Farkas et al. identify monotopic hairpin proteins as novel clients of the guided entry of tail-anchored proteins (GET) pathway. They demonstrate that high efficiency GET pathway–mediated ER targeting of the budding yeast hairpin protein Erg1, a key enzyme of sterol synthesis, becomes essential under conditions requiring increased Erg1 biogenetic flux. get3 cells and demonstrate the sensitivity of GET pathway mutants to the sterol synthesis inhibiting drug terbinafine. Our data uncover a key enzyme of sterol synthesis, the hairpin membrane protein squalene monooxygenase (Erg1), as a non-canonical GET pathway client, thus rationalizing the lipotoxicity phenotypes of GET pathway mutants. Get3 recognizes the hairpin targeting element of Erg1 via its classical client-binding pocket. Intriguingly, we find that the GET pathway is especially important for the acute upregulation of Erg1 induced by low sterol conditions. We further identify several other proteins anchored to the endoplasmic reticulum (ER) membrane exclusively via a hairpin as putative clients of the GET pathway. Our findings emphasize the necessity of dedicated targeting pathways for high-efficiency targeting of particular clients during dynamic cellular adaptation and highlight hairpin proteins as a potential novel class of GET clients.The guided entry of tail-anchored proteins (GET) pathway targets C-terminally anchored transmembrane proteins and protects cells from lipotoxicity. Here, we reveal perturbed ergosterol production in ∆ Biological membranes are composed of lipids and proteins. The specific combination of lipids and membrane proteins endows different cellular membranes, such as the plasma membrane and intracellular membranes of eukaryotic cells, with appropriate functional properties . A huge Saccharomyces cerevisiae (yeast), or SGTA, TRC35, UBL4A, and BAG6 in mammals, mediates the initial capture of nascent TA proteins  after synthesis at the ribosome . A cytosproteins . They arproteins . Severalproteins . In combproteins . The steproteins . Evidencget1, Δget2, and Δget3 mutants as substantially sensitive to the monounsaturated fatty acid palmitoleate  on a medium containing terbinafine. In line with the hypothesis that sterol synthesis is impaired in Δget3 mutants, the strain was strongly terbinafine sensitive  and synthetic complete (SC) media  media , as mediC) media . The funs tested . This id strains . Decreas strains  but also strains . Consistnditions . The ergnditions  commencenditions . This inget3 mutant may reflect Get3-dependence of one or several of the enzymes involved in sterol biosynthesis . Pretargeting complex components Get4, Get5, and Sgt2, which more stably interact with Get3 in the presence of clients  within 30 amino acids of their C-terminus , Erg1 fe2CO3), which were sufficient to extract Get3, which is peripherally associated with the ER membrane via its receptor  with Get3 was analyzed. As expected for a Get3 client, full length, but not truncated, Erg1 was recovered . To furterg1 deletion mutant in the background of a URA3-carrying plasmid driving the expression of Erg1 tagged with a fluorescent protein. Strains were transformed with a second plasmid driving the expression of GFP-tagged Erg1 lacking its C-terminal hairpin (ΔC) or having it replaced by the TMS of Erg9 (ΔC Erg9TMS). A plasmid expressing full-length Erg1 served as the positive and the empty vector as the negative controls. Counterselection of the URA3-containing Erg1 expression plasmid on a 5′-fluoroorotic acid (5-FOA) containing plate revealed that both Erg1 ΔC and Erg1 ΔC Erg9TMS failed to functionally rescue ∆erg1  or lower (negative skewness) values compared to the mean, the presence of bright pixels in the cells corresponding to ER and lipid droplet localization of Erg1 is reflected in a higher positive skewness as opposed to diffuse cytosolic staining. Indeed, a clear difference could be measured in the distribution of the Erg1–GFP signal between logarithmically growing WT and Δget3 cells  to monitor ERG1 mRNA levels. This confirmed a comparable increase of mRNA production from the ERG1 gene in the presence of terbinafine in both WT and Δget3 strains  cells were electroporated with the ligation mixture, plated onto appropriate selection plates, and incubated for 1 d at 37°C. Individual colonies from the plate were used to amplify plasmids, which were confirmed by Sanger sequencing. For Gibson assembly, the plasmid backbone was prepared as described for T4 ligation and then fused with PCR-amplified inserts in Gibson assembly reaction mixture for 1 h at 50°C. Starting with the electroporation of bacterial cells, cloning proceeded identically to the T4 ligation method described above.All plasmids used in this study are listed in is study . Plasmidis study  or T4 liUnless indicated otherwise, all yeast strains used in this study are from the By4741 genetic background  and are erg1 pRS416 MET15pr::Erg1-mTagBFP2 strain, the By4741 strain carrying the plasmid was used to replace ERG1 with the NAT resistance cassette due to the ERG1 gene being essential.To generate the ΔERG1 strain was created by transforming Δerg1 pRS416 MET15pr::Erg1-mTagBFP2 with a cassette encoding HA–ERG1 containing ∼40 base pairs on both 5′ and 3′ ends homologous to the genomic sequence directly adjacent to the ERG1 gene. After growth on a YPD plate, the yeast was replica-copied onto a YPD plate containing 1 mg/ml 5-fluoroorotic acid (Thermo Fisher Scientific) to select colonies capable of losing the pRS416 MET15pr::Erg1-mTagBFP2 plasmid indicating correct genomic integration of the HA–ERG1 cassette.HA–NOP1 promoter were selected from the previously published SWAT library (GET3 were generated with a synthetic genetic array (SGA)-based method using an SGA-compatible MATα strain as described previously  dissolved in methanol was applied at a final concentration of 50 µg/ml.600 0.8. A total of 5 µl of the normalized culture was spotted out in a 1:5 dilution series onto YPD plates with the appropriate composition. Strains containing plasmids were spotted onto synthetic dropout plates lacking the appropriate auxotrophic marker. Control plates for growth on terbinafine (Sigma-Aldrich) contained an equivalent amount of methanol instead. Plates were incubated at 30°C for 1 d, except for terbinafine-containing plates, which were incubated for 2 d to account for generally slower growth of yeast in presence of the inhibitor.Logarithmically growing strains were normalized to ODAfter the transformation of the test strain with the indicated pRS415-derived plasmids, single colonies were streaked out onto a synthetic dropout plate lacking leucine containing 1 mg/ml 5-fluoroorotic acid (Thermo Fisher Scientific) and allowed to grow for 3 d at 30°C.d-glucose. Synthetic dropout media lacking uracil containing 10% galactose was added to obtain a final concentration of 2% galactose in the culture, and the culture was allowed to grow for 1 h before imaging the strains.Yeast was grown to logarithmic phase in synthetic dropout media lacking uracil containing 2% raffinose instead of For the screens of N- and C-terminally GFP-tagged proteins, stationary-phase cells were diluted into 384-well glass-bottom microtiter plates (Brooks Life Science Systems) containing low fluorescence minimal media (FORMEDIUM Ltd.) supplemented with 2% glucose, methionine, histidine, uracil, and leucine, and allowed to grow for 4 h at 30°C. For all other images, strains were grown to logarithmic phase in an appropriate selection media while shaking in tubes at 30°C. About 10 µl of the culture was diluted in 50 µl synthetic dropout media in 384-well glass-bottom microtiter plates (Brooks Life Science Systems) and imaged immediately.For lipid droplet staining, MDH (abcepta) was added to cultures to a final concentration of 2 µM from a 200 µM stock in dimethylsulfoxide, incubated for 5 min, and imaged immediately.For mitochondrial staining, MitoTracker Orange CMTMRos (Molecular Probes) was added to cultures to a final concentration of 100 nM from a 10 µM stock in dimethylsulfoxide, incubated for 20 min and imaged immediately.Images were acquired at room temperature with a Nikon Ti2 2-E inverted microscope equipped with a computer-controlled stage, a Lumencor Spectra X light source, a pco.edge 5.5 M-AIR-CL-PCO sCMOS camera  using NIS-Elements software (Nikon). The focal plane was detected with the Perfect Focus System (Nikon). Images were acquired with a CFI Plan Apo Lambda 100×/1.45 oil objective using settings appropriate for GFP , MitoTracker , or MDH and mTagBFP2 . Image cropping, brightness adjustment, and conversion from 16-bit to RGB encoding was done in ImageJ  to generTo quantify the distribution of Erg1-GFP signal, 100 cells with a visible ER signal were selected for each sample. and the distribution of the pixel intensities was quantified using ImageJ . The aveg for 10 min at 4°C. The resulting supernatant was centrifuged again at 500 g for 5 min at 4°C. The supernatant was centrifuged at 100,000 g for 50 min at 4°C, and the pellet was resuspended in a storage buffer with 33% glycerol equivalent to 50% of the volume before ultracentrifugation and then stored at −80°C. Protein concentration of the microsomes was determined with Pierce BCA protein assay (Thermo Fisher Scientific).Microsomes were prepared and proteins extracted based on previously published protocols . Yeast c2CO3, 1% IGEPAL (Sigma-Aldrich), or 1% Triton X-100 (Sigma-Aldrich), followed by centrifugation at 100,000 g for 50 min at 4°C. Proteins in the supernatant and the pellet were precipitated by incubating the samples with 12.5% trichloroacetic acid for 10 min on ice, then centrifuged at 15,000 g for 10 min at 4°C. The protein pellet was washed twice with acetone, dried at room temperature, and resuspended in NuPAGE LDS Sample Buffer (Thermo Fisher Scientific) to a final protein concentration of 5 µg/µl. Samples corresponding to 25 µg protein were analyzed by immunoblotting.Proteins were extracted by incubating microsomes corresponding to 500 µg protein for 1 h on ice in storage buffer, 0.5 M NaCl, 2.5 M Urea, 0.1 M Na600 0.8 in synthetic dropout media lacking appropriate auxotrophic selection markers. A total of 200 ml of each strain was harvested, and the cell pellet was stored at −80°C. Pellets were thawed and resuspended in 3 ml lysis buffer , then crushed in a mortar with pestle under liquid nitrogen. Thawed lysates were centrifuged at 500 g for 5 min at 4°C and the supernatant was transferred to a new tube and centrifuged at 500 g for 5 min at 4°C. Protein concentration of the lysate was determined with Pierce BCA protein assay (Thermo Fisher Scientific) and 3 mg of total protein was incubated with 50 µl µMACS Anti-GFP MicroBeads (Miltenyi Biotec) while shaking for 1 h at 4°C. Samples were loaded onto µ Columns (Miltenyi Biotec) in a µMACS Separator (Miltenyi Biotec) and washed twice with 300 µl TEV cleavage buffer . About 25 µl TEV cleavage buffer containing 5 µg TEV protease (custom-made) was added onto the columns and incubated for 1 h at room temperature. Input and eluate samples were mixed with NuPAGE LDS Sample Buffer (Thermo Fisher Scientific), incubated at 70°C for 5 min, and stored at −20°C until analysis by Western blotting or mass spectrometry.Strains were grown to ODS. cerevisiae proteins, potential contaminants and decoy peptides were removed along with proteins not identified in all three replicates of Get3 DE-TEV-GFP. Intensity values were log2 transformed, and the missing values were imputed with values based on a normal distribution  using Perseus  differentially and on average at least eightfold between the Get3 DE-TEV-GFP and Get3 DE FIDD-TEV-GFP samples.Eluates from immunoprecipitation of TEV-GFP tagged proteins were resuspended in NuPAGE LDS Sample Buffer (Thermo Fisher Scientific), incubated at 70°C for 5 min, and resolved on NuPAGE 4–12% Bis-Tris gels. A total of 23 gel slices were cut from each gel using a custom-made cutter. Proteins in each gel fraction were reduced by incubating with 10 mM dithiothreitol at 56°C for 50 min, alkylated by incubation with 55 mM iodoacetamide for 20 min at RT in the dark, and digested using an MS-grade trypsin (Sigma-Aldrich). Digested peptides were extracted using acetonitrile and 5% formic acid and dried in a vacuum concentrator. Dried peptide samples were redissolved in 2% (v/v) acetonitrile (ACN), 0.1% (v/v) formic acid (FA) loading buffer and injected into a nano-LC system operated by UltiMate 3000 RSLC (Thermo Fisher Scientific). The LC was equipped with a C18 PepMap100-trapping column  connected to an in-house packed C18 analytical column . The LC was equilibrated using 5% (v/v) buffer B (80% [v/v] ACN 0.1% [v/v] FA in water) and 95% (v/v) buffer A (0.1% [v/v] FA in water). The peptides were eluted using the following gradient: (i) 10–45% linear increase of buffer B over 43 min; (ii) wash-out at 90% buffer B for 6 min; (iii) re-equilibration at 5% buffer B for 6 min. Eluting peptides were sprayed into Q Exactive HF (Thermo Fisher Scientific) mass spectrometer operated in a data-dependent acquisition mode. MS1 scans of 350–1,600 m/z range were collected at 60,000 resolution and an automatic gain control (AGC) target of 1e6 and a maximum injection time (MaxIT) of 50 ms. Thirty most intense precursor ions of charge 2–5 were subjected to fragmentation using normalized collision energy (NCE) of 30%. MS2 scans were acquired at a resolution of 15,000, 1e5 AGC target, and MaxIT of 54 ms. The dynamic exclusion was set to 25 s. Of the identified  Perseus . Two-taig, and resuspended in NuPAGE LDS Sample Buffer (Thermo Fisher Scientific) corresponding in µl to 100 × OD600 of the NaOH solution containing the samples. After incubation at 70°C for 5 min, the samples were centrifuged at 16,000 g for 30 s and stored at −20°C until later use. A total of 7 µl was used for Western blot analysis.Sample preparation was adapted from the previously described NaOH lysis protocol . BrieflySamples were resolved in Bis-Tris gels and transferred onto PVDF membranes. The membrane was blocked in Tris-buffered saline (TBS) containing 5% milk for 1 h followed by incubation with primary antibodies in TBS containing 0.1% Tween-20 (Roth) overnight at 4°C. After rinsing the membranes in TBS containing 0.1% Tween-20, incubation with secondary antibodies followed in TBS containing 0.1% Tween-20 and 0.01% sodium dodecyl sulfate. Membranes were scanned in a LI-COR Odyssey scanner and quantified in Image Studio Lite 5.2.5 (LI-COR).Primary antibodies used include polyclonal guinea pig anti-Get3 diluted 3,000-fold , polycloSecondary antibodies were diluted 10,000-fold and included IRDye 680LT anti-mouse IgG1 , IRDye 680LT anti-rabbit , IRDye 800CW anti-rabbit , and IRDye 800CW anti-guinea pig .600 units of logarithmically growing cells in SC or YPD media were pelleted and washed three times in 155 mM ammonium bicarbonate buffer. Cell pellets were snap-frozen in liquid nitrogen and stored at −80°. Pellets thawed on ice were resuspended in 155 mM ammonium bicarbonate buffer and lysed with glass beads by vortexing three times for 5 min at 4°C, with short pauses between each round. The lysate was snap-frozen in liquid nitrogen and stored at −80°. After thawing, samples were pelleted at 350 g for 2 min, the supernatant was taken, frozen, and MS-based lipid analysis was performed by Lipotype GmbH as described  and GTC mix  N-lauroylsarcosine, 150 mM β-mercaptoethanol) for 5 min at 4°C. Extracts were incubated at 65°C for 5 min and then on ice for 5 min before the addition of 1/2 volume of chloroform and 20 mM sodium acetate, 0.2 mM EDTA, and 2 mM Tris-HCl pH 8.0. After centrifugation, the upper aqueous phase was transferred to a fresh tube and an equal volume of phenol/chloroform/isoamylalcohol (25:24:1) was added. Samples were vortexed and centrifuged, and the upper aqueous phase was mixed with an equal volume of chloroform. After vortexing and centrifuging, RNA in the upper phase was precipitated by the addition of 300 mM sodium acetate pH 5.2 and 3 volumes of ethanol. Following centrifugation, the RNA pellet was washed with 70% ethanol and resuspended in RNase-free water.260/A280 ratio. DNase treatment was performed on 10 µg total RNA using 2 U TURBO DNase (Thermo Fisher Scientific) in the presence of 40 U RiboLock RNase inhibitor (Thermo Fisher Scientific) for 15 min at 37°C. DNase-treated RNA was purified using the RNA clean and concentrator kit (Zymo Research) according to the manufacturer’s instructions.RNA was quantified and quality-controlled with the AcDNA was synthesized from 2 µg total RNA in 20 µl total volume in the presence of 2.5 µM oligo-dT primer (T24VN), 0.5 mM dNTP mix, 5 mM DTT, 40 U RiboLock RNase inhibitor (Thermo Fisher Scientific), 200 U Superscript III reverse transcriptase (Thermo Fisher Scientific) and 1× Superscript III reverse transcriptase first strand buffer (Thermo Fisher Scientific). After incubation at 50°C for 1 h, the enzyme was deactivated at 70°C for 15 min. Equal amounts of cDNA were used for qPCR experiments.TAF10 and converted using the formula 2Ct. Finally, converted values were normalized to the average of untreated WT samples.Primer pairs were designed to amplify an amplicon of ∼130–150 bases. A product melting curve was used to test the amplification of a single amplicon for each primer pair. The range of linearity of cDNA amplification was confirmed, and all used primer pairs had an amplification efficiency of >90%. qPCR was performed using LightCycler 480 SYBR Green I Master (Roche), 0.3 µM primers, and 1× ROX  in a Mx3000P qPCR machine (Agilent Technologies). Cycling conditions were 95°C for 5 min, 40 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s, followed by 95°C for 1 min, 55°C for 30 s and 95°C for 30 s. qPCR data were analyzed using MxPro software. Technical triplicate reactions were performed and values differing from the mean by >0.5 Ct were excluded. Obtained values were normalized to those of 6C6P was retrieved from RCSB PDB (www.rcsb.org) and visualized using VMD  and human SQLE (Uniprot ID: Q14534-1) sequences were retrieved from Uniprot (www.uniprot.org), aligned with Clustal Omega  were calculated using Excel following standard procedures. Data distribution was assumed to be normal, but this was not formally tested. Differences between samples with P < 0.05 were considered statistically significant.Mean, standard deviation, and statistical significance derived from Welch’s unequal variances Review HistoryClick here for additional data file.Table S1get3 strains grown in SC and YPD media.list lipidomic analysis of WT and ∆Click here for additional data file.Table S2list mass spectrometry analysis of Get3-TEV-GFP DE and Get3-TEV-GFP DE FIDD immunoprecipitates.Click here for additional data file.Table S3list yeast proteins in the SWAT library predicted to contain a single hairpin.Click here for additional data file.Table S4list of plasmids, yeast strains, and oligos used in this study.Click here for additional data file.SourceData F2is the source file for Fig. 2.Click here for additional data file.SourceData F3is the source file for Fig. 3.Click here for additional data file.SourceData F4is the source file for Fig. 4.Click here for additional data file.SourceData F5is the source file for Fig. 5.Click here for additional data file.SourceData F7is the source file for Fig. 7.Click here for additional data file.SourceData FS4is the source file for Fig. S4.Click here for additional data file.


Variability in cytochrome P450 3A4 (CYP3A4) metabolism is mainly caused by non-genetic factors, hence providing a need for accurate phenotype biomarkers. Although 4β-hydroxycholesterol (4βOHC) is a promising endogenous CYP3A4 biomarker, additional investigations are required to evaluate its ability to predict CYP3A4 activity. This study investigated the correlations between 4βOHC concentrations and hepatic and intestinal CYP3A4 protein expression and ex vivo microsomal activity in paired liver and jejunum samples, as well as in vivo CYP3A4 phenotyping (midazolam) in patients with a wide body weight range.n = 96; 78 with obesity and 18 normal or overweight individuals) were included from the COCKTAIL-study (NCT02386917). Plasma samples for analysis of 4βOHC and midazolam concentrations, and liver (n = 56) and jejunal (n = 38) biopsies were obtained. The biopsies for determination of CYP3A4 protein concentration and microsomal activity were obtained during gastric bypass or cholecystectomy. In vivo CYP3A4 phenotyping was performed using semi-simultaneous oral (1.5 mg) and intravenous (1.0 mg) midazolam.The patients , and hepatic CYP3A4 concentrations , but not with intestinal CYP3A4 concentrations  or intestinal microsomal CYP3A4 activity . 4βOHC concentrations correlated weakly with midazolam absolute bioavailability  and apparent oral clearance , but not with systemic clearance .4βOHC concentrations were positively correlated with hepatic microsomal CYP3A4 activity (These findings suggest that 4βOHC concentrations reflect hepatic, but not intestinal, CYP3A4 activity. Further studies should investigate the potential value of 4βOHC as an endogenous biomarker for individual dose requirements of intravenously administered CYP3A4 substrate drugs.Clinical.Trials.gov identifier: NCT02386917.The online version contains supplementary material available at 10.1007/s00228-022-03336-9. The cytochrome P450 (CYP) 3A subfamily, consisting mainly of CYP3A4 and the polymorphic CYP3A5 , plays aint), the latter representing metabolic capacity [The current gold standard method to assess in vivo CYP3A4 activity is midazolam, a selective CYP3A4 phenotypic probe drug in vivo with a short half-life –16. Howecapacity –19. Prevcapacity , 21. Thucapacity , 23. Thecapacity –27. The capacity –30. Stilcapacity . Also, tcapacity . The potcapacity –35.Additional investigations are required before 4βOHC can be accepted as a validated biomarker. As part of the primary endpoint in the COCKTAIL study , we eval2 combined with at least one obesity-related comorbidity) scheduled for weight loss treatment with Roux-en-Y-gastric bypass (RYGB) (n = 38) or non-surgical calorie restriction (n = 40), and 18 mainly normal to overweight individuals scheduled for cholecystectomy (BMI 18.5–30 kg/m2). The patients with severe obesity were subjected to a 3-week low-energy diet  before the study investigation, whereas the normal to overweight individuals were not subjected to any defined diet beforehand. Medications, including statins, and/or other substances that are known to alter CYP3A4 activity/midazolam pharmacokinetics were discontinued at least seven half-lives before the investigational day, and none of the patients used any CYP3A4 inducers or time-dependent inhibitors. The study was approved by the Regional Committee for Medical and Health Research Ethics (2013/2379/REK) and complied with the Declaration of Helsinki. All patients signed a written informed consent before study participation.In total, 96 patients from the open-label, three-armed COCKTAIL-study (NCT02386917) were included in the present analysis , 37. Then = 38), and liver biopsies were obtained during cholecystectomy (n = 18), as previously described [Blood samples for determination of plasma 4βOHC concentrations and a 24-h pharmacokinetic investigation using semi-simultaneous oral and intravenous dosing of midazolam were collected from all patients. From 10:00 p.m. before the investigation, patients abstained from food, drink (except water), and drugs. On the investigational day, patients first met for blood sampling before 1.5 mg oral midazolam syrup was administered, followed by 1.0 mg intravenous midazolam 4 h later. Blood samples for determination of midazolam plasma concentrations were collected from a peripheral venous catheter before and at 0.25, 0.5, 1, 1.5, 2, 3, 4, 4.25, 2.5, 5, 5.5, 6, 8, 10, 12, 23, and 24 h following oral dosing. The pharmacokinetic investigation has been described in more detail previously . In vivoescribed , 38.Body composition was measured with Inbody 720, Body Composition Analyzer . The NAFLD liver fat score was calculated as suggested by Kotronen et al. . Standarn = 6) [4βOHC plasma concentrations were determined by a previously described assay  with an n = 6) . All samThe plasma concentrations of midazolam were determined by liquid–liquid sample extraction followed by a previously validated UHPLC-MS/MS method . The samThe UHPLC-MS/MS method was carried out in two labs due to limited capacity. The instrument in lab 1 was a Thermo Fisher Scientific Vanquish HPLC coupled to a Thermo Fisher Accucore (C18 2.1 × 50 mm) analytical column and a Thermo Fisher Scientific Altis QqQ MS. In lab 2, a Waters  Acquity UPLC with a BEH C18 RP-shield  analytical column coupled to a Micromass Quattro micro tandem MS was used. The chromatography was carried out with a flow rate of 0.4 mL/min and 0.2 mL/min and gradient elution for lab 1 and lab 2, respectively. Mobile phase A was 5% acetonitrile in 10 mM ammonium formate, and mobile phase B was 90% acetonitrile and 10% methanol. Positive electrospray ionization and selected reaction monitoring were performed with compound-tuned conditions. For both labs, acquisition was carried out by the MRM transitions m/z 326.1 -> m/z 249.1 and m/z 326.1 -> m/z 291.2 for midazolam, and m/z 332.1 -> m/z 252.1 and m/z 332.1 -> m/z 203.1 for midazolam-d6 in positive mode.Inter-laboratory cross-validation was performed using clinical study samples, and the bias was within ± 12.5%. Calibrators and quality control (QC) samples were prepared in blank plasma and analyzed in each analytical run. Eight calibrators in the range of 0.1 to 20 ng/mL were applied, and back-calculated values of calibrators within 80 to 120% were accepted. LLOQ was 0.1 ng/mL, and the upper limit of quantification (ULOQ) was 20 ng/mL. Samples with midazolam concentrations above ULOQ were diluted in blank plasma and reanalyzed. Dilution integrity with dilution factors of 1/2, 1/5, 1/10, 1/20, and 1/50 was established; mean accuracy ranged from 88.9 to 103.8%, and the imprecision was < 4.5%. Within-series and between-series performance were assessed with the resulting coefficient of variation < 12.3%, and the mean accuracy ranged from 99.3 to 104.3%.The biopsies were transferred into individual cryotubes, snap-frozen in liquid nitrogen directly upon sampling, and stored at − 80 °C until analysis.Proteins were extracted from liver and jejunum biopsies in an SDS-containing (2% w/v) lysis buffer and quantified as previously described . In shorMethods for quantifying CYP3A4 activity in liver and jejunum biopsies have been described previously . In brieCYP3A4 and CYP3A5 variant alleles were analyzed using Taqman-based real-time polymerase chain reaction assays implemented for routine pharmacogenetic analyses at the Center for Psychopharmacology, Diakonhjemmet Hospital, Norway. CYP3A4, the reduced function allele*22 (rs35599367), and CYP3A5, the null allele *3 (rs776746), were included in the analysis. All alleles were in Hardy–Weinberg equilibrium.Km) values were adjusted for the fraction of unbound drug in microsomes, which was predicted from the physicochemical properties of the substrates and microsomal protein concentration using the Simcyp prediction tool (https://members.simcyp.com/account/tools/fumic). The unbound CLint  was calculated from the ratio of maximum velocity (Vmax) to the unbound Km. Due to limited capacity, the hepatic and jejunum microsomal CLint,u were determined in 20 RYGB patients.In vivo absolute bioavailability, apparent oral clearance, and systemic clearance of midazolam from the pharmacokinetic investigation were determined using a previously developed population pharmacokinetic model . Brieflyp value < 0.05 was considered statically significant. Data are presented as median difference ) if not otherwise stated. Demographic data are presented as mean ± standard deviation (SD). All statistical analyses were performed using R for Windows (version 4.1.2) [Neither 4βOHC nor most of the other variables passed the normality tests , and non-parametric statistical tests were therefore used in the statistical analyses. Spearman’s rank-order correlation test was used to describe the rank-based measure of association. Wilcoxon rank-sum test was used to compare patients with obesity with normal to overweight individuals. A n 4.1.2) .CYP3A4 *1/*1), and 10% also expressed functional CYP3A5 (CYP3A5 *1/*3). In line with the inclusion criteria, mean BMI was higher in patients with obesity compared with normal to overweight individuals. Patients with obesity had lower serum total cholesterol compared with the normal to overweight individuals  but higher NAFLD liver fat score  and hs-CRP . Twenty percent of the patients received treatment with cholesterol-lowering drugs (mainly statins) .Patient characteristics at the investigational day are given in Table ρ = 0.30, p = 0.027) and hepatic microsomal CLint,u , but not with systemic midazolam clearance  , but no correlation was observed between systemic midazolam clearance and hepatic microsomal CLint,u , and a positive correlation with apparent oral clearance   Fig. . HepaticIn the patients with obesity, neither 4βOHC concentrations, systemic midazolam clearance, nor absolute bioavailability changed during the 3-week low-energy diet period  nor in patients with obesity (p = 0.33) , whereas in the normal to overweight individuals, the difference did not reach statistical significance (p = 0.12)   and ρ =  − 0.23 (p = 0.027), respectively  Fig. a. Both Nely Fig. b and c. In this study, a comprehensive investigation of 4βOHC as an endogenous biomarker for CYP3A4 activity was performed. To our knowledge, this is the first study where 4βOHC has been compared with three other CYP3A4 metrics: intestinal and hepatic microsomal CYP3A4 activity and protein expression in paired liver and jejunum samples, as well as absolute bioavailability, apparent oral clearance, and systemic clearance of midazolam in vivo. The main findings were that 4βOHC concentrations were positively correlated with hepatic microsomal CYP3A4 activity and hepatic CYP3A4 expression. Equally important, this study also provides evidence of a negligible contribution from intestinal CYP3A4 to the formation of 4βOHC.int,u) supports that 4βOHC concentrations reflect individual CYP3A4 metabolic capacity and provides evidence that 4βOHC is a suitable marker for hepatic CYP3A4 phenotype. However, there was no correlation between 4βOHC concentrations and systemic clearance of midazolam, the gold standard method to assess CYP3A4 activity. The correlation between these two metrics has been investigated in multiple studies previously, with conflicting findings varying from no correlation as in this study to a weak or moderate correlation [int,u) and in vivo systemic midazolam clearance, suggesting that systemic midazolam clearance does not solely reflect CYP3A4 activity in the present population. In contrast to hepatic microsomal CLint,u, in vivo systemic midazolam clearance may also be influenced by differences in hepatic blood flow given its medium to high extraction ratio [The moderate correlation between 4βOHC concentrations and hepatic microsomal activity  was correlated with 4βOHC concentrations. Even though there was a weak, but significant correlation between 4βOHC concentrations and (1) midazolam absolute bioavailability, and (2) apparent oral clearance, but no correlation with systemic clearance, these results indicate that intestinal CYP3A4 has a negligible role in the formation of 4βOHC. There have been some indications of a possible contribution from intestinal CYP3A4 in the formation of 4βOHC [Information regarding the role of intestinal CYP3A4 in 4βOHC formation has been lacking , 53, andof 4βOHC , 54. Howof 4βOHC , which iint,u using midazolam as a probe drug are also reported to be negatively correlated with BMI [2, although the authors suggested that this was due to type 2 diabetes, not obesity [2 and NAFLD [In this study, we also report a negative correlation between BMI and 4βOHC, and that patients with obesity have significantly lower 4βOHC concentrations compared with normal to overweight individuals. Previous studies have also found an inverse correlation between BMI and 4βOHC/C, and body weight and 4βOHC/C , 25. Intwith BMI , 9. Thiswith BMI . The norwith BMI , 51, 56.nd NAFLD , 58, andnd NAFLD . FurtherCYP3A5*1/*3) and non-functional CYP3A5 (CYP3A5*3/*3). Nevertheless, we do not believe that the heterozygote CYP3A5*1 carriers in our study population (~ 10%) have impacted the interpretation of the results in this study to any significant degree. This is supported by the fact that there were no statistically significant differences between the heterozygote CYP3A5*1 carriers and the homozygote CYP3A5*3 carriers in any of the other metrics investigated. Also, there was a tendency of lower 4βOHC concentrations in CYP3A4*22 carriers (reduced function), but the interpretation is challenged by the low number of individuals carrying this allele. Overall, these results are in line with previous studies [Most of the cholesterol formation to 4βOHC is accounted for by CYP3A4 metabolism, whereas the contribution from CYP3A5 is more ambiguous , 60, 61. studies , 25, 50 The major strength of this study includes the comprehensive investigation of three different CYP3A4 metrics in relation to 4βOHC, all obtained from the same individuals at the same time point. The patients with obesity were subjected to a 3-week low-energy diet before the study investigation, which may have influenced the results. However, as neither 4βOHC concentrations, absolute bioavailability, nor systemic clearance of midazolam changed from before to after the diet, a change in the CYP3A4 phenotype during this short period seems unlikely. Nevertheless, in addition to the fact that 20% of the patients used cholesterol-lowering drugs, cholesterol levels also decreased following the diet, which led to a significant increase in 4βOHC/C in the patients with obesity. Several studies report the 4βOHC/C ratio to account for abnormal or changing cholesterol levels. However, the concentration of 4βOHC is less than 0.002% of total cholesterol . We deciIn summary, this comprehensive study showed that 4βOHC plasma concentrations reflect hepatic, but not intestinal CYP3A4 activity well. Hence, 4βOHC may be a valuable biomarker for individualized dosing of intravenously administered CYP3A4 substrate drugs. Additionally, 4βOHC is a valuable supplement to traditional phenotyping with probe drugs such as midazolam given its easy implementation and complementary information when other factors, such as disease state or simultaneous physiological alterations, are expected to influence the pharmacokinetics of CYP3A4 probe drugs.Supplementary file1 (DOCX 1062 KB)Below is the link to the electronic supplementary material.


This study broke the synthesizability bottleneck of theoretically designed environment-friendly SM derivatives and advanced the mechanism of screening functional derivatives.Synthetic musks (SMs), as an indispensable odor additive, are widely used in various personal care products. However, due to their physico-chemical properties, SMs were detected in various environmental media, even in samples from arctic regions, leading to severe threats to human health . Environmentally friendly and functionally improved SMs have been theoretically designed in previous studies. However, the synthesizability of these derivatives has barely been proven. Thus, this study developed a method to verify the synthesizability of previously designed SM derivatives using machine learning, 2D-QSAR, 3D-QSAR, and high-throughput density functional theory in order to screen for synthesizable, high-performance (odor sensitivity), and environmentally friendly SM derivatives. In this study, three SM derivatives  were screened and recommended due to their good performances . In addition, the synthesizability mechanism of SM derivatives was also analyzed. Results revealed that high intramolecular hydrogen bond strength, electrostatic interaction, qH Synthetic musks (SMs), as a kind of additive with a particular smell, have been widely used in perfume, shower gel, shampoo, lotion, shampoo, shaving cream, soap, deodorant, sunscreen, nail polish, hair oil, hair dye, lip balm, aftershave softener, air freshener, washing powder, and other personal care products in daily life to enhance the fragrance and mask odors . SMs conThe environmentally friendly molecular design has been widely used for the source control of emerging pollutants. Zhou et al.  designedThe synthesizable studies of environmentally friendly emerging contaminant derivatives were barely reported. Density functional theory (DFT) has been widely used to predict the positive frequency value of emerging pollutant derivatives to verify whether the designed molecules can exist in the environment ,25,26,27In order to accurately verify the predicted synthesizability of SM derivatives, it is important to analyze the mechanism of the relationship between the molecular structures and descriptors and the synthesizability of SM derivatives. The integration of DFT and ML methods for predicting the synthesizability of SM derivatives is based on the comparative analysis of the topological parameters, electronic parameters, geometric parameters, and physico-chemical parameters of the SMs and SM derivatives. The sensitivity coefficient analysis was conducted using the two-dimensional quantitative structure-activity relationships (2D-QSAR) model for screening key feature molecular descriptors ,30. In aThe objective of this study is thus to summarize the environmentally friendly SM derivatives with improved functionalities described in the existing research, aiming to provide the primary mechanism analysis and synthetic strategy for the synthesizability of theoretically designed SM derivatives. This is the first attempt to combine DFT with multiple ML methods to accurately predict the synthesizability of SM derivatives. In-depth synthesizable mechanisms for designing environmentally friendly SMs were investigated by integrating the 2D-QSAR model, the 3D-QSAR model, and intramolecular hydrogen bond theory. This study constructed a cost-effective screening system for synthesizing theoretically designed SM derivatives, which is expected to provide theoretical guidance for replacing emerging pollutants and developing new materials.p = 0.6 in this study), and the characteristic values with strong correlations were automatically eliminated. As shown in +, EHOMO, energy gap (EG), dipole moment (DM), QYY, QZZ, QXY, QXZ, QYZ, positive frequency (Freq), Raman, GE, AATSC8c, GATS5c, GATS6c, and GATS3s. The PCC of the key descriptors were all within a reasonable range, and the dataset also meets the training requirements of the bagging-PU model in this paper [In this study, 1471 descriptors, including electronic parameters, structural parameters, topological parameters, physico-chemical parameters, and spectra of SMs and SM derivatives, were calculated by Gaussian 09 , PaDEL-Descriptor, and ChemBioDraw 12.0 software . After eliminating the descriptors without values, there were 1022 descriptors left, which are provided in is paper .In this paper, the 88 SMs and SM derivatives retrieved were divided into positive and unlabeled samples, of which 11 commercial SMs were marked as positive samples. The remaining 77 theoretically designed and yet-to-be synthesized environmentally friendly SM derivatives were labelled as “unlabeled samples”. In each iteration of the PU machine learning process, some unlabeled samples can be randomly marked as unlabeled, non-synthesizable samples. In addition, the key descriptors screened by Pearson correlation coefficients were used to construct a random forest base classifier for predicting the synthesizability of unlabeled samples. The above PU machine learning procedure was repeated for 23 iterations, and the unlabeled, non-synthesizable samples were relabeled in each iteration. Furthermore, the synthesizability of an unlabeled sample was defined as the average of the RF classifier prediction scores for all samples except that sample. The SM derivatives with synthesizability greater than 0.5 were marked as an unlabeled synthesizable sample, and those with less than 0.5 were marked as an unlabeled non-synthesizable sample . The modIn this paper, bagging-ERT ML was used to predict the synthesis of SM derivatives. The Extremely Randomized Tree Classifier (ERT) was used in the replacement algorithm of the PU machine learning prediction model to prove that the synthesis of SM derivatives does not depend on specific predictive models. Eleven commercial SMs were selected as positive samples, and the remaining 77 theoretically designed environmentally friendly SM derivatives that have not yet been synthesized were used as unlabeled samples. The bagging-ERT algorithm for predicting the synthesizability of SM derivatives was constructed. The evaluation score of the built model was 0.727 (>0.7), indicating that the model has good predictive performance . Among tThe gradient-boosting classifier (GBC) was selected as the basic classifier to construct the bagging-gradient-boosting classifier (bagging-GBC) model for further analyzing the molecular synthesis of SM derivatives. We aim to screen the SM derivatives with high predicted synthesizability that meet different models so that these screened environmentally friendly SM derivates could have the highest probability of synthesis. The synthesizability of the SM derivatives was predicted using the bagging-GBC algorithm. The constructed model showed an evaluation score of 0.770 (>0.7), indicating that the model has good predictive performance . Table 350 in the fish [The environmental risks and functional properties of seven SM derivatives screened by the bagging-GBC, bagging-ERT, and bagging-RF models were predicted by the 3D-QSAR models constructed by Li et al. ,23,24 an50 value in fish and reduced bioaccumulation ability in skin keratin, indicating that the environmental risks of D37 were lower than those of its precursor, HHCB. Therefore, D37, with its high synthesizability and lower environmental impacts, can be an alternative for HHCB. According to It was found that D37, as a substitute molecule for HHCB, had a 7.18% improvement in its functional properties (odor sensitivity) compared with HHCB. The docking scores of D37 to 1A52 and 1A28 decreased, indicating that the abortion risk induced by D37 was alleviated. In addition, D37 had an improved LC+, EHOMO, energy gap (EG), dipole moment (DM), QYY, QZZ, QXY, QXZ, QYZ, positive frequency (Freq), Raman, GE, AATSC8c, GATS5c, GATS6c, and GATS3s) screened by the Pearson correlation coefficient method in To further analyze the influence of the 16 key descriptors  for the sensitivity analysis. The above nine key descriptor values were used as independent variables, and the synthesizability of SM derivatives was used as a dependent variable for constructing three linear regression models by SPSS software. The correlation coefficients R of the bagging-RF, bagging-ERT, and bagging-GBC algorithms were 0.793, 0.825, and 0.805, respectively, all of which met the statistical requirements, and the Sig. of these models were all 0.000, passing the significance test [HOMO are positive, indicating a positive correlation between these three descriptors and the synthesizability of SM derivatives. The coefficients of the energy gap, qH+, dipole moment, QYY, QXY, and AATSC8c are negative, indicating a negative correlation between these three descriptors and the synthesizability of SM derivatives.Raman, energy gap, qHnce test . The lin+, dipole moment, and EHOMO were more prominent, and their sensitivity coefficients were all greater than 0.2. However, the characteristics of positive frequency, QYY, QXY, and AATSC8c descriptors were not obvious, and the sensitivity coefficients were all less than 0.05. Therefore, it can be inferred that the key descriptors  have a more significant impact on the synthesizability of SM derivatives and are in a higher position in the ranking order of the key descriptors’ correlations –(3) . As showelations . In cont+, dipole moment, and EHOMO significantly impact the synthesizability of SM derivatives when the change degree of key descriptors is less than 50%. In order to further analyze the changing trend of the synthesizability of SM derivatives when the parameter eigenvalues vary greatly, this paper analyzed the growth rate of the sensitivity coefficients of the characteristic parameters. Thus, the eigenvalues that most significantly affect the synthesizability of SM derivatives were screened, and the growth rates of the sensitivity coefficients of the three models were calculated , the top seven SM derivatives with high synthesizability, and the last seven SM derivatives with low synthesizability predicted by the three models were selected. The energy gap, qH+, dipole moment, and EHOMO values of the 14 SM derivatives and 11 commercially synthesized SMs are given in +, dipole moment, and EHOMO eigenvalues of the seven synthesizable SM derivatives changed by 3.72%, −0.66%, −5.70%, and −7.57%, respectively. The energy gap, qH+, dipole moment, and EHOMO eigenvalues of the seven none synthesizable SM derivatives changed by 3.72%, −0.66%, −5.70%, and −7.57%, respectively, compared with the 11 commercially synthesized SMs. The results further showed that the eigenvalues of qH+, dipole moment, and EHOMO have relatively significant effects on the synthesizable properties of SMs. The higher the value of qH+, the larger the value of the dipole moment, and the lower the value of EHOMO, the lower the synthesizability of SM derivatives, which is consistent with the conclusion of the sensitivity analysis of the constructed machine learning models.In 2 is 0.753 > 0.5, indicating that the constructed 3D-QSAR model had a good prediction ability [2 = 0.971 > 0.9 and close to 1.000) and the external test coefficient (r2pred = 0.940 > 0.6) further proved the good predictive ability and robustness of the generated models. The standard error of estimate (SEE) of the model was 0.047 < 0.95, which confirmed the good fit ability and predictive ability of the constructed 3D-QSAR model. In addition, in order to verify the rationality of setting the positive samples and unlabeled samples of the machine learning prediction model, this paper also used the constructed 3D-QSAR model to predict the synthesizability of the 11 positive samples . However, since the 3D-QSAR model can only predict molecules with a common skeleton  at position 3, which led to the decreased synthesizability of D67, D61, D62, D57, and D76. Previous studies have shown that adding strong electron-withdrawing groups can reduce the EHOMO value of the molecule [+ value of a molecule, the easier it is to accept electrons and generate electrophilic reactions. The lower the energy gap value of the molecule, the easier it is for nucleophilic and electrophilic reactions to occur, resulting in poor stability (or synthesizability) of the molecule [HOMO values of non-synthesizable SM derivatives  were all smaller than D50 and D51. Furthermore, D57, D61, D62, D67, and D76 substitute molecules had lower energy gap values and higher qH+ values than D50 and D51. The higher the value of qH+, the lower the value of EHOMO, and the smaller the energy gap value, the lower the synthesis ability of SMs substitute molecules. It showed that the higher the value of qH+, the lower the value of EHOMO, and the smaller the energy gap value, the lower the synthesizability of SM derivatives.In the contour maps of the CoMSIA model for the molecular synthesizability of environmentally friendly SM derivatives, the contributions of hydrophobic, electrostatic, hydrogen bond acceptor, hydrogen bond donor, and steric fields were 22.6%, 19.8%, 33.2%, 20.4%, and 4.0%, respectively. The results indicated that the hydrophobic, electrostatic, hydrogen bond acceptor, and hydrogen bond donor fields significantly impacted the synthesizability of SM derivatives. In this study, unlabeled sample D50 was taken as a template molecule for analysis . Seven dmolecule . Then thmolecule , the higmolecule ,48. Comp+, the larger the value of the dipole moment, the lower the value of EHOMO, and the smaller the value of the energy gap, the lower the synthesizability of SM molecules.Yellow patches appeared surrounding positions 2 and 4 of D50 in the contour map c, indicaIn order to further analyze the synthesizable mechanism of SM derivatives, they were screened based on bagging-RF, bagging-ERT, and bagging-GBC models. The intramolecular hydrogen bond differences of the top seven SM substitute molecules (D7 > D50 > D8 > D25 > D37 > D51 > D52) and the last seven non-synthesizable SM derivatives (D67 < D62 < D39 < D57 < D61 < D76 < D49) were compared to analyze the intrinsic factors affecting the internal reasons of synthesizability. HHCB and MK derivatives from the above 14 SM derivatives were selected as representatives. D7, D8 (synthesizable), and D39, D49 (non-synthesizable) were the derivatives of HHCB. D50, D51, and D52 (synthesizable) and D57 and D76 (non-synthesizable) were the derivatives of MK. For the synthesizable derivatives, D7 contained one intramolecular hydrogen bond C3−H23···O13 (H23···O13 bond length is 2.46 Å), D8 contained one intramolecular hydrogen bond C16–H29···O15 (H29···O15 bond length is 2.22 Å), D50 had five intramolecular hydrogen bonds C10–H28···O17 (H28···O17 bond length is 2.22 Å), C1−H24···O20 (H24···O20 bond length is 2.21 Å), C12−H36···O17 (H36···O17 bond length is 2.42 Å), C12−H35···O14 (H35···O14 bond length is 2.42 Å), C11–H33···O14 (H33···O14 O14 bond length is 2.22 Å), D51 had six intramolecular hydrogen bonds C10–H30···O19 (H30···O19 bond length is 2.36 Å), C13–H35···O22 (H35···O22 bond length is 2.70 Å), C10–H30···O18 (H30···O18 bond length is 2.30 Å), C1–H25···O21 (H25···O21 bond length is 2.82 Å), C9–H27 ···O16 (H27···O16 bond length is 2.73 Å), C8–H31···O16 (H31···O16 bond length is 2.54 Å), and D52 had four intramolecular hydrogen bonds C10−H31·· O20 (H31···O20 bond length is 2.31 Å), C9−H30···O20 (H30···O20 bond length is 2.43 Å), C14–H38···O17 (H38···O17 bond length is 2.49 Å), C12–H34···O23 (the bond length of H34···O23 is 2.34 Å). Among the non-synthesizable SM derivatives, D39 and D49 had no intramolecular hydrogen bonds, while D57 had five intramolecular hydrogen bonds C10–H33··O18 (the bond length of H33···O18 is 2.18 Å), C10–H33···O19 (H33···O19 bond length is 2.48 Å), C1–H28···O21 (H28···O21 bond length is 2.45 Å), C9−H29···O15 (H29···O15 bond length 2.67 Å), C9–H29···O16 (H29···O16 bond length is 2.34 Å), and D76 contained four intramolecular hydrogen bonds C10–H33···O21 (H33···O21 bond length is 2.22 Å), C12–H36···O24 (H36···O24 bond length is 2.51 Å), C9–H30···O19 (H30···O19 bond length is 2.27 Å), C1–H28···O13 (H28···O13 bond length is 2.19 Å) . Compari+, dipole moment, and EHOMO are key descriptors affecting the synthesizability of SM derivatives. Among them, the size of EHOMO and the energy gap are closely related to molecular stability, and qH+ affects the interaction between donor and acceptor in intramolecular hydrogen bonds [+ value, and energy gap value, the lower the EHOMO value, the more stable the SMs substitute molecule, and the higher the synthesis probability will be.Although the formation rate of intramolecular hydrogen bonds in non-synthesizable SM derivatives was only 50%, D57 and D76 still had intramolecular hydrogen bonds due to structural reasons. Therefore, we further analyzed the influences of intramolecular hydrogen bonds on the synthesizability of MK derivatives . Intramolecular hydrogen bond strength is difficult to calculate accurately due to the complex and strict operation process. If the system needs to be cut off and the cutoff point should be saturated, the structure needs to be adjusted to avoid severe steric hindrance. Emamian et al. [en bonds . TherefoA total of 88 SMs and their derivatives were retrieved, including 11 commercialized SMs  and 77 environmentally friendly SM derivatives that have been theoretically designed but have not yet been experimentally synthesized. The 77 SM derivatives consist of 19 functionally improved SM derivatives , 10 SM dSMs are widely used in personal care products. Due to the extensive use of SM, environmental and human health problems were accelerated. Therefore, it is of great practical significance to develop and design environmentally friendly SM derivatives. The process of developing SM derivatives takes a long time and much labor. The machine learning (ML) method can reduce unnecessary consumption in the early research stage by helping to discover and terminate the molecular design of SM derivatives with low synthesizability. Therefore, it is necessary to predict the synthesizability of the theoretically designed SM derivatives before synthesizing.The research found that structural parameters  can affect the physic-chemical properties of molecules ,52. DolzThe Pearson correlation coefficient is widely used in statistical analysis, pattern recognition, image processing, and other fields. The Pearson correlation matrix can be used to select suitable descriptors for multiple linear regression analysis  and analAmong which, p = 0.6 in this study), and the highly correlated descriptors were eliminated independently. The specific process code is shown in SPSS 18.0 software  is commonly used for Pearson correlation coefficient analysis of eigenvalues. However, since there are over 1000 descriptors of SMs and SM derivatives in this study, the Pearson correlation coefficient was calculated and analyzed by self-writing “code packages” in python software . The corIn this paper, the number of positive samples  used to construct the PU machine learning prediction model of synthesizability is small, and its number is much smaller than that of unlabeled samples . Thus, traditional classifiers such as the random forest (RF) method are more appropriate. A bagging classification model was constructed in this study based on the basic classifier  and the independent variables  .In the Bagging-RF classification method, the synthesized commercial SMs were set as positive samples (marked as “1”), and the theoretically designed yet unsynthesized environment-friendly SM derivatives were set as unlabeled samples (marked as “0”). The above samples were used to construct a PU machine learning classification model. During the model training process, unlabeled samples are divided into unlabeled synthesizable samples and unlabeled non-synthesizable samples, and their synthesizability can be output simultaneously. By adjusting the n_estimators, criterion, and random_state parameters inside the RF algorithm and the n_estimators, max_samples, random_state, and other parameters in the PU classifier, a PU classification model with a model evaluation score (out-of-bag (OOB) score) greater than 0.7 is obtained. Finally, the model with a high oob_score (greater than 0.7) is used to predict the synthesizability of the positive and unlabeled samples. In this way, the synthesizability of SM derivatives was obtained. When the synthesizability is >0.5, it is considered synthesizable, and when the synthesizability is <0.5, it is considered not synthesizable . The speIn this paper, the bagging-Extremely Randomized Tree Classifier (ERT) machine learning method was also used to predict the synthesizability of environmentally friendly SM derivatives. ERT was used as an alternative algorithm for PU machine learning predictive models to demonstrate that the synthesis of SM derivatives is not dependent on a specific predictive model. The ERT algorithm is a machine learning method based on tree structure for decision-making. Its algorithm is very similar to the random forest algorithm, which is composed of many decision trees. The RF uses randomly selected samples. In comparison, the ERT uses all samples with randomly selected molecular characteristics because the split is random. Therefore, to some extent, ERT is more appropriate than the prediction results simulated by the RF algorithm . The RF T is the number of random trees. For a given data point x and data set Dtrain, the feature vector is represented by f Dtrain, and the feature vector is represented by c, each tree learned a weak predictor x’, the probability of belonging to class c is calculated by the average of the probabilities on all trees, as shown in Equation (5):ERT is an extension of RF, where a further randomization stage is added for selecting cut points and, at the same time, randomizes the attributes in RF, randomly selecting attributes and splitting cut points . Accordiy = 1 in the training sample is t = 1, the formula was shown as follows:In this paper, the machine learning of the bagging-gradient boosting classifier (GBC) was used to construct and compare the predictive models for the synthesis of SM derivatives to prove once again that the synthetic properties of SM derivatives do not depend on specific prediction models. GBC is an ensemble classifier that performs well when the number of variables exceeds the number of samples  . GBC hasm = 1, 2, 3, …, M.The loss function negative gradient i = 1, 2, 3, …, N. The leaf nodes under the mth regression tree are Best-fit value The calculation of The best classifier The final computational form of the classifier model:The detailed code package is provided in Sensitivity analysis is mainly used to analyze the sensitivity of model output values to changes in characteristic parameters . In thisiSC represents the sensitivity coefficient of the input feature parameter i, ΔiX/iX indicates the change rate of the input feature parameters, ΔiY/iY is the synthesizability change rate of SM derivatives. The sensitivity analysis on each descriptor was conducted with the help of each descriptor’s coefficient in the linear regression model. The sensitivity coefficients for each descriptor varying by 10%, 20%, 30%, 40%, and 50% of its value were calculated, which helped to screen out the key descriptors.The sensitivity coefficient can be calculated as follows:The chemical structures  and their synthesizability were used as independent and dependent variables, respectively, for the establishment of 3D-QSAR models. The synthesizability of unlabeled samples  is predicted by the ML model with the highest model evaluation score. Using the SM derivative with the highest synthesizability as a template, a 3D-QSAR model about the synthesizability of environmentally friendly SM derivatives was constructed. The molecules of SM derivatives were optimized using the Tripos force field and Gasteiger-Hückel charges . The PowThe constructed 3D-QSAR model was then used to predict the synthesizability of positive samples  for verifying the molecular synthesizability of SMs and their derivatives predicted by the PU ML model. The plausibility of the positive and unlabeled samples used in the PU ML model was also verified. At the same time, the coupling of the force field information of the 3D-QSAR contour maps with the key descriptors affecting the molecular synthesis of SM derivatives was used to analyze the synthesizable mechanism of SM derivatives further.The stability of chemicals is closely related to the ease of synthesis , and int+ and energy gap can influence the synthesizability of SMs and their derivatives. This study analyzed the molecular structure and physico-chemical parameters of SM derivatives to accurately screen high-performance, environmentally friendly SM derivatives with high synthesizability. This research has remarkably improved in synthesizing high-performance alternatives for emerging contaminants from molecular aspects. This technology can also be applied to other fields, promoting the discovery and development of more advanced functional materials in various areas.This study investigated the synthesizability of environmentally friendly SM derivatives by integrating machine learning, 3D-QSAR, 2D-QSAR, and DFT methods. Seven of the 77 SM derivatives screened were found to have high synthesizability and low environmental risks. SM derivatives were recommended based on their performances. D37 and D52, with low abortion risks, can be used as alternatives to HHCB and MK. D52, with improved odor sensitivity (increased 10.77%), can be used as an alternative with improved functional properties. From the perspective of reducing the dermal exposure risks of SMs, D25 had a lower skin keratin enrichment (−17.25) and thus could be used as an alternative to PHAN. In addition, the synthesizability of SM derivatives was analyzed by 3D-QSAR, 2D-QSAR, and DFT methods. Based on the mechanism analysis, descriptors such as qH


FLC expression and delayed flowering. The delayed-flowering phenotype is independent of photoperiod and can be overcome by vernalization, confirming that the complex specifically functions in the autonomous pathway. Chromatin immunoprecipitation combined with sequencing indicated that, in the AuPC mutants, the histone modifications  associated with transcriptional activation are increased, and the histone modification (H3K27me3) associated with transcriptional repression is reduced, suggesting that the AuPC suppresses FLC expression at least partially by regulating these histone modifications. Moreover, we found that the AuPC component SDG26 associates with FLC chromatin via a previously uncharacterized DNA-binding domain and regulates FLC expression and flowering time independently of its histone methyltransferase activity. Together, these results provide a framework for understanding the molecular mechanism by which the autonomous pathway regulates flowering time.Although previous studies have identified several autonomous pathway components that are required for the promotion of flowering, little is known about how these components cooperate. Here, we identified an autonomous pathway complex (AuPC) containing both known components  and previously unknown components . Loss-of-function mutations of all of these components result in increased  Arabidopsis thaliana, the flowering time can be promoted by the autonomous, photoperiod, vernalization and gibberellin signaling pathways (Arabidopsis (FLC is activated by FRI (FRIGIDA), leading to a late-flowering phenotype  to detect proteins co-purified with the autonomous pathway component FLD; we found that FLD interacts not only with the known autonomous pathway components LD and SDG26 but also with three other components. We demonstrate that these components form a multi-subunit autonomous pathway complex (AuPC). In the mutants of these components, fld , ld , sdg26 , aprf1-9 (WiscDsLox489-492K11) and flc (SALK_003346) were obtained from the Arabidopsis Biological Resource Center. The mutants of efl2 and efl4 were generated by CRISPR/Cas9 using guide RNA 5′-GGAGATGTGTATTCTGGATT-3′ and 5′-GGAGATGTGTTGTCAGGATT-3′, respectively, in the pHEE401E-CRISPR/Cas9 system (pCAMBIA1305 vector and introducing the constructs into wild-type (WT) plants (Col-0) and mutant plants. 3×Flag or 3×HA was fused to the C-terminal of these proteins for western blot detection and affinity purification. Primers used for cloning these genes are listed in All plant materials used in this study were in the Col-0 background. The T-DNA insertion mutants of 9 system . The traFor flowering time analyses, numbers of rosette leaves produced by plants grown under long-day (16 h light/8 h dark) and short-day (8 h light/16 h dark) conditions were evaluated. For vernalization treatment, seeds were germinated on MS plates at 23°C for 3 days and then grown at 4°C for 50 days. After treatment, plants were transplanted to soil and grown at 23°C under long-day conditions.3×Flag-tagged FLD, SDG26, LD, EFL2, EFL4 and APRF1 transgenic plants in WT and mutant  backgrounds were collected and ground into fine powder in liquid nitrogen. The powder was homogenized in 15–20 ml of lysis buffer  in 50-ml Falcon tubes by rotating at 4°C. After centrifugation, the supernatant was transferred to a new tube and was incubated with 100 μl of ANTI-FLAG M2 Affinity Agarose Gel  at 4°C for 2.5 h. The agarose gel was washed five times with lysis buffer, and the proteins bound to the agarose gel were then eluted by incubation with 3×Flag peptides  at 4°C for 30 min. Finally, the purified proteins were analyzed by mass spectrometry as described previously . The immunoprecipitated proteins were detected by western blotting using Flag antibody  and HA antibody .For co-immunoprecipitation (co-IP), after transgenic plants expressing Flag-tagged and HA-tagged proteins were crossed, the proteins from ∼1 g of 2-week-old seedlings of the FFLD/SDG26/LD-3×Flag transgenic plants in the WT background was ground and resuspended in 2 ml of lysis buffer, and the suspension was centrifuged at maximum speed at 4°C. The supernatant was passed through a 0.22‐μm filter, and 500 μl of the filtrate was loaded onto a Superose 6 (10/300GL) . Fractions were collected and detected by western blotting using Flag antibody (Sigma).Gel filtration was performed as described previously . In brieFLD, LD, SDG26, SDG4, SDG7, SDG24, APRF1, ELF4, EFL1, EFL2, EFL3 and EFL4 as well as the truncated forms of FLD, LD and SDG26 were separately cloned in frame with the GAL4 activation domain (AD) in pGADT7 and/or with the GAL4 DNA-binding domain (BD) in pGBKT7. First, the pGADT7 and pGBKT7 plasmids were linearized by EcoRI and BamHI digestion, and the inserts were amplified by PCR using chimeric primers  and lacking Trp (SD-Trp), respectively. Next, the positive colonies of the transformed AH109 and Y187 were mixed together in the YPD medium for 16–20 h, and the mated cells were then grown on dropout medium lacking Leu and Trp (SD-Leu/Trp). Subsequently, the positive colonies on SD-Leu/Trp were spotted on dropout medium lacking Trp, Leu and His (SD-Leu/Trp/His). Growth of mated cells on SD-Leu/Trp/His indicates an interaction between the combined AD fusion protein and BD fusion protein. 3-Amino-1,2,4-triazole was applied to reduce the background growth on SD-Leu/Trp/His.The pGEX-6P-1 in frame with GST by using the ClonExpress II One Step Cloning Kit. These GST fusions were transformed and expressed in the Escherichia coli expression strain Transetta (DE3). GST-tagged proteins were purified from DE3 cultures with Glutathione Sepharose® 4B . The full-length cDNA sequences of LD, EFL2 and EFL4 in frame with 3×Flag were cloned into pAT424 between SalI and NotI restriction sites under the control of the pTDH3 promoter by using the ClonExpress II One Step Cloning Kit. The pAT424 constructs were introduced and expressed in the yeast strain YPH499 , 1 mM EDTA, 150 mM NaCl and 10% glycerol], the cells were ground into powder in liquid nitrogen and lysed in fresh Buffer B . Subsequently, Flag-tagged LD, EFL2 and EFL4 were purified by using ANTI-FLAG M2 Affinity Agarose Gel . The pAT424 plasmid (NBRP ID: BYP7586) and the yeast strain YPH499 (NBRP ID: BY21467) were provided by Yeast Genetic Resource Center/National BioResource Project (NBRP), Japan. Primers used for cloning the constructs are listed in The cDNA sequences encoding full-length EFL2, EFL4, FLD and SDG26 as well as the N-terminal (1–240 aa) and the C-terminal (320–492 aa) of SDG26 were cloned into n YPH499 . Similar® 4B at 4°C with gentle rotation for 1 h. After the Glutathione Sepharose® 4B was washed six times with 1 ml of pull-down buffer at 4°C, proteins bound on the beads were eluted with 200 μl of elution buffer  at 4°C for 30 min. Finally, the eluted and input samples were boiled with SDS loading buffer and separated on SDS-PAGE gels for western blotting with Flag antibody  and GST antibody  or Myc antibody .The purified Flag-tagged protein was mixed with the purified GST-tagged protein (or Myc-tagged APRF1) in 1 ml of pull-down buffer . A 100-μl volume of the mixture was used as input, and the remaining mixture was incubated with 40 μl of Glutathione Sepharose2, 1 mM DTT, 0.5% (w/v) bovine serum albumin and 5% (w/v) glycerol] at 25°C for 30 min. Then, the binding reaction mixture was loaded onto a 4% nondenaturing polyacrylamide gel at 80 V for 3 h, and the bound DNA signals were visualized by ethidium bromide staining. The synthesized oligos are listed in The double-stranded DNA probe was generated by PCR using synthesized oligos. For electrophoretic mobility shift assay (EMSA), 1 μg of each purified protein was incubated with the DNA probe in the binding buffer | ≥ 1 and P-value <0.01 using edgeR  from 10-day-old seedlings of (v2.1.0) . The rea(v2.1.0) . The dif2, 25% glycerol, 250 mM sucrose, 5 mM DTT, 1% protease inhibitor cocktail tablet (Roche) and 1 mM PMSF] and was then subjected to cross-linking with 1% formaldehyde . The sample was cross-linked for 20 min, and a concentration of 0.125 M glycine was added to stop the reaction. The mixture was then passed through two layers of Miracloth and centrifuged at 1500 × g for 20 min. The nuclei were washed in nuclear resuspension buffer  and then sonicated in sonication buffer  for 37 cycles  by Bioruptor . The sheared chromatin was diluted 1-fold with dilution buffer  and incubated with H3Ac antibody , H3K4me3 antibody , H3K27me3 antibody  and H3K36me3 antibody  coupled with Dynabeads Protein A  at 4°C overnight. The beads were washed five times with wash buffer  and then with TE buffer [10 mM Tris–HCl (pH 8.0) and 1 mM EDTA (pH 8.0)]. The bead-bound DNA–protein complexes were eluted from the beads and reverse cross-linked with NaCl and Proteinase K . The chromatin DNA was extracted using phenol/chloroform/isoamyl alcohol and was then subjected to library construction.The enrichment of the histone modifications, including H3Ac, H3K4me2, H3K4me3, H3K36me3 and H3K27me3, on chromatin was detected by chromatin immunoprecipitation combined with sequencing (ChIP-seq), as described previously . For ChI2, 0.1 mM PMSF, 1 mM DTT and 1% proteinase inhibitor cocktail tablets (Roche)] supplemented with 1% formaldehyde (Sigma). The materials were cross-linked for 12 min under vacuum conditions, and 0.125 M glycine was added to stop the reaction. Cross-linked samples were ground to a powder in liquid nitrogen, suspended in 20 ml of Buffer I and then subjected to rotation at 4°C for 20 min. The mixture was then passed through two layers of Miracloth and centrifuged at 1500 × g for 20 min. The nuclei pellet was washed with Buffer II . The washed pellet was resuspended in 1.2 ml of Buffer III , and then centrifuged at 13 000 × g at 4°C for 1 h. The nuclei suspended in sonication buffer  were sonicated for 21 cycles  by Bioruptor . Sonicated samples were centrifuged at 13 000 × g and diluted 2.3-fold with dilution buffer , and then incubated with GFP-binding protein coupled with agarose beads  overnight. Beads were sequentially washed with low-salt buffer , high-salt buffer , LiCl buffer  and TE buffer [1 mM EDTA (pH 8.0) and 10 mM Tris–HCl (pH 8.0)]. The beads were eluted with elution buffer (1% SDS and 0.1 M NaHCO3) at 65°C for 15 min. DNA was extracted from the eluted sample with the phenol/chloroform/isoamyl (25:24:1) reagent. Purified DNA was subjected to qPCR.ChIP for ELF2-GFP, APRF1-GFP, SDG26-GFP and SDG26-truncated-GFP was carried out as previously described , with mi® Ultra™ DNA Library Prep Kit for Illumina®  and were sequenced by Illumina-NovaSeq (sequencing method: PE150). For data analysis, the clean reads were aligned to the Arabidopsis genome (TAIR10) using Bowtie2 (v2.2.6)  at peaks, and the read counts were normalized to RPKM by the number of clean reads mapped to the genome in each library. When the log2FC value is 0.585, the FC is 1.5. Therefore, |log2FC| ≥ 0.585 was used as the standard to identify differentially expressed peaks. The boxplots and bar plots were drawn with R package ggplot2. Heatmaps were drawn with R package gplots.ChIP-seq libraries were constructed by Novogene  using the NEBNext(v2.2.6) . SICER , and APRF1 were all co-precipitated with each other  in the fld, ld, efl2 efl4 and sdg26 aprf1 mutants, respectively  were used, the downregulation of FT  and SOC1  was evident when the thresholds were slightly reduced   Figure . TherefoFLC represses flowering by preventing the transcription of FT and SOC1   of SDG26 bound to the double-stranded DNA from the FLC sequence (FLC-2), whereas EFL2, FLD and LD did not bind to the DNA sequence  . We geney Figure , suggeste Figure  and C. Ae Figure , the C-te Figure , a majore Figure , suggesti Figure , indicatArabidopsis plants and assessed the effect of the truncation on the association of SDG26 with FLC chromatin by ChIP-PCR. We found that the truncation significantly reduced the association of SDG26 with FLC chromatin  in the enrichment of histone modifications in the AuPC mutants. As expected, the changes in histone modifications were highly similar among the different AuPC mutants  associated with transcriptional activation were increased, and the histone modification (H3K27me3) associated with transcriptional repression was reduced in the AuPC mutants .gkac551_Supplemental_FilesClick here for additional data file.


MUL1 is a multifunctional E3 ubiquitin ligase that is involved in various pathophysiological processes including apoptosis, mitophagy, mitochondrial dynamics, and innate immune response. We uncovered a new function for MUL1 in the regulation of mitochondrial metabolism. We characterized the metabolic phenotype of MUL1(−/−) cells using metabolomic, lipidomic, gene expression profiling, metabolic flux, and mitochondrial respiration analyses. In addition, the mechanism by which MUL1 regulates metabolism was investigated, and the transcription factor HIF-1α, as well as the serine/threonine kinase Akt2, were identified as the mediators of the MUL1 function. MUL1 ligase, through K48-specific polyubiquitination, regulates both Akt2 and HIF-1α protein level, and the absence of MUL1 leads to the accumulation and activation of both substrates. We used specific chemical inhibitors and activators of HIF-1α and Akt2 proteins, as well as Akt2(−/−) cells, to investigate the individual contribution of HIF-1α and Akt2 proteins to the MUL1-specific phenotype. This study describes a new function of MUL1 in the regulation of mitochondrial metabolism and reveals how its downregulation/inactivation can affect mitochondrial respiration and cause a shift to a new metabolic and lipidomic state. Mitochohypoxia) . The reghypoxia) . MUL1 (ahypoxia) . MUL1 ishypoxia) . We havehypoxia) . In addihypoxia) . Here, wl-glutamine, 50 units/ml penicillin, and 50 μg/ml streptomycin . HeLa cells were maintained in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum , 50 units/ml penicillin, and 50 μg/ml streptomycin. Chemicals: perifosine, chetomin, DMOG, oligomycin, FCCP, antimycin A, and rotenone (SIGMA) were dissolved in DMSO, stored at -80°C, and used at the indicated concentrations. XF DMEM media pH 7.4, glucose, pyruvate, and glutamine solutions were obtained from Agilent Technologies. DMSO (0.1%) was used as a vehicle control.HEK293 wild type (WT), HEK293 MUL1(−/−), and HEK293 Akt2(−/−) cells were grown using Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal calf serum , 2 mM , chetomin, or DMOG were lysed using a Triton X-100-based lysis buffer , 2 mM EDTA) in the presence of protease inhibitors (Thermo Fisher Scientific). Approximately 40 μg of whole cell extract was resuspended in the SDS sample buffer, boiled for 4 min, and analyzed by SDS-PAGE (12% or 15% gels) and then transferred onto PVDF membranes (Genesee) using a semi-dry cell transfer blot (Bio-Rad). Nonfat dry milk (4%) or 5% BSA (for Akt2 and phospho-specific antibodies) in TBST buffer  was used to block the nonspecific binding of the membrane. The membranes were incubated with the indicated primary antibodies: HIF-1α , AKT (pan), Akt1, Akt2, Akt3, phospho-Akt-T308, phospho-GSK-3β-S9, ULK1 , GLUT1 , MFN2, and β-actin . MUL1 and UBXN7 rabbit polyclonal antibodies were homegrown and used at a 1:5,000 dilution. Secondary peroxidase-conjugated goat anti-rabbit or goat anti-mouse antibodies (Jackson ImmunoResearch) were used at a 1:10,000 dilution. The membrane was visualized by enhanced chemiluminescence (ECL) (Thermo Fisher Scientific).Control (untreated) and cells treated with perifosineAkt2 expression, the target sequence 5′–GACCCCATGGACTACAAGTG–3′ located in exon 4 was selected using the CRISPOR program  and cloned into the pSpCas9(BB)-2A-GFP (PX458) vector (Addgene), as previously described  or the empty PX458 control vector was transfected into HEK293 or HeLa cells, respectively. Forty-eight hours later, single GFP-positive cells were sorted into 96-well plates using a FACS ARIA II sorter (BD Biosciences). The clones were expanded, and Akt2 or MUL1 protein expression was monitored by the western blot analysis. In addition, genomic DNA was isolated and used for PCR amplification and DNA sequencing to verify the deletion of the target sequence surrounding exon 4 of the Akt2 gene or exon 1 of the Mul1 gene. Three independent HEK293 Akt2(−/−) and three HeLa MUL1(−/−) clones, as well as two HEK293 or HeLa WT control clones (transfected with a PX458 empty vector), were chosen for further experiments. The HEK293 MUL1(−/−) cells have been previously described (To ablate escribed . To knocescribed .2-free incubator. Extracellular acidification rate (ECAR) was measured under basal conditions and again after sequential injections of glucose (10 mM) in port A, ATP synthase inhibitor oligomycin (1.5 μM) in port B, and the glycolysis inhibitor 2-deoxyglucose (2-DG) (300 mM) in port C. Basal glycolysis was assessed by recording three measurements, following the addition of each compound by a 3-2-3 mix/measurement cycle. Glycolysis is the ECAR after the addition of glucose. Glycolytic capacity is the increase in ECAR after the injection of oligomycin following glucose. Glycolytic reserve is the difference between the glycolytic capacity and glycolysis. Data analysis was performed using Report Generator software for glycolysis stress test (Agilent Technologies).To monitor glycolysis in the cell lines, the glycolysis stress test was performed using an XFe24 extracellular flux analyzer (Agilent Technologies). HEK293 WT, MUL1(−/−), and Akt2(−/−) cells were seeded in triplicate on poly-lysine D-coated XFe24 microplates at a density of 60,000 cells per well in the assay medium (XF DMEM pH 7.4 containing 2 mM glutamine and 1 mM sodium pyruvate without glucose) for 1 h in a CO2-free incubator for 60 min. Three baseline measurements were recorded before the injection of the following compounds: 1.5 μM of oligomycin in port A, 1.0 μM of FCCP in port B, and 0.5 μM rotenone/antimycin in port C. Data analysis was performed using Cell Mito Stress Test Report Generator software (Agilent Technologies).Mitochondrial stress assay was performed using an XFe24 extracellular flux analyzer (Agilent Technologies) following the workflow provided by the manufacturer’s instructions. Briefly, for oxygen consumption rate (OCR) measurements, HEK293 WT and MUL1(−/−) cells were seeded in triplicate on poly-lysine D-coated XF24 microplates at a density of approximately 60,000 cells per well in the assay medium , followed by incubation at 37°C in a CO6 HEK293 WT, MUL1(−/−), or Akt2(−/−) cells were grown to confluency following treatment with perifosine or chetomin inhibitors, as well as with the HIF-1α activator DMOG, and DMSO was used as a vehicle control. A freshly prepared [U-13C]glucose tracer containing medium  was added to the cells for 6 h at 37°C. Cells were scraped from the plates, washed twice with cold PBS, and immediately snap-frozen in liquid nitrogen  for NMR analysis. The final volume of the sample (50 μl) consisted of 90% (v/v) deuterated 50 mM sodium phosphate buffer (pH 7), with 2 mM ethylene diamine tetra-acetic acid (EDTA), whereas 10% (v/v) was occupied by 5 mM D6-4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) and 0.2% sodium azide (NaN3) in D2O  and centrifuged at 10,000 × g for 15 min at 4°C, and the supernatant was removed and dried in a speedvac. The dried cell extracts of each sample were resuspended in 0.5 ml of acetonitrile:water  solution, vortexed, and centrifuged for 15 min at 10,000 × g at 4°C. The supernatant was transferred to a new tube and dried in a speedvac. The dried extract of each cell pellet was re-suspended in phosphate buffer prepared in deuterium oxide (D) in D2O .1H-NMR spectra were collected on an 800 MHz NMR, equipped with a 5 mm TXI CryoProbe and an Avance III console (Bruker Biospin), using TopSpin software (version 3.6.3). The 1H-NMR spectra were acquired using a noesypr1d pulse sequence consisting of a 1 s relaxation delay (d1) and a mixing time of 100 ms. A 4 s acquisition time (AQ) over a spectral width (sw) of 12 ppm gave a final time-to-repeat of 5.1 s. A total of 64 scans were acquired for each spectrum. The conventional 1H-decoupled 13C-NMR spectra were acquired for each sample at 150.13 MHz using a13C-optimized 1.5 mm high-temperature superconducting (HTS) probe on an Agilent NMR system with a magnetic field strength of 14 T. 13C-NMR spectra were recorded using an acquisition time (AQ) of 1.5 s, a relaxation delay (d1) of 1.5 s, a flip angle of 45°, and an acquired size (TD) of 54 k with a spectral width of 250 ppm . 1H-NMR spectra were Fourier-transformed (FT) with a line-broadening factor of 0.5 Hz, zero filling to 65,536 data points, and baseline correction with the spline method. 13C-NMR spectra were processed with the following processing parameters: zero filling to 128k data points, an exponential line broadening of 0.5 Hz, manual phase correction, and Whittaker smoother method for baseline correction. 250 ppm . All NMR13C-isotopomer distribution pattern of glutamate was determined by 13C-NMR. 13C-NMR resonances of various isotopomers of glutamate are identified from the 13C–13C J-coupling constants and quantified by line fitting to the glutamate resonances at C2, C3, C4, and C5 positions. The 13C-labeling was described first by the position of the 13C label and then by the multiplicity of the resonance and its origin. For example, C2S would denote the isotopomer of glutamate labeled only at the C2 position. The descriptor C2D12 describes the resonance of the C2 position that is split into a doublet (D) from coupling to an adjacent 13C label at the C1 position. The descriptor C2D23 denotes the resonance of the C2 position that is split into a doublet (D) from coupling to an adjacent 13C label at the C3 position. When a13C-labeled C2 is flanked by two other neighboring 13C-labeled positions (C1 and C3), the resonance is split twice by the J-coupling into a doublet of doublets or a quartet (Q), denoted C2Q. Similarly, other carbon resonances of glutamate were assigned, and peak areas were extracted for each of the isotopomers.The positional 13C-isotopomers of glutamate observed at carbon positions 2, 3, 4, and 5 in the 13C-NMR spectra of each cell sample. The tcaCALC program in MATLAB was utilized to perform an isotopomer analysis to estimate relative pathway fluxes , pyruvate carboxylase (YPC), pyruvate kinase (PK), and anaplerosis leading to succinyl-CoA (Ys). All flux rates are referenced to a citrate synthase (CS) flux of 1 and is equivalent to Kreb’s cycle flux. The input file contains the peak area ratios with initial parameters for PDH, PK, YPC, and Ys fluxes of 0.2, 03, 0.1, and 0.2, respectively.Metabolic flux relative to Krebs cycle turnover was calculated from the y fluxes . The metCell pellets of HEK293 WT or MUL1(−/−) cells were subjected to the Folch extraction procedure, which resulted in two phases, i.e., aqueous phase and chloroform phase, containing polar metabolites and non-polar lipids, respectively. The aqueous phase was analyzed using the Thermo Q-Exactive Orbitrap mass spectrometer with a Dionex UHPLC and an autosampler. All samples were analyzed in positive and negative heated electrospray ionization (HESI) with a mass resolution of 35,000 at m/z 200 as separate injections. Separation was achieved on an ACE 18-pfp 100 × 2.1 mm, 2 µm column with mobile phase A as 0.1% formic acid in water and mobile phase B was acetonitrile. The flow rate was 350 μl/min with a column temperature of 25°C. A 4 µl sample solution was injected for negative and 2 µl for positive ion mode LC-MS analysis. MZmine software was used to identify features and deisotopes, align features, and perform gap filling. The metabolomics data were searched against the SECIM’s internal retention time metabolite library. LC-MS analysis for lipidomics was performed on the chloroform phase of the Folch extracted cell samples, utilizing a Thermo Q-Exactive Orbitrap mass spectrometer with a Dionex UHPLC and an autosampler. All samples were analyzed in the HESI source with a mass resolution of 35,000 at m/z 200. LC separation was achieved on an Acquity BEH C18 1.7 µm, 100 × 2.1 mm column. The mobile phase A (60:40 acetonitrile:10 mM ammonium formate with 0.1% formic acid water) and mobile phase B (90:8:2 2-propanol: acetonitrile: 10 mM ammonium formate with 0.1% formic acid in water) were used for elution of the lipids. The flow rate was 500 μl/min, and the column temperature was maintained at 50°C. Lipidomics data were analyzed using LipidMatch software .https://www.metaboanalyst.ca) was used for the metabolomic data analysis, interpretation, and integration of the joint metabolic pathway analysis of genomics and metabolomic data. For multivariate statistical analysis, LC-MS data from positive and negative ion modes were imported into MetaboAnalyst software and subjected to normalization by the sum of the intensities, data transformation via log transformation, and pareto scaling. Joint metabolic pathway analysis was performed with gene and metabolite using uniport protein ID and HMDB ID, respectively. The fold change for MUL1(−/−) compared to that for WT genes and metabolites was used as an input to perform the joint pathway analysis. Metabolic pathways containing both metabolites and genes were used for the integrative pathway analysis along with Fisher’s exact test for the enrichment analysis. Simultaneously, an interactive network of pathway analysis, showing the connections between pathways and an individual pathway, was carried out, using significantly different metabolites between HEK293 WT and MUL1(−/−) cells using results from both positive and negative mode ionization. A quantitative metabolomic panel of significantly different metabolites between HEK293 WT and MUL1(−/−) cells was created, employing statistical analysis via t-test in MetaboAnalyst  was determined, and the data were scaled with the enrichment .High-resolution LC-MS/MS-based lipidomic profiling of lipid classes was carried out using the normalized individual lipid intensity to the total lipid intensity of each sample. The values for each lipid class and the top 10 most abundant lipids in each class are expressed as mean ± SEM (n = 4). Lion term enrichment analysis of HEK293 WT versus MUL1(−/−) cells in the ranking mode was carried out by lipid ontology (LION): a web-based interface used for the identification of lipid-associated terms in lipidomes . The cutn = 3 per group) were used for library preparation, DNA sequencing, and data analysis . Using DESeq2, a comparison of gene expression between the defined groups of samples was performed. The Wald test was used to generate p values and log2 fold changes. Genes with an adjusted p value <0.05 (p < 0.05) and absolute log2 fold change >1 were labeled as differentially expressed genes. Significantly differentially expressed genes were clustered by their gene ontology, and the enrichment of gene ontology terms was tested using the Fisher exact test. Heatmaps of the top 50, as well as genes specifically involved in lipid, glucose, carbohydrate, glutathione, and retinoic acid metabolomic processes, were analyzed using RStudio and Heatmap.2 software (Standard RNA sequencing for the gene profiling expression of protein-coding sequences (mRNA) was performed using HEK293 WT and MUL1(−/−) cells. Approximately 10 million HEK293 WT and MUL1(−/−) cells  (Agilent Technologies). For data analysis , a value of p ≤ 0.05 was considered significant.All quantitative data were expressed as mean ± SD or ±SEM of three or four independent experiments. Following the western blot analysis, the optical densities of blot bands were determined using ImageJ software. Protein/β-actin ratios were obtained from the densitometry data, and the differences among groups were analyzed by one-tailed Student’s MUL1-mediated K48 polyubiquitination invariably targets its substrates for proteasomal degradation and therefore regulates their protein level. There are four such known substrates: MFN2, ULK1, AKT, and the UBXN7 protein that controls the protein level of HIF-1α  Li et a. Figure e24 analyzer. Glycolysis, glycolytic capacity, and glycolytic reserve in HEK293 WT and MUL1(−/−) cells were monitored. The individual involvement of HIF-1α and/or Akt2 proteins, in this process, was investigated using a specific HIF-1α inhibitor, chetomin, or an Akt2 inhibitor, perifosine  treated with Akt2 or HIF-1α inhibitors and WT cells were labeled using a [U-13C]glucose tracer in the cell culture medium. After multiple turns of the TCA cycle, all carbon positions of the glutamate were labeled, resulting in a complex but well-defined pattern of 13C-NMR spectra , MUL1(−/−) + perifosine, and MUL1(−/−) + chetomin are summarized in 13C-isotope labeling patterns of the metabolites derived from the [U-13C]glucose metabolism after a single turn of the cycle [U-13C]glucose-derived [U-13C]pyruvate produces lactate via LDH and can enter the tricarboxylic acid (TCA) cycle through YPC (red arrow) and PDH flux (green arrow). In the first turn of the TCA cycle, the [U-13C]pyruvate oxidation through PDH produces labeled glutamate C4–C5 (green dots) positions, whereas YPC flux labels glutamate C2–C3 (red dots). Pyruvate cycling via phosphoenolpyruvate carboxykinase (PEPCK) and PK fluxes is also shown in 13C2] and lactate isotopomers is only possible via this pathway , and anaplerosis leading to succinyl-CoA (Ys) were estimated using glutamate 13C-isotopomer distribution data obtained from 13C-NMR analysis of the cell extracts. All flux rates are referenced to a citrate synthase (CS) flux of 1 and is equivalent to Kreb’s cycle flux. Using tcaCALC, multiple different metabolic models can be compared to each other, with the most parsimonious model selected  cells compared to untreated cells  and Akt2(−/−) + DMOG cells are summarized in 13C-labeling pattern in glutamate for C2D12 and C2D23 multiplets was significantly different between HEK293 Akt2(−/−) and Akt2(−/−) + DMOG cells  levels (s) was significantly lower in both Akt2(−/−) and Akt2(−/−) + DMOG cells compared to that of WT and MUL1(−/−) cells (We created HEK293 Akt2(−/−) cells using CRISPR-Cas9 (see Methods) in order to investigate the role of HIF-1α in glycolysis in the absence of the Akt2 protein. ECAR was measured for both HEK293 Akt2(−/−) and WT cells in the presence or absence of dimethyloxalyglycine (DMOG), a known activator of HIF-1α . HEK293 OG cells . MetabolS fluxes . The met) levels . Anapler−) cells . All fluR2 and Q2 parameter values were calculated for the PLS-DA model. The R2/Q2 values for the LC-MS positive and negative ion mode data were 0.98/0.70 and 0.90/0.30, respectively  and WT cells. The unsupervised principal component analysis (PCA) 2D score plots showed a clear separation in the metabolic profile between HEK293 WT and MUL1(−/−) cells in the LC-MS positive ion mode , whereasectively . The PLSectively . Heatmapectively . SignifiGenome-wide RNA sequencing data comparing HEK293 WT and MUL1(−/−) gene expression showed that 781 genes were downregulated, whereas 822 genes were upregulated in MUL1(−/−) cells . The heaTo monitor changes in the lipid profile, LC-MS analysis was performed on HEK293 WT and MUL1(−/−) cells. The profiling of various lipid classes such as triglycerides (TGs), diacylglycerols (DGs), phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), and ceramides shows strong changes in lipid metabolism enforced by the MUL1 knockout. The lipidomic profiling data demonstrated that the TGs and DGs are significantly higher in HEK293 MUL1(−/−) cells but there was no notable difference found for PCs, PEs, and ceramides . To provPC and PK flux in MUL1(−/−) cells. These metabolic changes can be reversed by specific Akt2 or HIF-1α inhibitors. Furthermore, our data indicate that the metabolic phenotype of MUL1(−/−) cells is distinct from the glycolytic state observed in cancer cells (Warburg effect), where increased pyruvate dehydrogenase (PDH) and lactate dehydrogenase (LDH) switch OXPHOS to glycolysis , such as hypoxia . Metabolycolysis . We perfOur multifaceted analysis shows that MUL1 is involved in the regulation of metabolism and its inactivation/downregulation can lead to a distinct metabolic state not previously described. The MUL1 protein level is known to be regulated, but the mechanism(s) is not fully characterized. Our previous studies identified Omi/HtrA2, a serine protease located in the mitochondrial intermembrane space (IMS), as a major regulator of the MUL1 protein level . Omi/HtrBesides the involvement of Akt2 and HIF-1α in the regulation of metabolism by MUL1, the participation of other MUL1-substrate proteins, such as the MFN2 and ULK1 proteins, cannot be excluded. Accumulation of MFN2 and ULK1 is also observed in MUL1(−/−) cells. In addition, MFN2 has been implicated in the regulation of metabolism in cancer cells and shown to interact with PKM2 . ULK1 plWe identified a new function for the mitochondrial MUL1 E3 ubiquitin ligase in the regulation of metabolism. The mechanism involves the K48-polyubiquitinating function of the ligase and the coregulation of Akt2 and HIF-1α proteins. The accumulation and co-activation of Akt2 and HIF-1α in MUL1(−/−) cells drive and maintain this new metabolic phenotype characterized by the activated pyruvate carboxylation and PK flux, along with increased aerobic glycolysis. In addition, MUL1(−/−) cells have a distinct lipid metabolism characterized by increased triglyceride and diacylglycerol storage. These results support a very important role for MUL1 ligase in the regulation of mitochondrial metabolism and lipogenesis including a new metabolic state of aerobic glycolysis.