3.7  according to the user’s manual. Briefly, short reads were aligned against the fully sequenced genome of R. rubrum to determine unique and total gene reads.Subtraction of ribosomal RNAs from a 10 µg total RNA sample using an Ambion MICROBExpress Kit , as well as subsequent sequencing of the enriched mRNA fraction  were performed at the W.M. Keck Center for Comparative and Functional Genomics (University of Illinois at Urbana-Champaign) using in-house protocols. The reads were on average between 70 and 80 nt, at a total of about 22 million reads for the RNA preparation from sulfate-grown cells and about 20 million reads for the RNA preparation from MTA-grown cells. RNAseq data were aligned against the Although there has been great progress in LC–MS-based metabolomics that enables one to extract biological insight from metabolite profiles, more remains to be done. Building upon existing technologies, we developed a bioinformatics platform that includes various functional modules: data pre-processing , automatic mass spectral peak grouping (peaks with isotopic patterns versus peaks without isotopic patterns), molecular formula determination, database searching and pathway activity evaluation based on MSEA . This novel approach enabled a streamlined process for the detection of actively changing metabolic pathways from raw LC–MS data . Mass spectral features refined by the data pre-processing module were classified into three groups. The primary group contained features with visible isotope peaks and with a >20 % abundance change between the experiment and control. The secondary group contained features with visible isotope peaks, but whose abundance change between groups was <20 %. The tertiary peak group had features with no visible isotopic pattern. Molecular formulas were determined for peaks in the primary and secondary groups using well-known round-robin and recursive backtracking algorithms  were also analyzed based on mass difference and retention time (RT) with tolerances  and stored as a list of matrices for molecular formula determination. Then, isotopologues (except monoisotopes) were removed from the peak list. We took notice of the limited usage of elements in the biological molecular formula in a previous study, conceptualized as the seven golden rules (Kind and Fiehn R. rubrum and 5–6 % of B. subtilis), suggesting further technical improvements are needed to detect isotopologues in untargeted metabolomics.In this study, a mass difference matrix was constructed through an all-by-all mass comparison among detected peaks to filter the redundant data and to recognize isotopic patterns. For each peak, possible adducts and multimers were eliminated by comparing their masses with the entire mass differences in the matrix. Isotopic patterns . Isotopic abundance patterns are known to be useful in reducing the number of potential elemental compositions and providing high confidence to the search hits . This was somewhat unexpected, because higher confidence is expected at the expense of coverage when combining mass and molecular formula information. However, the unavoidable incompleteness of available databases, and variable quality of the LC–MS data, may have affected the HRPP. In contrast, our approach consistently showed higher confidence and coverage, supporting the usefulness of the seed metabolite approach . Details are shown in Fig. To cope with these challenges, we used seed metabolites and the dynamic build-up of metabolite sets see Fig. . Also, wDynamically constructed metabolite sets  were further curated into subcategories manually and evaluated by the enrichment analysis process to detect interconnected pathways upon metabolic perturbation with MTA, a key metabolite in bacterial methionine salvage pathways.B. subtilis, a total of 26 implicated pathways were generated based on the concept of seed metabolites and the dynamic build-up process. Among them, only the purine salvage pathway  and the classical, subtilis-type methionine salvage pathway  were characterized as active pathways using the enrichment analysis approach  or detected accordingly in our experimental setup .In R. rubrum, a total of 53 implicated pathways were dynamically constructed. Among them, eight were considered as active pathways using the enrichment analysis approach , the rubrum-type methionine salvage pathway , and the non-mevalonate isoprenoid pathway  were clearly affected by MTA feeding. Thus, the unexpected link of MTA metabolism with the isoprenoid pathway in R. rubrum, as recently published by our group  , as evidenced by the strong up-regulation of S-adenosyl-l-homocysteine (SAH)  and SAM  at 10 min. However, metabolites of the AMC were down-regulated at 20 min. Similar to the AMC, glutathione metabolism was also up-regulated  at 10 min and down-regulated at 20 min at the level of metabolites. Notably, we observed that a glutathione-methylthiol adduct  was highly and consistently up-regulated by adding MTA to wild-type R. rubrum . However, this compound was not observed in the MTXu 5-P methylsulfurylase mutant , with an up-regulation at 10 min and a down-regulation at 20 min. Malate , 2-oxoglutarate  and fumarate  were highly up-regulated with statistical significances at 10 min. Butanoate metabolism, which involves these metabolites and downstream metabolites of the TCA cycle, was also up-regulated at 10 min , and sulfur metabolism was down-regulated at 20 min . The fold changes and p-values of their constituent metabolites in these active pathways are listed in Supplementary Material Table S5.In B. subtilis, genes of the classical, subtilis-type methionine and purine salvage pathways were investigated based on their activation observed upon MTA perturbation . In line with the metabolomics results, genes in the subtilis-type methionine salvage pathway were highly up-regulated by MTA perturbation . However, genes in the purine salvage pathway were not induced by MTA feeding, even though RNAs were detected under the growth conditions, suggesting that accumulation of metabolites in these pathways in vivo is not related to up-regulation of transcription.Subsets of genes detected in each active pathway have been manually validated upon MTA perturbation. Selected genes and primers are listed in Supplementary Material Table S6. After feeding MTA, gene expression levels were monitored at selected time points by qRT-PCR. In R. rubrum, genes in the rubrum-type methionine salvage pathway  and in the isoprenoid pathway , were highly up-regulated upon MTA feeding. Up-regulation of the MTXu 5-P methylsulfurylase gene  confirms its critical role in the novel MTA isoprenoid shunt. Similarly, the gene encoding the methylthioribulose-1-phosphate isomerase, a RuBisCO-like protein that provides the substrate for the MTXu 5-P methylsulfurylase, was also highly affected  upon MTA perturbation. Notably, 1-deoxy-d-xylulose-5-phosphate synthase , which converts d-glyceraldehyde-3-phosphate into DXP, was suppressed by MTA-feeding. Since this gene was expressed prior to MTA perturbation, the qRT-PCR results suggest that the non-mevalonate pathway is the major route to isoprenoid biosynthesis under physiological conditions. However, upon MTA perturbation, cells suppressed the use of the non-mevalonate pathway and instead activated the rubrum-type methionine salvage pathway to channel the carbon skeleton of methylthioxylulose-5-phosphate into DXP, indicating that the MTA-isoprenoid shunt is able to contribute significantly to DXP synthesis in R. rubrum. Expression levels of genes were stabilized to approximately basal levels after 60 min. Although the isoprenoid pathway intermediates were not affected in B. subtilis upon MTA perturbation, two genes of the non-mevalonate isoprenoid pathway were analyzed in their expression pattern to rule out the possibility that isoprenoid genes are induced in B. subtilis. Expression of the two marker genes was not induced, in line with the idea that methionine salvage and isoprenoid biosynthesis are indeed unlinked in B. subtilis  .As listed in Supplementary Material Table S8, in R. rubrum, genes of the purine salvage pathway  and de novo purine biosynthesis  were expressed constitutively. As observed for B. subtilis, these genes are apparently expressed under physiological conditions and are not affected by MTA perturbation.In contrast to the genes involved in methionine salvage and isoprenoid biosynthesis in trans-sulfuration pathway and the sources and sinks of glutathione in the context of R. rubrum MTA feeding; this required that the expression patterns of many more genes be checked. Hence, an RNAseq experiment was conducted for R. rubrum.Our qRT-PCR data show that the active pathways detected using our metabolomics analysis platform are reliable, whether they are accompanied by a change of gene expression upon MTA feeding, or by consistent expression of genes under growth conditions before MTA is added . qRT-PCR allows sensitive and specific assays for targeted genes, but it covers only a limited number of genes in the whole genome. We also felt it was important to clarify both the role of the R. rubrum, even though there were sensitivity differences between the two techniques. In addition, RNAseq provided some clues for the role of trans-sulfuration and for the multiple fates of methanethiol liberated during rubrum-type MTA salvage. RNAseq showed that glutathione metabolism was perturbed by MTA feeding, as evidenced by NADPH-glutathione reductase  and glutathione S-transferase . In line with the metabolomics data from this study , these gene expression patterns describe well the biochemistry of glutathione. Cysteine can also be metabolized to hydrogen sulfide and pyruvate by cystathionine β-synthase (CBS) and cystathionine γ-lyase (CGL)  clearly reconfirmed our qRT-PCR results for We describe an analytical strategy that combines untargeted metabolomics and transcriptomics  to decipher intertwined metabolic pathways of universal application. The LC–MS platform we developed facilitates the data analysis and includes several critical functional modules: data processing, isotope pattern analysis, molecular formula determination, database searching and pathway activity profiling. Together with the concepts of seed metabolites and the dynamic build-up of metabolite sets, this platform enables a streamlined process for detecting interconnected metabolic pathways from raw LC–MS data. Implicated pathways and their constituent metabolites are automatically annotated with high confidence and coverage by integrating different sources of information. Statistical evaluation using MSEA confers high confidence to the annotations of perturbed metabolites and their respective pathways.R. rubrum and B. subtilis by combining changes in bacterial growth conditions  and a genetic knockout. Our results revealed the coordinated regulation of several expected and unexpected metabolic pathways when MTA was provided to either organism , the key enzyme of the non-mevalonate pathway that converts d-glyceraldehyde-3-phosphate into DXP , and activated the flux into DXP through the rubrum-type methionine salvage pathway in which glutathione plays a critical role as a coenzyme in vivo when 1 MTXu 5-P methylsulfurylase catalyzes dethiomethylation of MTXu 5-P. The rubrum-type methionine salvage pathway is also interconnected with the AMC . This adduct can be metabolized through the detoxification process by γ-glutamyl transpeptidase (Rru_A0385) and leucyl aminopeptidase (Rru_A0454), which were not induced but constitutively expressed in our RNAseq experiment. Also, cysteine from the trans-sulfuration can be catabolized into pyruvate metabolism and the TCA cycle, as evidenced by the RNAseq and metabolomics data.In Fig. R. rubrum, metabolite profiles of glutathione-related compounds in the wild-type organism were compared with those of an MTXu 5-P methylsulfurylase mutant . In addition, a strong correlation between interconnected active pathways could be observed; the rubrum-type methionine salvage pathway was strongly intertwined with the purine salvage pathway, de novo purine biosynthesis pathway, glutathione pathway, TCA cycle and the AMC. Also, there was a moderate correlation between the isoprenoid pathway and other active pathways . Metabolites in sulfur metabolism showed an anti-correlation with the other active pathways. When MTA was used as the sole sulfur source, no sulfate source could be provided through conventional sulfur metabolism; hence, the down-regulation of metabolites in that pathway was expected and mirrored the effect from transcriptomics. In B. subtilis, although metabolites in each pathway showed a strong correlation, there was only a weak correlation between the classical methionine salvage pathway and purine salvage pathway .Interconnections identified using our approaches were also supported by metabolite–metabolite correlation analyses. Pearson correlation coefficients of percentage changes in metabolite abundances were clustered by agglomerative hierarchical clustering, clearly showing a high correlation between metabolites in each active pathway in R. rubrum. In R. rubrum the rubrum-type methionine salvage pathway, isoprenoid pathway, the AMC and the trans-sulfuration pathway were regulated hierarchically. In contrast, the purine salvage pathway, de novo purine biosynthesis pathway, and sulfur metabolism could be classified into metabolic regulation since corresponding genes were not induced by MTA feeding. Only the abundances of metabolites were changed upon MTA perturbation. In B. subtilis the subtilis-type methionine salvage pathway corresponded to hierarchical regulation. The purine salvage and de novo purine biosynthesis pathways were regulated only at the metabolite level.In addition, our analyses showed that there are two types of regulation, metabolic  and hierarchical  Supplementary material 2 (XLS 1066 kb)Supplementary material 3 (ZIP 23 kb)Below is the link to the electronic supplementary material.