Diplodia, Eutypa, Fomitiporia and Phaeomoniella. All in all, the fungal diversity and community composition did not differ between minimally and intensively pruned, esca-positive vines.Previous works on grapevine-trunk diseases indicate that minimal or non-pruning of the grapevine under certain circumstances can significantly reduce the risk of symptom expression. Nevertheless, knowledge of the mechanisms behind these observations are limited. Therefore, it was the aim of this study to investigate in more detail the effect of pruning intensity on the grapevine trunk by means of trunk integrity and the fungal community in the wood tissue. Two German vineyards partially trained in vertical-shoot position and semi-minimally pruned hedges were chosen for this survey due to the accessibility of multi-annual esca-monitoring data. The results revealed that only in one of the two vineyards was the incidence of external esca symptoms significantly reduced over a period of five years (2017–2021) by minimal pruning, which was up to 73.7% compared to intensive pruning. In both vineyards, the trunks of intensively pruned vines not only had more pruning wounds on the trunk  than minimally pruned vines, but also exhibited a larger  circumference of the trunk head. In addition, the percentage of white rot and necrosis in the trunks of esca-positive and esca-negative vines was analyzed and compared between the two pruning intensities; hereby, significant differences were only found for esca-negative ‘Dornfelder’ vines, in which the proportion of necrosis was higher for intensively pruned vines (23.0%) than for minimally pruned vines (11.5%). The fungal communities of the differently pruned vine trunks were mainly dominated by four genera, which are also associated with GTDs:  Eutypa lata being the most virulent and common [Botryosphaeria, Diplodia or Neofusicoccum, are associated with Botrysphaeria dieback [Phaeomoniella chlamydospora, Phaeoacremonium spp. and Cadophora spp. are frequently found in young, esca-affected vines [Fomitiporia spp., which is the causal agent of white rot [The esca complex is one of three major grapevine-trunk diseases (GTDs), which are a group of destructive wood diseases that cause massive losses every year in vine-growing regions all over the world ,2. The cd common ,8,9,10.  dieback ,12. Here dieback . For the dieback ,14,15,16ed vines ,20,21,22hite rot ,25,26,27hite rot ,31,32,33Although pathogenic fungi play a key role within the esca complex, there are several other biotic and abiotic factors that drive this longsome disease process ,35. For Another key driver frequently mentioned in the context of GTD management may lie in the pruning scheme as part of the grapevine-training system ,52. SincAlthough the assessments demonstrated significant differences regarding the susceptibility of intensively and minimally pruned vines against esca, data on the possible causes for these findings are rare. Therefore, it was the aim of this study to compare in more detail intensively and minimally pruned vines. Two German vineyards planted with VSP and SMPH vines were chosen for this study due to the multi-annual esca-monitoring data available. In these vineyards, the trunk morphologies of the two different pruning intensities were compared by means of pruning-wound quantity and size, as well as by trunk dimension, i.e., height, head and waist circumference. Furthermore, the inner-wood integrity of VSP and SMPH vines was assessed by measuring the relative amount of white rot and wood discoloration. Finally, the mycobiome in esca-affected intensively and minimally pruned vines was analyzed using a cultivation-independent method (next-generation sequencing). The obtained results may help to better understand in which way the pruning system influences the incidence of GLSD. In addition, the data collected in this study should help to develop pruning or training strategies that reduce the incidence of GLSD in vineyards.The vineyards chosen for this survey were located in the north of Rhineland-Palatinate, Germany: one younger vineyard that was planted in 2003 with the cultivar ‘Dornfelder’ and one older vineyard that was planted in 1984 with the cultivar ‘Müller-Thurgau’. The vineyards were about 28 km apart from another and were established on a plain surface with loamy soil. In both vineyards, the plants were originally trained in VSP and partially converted into SMPH in 2013 (‘Dornfelder’) and 2008 (‘Müller-Thurgau’). In 2020, 726 additional VSP vines in the ‘Dornfelder’ vineyard were converted into SMPH to study the effect of late pruning conversion, i.e., changing from intensive to minimal pruning in an advanced life stage (17 years) of the vineyard, on the incidence of esca. Further vineyard characteristics are shown in For VSP, the vines were pruned by hand at dormancy and old canes from the preseason were removed. One remaining, annual cane was horizontally attached to the trellis. The SMPH vines were roughly pruned with a mechanical trimmer on the sides and on top. As a result, the plants had a height of about 2 m and a canopy width of about 50 cm. The mechanical trimmer was also used for a summer pruning, which was performed on both pruning systems, VSP and SMPH.From 2015 to 2020 the ‘Müller-Thurgau’ vineyard was annually monitored for external esca symptoms, i.e., GLSD (“tiger-strips”) and apoplexy. Monitoring took place five times in a season from the beginning of July until the beginning of September in a two-week rhythm. Esca-affected plants were marked in a field map. The ‘Dornfelder’ vineyard was monitored the same way from 2017 to 2021.For statistical analysis of the esca incidence in the two pruning systems, Fisher’s exact test was applied in RStudio  using tht-test.The number of pruning wounds and combined wound sizes were determined in order to compare the impact on the grapevine trunk caused by the pruning systems. In addition, the trunk height, the circumference of the trunk head and the circumference of the trunk center (middle section) was measured. For the measurements, 20 vines per pruning system and study site were randomly picked. The obtained data were evaluated by using the program RStudio  and StudA total of 30 grapevine trunks per pruning system were removed from each study site by cutting the vines below the graft union. From these, 15 showed GLSD symptoms and 15 were asymptomatic during the whole survey. The trunks were brought to the lab for further processing and examination.With a band saw, the trunks were longitudinally cut into two halves and the inner view of both halves was documented with a Nikon digital camera D90. The pictures were processed by hand with the program paint.net v4.1.5 as follows: The background was erased, wood showing white rot was colored in green and discolored/necrotic wood was highlighted in blue. Afterwards the area size of white-rot and necrotic-wood tissue in relation to healthy wood was determined by using ImageJ version 1.8.0_172 . Statistwww.dlr.rlp.de, accessed on 10 January 2022).Weather conditions, especially the amount of rainfall, may play a significant role in the esca-disease process. Therefore, precipitation data at the two vineyard locations were obtained from weather stations in proximity to the study sites, which are run by the DLR Rhineland-Palatinate (From each vineyard (‘Dornfelder’ and ‘Müller-Thurgau’) and pruning system (VSP and SMPH), nine grapevine trunks  were collected by sawing the vine below the graft union. From the trunks, wood samples were taken using a core drill with a diameter of 12 mm and 300 mm length. For each vine, three holes were drilled into the trunk head and three holes into the center of the trunk. The core samples from each vine and trunk position were collected in a sterile plastic bag, resulting in a sample size of 72 . Samples were brought to the lab for further processing.3 in size, collected in a 50 mL falcon tube and stored at −20 °C.Under sterile laminar airflow, the samples were surface disinfected by successive submersion in 2.5% sodium hypochlorite, 70% ethanol and twice in sterile distilled water, each for 30 s . AfterwaFor the mycobiome analysis, a cultivation-independent method (next-generation sequencing) was chosen. For this, the gDNA from the wood samples was extracted using the cetyltrimethylammonium bromide (CTAB)/β-mercaptoethanol method . Prior tThe gDNA samples were shipped to Microsynth AG  for metabarcoding analysis. For library preparation, the ITS2 region was amplified with a Nextera two-step PCR using the primer pair ITS3 and ITS4 . The amphttps://www.ncbi.nlm.nih.gov/sra, accessed on 10 January 2022) with accession number PRJNA796210.Metabarcoding raw-data processing was conducted by an amplicon-denoising workflow based on the R package dada2 . Primerst-test was chosen to find significant differences in the relative abundance of the four selected, GTD-associated fungal genera  when comparing the mycobiome of VSP and SMPH vines. Here, the relative abundance is defined as the number of reads of one genus relative to the total number of reads in one sample.The statistical analyses of the mycobiome data were all performed using RStudio software  and the The esca monitoring produced inconclusive results in the two vineyards, ‘Dornfelder’ and ‘Müller-Thurgau’ . In the In 2020, additional VSP-trained vines (726) were converted into SMPH in the ‘Dornfelder’ vineyard. The esca monitoring in 2020 revealed an equal rate of symptomatic VSP vines (19.7%) and recently converted SMPH vines 18.3%; . The SMPFive properties were chosen in order to study the effect of pruning intensity on the grapevine trunk . (1) In Besides the above-mentioned grapevine-trunk properties, the inner-wood integrity of intensively and minimally pruned grapevines was compared by evaluating the relative area size of white rot and necrosis in the longitudinal sections. The analysis was performed with respect to the esca status of the examined grapevines, i.e., with or without external symptoms.Regarding white rot, esca-positive vines showed a higher proportion of symptomatic wood than esca-negative vines, irrespective of the pruning system and vineyard . In the The results of the wood-necrosis assessment were not in accordance with the white-rot assessment in terms of the esca status. Both VSP- and SMPH-trained and esca-negative ‘Müller-Thurgau’ vines showed a higher mean necrotic area with 36.4% and 42.4%, respectively, than the esca-positive ‘Müller-Thurgau’ vines . For the ‘Dornfelder’ vineyard, the mean necrotic area in esca-negative VSP vine was significantly higher (23.0%) compared to esca-positive VSP (13.8%), SMPH (11.9%), and esca-negative SMPH vines (11.5%).When combining white rot and dark necrosis, the mean area of the affected wood is higher for the intensively pruned (esca negative) ‘Dornfelder’ vines than for the minimally pruned (esca positive) ‘Dornfelder’ vines. However, in the ‘Müller-Thurgau’ vineyard, no differences were found irrespective of esca status or training system.In total 525 fungal operational-taxonomic units (OTUs) were identified from 72 grapevine wood samples by metabarcoding of the ITS2 region (ITS3-ITS4). After rarefaction, 111 OTUs were left in the ‘Dornfelder’ samples and 267 OTUs in the ‘Müller-Thurgau’ samples. The mean number of observed OTUs was about three times higher in the ‘Müller-Thurgau’ samples  compared to the ‘Dornfelder’ samples  as well as in the ‘Müller-Thurgau’ vineyard , indicating no influence of the pruning intensity on the mycobiome composition in the grapevine trunk.A PCoA of the wood samples collected from two different trunk origins (center and head) of VSP and SMPH vines (esca symptomatic) was performed to find possible differences in the fungal-community composition . The resPhaeomoniella (29%), Fomitiporia (23%), Eutypa (13%), Diplodia (9%), Pleosporales (3%), Cadophora (2%), Phellinopsis (2%), Kalmusia (2%), Seimatosporium (1%) and Lopadostoma (1%). Samples collected from ‘Dornfelder’ vines contained 267 OTUs, which were assigned to 95 genera. Here, the ten most abundant genera/order covered 84% of all reads: Phaeomoniella (37%), Fomitiporia (19%), Diplodia (11%), Helotiales (6%), Eutypa (4%), Seimatosporium (2%), Phaeoacremonium (2%), Neosetophoma (1%), Teichospora (1%) and Angustimassarina (1%).Wood samples collected from ‘Müller-Thurgau’ vines contained 111 OTUs and were assigned to 43 genera. The ten most abundantly identified genera/orders, which are not assigned to ‘Unknown’, covered 85% of all reads : PhaeomoDiplodia (Botryosphaeria dieback), Eutypa (Eutypa dieback), Fomitiporia (esca) and Phaeomoniella (esca). For the ‘Dornfelder’ samples, their relative abundance is not different when comparing intensive and minimal pruning (Diplodia was about ten times more abundant in the VSP samples (23.6%) than in the SMPH samples (2.8%). Furthermore, the abundance of Phaeomoniella was about twice as high in the SMPH samples (59.8%) compared to the VSP samples (29.5%).Four fungal genera, all associated with GTDs, dominate the fungal mycobiome in the collected wood samples:  pruning . HoweverPruning intensity can have an impact on the incidence of external esca symptoms in the grapevine trunk. While intensive pruning may favor symptom occurrence, minimal pruning may reduce the risk ,54,55,56The reason why minimal pruning under certain conditions can minimize the esca risk is most likely due to the reduced number of pruning wounds on the trunk. As expected, fewer pruning wounds were found on the SMPH vines than on VSP vines. Moreover, intensive pruning seems to increase the trunk’s head circumference; the heads of the VSP vines were 17% (‘Dornfelder’) and 12% (‘Müller-Thurgau’) larger compared to SMPH vines, respectively. Removing shoots from the trunk head is a stressful situation for vines; the wound has to be closed, new shoots formed and therefore the vascular system needs to be restructured. This probably leads to additional wood formation and callusing in the trunk’s head, thereby increasing the circumference. The annual restructuring of the vascular system caused by intensive pruning also provokes disturbance in the sap flow and induces occlusion, both of which are associated with external esca symptoms, i.e., GLSD and apoplexy ,67,68. HFomitiporia spp., no significant differences were found between the VSP and SMPH vines irrespective of the esca status. Nevertheless, the data demonstrate that the occurrence of external esca symptoms is consistent with the extent of white rot in the trunk, since white-rot phenomena were significantly increased in symptomatic vines. Therefore, external symptoms may be positively correlated with the presence of white-rot fungi such as Fomitiporia spp., which is in accordance with other observations [F. mediterranea in the trunk considerably decreased and external symptoms disappeared [Besides vessel restructuring by the plant, pathogenic fungi can also interfere with the vessel integrity, e.g., by tissue degradation or by formation of occlusion ,69,70. Srvations ,75,76,77appeared ,79. For appeared , who fouappeared ,80,81. IDiplodia, Eutypa, Fomitiporia, Phaeomoniella [Fomitiporia spp., is expected in these trunks [A comparison of the fungal diversity in the trunk of symptomatic vines resulted in no differences between intensive and minimal pruning. With more pruning wounds, a higher diversity was expected for intensively pruned vines than for minimally pruned vines due to an increased infection rate. An explanation could be that the grapevine community in the trunk is mainly ruled by a few dominating fungi that impede the growth of other microbes ,83. In tmoniella . This ismoniella ,85,86,87e trunks ,88.Fomitiporia mediterranea and Phaeomoniella chlamydospora are the most isolated fungi from their genus in the context of the mycobiome in German grapevine wood [Eutypa spp., E. lata is the most common representative of this genus in grapevine wood [Diplodia seriata is one of the most frequent fungi, irrespective of sample age or health status [D. seriata is highly abundant in grapevine wood, it expresses a low pathogenicity against grapevine compared to other botryosphaeriaceous fungi [The method (metabarcoding) for the mycobiome analysis as applied in the present work has some limitations, since the classification of some OTUs at the species level based on ITS sequences alone is often not possible. Nevertheless, the species assignment can often be assumed by the isolation frequencies according to previous works. For instance, the two esca pathogens ine wood . While tine wood ,88. Regaine wood ,88,89. Ih status ,84,86,88us fungi ,91,92. TCadophora spp. and Phaeoacremonium spp. are well-known grapevine pathogens that are mainly associated with Petri disease, with C. luteo-olivacea and P. minimum being the most prevalent [Diaporthe spp., anamorph Phomopsis spp., were frequently observed in our study. These fungi, primarily Diaporthe ampelina, are the causal agent of Phomopsis dieback, but also cause Phomopsis cane and leaf spot [Phellinopsis sp. dominated one particular sample of intensively pruned Dornfelder vine. Representatives of this genus are known to cause wood decay in forests and gardens [Kalmusia spp. were recently shown to be associated with decline (K. variispora) and vascular necrosis (K. longispora) of V. vinifera [Seimatosporium, Neofabrea, and Truncatella; related species known as pathogens on grapevine are S. vitis, N. kienholzii, T. angustata [Clonostachys, was found [Besides the four dominating fungal genera mentioned above, further GTD-associated genera were detected, although with reduced abundance. The genera revalent ,94,95,96eaf spot ,98,99. T gardens ,101. Kalvinifera ,102. Furngustata ,107,108.as found ,110.Minimal-pruning schemes may lead to a reduction in esca. This observation is probably linked to the integrity of the vascular system. By avoiding pruning wounds, the plant has no need to form new shoots on the trunk. Therefore, the blocking of former sap routes and restructuring of the vessel system in the trunk is not necessary, which reduces the risk of sap-flow disturbance and occlusion. In addition, fewer pruning wounds on the trunk decrease the risk of infection by GTD-associated fungal pathogens, which could also interfere with the vessel integrity. In conclusion, keeping the main vascular system intact by minimal pruning reduces the occurrence of external esca symptoms. Nevertheless, the timing of pruning conversion, from intensive to minimal pruning, seems to be a critical factor, as seen for the ‘Müller-Thurgau’ vineyard. If pruning conversion takes place in older vineyards (about >10 years), the damage to the vascular system by vessel restructuring and pathogenic fungi may be too advanced and symptom expression is more likely. However, since only two vineyards were compared in this study, and due to the complexity of the disease, with several influencing factors  involved, more research efforts need to be put towards this topic in order to verify these findings.


The toxicity of cigarette smoke (CS) is largely attributed to its ability to generate reactive oxygen species (ROS). Reportedly, CS generates superoxide in cell culture systems by stimulating the cells to produce superoxide and through direct chemical reactions with components of the culture media. In this study, we investigated CS-induced superoxide formation in biocompatible aqueous media and its characteristics. Cigarette smoke extract (CSE) and total particulate matter (TPM) were prepared from the mainstream smoke of 3R4F reference cigarettes. CSE and TPM generated superoxide in Hank’s balanced salt solution (HBSS), Dulbecco’s modified Eagle media (DMEM), and blood plasma, but not in distilled water and phosphate-buffered saline. Each constituent of HBSS in solution was tested, and bicarbonate was found to be responsible for the superoxide generation. More than half of the superoxide formation was abolished by pretreating CSE or TPM with peroxidase, indicating that the substrates of peroxidase, presumably peroxides and peroxy acids, mainly contributed to the superoxide production. In conclusion, the presence of bicarbonate in experimental conditions should be considered carefully in studies of the biological activity of CS. Furthermore, the local amount of bicarbonate in exposed tissues may be a determinant of tissue sensitivity to oxidative damage by CS. Smoking is a well-established risk factor for cardiovascular diseases, cancers, and respiratory disorders. Several studies conducted over the past few decades have uncovered smoking-activated pathogenic events, key aspects of which include cytotoxicity and altered cell growth, inflammation, genetic damage, hypoxia, cardiac and endothelial dysfunction, prothrombotic condition, and abnormal lipid accumulation . AlthougCS was observed to generate ROS in in vitro experiments. In a previous study, CS was found to be capable of stimulating cells to produce superoxide by activating NADPH oxidase . In the The mechanism of superoxide generation in a cell-free system was investigated with CS preparations. The standard reference cigarette 3R4F was used to generate CS ,14. CS imeta-chloroperoxybenzoic acid (mCPBA), and ferrous sulfate (FeSO4). Other chemicals and sources were as follows: coumarin boronic acid (CBA) ; 5-(diisopropoxyphosphoryl)-5-methyl-1-pyrroline-N-oxide (DIPPMPO) ; 2-(4-iodophenyl)-3-(4-nitro-phenyl)-5--2H-tetrazolium (WST-1) ; hydrogen peroxide (H2O2) ; InstantBlue Coomassie protein stain . All other chemicals used were of the highest purity available and were obtained from standard suppliers.3R4F reference cigarettes (9.4 mg tar and 0.73 mg nicotine) were purchased from the Center for Tobacco Reference Products of the University of Kentucky . Fetal bovine serum (FBS), Hank’s balanced salt solution (HBSS), and phosphate-buffered saline (PBS) were obtained from Thermo Fisher Scientific . The following chemicals were purchased from Sigma-Aldrich : Dulbecco’s modified Eagle’s medium , 4-hydroxy-2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPOL), superoxide dismutase (SOD), horseradish peroxidase (HRP), diethylenetriaminepentaacetic acid (DTPA), xanthine, xanthine oxidase, catalase, D-mannitol, Male Sprague-Dawley rats at 5 to 6 weeks of age were purchased from Daehan Biolink . The rats were housed in the laboratory animal facility and acclimated for more than a week before the experiments. The laboratory animal facility was maintained at a controlled temperature of 22 ± 3 °C and relative humidity of 50 ± 20% with ambient noise of less than 60 dB and a 12-h light–dark cycle. Food and water were provided ad libitum.g for 15 min, the plasma was obtained from the supernatant and used immediately.Blood plasma was prepared from 7-week-old Sprague-Dawley rats. The rats were anesthetized with ether, and blood was collected from the abdominal aorta using acid-citrate-dextrose  as an anticoagulant . After centrifugation of the blood at 250× CSE and TPM were prepared as described in our previous study with minor modifications . In acco2 and subcultured when they reached 80–90% confluence.Vascular smooth muscle cells (VSMC) were isolated from rat thoracic aortas by enzymatic digestion as previously described . Briefly2HPO4, and 1.06 mM KH2PO4), HBSS , DMEM, DMEM with 10% FBS, and rat blood plasma. For measurement in cultured cells, VSMC or A549 cells were seeded into 96-well plates at a density of 2 × 104 cells/well and grown for 24 h. Cells were treated with 10% CSE in the presence or absence of 10 U/mL SOD. The absorbance at 450 nm was measured for 60 min for CSE or for 30 min for TPM at 37 °C using a SpectraMax M3 microplate reader . To test the involvement of the HRP substrate in superoxide generation, CSE or TPM was preincubated with HRP for 30 min at 37 °C before superoxide measurement.Superoxide was measured with a WST-1 colorimetric probe. CSE or TPM were added to aqueous solutions containing 500 µM WST-1, which included distilled water (DW), PBS  working in X-band. The instrumental parameters were as follows: modulation frequency, 100 kHz; modulation amplitude, 1.0 G; receiver gain, 30 dB; microwave power, 9.4 mW; center field, 3435 G; conversion time, 20 ms; time constant, 1.28 ms; sweep time, 30 s. The presented spectra are the averages of five scans. To obtain the typical spectrum for the superoxide or hydroxyl radical, samples were prepared by treating the 44 mM NaHCO3 solution with 500 µM xanthine and 10 mU/mL xanthine oxidase or 1 mM FeSO4 and 10 mM H2O2.EPR spin-trapping experiments were performed as in a previous study . CSE was3 to a concentration of 1 mg/mL. These albumin solutions were treated with 10% CSE, and after a 5-min incubation, the reaction was terminated by adding 5 mM of TEMPOL. Carbonylation of the albumin was assessed using the OxyBlot protein oxidation detection kit (Merck Millipore) following the manufacturer’s instructions. Briefly, each sample was subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to a polyvinylidene difluoride membrane. The carbonylated albumin was probed with a primary antibody specific for 2,4-dinitrophenylhydrazone and a horseradish peroxidase-conjugated secondary antibody, and then visualized with Immobilon Western chemiluminescent HRP substrate (Merck Millipore). Chemiluminescence images were obtained and analyzed with a ChemiDoc XRS+ system equipped with Image Lab software . To confirm the amount of loaded albumin, the gels were additionally stained with InstantBlue Coomassie protein stain (Abcam).Human albumin was added to 100 µM DTPA-containing DMEM; DMEM with 100 U/mL SOD, 10 mM TEMPOL, or 500 U/mL catalase; or DMEM without NaHCOp-value less than 0.05 was considered statistically significant.The means ± standard errors were calculated for all experimental groups. The data were subjected to one-way analysis of variance followed by Dunnett’s test to determine the significance of the differences relative to the controls, except for the data for albumin carbonylation, which was subjected to two-way analysis of variance followed by Tukey’s honest significant difference test. All statistical analyses were performed using SigmaPlot software ver. 13 . A CSE was tested for its ability to produce superoxide in various aqueous media. CSE was used to treat DW, PBS, HBSS, DMEM, DMEM containing FBS, and blood plasma at a concentration of 10%, and superoxide formation was measured using a WST-1 superoxide probe . Adding In addition, superoxide generation was assessed in the presence of cells to compare the relative amount of superoxide formed in media and cells. The amount of superoxide generated in the presence of VSMC or A549 was approximately two fold greater than that in the absence of cells D. These 3, CaCl2, KH2PO4, Na2HPO4, MgSO4, and glucose. The concentrations of each constituent were equal to those in HBSS. CSE was added to each solution but superoxide was only produced in the NaHCO3 solution, and the amount of superoxide formed was comparable to that produced in HBSS , rather than sodium ions, are responsible for superoxide generation by CSE.Each constituent of the aqueous solutions was examined for its involvement in CSE-induced superoxide production to identify the culprit(s) responsible for superoxide formation. HBSS consists of only eight constituents with the simplest chemical composition among the tested media in which CSE significantly produced superoxide. Accordingly, superoxide formation was tested in eight solutions: NaCl, KCl, NaHCO in HBSS A. Moreov in DMEM B. A rela3 to the reaction, we tested the amount of superoxide generated in HBSS solutions, each lacking a single constituent. Consistent with previous results, the superoxide generation by CSE was abolished by removing NaHCO3 from HBSS . This raises the possibility that WST-1 may detect the hydroxyl radical in addition to superoxide. The specificity of WST-1 toward superoxide needs to be explored although our current observation may not be enough to contradict the general notion that WST-1 is a superoxide probe.WST-1 has been conventionally and widely used for the detection of superoxide . However


Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures , or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.The online version contains supplementary material available at 10.1007/s10916-022-01807-1. Reverse transcription polymerase chain reaction (RT-PCR)  remains ML methods offer very different ways to help confining the spread of infectious diseases –13, e.g.presence of domain shifts in COVID-19-related blood test datasets. Second, we evaluate the ML models for prediction of COVID-19 diagnosis and mortality risk with different assessment strategies to demonstrate that these domain shifts diminish the predictive performance. Third, we compare the expected and actual performance to show how model credibility is decreased by domain shifts.In this study, we first reveal the Good generalization of ML models is only possible if the training data and future (test) data arise from the same underlying distribution. Deviations between training and test data distribution are a well known challenge in medical  and biolThe same underlying distribution of training and future data also cannot be guaranteed during pandemics. Examples of potential domain shifts in COVID-19 related datasets are plotted in Fig. The domain shifts , 59, 60 degrading of predictive performance over time, because standard ML approaches are unable to cope with domain shifts over time [unreliable performance estimates. These performance estimates might be overoptimistic and can deviate significantly from the actual performance [Neglecting and insufficiently countering these domain shifts can lead to undesired consequences and failures of the models. The domain shifts can lead to ver time , 59, 60.formance .The ML models in our experiments do not require additional expensive features , 45–52. The findings of our work do not only apply to COVID-19 datasets, but also to future pandemics, other medical datasets and even to datasets from other fields, where domain shifts might play a role.Ethics approval for this study was obtained from the ethics committee of the Johannes Kepler University, Linz . In our study, we analyze anonymized data only. The dataset was collected, pre-processed and the blood tests were merged with the RT-PCR tests.As a first step, we plotted the statistics of the blood test parameters over time to visualize fluctuations of the statistics indicating the presence of domain shifts. To answer, whether domain shifts in the dataset cause degrading of predictive performance, we implemented different assessment strategies. To analyze the model credibility, a comparison of expected and actual performance was implemented and examined. Additional experiments and results are presented in the 2020 cohort, the blood test samples are discarded. Hence, the 2020 cohort is biased towards patients, who might already be suspect for being COVID-19 positive and therefore are tested. Additionally, all samples with a deviating RT-PCR test result within the next 48 h are discarded, as the label might be incorrect.The study is conducted on the dataset Table  of the K2019 cohort). The 2019 cohort does not contain COVID-19 tests, therefore, blood tests with a temporal distance of less than 48 h are aggregated. A temporal distance of 48 h is selected such that the 2019 cohort resembles the 2020 cohort. The samples with less than 15 features are dropped from the dataset, all other available blood tests from the year 2019 are incorporated in the dataset. We assume that all patients in the year 2019 have been COVID-19 negative, because the virus has not been detected in Austria at this time. With a large, diverse dataset, the data distribution of the COVID-19 negative samples is broadly covered and learnt by the ML model. The distribution of the negative samples provided to the model during training has to be similar to the test data distribution for high predictive performance. During deployment, the model will be confronted with negative blood tests from a broad spectrum of different health scenarios, therefore, the 2019 cohort is incorporated during training.Additionally, we incorporate pre-pandemic blood tests from the year 2019 as negatives to our dataset to cover a wide variety of COVID-19 negative blood tests . All other features and samples are discarded. Besides the measured blood test values, the selection of the acquired blood test parameters might also contain relevant information. Therefore, for each sample 100 additional binary entries are created, which indicate whether each of the features is missing or measured. The missing values are filled by median imputation. Hence, the models can be applied to blood tests with few measured values. In the full dataset (2019 and 2020 cohort) 58.0% and in the positives cohort 49.6% of the selected features are missing.Before the selection of the 100 most frequent features, we include all available blood test parameters from the Med Campus III in Linz. This ranges from standard blood test parameters, such as leucocyte count up to blood tests for rare tropical diseases. Only the COVID-19 antibody tests are discarded from the dataset, as these might be directly related to the COVID-19 status. For the prediction of the COVID-19 diagnosis, the 100 most frequent features in the positives cohort are displayed in Fig. positives cohort.Domain shifts are changes of the distribution over time, therefore, the mean, median and standard deviation, the first and third quantile of exemplary blood test features of the We investigate the capability of different ML model classes to predict the COVID-19 diagnoses and the mortality risk. To this end, the predictive performance of self-normalizing neural networks (SNN) , K-neareThe hyperparameters are selected via grid-search on a validation set or via nested cross-validation to avoid a hyperparameter selection bias Table . The traThe models are selected and evaluated based on the area under the receiver operating characteristic curve (ROC AUC) , which iIn this section, we evaluate whether domain shifts diminish the predictive performance of ML models. A flow chart about the assessments is shown in the supplementary information Fig. . Therefoi.assessed by random validation with pre-pandemic negatives.All patients are randomly shuffled and split regardless of the patient cohorts . Domain shifts are not considered in this experiment. This experiment is performed to obtain an estimate of the predictive performance if there were no domain shifts in the data. This also corresponds to the performance estimates provided in other studies , 37–43, ii.assessed by random validation with recent negatives.2019 cohort and 80%  of the 2020 cohort. The test set comprises the remaining samples (20%) of the 2020 cohort. Therefore, the performance is estimated on patients, who actually were tested for COVID-19. Domain shifts between the 2019 cohort and the 2020 cohort are considered. Domain shifts within the 2020 cohort are not considered. This experiment is executed in order to reveal the effects of biases and domain shifts between the 2019 and 2020 cohort.The training and validation sets include the iii.assessed by temporal validation.2019 cohort and the 2020 cohort before November . A prospective performance estimation is conducted for the test set with all samples from November and December 2020. By the temporal split, domain shifts over time are considered. In this experiment, it is investigated how the models would perform in real-world environment, where models can only be trained with data from the past and deployed on future data.The training and validation sets include the iv.assessed by nested cross-validation.positives cohort, which are the positive cases from the 2020 cohort. Due to the limited number of samples, predictive performance is estimated with five-fold nested cross validation. This experiment is conducted to show the performance estimates, when domain shifts over time within the positives cohort are not considered. We hypothesize, that these results, which correspond to the performance estimates in other studies [The training (60%), validation (20%) and test (20%) sets comprise the  studies –48, are v.assessed by temporal validation.The training and validation sets include the positive cases from 2020 before November . The test set comprises the cases from November and December. In this experiment, domain shifts over time are considered. In this experiment, by temporal validation, the performance of the models with consideration of the domain shifts is estimated.The performance estimates obtained by these different assessment strategies are compared. If the underlying distribution of the data remains similar over time, the performance estimates by random cross-validation and temporal cross-validation must also be similar. If the performance estimates of (ii) are different from (i), then former and more recent negatives follow different distributions and the ML models are affected by the domain shifts. If performance estimates from (iii) are lower than those of (i) and (ii), the distribution of the data changes over time, hence indicating the presence and diminishing effects of domain shifts on predictive performance. Equally, changing performance estimates from (iv) to (v) indicate a domain shift over time. The binomial test  is used In this experiment, we test whether domain shifts cause deviations of expected and actual performance. The predictive performance would remain similar without domain shifts, but in the presence of domain shifts, the performance could be significantly different and thus domain shifts may be exposed. If the expected and actual performance are different, the diminishing effect of domain shifts on model credibility are revealed.In this experiment, a standard ML approach is simulated in which a model is trained on data collected in a particular time-period (model training), then assessed on a hold-out set (expected performance) and then deployed  Fig. . For exaIn general, ML models are capable of diagnosing COVID-19 and predicting mortality risk with high ROC AUC values. XGB and RF outperform other model classes in the COVID-19 diagnosis and in the mortality prediction. The comparison of evaluations on different cohorts expose domain shifts and their diminishing effect on predictive performance. Results are reported in terms of threshold-independent performance metrics for the comparison of the models Tables  and 3 asi.assessed by random cross-validation with pre-pandemic negatives.In this experiment, the highest ROC AUC performance is achieved, however, domain shifts are not considered in the performance estimate. The threshold-dependent metrics for the RF for multiple thresholds are reported, which are determined by defining negative predictive values on the validation set Table .ii.assessed by random cross-validation with recent negatives.The test set of experiment (ii) only comprises cases from the year 2020, which have been tested for COVID-19 with an RT-PCR test. Pre-pandemic negatives are excluded from the test set and the model is evaluated on pandemic samples only, which causes a performance drop from experiment (i) to (ii) (P = 0.016), see Table iii.assessed by temporal cross-validation.In this experiment, the model is trained with samples until October and evaluated on samples from November and December. An additional performance drop in comparison to experiment (ii) (P = 0.016) is observed, which points to a domain shift over time which degrades predictive performance.iv.assessed by random cross-validation.The samples are randomly shuffled and a five-fold nested cross-validation is performed. Again, the threshold-dependent metrics are reported Table .v.assessed by temporal cross-validation.positives cohort). The performance drops from experiment (iv) to (v) (P = 0.016), revealing a domain shift and over time for mortality prediction. The domain shifts over time again decrease the predictive performance.In this experiment, the model is trained with samples until October and evaluated on samples from November and December for mortality prediction of COVID-19 positive patients , which leads to a further decrease in predictive performance. Same holds for the drop of the predictive performance due to prospective evaluation in the mortality prediction task from experiment (iv) to (v).The conducted experiments explore different levels of consideration of the domain shifts by different assessments. The evaluations are compared on the basis of ROC AUC as the PR AUC depends on the class prior, which varies in the different evaluation cohorts. The results expose the domain shifts and their diminishing effect on predictive performance, as the performance drops from experiment (i) to (ii) and even further to (iii), and also from experiment (iv) to (v). By comparing experiment (i) and (ii) we investigate if inclusion of pre-pandemic negatives in the test set leads to overoptimistic metrics, and indeed variations in the performance metrics can be observed. We attribute this to the fact that the This experiment investigates the difference of the expected to the actual performance. The expected and actual results are compared for different simulated deployment times (June until December 2020) Fig. . The expCredible and highly performant ML models for in-hospital applications require frequent re-training and re-assessments to combat the domain shift effects. Stronger weighting of more recent samples increases the predictive performance under domain shifts. More details on the methods and results to frequent re-training and stronger weighting of more recent samples are described in the Our set of experiments exposes the presence of domain shifts in COVID-19 blood test datasets as well as their detrimental effect on ML models. These domain shifts were insufficiently considered in previous works, which might have led to poor performance or even failure of the ML models in clinical practice. Therefore, our results suggest that the model performance should be frequently re-assessed. An up-to-date temporal evaluation appears indispensable to avoid unexpected behavior. The model should be frequently re-trained and more recent samples should be weighted stronger to exploit newly acquired samples and, thus, to counter the domain shift effect Below is the link to the electronic supplementary material.


Marmota himalayana burrows as well as whether there are differences in burrow traits under different terrains  are not well understood. From July to August 2019 (warm season), we used unmanned aerial vehicles to fly at low altitudes and slow speeds to locate 131 M. himalayana burrows  in the northeastern Qinghai‐Tibetan Plateau region. We then measured the physical characteristics  of these burrows on site. We found that terrain had a substantial influence on burrow density, orientation, and entrance size and on the angle of the burrow entrance; species richness had a substantial impact on path density and tunnel volume. The physical parameters of the M. himalayana burrows showed that they function to protect the marmots from natural enemies and bad weather, provide good drainage, and maintain a stable microclimate around the entrance. We discuss the ability of burrowing animals  to adapt to the external environment based on their burrow characteristics.Burrows provide burrowing animals with a place to hibernate, reproduce, and avoid predators and harsh weather conditions and thus have a vital impact on their survival. However, the general physical characteristics and ecological functions of  M. himalayana dens showed that they function to protect the marmots from natural enemies and bad weather, provide good drainage, and maintain a stable microclimate around the entrance. This is a result of the marmot’s adaptation to the harsh environment (cold and humidity) of the Qinghai‐Tibet Plateau.The physical parameters of the  Determining the adaptation mechanism of M. himalayana with respect to alpine meadows on the QTP in terms of its burrow characteristics could strengthen our understanding of the ecological significance of this species.Except for plateau rabbits  is conditioned by the presence of convex geomorphic features, which are due to their structure being suitable for the construction of permanent burrows, mainly hibernacula  are predominantly in talus patches, which provide shelter from predators and weather  prefer southern and eastern exposed locations  . This area is located in the northeast of the QTP. The climate is cold and humid, with only a warm season (May to September) and cold season (October to April) , Tibetan foxes (Vulpes ferrilata), and large raptors (Bubo bubo and Buteo hemilasius). Marmots are very cautious and often look up to observe the surrounding environment during foraging. Their area of activity is typically concentrated within 2–100 m of their burrow entrance  of 2019 by searching the study area from unmanned aerial vehicles (UAVs) flying 40–50 m above the ground at speeds of 30–50 km/h. Burrows located during aerial surveys were ground checked to verify their identity based on the presence of a large amount of marmot scat Figure , footpriM. himalayana burrows, we did not carefully distinguish between temporary burrows, summer‐living burrows, and hibernation burrows. We measured the following indicators of these burrows and the surrounding environment.During the entire study period, we investigated 131 burrows (51 on sunny slopes (facing south or west), 45 on shady slopes (facing north or east), and 35 in flat areas). To reveal the universal characteristics of (1) Burrow density: We calculated the burrow density for each terrain based on the area of the surveyed sites as scanned by UAVs  and the short axis (b). The entrance area (S) was calculated using the following formula:(2) Burrow entrance size: As the burrow entrance of (3) First tunnel length: We used a measuring tape to measure the length from the entrance to the first corner of the tunnel.R and r represent the upper and lower radius of the mound, respectively.(4) Burrow volume: We used the equal volume method to measure the tunnel volume based on the pile of dirt beside each burrow. The volume of the truncated cone‐shaped pile is approximately equal to the tunnel volume:N  position), NE (1°–89°), E (90°), SE (91°–179°), S (180°), SW (181°–269°), W (270°), and NW (271°–360°).(5) Burrow orientation and angle of burrow entrance: We used a rangefinder  to measure the orientation and angle of the entrance. Here we divided the burrow orientation into eight directions: (6) Path density near the burrow entrance: We determined the path density according to the trampled vegetation around the burrow entrance Figure .(7) Vegetation characteristics near the burrow entrance: For each burrow, to avoid any influence of the mound, we selected a quadrat  and/or distance  as fixed effects. One‐way analysis of variance (ANOVA) with a least significant difference (LSD) test for multiple comparisons was used to compare various indicators of marmot burrow characteristics among different terrains; for the data about plant species richness, species height, and aboveground biomass, we used a two‐way ANOVA. Figures were constructed using Origin 9.1.Data were analyzed using Statistical Package for the Social Sciences (SPSS) . Data were checked for a normal distribution using the Shapiro–Wilk test. Data for marmot burrow characteristics that were not indicated as being normally distributed were logPrincipal component analysis (PCA) was performed to study the relationship between the environmental variables (terrain and plant traits) and the burrow characteristics. PCA was performed using CANOCO version 5.0  . The average length of the long axis among entrances in flat areas (31.00 ± 4.27 cm) was significantly longer than that on sloped terrain (27.21 ± 3.71 cm) . There was no significant difference in the length of the short axis of the entrance among different terrains . In addition, there was no significant difference in the entrance area among the shady slopes (0.19 ± 0.03 m2), the sunny slopes (0.19 ± 0.04 m2), and the flat areas (0.22 ± 0.06 m2)   of the oval‐shaped entrances was significantly longer than the short axis (22.95 ± 3.57 cm)    and sunny slopes (0.32 ± 0.15) and in flat areas (0.29 ± 0.17) . However, the angle of the burrow entrance was not significantly different between those on sunny slopes and in flat areas   and in flat areas (38.13 ± 3.92°)  . Species richness in activity areas near burrows in flat areas was significantly higher than that on sloping terrain . There was no significant difference in species richness near the entrance of the burrows among the three terrains   was significantly lower than that in the activity area (31.92 ± 3.43 species) across all burrows  Figure .F1, 129 = 2.92, p < .05). Species height near the entrance on shady slopes (26.96 ± 4.67 cm) was significantly higher than that near the entrance on sunny slopes (11.00 ± 2.12 cm) and in flat areas (19.71 ± 3.74 cm)   was significantly lower than that in the activity area quadrat (28.30 ± 3.52 cm)  Figure .F1, 129 = 2.78, p < .05). The aboveground biomass near the burrow entrance on sunny slopes (113.03 ± 24.17 g) was significantly lower than that on shady slopes (162.81 ± 17.96 g) and in flat areas (166.91 ± 16.70 g)   was significantly higher than that near the burrow entrance (150.77 ± 30.62 g)  Figure .3.9In a PCA of these data, principal component PC1 and PC2 explained 89.11% of the total variation in the environmental characteristics . Terrain had a substantial influence on burrow density, orientation, and entrance size and on the angle of the burrow entrance. In addition, species richness had a substantial impact on path density and tunnel volume Figure .44.1The underground structure of marmot burrows is very complex, and digging a complete burrow requires a lot of work. Animals tend to use the smallest investment of energy to get the greatest return  that inhabits the Ukraine  is larger than that of steppe marmots (3.85 kg). The shape and size of the burrow entrance facilitate the rapid entry of the marmot while preventing predators from entering  and thus ensures that they can enter the burrow quickly to avoid predators  in other parts of the QTP  are the most numerous and most common species of plants around the burrows. We found that species richness in marmot activity areas was significantly higher than that near the burrow entrance ; Investigation (lead). Fujiang Hou: Funding acquisition (supporting).App S1Click here for additional data file.


Moreover, a sensory evaluation was conducted to assess potential consumer acceptance. Based on the consumer’s perception, the overall acceptance of bread with 15% w/w of coconut and chestnut flour was in privilege compared to the control sample. As a result, taking all of the tested parameters into account, the breads with 5, 10, and 15% supplementation of chestnut or coconut flour were still of good quality compared to the wheat bread and their fibre content was significantly higher.Wheat bread, produced by the single-phase method, is a common food consumed all over the world. Due to changes in lifestyle and nutritional trends, alternative raw materials are sought to increase the nutritional value and improve the taste of daily consumed products. Additionally, customers seek a wide variety of foods, especially when it comes to basic foods. Nuts, such as coconuts or chestnuts, might provide an attractive flavour with benefits to the nutritional quality. In this study, the effect of substituting wheat flour with coconut or chestnut flour (flour contribution level: 5, 10, 15, 30, 50%  For most people in Europe bread is a basic and essential food product, which is consumed in everyday diet . A greatCastanea sativa Mill.) is mainly grown in continental European regions and has represented one of the most important and sustainable food resources of rural areas for many centuries. Chestnut flour (CH) is obtained by grinding dried chestnuts and it has a high starch content (50–60%), relatively high amount of sucrose (20–32%), high-quality proteins with essential amino acids (5–8%), dietary fibres (4–10%), and a low amount of lipids (2–4%) [Chestnuts and coconuts are nuts cultivated in many regions of the world. They are globally popular for their flavour, and nutritional and health properties. European sweet chestnut  . Chestnu acids 5–%, dietars (2–4%)  reportedCocos nucifera L.), needs a much warmer, tropical climate so it is mainly cultivated in Central and South America, Africa, and Asia. Coconuts in the form of whole fruits, as well as their parts and by-products obtained on daily basis by industries, is usually the main source of income for producing countries [Coconut, as a fruit of palm trees  of the crumb of the CH bread decreased proportionally to the addition of the CH flour. An opposite effect was noticed for the CO flour—the more flour share, the lighter the bread . As chesThe hardness and gumminess of bread with 50% of CO and CH flour were the highest compared to other tested samples, and it increased significantly after 48 and 72 h of storage . A similThe results of the sensory evaluation indicate a significant effect of the bread supplementation with CH or CO flours on the sensory quality and overall consumer acceptability. A wheat flour replacement with 50% of CO or CH flours has a negative impact on all the parameters  evaluateIn order to demonstrate comprehensively the alterations occurring in the types of the obtained breads, a chemometric analysis in the form of PCA was conducted on the qualified gathered data. In the PCA analysis, ten principal components were created, of which the first two explained 88.71% of the total variability. Due to the different chemical compositions, the effect of CH flour on the physicochemical and sensory characteristics of wheat bread was clearly different from that of the CO flour A. The atAll of the reagents and solvents used, of the reagent grade purity, they were obtained from Avantor Performance Materials Poland S.A. .Castanea sativa Mill., country of origin: Emilia-Romagna, Italy) flour, coconut  flour, dried baker’s yeast (Saccharomyces cerevisiae L.), salt and water were added to obtain a dough of a yield of 165%  flour type 750 , chestnut  was used. The carbohydrates content was calculated by subtracting the values of the protein, fat, moisture, total dietary fibre, and ash content from 100. The energy values  were calculated using conversion factors according to EU Regulation No 1169/2011 . The conversion factor for kilojoules is 1 kcal = 4.184 kJ.Protein content was determined according to the Kjeldahl method  , and calibrated against black and white plate standards. Crumb colour was determined at the middle point of the central 2 cm thick slice. The parameters of the device in the reflection mode were set as follows: a standard observer of 10°, and an illuminate D65. The measurements were performed at room temperature through a diaphragm of 3 cm diameter. The colour was expressed in the CIE L*a*b* scale, and the parameters determined were: L* (lightness), a* (redness), and b* (yellowness).The crumb texture was evaluated applying texture profile analysis (TPA) . TPA test was carried out using a cylindrical mandrel of a diameter of 25 mm, which penetrated the crumb to a depth of 9 mm with a crosshead speed of 40 mm/min. Measurements were carried out on middle slices of 20 mm thickness taken from the centre of each kind of experimental loaves (in triplicate). The bread samples were packed in a plastic box and kept in dark at room temperature (at 25 °C and 60% RH). Measurements were taken 24, 48, and 72 h after baking. Each slice was compressed twice to give texture profile charts obtained in the Texture Expert Exceed software, from which following textural parameters were determined: hardness , gumminess , resilience , cohesiveness , and chewiness  .Sensory evaluation of breads was carried out by ten trained panelists on the day of production (2 h after baking). The questionnaire included a 10-point scaling method based on the Polish norm . The same group of ten panelists received samples of coded bread in two sessions—at the rate of 6 per session (control and coconut or chestnut supplemented samples). During the testing sessions, panelists had access to drinking water to clean the palate between evaluations. Panelists were trained using ISO 8586-1:2012, ISO 8586-2:2014, and ISO 11036:2020. The following characteristics were assessed: crust colour (0 = yellowish and 10 = brownish), taste (0 = sweet and 10 = bitter), flavour , crumb porosity , and an overall acceptability (0 = dislike very much and 10 = like very much).All data in tables were presented as the mean with standard deviations. Unless otherwise stated, determinations were performed in triplicate. The statistical program Statistica 13.3  was used to develop the results. The effect of the type of flour, as well as the effect of the wheat flour replacement with coconut or chestnut flours on nutritious composition and/or physicochemical properties was analysed using the one-way analysis of variance (ANOVA). To evaluate the differences between average values for data that were normally distributed, the Tukey HSD test was used with a significance level of α = 0.05. If the tested data did not come from a normal distribution, the Kruskal–Wallis test was used instead.The gathered quantitative data were used in the chemometric analysis in principal component analysis (PCA) in order to show the affinity of the obtained wheat-coconut or wheat-chestnut bread loaves towards wheat bread treated as a control sample. The results of the determinations performed were qualified for PCA analysis based on a correlation score with the first or second principal component at a level of at least 0.7. According to the generated factor loadings matrix, data from the following analyses of breads were qualified for PCA classification: insoluble and soluble fibre, protein and fat content, specific volume, total colour difference ΔE*of crumb, and sensory characteristics .Among the analysed flours, the lowest moisture, carbohydrates, soluble dietary fibre content, and the highest ash, protein, fat, and insoluble dietary fibre content were specified in coconut flour. The insoluble dietary fibre fraction content of prepared breads was significantly increased by replacing part of the wheat flour with CH or CO flour. The volumes of breads with CO or CH flour supplementation decreased proportionally to the flour content, compared to wheat bread. A sensory evaluation points out that breads with 30 and 50% of CO and CH flour did not have a positive effect on the tested parameters, however, the supplementation with 5, 10, and 15% improved a breads taste, flavour, and overall acceptability. As a result, taking all the tested parameters into account, the breads with 5, 10, and 15% supplementation of CH or CO flours were still of good quality compared to the wheat bread, and their fibre content was significantly higher. Therefore, chestnut flour, as well as coconut flour, can be great ingredients for making nutritionally enhanced foods as well as functional foods.


Vibrio species, many of which are pathogens, have been regarded as an important virulence factor. However, there is little information on the structure and collagenolytic mechanism of Vibrio collagenase. Here, we report the crystal structure of the collagenase module (CM) of Vibrio collagenase VhaC and the conformation of VhaC in solution. Structural and biochemical analyses and molecular dynamics studies reveal that triple-helical collagen is initially recognized by the activator domain, followed by subsequent cleavage by the peptidase domain along with the closing movement of CM. This is different from the peptidolytic mode or the proposed collagenolysis of Clostridium collagenase. We propose a model for the integrated collagenolytic mechanism of VhaC, integrating the functions of VhaC accessory domains and its collagen degradation pattern. This study provides insight into the mechanism of bacterial collagenolysis and helps in structure-based drug design targeting of the Vibrio collagenase.The collagenases of  Vibrio collagenase, a virulence factor, remains unclear. Here, the authors report the structure of Vibrio collagenase VhaC and propose the mechanism for collagen recognition and degradation, providing new insight into bacterial collagenolysis.The collagenolytic mechanism of  Of the 28 types of collagens, type I collagen is the most widely occurring and has a hierarchical structure2. Monomeric type I collagen  is a righthanded triple helix comprising three α chains (two α1 chains and one α2 chain). Each chain consists of Gly-X-Y repeating triplets where X and Y are often occupied by proline and hydroxyproline, respectively. The N- and C-terminal telopeptides, located on the flanks of the triple-helical region, constitute the non-triple helical regions of tropocollagen1. Every five tropocollagens form one quasi-hexagonal microfibril unit4. Microfibrils are interdigitated regularly with each other, forming mature collagen fibrils. Various covalent cross-links, such as pyridinium compounds and pyrroles within or between microfibrils, maintain the strength and stability of the fibrils5. Collagen fibrils further aggregate with proteoglycans to form collagen fibers and other super structures in the ECM7. Due to its tight structure and hierarchical assembly, collagen can only be degraded by a very few proteases8.Collagens are the most abundant proteins in mammals, mainly in the extracellular matrix (ECM)11, especially for MMP-1. MMP-1 cleaves type I tropocollagen chain at a single Gly-Ile/Leu site generating 1/4 and 3/4 length fragments via a complicated molecular mechanism18. Bacterial collagenolytic proteases, that are capable of hydrolyzing native collagen at multiple sites, are distributed in the M9, S1, and S8 MEROPS peptidase families, some of which are strongly linked to bacterial pathogenesis21. Most known bacterial collagenases belong to the M9 family, which is subdivided into the M9A (Vibrio collagenases) and M9B (Clostridium collagenases) subfamilies. Up to now, information on the three-dimensional structure of Vibrio collagenase is still lacking. As for Clostridium collagenases, only the crystal structure of the collagenase module (CM) of ColG22 and those of the peptidase domains of ColH and ColT have been solved23. Eckhard et al.22 proposed a “chew and digest” collagenolytic model based on the saddle-shaped architecture of the CM of ColG. In this model, triple-helical collagen initially contacts the peptidase domain in the open state; in the closed state, triple-helical collagen interacts with both the activator and the peptidase domains in the CM and then is unwound and degraded progressively. However, due to the lack of sufficient experimental evidence for this model, how triple-helical collagen is recognized by the activator and the peptidase domains is still largely unclear. Thus, the mechanism of collagen recognition by M9 bacterial collagenases warrants further study in order to better understand its role in pathogenesis.Collagenolytic proteases, capable of hydrolyzing native collagen under physiological conditions, are found in animals and microbes. Mammalian matrix metalloproteinases (MMPs), belonging to the M10A subfamily, play crucial roles in the interstitial collagen catabolism and their structures and collagenolytic mechanisms have been extensively studiedVibrio species accelerate bacterial dissemination and facilitate diffusion of other toxins through hydrolysis of the collagenous components of the ECM, they are therefore regarded as an important virulence factor24. So far, studies have largely been focused on the physicochemical properties28, cytotoxicity, and fish pathogenicity29 of Vibrio collagenases. Except for an early report that Vibrio collagenase attacked the collagen molecule at 3/4 from the N-terminus by digesting the preferential peptide bond, Y-Gly, in the first step of collagen degradation30, information on the collagen cleavage pattern of Vibrio collagenase is scarce. Like Clostridium collagenases, the characteristic domain architecture of Vibrio collagenase consists of a peptidase M9N domain (activator domain) and a peptidase M9 domain (peptidase domain), forming a CM31. Some Vibrio collagenases contain additional C-terminal accessory domains, such as the polycystic kidney disease-like domain (PKD-like domain) and the bacterial prepeptidase C-terminal domain (PPC domain)20. The PPC domain of Vibrio collagenase Ghcol from Grimontia hollisae (formerly known as Vibrio hollisae) has been demonstrated to function as a collagen binding domain (CBD)32. Despite these studies, little is known about the mechanism of Vibrio collagenase for collagen recognition and degradation due to the lack of structural information.Collagenases of certain pathogenic Vibrio collagenase VhaC from Vibrio harveyi VHJR7, which is isolated from an infected fish34. Protease VhaC is a multi-domain enzyme composed of a CM containing an activator domain and a peptidase domain, a PKD-like domain and a PPC domain. The crystal structure of the CM is solved at 1.8 Å resolution and the overall conformation and interdomain arrangement of VhaC in solution is determined by small angle x-ray scattering (SAXS). The mechanism of VhaC for the recognition and catalysis of triple-helical collagen is illustrated based on structural and biochemical analyses and molecular dynamics studies, and the functions of the C-terminal accessory PKD-like and PPC domains in collagenolysis are analyzed. Finally, combined with its collagen degradation pattern, a model for collagen fibers degradation by VhaC is proposed. These results reveal the structure of Vibrio collagenase and its mechanism for collagen recognition and degradation, providing insight into the mechanism of bacterial collagenolysis and helping in the development of strategies to prevent and treat infection caused by pathogenic Vibrio species.In this work, we report the structure and propose the collagenolytic mechanism of a vhaC gene from Vibrio harveyi VHJR7 was predicted to encode a putative M9A collagenase (GenBank accession No. WP_047516938.1) of 814 amino acid residues. Mature VhaC protein contains 745 residues from Ser70 to Gln814 with the N-terminal signal peptide and propeptide being removed, which presented as a monomer in solution , a peptidase domain , a PKD-like domain (Ser606-Ala698), and a PPC domain (Leu699-Gln814) Fig. . The acture Fig. , which iure Fig. . The acture Fig. . The pepure Fig. . The CM ure Fig. , which i22. Correspondingly, in the CM structure of VhaC, the C-terminal residues of peptide P1 also bind to the conserved Gly441-Gly442 motif, representing the S1′ peptide recognition site  derived from the propeptide of VhaC via hydrogen bonds and hydrophobic interactions Fig. . While pite Fig. . Therefoite Fig. . In the  P1 Fig.  instead 446 Fig. . The Ca237.Because the structure of full-length VhaC could not be obtained via crystallization, VhaC was alternatively subjected to SAXS measurement to analyze its overall conformation and interdomain arrangement in solution. Related SAXS-derived molecular parameters are shown in Supplementary Table 10]3, and 4-phenylazobenzyloixycarbonyl-Pro-Leu-Gly-Pro-o-Arg (Pz peptide) were compared. VhaC showed evident activity against all three substrates, whereas trypsin (control) showed negligible activity against collagen fibers and no activity against [(POG)10]3 or Pz peptide 10]3 and only 27% activity against type I collagen fibers  against insoluble type I collagen fibers, triple-helical peptide (THP) 3, but this failed. Instead, the structure of unbound-CM (the structure of CM with peptides P1 and P2 being removed) in complex with a THP (PDB: 1K6F) was modeled by molecular docking. According to the binding mode with the highest score, THP is bound to the potential substrate-recognition interface of the activator domain by hydrophobic interactions 10]3 ons Fig. , away frain Fig.  but not ain Fig. . The keyain Fig. , and theain Fig. , indicat0]3 Fig. . These rlue Fig. . Circulalue Fig. , suggestTogether, these results indicate that VhaC adopts different strategies for the recognition of triple-helical collagen and peptide substrates. Triple-helical collagen is initially recognized by the activator domain, whereas peptide is directly recognized by the peptidase domain.22, in which the triple-helical collagen is initially recognized by the peptidase domain, the activator domain, and the peptidase domain of ColG were expressed in E. coli, and the collagen-binding ability of the recombinant proteins was analyzed. Similarly, the activator domain of ColG showed evident binding ability towards collagen fibers  of enzyme molecule  for 1000 ns. The RMSD profiles of the backbone atoms of the unbound-CM structure and its complex structure with THP show that all simulations generated stable trajectories Fig. , indicatule Fig.  and the ule Fig.  also shoule Fig.  and the ule Fig.  decreaseule Fig. , an unwi10]3, or Pz peptide  to Ala also completely abolished the activity of VhaC towards these three substrates 10]3 and Pz peptide. Furthermore, mutations of Gly441 and Gly442 to Val almost completely abolished the activity of VhaC against the three substrates 10]3 but only 35% towards collagen fibers  and charged residues  in the PPC domain were selected based on sequence alignment  at 30 °C for 5 h  VhaVibrio collagenases of the M9A subfamily, neither a domain nor an intact enzyme has three-dimensional structure information. VhaC from Vibrio harveyi VHJR7 is a true Vibrio collagenase composed of a CM containing an activator domain and a peptidase domain, a PKD-like domain, and a PPC domain. In this study, the crystal structure of the CM was solved, it having a more contracted saddle-shaped architecture and lacking the catalytic helper subdomain, when compared to that of Clostridium collagenase ColG, the only CM structure in the M9 family22. Moreover, the overall conformation and interdomain arrangement of the full-length VhaC in solution was analyzed by SAXS. The side-by-side arrangement of the CM, the PKD-like domain, and the PPC domain results in a long and flat overall conformation in solution, which is significantly different from the tapered form of the Clostridium collagenase, ColH . For ColH, the compact arrangement of the CM and the PKD-like domain 1 constitutes its swollen head, and the PKD-like domain 2 and the PPC domain extending to the C-terminus form its tapered tail37. Among MMPs, the structure of MMP-1 and its complex with triple-helical collagen have been extensively studied by X-ray diffraction50 and nuclear magnetic resonance (NMR)17. The mature enzyme structure of MMP-1 shows a compact arrangement of the catalytic (CAT) domain and the C-terminal hemopexin (HPX)-like domain connected by a linker12, and the catalytic and structural Zn2+ ions play an important role in the structural stability and the dynamics of the MMP-1: THP complex51. There is no sequence or structural similarity between MMP-1 and VhaC.Although many bacterial collagenases of the M9 family have been identified, structural information on the M9 bacterial collagenases is still limited. For Clostridium collagenase ColG proposed by Eckhard et al.22. In this model, the CM of ColG composed of the activator domain and the peptidase domain is suggested to switch between open and closed states for collagen hydrolysis. In the open state, triple-helical collagen initially docks to the peptidase domain. Followed by closing of the CM, triple-helical collagen is able to interact with both the activator domain and the peptidase domain. Subsequently, an entropy-driven unwinding of triple-helical collagen occurs, and the cleavage of the peptide chain follows22. Based on this model, only in the closed state can the activator domain interact with the triple-helical collagen bound to the peptidase domain and this assists the peptidase domain to complete the substrate unwinding22. In this study, molecular docking on the CM of Vibrio collagenase VhaC with a THP (PDB: 1K6F) suggested that triple-helical collagen is bound to the activator domain by hydrophobic interactions, but not the peptidase domain. Consistently, biochemical analysis showed that the activator domain of VhaC had binding ability to both THP [(POG)10]3 and collagen fibers, but its peptidase domain had neither. Moreover, it was also found that the activator domain of ColG showed evident binding ability towards [(POG)10]3 and collagen fibers, but its peptidase domain did not. Therefore, during collagen degradation by the M9 bacterial collagenases, such as VhaC and ColG, it is most likely that the activator domain is responsible for the initial recognition of triple-helical collagen, rather than the peptidase domain. This is different from the peptide recognition mode of the M9 collagenases. Both the results of this study and those reported on the M9B collagenases suggest that, for peptide degradation, the peptidase domain of the M9 collagenases alone can finish the recognition and catalysis of peptides23. However, for collagen degradation, the initial recognition of triple-helical collagen by the activator domain is indispensable.Based on structural and biochemical analyses, the function of each domain of VhaC in collagenolysis was investigated and the mechanism of VhaC for triple-helical collagen recognition and catalysis was proposed. Presently, our understanding of collagen recognition and degradation by M9 collagenases largely comes from the chew-and-digest model of the 4. Similarly, Vibrio collagenase VhaC is likely to initially attack the C-telopeptide region to release tropocollagen fragments for further hydrolysis. However, the recognition and catalytic mechanisms of MMP-1 and VhaC on the triple-helical collagen monomer are significantly different. In addition to the CAT domain, the hydrolysis of triple-helical collagen by MMP-1 requires the exosite interaction of the HPX domain with the substrate50 and the interdomain flexibility modulated by the linker52. MMP-1 exhibits an equilibrium between open and closed conformations in solution16, and the binding of triple-helical collagen in the open conformation is productive53. MMP-1 in the open state binds on the triple-helical collagen via the HPX domain and the guidance of the flexible linker positions the CAT domain toward the intertwined cleavage site17. Back-rotation of the CAT and HPX domains to the closed conformation leads to the local unwinding of the triple helix, allowing a single-chain to enter the catalytic cleft and be cleaved17. The unwinding of triple-helical collagen , which is stimulated by heat and compensated by entropy54, is a prerequisite for a single chain to enter the catalytic cleft (~5 Å) of MMP-1. In contrast, the hydrolysis of triple-helical collagen by VhaC is performed only by its CM that easily accommodates a triple-helical collagen monomer in its large catalytic cavity (~40 Å) and either the PKD-like domain or the PPC domain is not necessary. The activator domain in the CM initially binds the triple-helical collagen and the subsequent closing movement of the CM suggested by MDS studies enables the triple helix close to the catalytic center of the peptidase domain for hydrolysis. During this process, a local unwinding of the triple-helical collagen may occur. Eckhard and coworkers speculated that the final hydrolysis of triple-helical collagen by the CM of Clostridium collagenase ColG depends on an entropy-driven unwinding and the mechanical energy may come from the release of stored ordered water for substrate hydrolysis22. Santra et al.56 reported that Vibrio collagenase VMC that contains only a CM is likely to cause unwinding and structural destabilization of the triple-helical collagen molecule via its C-terminal FAXWXXT motif prior to hydrolysis. The key residues in this motif are conserved in Vibrio collagenases, providing information for revealing the underlying triple helix unwinding mechanism of Vibrio collagenases. Even so, how the triple-helical collagen is unwound before hydrolysis in the CM of the M9 collagenase still needs further study.Mammalian collagenase MMP-1 initially hydrolyzes the C-telopeptide on the outer edge of the collagen fibril to expose the cleavage site and permit interactions of the enzyme with the adjacent tropocollagen triple helixVibrio collagenase of the M9A subfamily has been identified as an important virulence factor due to its ability to digest native collagen of the ECM24. This study reveals the structure of a Vibrio collagenase, VhaC, and its integrated mechanism of collagen recognition and degradation. This information will be crucial for future development of Vibrio collagenase as a drug target. In addition, functional domains that effectively bind to collagen have broader biomedical and clinical applications, such as developing collagen-targeted therapy. For example, CBDs of Clostridium collagenases are tailored for collagen-targeted drug delivery by fusing to growth factors58. The activator domain of VhaC consisting of an array of tandem α-helices has such a potential for its ability to target and bind collagen, which may be a promising target for designing drug delivery models. Sequence alignment shows that the key residues responsible for collagen binding are highly conserved in the M9 family, suggesting that the activator domains of other M9 collagenases, just like that of ColG, may also have collagen-binding function and act as such collagen-binding moiety in the drug delivery model.59. Briefly, shredded codfish skins bought from Shandong Oriental Ocean Co., Ltd. (China) were soaked with 0.1 M NaOH for 1 d, and then were treated with acetone by stirring for 3 h. The samples were then washed with distilled water and dissolved in 0.5 M acetic acid for 3 d with continuous stirring. After centrifugation, NaCl powder was added to the supernatant to a final concentration of 0.5 M. The precipitated collagen was dialyzed against distilled water and then freeze-dried. Type I collagen fibers from bovine achilles tendon were purchased from Worthington Biochemical Co., (USA). Collagens of types II-V, gelatin, casein, and Pz peptide were purchased from Sigma–Aldrich (USA). Collagenous peptide (POG)10 was purchased from Peptide Institute, Inc (Japan). Trypsin was purchased from Shanghai aladdin Biochemical Technology Co., Ltd. (China).Fish collagen was extracted from codfish skin using the method described by Chen et al.vhaC and pET-22b-colG as templates, truncation mutations of VhaC and ColG were generated by PCR amplification or overlapping extension PCR60. The gene encoding EGFP amplified from the vector pEGFP-N1  was used to fuse with DNA fragments for expressing EGFP fusion proteins. Site-directed mutations were performed using a modified QuikChange kit . The plasmids and primers used in this study are listed in Supplementary Data The genes encoding VhaC WP_047516938.1) and ColG D87215.1) were synthesized by the BGI Tech Solutions Co., Ltd. (China) and cloned into the pET-22b vector  for protein expression. Using the recombinant plasmid pET-22b-6938.1 an15.1 wereE. coli strains used in this study are listed in Supplementary Data E. coli BL21 (DE3) which were induced by the addition of 0.35 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at 15 °C for 16 h in the Lysogeny-Broth (LB) medium. The selenomethionine (SeMet) derivative of CM was over-expressed in E. coli BL21 (DE3) under the induction of 0.35 mM IPTG in the M9 minimal medium supplemented with selenomethionine, lysine, valine, threonine, leucine, isoleucine, and phenylalanine61. After cultivation, E. coli cells were collected and disrupted by high-pressure cell cracker in the buffer containing 50 mM Tris-HCl (pH 8.0), 100 mM NaCl, and 0.5% glycerol. The recombinant proteins were first purified by affinity chromatography with nickel-nitrilotriacetic acid resin  and then by anion exchange chromatography using a Source 15Q column . The target fractions were further purified by gel filtration chromatography using a Superdex 200 column  eluted in the buffer containing 10 mM Tris-HCl (pH 8.0) and 100 mM NaCl. Protein concentration was determined using a BCA protein assay kit . Sedimentation velocity of the purified VhaC was performed on an XL-I analytical ultracentrifuge  at a speed of 210,000 × g. The sedimentation coefficients and molecular weight were calculated by SEDFIT V14.4 f software.The 47. For collagens of types II-V, the mixture of 0.05 mL enzyme solution at appropriate concentration and 0.25 mg substrate was incubated at 37 °C in 50 mM Tris-HCl (pH 8.0) for 0.5 h with continuous stirring. According to the method used by Philominathan et al.45, collagenous peptide (POG)10 was incubated with 50 mM Tris-HCl (pH 8.0) at 4 °C for 24 h before used for enzyme assays. The mixture of 5 μL enzyme solution at appropriate concentration and 5 μL [(POG)10]3 solution (1000 μM) was incubated at 25 °C for 30 min, and then 50% (v/v) of 1.25 M trichloroacetic acid (TCA) was added into the mixture to stop the reaction. The released amino acids were quantified using the colorimetric ninhydrin method with L-leucine as the standard. One unit of enzyme activity is defined as the amount of enzyme that releases 1 μmol leucine from types I-V collagens (fish collagen) in 1 h or [(POG)10]3 (gelatin) in 1 min. The activity toward casein was determined at 40 °C using the Folin-Ciocalteu method62. The activity toward Pz peptide was measured at 40 °C with the method described by Wuensch and Heidrich63 with modifications. Briefly, the mixture of 50 μL enzyme solution at appropriate concentration and 50 μL Pz peptide solution (1 mg/ml) was incubated at 40 °C for 30 min, and then 100 μL 1.25% (w/v) citric acid was added into the mixture to stop the reaction. After an addition of 1 mL ethyl acetate, the sample was mixed and centrifuged at 15,000 × g for 10 min. The absorbance of the upper phase was determined at 320 nm. One unit of enzyme activity is defined as the enzyme amount that increases 0.1 unit of absorbance at 320 nm per min. With type I collagen as the substrate, the optimum temperature for VhaC activity was determined in a range of 0-70 °C at pH 8.0. The optimum pH for VhaC activity was determined at 37 °C in Britton-Robinson buffers ranging from pH 3.0 to pH 12.0. Enzyme kinetic assays of wild-type CM and its mutants against [(POG)10]3 and Pz peptide were determined by nonlinear analysis. The initial rates were determined with 0 to 1500 μΜ [(POG)10]3 and 0 to 10 mg/ml Pz peptide in 50 mM Tris-HCl (pH 8.0). The Km values were calculated by the Michaelis-Menten equation using the OriginPro 8.5 software.Fish collagen, collagens of types I-V, gelatin, and casein were used as substrates for the enzyme specificity assays. The enzyme activities against type I collagen (fish collagen) and gelatin were determined using the colorimetric ninhydrin method with L-leucine as the standard64. The structure of CM was determined by single-wavelength anomalous dispersion phasing using a selenomethionine derivative. Experimental phases were solved using AutoSol in the Phenix program65. Initial mode was built using Phenix program AutoBuild. Refinement of the structure of CM was done by Phenix program Refine and Coot66 alternately.The protein concentration of CM for crystallization was 10 mg/mL in the buffer containing 10 mM Tris-HCl (pH 8.0) and 100 mM NaCl. Crystals of CM were obtained at 4 °C using the sitting drop method in the buffer containing 0.2 M sodium acetate trihydrate, 0.1 M Tris-HCl (pH 8.5), and 30% (w/v) polyethylene glycol 4000 after two weeks. Crystals of SeMet-CM were obtained at 4 °C using the sitting drop method in the buffer containing 0.2 M calcium acetate, 0.1 M imidazole/HCl (pH 8.0), and 10% (w/v) polyethylene glycol 8000 after two weeks. All the x-ray diffraction data were collected on the BL18U1 beamline at Shanghai Synchrotron Radiation Facility. The initial diffraction data sets were processed by the HKL3000 program67 and ATSAS 3.0.3 software package68. The scattering profile of the protein was obtained after subtracting the buffer profile. The forward scattering, I(0), and the radius of gyration, Rg, were evaluated using the Guinier approximation. The maximum dimension, Dmax, and the interatomic distance distribution function (P(r)) were calculated using the program GNOM69. The Kratky plot was used to analyze for protein flexibility70. Ab initio modeling of the molecular envelope was calculated and optimized by the program DAMMIF71 and DAMMIN72. Rigid body model construction to the experimental scattering data was performed using CORAL73, using the individual domain models of VhaC. Except for the crystal structure of CM, the PKD-like domain model was prepared by SWISS-MODEL74, using the crystal structure of the PKD-like domain from ColG (PDB: 4TN9) as the template. For the model of the PPC domain, solution structure of the PPC domain derived from the M4 metalloprotease vEP (PDB: 2LUW) was used. The modeled structures lacked structural information of the linker region between the PKD-like and the PPC domains (Thr691 to Gly712). Therefore, a poly-glycine segment was involved in the rigid body modeling process using CORAL, replacing this missing linker. The theoretical scattering curve from the atomic model was calculated and compared with the experimental curve by CRYSOL75. Atomic model was docked into ab initio envelope with the program SUBCOMB76.SAXS data from solutions of VhaC were collected on the BL19U2 beamline at Shanghai Synchrotron Radiation Facility, equipped with a PILATUS 1 M detector  (Supplementary Table 10 was incubated in the buffer of 10 mM Tris-HCl (pH 8.0) containing 100 mM NaCl at 4 °C for 24 h before ITC measurement. Titrations were performed by adding 0.4 μL of [(POG)10]3 solution for the first injection and 3 μL for the following 12 injections to the protein solution with stirring using a MicroCal PEAQ-ITC system  at 25 °C. The concentrations of [(POG)10]3 were 1000 μM for the activator domain and the peptidase domain-E478A proteins (100 μM) and 262 μM for the ColG-activator domain and the ColG-peptidase domain-E524A proteins (150 μM). The experimental data were analyzed using Microcal PEAQ-ITC analysis software. For the binding of insoluble type I collagen fibers, the individual domains fused with EGFP at a concentration of 12.5 μM were incubated with various amounts of collagen fibers  in 50 mM Tris-HCl (pH 8.0) at 30 °C for 2 h with stirring. The free fluorescence intensity in the supernatant was detected on an FP-6500 spectrofluorometer  at an excitation wavelength of 489 nm and an emission wavelength of 510 nm. The collagen-binding ability of fusion proteins and their site-directed mutants was determined with the free fluorescence intensity in the supernatant before and after incubation with 5 mg collagen fibers. Binding affinity (%)=(X-Y)/X*100%, where X and Y represent the free fluorescence intensity in the solution before and after incubation, respectively.Collagenous peptide (POG)As for collagen-swelling assay, insoluble type I collagen fibers (5 mg) were incubated with 12.5 μM EGFP-PKD or EGFP-PPC in 1 mL 50 mM Tris-HCl (pH 8.0) at 30 °C for 12 h with continuous stirring. Then, the collagen-swelling effects of the two fusion proteins were checked based on visual inspection.CD spectra of the purified VhaC and its mutants (0.5 mg/mL) in the buffer containing 10 mM Tris-HCl (pH 8.0) and 100 mM NaCl were recorded from 250 nm to 200 nm at a scanning rate of 200 nm/min with a bandwidth of 1 nm on a J1500 spectropolarimeter  at 25 °C. JASCO Spectra Manager was used for data collection and analysis.http://zdock.umassmed.edu/) to generate probable binding modes with default parameters. Unbound-CM and the most reasonable complex structure of unbound-CM with THP were subjected to the software package GROMACS 201977 for a 1000-ns MDS, respectively, with the AMBER99SB-ILDN78 force field adopted. All simulations were performed under the NPT ensemble with periodic boundary conditions and a time step of 2 fs. The temperature of the system was kept at 298 K using the v-rescale method, and the pressure was kept at 1 bar using the Parrinello-Rahman method. According to the backbone-atom RMSD plot, trajectories that reached the equilibrium state (500-1000 ns) were used for further principal component and cluster analyses.Unbound-CM was constructed by manually removing peptide P1 and P2 from the structure of CM with PyMOL. The structure of a THP was obtained from the Protein Data Bank (ID: 1K6F). Unbound-CM and THP were subjected to Z-Dock  with a J-type scanner. AFM imaging data were collected using Nanoscope v. 8.10. AFM data analysis was performed with NanoscopeAnalysis v. 1.40.AFM was used to observe the collagen fibers before and after enzymatic treatment. Insoluble bovine type I collagen fibers (5 mg) were treated with 0.1 μM VhaC or 50 mM Tris-HCl (pH 8.0) (control) at 30 °C for 5 h with continuous stirring. After treatment, the samples were centrifuged at 10,000 × The cleavage sites of VhaC on tropocollagen α1 and α2 chains were analyzed by nano LC-MS. Insoluble type I collagen fibers (10 mg) were incubated with 0.2 μM VhaC in 50 mM Tris-HCl (pH 8.0) at 30 °C for 5 h with continuous stirring. The reaction was stopped by the addition of 1% trifluoroacetic acid and the digested product was analyzed by nano LC system  equipped with a high-resolution tandem mass spectroscopy system . The sequences of released peptides were analyzed by Proteome Discoverer software 1.4  with the Sequest HT search engine against ipi.BOVIN.v3.69.fasta database for data processing.79. The amino acid concentration in the supernatant released from collagen fibers was quantitatively analyzed by the colorimetric ninhydrin method with L-leucine as the standard. Collagen fibers (30 mg) incubated with 0.5 μM trypsin or 50 mM Tris-HCl (pH 7.5) at 37 °C for 1 h with continuous stirring were used as the control.Thirty milligrams of insoluble type I collagen fibers were incubated with 0.5 μM VhaC or 50 mM Tris-HCl (pH 8.0) at 37 °C for 1 h with continuous stirring. Pyridinolines released from the covalent cross-links within collagen fibrils in the supernatant were detected on an FP-6500 spectrometer  at an excitation wavelength of 325 nm and an emission wavelength of 400 nm80.SignalP 5.0 was used to predict the potential signal peptide of VhaC. NCBI Conserved Domain Database was used to analyze the conserved domain structure of VhaC. Multiple sequence alignment was performed with CLC Sequence Viewer 6 and edited with ESPript 3.0t-test where appropriate. For ITC analysis, CD spectroscopy analysis, AFM observation, and nano LC-MS analysis, similar results were obtained from three independent experiments. Detailed data analyses are described in the text.Data analyses were carried out using Microsoft Excel 2019, OriginPro 8.5, and OriginPro 2018C. All data shown are means ± SD and were analyzed using two-tailed Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Movie 1Supplementary Movie 2Reporting Summary