Constitution of biobank of human tissues requires careful handling and storage of biological material, to guarantee the quality of samples. Tissue preparation is also critical for further applications such as transcriptomic profiling. In this study, our aim was to evaluate the impact of different disruption techniques  and homogenizing buffers (RLT versus QIAzol) on RNA purity and quality of metabolic tissues  present in the COMET Biobank. For all homogenization methods used and tissue types, the A260/280 ratios reached values ≥ 1.8, which are in the range of what is found in human tissues and cell lines, while the A260/230 ratios were however ≤ 1.8, with the lowest value obtained with GentleMACS Dissociator. In addition, GentleMACS Dissociator combined with QIAzol reagent gave the highest RIN value and 28S/18S ratio for all tissues tested, except for muscle. Performing RT-qPCR, Ct values for different housekeeping genes can be influenced by extraction methods and RNA quality of samples. In conclusion, we have demonstrated that different disruption techniques and homogenizing buffers impact the purity and some quality markers of RNA, and can also impact quantification of mRNAs by RT-qPCR in human metabolic tissues. Biobanks contain large sample diversity , collected from healthy donors and patients with pathologies of interest. The COMET biobank is a biorepository of metabolic tissues collected in morbidly obese patients with different metabolic status (https://cometbiobank.com) and is transferable to the scientific community for research in the field of type 2 diabetes (T2D) and associated comorbidities. The biobank contains a variety of tissues including blood derivatives, adipose tissue, liver and skeletal muscle.With the development of personalized medicine, biobank facilities have clearly increased over the past decades, to meet to the need of therapeutic innovations in human diseases such as the discovery of new drugs or biomarkers1. Nonetheless, among methods aimed at studying nucleic acids, transcriptomic analyses are the most critical techniques since RNA can be degraded by RNases during tissue processing and nucleic acid extraction. During tissue preparation, ex vivo warm ischemia, which refers to the period after surgical extirpation of the biopsy, can influence RNA integrity and possibly, gene expression analysis. Indeed, using transcriptomic analysis, it was found that gene expression profiling can be influenced in certain tissues by ischemia time and temperature3. Concerning the delay between tissue removal and snap-freezing, Song et al. found that RNA integrity was unaltered when exposed to ischemia for 1 h4, whereas other studies found that RNA quality was stable up to 180 min5. Current biobanking practice suggests freezing samples within 30 min, although no consensus arises from the literature, as some authors found that RNA remained intact up to 16 h after resection6, while others found no correlation between the duration of ischemia and RNA quality7. Regarding the temperature at which the tissue is sampled, RNA quality appears more preserved when the tissue is maintained on ice (“cold-ischemia”) rather than at room temperature or in saline9. Despite this, some authors found no influence of temperature6. Moreover, RNA stability can vary between tissue types, pathology, and localization, with some tissue being more sensitive than others, such as the liver10.The constitution of a biocollection requires careful handling and storage of biological material, to guarantee samples quality for further applications. Among these applications, molecular profiling using –omics approaches such as genomics, transcriptomics, proteomics and metabolomics, are widely used to generate large-scale data that can be correlated with patients’ clinical features11. In this study, we have selected three mechanical methods that have shown a high efficiency to disrupt a wide range of cells/tissues to compare their performances across 4 selected metabolic tissues: subcutaneous (SCAT) and visceral (VAT) adipose tissues, which are soft tissues with high lipid content; liver, which is a highly vascularized and relatively fibrous tissue; and skeletal muscle, which is a highly fibrous and hard tissue.Sample preparation using disruption methods for nucleic acid extraction can also impact RNA quality. Several methods for tissue homogenization can be used, according to the density of the tissue and its intrinsic content of RNases. While non-mechanical methods  are more suitable for single cell homogenization, tissue disruption often requires mechanical techniques, such as high-pressure homogenizer, tissue grinder, bead beating-based homogenization, or rotor/stator-based homogenization11. The rotor/stator-based system (GentleMACS dissociator) is based on a rotating paddle which is combined to a fixed stator with a narrow gap between the stator teeth and the rotor to allow tissue dissociation. Another available method is based on forcing the tissue to pass through a narrow gauge needle using a syringe barrel and plunger, which could be an alternative method in the absence of the aforementioned dedicated equipment.The bead beating method (FastPrep-24 instrument) uses tiny beads that are agitated and accelerated against the tissue, thus creating shear forces that disrupt tissues and release intracellular componentsMoreover, homogenizing reagents can also impact the quality and quantity of RNA recovered. Commonly used buffers often contain guanidine (iso)thiocyanate combined with phenol (QIAzol or TRIzol buffer) or β-mercaptoethanol (Qiagen RLT buffer), to allow both cell lysis and RNase inhibition. In this study, we compared two homogenization buffers. For muscle and liver, we used RLT buffer which contains a high concentration of guanidine isothiocyanate, a chaotropic salt which allows cell lysis and binding of RNA to the silica membrane. β-Mercaptoethanol is added to RLT buffer to increase inhibition of RNases. For muscle, a step of protein digestion by proteinase K is included in the extraction kit. For adipose tissues and liver, we used QIAzol reagent, a lysis buffer composed of phenol and guanidine thiocyanate to permit chaotropic disruption of cells and organic extraction of intracellular components. This reagent is recommended for the lysis of fatty tissues but can be applied to all types of tissues.In the present study, our aim is to evaluate several homogenization methods using different disruption techniques and homogenizing buffers in order to optimize extraction of total RNA for the different metabolic tissues present in COMET Biobank. The influence of these methods on the purity and quality of RNA and subsequent mRNA quantification of housekeeping genes will also be investigated.Using FastPrep-24 Instrument, the number of cycles was determined visually to allow complete dissociation of the tissue. Two to four cycles of 30–40 s separated by incubation on ice were necessary to dissociate the tissues , regardless of the homogenization buffer used. With GentleMACS Dissociator, a single cycle of a predefined program allowed the complete dissociation of all tissues, independent of the buffer used. Alternatively, a complete dissociation of all tissues tested was achieved after several passages through the needle and syringe, except for skeletal muscle.P = 0.02, Table P < 0.05) or GentleMACS Dissociator (P = 0.06). The type of the tissue also impacted RNA levels  and muscle (P = 0.06).Despite equivalent quantity of tissue sampled in each cryotube, the amount of total RNA extracted (141 ± 27 ng/mL) was highly variable from one tissue biopsy to another . Values were 1.93 ± 0.02 with FastPrep-24 Instrument, 1.91 ± 0.04 with GentleMACS Dissociator and 1.98 ± 0.02 with syringe/needle. There was also differences across tissues (P = 0.07). More specifically, the ratio was higher in liver tissue compared to SCAT (P < 0.05), which may reflect a difference in RNA nucleotide composition between tissue types12.According to Nanodrop technical bulletinP = 0.43, Table P = 0.001) with a significant interaction with tissue type (adipose vs non adipose tissues) (P = 0.003). Indeed, the mean A260/230 ratio was significantly lower with GentleMACS Dissociator (0.71 ± 0.1), than with FastPrep-24 Instrument  or syringe/needle . In non-adipose tissues, i.e., liver, the syringe/needle gave a higher A260/230 ratio than with GentleMACS Dissociator (P < 0.01) or FastPrep-24 Instrument (P < 0.05), which was not the case for adipose tissues.The A260/230 ratio reached an average of 0.98 ± 0.09, suggesting that few residues of extraction buffer  remained in the preparation. The A260/230 ratio was more heterogeneous across samples than the A260/280 ratio  or tissues (P = 0.16). However, excluding skeletal muscle samples, which were not eligible for dissociation with syringe/needle, we found that RIN was affected by tissue (P = 0.02) and disruption method (P = 0.014), as higher RINs were observed with GentleMACS Dissociator as compared to FastPrep-24 Instrument (P < 0.05). For VAT, RIN achieved 8.5 ± 0.3 with GentleMACS Dissociator versus 6.1 ± 0 with FastPrep-24 Instrument (P < 0.01). Concerning SCAT, we observed RIN reaching 8.1 ± 0.3 with GentleMACS Dissociator versus 6.2 ± 0.2 with FastPrep-24 Instrument (P < 0.05) . RIN for liver were lower than the other tissues (P < 0.05 vs VAT or SCAT), suggesting that hepatic tissue is more sensitive to RNA degradation than other tissue types . Concerning muscle, homogenization with RLT buffer and FastPrep-24 Instrument gave equivalent RIN as with QIAzol reagent and GentleMACS Dissociator (P = 0.5), probably because this tissue is more resistant to degradation during homogenization  was not statistically different across disruption methods (P = 0.01) and tissue (P = 0.03). Again, the 28S/18S ratio was greater with GentleMACS Dissociator or syringe/needle, when compared to FastPrep-24 Instrument . In fact, the three techniques provided similar results across tissue types, with the exception of a lower ratio with the FastPrep-24 Instrument in adipose tissues  , confirming that this tissue is less sensitive to degradation , followed by TBP (P < 0.05 vs GAPDH), and finally GAPDH (Table P < 0.001) , with the needle/syringe resulting in higher Cts than other methods. There were higher Cts with QIAzol vs RLT buffer in non-adipose tissues only for GAPDH (P = 0.004), when limiting the analysis to the same disruption method (GentleMACS Dissociator).In all tissues tested, threshold cycles (Cts) were found the lowest for β-actin (P P < 0.0 vs GAPDHP < 0.01) and syringe/needle (P < 0.05). The Ct/RIN ratio was also lower with QIAzol (P = 0.007) in non-adipose tissues, similarly when limiting the analysis to the GentleMACS instrument (P = 0.01). For liver, a significantly lower Ct/RIN ratio could be observed as compared to the three other tissues (P < 0.05), confirming that this tissue is more prone to degradation. In addition, an inverse correlation between Cts and RIN could be observed for β-actin in non-adipose tissues (P = 0.01), but not for other genes and tissues (P > 0.05).We also calculated the Ct/RIN ratio as a summary measure including both indications of Ct for a specific transcript and RIN for the sample from which it is derived. This measure allows to better compare Cts obtained from samples with different RIN , we also evaluated the possibility to immerge the sample in RNAlater, 24 h before freezing. RNAlater, composed of ammonium sulfate and EDTA, is an RNA protector that penetrates the tissue and induces RNases precipitation and inhibition by metal chelation. In addition, tissue stored in RNA later also develop a “hard, rubbery texture, and may be more difficult to homogenize thoroughly than would fresh tissue” . When using FastPrep-24 Instrument, we observed an improvement of RIN for liver (with RLT buffer) or adipose tissues (with QIAzol reagent), without change in RNA yield Fig. .Figure 6Results obtained when controlling the influence of the study design  were virtually identical. Still, because the needle approach was performed on a different set of patients, the conclusions of this method cannot be fully separated from a potential patient effect.In the present study, we have used human biopsies collected in the frame of COMET biobank to evaluate the impact of several homogenization methods on total RNA recovery. We have found that different disruption techniques and homogenizing buffers influence the purity and some quality markers of RNA and can also impact quantification of mRNAs by RT-qPCR.13. Manual disruption by syringe/needle can be a good alternative method which provides RNA with comparable quality to GentleMACS Dissociator, but only in case of soft tissues14 or isolated cells15. As also shown for skin biopsies13, we observe that RNA yields tend to be higher with GentleMACS Dissociator, but no statistical correlation can be found between biopsy weight and RNA levels, i.e., larger biopsies does not necessarily yield to the highest amounts of RNA. This variation can be due to heterogeneity in cellular composition and transcriptional activity in human tissues/organs, with implications on RNA profiles. In human liver biopsies16, numerous cell types can be identified by single cell RNA sequencing, including but not limited to hepatocytes, duct cells, endothelial cells, hepatic stellate cells, Kupffer cells, and immune cells. Moreover, the authors also found a transcriptional heterogeneity between hepatocytes according to their spatial localization along the liver lobule16. Adipose tissues are also heterogeneous in their composition, as they contain adipocytes, preadipocytes, mesenchymal stem cells, vascular cells, and inflammatory cells17. Furthermore, at least three distinct subpopulations of white preadipocytes have been evidenced in mice, with unique gene expression profile18. In skeletal muscles, single nuclear sequencing of different nuclei present in synticial myofibers revealed transcriptomic heterogeneity is not related to fiber type differences19.Our study shows that homogenization methods clearly impact the recovery of RNAs from human tissues. GentleMACS Dissociator combined to QIAzol reagent appears to be the best method to obtain the highest RIN value and 28S/18S ratio for all the tissues tested, except for muscle. As compared to FastPrep-24 Instrument, GentleMACS Dissociator seems to be more efficient to dissociate human tissues in one cycle, avoiding repeated lysis cycles and thus preserving RNA integrity. Such a difference was also evidenced on skin biopsies (which are very difficult to dissociate), where GentleMACS Dissociator is the only system allowing a complete homogenization of human skin samples, as compared to FastPrep-24 Instrument20, suggesting limited protein contamination. The A260/230 ratio is however ≤ 1.8, with the lowest value obtained with GentleMACS Dissociator. This difference is also observed for DNA extraction, where GentleMACS Dissociator leads to inferior an A260/230 ratio when compared to bead beating-based homogenization methods21. A low A260/230 ratio is indicative of the presence of organic compounds such as phenol or guanidine, which is inherent to methods using chaotropic lysis buffers12. Nonetheless, guanidine thiocyanate contamination was shown not to interfere with downstream molecular applications, up to 100 mM in RNA samples22. Indeed, applications such as cDNA synthesis, in vitro transcription20, sequencing23, or microarrays24 were not affected by low A260/230 ratios.For all homogenization methods used and tissue types, the A260/280 ratio reaches values ≥ 1.8, which are in the range of what is found in human tissues and cell lines20. Nonetheless, for RNA extracted from adipose tissues with FastPrep-24 Instrument, the 28S/18S ratio indicated the absence of the 28S rRNA band, preventing the calculation of a RIN value, which may correspond to a mechanical breaking of 28S rRNA. Indeed, this rRNA is more susceptible to be affected by enzymatic degradation, mechanical shearing, or freezing procedure than 18S rRNA, as a result of its important size and the presence of “hidden break” in its polynucleotide chain26. In addition, we observed that RINs were lower in liver than in other tissues, probably due to the fact that hepatic tissue contains high levels of RNAses10.To assess RNA quality, we used the RIN value, now preferred to the 28S/18S ratio that shows a high variability for RNA extracted from human samples, which is not necessarily related to poor RNA quality27. In our experiments, we observe a certain degree of variability between patients’ samples, suggesting an impact of their pathophysiological conditions on RNA profile. However, we find some housekeeping genes suitable for normalization of qPCR experiments such as β-actin in adipose tissues and muscle or TBP in liver. Nonetheless, when using human samples, variability between patients can only be minimized, as shown by Kim et al.28. The authors observed that no housekeeping gene was found to vary by less than twofold in liver diseases such as cirrhosis or hepatocellular carcinoma.RT-qPCR is another method used to evaluate RNA integrity through analysis of mRNA expression of target genes. To avoid underestimation of RNA fragmentation/degradation, we used oligo(dT) primers for reverse transcription, rather than random primers, as previously suggested29. In addition, we cannot exclude that RNA isolation methods differentially influence the recovery of some mRNA species, as previously suggested in studies comparing phenol extraction or RNA isolation kits31. For adipose tissue, Cts remains unchanged regardless RIN observed. This low RIN may not necessarily reflect low quality mRNA despite the observed degradation of 28S rRNA. This suggests that, in adipose tissues, 28S rRNAs undergo degradation by mechanical breaking rather than degradation by enzymatic activity of RNases, with consequential reduction in RIN.When normalizing Cts according to RIN (the Ct/RIN ratio), QIAzol and GentleMACS Dissociator gave improved results as compared to other buffer and extraction methods. In addition, an inverse correlation between RIN and Cts is found for β-actin in liver and muscle but not for other genes and tissues. To explain such discrepancies, we suggest that the β-actin mRNA is more sensitive to degradation by RNases or has a shorter half-life in these tissues, as it was shown previously in human cell lines26. Concerning human samples, Imbeaud et al.20 found that RNA quality metrics  are predictive of relative gene expression analysis in RNA samples of disparate quality. In another study performed on a lung biobank5, the authors observed that gene expression measurements are more influenced by interpatient differences than other variables. Even for two samples issued from the same individuals, they observed that variability in mRNA expression levels was mostly within the range of a factor of 2.In the literature, the relationship between RIN and subsequent analysis of mRNA expression levels by RT-qPCR is not clear. For instance, a correlation between RIN and apparent expression levels of housekeeping genes is found during the progressive degradation of RNA samples, of unknown tissue origin32. When RIN are lower than 5, they suggest that samples can be also used for RT-qPCR, but only for amplification of small size amplicons32. For genome-wide studies such as microarrays or RNA sequencing, inclusion of samples with highest quality metrics is recommended to avoid experimental bias, as suggested by the literature35.Nonetheless, Kap et al. propose to divide RNA samples into “fit for purpose” groups according to RIN, with samples having RIN above 5 suitable for RT-qPCR6. For COMET biobank, we have chosen direct freezing of tissues to allow several applications to be performed on the same type of sample, such as genomics, proteomics, metabolomics, and histological analysis. However, to avoid tissue damage, we have reduced the time of ex-vivo warm ischemia to a minimum, as the biopsy is immediately collected after removal from the patient. Moreover, we prepared samples at low temperature within a short period of time before snap-freezing (15 or 20 min depending on the tissues). However, using RNA stabilizing reagents have the advantage of avoiding the need of freezing facilities in the clinic surgical block. Nonetheless, the impact of protective reagents is not clear in the literature, as a previous study showed that gene expression analysis is not altered after cold ischemia of 180 min, independently of pre-treatment with RNAlater5. Therefore, RNAlater does not necessarily improve RNA quality, if samples are prepared in a limited period of time before freezing. This data is in accordance with our experiments showing good quality of RNA when using QIAzol/ GentleMACS Dissociator. The effect of RNAlater is more obvious in fat and liver tissues when tissue homogenization is performed with FastPrep-24 Instrument, with both tissue types becoming harder and thus more resistant to the stress induced by mechanical dissociation.We also evaluated an alternative method for sample preparation for biobanking, using a 24 h-incubation in RNA stabilizing reagents like RNAlater before freezingIn conclusion, we have demonstrated that different disruption techniques and homogenizing buffers impact the purity and some quality markers of RNA isolated from metabolic tissues and can also affect mRNA quantification by RT-qPCR. To optimize quality of RNA preparation, we suggest to prepare human samples at low temperature in a short period of time and to implement a gentle homogenization method like GentleMACS Dissociator combined to QIAzol reagent.http://www.clinicaltrials.gov). The study was sponsored by the Montpellier University Hospital in collaboration with University of Montpellier. When included into the clinical trial, patients signed a written informed consent for the collection and storage of tissue samples and associated data.Human tissues were collected in the frame of COMET clinical trial, initiated in 2015. The protocol was performed in compliance with various French regulations, such as the provisions relating to biomedical research, the Public Health Act no. 2004-806, the Data Protection Act and the Bioethics Act. It was also performed in accordance with the Declaration of Helsinki and Good Clinical Practice, after approval by the French National Agency for the Safety of Medicines and Health Products (ANSM) and the Sud Méditerranée I Ethics Committee (registration number NCT02861781 on 2) from 18 to 65 years old undergoing bariatric surgery (mainly sleeve gastrectomy) and stratified in 3 groups according to their metabolic status: normal insulin sensitivity (Homeostatic Model Assessment of Insulin Resistance [HOMA-IR] < 3), insulin resistance without T2D (HOMA-IR ≥ 3), or T2D (according to American Diabetes Association (ADA) criteria12). Insulin-sensitive tissues  and blood sample derivates  were collected in order to constitute the tissue biobank.The protocol has included 270 obese patients  ≥ 35 kg/mFor this study, we have selected 11 female patients, with a mean (± standard deviation) age of 50 ± 10 and BMI of 44.8 ± 5.6, with or without T2D and Non-Alcoholic Steato-Hepatitis (NASH) , managed by Montpellier University Hospital.Samples were stored at the Tissue Bank from the Biological Resource Centre (CRB) (BB-0033-00031) and related data were registered on Tumorotek software , designed for biospecimens management. Traceability data related to biopsy sampling, transport and storage were resumed in a written “patient life-sheet”.All tissue samples were handled following a Standard Operation Procedure (SOP) defined at the beginning of the study. During the surgery, large biopsies of sub-cutaneous and visceral adipose tissues, hepatic and skeletal muscular tissues and epiploic artery (500 mg to 1 g) were collected, immediately sampled on ice into small pieces of 20 (for muscle and liver) or 50 mg (for adipose tissues) and snap-frozen in liquid nitrogen in labelled cryovials within 15 min (for muscle and liver) or 20 min (for adipose tissues). Frozen samples were transferred in a liquid nitrogen dry shipper, the inner wall of which is covered by a porous material that absorbs a certain volume of liquid nitrogen. The container saturates with cold nitrogen gas which allows the maintenance of a stable temperature at − 140 °C, constantly registered by a sensor located in the top of the container. Samples were transported from the surgical block to the Tissue Bank for a long-term storage at − 80 °C, according to the technical protocol (DT-COMET-01), and two procedures .For evaluation, some samples were alternatively pre-treated in the RNA stabilization solution RNAlater (Sigma-Aldrich) before freezing.For bead beating-based homogenization , samples were put in a 2 mL RNase DNase free tube containing 0.5 mL of 0.5 mm silica/zirconium beads (BioSpec—Cat No. 11079105z) and (i) 300 µL of RLT buffer + β-mercaptoethanol (1%) or (ii) 1 mL of QIAzol buffer. Homogenization was performed on FastPrep-24 using 2–4 cycles at 6 m/s for 30 to 40 s with 5 min incubation time on ice between cycles.For rotor/stator-based homogenization , samples were introduced in GentleMACS M tube (Miltenyi Biotec) containing (i) 300µL of RLT buffer + β-mercaptoethanol (1%) or (ii) 1 mL of QIAzol buffer. The tissue was homogenized using the dissociation template RNA 02.01 program.For needle dissociation, trimmed samples were introduced in a 2 mL RNase DNase free tube containing (i) 300µL of RLT buffer + β-mercaptoethanol (1%) or (ii) 1 mL of QIAzol buffer. Homogenization was performed by several back and forth movements through a syringe and needle (18G). Skeletal muscle could not be prepared using this method as it is too compact.After homogenization, the samples were centrifuged for 15 min at 3500 rpm at 4 °C in order to separate the tissue lysate from cellular debris.For each condition tested, 50 mg of sub-cutaneous (SCAT) and visceral (VAT) adipose tissues and 20 mg of liver and muscle were used, for a total of 39 samples. The tissues were disrupted using three alternative technologies in combination with two different homogenizing reagents. A summary of the experimental protocol applied to each tissue and patient is shown in Table RNA extractions were performed using RNeasy mini kit (Qiagen) for hepatic tissue, RNeasy lipid tissue mini kit (Qiagen) for adipose tissues, and RNeasy fibrous tissue mini kit (Qiagen) for muscular tissue, according to the manufacturer’ instructions. At the end of the extraction protocol, RNA was eluted in 30 µL buffer. Nucleic acid concentration, A260/280 and A260/230 ratios were measured using NanoDrop One/OneC Spectrophotometer (ThermoFischer Scientific).23. The RIN number was calculated for the 39 samples subjected to different disruption/homogenization methods. For each tissue type, we used the mean value obtained for samples issued from 3 selected patients , measured by 2100 Bioanalyzer (Agilent Technologies) using RNA 6000 Nano kit, following the manufacturer’s protocol. This automated system is based on electrophoretic separation of RNA, which allows calculating RIN by applying an algorithm on the ratio of 18S/28S ribosomal RNA traces- Reverse Transcriptase (Invitrogen), in a final volume of 20 µL, according to manufacturer’s instructions. Then, 2 µL of 1:5 diluted cDNA was amplified in a final volume of 10 µL by qPCR using LightCycler 480 SybrGreen I Master kit (Roche). We used primers targeting non coding 5′ or 3′UTR regions common in all splice variants described in different tissues , β-actin  or glyceraldehyde-3 phosphate dehydrogenase . For this, 200 ng RNA were reverse transcribed into cDNA in the presence of 1 µg oligo(dT) primers and SuperScript II RNAse HAll data are expressed mean ± SEM, unless otherwise stated. Statistical analyses were performed using GraphPad Prism v5.0  or SAS v9.4 . We analysed the effect of tissue, disruption method and the interaction disruption method × tissue type (adipose or non-adipose tissue) by ANOVA followed by Tukey’s post-hoc tests. For RT-qPCR results, the effect of genes  and the interactions gene × tissue type, gene × disruption method and gene × tissue type × disruption method were added to the model. To check the robustness of results, the effect of disruption method was assessed after exclusion of muscle samples (not submitted to the needle approach). The effect of buffer was assessed in non-adipose tissues only, with or without exclusion of the FastPrep method. The effect of tissue origin across the GentleMACS and QIAzol condition was also analysed. Lastly, mixed models with a random intercept by patient were applied to control for the non-independency of observations . Tests were considered statistically significant at P < 0.05 .Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.


Using the database of the German Cartilage Registry (KnorpelRegister DGOU), this study aims to present patient- and joint-related baseline data in a large cohort of patients with cam-derived femoroacetabular impingement syndrome (FAI) and to detect symptom-determining factors.Requiring cam morphology as the primary pathology, 362 patients were found to be eligible for inclusion in the study. The assessment of preoperative baseline data was performed using the patient-reported outcome measure—International Hip Outcome Tool (iHOT-33). Descriptive statistics were performed to present baseline data. Univariate and multiple regression with post hoc testing were used to identify patient- and joint-related factors that might affect the preoperative iHOT-33 and its subscores, respectively.p < 0.001), female sex (p < 0.001), BMI ≥ 25 kg/m2 (p = 0.002) and in patients with previous surgery on the affected hip (p = 0.022). In contrast, the parameters defect grade and size, labral tears, and symptom duration delivered no significant results.The study collective’s mean age was 36.71 ± 10.89 years, with 246 (68%) of them being male. The preoperative mean iHOT-33 total was 46.31 ± 20.33 with the subsection “sports and recreational activities” presenting the strongest decline (26.49 ± 20.68). The parameters “age,” “sex,” “body mass index” (BMI), and the confirmation of “previous surgery on the affected hip” were identified to statistically affect the preoperative iHOT-33. In fact, a significantly lower mean baseline score was found in patients aged > 40 years . Registered 3 January 2014—retrospectively registered. The registration of data was approved by the local ethics committees of every participating institution. Primary approval was given by the ethics committee at the University of Freiburg (No. 105/13). https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005617 Over the last two decades, cam morphology and the associated femoroacetabular impingement syndrome (FAI) have been identified as a common source of hip pain and premature cartilage damage –4. AlthoThe symptoms commonly described in cases of symptomatic FAI include motion-related or position-related pain in the hip and groin and functional limitations such as a reduced range of motion and even alterations in gait parameters , 15, 16.To reliably evaluate pre- and postoperative hip function in patients with chondral defects due to multiple causes, the iHOT-33 is also used in the German Cartilage Registry (KnorpelRegister DGOU). This multicenter, nationwide register aims at collecting baseline and follow-up data in connection with the treatment of osteochondral defects of the hip, as well as the knee and the ankle joint.Concerning the parameters affecting hip function in patients with symptomatic FAI in general and with cam-derived FAI in particular, studies usually focus on the assessment of therapy outcome. In these studies, several factors, such as age, sex, or body mass index (BMI), that influence treatment results have so far been identified –26. HoweData for the present study were taken from the German Cartilage Registry (KnorpelRegister DGOU). The German Cartilage Registry is a multicenter register with over 70 participating clinics that was set up in 2013 to evaluate the long-term outcomes of different methods of surgical cartilage repair on the hip, knee, and ankle joint. The registry is conducted in accordance with the Declaration of Helsinki and was registered in the German Clinical Trials Register. Approval was received from each ethics committee before the enrollment of the first patients. The participants were generally included prior to surgery with informed consent given by every patient. Data collection was performed via web-based input.Up to August 2019, the registry’s subsection “hip” contained data from 1461 patients that underwent hip surgery, particularly for reasons of cartilage damage. To solely assess patients with cam-derived FAI, only patients with documentation of isolated cam morphology that was declared causal of cartilage damage were included. Patients with record of other or multiple hip pathologies were excluded. With regard to FAI in particular, cases of isolated Pincer- or combined Cam-/Pincer morphology were excluded as well, though, in cases of isolated cam morphology, cartilage damage and labral tears were judged as cam-derived and consequently kept for evaluation. Further exclusion was made on the basis of missing documentation of the cartilage status. Consequently, 551 patients were generally considered eligible for inclusion. However, as several entries presented with incomplete datasets, these were excluded as well, ultimately resulting in 362 patients to form the final study group. Detailed information on the inclusion/exclusion procedure is provided in Fig. n = 259) or arthroscopically assisted (n = 94). Overall hip function was measured using the iHOT-33 and its subsections. The total scores, as well as the subscores, were calculated as a mean of the specific item responses ranging from 0 to 100, with 100 representing the best possible quality-of-life score. Only preoperative baseline data were used in this analysis.General patient data as well as pre- and postoperative data collection was performed via web-based remote data entry system. Perioperative data, including defect- and joint-specific characteristics, procedure-related information, and diagnosis confirmation, were provided by the treating surgeon on the basis of the intraoperative findings. Except one open procedure, all other study patients were treated by means of hip arthroscopy . Descriptive data are presented as mean (± standard deviation (SD) and 95% confidence intervals (CI)), percentage of the total and in total numbers. To detect factors possibly affecting preoperative iHOT-33, univariate and multiple regression analysis with simultaneous entry was performed. Metric and dichotomous variables were directly entered in the regression model; categorical variables were dummy coded first. Post hoc Tukey’s test and Student’s n = 119/32.9%) or multilocular (n = 240/66.3%) cartilage lesions. Concomitant labral tears were found in 319 patients (88.1%). Detailed patient characteristics are presented in Table Sex distribution of the study group was nearly 2:1 , with an overall mean age of 36.71 (± 10.89) years. The mean symptom duration at the date of surgery was 23.42 (± 23.02) months. Three patients (0.8%) presented with isolated femoral cartilage damage. All other study participants had isolated acetabular . The corresponding subscores were also distinctly lowered, with the greatest decrease for the “sports and recreational activities” score 0.33. Thep < 0.001), “male sex” (p < 0.001), “lower BMI” (p = 0.030), and the absence of “previous surgeries on the affected hip” (p = 0.003). All other analyzed parameters were shown to be not significantly associated with certain tendencies in iHOT-33 scoring, among others, the cartilage parameters “defect size” (p = 0.622) and “defect grade” (p = 0.405), the presence of “labral tears” (p = 0.534), and the overall “symptom duration” (p = 0.279) .Descriptive statistics for the parameter “age” revealed that increasing patient age almost constantly decreases the mean baseline iHOT-33 Fig. . Here, tp < 0.001). For “age” and “sex,” results showed a similar pattern for the according subscores except for “sports and recreational activities.” As lower BMI was revealed to be positively associated with higher iHOT-33 scores, the study collective was subdivided according to the categories proposed by the World Health Organization (WHO). Study patients with a BMI below pre-obesity (< 25 kg/m2) were proven to have significantly better baseline iHOT scores than those with a BMI ≥ 25 kg/m2 . Regarding “previous surgery on the affected hip,” patients without previous operations had a mean preoperative iHOT-33 of 46.99 (± 20.26), whereas those with previous hip surgery in their medical history had a significantly lower mean score of 37.51 . Detailed statistics of the affecting parameters are presented in Table Analysis of the parameter “sex” showed males to have a mean preoperative iHOT-33 of 50.61 (± 18.78) compared with 37.20 (± 20.55) for females  correspond very closely with this study’s findings as ages < 40 years and BMI < 25 kg/m2 had a significantly better baseline iHOT-33 total. Apart from this, there are further publications that indicate the influential role of patients’ sex, age, and BMI on FAI-related treatment outcome [This study’s second objective was to identify factors that may influence preoperative hip function in patients with FAI due to cam morphology. Again, the iHOT-33 was taken as a measure for quantification of joint-related functional restrictions. Using univariate and multiple regression analysis, four factors were detected to be significantly correlated to the preoperative iHOT-33 score. These were the parameters “age,” “sex,” “body mass index,” and the confirmation of “previous surgeries on the affected joint.” In fact, the factors younger age, male sex, BMI < 25 kg/m outcome . Further outcome , 39–41.  outcome , 39–41.  outcome . However outcome  In lightAs concerns the factors identified to possibly influence the baseline iHOT, it is known that the ageing of organisms is accompanied by an ongoing reduction in the ability of tissues to maintain themselves . ObviousThe findings of this study have to be seen in the light of some limitations. First, the study only assesses patients with cam-type FAI. Results might have been different for FAI patients in general including Pincer and combined Cam + Pincer morphologies. Then, as the used registry focuses on cartilage therapy, all patients were characterized by concomitant chondral defects. Again, results might have been different for patients with cam morphology in absence of cartilage damage. As patients with “previous surgery on the affected hip” were included in the statistical analysis, it can be questioned whether their data should be used when assessing baseline data. However, as none of the statistics were substantially changed hereby and as this aspect is of some importance, these patients were deliberately included in the study. Finally, the findings are based on the analysis of register data. Any parameters not evaluated  or not (sufficiently) recorded by the register might have been unintentionally ignored or misinterpreted. Selection bias and non-response to follow-up questionnaires might also have led to biased results , 51.2,” and the confirmation of “previous surgery on the affected hip” were detected as associated with significantly lowered preoperative iHOT-33 scores. These findings help to identify symptom-defining baseline characteristics of cam-derived FAI syndrome. Moreover, as these factors have previously been identified to be associated with poorer postoperative outcome, additional information on chances and expectations of surgical FAI treatment for physicians and patients is provided.The study revealed a distinct reduction in the baseline iHOT-33, with mean total scores being more than halved. The parameters “age > 40 years,” “female sex,” “BMI ≥ 25 kg/m


Two emblematic molecules, i.e., CO and N2, which in many respects display similar electric response, havebeen studied in detail. The substantial differences, which have beenhighlighted in the topology of the parallel and perpendicular polarizabilitydensity tensor components of CO and N2, are grossly hiddenby compensation, when integration is carried out to get the molecularproperties.The notion of theelectric dipole polarizability density functionof atoms and molecules has been considered. The current density inducedby the time derivative of the electric field of monochromatic lightallows for a new definition of the electric dipole polarizabilitydensity, which is translationally invariant. This translational invarianceprovides the physical meaning that was lacking in previous definedpolarizability densities. The new polarizability density has beenimplemented at the TD-DFT level of theory. The origin independencehas been proven  Within the long-wavelength approximation,4 the leading contribution to light scattering is provided by theoscillating electric dipole μ(t) induced by the electric field of the monochromatic wave,whose strength E(t) = E0 cos(ωt) is assumed to be spatially uniform all over themolecular domain. If we limit ourselves to consider linear response,the induced dipole is expressed by the relationshipαβ(ω),symmetric under theexchange α ↔ β, represents the frequency-dependentelectric dipole polarizability.A beam of lightinduces oscillatingcharge and current distributions in the electron cloud of a molecule.αβ(0) ≡ ααβ, anddynamic ααβ(ω)are possibly the most intensively studied linear-response propertiesof atoms and molecules, at the experimental as well as the theoreticallevel. They are used to rationalize basic optical properties, e.g.,refraction indices and absorption coefficients.9 Moreover, the Rayleigh scattering depolarizationratio of gases depends on molecular polarizability anisotropy.10Static, i.e., α11 Static polarizability anisotropies of linear and symmetrictop molecules, determined by Alms and co-workers measuring the depolarizationratio of Rayleigh scattered light,12 canalso be determined from the second-order Stark shift in microwavespectroscopy.13The Stark effect is observed in the purerotational spectrum ofmolecules and in the electronic spectra of atoms.15 electric polarizabilities arerelated by perturbation theory to the charge distribution and polarizabilitiesof the isolated monomers.16Within the theoryof intermolecular forces and long-range interactions,μ(t) in the presence of E(t) in relation to diverse regions of a molecule.In order to understand mechanisms and causes that come into play tobring about polarization of the electron cloud and to understand therole of different substituents and moieties entering a given molecularsubstrate, and related push–pull effects, attempts have beenmade to define reliable resolutions of the electric dipole polarizabilityinto contributions arising from various molecular domains, with theaim that they be movable from molecule to molecule. Accordingly, partitionsinto transferable additive terms have been defined allowing for variouscriteria.22 A simple scheme for separating molecular polarizability into atomicand bond contributions has been obtained by employing the electricdipole acceleration gauge.19A muchinvestigated problem, yet unsolved to a satisfactory extentin the theory of electric polarization, is that of determining themain contributions to the induced dipole 23 introducing a dynamic charge density susceptibilitywhich has been decomposed using regular solid harmonics.20A valid and rigorousapproach has been proposed relying on powerfultools provided by the quantum theory of many-particle systems,24 has beenwidely discussed and applied by Hunt and co-workers in a series ofpapers.30 Sipe and Van Kranendonk analyzed some limitations of the conceptof polarizability density applied to atoms and molecules.31 Polarizability densities within simple one-electronatomic systems have been investigated by Orttung and Vosooghi.32The related notion of nonlocal polarizabilitydensity, first introducedby Maaskant and Oosterhoff,33 The nuclear magnetic shielding density functionwas used as a typical example.34The general concept of a density functionfor a molecular electronicproperty has been considered by Jameson and Buckingham, who investigatedthe origin dependence of some electric and magnetic property densities.20 within Bader’s theoryof atoms in molecules (AIM).37 Recently, an origin-independentdecomposition of static polarizability based on AIM has been put forwardby Montilla and co-workers.21 Alparonereported calculated polarizabilities and hyperpolarizabilities obtainedintegrating origin-dependent perturbed electron densities, at firstand second order, respectively, employing a Coulomb-attenuating DensityFunctional Theory (DFT) method.38Frequency-dependent distributed polarizabilities have been computedby time-dependent Hartree–Fock (TDHF) calculations by Hättiget al.,39 and a partition into fluctuating charge and induced atomicdipole contributions has been put forward by Mei et al.40 An extension to consider also hyperpolarizabilitydensity has been advanced and applied by Nakano et al.,41 Yamada et al.,42 andAlparone.38A Hirshfeld-based scheme for resolving the intrinsic polarizabilitydensities has been proposed by Otero and co-workers,r in molecules, via planar maps and 3D-perspective views ofisosurfaces, quite useful to indicate molecular regions most involvedin the electric field-induced polarization of the electron cloud.Taking into account thisstate of affairs, we are concerned inthis article with a novel theoretical method for computing and representingpolarizability density functions of position 42 yield visualizationsof polarizability density depending on the origin of coordinate system,which reduces their physical relevance, the computational techniqueoutlined in this work provides an effective, translationally invariant,definition of static and dynamic polarizability densities.Whereas approaches presented so far43 for a molecule with n electronsand N clamped nuclei, charge, mass, position, andcanonical momentum of the k-th electron are indicated,in the configuration space, by −e, me, rk, p̂k, k = 1, 2, ..., n, using boldface letters for electronic operators. Analogous quantitiesfor nucleus I are ZIe, MI, RI for I = 1, 2, ..., N.Herein the following we outline the notation and the theoreticalmethod. Within the Born–Oppenheimer (BO) approximation,αβγ.The imaginary unit is represented by a Roman i. Throughout thispaper, SI units are used and the standard tensor formalism is employed;e.g., the Einstein convention of implicit summation over two repeatedGreek indices is applied. The third-rank Levi–Civita pseudotensoris indicated by ϵn-electron vectoroperators, e.g.,for position and canonical momentum,μ̂ = −eR̂.Capitals denote 44 assuming that the eigenvalue problem for thetime-independent BO electronic Hamiltonian Ĥ(0)Ψj(0) = Ej(0)Ψj(0) has been solved, determining a set of eigenfunctions Ψj(0) and corresponding energy eigenvalues Ej(0). The reference (ground) state is indicated by Ψa(0) and the natural transition frequencies are ωja = (Ej(0) – Ea(0))/ℏ.Expressionsfor the polarization charge density and current densityinduced in the electrons of a molecule by a monochromatic plane waveare obtained by time-dependent quantum mechanical perturbation theory,n-electrondensity functions45xk = rk ⊗ sk, k = 1, 2, ..., n, wheres1, one gets from eqs a(0) of the molecule. Theprobability current density45 is obtainedfrom r′ = r after operatingwith the electronic canonical momentum p^ = −iℏ∇. The interaction Hamiltonianconsidered in the present work does not contain terms depending onelectron spin; therefore the probability current density (r) = −eγ(0)(r)be the electronic charge density in the absence of perturbation. Thetime-dependent electric field E(t) carried by a monochromatic plane wave with frequencyω, assumed spatially uniform within the electric dipole approximation,4 induces an oscillating polarization of the electronic distribution.If the intensity of E is weak enough,first-order time-dependent perturbation theory44 can be applied to describe the interacting system.46 For instance, the total electronic charge densitycan be expressed as a truncated series47E, the electric dipole moment inducedin the electron distributionis given byLet ρIn polarizability density tensorfunction and Ψj(0), thus fulfilling the constraint of chargeconservation,47 i.e.,α(t) is independentof the origin of the rα vector.Accordingly, no such origin appears in this equation.It is easily understood that the spaceintegral of the polarizationdensity vector  all overintegral  for μα, obtained by differentiation of the currentdensity vector,46In the present context, we will make use of polarizability density function,47 alternative to—and more meaningful from the physicalviewpoint than—the widely adopted ,,17), oneinvariantof the origin. They are valid for any value of ω, includingω → 0, i.e., for a static electric field. Plots of densities(The great advantage offeredby definitions  and 20)20) with ensities and (20)E = E0 cos(ωt) or The tensor JĖ, quite important for the interpretation of static and dynamic properties.In the static case we have E = E0 and Ė = 0; therefore, visualizationsof the polarizability density provide important information on thedipole moment induced in the molecule. For ω ≠ 0, These relations define thedipole density vector 57 and TD-DFT61 frameworks. For that purpose, we rewrite The theoretical formulation of the polarizability density functiondescribed above can be straightforwardly implemented within the randomphase approximation (RPA) formulation of the TD-HF see ref .i and m are used foroccupied and virtual orbitals, respectively, and q denotes basis set functions. Then, orbitals ψi are expanded as linear combinations of basis setfunctions χqCqi(0), Tj and corresponding transitionenergies ωja are obtained by meansof a TD-DFT (or TD-HF ≡ RPA) calculation. In particular, the T matrix is defined as in ref  method and the BHandHLYP functional,64 recently shown to provide good linear response properties,54 adopting basis sets of contracted functionswhich include terms of high angular momentum, taken from BSE.65 For C, N, and O atoms we have adopted the aug-cc-pV7Z,which corresponds to a (9s8p7d6f5g4h3i2j) basis set. BHandHLYP transitionmatrix elements of operators R̂ and ∇, amplitudes Sj and Tj, and energies ωja, appearing in 66 using the TD =  6d 10f keywords. Moleculargeometries were optimized at the BHandHLYP/aug-cc-pVTZ level. Polarizabilitydensities have been evaluated using the SYSMOIC program package.63 Spatial integration of the density functionshas been performed using the Becke scheme,67 adopting 131 angular points for the Lebedev’s quadratureof 59th order of accuracy68 and 131 radialpoints for the Gauss–Chebyshev radial quadrature of secondkind.69In order to show some preliminary applications ofthe theory exposedin this paper, three simple model systems have been considered forthe calculation of static and dynamic electric dipole polarizabilitydensities at the TD-DFT level of approximation. They are HP,R)and formalisms, in comparison with the conventional origin-dependent polarizabilitydensity r, ω),and see R,R) formalism. This is a quiteinteresting point since translational invariance is a fundamentalrequirement for any physically meaningful density, irrespectiveof basis set choice. To investigate this aspect, we havecalculated the isotropic value of the polarizability densities ofthe H2O molecule for two different origins, adopting therather small 6-31G basis set at the TD-HF level of theory. Theorigin hereafter denoted “000” has been chosen by makingit coincide with the center of positive charges. The second origin,referred to as “123”, has been set shifting the previousone by 1, 2, and 3 bohr along x, y, and z, respectively.Let us first consider theorigin independence of the polarizabilitydensities defined by means of a,evaluated via P,R) and  formalisms, are clearly origin-independent with respectto both passive and active translations. These findings are very appealing,also in consideration of the fact that translational invariance of b and c is independent of basis set quality.The results are shownin 1.b and c would be exactly the same in the limit of complete basisset. In Densities 2.αβ dependon basis set quality.Upon integration, all densities, for both origins, would convergeto the same value of dipole polarizability in the complete basis setlimit.Computed tensorcomponents α3.a dependson the origin, although the corresponding electric dipole polarizabilitydoes not, but improves on increasing basis set quality toward thecomplete basis set result.The density Some more points of interest are70 However, these molecules contain distinctatomic species and different polarizability densities are expectedto characterize them, in connection with diverse topology of inducedcurrent density fields. To highlight this point, in 2,respectively, calculated for the static case and for two radiationfrequencies which bracket, one less and one greater than, the excitationenergy associated with the first nonzero transition moment of thecorresponding tensor component.Carbon monoxideis similar to the nitrogen molecule, in that ithas the same number of electrons, chemical bonds, and lone pairs.To some extent, also their electric response properties, permanentelectric dipole moment, and electric dipole polarizability, are similar;consider, for instance, the very small dipole moment of CO, 0.122D.71 Therefore, we havecarefully chosen the frequency values to keep the polarizability tensorcomponents below reasonable limits within the transparent region.The captions to ∥ and α⊥ obtainedby integrating the polarizability densities displayed. As can be observed,the absolute value of the integrated polarizability components isalways less than 75 au.For ω ≠0, we stress that our approach is not validin the near-resonant region, since the sum-over-states (SOS) relationsused here, 2 are characterized by similar negativedomains of small extension in the vicinity of the nuclei, embeddedwithin a positive region, which in carbon monoxide is more conspicuousabout the oxygen nucleus  and N2 (14.56 au),despite the different density distributions.For the static case, we found that theparallel polarizabilitydensities of CO and N2.The perpendicularcomponent of the polarizability density presentspairs of “bubbles” of opposite sign near each nucleus,resembling a p orbital oriented along the bond. Notably, the bubblesize depends on the nuclear species, being larger about C rather thanO in carbon monoxide. In the nitrogen molecule, the bubbles have smallersize and are characterized by N-centered negative cores. In any case,positive (red) regions extend in space much more than negative (blue)ones. Moreover, the bubble pairs are aligned in CO, whereas they areantialigned in N⊥(0) turns out to be smaller than α∥(0), a result that would be difficult to explain without the correspondingmaps of polarizability density components shown in 2, respectively, whereas computed α∥(0) for these molecules are 14.67 and 14.56 au, respectively.Remarkably, the domains of juxtaposedbubbles with opposite signtend to offset one another upon integration. Therefore, their contributionto the integrated property almost vanishes. As a consequence, α73 we notea general good agreement, with the relevant exception of α∥ in CO for which a discrepancy as large as 6% is observed.From this point of view, it seems clear that for this component theelectronic correlation provides a sensible contribution, larger inCO than in N2, as evidenced also by the quite small dipolemoment estimated here 4 × 10–4 D, at leastwith the correct sign.Comparing our results with the coupled-cluster (CCSDT) static polarizabilitycalculated by Hammond et al.,j|R̂⊥|a⟩ is less than the first transition energy for the parallelmatrix element ⟨j|R̂∥|a⟩ in both molecules,α⊥(ω) becomes larger than α∥(ω) as the radiation frequency ω approachesthe first transition pole, as also noted in ref  and  formalisms (not shown here for thesake of space) do not display any significant differences with respectto those calculated at higher levels. A more complete study on thebasis set convergence for the different gauges using a few suitablechosen molecules is under way.Since it is conventionally accepted that velocitygauges in generalshow slower basis set convergence than length gauges, we have performeda brief preliminary study on the water molecule, calculating the staticpolarizability density tensor components using a number of basis setsof increasing quality from the same source.αβ(ω)of a molecule has sometimes been expressed in the form of integralsover three-dimensional space of second-rank density functions, e.g., r, ω)in αβ(ω) components.In summary, withinthe transparent region, the electric dipolepolarizability ααβR,P)((ω) polarizability within mixed dipole length-dipolevelocity gauge, coinciding with ααβR,R)((ω) customarily considered, if off-diagonalhypervirial theorems, Much more promising appears the alternative proposal of a translationallyinvariant density function in The theoretical methodsinvestigated in our paper are invariantof the origin within the algebraic approximation (for any basis set),as proven via numerical tests, and quite practical for computing andrationalizing dynamic polarizabilities. They are also applicable inthe limit ω → 0 to calculate static polarizabilities.⊥(0) is smaller than α∥(0) in both molecules,and why the two components change with the radiation frequency ω.The computational scheme implemented in the present research hasbeen applied to investigate the carbon monoxide and nitrogen moleculesby using very large basis sets. The results obtained have been representedby means of diverging color maps documenting efficiency and powerof electric dipole polarizability density plots defined via the 36 or the LoProp method,22 would be very useful for assessing local quotasof the total electric dipole polarizability of a molecule in quantitativeterms.Future applications of the theoretical procedure reported hereare expected to provide important contributions to the comprehensionof mechanisms which operate alongside polarization of the electroniccloud in atoms and molecules responding to static and optical electricfields, as well as fundamental information on the role played by differentmolecular regions. In particular, integration of the


To further annotate the main QTL intervals and identify candidate genes, we used Oxford Nanopore long-read sequencing in combination with Illumina short-read resequencing, to assemble the parental genomes de-novo. In addition to 2.5 million genome-wide SNPs and short InDels detected between the parents, we also highlight here the structural variation between these lines and the reference melon genome. Through systematic multi-layered prioritization process, we identified 18 potential polymorphisms in candidate genes within multi-trait QTLs. The associations of selected SNPs with earliness and ripening traits were further validated across a panel of 177 diverse melon accessions and across a diallel population of 190 F1 hybrids derived from a core subset of 20 diverse parents. The combination of advanced genomic tools with diverse germplasm and targeted mapping populations is demonstrated as a way to leverage forward genetics strategies to dissect complex horticulturally important traits.Earliness and ripening behavior are important attributes of fruits on and off the vine, and affect quality and preference of both growers and consumers. Fruit ripening is a complex physiological process that involves metabolic shifts affecting fruit color, firmness, and aroma production. Melon is a promising model crop for the study of fruit ripening, as the full spectrum of climacteric behavior is represented across the natural variation. Using Recombinant Inbred Lines (RILs) population derived from the parental lines “Dulce” ( Earliness of maturity is an important trait of crop plants with a direct impact on production efficiency and stress tolerance. Horticultural earliness, also referred to as days to harvest (DtH), was previously dissected in tomato to its components – time from sowing to first female flower (flowering time), and number of days for fruit development and ripening . Floweriinodorus group and climacteric  lines have enabled QTL mapping of abscission formation [CmNAC-NOR, an orthologue to the tomato ripening mutant NOR gene [CmACS, CmACO), cell wall integrity, carotenoid accumulation and sugar metabolism [In fleshy fruits, fruit development and ripening are considered as the main components determining earliness. Fruit development consists of carpel cells expansion and differentiation, and ripening is a complex process that typically includes modifications in fruit color, texture, composition and profile of sugars, acids, and volatiles , 4. Ripeormation , ethylenormation –19, follNOR gene . AnotherNOR gene . Comparatabolism . Varioustabolism , 23.Flowering initiation is an integrated response to environmental and endogenous cues through a network of pathways responding to factors such as photoperiod, vernalization, aging, autonomous flowering, and gibberellic acid (GA) . RecentlThe genomic resources for melon are constantly improving. Since the first melon reference genome, published in 2012 , updatedDe novo assemblies are becoming more common for model and non-model organisms, and pan-genomes are becoming the new references [de novo assembly of the semi-climacteric “Harukei-3” provides insight to the effect of transposable elements on ripening related gene expression [Recent advances in long-read sequencing have presented an important addition to the available tools that simplify assemblies and can further elucidate genomic context of QTLs. ferences –44. The ferences –47, and ferences . In meloferences . A recenferences . The genferences . A recenpression .de novo assemblies of their genomes and highlight the structural variations between them, some of which are in context of the detected QTLs. Associations of selected candidate genes and polymorphisms within them were validated across a diverse collection and a large diallele population.In the current study, we used a RILs population derived from melon inbred lines differing in their earliness and ripening behavior, to map QTLs related to these traits. Resequencing of parental genomes facilitated detailed genomic analysis of QTL intervals, an expansion of the genomic comparison between our parental lines and an improved QTLs annotation. We also present ,Table 1,r = 0.55–0.6, h2 = 0.56–0.72, h2 = 0.34.The TAD×DUL RILs population was analyzed over three years for earliness and ripening related traits. We characterize agronomic earliness as days from transplanting to harvest (DtH) and further break it down to its components – days to flower (DtF) and fruit development time . A total of 3963 fruits were sampled for DtH across the different experiments, averaging 10 fruits per line per year. Variation in DtH, analyzed on line-mean basis, is substantial and distributes in a transgressive manner across nearly 20 days  of the RILs, parental lines and their F1 hybrid was limited to two fruits from separate plants per replicate. A total of 1258 fruits were sampled in the open field experiments and 536 fruits from the net house, averaging 11 fruits per line. This trait was also found to be highly heritable (eriments . The F1 periment .r = 0.57) with RF values in the open field between 3–26 kg cm −2 and in the net house 3–17 kg cm −2. FF values range between 0.6 and 2.7 kg cm −2, with “Dulce” about twice as firm compared to “Tam Dew” in both tissues. RF values display a much wider range than FF . Both traits are characterized by transgressive segregation across the population, with approximately a third of the RILs softer or harder than the parents. In the net house, both RF and FF display dominant inheritance with the F1 fruits not significantly different from “Tam Dew”  was evaluated in one open field experiment (2017) and in the net house experiment (2018), while flesh firmness (FF) was only evaluated in the net house experiment. There is moderate positive correlation between the open field and the net house  was measured on 3510 mature fruits across all experiments with a mean of 8 fruits per line in the open field experiments and 4 fruits per line in the net house. Interestingly, while both parents have high TSS, with “Tam Dew” constantly a couple of degrees brix sweeter than “Dulce” (~15 vs 13°brix), substantial transgressive segregation is observed across the RILs (9.4–16.4°brix). The environmental effects and G × E interactions in this trait are apparent, as distributions are moderately correlated between the open field experiments but not so between the open field and the net house, where TSS values are lower 1b. TSS det house .p = 0.0004). A stronger negative correlation with EtE is observed for both FtH and DtH in the open field, ranging between r = −0.60 and − 0.65. This negative relation is even more pronounced in the net house . Interestingly, this analysis also shows that ripe fruit TSS is not correlated with ripening behavior or with earliness traits . Significant epistatic interaction between FtH3.3 and FtH8.2 was detected only in the net house , to support the validity of QTLs for this trait, we analyzed the correlation between the five replications in the net house experiment. All correlations were significant and above r = 0.55 (FF5.1 and FF8.3 can significantly distinguish between three levels of flesh firmness (1.16–1.74 KgF cm−2) and account for a third of the total genetic variation in this trait , out of 41 genes annotated across the confidence interval, MELO3C011432, a WRKY family transcription factor, received a high score, with a codon deletion in “Dulce” . In the HDA20 population, similar allelic effect on DtH is shown with a clear additive mode of inheritance, where heterozygotes are intermediate to the homozygote genotypes . The effect of this locus on EtE was validated in the HDA10 population where the deletion alleles are associated with increased ethylene production by nearly 60 μL kg−1 h−1, with additive mode of inheritance . Significant association of this gene with fruit firmness was shown also across the HDA20 population, where the deletion alleles were softer in 3 Kg cm−2 than wild-type  and heterozygotes are intermediate to both homozygotes .MELO3C011365. Another candidate gene in EtE3.3 QTL is MELO3C011365, a transducin/WD40 repeat-like superfamily protein, described as a large family of proteins involved in signal transduction and coordinating protein–protein interactions. Forty-eight genes are annotated within EtE3.3 and MELO3C011365 is located 20 Kb from the QTL peak. We detected several high impact polymorphisms in this gene, including a nonsense mutation, leading to a premature stop codon, two missense mutations, and a splice site region SNP . To test the combined effects of EtE3.3 and EtE8.2 across our diallel population, we analyzed MELO3C011365 with MELO3C24520–a recently suggested EtE candidate located within EtE8.2 [HDA10 population, with a difference of 120 μL kg−1 h−1 between the combination of contrasting alleles at both loci  and in ripe fruits  substitution in exon 5  and shares a minor QTL with DtF8.2 , to test the association of this polymorphism. Significant association was found between SNP S08_4442666 at MELO3C007661 and DtH across the multi-allelic HDA20 population  with the FtH3.3 candidate, MELO3C011432, was tested and significantly accounted for 36% of the genetic variation in DtH  reads of “Tam Dew” and 23.3 Gb of “Dulce”, representing ~43× and ~ 64× coverage of the estimated 400 Mb melon genome, respectively. N50 for ONT read lengths was 16.3Kb and 20.2Kb for “Tam Dew” and “Dulce”, respectively. The initial assembly of “Tam Dew” was comprised of 386 contigs with an N50 of 3.4 Mb and “Dulce” assembly was comprised of 190 contigs with an N50 of 7 Mb , we manually scanned for inversions and translocations. To increase the confidence in the reported events, we only considered segments with at least two anchor sequences present. Between “Tam Dew” and the reference genome we identified 42 events in total, 12 inversions of which 3 were larger than 1 Mb, and 18 translocations between chromosomes, 4 larger than 1 Mb – the largest being 3.6 Mb from chromosome 1 in the reference to chromosome 2 in “Tam Dew”. In Dulce we identified 32 events in total, 17 inversions of which one was larger than 1 Mb, and 6 translocations between chromosomes, none larger than 1 Mb. Interestingly, the most substantial SV that we detected was on chromosome 6 where we report five large translocation events encompassing nearly half the chromosome , yielded 7973 structural variants encompassing 27 Mb. Here too, the majority (66%) were within repetitive elements, 7% larger than 50 Kb, and 28% were InDels, the largest between 10 Kb and 50 Kb, altogether encompassing approximately 2 Mb Fig. 6d, and 11. ,We further analyzed how the structural variation between “Tam Dew” and “Dulce” affected genome-wide gene distribution, and we report that 1119 genes common to both parents (96% single-copy), were re-localized to different chromosomes. 305 genes from chromosome 8 of “Tam Dew” are located on different chromosomes of “Dulce”, mainly on chromosomes 3 and 4. 292 genes from Dulce’s chromosome 1 were mainly translocated to chromosomes 2 and 7 of Tam Dew . These rFig. 6dinodorus type) melon, and climacteric types . In the current study, we used a RILs population originating from a cross between the inodorus line, “Tam Dew” (a Honey Dew variety) and the climacteric line, “Dulce” (reticulatus type). A comparison between EtE from our RILs to a recent study using RILs derived from “Piel de Sapo” (inodorus) and a cantalupensis variety, “Vedrantais” [−1 h−1, compared to 0–286 μL kg−1 h−1) a difference that may be attributed to the fact that “Vedrantais” is much more climacteric than “Dulce” (225 vs 90 μL kg−1 h−1). Nevertheless, several common genetic loci related to ripening were mapped in both populations. Earliness and ripening related traits displayed transgressive segregation across our population, as RILs surpassed the parental range  and Makuwa (agrestis) parents [RF2.1, “Dulce” allele is associated with firmer fruit, while in RF8.2, “Tam Dew” allele is associated with increased firmness. These two QTLs are acting additively (no interaction) and therefore the trans-allelic combination DULRF8.2TADRF2.1 is significantly firmer than all other combinations between these QTLs  and 8 (QTL8.2) provide a genetic explanation for the correlations between the different earliness and ripening-related traits. These two multi-trait QTLs are responsible for more than 30% of the genetic variation  we identified possible candidate genes and polymorphisms. We combined detailed genotypic profile of parental genomes with functional annotations of sequence variation. Gene expression information was also included in the process. Another important layer was the validation of significant associations in two additional multi-allelic populations derived from our diverse melon collection  protein involved in degradative sorting mechanism of plasma membrane proteins [2 in our population  [C. melo ssp. melo), we report here a similar PAV between these lines in Vat proteins, as six open reading frames on chromosome 5 are present in “Tam Dew” but missing from the “Dulce” genome . Out of the 1412 genes missing from either “Tam Dew” or “Dulce”, none were found within a QTL interval. Nonetheless, recent publications report on PAVs related to melon domestication in a region on chromosome 5 containing resistance genes, such as the protein coding mission) , 50. Tho” genome .MELO3C007661 gene was missing from “Tam Dew” annotation, and was manually added after the PCR validation. It is possible that ab-initio gene annotation supported by expression data originating from each of the parents would greatly increase the confidence of both SV and gene annotation from their present draft status.We believe that further examination of the genomic data generated in this study will expose additional cases of SVs within genes, some of which with potential impact on phenotypic variation. However, the lift-over process used in this work is limited to the reference transcriptome, and at times found inaccurate—e.g. the above mentioned de novo assemblies of parental lines of a segregating population is an effective way to identify and prioritize candidate genes within QTL intervals, towards the complementary use of reverse genetic approaches (e.g CRISPR-Cas9 mediated genome editing) for breeding improved varieties.Earliness and ripening behavior in melon are shown here and in other studies to be under complex genetic control , 16–18. 7 recombinant inbred lines originating from a cross between the late non-climacteric “Tam Dew”  and the early climacteric “Dulce”  growing conditions and experimental design previously described in Oren et al., 2020 [1 and their parents were represented by five plants per plot in two replicates and grown in a randomized block design (RCBD) in the open field in the summers of 2016 and 2017. In the summer of 2018, each line was represented by five replicates of a single plant and were grown in a 50-mesh net-house in RCBD. The second population, Melo180 GWAS panel, is composed of 177 diverse accessions representing the two melon subspecies (ssp. agrestis and ssp. melo) and eleven horticultural groups. Here, each line was represented by three plots of five plants each in an RCBD in the open field in the summer of 2015 [HDA20 –multi-allelic population of 190 F1 hybrids derived from intercrossing in a half-diallele mating scheme of 20 diverse core accessions, selected to represent the genetic variation in our Melo180 GWAS panel [HDA10 is a core subset of 10 parents and 45 half-diallele F1 hybrids that are included in the HDA20 populations.The germplasm in this study included three sets which were grown at Newe-Ya’ar Research Center, northern Israel (32°43′05.4″N 35°10′47.7″E). The first population, TAD×DUL RILs, is composed of 164 Fl., 2020 . Briefly of 2015 . The thiAS panel . The 190http://www.restek.com/). Ethylene emission rate (EtE)—μL Kg−1 fresh weight per hour—was calculated from the sample peak area based on the standard peak area (1 ml of 1 ppm ethylene in N2). Fruits were then cut along the longitudinal section, and firmness — KgF cm−2 —was measured on each fruit at two opposite points in both flesh and rind, using a digital force gauge . Fruit rind and flesh firmness  scores were an average of the two sampling points. Flesh sugar content, evaluated as total soluble solids (TSS) was measured by refractometer  in juice squeezed from five fruits per plot. Genotype least square means for EtE, RF, FF and TSS were calculated on a minimum of four fruits per genotype.At maturity a single fruit from each plant was harvested at maturity based on abscission in climacteric fruits, or rind color and days after fruit set (45–50 days) and rind color in non-climacteric fruits, giving a total of five mature fruits per plot (10 per genotype). In the open field, female flowers were routinely tagged at anthesis, over the course of three weeks, and the flowering date of tagged fruits was collected during harvest. Earliness (DtH) is defined as the number of days from sowing to harvest. Time to flower (DtF) is the number of days from sowing to anthesis and fruit development time (FtH) was the number of days from anthesis to harvest. In the net house, flowers were manually pollinated and due to variation in setting, DtF data from this experiment was not reliable enough, therefore only FtH data was used . Ethylenh2) was estimated for each trait in each year separately using ANOVA based variance components [JMP ver. 14.1 statistical package  was used for statistical analyses as described in Oren et al., 2020 . Brieflymponents . Trait cExtraction of DNA was done using the GenElute™ Plant Genomic Miniprep Kit , and the quantity and quality was determined using Nanodrop spectrophotometer ND-1000 , electrophoresis on agarose gel (1.0%) and Qubit® dsDNA BR Assay Kit .et al. [. Map construction was based on 89 343 SNPs across 146 lines. SNP filtration were done with TASSEL v.5.2.43 [et al. [HDA20 population was extracted and shipped to the Genomic Diversity Facility at Cornell University  for WGS to an estimated 30× coverage, yielding 4 million informative SNPs as previously described [Genotyping of the TAD×DUL RILs was based on GBS, and map construction were previously described by Oren et al. . Map conv.5.2.43  and linkv.5.2.43 . Genotyp [et al.  and the escribed .et al. [For expression analysis, fruit rind tissue was sampled into two biological replicates from “Tam Dew” and “Dulce” at flowering day, 5, 10, 15 and 20 days after anthesis (DAA) and at mature stage. Each biological replication consisted of bulked tissue from three fruits sampled from different plants from each line. Fruit tissue was frozen in liquid nitrogen and stored in −80°C. Total RNA was extracted from 24 tissue samples  as previously described  and 50 μet al.  . In esset al. ) and foret al. [A modified CTAB protocol based on Fulton et al.  was usedHigh-quality HMW DNA libraries for Oxford Nanopore MinION were constructed and DNA size selection was performed using BluePippin system . Library preparation was performed with 1–1.7 μg DNA using the Ligation Sequencing Kit SQK-LSK109  following manufacturer’s guidelines. Libraries were loaded on MinION FLO-MIN106D flow cell. Base calling was done using the GPU version of Guppy v2.1. “Dulce” samples produced 1.7 million sequences with a sum length of 23.3 Gb between 70 bp and– 148 592 bp with an average length of 13 729 bp. “Tam Dew” produced 1.7 million sequences with a sum length of 15.7 Gb between 76 bp and 117 396 bp with an average length of 117 396 bp. Mean read qualities for both samples were equal or above Q10.De-novo assemblies and their annotations were created for both parental lines of the TAD×DUL RILs. The assembly workflow is described in http://broadinstitute.github.io/picard/; Broad Institute). Both long and short-reads were aligned using Minimap2 (v2.17) [ (v2.17)  with par (v2.17) . Polishe (v2.17)  using Ra (v2.17) . Assembl (v2.17)  and QUAS (v2.17) . Finally (v2.17) .de-novo repetitive elements in both “Dulce” and “Tam Dew” genomes. Gene models were annotated using a lift-over approach based on the Melon v4.0 data previously published [RepeatModeler2  was usedublished , 33. We ublished  based onublished  using deTo observe SV variation between “Dulce”, “Tam Dew” and Melon genome V4.0, we first aligned the assemblies to each other and to the reference using Nucmer , with 1000 permutations and p-value of 0.05 as detection threshold using 1.5 LOD scores confidence intervals. Composite interval mapping (CIM) was done based on a 10 cM marker window size.QTLs were analyzed as previously described . In brieTo classify and rank polymorphisms within predicted genes, we used SnpEff  that preWe wish to thank Uzi Saar, Fabian Baumkoler and the Newe-Yaar farm team for technical assistance in setting the field trials and for plant maintenance. We thank Dr. Vitaly Portnoy for technical assistance in ethylene emission measurements, and Dr. Nurit Katzir for sharing raw data on the PI414xDUL RILs population. Funding for this research was provided by the United States-Israel Binational Agricultural Research and Development Fund (BARD) grant no. IS-4911-16 and by the Israeli Ministry of Agriculture Chief Scientist grants no. 20-01-0141.de novo assembly pipeline. Bioinformatic, genomic and statistical support were provided by YE, SF, YT and AAS. EO analyzed the results. EO and AG wrote the manuscript. All authors discussed the results and approved the final version of the manuscript.EO and AG conceived the research plan and designed the experiments. YB, AD and AG were responsible for development of plant genetic materials. EO, AD, GT, TI and AG performed the experiments and collected the data. ERR, BS and ESB contributed to the genomic sequencing and sec id="sec35"><title>Funding</title><p>Amit Gur, United States - Israel Binational Agricultural Research and Development Fund (BARD), Funder id 100006031, IS-4911-16.</p><p>Amit Gur, Ministry of Agriculture and Rural Development , Funder id 501100004576, 20-01-0141.</p></secThe data supporting the findings of this study are available within the paper and within its supplementary materials published online. Raw sequences and FASTA files of genome assemblies can be found at NCBI BioProject: PRJNA775626.The authors declare that they have no conflict of interest.at Horticulture Research Journal online.Web_Material_uhab081Click here for additional data file.