Highly-selective fluorogenic substrate and activity-based probe for monitoring cathepsin L activity in the breast cancer cell line MDA-MB-231. Cysteine cathepsins normally function in the lysosomal degradation system where they are critical for the maintenance of cellular homeostasis and the MHC II immune response, and have been found to have major roles in several diseases and in tumor progression. Selective visualization of individual protease activity within a complex proteome is of major importance to establish their roles in both normal and tumor cells, thereby facilitating our understanding of the regulation of proteolytic networks. A generally accepted means to monitor protease activity is the use of small molecule substrates and activity-based probes. However, there are eleven human cysteine cathepsins, with a few of them displaying overlapping substrate specificity, making the development of small molecules that selectively target a single cathepsin very challenging. Here, we utilized HyCoSuL, a positional scanning substrate approach, to develop a highly-selective fluorogenic substrate and activity-based probe for monitoring cathepsin L activity in the breast cancer cell line MDA-MB-231. Use of this probe enabled us to distinguish the activity of cathepsin L from that of other cathepsins, particularly cathepsin B, which is abundant and ubiquitously expressed in normal and transformed cell types. We found that cathepsin L localization in MDA-MB-231 cells greatly overlaps with that of cathepsin B, however, several cathepsin L-rich lysosomes lacked cathepsin B activity. Overall, these studies demonstrate that HyCoSuL-derived small molecule probes are valuable tools to image cathepsin L activity in living cells. This approach thus enables evaluation of cathepsin L function in tumorigenesis and is applicable to other cysteine cathepsins. Both cathepsins L and V have maximum activity in the same pH range and share 79.5% amino acid identity.in situ. Among the latter are selective substrates and activity-based probes (ABPs).In order to be able to discriminate between cathepsin L and related cathepsins and to correctly address their physiological functions, one has to understand their specificity.ABPs are mostly short peptides coupled to a reactive group (warhead) that binds to catalytic machinery, with a detectable tag .Currently, the most successful approach to rapidly identify sequences with high activity and selectivity for individual proteases is HyCoSuL, which utilizes a combination of natural and unnatural amino acids and positional scanning library technology to explore an extended chemical space around protease active sites.2.2.1.–1, Iris Biotech GmbH), Fmoc-protected amino acids , N-hydroxybenzotriazole , N,N-diisopropylethylamine , diisopropylcarbodiimide , HATU and HBTU , 2,4,6-trimethylpyridine , N,N′-dimethylformamide , acetonitrile , piperidine , trifluoroacetic acid , triisopropylsilane , methanol , dichloromethane , diethyl ether , acetic acid  and phosphorus pentoxide . Fmoc-6-ahx-OH, biotin and 2-chlorotrityl chloride resin  were purchased from Iris Biotech GmbH. TFE , HBr (30% wt. in AcOH), anhydrous THF (tetrahydrofuran), NMM (4-methylmorpholine), IBCF (isobutylchloroformate) and 2,6-DMBA  were from Sigma-Aldrich. Fluorescent tags  were purchased from Lumiprobe. Diazomethane was generated according to the Aldrich Technical Bulletin (AL-180) protocol.All chemicals were purchased from commercial suppliers and used without further purification. For ACC-labeled individual substrate synthesis, we used Rink amide RA resin (loading 0.48 mmol g2.2.via active site titration with E-64 inhibitor (Peptide Institute).A detailed description of expression and purification can be found elsewhere.2.3.A detailed protocol for the synthesis of the P4–P2 fluorogenic substrate combinatorial library is provided elsewhere.552.4.All kinetic experiments were performed using fMax (Molecular Devices) and CLARIOStar (BMG LABTECH) spectrofluorimeters operating in kinetic mode using 96-well plates. ACC-labeled substrates were screened using 355 nm and 460 nm wavelengths . The cathepsin assay buffer  contained 100 mM sodium acetate, 100 mM sodium chloride, 10 mM DTT and 1 mM EDTA and had a pH of 5.5. The buffer was prepared at room temperature and the enzyme kinetic studies were performed at 37 °C.2.5.–1) calculations. Each sub-library screening was repeated at least 3 times, and the average value was used to create the substrate specificity matrix. The best recognized amino acid at each position was set to 100% and other amino acids were normalized accordingly.P4, P3 and P2 sub-libraries were each screened at 100 μM concentration with cathepsin L in 100 μL final volume. The active enzyme concentration was in the 0.5–2.0 nM range depending on the sub-library. The assay time was 30 min, but only the linear portions of the progression curves (5–15 min) were used for the velocity  and 200 μM . The substrate cleavage assay was carried out for ∼30 min, but only the linear portions of the hydrolysis curves (10–15 min) were analyzed. Both screenings were repeated at least three times and average values are presented. The Ac-Ala-Arg-Leu-Arg-ACC substrate served as a positive control, and the rate of its hydrolysis was set as 100%. Values for the other substrates were normalized to the P1 Arg substrate.To determine cathepsin L preferences in the P1 position we used a fluorogenic substrate library of 139 components with the general formula Ac-Ala-Arg-Leu-P1-ACC. The library contains 19 natural and 120 unnatural amino acids. The synthesis and detailed structure of the library are found elsewhere.2.7.Each ACC-labeled cathepsin L substrate analyzed was synthesized and purified as described elsewhere.562.8.–1) are presented as average values from at least three screenings.All potentially selective cathepsin L substrates were tested for their selectivity against a panel of five different cathepsins, containing cathepsins L, V, S, K and V, at concentrations of 2 μM and 100 μM. To obtain reliable results the concentration of each cathepsin was 5 nM. Substrate hydrolysis was carried out for 10 min (for cathepsin L) and for up to 60 min (for other cathepsins). Hydrolysis rates -Phe(F5)-OH peptide was synthesized on a solid support using a 2-chlorotrityl chloride resin and used for further synthesis without purification. In parallel, two amino acyl-warhead amino acids were conjugated to electrophilic binding groups. Boc-Lys(Cbz)-AOMK and Boc-Arg(Cbz)2-AOMK were synthesized in solution using diazomethane in diethyl ether (Lys/Arg-CH2N2), HBr in AcOH (Lys/Arg-CH2Br) and 2,6-DMBA in DMF (Lys/Arg-AOMK). Next, Boc-Lys(Bzl)-AOMK and Boc-Arg(Cbz)2-AOMK were N-terminally de-protected in TFA/DCM  and coupled with the above peptide to obtain (1) biotin-6-ahx-His(Trt)-dThr(tBu)-Phe(F5)-Lys(Cbz)-AOMK and (2) biotin-6-ahx-His(Trt)-dThr(tBu)-Phe(F5)-Arg(Cbz)2-AOMK, followed by purification on HPLC. Next, Trt/tBu protecting groups were removed in TFA/DCM and Cbz groups were removed via hydrogenolysis . The final compounds were purified again on HPLC. Fluorescence probes were similarly synthesized. Cy5-labeled MP-cL3 probe: Boc-6-ahx-His(Trt)-dThr(tBu)-Phe(F5)-OH peptide was synthesized on a solid support and used without further purification. Boc-Cys(Bzl)-AOMK was synthesized and N-terminally de-protected as described above. Next, the peptide and H2N-Cys(Bzl)-AOMK were coupled in DMF to obtain Boc-6-ahx-His(Trt)-dThr(tBu)-Phe(F5)-Cys(Bzl)-AOMK. All protecting groups were then removed in TFA/DCM , and excess TFA was removed in a rotary evaporator under reduced pressure . The crude product (1 eq.) was dried over P2O5 in a desiccator for several hours. In a separate flask, 1 eq. of Cy5-NHS was dissolved in DMF and 5 eq. of DIPEA were then added. The solution was immediately mixed with crude peptide-AOMK (1 eq.) and the reaction was carried out at room temperature for 30 min. The reaction progress was monitored by HPLC (220 nm for peptide-AOMK or 646 nm for Cy5-NHS). The final product was purified using HPLC to obtain Cy5-6-ahx-His-dThr-Phe(F5)-Cys(Bzl)-AOMK. Cy5/BDPFL/Cy3-labeled MP-pc1/2/3 pan-cathepsin specific probes: first, the Boc-6-ahx-Ala-Arg(Pbf)-Leu-OH peptide was synthesized on a solid support and used without further purification. In parallel, Boc-Arg(Boc)2-AOMK was synthesized and N-terminally de-protected as described above. Next, the peptide and warhead were coupled in DMF to obtain Boc-6-ahx-Ala-Arg(Pbf)-Leu-Arg(Boc)2-AOMK. The crude product was then purified using HPLC, de-protected with TFA/DCM and dried over P2O5 to obtain H2N-6-ahx-Ala-Arg-Leu-Arg-AOMK. Finally, the fluorescent tag (BDPFL/Cy3) was attached to the N-terminus and the product was purified using HPLC to obtain BDPFL-6-ahx-Ala-Arg-Leu-Arg-AOMK (MP-pc2) and Cy3-6-ahx-Ala-Arg-Leu-Arg-AOMK (MP-pc3). Cy5-Ala-Arg-Leu-Arg-AOMK (MP-pc1) was synthesized without a linker and thus first the Boc-Ala-Arg(Pbf)-Leu-OH peptide was synthesized and then coupled to the H2N-Arg-AOMK warhead. Next, the compound was de-protected and labeled with a Cy5 tag in a manner similar to that for the BDPFL and Cy3 probes.Two biotin-labeled probes (MP-cL1 and MP-cL2) were synthesized as described by Poreba 2.11.kobs/I parameter was measured under pseudo-first order kinetic conditions, as described.For the kinetic studies, five recombinant human cathepsins  were expressed and purified and active site titrated using an E64 inhibitor. The 2.12.To evaluate the applicability of the bMP-cL1 and bMP-cL2 probes using affinity-based enrichment assays, HEK293T cells were seeded on two 10 cm plates and cultured for 2 days. The medium was changed and the cells were then transfected using the pcDNA3:ppCatL construct  and Lipofectamine 2000 ™ based on the manufacturer’s guidelines (Thermo Scientific). Forty-eight hours later, the cells were harvested and whole cell lysates were prepared in a lysis buffer maintaining the cathepsin activity  and pH 6.0) as described elsewhere.2.13.To evaluate probe uptake, 250 000 HEK293T cells transfected with pcDNA3:ppCatL were seeded into 6-well plates and incubated with various concentrations of bMP-cL1 and bMP-cL2 (0–10 μM) for up to 24 hours. In control experiments the cells were preincubated with 25 μM E-64d, a pan-cathepsin inhibitor, for 2 hours prior to probe addition. After incubation samples were collected and immunoblotted. Biotinylated proteins were detected as described above.2.14.To evaluate MP-cL1 and MP-cL2 selectivity, 250 000 HEK293T cells overexpressing cathepsin L were plated and treated with probes and inhibitors as described above. Streptavidin-bound magnetic beads were prepared in separate tubes according to the manufacturer’s guidelines . Briefly, 50 μL beads were washed with sample lysis buffer and then the lysates were added. Tubes were incubated at room temperature on a rocking platform for 2 hours. The beads were then extensively washed with 50 mM Tris-buffered saline containing 0.1% Tween-20 and collected between each washing step. The beads were then re-suspended in 1× SDS loading buffer at a final volume of 100 μL, and the samples were placed at 95 °C and prepared for immunoblotting as described above to detect biotinylated proteins in the lysates, as well as in the unbound and eluted fractions. To confirm cathepsin L/B selectivity, we seeded HEK293T cells as described above. The probes were added at a 5 μM final concentration and incubated for 18 hours before the cells were harvested and lysed to isolate biotinylated proteins using streptavidin magnetic beads, as described above. The lysates plus the unbound and eluting fractions were then evaluated to detect cathepsin B  or 100 nM (experiment B), and then mixed with various concentrations of either the MP-cL3 or MP-pc1 probe for 30 min. In experiment A, the probe concentrations were 2 nM, 10 nM, 50 nM and 250 nM, while in experiment B the probe concentrations were 20 nM, 100 nM, 500 nM and 2.5 μM. The reaction volume for all the samples was 200 μL. After 30 min, 100 μL of 3× SDS/DTT was added to the reaction mixtures, the samples were boiled for 5 min, and then 20 μL of the sample was run on 4–12% Bis-Tris Plus 15-well gels for 30 min at 200 V with 2 μL of PageRuler™ Prestained Protein Ladder. The gels were then directly scanned at 700 nm  using an Odyssey fluorescence imaging system (LI-COR) and the images were analyzed using Image Studio software.2.16.l-glutamine, 100 units per mL penicillin and 100 μg mL–1 streptomycin. The next day, the MP-cL3 and MP-pc1 probes (1 μM) were added to the medium and the cells were incubated for 0–18 hours. To control for non-specific binding, the cells were preincubated with E64d (25 μM) for 2 hours prior to probe addition. At various time points, the medium was aspirated and the cells were gently washed twice with 1 mL of Dulbecco’s Phosphate-Buffered Saline (DPBS) and then trypsinized, harvested and centrifuged for 10 min (500 × g). The supernatants were removed by aspiration and the pellets washed with 1 mL of DPBS and centrifuged . The supernatants were again removed and the pellets solubilized in 100 μL 1× SDS/DTT and then boiled for 5 min. Each sample was then cooled to room temperature and sonicated  and then 30 μL was loaded on 4–12% Bis-Tris Plus gels and run for 30 min at 200 V with a SDS-PAGE Standards/Broad Range ladder. Proteins were transferred to a 0.2 μm nitrocellulose membrane for 1 hour at 10 V and then Ponceau staining was used to verify equal loading. Membranes were blocked with 2.5% BSA in Tris-buffered saline/0.1% v/v Tween 20 (TBS-T) for 1 hour at room temperature. Cathepsins L and B were detected by primary goat anti-hCathepsin L  and goat anti-hCathepsin B  antibodies, respectively, followed by incubation with a secondary antibody . The membranes were incubated with primary antibodies for 2 hours, and with (fluorescent) secondary antibodies for 30 min (both in TBS-T at room temperature). The fluorescence was read at 700 nm  and 800 nm  using an Odyssey fluorescence imaging system (LI-COR). The images were analyzed using Image Studio software.Low passage MDA-MB-231 cells were seeded on 12-well plates (200 000 cells per well per 1 mL of medium) and cultured in Dulbecco’s Modification of Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM of 2.17.d-lysine  for 30 minutes. The poly-d-lysine was aspirated and the coverslips were washed twice with sterile water and air-dried. Next, low passage MDA-MB-231 cells in DMEM (100 000 cells per mL) were added to wells (1 mL per well) and allowed to attach for 2 hours. Cells were then treated with Cy5-labeled probes (1 μM) for 8 or 24 hours. The control cells were preincubated with E64d for 2 hours. After various incubation times, the medium was removed and the cells were gently washed twice with DPBS (1 mL each). The cells were then fixed with ice-cold methanol (500 μL per well) for 15 minutes at –20 °C. The methanol was gently removed and the fixed cells were washed twice with DPBS, blocked and permeabilized with 10% BSA in DPBS  for 1 hour at room temperature, and washed twice with 3% BSA in DPBS . Cathepsins L and B were detected by primary goat anti-hCathepsin L  and goat anti-hCathepsin B , respectively, in 3% BSA in DPBS  for 2 hours at room temperature. The antibodies were removed and the cells were washed twice with 3% BSA in DPBS  and labeled with a secondary antibody (Alexa Fluor™ 488 donkey anti-goat IgG (H + L), A11055, Invitrogen, 1 : 500) in DPBS  for 1 hour at room temperature. Next, the cells were washed twice with DPBS, and coverslips were mounted using Vectashield mounting medium fluorescence containing DAPI (Vector Lab. H-1000). In brief, a drop of mounting medium was placed on the Superfrost Plus microscope slide  and the coverslip was placed on the drop upside-down. Excess mounting medium was removed with a kimwipe and nail polish was used to seal the coverslips before subjecting the slides to confocal microscope analysis . DAPI was detected using the UV channel, the Cy5 probe was detected with a Cy5 filter (excitation at 647 nm (Axio Observer) or 633 nm (Zeiss LSM), emission at 667 nm; red channel) and cathepsins/secondary antibodies were read using FITC filters (excitation at 495 nm (Axio Observer) or 488 nm (Zeiss LSM), emission at 517 nm; red channel; green channel). All images were acquired in .tiff format in MetaMorph software or in .lsm format in ZEN software and analyzed using ImageJ and ZEN software. The images are representative views of the cells from two coverslips.Glass coverslips 15 mm in diameter were placed in 12-well plates and coated with poly-2.18.To quantify ABP/cathepsin colocalization we calculated weighted colocalization coefficients between the MP-cL3 ABP (red) and cathepsin L (green) and between the MP-cL3 ABP (red) and cathepsin B (green) using ZEN 2011 software. These calculations were performed by summing the pixels in the colocalized regions (red and green) and dividing by the sum of the pixels in the red channel (ABP). The value of each pixel was equal to its intensity value (from 0 to 1), thus a weighted colocalization coefficient is more accurate here than the standard colocalization coefficient where all the pixels, regardless of their intensity, have a value of 1. To eliminate the red and green staining background in these calculations, we set crosshairs according to single label controls. A graphical example of the calculated weighted colocalization coefficient is presented in Fig. S12.3.3.1.To develop novel active and selective substrates and ABPs for cathepsin L, we undertook a combined approach employing HyCoSuL and a P1-individual substrate library containing unnatural amino acids. 3.2.et al.;d-amino acids were better recognized by cathepsin L than their l-enantiomers, indicating that the S3 pocket is not stereochemically specific. Moreover, several amino acids (mostly Phe derivatives) were completely ignored by cathepsin L. Similarly to P3, P4 also displayed a broad specificity with preference for basic amino acids  and the ability to tolerate d-amino acids. Among natural amino acids, only branched Leu, Ile and Val and hydrophobic Phe and Tyr were very poorly recognized by cathepsin L. Among unnatural amino acids, only Phe derivatives were not recognized.Cathepsin L substrate specificity was determined using a substrate combinatorial library containing Arg at P1.kcat/KM profiles to the HyCoSuL screening data, we found that cathepsin L displays almost no S4–S2 subsite cooperativity ; however, other amino acids are tolerated by this protease in the S1 pocket. Choe 3.4.kcat/KM) than the reference substrate. Next, we synthesized a 10-membered library of substrates in which the P4–P2 region was fixed with the best recognition sequence, while the P1 position was varied -Cys(MeOBzl)-ACC and Ac-Dap-Orn-Phe(3-Cl)-Nle(OBzl)-ACC, which are the most potent cathepsin L tetrapeptide substrates reported to date: both are hydrolyzed by cathepsin L almost 4-times faster than the Ac-His-Arg-Phe-Arg-ACC substrate is.To develop active cathepsin L substrates we combined the HyCoSuL approach with the results from the P1 library screening . Initials varied . kcat/KM3.5.P1-ACC substrates are very potent cathepsin L substrates, they display only moderate or even poor selectivity towards other cysteine cathepsins (data not shown). Next, to develop a highly selective cathepsin L substrate, we synthesized eleven tetrapeptide substrates with various P4–P1 sequences . We then hydrolyzed these first-generation substrates (100 μM each) with 5 nM of cysteine cathepsins B, K, L, S and V -Arg-ACC) as a candidate for further structure optimization. We next synthesized a second-generation library of substrates, replacing one position at a time (from P4 to P1) . Moreover, P2 was critical in terms of discriminating cathepsin L from other cysteine cathepsins. Mono- and di-substituted Phe are more active than 2,3,4,5,6-pentafluorophenylalanine (Phe(F5)) in the P2 position on cathepsin L, however, Phe(F5) is poorly recognized by other cathepsins, rendering Phe(F5) in the P2 position the most selective amino acid for cathepsin L.Although the family of Ac-Dap-Orn-Phe(3-Cl)-equences . For eacequences . For the Table S4 and util4 to P1) . For P4 rements” . For P4,dThr-Phe(F5)-Arg-ACC, which displayed almost non-detectable hydrolysis when tested with other cysteine cathepsins   . Thus weepsin B)  Panel A.epsin B) . Furtherepsin B) . Strong epsin B) , but onlepsin B) . Based oepsin B) .3.8.dThr-Phe(F5)) to construct a fluorescently-tagged probe (MP-cL3). To confirm probe specificity . Both probes were incubated separately at various concentrations with active site-titrated cathepsins (10 nM or 100 nM) for 30 min, and SDS-PAGE analysis was then carried out  and cathepsins B and L share some substrate preferences. Our initial analyses indicate that cathepsin L can be distinguished from cathepsin B . To valiWe next challenged the selectivity of the MP-cL3 probe in MDA-MB-231 cells, similarly to in HEK293T cells, since these cells contain more cathepsin B. We found that MP-cL3 is rapidly taken up by cells, is detectable after 1 hour and demonstrated no labeling of cathepsin B up to 8 hours  and S4†.3.10.Our data indicate that we could achieve high selectivity for cathepsin L by incubating MP-cL3 in cells for up to 8 hours . We util3.11.To investigate the colocalization of MP-cL3 with the cathepsin L protein in MDA-MB-231 cells we performed a quantitative pixel-by-pixel analysis of multiple fluorescence microscopy images. A reliable parameter for such analysis is a weighted colocalization coefficient (wcc), which provides information not only about single pixels overlapping, but also correlates their intensities, making the analysis more accurate.4.dThr-Phe(F5)-Arg-ACC). Notably, given that we found that the selectivity towards cathepsin L lies in the P4–P2 sequence, the Arg at position P1 could be exchanged for non-charged amino acids to (1) make the substrate and inhibitor more active in the presence of cathepsin L and (2) to avoid potential cross-reactivity with the numerous proteases that prefer Arg at P1. We then synthesized three small molecule probes, with MP-cL3 (Cy5-6-ahx-His-dThr-Phe(F5)-Cys(Bzl)-AOMK) being the most potent and selective toward cathepsin L. This probe bound exclusively to cathepsin L in an in vitro experiment testing five recombinant cysteine cathepsins. To demonstrate that the enzymes were active, we also used a broad spectrum cathepsin probe, MP-pc1 (Cy5-Ala-Arg-Leu-Arg-AOMK). More importantly, we showed that the MP-cL3 probe selectively labels cathepsin L in HEK293T and MDA-MB-231 cell lines, both of which express cathepsins L and B at different levels. The MP-cL3 probe selectivity for cathepsin L over cathepsin B is of special importance, as cathepsin B is overexpressed in many types of cancers and masks cathepsin L proteolytic activity, making its analysis more complex. Finally, using the MP-cL3 probe, we not only showed specific labeling of cathepsin L in cells but performed quantitative pixel-by-pixel localization studies using fluorescence microscopy. That analysis indicated that cathepsin L co-localizes with cathepsin B in MDA-MB-231 cancer cells and that some cathepsin B-rich lysosomes lack cathepsin L. We also prepared two biotinylated cathepsin L probes that have only minor cross-reactivity with cathepsin B and could serve as tools in diverse biological experiments. In summary, we have developed the first selective cathepsin L ABP, which allows accurate detection of cathepsin L activity in cancer cells. Given that this probe does not react with other cathepsins, it has the potential to facilitate the understanding of cathepsin L function and provide new information about the biological framework of cathepsin L-dependent or -related cancer progression.The development of protease-specific substrates offering direct readout (fluorogenic or chromogenic) is of great significance since such tools could be extremely useful in biochemical and biological studies of proteases. Starting from broad spectrum chromogenic substrates developed in the early 1940s and 1950s, the field has progressed through individual internally quenched fluorescence substrate libraries and combinatorial mixture libraries with natural amino acids.M. P., G. S. S. and M. D. designed the research; M. P., W. R., M. V., K. G. and P. K. performed the research and collected data; D. F., K. V., D. T. and B. T. contributed new reagents and enzymes; M. P., G. S. S. and M. D. analysed and interpreted the data and wrote the manuscript; and W. R., M. V., K. G., P. K., D. F., K. V., D. T. and B. T. critically revised the manuscript.There are no conflicts to declare.Supplementary informationClick here for additional data file.


Drosophila melanogaster. In our method, a k-nearest neighbors algorithm automatically classifies fly behavior and finds grooming events with over 90% accuracy in diverse genotypes. Our data show that flies spend ~13% of their waking time grooming, driven largely by two major internal programs. One of these programs regulates the timing of grooming and involves the core circadian clock components cycle, clock, and period. The second program regulates the duration of grooming and, while dependent on cycle and clock, appears to be independent of period. This emerging dual control model in which one program controls timing and another controls duration, resembles the two-process regulatory model of sleep. Together, our quantitative approach presents the opportunity for further dissection of mechanisms controlling long-term grooming in Drosophila.Despite being pervasive, the control of programmed grooming is poorly understood. We addressed this gap by developing a high-throughput platform that allows long-term detection of grooming in  From birds that preen their feathers to dogs that lick their fur, many animals groom themselves. They do so to stay clean, but routine grooming also has a range of other uses, such as social communication or controlling body temperature. Despite its importance, grooming remains poorly understood; it is especially unclear how this behavior is regulated.Fruit flies could be a good model to study grooming because they are often used in laboratories to look into the genetic and brain mechanisms that control behavior. Flies clean themselves by sweeping their legs over their wings and body, but little is known about how the insects groom ‘naturally’ over long periods of time. This is partly because scientists have had to recognize and classify grooming behavior by eye, which is highly time-consuming.Here, Qiao, Li et al. have created a system to automatically detect grooming behavior in fruit flies over time. First, a camera records the movement of an individual insect. A computer then analyzes the images and picks out general features of the fly’s movement that can help work out what the insect is doing. For example, if a fly is moving its limbs, but not the main part of its body, it is probably grooming itself. Qiao, Li et al. then borrowed an algorithm from an area of computer science known as ‘machine learning’ to teach the computer how to classify each fly’s behavior automatically.The new system successfully recognized grooming behavior in over 90% of cases, and it revealed that fruit flies spend about 13% of their waking life grooming. It also showed that grooming seems to be controlled by two potentially independent internal programs. One program is tied to the internal body clock of the fly, and regulates when the insect grooms during the day. The other commands how long the fly cleans itself, and balances the amount of time spent on grooming with other behaviors.Cleaning oneself is not just important for animals to stay disease-free: it also reflects the general health state of an individual. For example, a loss of grooming is associated with sickness, old age, and, in humans, with mental illness. If scientists can understand how grooming is controlled at the brain and molecular levels, this may give an insight into how these mechanisms relate to diseases. The system created by Qiao, Li et al. could help to make such studies possible. Grooming is broadly defined as a class of behaviors directed at the external surface of the body. Most animals spend considerable time grooming , and thiMany animal behaviors, such as locomotion, have been shown to be controlled by both external stimuli (stimulated behavior) and internal programs (programmed behavior). An example of stimulated locomotor activity is the abrupt evasive response triggered by the sudden appearance of a predator. In contrast, programmed locomotor activities, such as daily foraging for food, are essential to maintain vital functions of the organism . SimilarDrosophila melanogaster is an ideal model organism with which to dissect the fundamental mechanisms of grooming and its relationship to other behaviors. The fly is known to be a frequent groomer with a rich repertoire of behaviors and a sophisticated genetic toolkit developed to study them  method, a machine learning technique, to classify each video frame as grooming, locomotion or rest. Results from multi-day recordings reveal that Drosophila spend approximately 13% of their waking time grooming, and the temporal pattern of grooming behavior is tightly regulated by the fly’s internal circadian pacemaker. These findings suggest that grooming, similar to feeding and rest, likely serves one or more critical functions in Drosophila. Additionally, genetic perturbations reveal that the transcription factors CYCLE and CLOCK are critical parts of an internal program that controls the amount of Drosophila grooming. These grooming data, the easily implementable hardware, and the automated analysis package together permit the construction of high-resolution ethograms of stereotypical fly behavior over the circadian time-scale.To overcome limitations of currently available methods, we developed a new platform for long-term video-tracking and automated analysis of fly grooming. The layout of our hardware takes advantage of a basic design for housing individual flies that is widely used in locomotion and sleep studies . Here, wWe used a custom-designed video set-up to monitor fly behavior. Within the set-up, insects were placed individually in cylindrical glass tubes 6 cm long and 5 mm wide with food and cotton at opposite ends . Tubes wWe then developed an automated video image analysis package that classifies fly behavior into grooming, locomotion, or rest. ‘Grooming’ in our algorithm is defined as fly legs rubbing against each other or sweeping over the surface of the body and wings  Videos  and 3, ‘To classify behavior, raw videos were processed through four major automated steps: fly identification, feature extraction, classifier training , and subset behavior classification . First, C0  equal to0 pixels . Based olhouette , right. Second, we performed feature extraction to distinguish three specific types of behaviors, which are grooming, locomotion, and rest, performed by the individual fly. The features we used were: (1) periphery movement (PM), which characterizes movements of the legs, head and wings; (2) core movement (CM), which quantifies movements of the thorax and abdomen; and (3) centroid displacement (CD), which quantifies whole body displacement. Extracting these three features allowed us to identify patterns corresponding to different types of behavior.To extract PM and CM, we split each fly’s body into a core and a periphery. Based on the grayscale distributions of the two parts , we set To extract CD, we calculated the average position of all pixels from the individual fly and defined changes in that quantity between every two consecutive frames as CD. Since the fly moves in essentially one dimension through the narrow tube, we ignored movements perpendicular to the long axis of the tube when calculating centroid movement. In subsequent analysis, fly location was represented by its centroid position. Noise in the apparatus may slightly change the centroid position even when a fly is stationary. By extracting these three features , we were able to distinguish between locomotion, rest, and grooming. As shown in k-nearest neighbors (kNN) technique to the normalized features  and mapping them onto a three-dimensional feature space where the axes correspond to normalized PM, CM and CD . As expected, comparison between 8/15, 10/15, 12/15 and 14/15 shows  than the one used in training the classifier. The comparisons showed that, of the grooming events picked out by our algorithm, 92.1% were manually verified as true grooming events . As shown in Drosophila melanogaster strains.The accuracy of our algorithm was evaluated by comparing the computer-identified grooming with manually labeled grooming identified by visual inspection. We tested a total of 450 min of videos from a different set of with food  was accepted as a proxy for feeding. Rest periods lasting +iso31 fly under 12 hr light:12 hr dark (LD) conditions spent approximately 6% of its daily time grooming,~24% time locomoting, ~3% time feeding, ~16% resting, and the remaining ~51% sleeping  while locomotion (r = 0.26 and −0.13) and feeding (r = 0.27 and 0.06) were both less reliable in predicting grooming.Since sleep and wake are complementary states, we expected fractional time spent in sleep to negatively correlate with that of the four wake behaviors our method tracks. Pair-wise comparisons  of individual fly sleep with grooming, locomotion, short rest or feeding, showed the expected negative relationships . InteresThe weaker grooming-locomotion and grooming-feeding correlations were unexpected for two reasons. First, daily variations in grooming levels had appeared to closely follow those in locomotion , suggestDrosophila ethograms in our system, neither locomotor activity nor feeding is likely to be a primary driver of basal grooming.To focus on temporal dynamics at longer timescales, we binned multi-day data in 30 min  and applactivity . Resultscomotion , indicatcomotion , indicatcomotion . The larTo identify major drivers of basal grooming, we noted that multi-day time series of the behaviors showed time-of-day-dependent changes in each behavior . The appWe set out to determine whether the circadian clock drives rhythmic modulations in fly daily grooming independent of other circadian-regulated behaviors--that is, to test whether grooming exhibits circadian oscillations simply because individual grooming events are mutually exclusive of other individual wake activities. We recognized that the mutual exclusivity of the behaviors seen at the level of individual events  did not To test the independence of rhythms, we performed a series of ‘shuffling experiments’ using well-established  rhythmic+iso31 + to 2 days of alternating light-dark cycles and then monitored their behavior over multiple days in constant darkness (DD). In the absence of light cues, locomotor, feeding and sleep showed the familiar clock-driven rhythms in their daily timing  is one of the core components and whose transcription is controlled by the primary transcription factors Clock (clk) and Cycle (cyc)  . The perbehavior . Populatectively . The perectively  and in aectively . Consistrhythmic . Moreovec . Consistent with the original studies  and between locomotion and grooming (e.g. Jrkclk vs. 01cyc) became more evident when individuals of different genotypes were compared together , and surprisingly, that grooming behavior is observed even during periods of reduced locomotor activity . This suDrosophila. To our knowledge, the existence of programmed grooming, first proposed in the mid 60s, still remains unreported.A few recent studies  have shoData from this study suggest that a significant portion of daily fly grooming is driven by internal programs. Flies in our experiments are active for ~34% of the time within a 24 hr period, during which they mostly engage in grooming, locomotion and feeding. Behavioral analysis showed that, like sleep, locomotion and feeding, fly grooming behavior is modulated by oscillations of the circadian clock . This fiLper and Sper and abolished in arrhythmic 0per flies PGRP-SA did not show a significant change. Further studies are required to clarify these initial results and elucidate the biological function of programmed grooming in Drosophila.Finally, why are flies innately programmed to groom? The present study does not directly address this important question, but given that microscopic pathogens can sporulate on the fly cuticle and eventually infect the insect , persistbehavior . Consistd mutant , althougDrosophila and suggest that this innate behavior is driven by two possibly distinct sets of regulatory systems. The circadian system temporally segregates time-dependent variations in grooming from those of other essential behavioral outputs like feeding and sleep. Circadian coordination of grooming underscores a previously under-appreciated importance of this behavior in the daily routine of the fruit fly. The second regulatory system adjusts the level of grooming relative to other behaviors. This set of regulation likely confers adaptability on the animal by allowing it to up- or downregulate grooming as necessitated by internal and external conditions. The dual control mechanism of grooming proposed here is highly reminiscent of the two-process framework---circadian and homeostatic---that is widely used in understanding sleep regulation . 01cyc flies, gifts from William Ja (The Scripps Research Institute), have the Canton S background. JrkClk flies were backcrossed for five generations to iso31. P1sss mutant flies, gifts from Amita Sehgal (Perelman School of Medicine at the University of Pennsylvania), have the iso31 background. fumin mutants, gifts from F. Rob Jackson (Tufts University School of Medicine), have the 1118w background. Flies were bred and raised at 23°C and 40% relative humidity on standard cornmeal and molasses food. All experiments were done with 5–8 days old males at 260C and 70–80% relative humidity in a custom-built behavior tracking chamber  with food and a cotton plug at opposite ends. Twenty tubes were placed on a custom-designed acrylic plate inside a transparent acrylic cuboid box for simultaneous imaging. Temperature and humidity were monitored every 5 min with a digital thermometer  and a humidity sensor , respectively, while a wet sponge inside the chamber kept the relative humidity around 70–80% .The chamber was illuminated by two sets of light-emitting diode (LED) strips. White LEDs  producing ~700 lux were used to simulate daytime conditions and infrared LEDs  were used to visualize the flies at all times.A CCD monochrome camera  fitted with a varifocal lens  was used for video imaging. To minimize influence of chamber’s light/dark conditions on video quality, we put a 780 nm long pass filter  in front of the lens. Videos were saved as 8-bit images in. avi format with 1280 × 960 resolution at 10 Hz and down-sampled as needed.Using a desktop computer with Intel Core i7-4770 3.4 GHz processer and 4 × 4 G DDR3 1600 MHz RAM, it takes ~7 hr to extract grooming, locomotion and rest data from an 8 hr video of 20 flies recorded in 10 Hz  at 1280 pixel ×960 pixel resolution. Videos are analyzed every two frames (5 Hz), which is sufficient to capture grooming events.https://github.com/sbadvance/Drosophila-Grooming-Tracking.git (https://github.com/elifesciences-publications/Drosophila-Grooming-Tracking).All computational analyses were done with custom-written Matlab scripts that will be available at Fly shape extraction. Fly shape was extracted by applying a background subtraction algorithm. The background or reference frame is constructed randomly picking two frames, a template and a contrast, and comparing their pixel grayscale values and erasing all moving objects from the template frame. To remove the fly from the template frame, we replace the pixels belonging to the fly with corresponding pixels from contrast frames, relying on the fact that a fly is always darker than the surrounding objects. The template frame with no fly present then becomes the background frame. Additionally, because a fly’s surroundings, including food debris, change substantially during the course of an experiment   in the template as To reduce effects of charge coupled device (CCD) image noise and fluctuations in the system, we set a minimum change =10, which excludes 99.99% of noise-related changes in grayscale values.While increasing threshold Feature normalization. Since PM and CM both represent areas (number of pixels in area), while CD represents distance, we take the square root of PM and CM to make the dimensions of the features homogeneous. In addition, fly size varies between individuals and across experimental settings. To facilitate comparison of data in feature space, we therefore normalize PM, CM and CD of each fly with a scale parameter SP equal to the square root of the area of that fly. Thus, the final form of normalized features arep is the p-value and N is the number of frequencies computed in Lomb-Scargle periodogram.N). This is because in Lomb-Scargle periodogram, cut-off power grows as log (N) while peak power grows as N.To test the effect of binning on rhythmicity, we binned grooming activity of individual flies in 30 min, 5 min, and 1 min bin sizes and ran Lomb-Scargle periodogram analysis on these binned data, as well as raw data without any additional binning. Examples of 5 individual spectra of each bin size are shown in In Modified locomotion = original locomotion+original grooming – randomized groomingModified wake = original wakefulness+original grooming – randomized groomingThese manipulations modified either locomotion or wake while keeping the other unchanged.No sample size estimation was performed when the study was being designed. Unless otherwise specified, quantitative experiments with statistical analysis were repeated at least three times independently. Exclusion of data applies to flies which were physically damaged , physically confined , or dead during experiments. For testing statistical significance of differences between groups, we first tested the normality of data by one-sample Kolmogorov-Smirnov test. Two-sample F-test was applied for equal variances test. Samples with equal variances were compared using two-tailed t-test. Satterthwaite's approximation for the effective degrees of freedom was applied for samples with unequal variances. Results were expressed as mean ± s.d., unless otherwise specified. *p<0.05, **p<0.01, ***p<0.001 were considered statistically significant.X and Y are time spent in two behaviors X and Y, In led test . p-value In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.Drosophila using a k-nearest neighbors classifier" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Benjamin de Bivort (Reviewer #1); Daniel Cavanaugh (Reviewer #3).Thank you for submitting your work entitled "Automated analysis of internally programmed grooming behavior in eLife. Although all three reviewers found aspects of the work exciting, there were concerns about the manuscript as a methods paper and about the main biological finding that grooming is under circadian control preclude publication at this time.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in Reviewer #1:In this manuscript Qiao and Li et al. develop an automated annotation procedure for scoring grooming behavior in flies. They use this approach to then measure the circadian dependence of grooming, examining the effects of mutation in period, clock and cycle genes on grooming patterns. They find that the pattern of grooming is circadian and is disrupted by mutations that disrupt the circadian pattern of locomotion. They also examine the effect of stress on grooming. The major scientific result seems to be that control of grooming timing and duration are under independent genetic control, which I find plausible.This work seems to be well done technically, and relatively clearly presented. The grooming detection method will be useful to other researchers. I am not sure of the significance of the scientific findings. It seems like it could be trivially explained by the circadian clock driving arousal, and once an animal is resolved it has to make an exclusive "choice" between grooming or locomoting. Thus, even if the circadian control of grooming is indirect, falling out of the direct control of locomotion by the circadian rhythm, the authors might see these results. Still maybe this counts as circadian control of grooming.It doesn't feel like the paper is either clearly a methods paper or a circadian investigation. If it is the former, as suggested by the title, more work needs to be done to convey the generality and robustness of the classifier. If it's the latter, as suggested by the Abstract and the amount of Results section narrative devoted to the method (2p) vs circadian experiments (7.5), it seems fine but maybe in the wrong article category.Lastly, as my expertise is not in circadian behavioral genetics, I can only say that their results in this area seem plausible and well performed but cannot evaluate them in the broader context of that subfield.1) Were the flies used to assess the classifier accuracy all from the same genotype? If so which one, and do the authors have evidence that the classifier will generalize to other genotypes? Genotypes vary by size and pigmentation and this has the potential to change the value of the classification features and hence the classifier accuracy. Since the paper is given as a methods paper, more effort should be given to conveying the generality and robustness of the approach.It should be easy to see if classification accuracy changes with the size of the segmented fly body from the data they have in hand, if there's enough natural variation in body size. But I don't think it's unreasonable to explore these questions with new measurements.Drosophila” – Using distributions to assess behavioral stereotypy feels misguided, particularly as the authors have individual-level data. Individual flies have different behavioral biases in essentially all behaviors, so it's likely this is true for grooming. This could be true and nevertheless produce a normalized histogram that is relatively narrow. To assess, the authors could look at individual-level data of grooming rates across days. If every individual fly fills out the population distribution (i.e. the behavior is ergodic), then their argument would be supported.2) Subsection “Grooming plays an important role in the daily life of Drosophila” seems particularly speculative. More appropriate for the discussion? Even there, I don't see any reason why spontaneous behavior should be less idiosyncratic than stimulus evoked behavior. Perhaps it would even go the other way. All in all, this argument is not compelling.The sentence in Subsection “Grooming plays an important role in the daily life of The authors might want to, just for curiosity, look at the individual-level correlation between grooming and sleep.3) Subsection “Temporal pattern of grooming is under control of the circadian clock” – The authors should clarify what they mean about testing whether grooming is under circadian control. In particular, if locomotion is given as under circadian control, and locomotion and grooming are definitionally exclusive, how can grooming *not be under circadian control? i.e. What is the result which would falsify the hypothesis that grooming is under circadian control?As the authors go onto conclude in Subsection “Temporal pattern of grooming is under control of the circadian clock” that circadian systems regulate grooming it's particularly important to clarify what they mean early on.The reported lag between peak locomotion and peak grooming of 2 hours as suggested by Also, the data presented for a lag between grooming and running aren't particularly convincing as they appear to only happen 2/4 times. The sample size should be shown in Reviewer #2:Drosophila. They do so by using a trained classifier that relies on k-nearest neighbors clustering, to detect whether the fly periphery moves more than the core (grooming), the periphery and core move similarly (locomotion) or whether neither move much at all (rest). This is all performed on data from flies moving in small tubes, similar to those used extensively for circadian research. The authors manually label some data to assess the false positive and negative rates of their automated method – the algorithm is simple but nonetheless performs robustly with low false positive and negative rates. They then go on to collect data on fly grooming and locomotion over days and report on the statistics of these two behaviors over time, and to investigate whether grooming is modulated by circadian cycle. To do so, they use constant darkness and mutations in the period gene and find that they alter the rhythm of grooming but not the total time spent grooming each day. Finally, they examine mutations in cycle and clock genes, along with starvation conditions, and their effect on grooming rhythms, taking care to account for how flies change locomotor and feeding behavior concomitantly. This leads to the conclusion that there are two separate mechanisms that shape the rhythms of grooming (when flies groom throughout the day) and the duration of grooming (how long they spend grooming). I find the paper overall compelling for establishing a pipeline for studying fly grooming over long timescales – this setup should facilitate studies on the genetic and neural mechanisms underlying the regulation of grooming  over the course of days. However, there are a number of issues that I feel preclude publication of the manuscript in its current state.The authors develop methods to automate the detection of grooming (versus rest and locomotion) in 1) After reading the title I expected a methods-heavy paper – however, much of the details on the automated grooming detector are in the Materials and methods section – and there is scant information describing the choices the authors made in how they extracted fly shape or optimized the classifier. Additionally, while the algorithm seems useful for studying grooming behaviors in these particular tubes under IR light, it is not clear how the algorithm would perform if the flies were in other  environments. The algorithm itself does not seem to be sufficiently innovative on its own to be of general interest, so it seems important for the authors to demonstrate its ability to detect grooming behaviors robustly in a number of conditions . On a related note, the statistics on grooming and locomotor behaviors reported in 2) The authors bin the locomotor and grooming data into ~3 minute bins, and then investigate rhythmicity using the Lomb-Scargle periodogram (a robust method for identifying rhythms in sparsely sampled data). This corresponds to a sampling rate of 0.006Hz – thus pushing any high frequency power in the grooming or locomotor rhythm down into the low frequencies , and it 3) A central claim of the paper is that grooming is not simply a correlate of locomotion/activity but is under separate control by the circadian clock, and a model is used  to show 4) The reduced variability of the fraction of time spent grooming vs. locomotion  could reReviewer #3:Drosophila researchers. Particularly beneficial is the fact that their system has the ability to monitor basal grooming, as opposed to induced grooming, which should be very informative to the field. The authors have also done a nice job of explaining in detail how their algorithm works, which appears to accurately detect grooming events.The authors of this manuscript have made a significant advance in allowing for automated assessment of grooming behavior over long periods of time, and this will be generally useful to There are several major problems, however, with the specific application of this system and the resulting conclusion that grooming is under circadian control and that the amount of grooming is regulated by the clock and cycle genes. Most concerning is that the authors have failed to use standard assessments, such as periodogram analysis of individual fly grooming behavior, to confirm a circadian pattern of grooming and to determine the robustness of the rhythm. The only quantification of free-running grooming rhythms is Lomb-Scargle periodogram analysis on mean grooming data over 4 days in constant darkness. We therefore have no idea as to the periods and powers of behavior of individual flies. The raw data traces shown suggest very weak rhythms. They should also show how clock and cycle mutations affect the circadian pattern of grooming-it's curious that they only show total amount of grooming for these mutants when a loss of rhythmicity would bolster the idea that grooming behavior is under circadian control.In addition, the locomotor behavior of wildtype flies as determined by this monitoring system is unusual as it appears that a substantial amount  of activity is occurring during the dark period . This calls into question either the accuracy of the algorithm to determine locomotion, the lighting and noise controls during the experiment, or the genetic background of the wildtype flies used.Finally, it is not clear that the rhythms of grooming behavior are under direct circadian control as opposed to being a secondary product of sleep-wake cycles. Grooming can only occur when the flies are awake, which makes it very difficult to disentangle from sleep-wake rhythms. The authors have attempted to address this question by comparing the onset of grooming and locomotion during the evening , but locThe relationship between wakefulness and grooming might also explain why clkjrk and cyc01 mutants have increased grooming, as both mutants have decreased sleep overall . It would be useful to look at grooming behavior in other short-sleeping mutants to see if it is similarly increased.Overall, this manuscript represents a nice innovation that will benefit other researchers but has fallen short in using this innovation to uncover novel mechanisms regulating grooming behavior. At a minimum, additional analysis is needed to convincingly demonstrate circadian control of grooming.[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]Drosophila using a k-nearest neighbors classifier" for further consideration at eLife. Your revised article has been favorably evaluated by K VijayRaghavan (Senior editor), Kristin Scott (Reviewing editor), and three reviewers.Thank you for resubmitting your work entitled "Automated analysis of internally programmed grooming behavior in The manuscript has been improved but there are some remaining issues that need to be addressed as text changes before acceptance, as outlined below:1) Independence of grooming cycles from locomotion/wakefulness cycles is challenging to show definitively. At this stage, the authors have provided evidence that grooming is not *trivially* rhythmic because of wakefulness rhythmicity (i.e. there's not a wake/sleep cycle and 1/nth of the time you're awake you groom). Clearly these are coupled processes in that they are hierarchical – you have to be awake to groom. The authors should restate their discussion to say that they haven't definitively demonstrated that grooming rhythms are independent of the sleep-wake cycle.2) Separability of the modes of regulating grooming is not so clear, since the genes that seem to be require for the separate modes are all part of the core clock machinery. The justification for the idea of two separate programs in the current paper rests primarily on the fact that mutations in the per gene result in loss of grooming rhythms but not grooming amount, while mutations in clock and cyc affect both the rhythm and amount of grooming. The main problem here is that clock and cyc are core elements of the clock, and thus can't be separated from their role in driving rhythmicity. Furthermore, per is part of the negative arm of the clock while clock and cyc are part of the positive arm. Couldn't it be that the positive components, when missing, lead to increased grooming, but the negative components do not? The data do not prove that there are separate programs controlling the timing and amount of grooming. Without that additional evidence, this conclusion be toned down.3) To address concerns about binning their data, the authors now test 3 different bin sizes  and then search for low frequency rhythms – this seems okay, since for all cases the bin width is much smaller than the underlying rhythm they are trying to detect (24 hour circadian cycle). However, the authors should be able to run the Lomb-Scargle analysis on the data directly, without binning, and still uncover the rhythms, no? If so, this argues against a need for binning at all.Drosophila Activity Monitors (DAMs) with a custom video set-up". Based on my understanding, the only feature of the DAM system that the authors have incorporated is the 5mm diameter glass tubes to house the flies. It seems misleading to say that they are using features of the DAM system, which would imply use of the monitoring technology .4) In subsection “Automatic grooming detecting system”, the authors say: "we used a system that incorporates features from 5) In 6) There is no mention of the Pearson correlation test in the methods section. I would recommend that the authors include p values in addition to the correlation coefficient. In 7) Are the data from 8) This is a stylistic consideration, but some of the new figure captions (for example the caption for 9) There are some residual typos, such as missing spaces before references and sporadic double spaces. There are almost certainly other typos that I failed to see in reading it. So careful copy editing should be done to reduce the number of these that make it into print.Drosophila reveals independent modes of genetic control " This reflects a few suggestions: (1) I don't think it's essential to mention kNN in the title. Other clustering approaches would presumably work in that space, (2) "internally programmed behavior" still strikes me as an odd framing. I'd rather see a quick summary of their major science finding. Those are my two cents. Happy to leave this up to the authors.10) The authors might want to consider versions of the title along these lines: "Automated classification of grooming behavior in  [Editors’ note: the author responses to the first round of peer review follow.]Reviewer #1:1) Were the flies used to assess the classifier accuracy all from the same genotype? If so which one, and do the authors have evidence that the classifier will generalize to other genotypes? Genotypes vary by size and pigmentation and this has the potential to change the value of the classification features and hence the classifier accuracy. Since the paper is given as a methods paper, more effort should be given to conveying the generality and robustness of the approach.It should be easy to see if classification accuracy changes with the size of the segmented fly body from the data they have in hand, if there's enough natural variation in body size. But I don't think it's unreasonable to explore these questions with new measurements.w1118 flies (N=20). Since individual body sizes vary even within the same genotype, we created the PM, CD and CM feature space . As shown in Drosophila melanogaster strains.”“Since size and pigmentation differences between genotypes can potentially affect behavioral classification, we investigated robustness of our 2) Subsection “Grooming plays an important role in the daily life of Drosophila” – Using distributions to assess behavioral stereotypy feels misguided, particularly as the authors have individual-level data. Individual flies have different behavioral biases in essentially all behaviors, so it's likely this is true for grooming. This could be true and nevertheless produce a normalized histogram that is relatively narrow. To assess, the authors could look at individual-level data of grooming rates across days. If every individual fly fills out the population distribution (i.e. the behavior is ergodic), then their argument would be supported.We thank the reviewer for raising concerns about the presentation. We should not have presented the data using a normal distribution as it clearly conveyed the wrong point and was not appropriate for the purpose. Our intention was simply to draw attention to the observation that grooming has a smaller population-wide variance. In the revised version, because of the new structure of our work, this comparison between locomotion and grooming is removed from the manuscript. In In the panel on right, inter-individual differences in daily grooming and locomotion are presented. Each point is standardized daily grooming or locomotion of an individual fly, which is calculated by dividing daily among of grooming(green) or locomotion(gray) of an individual fly by the respective population average. The variation in standardized daily grooming time among individuals (coefficient of variation) is significantly less than in locomotion. The coefficient of variation of grooming is 0.14 compared with 0.34 for locomotion. N=66 flies. In the previous version (left panel), standardized daily grooming or locomotion of individual flies were fitted to normal distributions.The sentence in Subsection “Grooming plays an important role in the daily life of Drosophila” seems particularly speculative. More appropriate for the discussion? Even there, I don't see any reason why spontaneous behavior should be less idiosyncratic than stimulus evoked behavior. Perhaps it would even go the other way. All in all, this argument is not compelling.The authors might want to, just for curiosity, look at the individual-level correlation between grooming and sleep.We have removed the comparison of population-wide variance between grooming and locomotion in the revised manuscript, as mentioned above. Thus, this sentence is no longer there.iso31+) and in Canton S). In addition, correlation between grooming and locomotion and between grooming and sleep in individual sleep mutants sss and fumin, and circadian mutants 0, clkJRKper and 01cycflies are provided in We thank the reviewer for the advice to look at individual-level correlations. We have added individual-level correlation among grooming, locomotion, feeding, short rest, and sleep of wt to 3) Subsection “Temporal pattern of grooming is under control of the circadian clock” – The authors should clarify what they mean about testing whether grooming is under circadian control. In particular, if locomotion is given as under circadian control, and locomotion and grooming are definitionally exclusive, how can grooming *not be under circadian control? i.e. What is the result which would falsify the hypothesis that grooming is under circadian control?As the authors go onto conclude in Subsection “Temporal pattern of grooming is under control of the circadian clock” that circadian systems regulate grooming it's particularly important to clarify what they mean early on.The issue of circadian control of grooming is an important result of this work and we thank the reviewer for pointing out insufficient clarity in the definitions and test criteria. Previous wording may have incorrectly implied that locomotion and grooming are always mutually exclusive. They are mutually exclusive at the level of individual events but not at the level of fractional duration in each behavior. The latter quantity is what we show in time-series and where any long-term oscillations would appear. Within the state of wakefulness, a fly can locomote, groom, feeding or rest. As a result, wakefulness can be rhythmic as long as only one of the four is rhythmic. Therefore, if we find locomotion to be rhythmic, there is no requirement on either grooming or rest to be rhythmic. To test the presence of circadian rhythmicity in grooming behavior, it was therefore necessary to monitor the behavior in constant environment  and check for its rhythmicity.Much of this reasoning was missing in the previous version. But, prompted by the referee’s questions, we have now added substantial additional text starting in the last paragraph of subsection “Flies spend a significant portion of their awake time grooming”. We have also redesigned figures and added new figures to address this important question: The reported lag between peak locomotion and peak grooming of 2 hours as suggested by Also, the data presented for a lag between grooming and running aren't particularly convincing as they appear to only happen 2/4 times. The sample size should be shown in iso31+ flies.We updated number of samples and now this figure has been moved to Reviewer #2:1) After reading the title I expected a methods-heavy paper – however, much of the details on the automated grooming detector are in the Materials and methods section – and there is scant information describing the choices the authors made in how they extracted fly shape or optimized the classifier. Additionally, while the algorithm seems useful for studying grooming behaviors in these particular tubes under IR light, it is not clear how the algorithm would perform if the flies were in other  environments. The algorithm itself does not seem to be sufficiently innovative on its own to be of general interest, so it seems important for the authors to demonstrate its ability to detect grooming behaviors robustly in a number of conditions . On a related note, the statistics on grooming and locomotor behaviors reported in a) We agree with the reviewer that more information about our approach ought to be included outside of Methods and in the main body of the manuscript. We have added more details on fly shape extraction, features extraction, model optimization, and validation in the Results section. We have put more data on pruning filter size optimization in Drosophila Activity Monitor (DAM)), we attempted to retain as much of that design as possible in designing the new platform. The choice of ~6 cm long glass tubes to house individual animals retains an important feature of ongoing fly circadian experiments and lowers the technical hurdle of setting up the platform for the interested researcher . This also means the same fly preliminarily measured using DAM can be promptly placed in the new platform without the need for any special handling/transfer steps. Also, a shared arena design means that conclusions from one platform on some aspects of locomotion and sleep can be assumed to be valid in another, thus speeding up progress.b) This study intended to devise a platform that would add high-throughput detection of fly grooming to the current list of fly circadian behaviors studied in the laboratory. Since locomotor activity and sleep are two of the most commonly studied circadian behaviors and the studies use the same experimental design  Thank you for bringing up this important point. We agree that some of the behavioral statistics are potentially specific to the environment in which the flies are housed. Some features are clearly dependent on the genotype as well, as revealed by our data 2) The authors bin the locomotor and grooming data into ~3 minute bins, and then investigate rhythmicity using the Lomb-Scargle periodogram (a robust method for identifying rhythms in sparsely sampled data). This corresponds to a sampling rate of 0.006Hz – thus pushing any high frequency power in the grooming or locomotor rhythm down into the low frequencies , and it We apologize for not reporting significance metrics for the circadian peaks in the original manuscript. We now include a few example power spectra  and detailed statistics of all tested control and mutants in N). This is because in Lomb-Scargle periodogram, cut-off power grows as ln(N) while peak power grows as N. This detail has been added to Methods section.To test the effect of binning on rhythmicity, we binned grooming activity of individual flies in 30minutes, 5-minutes, and 1-minute bin sizes and ran Lomb-Scargle periodogram analysis on these binned data. Examples of 5 individual spectra of each bin size are shown in 3) A central claim of the paper is that grooming is not simply a correlate of locomotion/activity but is under separate control by the circadian clock, and a model is used  to show We thank the reviewer for pointing out this missing important detail. A summary about the model and method, including analytic functions used in the fitting procedure and example fits, is now in Figure 4—figure supplement 4. In addition, fitting errors are shown in the accompanying Related to this issue, differences in the temporal patterning of locomotion vs grooming only become apparent in the averaged and normalized data shown in the supplement . But the normalization could introduce artifacts. For instance, the absolute basal rates for both behaviors appear similar in the raw data . After nThe actual fittings of the data were done on the raw activity and power spectrum of individual flies, without any normalization. It is true that the absolute value of basal rate of grooming is lower than that of locomotion previous . The poi4) The reduced variability of the fraction of time spent grooming vs. locomotion  could reWe thank the referee for this advice. Substantial revision and streamlining precluded, this comparison between locomotion and grooming from the current manuscript.But regarding the now excluded figure: It is true that variance and mean of grooming and locomotion of individual flies could be correlated. In the previous Reviewer #3:There are several major problems, however, with the specific application of this system and the resulting conclusion that grooming is under circadian control and that the amount of grooming is regulated by the clock and cycle genes. Most concerning is that the authors have failed to use standard assessments, such as periodogram analysis of individual fly grooming behavior, to confirm a circadian pattern of grooming and to determine the robustness of the rhythm. The only quantification of free-running grooming rhythms is Lomb-Scargle periodogram analysis on mean grooming data over 4 days in constant darkness. We therefore have no idea as to the periods and powers of behavior of individual flies. The raw data traces shown suggest very weak rhythms. They should also show how clock and cycle mutations affect the circadian pattern of grooming-it's curious that they only show total amount of grooming for these mutants when a loss of rhythmicity would bolster the idea that grooming behavior is under circadian control.JRKclk and 01cycflies. All single fly power spectra now appear with power at the p=0.05 and p=0.01 levels  indicated.We apologize for this important omission. We have updated These details now offer clearer picture of circadian rhythmicity in grooming of wild-type and period-shifted mutants and their absence in canonical arrhythmic mutants.In addition, the locomotor behavior of wildtype flies as determined by this monitoring system is unusual as it appears that a substantial amount  of activity is occurring during the dark period . This calls into question either the accuracy of the algorithm to determine locomotion, the lighting and noise controls during the experiment, or the genetic background of the wildtype flies used.iso31+). Based on raw data from Figure 3—figure supplement 1C  a fly can be engaged in multiple behaviors, as long as the first four (constituting wakefulness) and sleep add up to 1 .Much of this reasoning was missing in the previous version. But, prompted by the referee’s question, we have now added additional text (subsection “Flies spend a significant portion of their awake time grooming”), Previous Figure panel 4E has been moved to The relationship between wakefulness and grooming might also explain why clkjrk and cyc01 mutants have increased grooming, as both mutants have decreased sleep overall . It would be useful to look at grooming behavior in other short-sleeping mutants to see if it is similarly increased.JRKclk flies groom much more but sleep significantly less than their controls (JRKclk groom 9% and sleep 32% as opposed to control grooming at 6% and sleep at 56%). In contrast, 01cyc flies groom more compared to their control strain (16% vs. 9%) but sleep about the same (51% vs. 52%). This indicates that increased grooming is not necessarily a result of increased wakefulness (or decreased sleep).Our fumin and sleepless. Compared with wt flies, sleep time in fumin flies decreases significantly, while daily grooming increases only ~1.7%  Independence of grooming cycles from locomotion/wakefulness cycles is challenging to show definitively. At this stage, the authors have provided evidence that grooming is not *trivially* rhythmic because of wakefulness rhythmicity (i.e. there's not a wake/sleep cycle and 1/nth of the time you're awake you groom). Clearly these are coupled processes in that they are hierarchical – you have to be awake to groom. The authors should restate their discussion to say that they haven't definitively demonstrated that grooming rhythms are independent of the sleep-wake cycle.We agree with the reviewer that a more thorough study is required to show complete independence of rhythms in grooming and wakefulness. We simply wish to make the reader aware that rhythmic wakefulness does not directly imply rhythmic grooming – a level of independence permitted within the hierarchical relationship between the two behaviors – only to motivate a need for grooming experiments in constant darkness. Independence in the opposite direction is not critical to our work. We have added the following statements to further alert the reader of the incompleteness of our results :“It should be noted here that our simulation results do not demonstrate bidirectional independence of rhythmicity in wakefulness and grooming but, only that rhythmicity of wakefulness does not depend on that of grooming. Demonstration of fully independent rhythms in the two behaviors is beyond the scope of the present study.”2) Separability of the modes of regulating grooming is not so clear, since the genes that seem to be require for the separate modes are all part of the core clock machinery. The justification for the idea of two separate programs in the current paper rests primarily on the fact that mutations in the per gene result in loss of grooming rhythms but not grooming amount, while mutations in clock and cyc affect both the rhythm and amount of grooming. The main problem here is that clock and cyc are core elements of the clock, and thus can't be separated from their role in driving rhythmicity. Furthermore, per is part of the negative arm of the clock while clock and cyc are part of the positive arm. Couldn't it be that the positive components, when missing, lead to increased grooming, but the negative components do not? The data do not prove that there are separate programs controlling the timing and amount of grooming. Without that additional evidence, this conclusion be toned down.We agree with the reviewer that we have not shown conclusively that the two modes of grooming control are independent. The following changes have been made in response to this comment:Abstract: “One of these programs regulates the timing of grooming and involves the core circadian clock components cycle, clock, and period. The second program regulates the duration of grooming and, while dependent on cycle and clock, appears to be independent of period.”0 data, the results raise the possibility of non-circadian roles for cyc and clk in setting the duration of internally driven grooming in Drosophila.”Subsection “Grooming duration is controlled by cycle and clock”: “Importantly, together with perare operate independently.”Subsection “Grooming duration is controlled by cycle and clock”: “The apparent absence of per from the second regulatory mechanism is consistent with the possibility that the two control mechanisms are not may not be due to circadian defects. These data are consistent with the hypothesis that clock-independent but cyc- and clk- dependent pathways regulate the amount of programmed grooming behavior”.Discussion section: “…implying that the changes in grooming level Discussion section: suggest that this innate behavior is driven by two possibly distinct sets of regulatory systems.3) To address concerns about binning their data, the authors now test 3 different bin sizes  and then search for low frequency rhythms – this seems okay, since for all cases the bin width is much smaller than the underlying rhythm they are trying to detect (24 hour circadian cycle). However, the authors should be able to run the Lomb-Scargle analysis on the data directly, without binning, and still uncover the rhythms, no? If so, this argues against a need for binning at all.N while the cut-off power grows as log N.We thank the reviewer for highlighting this point. The time series in 4) In subsection “Automatic grooming detecting system”, the authors say: "We used a system that incorporates features from Drosophila Activity Monitors (DAMs) with a custom video set-up". Based on my understanding, the only feature of the DAM system that the authors have incorporated is the 5mm diameter glass tubes to house the flies. It seems misleading to say that they are using features of the DAM system, which would imply use of the monitoring technology .We concur with the reviewer that the sentence may lead to confusion. The statement has been replaced with a new one that does not refer to the DAM system:“We used a custom-designed video set-up to monitor fly behavior.”5) In We thank the reviewer for pointing out the error. The spelling has been corrected.6) There is no mention of the Pearson correlation test in the methods section. I would recommend that the authors include p values in addition to the correlation coefficient. In We apologize for not reporting significance metrics for the Pearson correlation coefficient in the manuscript. We now include p-values for Pearson coefficient in all correlation analyses. When we calculated the bivariate normality of pairs of data in each correlation graph, we noticed that some pairs deviated strongly from a normal distribution. Thus, instead of testing significance of r values with Student’s t-test, we applied the bootstrap method for calculation of p-values. We now include example empirical distribution of r values in “In rX,Y=EX-μXY-μYσXσYwhere X and Y are time spent in two behaviors X and Y, rX,Y is the Pearson correlation coefficient between two behaviors, E is the expectation value, μ and σ are, respectively, mean value and standard deviation of a behavior. The statistical significance of r was estimated through bootstrapping. For each two behaviors, we randomly paired data from n flies (n=84 for iso31+ and n=76 for Canton S) and calculated a correlation coefficient r. This process was repeated 100000 times and the empirical distribution of the randomly paired r values were used for a two-tailed test . p-valueAlso, the order of the Pearson correlation graphs in 7) Are the data from Yes, they are. We noticed that this information was before mentioned only in the main text. Now, it is specified in the figure caption as well.“Time series in the four examples were taken in constant darkness (DD) and[…]”8) This is a stylistic consideration, but some of the new figure captions , locomotion (gray), sleep (dark gray), short rest (purple) and feeding (blue). Here, numerical values for fractional time spent in behavior are indicated only for grooming, locomotion and sleep[…]”9) There are some residual typos, such as missing spaces before references and sporadic double spaces. There are almost certainly other typos that I failed to see in reading it. So careful copy editing should be done to reduce the number of these that make it into print.We thank the reviewer for this comment and have now corrected 14 similar typos.10) The authors might want to consider versions of the title along these lines: "Automated classification of grooming behavior in Drosophila reveals independent modes of genetic control " This reflects a few suggestions: (1) I don't think it's essential to mention kNN in the title. Other clustering approaches would presumably work in that space, (2) "internally programmed behavior" still strikes me as an odd framing. I'd rather see a quick summary of their major science finding. Those are my two cents. Happy to leave this up to the authors.We appreciate the referee’s suggestion. Considering (1) this is a methods/technique paper (2) referee comment above about further ‘toning down’ of the grooming control model we propose, and (3) the suggestion to replace ‘internally programmed’ from title, we decided to keep ‘kNN’, not mention ‘independent control’ and replace ‘internally programmed’ with ‘long-term’ in the title. The new title is:Drosophila using a k-nearest neighbors classifier”“Automated analysis of long-term grooming behavior in


Work-related noise exposure is one of the major factors contributing to the development of adult-onset hearing loss and tinnitus. The aim of this study was to analyze, in patients with chronic tinnitus and long-term occupational noise exposure, (A) characteristics of hearing loss, tinnitus, comorbidities, demographic characteristics and a history of work-related noise exposure and (B) differences among individuals employed in occupations with high and low risk of developing work-related noise-induced hearing loss (NIHL). One hundred thirty six patients with chronic tinnitus and at least a 10 year-long working history were divided into two groups based on the risk of their profession to induce NIHL. Individuals employed in jobs at high risk for NIHL were mostly males and exhibited a poorer hearing threshold, more evident in the left ear. Tinnitus was mostly bilateral; the next largest presentation was left-sided; patients described their tinnitus as buzzing or high-pitched. Correlation between age, length of tinnitus and worse hearing was found. Patients with a higher degree of hearing impairment were mostly males and were more likely to have a family history of hearing loss and at least one cardiovascular comorbidity. Our study shows some differences in individuals with tinnitus and a history of a profession associated with increased exposure to NIHL compared to those without such a history. Noise-induced hearing loss (NIHL), commonly defined as a hearing threshold worse than 25 dB HL at the high-frequency range , is a maChronic exposure to loud noise induces a progressive destruction of inner and outer hair cells in the organ of Corti and alterations to the stria vascularis and spiral ganglion neurons. The mechanism of noise-induced hearing loss begins with outer and, to a lesser extent, inner hair cell loss in the high-frequency base of the cochlea, followed by a progression of hair cell loss toward the low-frequency apex of the cochlea ,7,8. OxiTinnitus is defined as the perception of sound without an external auditory stimulus. Approximately 2% of the population in industrialized countries is reported to experience incessant tinnitus . TinnituThe effects of long-term occupational noise in patients suffering from chronic tinnitus have rarely been studied, and limited information is available for specific occupation groups . The aimIn this study, we included 136 patients aged 26–84 years with chronic tinnitus (>12 months) and anamnestic history of having worked at least 10 years during the previous 20 years, presenting at the Tinnitus Unit of the Sapienza State University Hospital Policlinico Umberto I in Rome, Italy, during a 4-year period from January 2013–January 2017. n = 68) and patients with tinnitus and a history of employment in industries and occupations reported to have lower risks for hearing impairment . Patients were included in the HIGH-RISK group if they had a history of employment in one of the following professions: armed forces [Based on working history, patients were divided into two groups: patients with tinnitus and a history of employment in one of the professions associated with an increased exposure to occupationally-acquired noise-induced hearing loss  and the acoustic immittance (AI) test. PTA was measured at frequencies of 0.50, 1, 2, 4 and 8 kHz.Detailed work and noise-exposure history data were collected including type of work and family history for hearing loss and tinnitus. The presence of cardiovascular comorbidities such as diabetes, heart disease and hypertension was investigated.Self-assessment questionnaires regarding tinnitus (Tinnitus Handicap Inventory (THI)) , hearingp-values were reported. A multivariate binary logistic regression analysis was performed to investigate specific variables associated with a higher degree of hearing loss in tinnitus patients according to demographic characteristics such as age and sex, comorbidities, family history for hearing loss and self-administered questionnaire scores. The results of logistic regression were reported in the odds ratio scale along with a 95% confidence interval and p-values. A p-value of 0.05 was used as the cutoff for statistical significance.The mean and standard deviation (SD) for numeric and frequency and percentage for categorical demographic characteristics, such as sex, age, family history of hearing loss and comorbidities, distribution of tinnitus characteristics and self-administered questionnaire results, and PTA differences between high-risk and low-risk subjects were calculated. The chi-square test of association was used to analyze differences between the LOW-RISK and HIGH-RISK groups for demographic variables  and tinnitus characteristics; p < 0.001). In the LOW-RISK group, 31/68 were males (45.59%) and 37/68 were females (54.41%) (p < 0.001). The study included 136 patients: 86 males (63.2%) and 50 females (36.7%). Males were significantly more prevalent in the HIGH-RISK group . Mean age was 55.1 years (range 26–84 years). Individuals in the HIGH-RISK group were older  compared to individuals in the LOW-RISK group  (p = 0.72).Mean time of noise exposure was 18.4 years in the LOW-RISK group and 19.3 years in the HIGH-RISK group. No statistically-significant difference was found between groups (p = 0.253). Family history for hearing loss was found in 14/68 (20.6%) individuals in the HIGH-RISK group and in 9/68 (13.2%) in the LOW-RISK group; the difference was not statistically significant (p = 0.60); several patients presented more than one comorbidity. The most common comorbidity was hypertension, followed by heart and vascular diseases. Data are shown in At least one comorbidity among diabetes, heart and vascular diseases and hypertension was found in 27/68 (39.7%) patients in the HIGH-RISK group and in 24/68 (35.3%) in the LOW-RISK group (Frequency-specific hearing thresholds are shown in p = 0.086) and side (p = 0.64) were found within the same groups; however, the left ear showed poorer mean auditory thresholds for higher frequencies in the HIGH-RISK group compared to the right ear. Although worse hearing, especially for high frequencies, was found in the HIGH-RISK group compared to the LOW-RISK group for both males and females, a larger and statistically-significant difference was found for males (p < 0.001), not for females (p = 0.12).p = 0.726). Tinnitus was bilateral in 46/68 (67.6%) patients in the HIGH-RISK group and in 36/68 (52.9%) in the LOW-RISK group (p = 0.05). Unilateral tinnitus was significantly more prevalent in the left ear; left-sided tinnitus was found in 18/22 (81.8%) individuals in the HIGH-RISK group and in 19/32 (59.3%) in the LOW-RISK group (p = 0.05). Tinnitus was described as “whistle” in 46/136 (33.8%) patients, “buzzing” in 30/136 (22.1%), “high-pitched” in 26/136 (19.1%), “low-pitched” in 15/136 (11%) and “other” in 19/136 (13.9%) (p = 0.06). “Buzzing” and “high-pitched” tinnitus sounds were more common among HIGH-RISK individuals, and “whistle” was more common among patients in the LOW-RISK group.Average duration of tinnitus at the time of first admission to our center was 10.9 years for the HIGH-RISK group and 9.2 years in the LOW-RISK group. The difference was not statistically significant (p = 0.22) and HQ (p = 0.12); a statistically-significant difference was found for HHI (p < 0.001). Mean THI score was 33.1 in the HIGH-RISK group and 30.6 in the LOW-RISK group; the mean HHI score was 18.8 in the HIGH-RISK group and 9.4 in the LOW-RISK group; the HQ score was 13.4 in the HIGH-RISK group versus 11.8 in the LOW-RISK group. The difference was not significant for THI , miners (75%), construction workers (73.3%) and armed forces (72.7%); unilateral in railroaders (66.6%) and musicians (100%). The worst THI scores were found for school teachers (50.5) and best among musicians (21) and armed forces (24.1). Manufacturing workers (23.5) and construction workers (23.4) scored worst for HHI. Surprisingly, railroaders had the best HHI score (2.6). Worst hearing thresholds were found in miners (47.5 dB for 0.5–2 kHz and 78.1 dB for 4–8 kHz) and railroaders (31.6 dB for 0.5–2 kHz and 65.8 dB for 4–8 kHz). Musicians had the best hearing threshold among individuals in the HIGH-RISK group (11.6 dB for 0.5–2 kHz and 33.7 dB for 4–8 kHz). In the LOW-RISK group, bilateral tinnitus was more prevalent among entrepreneurs (63.6%) and office workers (54.2%) and unilateral among hospital workers (75%). The worst THI score was found among office workers (33.7); the worst HHI score among entrepreneurs (13.18). The worst hearing thresholds were found for professionals (23.2 dB for 0.5–2 kHz and 40.9 dB for 4–8 kHz); hospital workers had the best hearing among individuals in the LOW-RISK group (13.3 dB for 0.5–2 kHz and 15 dB for 4–8 kHz). Data sorted by type of work are shown in p = 0.001) and PTA for the 0.5–2-kHz (p = 0.05) and the 4–8-kHz frequency range (p < 0.001) compared to older subjects (>60 years). No significant differences were found for HQ score and tinnitus length. In the HIGH-RISK group, compared to participants older than 60 years, patients younger than 45 years showed a significantly lower length of tinnitus (p = 0.02), PTA for the 0.5–2-kHz (p < 0.001) and the 4–8-kHz frequency range (p < 0.001). No significant differences were found for THI, HHI and HQ scores  showed significantly lower THI and HHI scores (Q scores . p < 0.001)  individuals showed significantly worse hearing in the HIGH-RISK group compared to the LOW-RISK group for all frequencies above 500 Hz (< 0.001) .A multivariate binary logistic regression analysis was used to investigate specific variables associated with a higher degree of hearing loss in tinnitus patients according to demographic characteristics such as age and sex, comorbidities, family history for hearing loss and the HHI self-administered questionnaire score. Analysis indicated that patients with a higher degree of hearing loss: (A) were 3.54-times more probable to come from male populations; (B) were 1.7-times more likely to have a family history of hearing loss; and (C) were 1.2-times more likely to have at least one comorbidity .The association between hearing loss, tinnitus and occupation has been previously demonstrated ,69,70,71The main demographic difference found among our groups was for the male gender. The larger prevalence of males found between individuals in the HIGH-RISK group compared to the LOW-RISK group (80.8% vs. 45.6%) is in accordance with other studies that show that men are mostly involved in jobs with elevated noise exposure ,72,73. WMean age did not differ between groups; however, a significant difference was found between patients younger than 45 years and older than 60 years for auditory thresholds and length of tinnitus. Older individuals had worse hearing thresholds and experienced tinnitus for a longer time. This is consistent with the literature, which reports greater incidence of tinnitus and hearing loss with age ,68,69,74Although the degree of NIHL has been shown to be significantly influenced by environmental factors, strong evidence has been gathered through various animal and human studies about the role of genetic predisposition ,76,77. IThe presence of cardiovascular comorbidities in individuals with NIHL has been previously described ,79,80,81Among individuals with chronic tinnitus, hearing thresholds were significantly worse in patients in the HIGH-RISK group compared to those in the LOW-RISK group. This finding is in accordance with the literature ,70,74,88One possible explanation for this phenomenon is the different shielding of the right ear from noise in specific occupations. An example of a work environment resulting in asymmetrical noise exposure is tractor drivers, in which the left ear is more frequently affected than the right ear, as these operators monitor equipment mounted on the rear side looking over their right shoulder and therefore exposing their left ear to the noise while their right ear is shielded by head shadow. The acoustic shielding of the head is also usually found in right-handed shooters that have a more severe hearing loss in the left ear. The handedness of the subject could thus be of relevance; however, studies assessing the impact of handedness on hearing loss showed no correlation between the ear with the asymmetry and the individual’s handedness . To dateThe main difference in tinnitus characteristics among individuals in the HIGH-RISK and LOW-RISK groups was laterality. A significantly higher number of individuals in the HIGH-RISK group had bilateral tinnitus. Among patients with unilateral tinnitus, a strong prevalence of left ear tinnitus was found in patients in the HIGH-RISK group (81.8% vs. 59.3%). Our findings are in accordance with other studies ,69,70,88Consistent with findings in a recent paper by Flores , no assoNo significant differences were found for mean THI questionnaire scores between our groups, in contrast to other authors who showed a higher tinnitus discomfort in individuals with NIHL ,90. WhenThis is one of the few studies on work-related noise exposure to include only individuals with chronic tinnitus and a long working history. Accurate audiological and tinnitus evaluation was uniformly performed among groups, although it was limited to PTA and did not investigate outer hair cell functions with otoacoustic emissions. Acuphenometry for pitch and loudness of tinnitus was not performed; pitch was investigated through an anamnestic interview; psychometric scores were used to assess the degree of tinnitus severity instead of investigating its psychoacoustic characteristics. Studies report that mood disorder comorbidity among individuals with tinnitus can be as high as 60–80% and can lead to increases in measures of tinnitus annoyance ,94. TherA limitation of this study is the lack of information about the loudness of noise exposure and about the degree to which workplace prophylaxis might have been used to mitigate the work-related hazard for individuals included in the study. However, assignment to the HIGH-RISK or LOW-RISK groups was done according to extensive evidence reported in large demographical studies ,34,38,43Hearing loss in the range of 10–16 kHz was not investigated in the present study. Such high-frequency hearing loss can be found in many individuals above the age of 40 and is common in noise-exposed subjects ,9. HeariThe relatively small size of our study cohort did not allow a uniform distribution of individuals among the different job categories. A large heterogeneity of noise exposure levels and timing of exposure can be found in our sample and may have biased results. A larger sample size may have improved the significance of our data and allowed us to examine a larger number of occupations. Furthermore, although no significant differences for length of noise exposure between groups were found, correlation between time of occupational noise exposure and audiological and tinnitus characteristics in exposed subjects was not performed in our sample and could be further explored in future studies.No historical audiological data were collected for patients, preventing us from differentiating hearing losses due to noise exposure, ototoxic agents or a combination of exposures and, therefore, to correlate the degree of hearing loss found in our study exclusively with work-related noise exposure. Our study shows some differences in individuals with tinnitus and a history of a profession associated with an increased exposure to occupationally-acquired noise-induced hearing loss compared to those who had no such history. Individuals employed in jobs at high risk for NIHL were mostly males and had a poorer hearing threshold, more evident in the left ear; tinnitus was mostly bilateral, followed by left-sided, described as buzzing or high-pitched. Correlation between age, length of tinnitus and worse hearing was found. Patients with a higher degree of hearing loss were mostly males and were more likely to have a family history of hearing loss and at least one cardiovascular comorbidity.