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.”


Grasping requires translating object geometries into appropriate hand shapes. How the brain computes these transformations is currently unclear. We investigated three key areas of the macaque cortical grasping circuit with microelectrode arrays and found cooperative but anatomically separated visual and motor processes. The parietal area AIP operated primarily in a visual mode. Its neuronal population revealed a specialization for shape processing, even for abstract geometries, and processed object features ultimately important for grasping. Premotor area F5 acted as a hub that shared the visual coding of AIP only temporarily and switched to highly dominant motor signals towards movement planning and execution. We visualize these non-discrete premotor signals that drive the primary motor cortex M1 to reflect the movement of the grasping hand. Our results reveal visual and motor features encoded in the grasping circuit and their communication to achieve transformation for grasping.DOI:http://dx.doi.org/10.7554/eLife.15278.001 In order to grasp and manipulate objects, our brains have to transform information about an object  into commands about movement that are sent to our hands. Previous work suggests that in primates (including humans and monkeys), this transformation is coordinated in three key brain areas: the parietal cortex, the premotor cortex and the motor cortex. But exactly how these transformations are computed is still not clear.Schaffelhofer and Scherberger attempted to find out how this transformation happens by recording the electrical activity from different brain areas as monkeys reached out to grasp different objects. The specific brain areas studied were the anterior intraparietal (AIP) area of the parietal cortex, a part of the premotor cortex known as F5, and the region of the motor cortex that controls hand movements. The exact movement made by the monkeys’ hands was also recorded.Analysing the recorded brain activity revealed that the three brain regions worked together to transform information about an object into commands for the hand, although each region also had its own specific, separate role in this process. Neurons in the AIP area of the parietal cortex mostly dealt with visual information about the object. These neurons specialized in processing information about the shape of an object, including information that was ultimately important for grasping it. In contrast, the premotor area F5 represented visual information about the object only briefly, quickly switching to representing information about the upcoming movement as it was planned and carried out. Finally, the neurons in the primary motor cortex were only active during the actual hand movement, and their activity strongly reflected the action of hand as it grasped the object.Overall, the results presented by Schaffelhofer and Scherberger suggest that grasping movements are generated from visual information about the object via AIP and F5 neurons communicating with each other. The strong links between the premotor and motor cortex also suggest that a common network related to movement executes and refines the prepared plan of movement. Further investigations are now needed to reveal how such networks process the information they receive.DOI:http://dx.doi.org/10.7554/eLife.15278.002 Grasping objects of different shapes and sizes appears trivial in daily life. We can distinguish between thousands of objects  and shapIn the primate brain, these processes are linked to the anterior intraparietal (AIP), the ventral premotor (F5), and the primary motor cortex (M1) . Within These electrophysiological and anatomical observations lead to our current understanding of the fronto-parietal network as the main circuitry for translating object attributes into motor commands . In detaTo create a deeper understanding of how visual information is transformed into motor commands, a precise identification and differentiation of visual and motor processes within the grasping network is required. Previous important grasping studies classified visual-dominant, visual-motor or motor-dominant neurons primarily on the phase of their activation  Tog“Discussion – Visual processing for grasping”, paragraph 3 and 4:“We emphasize that visual coding at the population level does not preclude motor coding of some individual cells, as suggested previously . Rather, the evidence of bidirectional connections to F5 suggests that subpopulations in AIP exist that reflect feedback motor signals from F5.”“These modulations could reflect either motor  or visual transformations required for grasping. […] These differentiation schemes support our hypothesis of visual rather than motor transformations in AIP.”B) Intermediate concernsB-1) in the Introduction, the authors state that prior work did not dissociate between visual and motor features. At first, I thought this was inconsistent with the work of Sakata and colleagues – describing visual dominant, visuomotor, and motor dominant cell types. I think some discussion of this work upfront, including how the current approach critically differs, would be helpful.Previous approaches separated neurons into visual, visuomotor and motor cells primarily based on their responses to objects in light or dark condition (activation or no activation). According to Sakata and co-workers, Motor dominant cells become activated during grasping and holding in both light and dark. They do not fire during object fixation. Visual-motor neurons discharge stronger during grasping in light than in the dark. Visual-dominant neurons discharge during object fixation and when grasping in light. Thus, the epoch of activation has been the main criteria for classification.Our work took a significantly different approach by comparing the coding (modulation patterns) of neurons and of neuronal populations with respect to highly variable objects and the measured hand kinematics in order to separate visual from motor processes. Our work demonstrated that activations in visual epochs can reflect motor processing, whereas grasp modulation can reflect object geometries.We followed your suggestion and entirely revised paragraph 4-6 of the Introduction:“To create a deeper understanding of how visual information is transformed into motor commands, a precise identification and differentiation of visual and motor processes within the grasping network is required. […] Our data revealed distinct roles of the grasping network in translating visual object attributes (AIP) into planning (F5) and execution signals (M1) and allowed visualizing the propagation of these features for grasping. “B-2) Introduction – last paragraph – it isn't clear to me what the authors mean to say here, without first reading the rest of the paper. I think this section could be made clearer.We revised the last paragraph accordingly (see above). We believe, that the concept and intention of our experiments are now better explained.B-3) I'm not sure I understand their explanation of the shape-specificity prior to fixation. Please try to make this clearer.Shape-specificity during the fixation epoch is likely due to our block design, in which we presented objects of similar shape on the same turntable. This fact is a small detail, however it could raise concerns on whether coding in AIP was really due to object shape, or rather to the object presentation order . We revised our arguments to make this clearer:“The large number of objects presented in one recording session required separating the 48 objects on different turntables see , often oB-4) At first I thought I missed something – it seemed as though AIP was not looked at in terms of J- and N-space encoding in the same way that F5 and M1 were. However, later it is clear that they did analyse AIP in the same way. Why not also show these data, as they do for F5 , 5 and MFor the complete N-space the AIP, F5, and M1 populations were analyzed and visualized in the same way. In the reduced space the areas were visualized differently to represent the full dimensional space  best. That is, showing the object separation of AIP in 3D to reveal the object-shape clusters best, whereas F5 and M1 populations were shown in 2D and aligned (rotated) to the J-space (with PCRA) to allow direct comparison of both representations next to each other. We believe that this kind of visualization reflects and explains the full-dimensional results best.B-5) Premotor area F5 provided higher modulations (separation of conditions) in the grasping epoch, when the hand was approaching the object. This is also shown and discussed in B-6) We find both studies most helpful. Umilta et al. and Spinks et al. report similarity between F5 and AIP with respect to grasp coding at the LFP and single unit level in support of our feature code correlation results. We now discuss and reference their work in the following sections:Chapter “Feature coding in area M1”, first paragraph:“In agreement with the general population response , single “Discussion”, last paragraph:“The high similarity of the F5 and M1 population during movement execution  is in agAlso, is it not surprising to find so little evidence for correspondence between AIP and F5 during planning?The applied analysis reflects the coding similarity between both areas, not their physical connectivity. In other words, when two areas share the same feature coding with respect to the 50 conditions the correlation coefficient increases. In fact, the correlation analysis results, although exploring the data with an entirely different method, match the identified visual and motor coding (and their proportion) from the population analysis. In detail, only an F5 subpopulation coded objects in visual terms, and therefore similar with respect to AIP see . This exIn response to your comment, we added the following sentences to chapter “Results/Feature code correlation”:“These results are consistent with the proportion of F5 visual cells identified in animal Z and M , which oHow does the relationship between AIP and F5 that the authors suggest according to their new results compare with prior accounts – e.g. Jeannerod et al., 1995; Fagg and Arbib, 1998?Existing models of the grasping network share the same basic interpretation of the F5-AIP network as the main circuit for visuomotor transformation . In support, our data provides important electrophysiological evidence that object features are indeed transformed into motor representations between both areas.Previous models were, however, uncertain on how and where visual information is exactly translated into motor commands and whether both areas perform both visual and motor computations. Fagg et Arbib (1998) and Rizzolatti et Luppino (2001) consider grasp transforms already in AIP. They propose that AIP is using the visual input to generate multiple affordances passed to F5, which selects the final grip type.Our data suggest a slightly different information flow in which visual and motor processes are organized more separately. In this understanding, AIP is responsible for providing object properties to F5 (F5a) thereby activating motor solutions that, in return to AIP, help further extracting geometrical features relevant for grasping. F5 with its connection to visual input (F5a) and motor output (F5p), could be the main hub for coordinating visual feature extraction with AIP and motor planning and subsequent execution with M1.We believe that our edits in response to this reviewer’s comment a) (see above) addresses this question.How do the authors interpret the increased AIP-M1 correspondence during grasp and hold?This comment is related to comment (1b)-d. As discussed above, visual representations of objects and motor features of the hand cannot be entirely disassociated, because the object geometries define the shape of the grasping hand. Even pure motor representations, such as the recorded hand kinematics, can therefore share similarities with an object space. When M1 changes from a mostly unmodulated (planning) to a highly modulated state (grasp epoch) the similarity to an object space increases. In other words, we interpret the increase of similarity as a result of the highly increased modulations in M1.B-7) What do the authors mean by the statement regarding M1 on p.26 "…its neuronal population has never been studied explicitly"? What about, for e.g., the work of Umilta et al., 2007; Spinks et al., 2008?We tempered this statement: “Although the motor relevance of the hand area of M1 has been described extensively with electrophysiological  and anatomical methods , it has been unclear how versatile hand configurations are encoded at the population level.”


Aspergillus carbonarius, Aspergillus ochraceus, and Penicillium verrucosum—is one of the most-abundant food-contaminating mycotoxins. To avoid the risk of OTA consumption for humans and animals, the rapid detection and quantitation of OTA level in different commodities are of great importance. In this work, an impedimetric immunosensor for ochratoxin A (OTA) detection, a common toxic botanical contaminant, was developed via the immobilization of anti-OTA antibody on bovine serum albumin modified gold electrodes. A four-step reaction protocol was tested to modify the gold electrode and obtain the sensing substrate. All the steps of the immunosensor elaboration and also the immunochemical reaction between surface-bound antibody and ochratoxin A were analyzed using cyclic voltammetry and electrochemical impedance spectroscopy. Modification of the impedance due to the specific antigen-antibody reaction at immunosensor surface, was used in order to detect ochratoxin A. Linear proportionality of the charge transfer resistance to the concentration of OTA allows ochratoxin A detection in the range of 2.5–100 ng/mL.Ochratoxin A (OTA)—a toxin produced by  Aspergillus ochraceus, Aspergilius niger and Penicillium verrucosum, found as contaminants of a variety of food, such as cereals, coffee beans, beans, grapes and dried fruit. OTA is one of the most toxic and widespread compound from the ochratoxins group  and 5 mM K4[Fe(CN)6] was used in cyclic voltammetry and electrochemical impedance spectroscopy measurements. Blocking buffer solution consisted of ethanolamine 0.1 M in water and NHS and EDC solution were also prepared in deionized water.A solution containing 0.1 M KCl, 5 mM KOTA 5 mg/mL was diluted in different concentration in acetate buffer.4 to 10−1 Hz, superimposed on +0.178 V dc potential, that is the potential of the ferrocyanide/ferricyanide couple [Fe(CN)6]4−/3−. The spectra were taken in 1mM ferrocyanide/ferricyanide solution (1:1 mixture) in 0.1M KCl as background electrolyte at room temperature. All the measurements were performed in a solution of The FRA 4.9 software calculates and records the real and imaginary parts of electrochemical impedance (Z’ and Z”) together with the phase and represents them in Nyquist and Bode diagrams.An Autolab PGSTAT100 Eco Chemie (Netherlands) potentiostat was used to carry out the impedance spectra at 10 mV sinusoidal ac potential perturbation in the frequency range from 10EIS using the classic ferricyanide/ferrocyanide redox probe was frequently used for quantitation of various molecules with biosensors, including with real samples. A few recent examples for ochratoxin A were included in ct following incubation with standard/sample was calculated as ΔRct = Rct(after)-Rct(before) and was correlated with the concentration of aflatoxin in the sample.Based upon the principles of electrochemical spectroscopy, the equivalent electric circuit that best fits the experimental data was found and optimum electrical parameters were obtained: electrical resistance of the solution, charge transfer resistance, constant phase element and Warburg impedance. For each modified electrode, the impedance spectra were recorded before and after incubation with the standard or sample solution. The variation in the R2SO4 solution until the voltammogram characteristic for a clean Au surface was obtained.Before modification, Dropsens gold electrode have been subjected to electrochemical pretreatment by applying 10 potential cycles between -0.3 and +1.5 V / pseudo silver reference electrode with 100 mV/s scan rate in 0.5 M HThe clean gold electrode was first modified using BSA/EDC/NHS, creating a cross-linked film that prevents the non-specific binding of OTA on gold, and allows further covalent attachment of antibody. Electrode modification was performed as described by Polonschii et al. , 5 mg/ mNext, the terminal carboxylic groups on BSA film were activated by dropping 10 μL of a 1:1 mixture of EDC/NHS onto the sensor surface, allowing the reaction to proceed, allowing it to react for 40 min at room temperature in a humid dark room. The electrode surface was rinsed after each step thoroughly with copious amounts of water for removing the unbound material. After this, the antibody anti-OTA immobilization was done by covering the modified electrode surface with a 10 μL droplet of the 5 μg/mL antibody solution in acetate buffer, allowing it to react in a water-saturated atmosphere for 1 h at room temperature. After incubation the electrode was rinsed in water to remove unbound antibodies and 75 μL ethanolamine 1M solution was drop cast onto the modified surface and incubated 15 min with the aim to deactivate the remaining succinimide groups and also to block unreacted active sites. These modified electrodes can then be stored dry several days at 4°C without a decrease in the sensitivity, or they can be subjected to immunochemical reaction.A schematic representation of the analytical principle of this electrochemical immunoassay is shown in For the OTA measurement, 10 μL of either sample or OTA standard solutions of different concentrations in acetate buffer pH 5.6 were pipetted onto the working electrode area and allowed to stand at room temperature for 45 min in a humid atmosphere (to prevent evaporation). The immunosensor was rinsed with a large amount of water before the electrochemical measurements.Parameters such as the incubation time and the amount of antibody/electrode were optimized to obtain good analytical characteristics, appropriate for Ochratoxin A detection in real samples.We have performed Faradic electrochemical impedance spectroscopy measurements using the classic redox probe ferricyanide/ferrocyanide, at the formal potential of this reversible redox couple, in order to induce the slightest possible perturbation in the system, as was recommended by Bard and Faulkner .ct increased significantly (red curves), due to the deposition on the electrode surface of an organic layer with negatively charged terminal carboxylic groups COO− (the isoelectric point of BSA being 4.7).After the grafting of the BSA film on the electrode, R6]4−/3− anions, preventing redox probe to reach the electrode surface and slowing down the electron transfer kinetic between the probe and the electrode. Similar approaches were also used by Radi and colab. [ct was observed (blue curves) because the negative charge of BSA- modified electrode is partly neutralized by the covalent attachment of the antibody. Afterwards, the Rct increased when the sensor was used for OTA detection (magenta curves), as the OTA binding to surface-bound antibody created an additional barrier to the electron transfer at the interface.The protein layer acts as a physical and an electrostatic barrier for the [Fe(CN)d colab. . Next, as), the charge transfer resistance (Rct), the constant phase element (Q) and the Warburg impedance element (W).Variants of equivalent electrical circuits were tested before choosing the most appropriate circuit for our experimental curves from Ideally, W and Rs represent the properties of the electrolyte and diffusion features of the redox probe in this solution and they are not affected by modifications at the electrode surface. Q value depends very much on the dielectric properties of the layer that separates the electrode surface and the ionic charges, the thickness of the separation layer and surface area of the electrode. A large increase in the Q value was noted when bare Au electrode was covered with BSA-EDC-NHS layer, whereas a Q decrease was observed upon further attachment of the antibody or of OTA to the sensor interface.ct value depends on the insulating properties at the electrolyte/electrode interface. Rct changes were much larger than those in other impedance components, and thus Rct can be considered an adequate signal for the determination of the interfacial properties for the prepared immunosensor.RThe experiments were run in triplicated and statistical analysis was used. EIS is a sensitive tool for monitoring affinity interactions at surfaces, but particularly due to this high sensitivity it is highly recommended to confront the impedance results with other electrochemical techniques , and to record a good parallel control of the samples .It was observed that our results are consistent with the cyclic voltammetry curves shown in The cyclic voltammograms of soluble electroactive species provide a convenient tool for monitor the various stages of the immunosensor building on gold electrode. The CV-s were performed after all the step of electrode modification and also after toxin adding on electrode surface. The cyclic voltammograms are strongly affected by the deposited layers, the difference between the anodic and cathodic peak potentials does not remained constant, whereas the peak current modifies significantly. It can be seen initial the characteristic quasireversible redox cycle for a bare Au electrode and after its functionalization with BSA/EDC/NHS, the electron transfer between the redox probe and electrode surface was severely affected and an obvious decreasing of the anodic and cathodic peaks was observed. After the Ab immobilization on the functionalized electrode surface, the peak currents of the redox couple of ferricyanide/ferrocyanide increases again. Immunochemical reaction of OTA molecules with the antibody film determined a decrease in the Faradaic response. It was observed also an increase in the peak-to-peak separation between the cathodic and anodic waves of the redox probe, indicating that the electron-transfer kinetics of ferricyanide/ferrocyanide is obstructed. All the observations are in accordance with results of EIS analyses about studied electrodes and these two techniques allow a good parallel control of the samples .ct parameter with the adding of OTA -Rct (before). As it can be seen in ct with the OTA concentration. The calibration curve in The difference between RIn this work, we immobilized monoclonal antibody anti-Ochratoxin A from Novus Biologicals (Canada) in order to propose a rapid method for ochratoxin A detection using antibody-immobilized on BSA-functionalized gold electrodes. We chose to do as this because, even that the cost of biosensor could increase, this choosing minimized the cross-reactivity (previous tests were done to check cross reactivity using aflatoxin B1—data not shown in this paper–and no cross-reactivity was observed).Some control experiments were performed with a sensor fabricated without antibody, aiming to confirm that the impedance changes were indeed due to specific interaction between OTA and its antibody, and were not caused by nonspecific adsorption. This second sensor was prepared using an identical protocol with identical conditions, buffers, concentrations, etc. as used for the specific antibody electrodes.We have proven the specificity of the interaction with OTA and the fact that non-specific adsorption effects are insignificant by making EIS determinations with a biosensor unmodified with antibody anti-OTA.The liquorice obtained from local market was crushed into mortar with pestle and 1 g of powder was mixed for 6 minutes with 5 mL of acetate buffer (pH 5.6). The mixture was kept at rest for 5 minutes and then was filtered through absorbent filter paper and 0.2 μm Nylon syringe filter. This extract (stock solution) was further diluted 1:1000 in acetate buffer in order to be used for the experiment.50 μL plant extract was mixed with 50 μL OTA in different concentrations before analysis with the electrochemical immunosensor. A volume of 75 μL of each sample was dropped on the surface of antibody- modified electrodes and allowed to incubate for 30 minutes. The Nyquist diagrams were recorded before and after incubation of the sensors with the plant extracts. The impedance data were fitted to equivalent circuit shown in ct  the OTA concentration of plant extracts was determined (Using OTA calibration curve and ΔRtermined .In this way, our new label-free, sensitive, cost-effective and fast EIS immunosensor can be utilized for OTA detection. The sensor based on screen-printed gold electrodes was easily modified with a cross-linked film of BSA that further serves as “anchor” for the covalent immobilization of the anti-OTA antibody. The casting of the protective BSA layer on the gold electrode prevents any nonspecific binding between OTA and the gold surface.The specific interaction between antibody and OTA induces an increase in electron transfer resistance at the interface sensor-solution that is correlated with the concentration of OTA in the sample. The detection of OTA was achieved by EIS on the linear range 2.5–100 ng/mL. Obtained results have the advantage of larger linear range, which include the maximum levels of OTA allowed by EC in various food products and are similar with some others obtained using immunosensors and EIS/SPR detection . The he A new label-free immunosensor for ochratoxin A detection was developed. This sensitive, fast and cost-effective sensor based on a screen gold electrode, which was easily modified in order to immobilize the monoclonal antibody anti-OTA, induced an increase in electron transfer resistance at the interface immunosensor-solution that is related to ochratoxin A concentration in the sample. The method could be successfully used for detection of ochratoxin A from plant extracts using bioanalysis and biosensing.


The enrichment of miRNA seed regions in exosomal lncRNAs is matched with a concomitant high expression of the same miRNA. In addition, the exosomal lncRNAs also showed an over representation of RNA binding protein binding motifs. The two most common motifs belonged to ELAVL1 and RBMX. Given the enrichment of miRNA and RBP sites on exosomal lncRNAs, their interplay may suggest a possible function in prostate cancer carcinogenesis.Long non-coding RNAs (lncRNAs) form the largest transcript class in the human transcriptome. These lncRNA are expressed not only in the cells, but they are also present in the cell-derived extracellular vesicles such as exosomes. The function of these lncRNAs in cancer biology is not entirely clear, but they appear to be modulators of gene expression. In this study, we characterize the expression of lncRNAs in several prostate cancer exosomes and their parental cell lines. We show that certain lncRNAs are enriched in cancer exosomes with the overall expression signatures varying across cell lines. These exosomal lncRNAs are themselves enriched for miRNA seeds with a preference for  For many decades, cancer has been thought as a disease resulting from DNA damage. More than often, these resulting DNA products are aberrantly overexpressed or deleted in an individual to promote the growth of cancer cells. Although these protein-coding genes have been widely characterized for their role in tumorigenesis, these DNA regions represent only 2% of the human genome. A large proportion is non-coding and its expression and function have been forgotten until the discovery of non-coding RNAs.The term non-coding RNA (ncRNAs) is commonly associated with RNA which is not translated into a protein. Indeed many ncRNAs are now understood to have important biological regulatory functions which regulate gene expression at multiple steps/levelsThe lncRNAs can range from several hundred to kilobase size species and evidence now suggests that large non-coding regions of the human genome are transcribed during normal and diseased cellular function235Prostate carcinoma represents one of the biggest challenges to the scientific and clinical community as it remains the most common malignancy in men in the western world, where it is still the second leading cause of cancer deathDue to the heterogeneous nature of the disease, this has impeded the discovery of effective clinical markers and the development of novel therapies. The majority of studies to understand this disease have focused on proteins, mRNAs and miRNAs. However, there is limited data on the characterization of the lncRNAs and their role in prostate cancer.One of the first lncRNAs described in prostate cancer was the prostate cancer antigen 3 (PCA3)More recently, exosomesOur study examines the expression of lncRNAs in several prostate cancer cell lines but also measures the levels of these lncRNAs in the released exosomes. We show that specific lncRNAs are enriched in cancer exosomes and furthermore these sequences harbour miRNA seed regions and appear to be enriched for specific RNA binding motifs.This study used four common prostate cancer cell lines  and one normal epithelial line (PNT2) to characterize the expression of exosomal lncRNAs. We isolated these exosomes using ultracentrifugationLncRNA expression was then examined in these exosomes and their parent cell lines. This was performed using a Human 8 × 60 K LncRNA expression array  which contained 33,045 LncRNAs and 30,215 coding transcripts. Results from the array suggested that hundreds of lncRNAs are readily found in exosomes released from prostate cancer cells. We applied a two-fold threshold for expression to identify the most abundant and common lncRNAs in these exosomes and their parent cells .Hierarchical clustering (HCL) analysis of the exosomal lncRNAs indicated that the populations of these lncRNAs could discriminate a normal cell line (PNT2) from a prostate cancer cells . The resWe furthered examined the miRNA expression in both exosomes and cells in prostate cancer cells and healthy cells using an Affymetrix miRNA microarray GeneChip® platform. Using Euclidean metrics for HCL analysis, clustering was initially performed on the exosomal data alone, followed by the analysis on the cellular data and lastly combining the two data sets. In the first analysis, the exosomal miRNAs could separate out the four different prostate cancer cell lines from the normal PNT2 line. The VCaP and LNCaP cells branch off a common node, which suggest they may have similar populations of exosomal miRNAs. In contrast, the DU145 and PC3 are unrelated . WhereasWhen combining the two data sets there are two major clusters, exosomes and cells. The cellular signature of PNT2, DU145, LNCaP and PC3 are distinct from their exosomal miRNAs . FurtherTo further understand the role of these exosomal lncRNAs, we hypothesized that they harbored miRNA seed regions as to sequester and bind mature miRNA sequences. To identify these motifs, we performed the enrichment analysis for each of the four cell lines. The data was filtered before analysis and only included transcripts, which were highly enriched in the exosome of each cell line. Several motifs were identified and then aligned to known miRNA sequences. Any motifs, which did not show perfect seed region alignment, were discarded from further analysis.let-7a, let-7b and let-7c, which are the top 3 highly overrepresented miRNAs found in the VCaP exosomes.By using the miRNA expression data, we then measured the miRNA levels in these prostate cancer exosomes and identified the extreme fold change in expression values for these miRNAs in each cell line. In the next step, these motifs were then mapped to the miRNAs showing the highest fold change to identify a perfect seed region alignment. Interestingly, most of highly enriched motifs in each of the cell lines are aligned to at least one miRNA which is highly represented in the exosome of the same cell line. For example, in VCaP, out of the 31 identified highly enriched motifs, 14 of them showed a perfect seed match to at least one miRNA which was also highly elevated in the exosome . In the let-7a, let-7b, let-7c, let-7d, let-7e and let-7i as well as the miR-17 family members including miR-17, miR-18a, miR-20a, miR-93 and miR-106b. Upon closer inspection, we observed that all the above-mentioned miRNAs have a perfect seed region match to the motif UGUUUU which is one of the most highly enriched motifs found on these exosomal lncRNAs.We also observe a similar phenomenon in the PC3 cell line. From the 28 identified motifs, eight of them show perfect reverse complementary binding site of 9 highly overexpressed miRNAs. This includes miR-221-5p, miR-762, miR-30b, miR-30c, miR-30d, miR-185, miR-151-5p, miR-130b and miR-149* . In the let-7 family. Concomitantly these miRNAs were also over expressed in the exosomes. Only one motif, AGCUGG was unique to PNT2 whose seed region is complementary to miR-149-5p. Although these motifs were not entirely unique to PNT2, we did observe that, exosomal lncRNAs only from cancer cells had a higher chance (3–9 fold increase) for seed matching.When we extended our enrichment analysis to the normal PNT2 line, we identified 40 over represented motifs in the exosomal lncRNAs. Of these, only three motifs had seed regions to six miRNAs as well as the let-7 family and miR-17 families could be associated to U rich motif overrepresented in the lncRNA transcripts expressed in the exosomes.These results suggest that lncRNAs, which are enriched in cancer exosomes, also harbor miRNA seed regions and there is a concomitant high representation of that same miRNA in the exosome. Furthermore we observed that From our analysis, , the motOut of these ten miRNAs, the only miRNA which we had the expression data available was for miR-149*. When we examined the levels of miR-149* in the exosomes, we realized that the level of this miRNA is significantly  high in DU145, LNCaP, PC3 and VCaP with 4.52, 4.03, 3.28 and 1.5 fold change difference respectively (This fold change was the level of miR-149* in the exosome relative to its parent cell). Our observation may suggest that the enrichment of miR-149* seed match motif found in the exosomal lncRNAs is directly related to the high level of miR-149* also found in the exosomes of the prostate cancer cells. To demonstrate the motif is unique to exosomal lncRNAs only, we performed an additional enrichment analysis on exosomal lncRNAs compared to the cellular lncRNAs  longer than the average length of lncRNAs found inside their parent cells. Further, the DU145 cells did not show a significant mean difference between the length of exosomal and cellular transcripts , even though the average length of exosomal lncRNAs is 150 bp longer than the average length of its’ cellular transcripts. In contrast, in the PC3 cells, the mean length of exosomal lncRNAs was considerably less (570 bp difference) than the mean length of cellular lncRNAs. Performing Kendall’s tau-b two-tailed test in SPSS statistical tool, we found no strong correlation between lncRNA sequence lengths and miRNA seed enrichment . These aWe then sought to investigate if there existed short motifs, which could associate with RNA binding proteins. Our first analysis was restricted to the exosomal lncRNAs. We performed RBP binding site enrichment analysis on different sets of exosomal lncRNAs transcripts including lncRNAs which are enriched in all prostate cancer cell lines (N = 26), lncRNAs which are enriched in the exosome of healthy prostate cells (N = 32), and the set of lncRNAs which are enriched in the cell of at least one prostate cancer cell line N = 141, , and theUsing our algorithm, we then examined if the over-represented motifs in the exosomal transcripts compared to cellular transcripts shows a significant reduction in the binding site availability for RBPs. Using a set of 19 lncRNAs which were common to all cancer cell lines but not enriched in exosomes , we founOur analysis on the enrichment of healthy cells also shows significant reduction of the binding site of these RBPs in the highly enriched lncRNAs of exosomes compared to the ones in the cells. We did not identify any RBP which uniquely target either the exosomal or the cellular lncRNAs, however there is a significant difference in the number of binding sites for RBMX, ELAVL1 and SFRS1 in the exosomal lncRNAs compared to cellular lncRNAs of both healthy and cancer cell lines.This study measured the levels of long non-coding RNAs in exosomes released from prostate cancer cells and healthy cells. The four prostate cancer cell lines represent common metastatic lines isolated from different tissue sites. They have different metastatic potential and are either hormone sensitive/resistant. The array data indicated that lncRNAs are present in abundance in both healthy and prostate cancer exosomes. Furthermore, certain lncRNAs are specifically enriched only in the exosomes. When we applied HCL analysis, these exosomal lncRNAs were able to cluster the normal cell (PNT2) from the cancer cell lines. More so, LNCaP and PC3 have similar expression when compared to the DU145 and VCaP cells. We put forward the idea that exosomal lncRNAs signatures may represent a unique set of ncRNAs, which can be exploited for biomarker research. There have been a plethora of studies suggesting lncRNAs as biomarkers for prostate cancer, but only a few studies have characterized their presence in exosomes16let-7 family members. While in the LNCaP exosomes seeds sequences for let-7 and miR-17 members were highly represented on the exosomal lncRNAs. Another study demonstrated that let-7 was highly expressed in the blood of prostate cancer patients and also enriched in the exosomes of PC3 cellslet-7 can be selectively package into exosomes derived from metastatic gastric cancer cell linesSeveral studies have suggested that lncRNAs can act as sponges for miRNAs18Upon further analysis, we found 40 motifs with perfect miRNA seed regions, which were highly represented in the exosomal lncRNAs. In contrast only a solitary motif was identified in the cellular lncRNAs. In particular, CCUCCC was highly abundant with this region corresponding to the miR-149* seed. Interestingly, miRNA expression data indicated that miR-149* was significantly  elevated only in the exosomes and not the cell lines. The apparent enrichment of seed regions may suggest a possible role for these exosomal lncRNAs as RNA sponges for specific miRNAs.RNA-binding proteins (RBPs) are important regulators of many post-transcriptional events, including RNA splicing, transport and stability. The RBP ELAVL1  tends to stabilize RNA transcripts; while AUF1, may induce rapid degradation of RNA transcriptsELAVL1 is a member of the ELAVL family of RBP which posses RNA recognition motifs to selectively bind AU-rich elements (AREs) in the 3′ UTR regions of mRNAs. Binding of these AREs by ELAVL1 prevents the signalling events required for degradation thus stabilizing the mRNA transcript. It may be possible that ELAVL1 can extend its function to bind lncRNAs with abundant AREs to stabilize the transcript. Evidence for this scenario was documented when the lncRNA-UFC1 could directly interact with ELAVL1 in hepatocellular carcinoma cells2526RBMX is a ubiquitous nuclear ribonucleoprotein which interacts with the spliceosome, binds RNA, and is involved in pre-mRNA splicing29in vitro conditions to a confluence of 70–80% with the rationale that, it would reflect fast growing cancer cells in vivo. We have avoided 100% confluence, as cells would likely exit this exponential growth phase. It has been shown that altering the growth conditions in vitro can significantly change the cellular miRNA compositionIt is important to also recognize the caveats in our study. The cell lines were grown In summary, our study has catalogued the presence of lncRNAs in exosomes from prostate cancer and normal cell lines. These exosomal lncRNAs seem to be enriched in exosomes and harbor both miRNA and RBP binding sites. The populations of exosomal lncRNAs as indicated by HCL derived from prostate cancer cells are distinct from normal cells. This suggests that exosomal lncRNAs are disease specific and may provide a source of biomarkers for prostate cancer diagnosis.let-7. Several lncRNAs have been shown to have a sponge effect33The mechanism for loading exosomal lncRNAs into exosomes is currently unknown. Given the enrichment of distinct RBP motifs on exosomal lncRNAs, we predict that specific RBPs such as ELAVL1 may play an important role in directing lncRNAs for exosomal transport. Many of these exosomal lncRNAs also harbored miRNA binding regions with an over representation for specific miRNA families such as The fact that exosomal lncRNAs have both an enrichment of miRNA and RBP does elute to a possible function in prostate cancer cells. However, further exploration will be needed to fully understand the interplay between these exosomal lncRNAs, miRNAs and RBPs.2, and supplemented with 1% v/v penicillin, streptomycin, glutamine (PSG) and 10% v/v fetal calf serum (FCS).The prostate cancer cell lines, LNCaP, PC3, and DU145 were grown in RPMI, whereas VCaP cells were grown in F12:DMEM and PNT2 in defined KSFM . All cells were incubated at 37 °C in 5% COet al.2 flasks containing 25 mL of depleted media at an initial confluence of 15–20% and allowed to grow to 70–80% confluence, at which point the culture supernatant and cells were harvested. Once culture supernatant was harvested it was either stored at 4 °C for one to five days prior to exosome extraction by ultracentrifugation or frozen at −80 °C and thawed the day before extraction. All exosome preparations were performed according to the protocol described by Théry et al.14As FCS contains exosomes of bovine origin all media were first depleted of these bovine exosomes. To remove bovine exosomes, we utilized the ultracentrifugation protocol devised by Théry, C. RNAzol RT  was used to extract total RNA from both cellular and exosomal sources. Given the small size pellet for the exosomes we had to modify the manufacture’s protocol. In brief, 500 μL aliquot of RNAzol RT was used to resuspend the exosome pellet. The same mixture was then transferred to a new pellet and resuspended. This method was used to concentrate the total RNA from multiple exosome pellets. Total RNA isolation was then completed using the manufacture’s protocol with the addition of glycogen (25 μg) as a carrier and an overnight precipitation step. Total RNA was isolated from cells using the manufacture’s protocol. RNA quality was the assessed using an Agilent 2100 bioanalyser and small RNA chip . Samples that reached the 50 ng/μL cut-off, and a RIN value of 8–10 were subjected to lncRNA expression profiling. Total RNA from the exosomes and their parent cell were subjected to lncRNA profiling using the Human 8 × 60 K LncRNA expression array . Each array contained 33,045 LncRNAs and 30,215 coding transcripts and required a minimum of 2 μg of total RNA.The algorithm used in this study takes two sets of sequences including exosomal (test) and cellular (control) transcripts as input and identifies a set of 6-mers which are a) highly enriched in exosomal transcripts compared to the cellular transcripts and b) harbor the reverse complementary match of the seed region of at least one known miRNA. For each set, a sliding window of length six scans the whole length of all given sequences and extracts all possible motifs of length six. The reason we selected the length of six nucleotides is for the importance of the miRNA seed region match in governing miRNA binding to the target transcript. Next, all 6-mers of each set with a frequency occurrence of one are discarded. Among identified 6-mers, those motifs which have an equal occurrence frequency in both sets are discarded. At this stage, we performed motif enrichment analysis on the sequences of each set using MEMENext, among all identified motifs in each set, all motifs which show a perfect reverse complementary Watson-Crick match to the seed region of at least one miRNA annotated in the miRBase v20http://discover.nci.nih.gov). The enrichment logo was designed using WebLogoGenomic sequences for transcripts were obtained from Ensembl or NCBI nucleotide database according to their accession IDs. For those transcripts for which the genomic sequence was not available on any publicly available database, the genomic sequence was prepared in BioPython. All poly-Adenylation tails were removed from the sequences in order to optimize the result of the algorithm. MicroRNA and lncRNA expression Heat maps were designed using CIMminer .


Paramecium tetraurelia, i.e. FLAG-MKS3 is found associated slightly above each basal body and RNAi for MKS3 leads to loss of cilia. RNAi for MKS3 also leads to the disorganization of rows of basal bodies that run from anterior to posterior. In areas of misalignments, basal bodies with their post ciliary and transverse rootlets are found out of their expected rows. However, the rootlets are attached to the basal bodies at the expected angles relative to each other.Meckelin (MKS3) functions in ciliogenesis and ciliary gating. MKS3 appears to have similar functions in We propose that MKS3 guides new basal bodies as they move toward the anterior of the cell along the striated rootlet (SR) of the parent basal body. Basal bodies without MKS3 lose their interactions with the parent's SR. Without this guide to maintain orientation, new basal bodies migrate off the expected line and, when they form their SRs, these too cannot project toward the anterior as expected.We tagged 13 potential SR components and examined their location. Nine were associated with the SR, often with non-uniform distributions.Chlamydomonas rootlet proteins) coded for proteins that we found in the Paramecium SRs; those without this domain were not in the rootlets.Those sequences with SF assemblin domains (similar to those in the Paramecium MKS3.MKS3 interacted sufficiently with the epitope tagged striated rootlet proteins to be pulled down with a GST-fusion of the 252 C-terminal residues of the MKS3 interacts with a subset of SR proteins.