O-acetylation of a diverse range of carbohydrates across all domains of life. In bacteria they are essential in processes including symbiosis, resistance to antimicrobials, and biosynthesis of antibiotics. Their mechanism of action, however, is poorly characterized. We analyzed two acetyltransferases as models for this important family of membrane proteins, which modify carbohydrates on the surface of the pathogen Salmonella enterica, affecting immunogenicity, virulence, and bacteriophage resistance. We show that when these AT3 domains are fused to a periplasmic partner domain, both domains are required for substrate acetylation. The data show conserved elements in the AT3 domain and unique structural features of the periplasmic domain. Our data provide a working model to probe the mechanism and function of the diverse and important members of the widespread AT3 protein family, which are required for biologically significant modifications of cell-surface carbohydrates.Acyltransferase-3 (AT3) domain-containing membrane proteins are involved in  Salmonella spp. can generate a specific immune response upon infection and can influence bacteriophage interactions. This study integrates in situ and in vitro functional analyses of two of these proteins, OafA and OafB (formerly F2GtrC), which display an “AT3-SGNH fused” domain architecture, where an integral membrane AT3 domain is fused to an extracytoplasmic SGNH domain. An in silico-inspired mutagenesis approach of the AT3 domain identified seven residues which are fundamental for the mechanism of action of OafA, with a particularly conserved motif in TMH1 indicating a potential acyl donor interaction site. Genetic and in vitro evidence demonstrate that the SGNH domain is both necessary and sufficient for lipopolysaccharide acetylation. The structure of the periplasmic SGNH domain of OafB identified features not previously reported for SGNH proteins. In particular, the periplasmic portion of the interdomain linking region is structured. Significantly, this region constrains acceptor substrate specificity, apparently by limiting access to the active site. Coevolution analysis of the two domains suggests possible interdomain interactions. Combining these data, we propose a refined model of the AT3-SGNH proteins, with structurally constrained orientations of the two domains. These findings enhance our understanding of how cells can transfer acyl groups from the cytoplasm to specific extracellular carbohydrates.Membrane bound acyltransferase-3 (AT3) domain-containing proteins are implicated in a wide range of carbohydrate O-acyl modifications, but their mechanism of action is largely unknown. O-antigen acetylation by AT3 domain-containing acetyltransferases of  Salmonella infections are a considerable public health burden in both developing and developed countries. Salmonella enterica subspecies enterica serovar Typhimurium is estimated to cause more than 150,000 human deaths from gastroenteritis each year  bloodstream infections in Africa  is an important virulence factor. The O-antigen, the most distal and variable portion of LPS, is composed of repeating oligosaccharide units whose composition and structure vary between species and, in the case of  acetate , 8 can i acetate –12.O-acetylated by acyltransferase proteins which contain a 10-transmembrane helix (TMH) acyltransferase-3 (AT3) . This family of proteins is widespread in eukaryotes and prokaryotes and is involved in a range of acylation modifications. Examples of AT3-containing acetyltransferases from prokaryotes include those mediating peptidoglycan acetylation and contributing to lysozyme resistance  is an example of an AT3-only protein that is essential for O-antigen acetylation  and an N-terminal AT3 domain linked to an extracytoplasmic domain, commonly an SGNH domain (AT3-SGNH fused). The SGNH domain is fused through addition of an 11th TMH and linking region. Oac  Are there essential residues in the membrane-bound AT3 domain that can give clues to their role in acetyl transfer? (ii) Can we obtain insight into the architecture of these proteins by elucidating the structure of the SGNH domain and its N-terminal extension? (iii) What is the function of the SGNH domain, and can it function independently of the AT3 domain?In this work, using S. Typhimurium O-antigen acetyltransferases OafA (PF01757) fused to an SGNH domain  (1432–O-acetylation (The ses OafA  and OafBses OafA  (formerlPF13472) , 29  . The AT3PF13472)  30); th; thS. TyPF13472) , a compa13472) 1417, 32–5572) 1432–, and thu72) 1432–. In AT3-tylation . To test10.1128/mBio.01364-20.8TABLE S1Table S1, PDF file, 0.4 MB.Experimentally characterized bacterial AT3 acetyltransferases. Download Copyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. enterica serovar Paratyphi A OafB homologue revealed that only 4 amino acids are invariant across all 31 proteins, OafAH25, OafAF41, OafAG46, and OafAG202  in loop 2-3 is essential for activity in Shigella flexneri Oac     and OafB motif R6,R72 in l10.1128/mBio.01364-20.1FIG S1Salmonella enterica serovar Paratyphi A OafB WP_00400612. SGNH fused acetyltransferases are indicated by a gray box. Asterisks mark residues selected for mutation from this alignment. Download FIG S1, PDF file, 1.8 MB.Alignment of characterized AT3 acetyltransferases. Protein sequences are in the same order as in Copyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the G33-D39), which replaces a longer and more divergent loop region between TMH1-2 in the nonfused AT3 proteins. Further conserved residues are seen in the periplasmic loop between TMH3 and -4, including OafAS112, OafAN113, and OafAY122. Together, these observations suggest shared key residues in both AT3-only and AT3-SGNH fused proteins and possible adaption of AT3 domains in AT3-SGNH fused acetyltransferases toward their function together with the fused SGNH domain.We next examined features unique to the AT3 domains of AT3-SGNH fused acetyltransferases; these 11 sequences derive from diverse Gram-positive and Gram-negative bacteria . The mosS. Typhimurium OafA (OafA-STM), we developed an in situ functional assay using a double antibody LPS immunoblot. The assay quantifies both the level of acetylated abequose (O:5) and the amount of LPS based on the O-antigen core   LPS western blot with crude LPS extracts from Salmonella enterica serovar Typhimurium basal O-antigen strain expressing OafA point mutant variants in (A) the membrane domain and (B) the periplasmic domain. O:5 antibody binding (blue) shows abequose acetylation, and Salmonella LPS core antibody binding (green) acts as a loading control. (Right) Corresponding anti-His Western blot for expression of His-tagged OafA. The arrow indicates the full-length OafA protein. Download FIG S2, PDF file, 0.7 MB.Functional analysis of OafA membrane-bound domain point mutants Copyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01364-20.9TABLE S2Table S2, PDF file, 0.6 MB.Molecular biology materials. Bacterial strains and primers used in this study. Primers for cloning of OafA and OafB constructs and creation of OafA point mutant variants on the pBADcLIC_WT-OafA plasmid. Amp, ampicillin 100 μg/ml; Kan, kanamycin 50 μg/ml. Download Copyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in situ as a result of mutant protein expression is summarized in O-acetylation.Twenty positions in the membrane-bound domain of OafA were individually engineered to replace the wild-type amino acid with alanine. The level of O-antigen acetylation 10-H motif (OafAR14 and OafAH25) are essential for function. These residues are predicted to be on the same surface of the alpha helix with spacing similar to the predicted distance between the 3′ phosphate and the thioester bond of one coenzyme A molecule (∼19 Å). Thus, we hypothesize that these residues provide a potential acetyl-CoA interaction site within the AT3 domain. The 100% conserved glycines (OafAG46 and OafAG202) could be replaced with alanine with no detriment. As expected, both arginines in the TMH3 RXXR motif    were essR69,R72) , Fig. 4.F35 and OafAD39 caused significant reduction and complete loss of OafA activity, respectively. OafAS112A mutation also caused complete loss of OafA activity (E325) is invariant across AT3-SGNH protein sequences; mutation of this residue (OafAE325A) resulted in significant reduction in OafA activity  and residues 377 to 640 from S. Paratyphi OafB (OafB-SPAC-long), which have 31% sequence identity  and Clostridium thermocellum (PDB 2VPT); each has a root mean square deviation (RMSD) of 2.5 Å over 207 and 201 backbone residues, respectively.The core structure of OafB-SPA-helices . Two dis-helices  and wereC-long compared to its closest structural homologues and the only other SGNH domain from a fused acyltransferase with a solved crystal structure, OatA-SGNH (PDB 5UFY) . This additional volume in the fold is contributed by two separate noncontiguous parts of the structure, the first being helix α8, which comprises 10% of the SGNH domain volume (ext). The SGNHext interacts extensively with the SGNH domain covering 1,500 Å2 of the SGNH domain, including interactions with helix α8; 38 amino acids of the SGNH domain interact with 32 (of 48) residues in the extension. Removal of the most N-terminal half of the SGNHext  , is thatn volume . A strucn volume , Fig. S3AC-short ), resultH domain . These o10.1128/mBio.01364-20.3FIG S3FIG S3, PDF file, 0.1 MB.Structure-based sequence alignment of OafB, OafA, and the closest structural homologues. Residues conserved in >50% are highlighted in blue, and catalytic and oxyanion hole residues are indicated by an arrow. Abbreviations and details of the sequences used are in Materials and Methods. Download Copyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01364-20.4FIG S4C-long, OafA-STMC-short, OafB-SPAC-long, and OafB-SPAC-short, with melting temperatures of OafA-STMC-long = 63.8°C, OafA-STMC-short = 58.1°C, OafB-SPAC-long = 58.9°C, and OafB-SPAC-short = 50.0°C. Download FIG S4, PDF file, 0.4 MB.Melting curves of OafA-STMCopyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the In situ functional analysis of catalytic triad mutants OafAS412A and OafAH590A showed almost complete loss of function, whereas OafAD587A showed reduced activity , block II (G), block III (GxND), and block V (DxxH) (where x is any nonproline residue) . The stractivity , Fig. S2activity , 62.N459). The structure of OafB-SPAC-long shows OafBN459 to be within hydrogen-bonding distance of a cocrystallized sulfate ion  and asparagine (block III), are not . Analysifate ion , suggestPAC-long  suggestsype OafA , Fig. S2ype OafA , 61, 63.10.1128/mBio.01364-20.5FIG S5C-long (yellow) modelled on the structure of OafB . Catalytic triad and potential oxyanion hole residues are shown as sticks. Residues indicated with OafA first. Both the Ser 437 side chain and the Leu 438 backbone amide are in close proximity to catalytic triad and active site sulfate. (B) Sequence alignment of OafA from Salmonella enterica serovar Typhimurium and OafB from the Typhimurium and Paratyphi A serovars of Salmonella enterica from OafA residues 410 to 450. Alignments were carried out using T-coffee with default settings. The red box highlights predicted replacements for catalytic block II glycine. Download FIG S5, PDF file, 0.5 MB.Comparison of potential oxyanion hole residues in OafA and OafB. (A) Homology model of OafA-STMCopyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C-long contains a GTNG motif (OafBG502-G505) close to sequence block III (The GxND motif (block III), where Asn is typically involved in oxyanion hole formation , is not lock III , but thelock III . These oext) appears to occlude the active site and results in significantly lower solvent-accessible surface area (SASA) of the catalytic triad residues (40 Å) than in OatA, 2VPT, and 5B5S   . Removinext (OafA-STMC-long and OafB-SPAC-long) and those with half the SGNHext (OafA-STMC-short and OafB-SPAC-short)   . In vitr(pNP-Ac) , an assa(pNP-Ac) , 65. Thiive site .10.1128/mBio.01364-20.6FIG S6In vitro acetyl-esterase activity of C-terminal OafA and OafB assessed by hydrolysis of pNitrophenyl acetate (pNPA). Solid line, active protein; dashed line, heat-treated protein. Error bars = SEM, n = 3. Some error bars are obscured by point markers. “C-long” constructs comprise the SGNH domain with full SGNHext, and “C-short” constructs comprise the SGNH domain with fewer SGNHext residues to expose the SGNH domain active site. See FIG S6, PDF file, 0.5 MB.Copyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the ext affects the in vitro acceptor substrate specificity of OafA-STMC-term and OafB-SPAC-term proteins, purified proteins were incubated with pNP-Ac (acetyl group donor) and unmodified S. Typhimurium LPS   . First, The discovery that the “linker” region that is present between the more clearly defined AT3 and SGNH domains is, in fact, a long structured component of the SGNH domain means that the SGNH is much more constrained and proximal to the membrane than initially proposed if this region was a long flexible linker. The discovery that there are residues in the AT3 loop between TMH3 and -4 that are only conserved in the AT3-SGNH fused proteins suggests potential protein-protein contacts between the two domains during catalysis. To test this hypothesis, we used a coevolution analysis of the OafA-B type acetyltransferases to assess whether there was any evidence for correlated changes in the two domains consistent with a physiological interaction . While t10.1128/mBio.01364-20.7FIG S7C-long with residues (542 to 546) predicted to interact with the acyltransferase domain (blue). The extension is colored orange, the additional helix colored teal, and the catalytic triad is colored red. Download FIG S7, PDF file, 0.1 MB.(A) Predicted contact map for OafB based on a correlated mutation analysis using the RaptorX Web server. The horizontal/vertical line marks residue 377, which forms the boundary at the end of the AT3 domain. High-confidence interactions occur within the AT3 domain (top left) and the SGNH domain (bottom right), while a single high-scoring interaction between the AT3 (93 to 97) and SGNH (524 to 546) is marked (bottom left). (B) Structure of OafB-SPACopyright © 2020 Pearson et al.2020Pearson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Salmonella enterica, Listeria monocytogenes, Haemophilus influenzae, and Streptococcus pneumoniae, these proteins are implicated in acetylation of extracytoplasmic polysaccharides, which can have significance for interactions with phage and hosts and can affect virulence and antibiotic resistance  are a ubiquitous family of proteins involved in diverse carbohydrate modifications across the domains of life. Prokaryotic members of this family play roles in modification of antibiotics and antitumor drugs, as well as initiation of microbial symbioses with plants , 66, 67 sistance , 36, 38.in situ functional assays and structure-function assessment of the SGNH domain  rather than AT3 domain . These our model . AT3 domur model , 74, 75;F35 and OafAD39 in TMH2 and OafAS112 between TMH3 and -4) are located toward the periplasmic side of the AT3 domain    . Convers(OacG53)  31, 57), 57F35 aSalmonella in vitro without the presence of its cognate fused AT3 domain. This supports the predicted role for SGNH in the final step of acetyl group transfer to the acceptor substrate in fused acetyltransferases .A multiple sequence alignment of AT3 SGNH domain fused proteins was constructed using the MUSCLE alignment tool based on 1,188 full-length sequences from the UniProt reference proteomes. This alignment was used to construct a profile hidden Markov model (HMM) to detect further homologues in the UniProt reference proteome set as well as within the MGnify protein sequence set. We required that all matches to this profile-HMM had a sequence and domain threshold of 27 bits. We also required that the sequence matched >90% of the HMM match states to ensure that homologues with only one of the two domains were not included in the alignment.A total of 2,713 homologues were identified from the UniProt reference proteome set, and 9,757 homologues were identified from the MGnify metagenomics sequences. A large sequence alignment was constructed using OafB as the master with no indels with all the sequence matches aligned to it using the hmmalign package and a custom Perl script to format the alignment for contact prediction. The alignment was submitted to the RaptorX contact prediction server .S. Typhimurium strain 293 (Primers (Sigma-Aldrich) are listed in rain 293  for analoafA-STM and oafB-SPA sequences for protein expression were cloned into pETFPP_2  using in-fusion cloning (Clontech) to add a 3C-protease cleavable N-terminal His-MBP tag. Plasmid pMV433 (C-long (residues 366 to 609) and OafA-STMC-short (residues 379 to 609). oafB-SPA (UniProt A0A0H2WM30), amino acid residues 377 to 640 for OafB-SPAC-long, was codon-optimized for E. coli and synthesized by Genewiz in a pUC57-Kan vector. This vector was then used as a template for the sequence encoding OafB-SPAC-short (residues 399 to 640); see All d pMV433  was usedin situ functional analyses of OafA variants cloned into pBADcLIC were carried out in strain Path293  of 3.0 per ml for LPS and protein extraction.All  Path293   overnight culture was pelleted for 5 min at 16,000 × g. Cell pellets were resuspended in 100 μl LPS sample buffer  containing 2% (wt/vol) SDS and then boiled at 100°C for 5 min. Then, 400 μl of LPS buffer was used to dilute the solution before RNase (Roche) and DNase (Sigma) treatment at 37°C for 16 h. Samples were then treated with 100 μg proteinase K for 16 h at 50°C, and 7.5 μl of crude LPS extracts were run on 1.0 mm Tricine SDS poly acrylamide gel electrophoresis (TSDS-PAGE gel) for analysis by immunoblotting.The method was adapted from Davies et al. . First, g. Soluble and insoluble fractions were isolated from cell pellets using BugBuster solution (Novagen) following the manufacturer’s instructions for soluble protein extraction. The insoluble pellet was resuspended in 75 μl of sample buffer , heated to 60°C for 10 min, and centrifuged for 10 min at 16,000 × g. Then, 10 μl of insoluble fraction samples was loaded onto a 12% acrylamide 1.0-mm SDS-PAGE gel for analysis.First, 1 ml of OD-normalized culture was pelleted for 5 min at 16,000 × Salmonella core antigen (1:200)  were used as the primary antibodies and goat anti-rabbit IgG StarBright Blue700  (Bio-Rad) and goat anti-mouse IgG (H+L) DyLight 800  as the respective secondary antibodies. LPS antibodies were diluted in 5% milk phosphate-buffered saline with Tween 20 (PBS-T). The ChemiDoc MP imaging system (Bio-Rad) and Image Lab (Bio-Rad) were used for image capture and analysis. The in situ activity of OafA mutant relative to the wild-type protein was derived from quantification of the O:5 signal in each lane, standardized to the intensity of the single O-antigen repeat band for the Salmonella core signal on LPS immunoblots. Assay validation demonstrated that <1% of the O:5 signal with respect to the wild type was within the background variation. Variation increased significantly for signal intensities in the higher range; therefore, the O:5 signal recorded between 50 and 100% relative to wild type was not interpreted further.First, 7.5 μl of crude LPS extracts was run on 1.0 mm Tricine SDS polyacrylamide gel electrophoresis (TSDS-PAGE) gel for analysis by immunoblotting. The TSDS-PAGE-separated LPS samples and SDS-PAGE-separated protein samples were transferred onto Immobilon-P polyvinylidene difluoride (PVDF) membrane (Merck-Millipore). For His-tagged protein detection, the primary antibody was Tetra·His antibody  , and the secondary antibody was goat anti-mouse IgG-HRP  . The blot was developed using Luminata Classico Western HRP substrate (Merck-Millipore). For LPS detection, O:5 serotyping antibody   and C-term and OafB-SPAC-term constructs  and 100 mM NaCl. Proteins were heated from 20°C to 95°C with a heating rate of 2°C/min. The fluorescence at 330 and 350 nm was measured every 0.05°C.C-term, 40 μM OafB-STMC-term, or 0.04 U/ml acetyl xylan esterase) or appropriate control buffers were added to relevant wells of a 96-well plate and incubated at 37°C for 10 min prior to addition of pNP-Ac. Then, 100 μl of 1 mM pNPA in the corresponding buffer was then added to matching sample and control wells and immediately placed into a plate reader incubated at 37°C. Absorbance at 405 nm was measured at T = 0, and then at 5 min intervals.The catalytic activity of OafA and OafB C-terminal constructs was confirmed by acetyl esterase activity using pNP-Ac as a substrate. One hundred μl of enzyme solution (10 μM OafA-STMS. Typhimurium strain LT2 (Path993) was heated at 100°C for 20 min to inactivate the proteinase K (see above). Heat-treated LPS was mixed 1:1 with KPi buffer . Next, 10 μM OafA-STMC-term and 20 μM OafB-SPAC-term were incubated at 4°C in the LPS-KPi mixture with 4 mM pNP-Ac dissolved in ethanol . Samples of the reaction mix were taken after specified time points and inactivated by boiling for 10 min.Crude LPS extracted from OafA-negative Salmonella core antigen was followed as per immunoblotting, following removal of the membrane from the apparatus after sample loading.Then, 5 μl of LPS reaction samples were loaded onto methanol-activated PVDF membrane using a Bio-Rad Bio-Dot microfiltration apparatus. The protocol for LPS detection with O:5 serotyping antibodies and C-long, a hanging-drop vapor diffusion method was used with 20 mg/ml OafB-SPAC-long in a drop ratio of 1:1 protein:reservoir solution. After incubation for 24 h at 20°C, crystals grown in 100 mM BisTris (pH 5.5), 0.25 M lithium sulfate, and 25% PEG 3350 were cryoprotected by addition of glycerol to a final concentration of 20% and vitrified in liquid nitrogen.To crystallize OafB-SPAC-long were collected on beamline I04-1  at a wavelength of 0.9282 Å using a Pilatus 6M-F detector. Data were integrated with XDS .The atomic coordinates and structure factors have been deposited in the Protein Data Bank (PDB ID code


The rapid development of coronavirus disease 2019 (COVID-19) pandemic has become a great threat to global health. Its mortality is associated with inflammation-related airway mucus hypersecretion and dysfunction of expectoration, and the subsequent mucus blockage of the bronchioles at critical stage is attributed to hypoxemia, complications, and even death. Traditional Chinese medicine (TCM) has rich experience in expectorant, including treatment of COVID-19 patients with airway mucus dysfunction, yet little is known about the mechanisms. This study is aiming to explore the potential biological basis of TCM herbal expectorant for treating COVID-19.To get core herbs with high used frequency applications in the actions of expectoration by using association rule algorithm and to investigate the multitarget mechanisms of core herbs in expectorant formulae for COVID-19 therapies.Forty prescriptions for expectorant were retrieved from TCM Formulae. The ingredient compounds and targets of core herbs were collected from the TCMSP database, Gene-Cards, and NCBI. The protein interaction network (PPI) was constructed by SRING, and the network analysis was done by Cytoscape software. Bioconductor was applied for functional enrichment analysis of targets.The core herbs of expectorant could regulate core pathways  via interactions of ingredients  on mucin family to eliminate phlegm.TCM herbal expectorant could regulate MAPK and cytokine-related pathways, thereby modulating Mucin-family to affect mucus generation and clearance and eventually retarding the deterioration of COVID-19 disease. The recent pandemic of pneumonia caused by a novel coronavirus has been rampant across the world, with the number of victims increased daily ; so far,In the lungs of healthy people, mucus is barely secreted and constantly cleared by ciliated epithelium via expectoration or being swallowed into the throat. High mucus secretion is usually a clinical feature of severe respiratory disease. When a viral infection and a subsequent inflammation occurs, loads of filamentous actin (F-actin), neutrophil-derived DNA, dead cells, and cellular debris are accumulated to sequester pathogens, forming mucous purulence, often called phlegm after being coughed up . The patThe traditional Chinese medicine (TCM) has rich experiences in combating epidemic over the past thousands of years. In fact, the mortality rate of COVID-19 has been notably reduced in many regions of China after the implementation of TCM, thanks to its remarkable achievements in preventing the deterioration into severe or critical stage . From TCThe whole workflow is illustrated in Formula of TCM  were obtained from the a of TCM ; a finalHerb-usage frequency analysis was carried out for the expectorant formulae, and herbs were ranked according to their usage frequency.On the basis of the analysis on herb-usage frequency of expectorant formulae, the support degree was set to 15%  to obtain the high-frequently used herbs of the expectorant formulae by using the association rule algorithm, and displayed in Cytoscape3.7.2.Pinellia ternata), which are the most commonly used herbs of the expectorant formulae, were searched out from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP)1. Oral bioavailability (OB) greater than or equivalent to 30% and drug-likeness (DL) greater than or equivalent to 0.18 were set to be the screening parameters . The Cytoscape3.7.25 was used for constructing the network of herbs–compound components–action targets–disease.The potential targets were constructed to be a target protein interaction (PPI) network through the search tool for the retrieval of interacting genes/proteins (STRING6 to perform Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and visualize the results of the enrichment analysis.The analysis on the function enrichment and the main action pathway of the core target genes was carried out by using BioconductorP. ternata); of the 329 involved Chinese herbs, the top 20 ranked with relatively high usage frequency are as shown in According to statistics of expectorant formulae, the most frequently used is Banxia  to max 10.0 (purple)], and the degrees of association between the herbs were represented by the thickness of the lines.P. ternata, Citri Reticulatae Pericarpium, Poria, Glycyrrhizae Radix Et Rhizoma, and Zingiberis Rhizoma Recens. The pharmacological actions of high-frequency usage herbs are listed in the Pharmacopoeia of the People’s Republic of China ,” which is based on the accumulating experience in the diagnosis and treatment. Qingfei Paidu Decoction (QFPD) was one of the most recommended Chinese medicine prescriptions, encompassing all five core herbs . The clinical trials with QFPD and others are shown in 7, shown in Moreover, a large-scale report of 1,265 patients from 10 provinces has 98% curative rate with no conversion to severe stage, according to the National Administration of Traditional Chinese Medicine . There aP. ternata by TCMSP platform. One hundred forty main active compounds were screened according to the conditions of OB greater than or equivalent to 30% and DL greater than or equivalent to 0.18. Within all the 140 main active compounds, 13 were from P. ternata, 5 from Citri Reticulatae Pericarpium, 25 from Poria, 92 from Glycyrrhizae Radix Et Rhizoma, and 5 from Zingiberis Rhizoma Recens  in Gene-Cards. Thirty-six of them were intersected with the obtained 47 targets in the treatment of COVID-19  regulate the production of mucin MUC5A (as shown in p < 0.01), wherein a histogram and a bubble chart were drawn for the top 20 GO items  (p < 0.01), 20 with relatively lower p values were selected  receptor, the same host cell receptor for SARS-CoV, except for much high binding affinity of the S-protein . In thisIt could be seen from the results of network pharmacology analysis that the compounds, such as quercetin, naringenin, beta-sitosterol, kaempferol, nobiletin, licochalcone A, scutellarin, formononetin, and isorhamnetin, have the highest comprehensive score of the core herb compounds. They played a key role on the targets that induced the production of mucin, such as MAPK14 and MAPK1. The previous studies revealed that 6-gingerol in ginger (Zingiberis Rhizoma Recens) decreased MUC5A through ERK and MAPK pathways . Furtherp < 0.01). Target genes were enriched more in IL-17 and TNF signaling pathways, which directly or indirectly regulate the expression of MUC5AC. Moreover, these pathways were related to virus infection, including pertussis, leishmaniasis, hepatitis B, influenza A, tuberculosis, Kaposi’s sarcoma-related herpesvirus infection, human cytomegalovirus infection and measles. Overexpression of inflammatory factors    could inThe so-called “mucus plugs” or “phlegm plugs” in the anatomical results of COVID-19 patients were formed by the pulmonary mucus lesions. Mucus is a viscous, gel-like material consisting of various macromolecules, inorganic salts, and water. Normal mucus and mucociliary clearance are the key components of lung innate immune function . Airway The inflammatory process caused the loss of the functions of cells and cilia, and the airway phospholipase destroyed the surface-active layer and changed the biological and physical characteristics of the surface mucus of the airway. The characteristic gel-like property of mucus is believed to be attributable mainly to the presence of polydisperse glycoproteins or mucins. There are 17 genes in the encoding mucin of the human body. Five of the secreted mucins have terminal cysteine-rich domains that can form disulfide bonds resulting in polymers that impart the properties of a gel. MUC5AC and MUC5B are gel-forming airway mucins and are strongly expressed in the human respiratory tract and display the characteristics of mucociliary clearance and barrier functions. The MUC5AC is an airway mucin produced by the goblet cells of the surface epithelium , and MUCHypersecretion of mucus in the bronchioles and air sacs of the patients with COVID-19 blocks the small airway, which is related to the uncontrolled MUC5B defense mechanism  and the P. ternata, Poria, and Glycyrrhizae Radix Et Rhizoma, forming the major part of Er-Chen decoction  and immunity status and boosting functions for anti-inflammation and antivirus, provided an integrative solution to combat the COVID-19.P. ternate, Citri Reticulatae Pericarpium, Poria, Glycyrrhizae Radix Et Rhizoma, and Zingiberis Rhizoma Recens) were most frequently used and highly related to expectorant. Interestingly, these top 5 herbs were coincidently a part of the 21 herbs of QFPD decoction and being a major part of Er-Chen decoction, the typical expectorant TCM remedy. QFPD decoction has been proven effective in the remission of COVID-19 infection. The QFPD was specifically designed to treat major symptoms from COVID-19, including expectorant. However, other functions of QFPD, e.g., anti-inflammation, antiviral, immunity-boosting, etc., have been mechanistically investigated (In this study, we have systematically screened the expectorant remedies/herbs in TCM, using association rule algorithm analysis and network pharmacology. Top 5 herbs (stigated , except All datasets presented in this study are included in the article/TL, YfZ, and ZW contributed to conception and design of the study. YfZ and ZW analyzed the data and produced the figures. YfZ wrote the manuscript. YlZ, YZ, and HT contributed to manuscript revision. All authors contributed to read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Staphylococcus aureus α-toxin. Complex formation between PLEKHA7, PDZ domain–containing 11 (PDZD11), tetraspanin 33, and the α-toxin receptor ADAM metallopeptidase domain 10 (ADAM10) promotes junctional clustering of ADAM10 and α-toxin–mediated pore formation. However, how the N-terminal region of PDZD11 interacts with the N-terminal tandem WW domains of PLEKHA7 and how this interaction promotes tetraspanin 33 binding to the WW1 domain is unclear. Here, we used site-directed mutagenesis, glutathione S-transferase pulldown experiments, immunofluorescence, molecular modeling, and docking experiments to characterize the mechanisms driving these interactions. We found that Asp-30 of WW1 and His-75 of WW2 interact through a hydrogen bond and, together with Thr-35 of WW1, form a binding pocket that accommodates a polyproline stretch within the N-terminal PDZD11 region. By strengthening the interactions of the ternary complex, the WW2 domain stabilized the WW1 domain and cooperatively promoted the interaction with PDZD11. Modeling results indicated that, in turn, PDZD11 binding induces a conformational rearrangement, which strengthens the ternary complex, and contributes to enlarging a “hydrophobic hot spot” region on the WW1 domain. The last two lipophilic residues of tetraspanin 33, Trp-283 and Tyr-282, were required for its interaction with PLEKHA7. Docking of the tetraspanin 33 C terminus revealed that it fits into the hydrophobic hot spot region of the accessible surface of WW1. We conclude that communication between the two tandem WW domains of PLEKHA7 and the PLEKHA7–PDZD11 interaction modulate the ligand-binding properties of PLEKHA7.Pleckstrin homology domain–containing A7 (PLEKHA7) is a cytoplasmic protein at adherens junctions that has been implicated in hypertension, glaucoma, and responses to  Staphylococcus aureus α-toxin, through a mechanism that involves junctional clustering of ADAM10, mediated by tetraspanin 33 (Tspan33)  .Staphylococcus aureus α-toxin receptor ADAM10 at junctions, through binding of the WW1 domain of PLEKHA7 to the ADAM10 chaperone Tspan33  (B) and WW2  domains are highly conserved, because >90% of residues are identical between different species . To identify the residues involved in the WW1-PDZD11 interaction and the mechanism through which the WW2 domain promotes binding of PDZD11 to PLEKHA7 we used site-directed mutagenesis. We generated alanine mutants within the WW1 domain at amino acid positions 15, 17, 19, 22, 25, 27, 29, 30, 32, and 35 (C). The WT and mutant WW1 sequences were fused downstream of GST to generate baits for pulldown assays, where the preys were either PDZD11-HA or CFP-HA (negative control) (C). Immunoblot analysis showed that the WT sequence and the mutants Y17A, D22A, F27A, and L32A interacted with PDZD11, whereas the W15A, V19A, V25A, N29A, D30A, and T35A mutants failed to detectably interact with PDZD11 . None of the baits interacted with negative control HA-tagged CFP . Ponceau S staining of the blots revealed that all the WW1 mutant constructs migrated as multiple bands, typically three polypeptides, with different relative intensities , suggesting that the GST fusion proteins are unstable, and subjected to partial proteolytic degradation. Among mutants that failed to interact with PDZD11, T35A showed the least degree of degradation and migrated similar to WT, as indicated by the prevalence of the higher molecular size polypeptide . Next, we asked whether mutations of the WW1 domain that resulted in decreased interaction with PDZD11 were also effective in the context of constructs comprising both WW1 and WW2 domains. Thus, we generated fusion proteins comprising the first 160 residues of PLEKHA7 , either with WT sequence, or with the single amino acid mutations within the WW1 domain . Immunoblot analysis showed that all mutant baits interacted with PDZD11 as efficiently as WT, and none with CFP-HA . In addition, Ponceau S staining of GST fusion proteins showed no degradation, because all fusion proteins migrated as one major polypeptide . These results suggest that the presence of the WW2 domain stabilizes the WW1 domain. Next, to obtain separate evidence that these results reflect physiologically relevant mechanisms and interactions, we examined the effect of WW1 mutations on the interaction between full-length PLEKHA7 and PDZD11 in cells. PLEKHA7 recruits PDZD11 to junctions . In contrast, endogenous PDZD11 was rescued to junctions when PLEKHA7-KO cells where transfected with GFP-tagged full-length WT PLEKHA7  . Yet, deletion of the WW1 domain prevented the rescue of the junctional localization of PDZD11, confirming the critical role of the WW1 domain in PLEKHA7 interaction with PDZD11 . In summary, these results identify residues in the WW1 domain that are required for the interaction with PDZD11 by GST pulldown, suggesting that they are either directly implicated in the interaction, or they are required to maintain the correct folding of the WW1 domain. Moreover, because the presence of the WW2 domain offsets the effects of specific mutations on the interaction between PLEKHA7-PDZD11 both by GST pulldown and in cells, we conclude that the WW2 domain protects the WW1 domain from the de-stabilizing effects of the WW1 mutations. Therefore, we next wondered whether mutations within the WW2 domain might affect the ability of the WW1 domain to bind to PDZD11. Thus, we generated mutations within the WW2 domain, at positions 59, 60, 61, 65, 68, 71, 73, 75, 76, 84, and 85 . None of the mutations affected the ability of the WW1 + WW2 domain to interact with PDZD11, either by GST pulldown  or in cells . These results indicate that the structure of the WW1 domain and its ability to interact with PDZD11 is resistant to several mutations within the conserved residues of the WW2 domain.The N-terminal region of PDZD11 interacts with the WW1, but not the WW2 domain of PLEKHA7 A 15). H. HA) (15A) , 15. TheA) B and WW2, and 35 C. The WTcontrol) C. Immunoh PDZD11 C, top. Ngged CFP C, bottomC), or the nearby D30A mutation, which also showed strongly decreased interaction with PDZD11 (C). When we combined either the T35A or D30A mutations in the WW1 domain with different mutations within the WW2 domain, only T35A + H75A and D30A + H75A combinations resulted in the complete loss of binding to the PDZD11 prey by GST pulldown assay , and in a failure to rescue the junctional localization of PDZD11 within cells . Other combinations of mutations could rescue junctional PDZD11 , demonstrating that the results of GST pulldowns reflect physiologically relevant interactions and are not artifacts. Finally, when combining together either the D30A or T35A mutations with mutations of the residues flanking His-75 within the WW2 domain, e.g. either D74A or N76A, the interaction with PDZD11 was maintained . These results were confirmed when we switched bait and prey, e.g. we used GST-PDZD11 as a bait, and either WT or mutant constructs of the WW1 + WW2 domains of PLEKHA7 as preys. The single mutations D30A, T35A, and H75A did not affect PDZD11 interaction with the WW1 + WW2 domains , but the double mutants, either D30A + H75A, or T35A+H75A resulted in loss of interaction with PDZD11 .Next, we sought to clarify the mechanism of WW1 stabilization by WW2, by attempting to find combinations of mutations within the two domains that would affect the interaction with PDZD11. We selected two mutations within the WW1 domain: the T35A mutation, which showed the least degradation of the recombinant bait protein C, or theh PDZD11 C. When wA and BFig. S3, ) . Based on the models and on the experimental results highlighting the importance of the His-75 of the WW2 domain together with Asp-30 and Thr-35 of the WW1 domain, we carried out a series of molecular docking analyses, which allowed to model the interaction between the WW1 and WW2 domains (C and D). According to this model, His-75 of WW2 is accommodated in a small pocket of the WW1 domain formed by residues Asp-30, Arg-33, Tyr-17, and Ser-16 . Although the contact surface between the two domains is small (549 Å2) compared to the overall accessible surface area of the complex (5546.75 Å2), the electrostatic potential surface at the interface of the two domains shows good complementarity . Residues Asn-76, Glu-77, and Glu-78, at the interface of WW2 domain, electrostatically contribute to strengthening the interaction . In addition, His-75 forms a hydrogen bond with Asp-30 of WW1, thus generating a “binding pocket” suitable for accommodating PDZD11 at the interface between the two domains . The specific role for His-75 was highlighted by the observation that mutations in neighboring amino acids  had no effects on the interaction with PDZD11, either alone or in combination with either D30A or T35A . In agreement, structural inspection of the models reveals that Asp-74 and Asn-76 face away from WW1 domain, suggesting that they are not involved in the interaction between the two domains. Because in our model Arg-33 is localized in the binding pocket close to Asp-30 , we tested its relevance by combining the mutation R33A with mutations of D74A, H75A, or N76A. These double mutants interacted well with PDZD11 by GST pulldown and in cells , indicating that His-75 of WW2 is the main interactor of Asp-30 of WW1 .Next, we combined the information derived from these mutagenesis studies with structural modeling to gain further understanding on the structural basis of the PLEKHA7-PDZD11 interaction. Based on the homology of the WW1 and WW2 domains with templates of the ubiquitin ligase Nedd4 (48% sequence identity with WW1) and the Syntaxin-binding protein (58% sequence identity with WW2), we modeled a three antiparallel β-sheet structures of the WW1 and WW2 domains (A and B) . As a va domains , C and DTable S1), specifically in the last 60 ns of the trajectory -WT). The maintenance of a close distance between the two domains is shown also by the clustered structures from MD simulation, showing the representative conformations assumed by the WW(1 + 2)-WT complex along the simulating time . In agreement, hydrogen bond frequencies revealed that the main interacting residues were localized at the interface between the two domains, which concurred at the maintenance of the complex stability (Table S2). Importantly, molecular dynamic simulations also showed that the WW1–WW2 system carrying the double point mutations T35A+H75A and D30A+H75A appear more unstable in terms of RMSD values compared either to the WT system or to single mutants D30A and T35A . As was expected by analyzing the overall complex, we noticed that the residues located at the N-terminal, C-terminal, and loop regions are more prone to movements, compared with the residues forming the β-sheets . To test whether a specific geometry of arrangement between WW1 and WW2 is required to generate the binding pocket, we asked whether the isolated WW domains can directly bind to each other or to the WW1+WW2 domain. GST pulldown showed that neither WW1, WW2, nor the WW1 + 2 domains interact with either the isolated WW2 domain or the WW1 + 2 domain . Furthermore, the WW2 domain did not, when isolated, promote the interaction of the WW1 domain with PDZD11 . Together, these data indicate that the tandem WW domains of PLEKHA7 do not dimerize, but interact weakly, when in a specific spatial geometry, through a small complementary surface, and Asp-30, Thr-35, and His-75 drive the creation of a pocket that allows high-affinity binding to PDZD11 (C and D). Interestingly, the residue His-75 and even more so the Thr-35 are well conserved within tandem WW domains , suggesting that they may play a similar role in stabilizing potential interactions between tandem domains of additional proteins.The WW1–WW2 complex resulted to be stable in time during the 120 ns of molecular dynamic (MD) simulations, as shown by the low values of root mean square deviation (RMSD) . Immunoblot analysis showed that deletion of up to 14 N-terminal residues  of full-length PDZD11 did not impact the binding to PLEKHA7, when compared with WT PDZD11, whereas deletion of 19, 24, or 29 residues  resulted in either decreased or no interaction above background . Furthermore, a bait construct consisting of the first N-terminal 30 residues of PDZD11 was sufficient to interact with the PLEKHA7 prey . This analysis suggested that residues 16-30 of PDZD11 are essential for its interaction with PLEKHA7.Next, we sought to identify the sequences within PDZD11, which are required for its interaction with PLEKHA7. Previously, we showed that the isolated N-terminal domain of PDZD11  is sufficient for the interaction with the WW1 domain of PLEKHA7 . Thus, wFig. 3B, red box). Because polyproline sequences are key ligands for WW domains . In addition, we also generated alanine mutations of the highly conserved Tyr-18, Glu-19, and Asn-20 residues . GST fusion proteins were generated either in the context of a short construct of the first 30 residues of PDZD11 , or in the context of full-length PDZD11 , this latter to provide the binding region with the stabilizing effect of the downstream sequence. The ability of these fusion proteins to interact with the PLEKHA7 prey was examined by GST pulldown (C and D). Immunoblot analysis showed that the E19A,N20A, and P26A + P27A mutants interacted with PLEKHA7 similarly to WT (C and D). The P16A and P21A,P22A mutants showed reduced interaction with PLEKHA7 only when in the context of the short , but not in the context of full-length PDZD11 . In contrast, the mutation Y18A and the combined mutations of either P16,21,22A, or P21,22,26,27A, or P16,26,27A or P16,21,22,26,27A resulted in either decreased or undetectable interaction (polyproline mutations) with PLEKHA7 both in the context of the short N-terminal construct, and in the context of full-length PDZD11 (C and D). These results indicate that the residues that affect interaction with PLEKHA7 are either required to maintain the correct folding of the N-terminal domain of PDZD11 or are directly implicated in the interaction with PLEKHA7. Ponceau S staining of the blots showed that within the short N-terminal bait, the proline mutants but not the Y18A mutant migrated as two main polypeptides , whereas within the full-length molecule, they all migrated as a single band . This observation suggests that proline mutations destabilize the conformation of the short peptide, rendering it susceptible to proteolytic degradation, whereas when the mutations are in the context of the full-length molecule, the structure is stabilized.To identify more precisely the residues of PDZD11 involved in interaction with PLEKHA7 we performed mutagenesis of residues within the 16-30 PDZD11 sequence. Analysis of the sequence conservation of the N terminus of PDZD11 in 100 vertebrate species revealed that 27/31 residues are identical, including 5 proline residues, 4 of which  in the form of two consecutive prolines . In cells transiently transfected with GFP alone, GFP labeling was detected in cytoplasm . In cells transfected with either GFP-tagged PDZD11 WT, or with the mutants P16A, E19A, N20A, P21,22A and P26,27A exogenous PDZD11, labeled by GFP, was colocalized with endogenous PLEKHA7 at junctions . The GFP-tagged construct of PDZD11 with the mutations at Y18A was localized both at junctions and in the cytoplasm , whereas GFP-tagged constructs of PDZD11 with mutations at P16,21,22A, or P16,26,27A, or P21,22,26,27A, or P16,21,22,26,27A, were localized predominantly in the cytoplasm . Together, these results identify the polyproline stretches 16, 21, 22, and 26, 27, together with Tyr-18, as the key residues of PDZD11 that are implicated in recruitment by PLEKHA7 at the junction in cells.Next, we asked whether the PDZD11 mutations influenced the junctional localization of exogenous PDZD11 in WT cells E. In celytoplasm E, +GFP. unctions E, arrowsF). The experimental constraints used for docking are the identified interacting residues on WW1 and WW2. In the model, the peptide displays an N-terminal/C-terminal orientation similar to what is known for other proline-rich WW domain ligand (F) or with a hydrogen bond . In the second mode, the peptide shifts toward the right, and the Tyr-18 can form a hydrogen bond with the backbone of Arg-33 . Our experimental data do not test the existence or relative populations of these modes. The PDZD11 peptide also interacts with WW2, reinforcing the overall complex stability. The tandem prolines 26-27 and Trp-24 are accommodated in the hydrophobic and solvent-exposed face (formed by Phe-64 and Phe-72) of the WW2 domain (F). Finally, the stability of the complex was monitored measuring the central mass distance between the PDZD11 peptide (residues 16-22) and, respectively, WW1 and WW2 domains during 120 ns of MD simulation . The PDZD11 peptide strongly maintains the interaction with the WW1 domain, but not the one with WW2, which is restored only in the final steps of the simulation . Although the short MD simulation does not permit full evaluation of large scale motions and populations of side chain rotamers, these results suggest that the binding of PDZD11 to the tandem WW domains can cause a conformational rearrangement of the tandem WW1–WW2 domains, which can favor the successive binding of other effector proteins, as Tspan33.Finally, based on these experimental data, we docked the N-terminal heptapeptide of PDZD11 (residues 16 to 26) into the binding pocket of the WW1–WW2 model of PLEKHA7 F. The exn ligand  placing n ligand F or with2 domain F. FinallA) or presence  of PDZD11. PDZD11  binds to the pocket generated at the interface between WW1 and WW2 domains . We noticed that the WW1 domain displays a highly lipophilic region at the solvent-accessible surface, which resulted in a “hydrophobic hot spot” for protein recognition . The lipophilic cavity is formed mainly by the conserved residues Leu-10, Trp-15, Tyr-17, Phe-27, Leu-32, and Leu-38, which are not directly involved in the interaction with PDZD11. Strikingly, the presence of PDZD11 significantly increased (by 73.26 Å2) the size of this lipophilic surface and thus, extending the hydrophobic hot spot region . Previously, we showed that the isolated C-terminal domain of Tspan33 (residues 256-283) is sufficient for the interaction with the WW1 domain of PLEKHA7 . Immunoblot analysis showed that deletion of the last 2 residues of Tspan33 (Trp-Tyr) was sufficient to abolish the interaction between Tspan33 and PLEKHA7 . Interestingly, the C-terminal region of Tspan33 presents an excellent complementarity in terms of lipophilic surface with respect to the hydrophobic hot spot region of the WW1 domain . The docking of the C-term Tspan33 decapeptide perfectly places the last two residues, Trp-282 and Tyr-283, in the hydrophobic region of the WW1 domain . Indeed, Trp-282 and Tyr-283 are the main contributors to the lipophilic surface of the Tpsan33 C-term, suggesting that the recognition of these macromolecules (WW1–WW2-PDZD11 and Tspan33) is mediated especially by hydrophobic/hydrophilic forces. To confirm the role of these residues, we studied mutants of PLEKHA7, and examined how double and single mutations within the WW1 and WW2 domains affect interaction with the C terminus of Tspan33 by GST pulldown. Although the GST fusion of the last 28 residues of Tspan33  interacted well with the WW1 + WW2 prey, either the double mutations D30A+H75A or T35A+H75A  or the single mutations D30A, T35A, and H75A  abolished binding. This suggested that the lipophilic surface of WW1 that interacts with Tspan33 is exquisitely sensitive to the conformation of the tandem domain structure, and may require PDZD11 for stabilization. To test this, we compared the binding of the WW1 + WW2 prey to the Tspan33 C-terminal bait in the absence or presence of either PDZD11 or CFP, as a negative control (G and H). Immunoblot analysis showed that PDZD11 increases the binding of both WT and single mutant preys to the Tspan33 bait, but cannot rescue the loss of binding to the double mutant prey (G and H), consistent with the observation that it does not bind to the double mutant (A).By GST pulldown analysis, the N-terminal region of PLEKHA7 binds weakly to the C-terminal region of Tspan33, but this interaction is greatly enhanced in the presence of PDZD11 . In agreFig. 4see also F. We not PLEKHA7 . Here wee mutant A.The precise molecular mechanisms through which PLEKHA7 is implicated in disease , morphogenesis , and diverse cellular functions –11 are p–2), and these domains form a binding pocket that interacts with the N-terminal, proline-rich peptide of PDZD11. Three residues in the WW1 domain, Arg-21, Asp-30, and Thr-35, and one residue in the WW2 domain, His-75, are critically involved in promoting the interaction of PLEKHA7 with PDZD11.Proteins that contain multiple or tandem WW domains show different structural configurations and reciprocal functional interactions between these domains , 25. In 28–WW domains are classified based on the sequences of the interacting ligands in 4 groups: PPXY (I), PPLP (II), PR (III), or p(S/T)P (IV). The sequence of the WW-interacting region of PDZD11 does not precisely match any of these sequences. However, group II and group III WW domains not only recognize PPLP and PR-containing peptides, but also polyproline stretches containing glycine, methionine, or arginine . In addiThe PDZD11-dependent clustering of transmembrane proteins by PLEKHA7 appears to occur by at least two different mechanisms. In the case of nectins, their C terminus interacts with the PDZ domain of PDZD11 through a canonical PDZ-binding motif , indicatOur molecular studies provide a rationale explaining how PDZD11 promotes the binding of the C terminus of Tspan33 to PLEKHA7. Previously we showed that clustering of ADAM10 at junctions occurs through sequential and hierarchical multimolecular interactions. ADAM10 must first be docked to junctions through the interaction of its chaperone Tspan33 with the PLEKHA7-PDZD11 complex, before it can be “locked” by direct interaction with afadin . Here weIn summary, we characterize the structural basis of the interaction between the tandem PLEKHA7 domains and their interactions with both PDZD11 and Tspan33. Our model illustrates the mechanism through which the PLEKHA7-PDZD11 complex forms a scaffold for a transmembrane ligand with hydrophobic C-terminal sequences. In this model, cooperative interaction between WW1 and WW2 promotes binding of WW1 to PDZD11, and this in turn promotes Tspan33 binding to a different surface of WW1. This mechanism may be relevant to the functions of additional proteins with tandem WW domains.Mouse cortical collecting duct cells  were grown in Dulbecco's modified Eagle's medium (Gibco) supplemented with 20% heat-inactivated fetal bovine serum , 1× minimal essential medium nonessential amino acids . PLEKHA7-KO mCCD cells were obtained by CRISPR/Cas9 genomic editing . HEK celPrimary antibodies were: polyclonal guinea pig anti- PLEKHA7 ; monoclonal mouse anti-GFP , polyclonal rabbit anti-PDZD11 , mouse anti-HA , and mouse anti-His . Secondary antibodies were anti-mouse Alexa Fluor 488, anti-rabbit Alexa Fluor 488, anti-rabbit Cy3, anti-mouse Cy3, and anti-guinea pig Cy3 , and HRP-conjugated anti-mouse .Constructs of pTre2-Hyg containing GFP-Myc, GFP- PLEKHA7 (human) full-length (residues 1-1121), PLEKHA7-WW(1 + 2)-Myc (residues 1-162), PLEKHA7-WW(2)-Myc (residues 50-162), GFP-PDZD11 (human) full-length (residues 1-140), and constructs of pcDNA3.1 containing CFP-HA, PDZD11-HA (human) full-length, and constructs of pFASTBAC containing PLEKHA7-His and constructs of pGEX-4T1 containing GST alone, GST-PLEKHA7 (human) WW1 domain (residues 1-56), WW1 + WW2 domain (1-162), WW2 domain (50-162), GST-PDZD11 full-length (residues 1-140), and GST-Tetraspanin 33 C-terminal (residues 256-283) were described previously , 15. MutEscherichia coli (BL21-DE3) were induced with 0.1 mm isopropyl 1-thio-β-d-galactopyranoside (2 h at 37 °C). Bacterial pellets after centrifugation were lysed in PBS containing 1% Triton X-100, 5 mg/ml of antipain, 5 mg/ml of leupeptin, 5 mg/ml of pepstatin, 1 mm phenylmethylsulfonyl fluoride, and cell debris were removed by centrifugation at 13,000 rpm (15 min at 4 °C). The supernatants containing GST-tagged proteins were normalized for protein content by SDS-PAGE. For expression of prey proteins in mammalian cells, 2 × 106 HEK cells were plated in a 100-mm2 dish and transfected with 20 μg of the HA-tagged construct, lysed 2 days after transfection using co-IP buffer . Expression of PLEKHA7 in Sf9 insect cells (Sf9) was as described in Ref. For the production and purification of GST fusion protein baits, TM Polystyrene Microplates). 24 h after seeding cells were transfected, and 48 h later they were fixed with methanol (6 min at −20 °C), rehydrated in PBS (3×), permeabilized with 0.3% Triton X-100 in PBS for 5 min, and incubated with 0.03% Triton X-100, 1% BSA, 0.2% gelatin in PBS for 20 min. Cells were then incubated with primary antibodies (2 h at room temperature), washed with PBS, and incubated with 0.03% Triton X-100, 1% BSA, 0.2% gelatin in PBS for 15 min and incubated with secondary antibodies , washed 2× with 0.03% Triton X-100, 1× with cold PBS, and 1× with water. Coverslips were mounted with Vectashield containing DAPI (Reactolab).Cells were seeded at density of 40,000 cells/well onto 12-mm glass coverslips in 24-well-plates  (100 V for 80 min at 4 °C), and blots were incubated with primary antibody , followed by secondary HRP-labeled antibody , and visualized with ECL (Amersham Biosciences).For preparation of cell lysates, cells were washed with cold PBS, lysed with co-IP buffer with Roche inhibitor (1×) at 4 °C, and incubated for 15 min with gentle agitation. Lysates were sonicated 5 s at 66% power (3 bursts), centrifuged 20 min at 13,000 rpm, and supernatants were recovered. Proteins were mixed with SDS sample buffer and incubated at 95 °C for 5 min. Inputs of prey protein lysates and baits in bacterial lysates were normalized by analysis on SDS gels . For immunoblots, gels were transferred onto nitrocellulose (0.45 μn = 100) were retrieved with blastp, using either the human PLEKHA7 or human PDZD11 sequences as a query. Sequences were aligned using Weblogo (RRID:SCR_010236).To analyze protein sequence conservation, the sequences of the WW domain of PLEKHA7 or PDZD11 from all available vertebrate species (RRID:SCR_017590).The Prime program from Schrödinger package  was used to generate the homology models of WW1 and WW2 domains. The selection of the corresponding template for building the models was based on the sequence similarity study performed with BLAST . The solution structure of Nedd4 WW domain  was chosaK values were predicted using PROPKA at neutral pH and finally an all-atoms restrain minimization with a maximum RMSD of 0.3 Å was performed. The amino acids and the peptide sequences used for docking were prepared with the LigPrep tool that generates different energetically favored conformations, considering also the possible protonation states of the ligands. Glide program from Schrödinger package  was used to perform the molecular docking. The calculation considers a predefined target grid-box, and in the case of PDZD11 peptide docking we centered the box at the level of residues Asp-30, Arg-33, and Thr-35 for the WW1 domain, and His-75 for the WW2 domain. In the case of Tspan33 docking, the grid was centered in the lipophilic region of the WW1 domain considering the residues . Finally, the standard-precision mode was considered in the case of docking single amino acid residues and the SP peptide-mode was chosen for the peptides .The homology models were submitted to the Protein Preparation Wizard of Schrödinger tools package, where bond orders and atom types were assigned and also terminals were capped. The p-1 Å-2 positional restrains for protein and ions over the course of the first 80 ps at constant pressure (1 atm). The restrained forces were gradually turned off to zero in the subsequent 100 ps, allowing movement of all atoms. (iii) A final heating and equilibrating step was performed at 300 K at constant pressure over 820 ps. Finally, the molecular dynamic production was performed under NTP ensemble  using the PMEMD Cuda. The SHAKE algorithm was used to restrain the lengths of all bonds involving hydrogen atoms, allowing an increase of the time step up to 2 fs with a cut-off space of 9 Å. The default particle-mesh Ewald settings (which correspond to a grid spacing of ∼1 Å and a direct space tolerance of 10−6) were used to determine long-range charge interactions. The CPPTRAJ of Amber16 was used to process and analyze the molecular dynamic results. The surface area at the interface of the WW1 and WW2 domains was determined on the basis of the difference between the solvent accessible surface areas of the two domains separately (WW1 + WW2) or in a complex (WW1–WW2).The Amber16 suite programs  was usedsandra.citi@unige.ch), except for molecular docking and molecular dynamic simulation datasets, SRR9822082 which are available upon request from L.S. (leonardo.scapozza@unige.ch).Data and materials are available upon request from S.C. (


However, this mixture showed a small change in the heat flux response when analyzed with thermodynamic sensors (lower yeast activity and worse gas formation properties resulted from the sensor characteristic with a lower response). The 100% wheat flour had the highest response, and the second highest response was recorded for a mixture of wheat flour with 10% grape pomace and 10% mealworm larvae. This combination also often had one of the highest responses when measured with an experimental electronic nose, so this combination was considered as one of the most advantageous options for processing from the mixtures mentioned in the article.The present article dealt with the fortification of plain wheat flour by the addition of grape pomace flour and mealworm larvae powder, focusing on the mineral content and selected properties of the dough. The work also analyzed the properties of one mixture in a weight combination of 80% wheat flour, 10% grape pomace, and 10% mealworm. X-ray analysis was used to measure the mineral content of calcium, iron, copper, and zinc. The properties of the individual mixture were monitored using an experimental electronic nose and a thermodynamic sensor system during the leavening. The results showed that a combination of 50% grape pomace and 50% mealworm larvae was advantageous from the viewpoint of the favorable representation of minerals. The analyzed mixture contained a high proportion of calcium (3976.7 ± 362.9 mg·kg Nowadays, people are increasingly focusing on health and a healthy lifestyle. In the field of food, the fortification (enrichment) of food is one of the ways of improving the health condition of the population. The beginning of this dates back to the end of the 19th century, when, for example, in 1898, the Austrian professor Wagner von Jaureg proposed eliminating goiters by iodizing salt . The metAnother advantage is the possibility of balancing the nutritional value of a given bakery product, for example, by means of components with the presence of biologically active substances. Grape pomace is an example. It is further used in the production of alcohol, as a basic raw material to produce grape oil, and after drying and grinding as grape pomace flour (GPF). Grape pomace is rich in polyphenols, especially flavonoids such as gallic acid, catechin, and epicatechin , which cTenebrio molitor), which is one of the most studied species of edible insects, the crude protein content in the dry matter is reported to be 45.1% to 63.0%   .The sample  was put in a plastic measuring container , mixed, and then placed in the measuring glass box for analysis, as shown in Fermentation of various mixtures of fortified flours was analyzed using the thermodynamic sensors (TDS) described in Adámek et al. . The priIn the circuit, two measuring elements T1 and T2 are located as shown in The sample  was put in a plastic measuring cup, mixed, and then placed in the special measuring glass box for analysis . A samplThe data obtained for the determination of selected elements were evaluated using Microsoft Excel 2013 . Results were expressed as the mean (M) ± standard deviation (SD).Although two curves were determined for each type of mixture for the electronic nose and TDS monitoring, the curves were set to the same starting position, and the resulting average curve was calculated from them. Therefore, this is preliminary information.In the first part of the work, the representative elements  were analyzed in wheat flour, grape pomace flour, mealworm flour, and their mixtures. The results obtained are shown in −1) was detected by the handheld X-ray analyzer. The Czech Nutritional Database [−1 for the same type of flour (type 530). This is consistent with Ertl and Goessler [−1 to 50 mg·kg−1. A similar result was reported by Xing et al. [−1. The zinc content in the measured plain wheat flour was therefore similar to that of other authors. The detected zinc content of grape pomace flour was twice as high as that of wheat flour (44.3 ± 24.8 mg·kg−1), but even that was only 11% of the value determined for mealworm flour. However, the other monitored values of elements were higher in grape pomace flour which had, in particular, a high proportion of calcium and iron.In plain wheat flour, none of the monitored elements except zinc , 190 ± 50 mg·kg−1 for wheat flour (type 700), and 390 ± 80 mg·kg−1 for whole wheat flour. In contrast, Sousa et al. [−1 or Bennemann et al. [−1 to 4295 mg·kg−1 in grape pomace flour. This was comparable to the measured sample of grape pomace flour that was more than seven times higher than the calcium content of mealworm flour and may be up to 30 times higher than that of commercial plain wheat flour.In the FoodData Central (USDA) database, calcium content is reported most frequently in the range of 150–400 mg·kgDatabase  reports e et al.  performeGoessler  determina et al.  determinn et al.  from 158−1 in wheat flour type 530 and in the FoodData Central (USDA) database, the iron content is most often in the range of 15–50 mg·kg−1. Iron content in 100% plain wheat flour type 530  was more than seven times lower than in mealworm flour and more than 25 times lower in grape pomace flour. Ertl and Goessler [−1 for wheat flour (type 480), 11 ± 2 mg·kg−1 for wheat flour (type 700) and 34 ± 3 mg·kg−1 for whole grain wheat flour. Therefore, it can be generally stated that wheat flour containing 10% grape pomace flour reached similar values of iron to that of rye flour  [−1) and was slightly higher than that of wheat flour (type 700) [Except for wheat flour, iron values were recorded in all other flours. The Czech Nutritional Database  lists an−1 for wheat flour (type 480), 1.8 ± 0.2 mg·kg−1 for wheat flour (type 700), and 4.4 ± 0.6 mg·kg−1 for wholemeal wheat flour. Copper values for 100% mealworm flour and 100% grape pomace flour were at least 10–15 times higher than that for wheat flour.The value of copper in the sample of 100% plain wheat flour was below the detection limit. In the Czech and American Nutritional Databases, the copper content of the same type of flour was not found. Ertl and Goessler  found coThis was followed by monitoring the leavening using a thermodynamic sensor. The resulting average recorded curves are shown in 4, significantly decreased the measured curves below the signal level of 100% wheat flour, in the case of 100% grape pomace flour and 100% mealworm flour. In general, this was the case with all sensors and a 100% sample of mealworm flour. It was assumed that yeasts did not have a basic substrate for its activity in this sample. According to Simultaneously with the fermentation measurement using thermodynamic sensors, the changes in the gases produced by the dough were monitored using an experimental electronic nose. The MQ-8, MQ-135, and MQ-3 sensors  showed l2 and ethanol were used to monitor, but they were mirrored against the previous sensors . With this characteristic, it is possible to determine the volume of gas retained in the dough and the volume of gas released from the dough during leavening. Measurement of this characteristic can take several hours and individual gases are not recognized.In the study by González et al.  comparinThe first part of the present work was focused on the analysis of elements in wheat flour with the addition of grape pomace flour and mealworm flour. Mealworm flour contained the highest proportion of zinc in comparison to the other flours analyzed. Calcium, iron, and copper were most represented in grape pomace flour. By combining these flours, commodities with a favorable representation of these elements can be achieved.In the next part of the work, the leavening of the doughs of said mixtures was measured using thermodynamic sensors. The sensors recorded the highest changes in the thermal properties of common wheat flour. From the point of view of leavening and the representation of minerals, the combination of common flour with the fortification of grape pomace flour and mealworm flour appeared to be the most suitable variant up to 20% of the total content of the addition. The 100% grape pomace flour showed only a very small change in heat flow. A larger change was recorded for the G50T50 mixture, but it soon decreased almost to the start value, similar to the G100 mixture. Although the T100 mixture had a slower increase at the beginning, it was equal to the maximum value of the W80G10T10 mixture.For the electronic nose, only a small response (in some cases no response) was found for the T100 mixture with all the sensors used. With a higher proportion of wheat flour, the response value was significantly higher. The same was true for the grape pomace and the TVOC sensor. For other sensors and G100 mixture measurements, the signal was comparable or only slightly lower. Due to the smaller signals in the W100 sample, it was possible to infer a maximum at a certain value of the addition of grape pomace flour or mealworm flour to wheat flour. However, this statement still needs to be tested on more types of mixtures and a larger number of samples.The work shows that in order to maintain good leavening dough properties, only a limited amount of mealworm flour and grape pomace flour can be added, but this has been shown to increase the element content of the bakery product.


The dynamic changes of the thawing and freezing processes of the active layer cause seasonal subsidence and uplift over a large area on the Qinghai–Tibet Plateau due to ongoing climate warming. To analyze and investigate the seasonal freeze–thaw process of the active layer, we employ the new small baseline subset (NSBAS) technique based on a piecewise displacement model, including seasonal deformation, as well as linear and residual deformation trends, to retrieve the surface deformation of the Beiluhe basin. We collect 35 Sentinel-1 images with a 12 days revisit time and 9 TerraSAR-X images with less-than two month revisit time from 2018 to 2019 to analyze the type of the amplitude of seasonal oscillation of different ground targets on the Beiluhe basin in detail. The Sentinel-1 results show that the amplitude of seasonal deformation is between −62.50 mm and 11.50 mm, and the linear deformation rate ranges from −24.50 mm/yr to 5.00 mm/yr (2018–2019) in the study area. The deformation trends in the Qinghai–Tibet Railway (QTR) and Qinghai–Tibet Highway (QTH) regions are stable, ranging from −18.00 mm to 6 mm. The InSAR results of Sentinel-1 and TerraSAR-X data show that seasonal deformation trends are consistent, exhibiting good correlations 0.78 and 0.84, and the seasonal and linear deformation rates of different ground targets are clearly different on the Beiluhe basin. Additionally, there are different time lags between the maximum freezing uplift or thawing subsidence and the maximum or minimum temperature for the different ground target areas. The deformation values of the alpine meadow and floodplain areas are higher compared with the alpine desert and barren areas, and the time lags of the freezing and thawing periods based on the Sentinel-1 results are longest in the alpine desert area, that is, 86 days and 65 days, respectively. Our research has important reference significance for the seasonal dynamic monitoring of different types of seasonal deformation and the extensive investigations of permafrost in Qinghai Tibet Plateau. The upp2 . The act2 . Additio2 ,8. Thus,2 , levelin2 . HoweverIn recent years, Synthetic Aperture Radar interferometry (InSAR) has begun to provide an opportunity to retrieve the surface deformation of the frozen soil on the QTP with the development of multiple, high resolution sensors with wide coverage and frequent acquisitions ,13,14. ITM) [TSInSAR algorithms include Permanent Scatterer (PSInSARTM) , InterfeTM) , StanforTM) , Quasi-PTM) , PersistTM) , and SpaTM) . To incrTM) ,32,33,34TM) ,17 emploTM) . Then ChTM) . Based oTM) ,36. YuanTM)  also useTM) ,21,38,39TM) . Hu and TM) ,23 incorTM) ,40,41, aTM) ,20,21. ETM) ,39,42,43TM) ,23.In this paper, this seasonal deformation model is introduced to implement the NSBAS process and to retrieve the surface deformation related to the freeze–thaw cycles of permafrost using Sentinel-1 and TerraSAR-X data at different scales over the QTP. This model includes the seasonally varying components (subsidence/uplift), as well as linear and residual deformation trends, based on the accumulated degree-days of freeze and thaw. Comparing the results of two sensors, the difference in the deformation results and the deformation law of different typical ground targets on the Beiluhe basin are explained in more detail. Spatiotemporal analysis is implemented to analyze the correlation between seasonal deformation, linear deformation rates, slope, and elevation in the area of interest. In addition, the daily air temperature dataset combined with the deformation results are used to analyze the whole freeze–thaw cycles, and the relationship between the seasonal deformation and the active layer thickness are also analyzed based on GPR data.The study area covering the Wudaoliang to Tuotuohe region is located in the northeast of the continuous permafrost zone on the QTP, belonging to Qinghai Province, China . Figure 2, and TerraSAR-X data were acquired by the staring spotlight (ST) mode along the N. 385 ascending track. Detailed information is shown in In this paper, 35 scenes of C-band Sentinel-1 images with a 12 days revisit time from 7 August 2018 to 25 October 2019 and 9 X-band TerraSAR-X images with less than two month of revisit time from 15 December 2018 to 8 October 2019 were collected to implement the NSBAS process. Sentinel-1 is a two-satellite constellation, i.e., Sentinel-1A and Sentinel-1B, which were launched by European Space Agency (ESA) . SentineIn addition, the one arc-second SRTM DEM (pixel size of 30 m) was adopted to remove the topographic phase. Additionally, we obtained the 2−m air temperature in our study area by downscaling the ERA5 monthly averaged reanalysis product. These measurements can be downloaded from ERA-5 repository of European Centre for Medium-Range Weather Forecasts . In thisIn this study, we employed the NSBAS approach based on the Stefan model ,23 to inThe DEM co-registration method was adopted for TerraSAR-X and Sentinel-1 data. Furthermore, the ESD method was used to refine the azimuth offset of the Sentinel-1 images . AdditioADDT) and freeze (ADDF) to account for the seasonal thaw subsidence and freeze uplift, respectively, and they are calculated based on the 2-m air temperature by Liu et al [Permafrost areas generally experience seasonal uplift and subsidence. The linear deformation model is not suitable to retrieve the freeze–thaw cycle deformation of the frozen soil . In thisiu et al . kF=1.4  content . nF=0.61o et al. .The gradual deformation change (subsidence/uplift) is primarily associated with the freezing and thawing of the active layer and the uppermost permafrost on the Beiluhe basin. Therefore, we adopted the NSBAS method based on the deformation model, including seasonal and long-term deformation changes, to retrieve the surface deformation of freeze–thaw cycles. The seasonal and long-term deformation model is shown in Equation (3)n and n + 1. Singular value decomposition (SVD) is used instead of least squares to retrieve the deformation time series in Equation (4). In the traditional SBAS approach, the set of interferometric phase observations is written as a linear combination of individual SAR scene phase values for each pixel independently  in EquatHowever, o by SVD . To overo by SVD  as(5)∀dIn this study, GIAnT software was used to implement the NSBAS process . FinallyThe proposed deformation model of The Sentinel-1 InSAR results from Wudaoliang to Tuotuohe are shown in The Beiluhe basin is located in the middle of our study area. To investigate the spatial distribution characteristics and deformation trends of the InSAR results, we extracted the N1-N2 and M1-M2 profiles from the amplitude of seasonal deformation, linear deformation rate, and residual deformation maps . We diviTo analyze the deformation trends of the different ground targets in more detail, the landform of the Beiluhe basin was divided into six ground targets : QTH RegTo study the freeze–thaw cycles of permafrost, we extracted the time series displacement maps of the four points marked in The thawing process began from the surface of the frozen soil in mid-April in 2019, and it ended in mid-September in 2018 or 2019. The displacement maps of the Sentinel-1 and TerraSAR-X data in To verify the validity of our work, we compared our study with other studies on the QTP . Li et aIn In this paper, the NSBAS method with the seasonal deformation model provided new insights into complex permafrost investigation and deformation monitoring. This deformation model based on the accumulated degree days of freeze and thaw appears capable of accurately approximating and modeling the ground surface deformation of the frozen soil.Our results show that the amplitude of seasonal deformation is between −62.15 mm and 11.5 mm, and the deformation rate ranged from −24.50 mm/yr to 5.00 mm/yr; the seasonal deformation and linear deformation rate are negatively correlated with elevation from Wudaoliang to Tuotuohe region. Additionally, the deformation results of six typical ground targets of the Beiluhe basin were analyzed in detail, and the seasonal deformation and linear deformation rates of the alpine meadow and floodplain were higher than those of alpine desert and barren. The QTR and QTH regions had a small subsidence trend, with maximums of 15.57 mm and 18.23 mm, respectively. The deformation results are also closely related to the ALT. In addition, the complete freeze–thaw cycle of the Beiluhe basin was analyzed by combining the daily air temperature data and the deformation map. The thawing process began from mid-April 2019 and ended in mid-September 2018 or 2019, and from the end of September 2018 or 2019, different typical ground targets began to freeze. Based on the TerraSAR-X and Sentinel-1 data, the time lags of the alpine desert are the longest, likely because the activity layer of the alpine desert is thicker than that of other regions. Based on the GPR data, there is a close relationship between seasonal deformation and the ALT. The seasonal deformation of the alpine meadow and the floodplain areas is relatively higher than the alpine desert area, with thinner ALT.


The flow stress increases with the extension of strain rate and decreases with the rising of deformation temperature. The dynamic recrystallization behavior happens during the hot compression deformation process. The hot deformation activation energy of the alloy can be calculated as 468.5 kJ/mol, and the high temperature deformation constitutive equation is confirmed. The hot processing map of the alloy is established on the basis of hot deformation behavior and hot working characteristics. With the optimal thermal deformation conditions of 940 to 970 °C and 0.01 to 10 s−1, the fine equiaxed grain and no holes are found in the matrix, which can provide significant guidance for hot deformation processing technology of Cu–Ni–Co–Si alloy.The Cu-1.7Ni-1.4Co-0.65Si (wt%) alloy is hot compressed by a Gleeble-1500D machine under a temperature range of 760 to 970 °C and a strain rate range of 0.01 to 10 s According to the trinary phase diagram of Cu–Ni–Si, Ni2Si intermetallic is precipitated at temperature 880 °C and the addition of Co to this system also results in the formation of the 2Si phase at a temperature of 1050 °C [2Si phases are higher than those of the Ni2Si phase, the deformation behavior, microstructure and microstructure evolution of the Cu–Ni–Co–Si alloy during hot processing may be different from those of the Cu–Ni–Si alloy, which have a great effect on the formability and comprehensive performance of the alloy.Cu–Ni–Si alloy is an ideal material for integrated circuit lead frames, connectors, elastic components and power conversion, which has high tensile strength, high softening temperature, and excellent electrical and thermal conductivity. The C70250 alloy is a typical Cu–Ni–Si alloy with tensile strength of 600–800 MPa and electrical conductivity of 35%–45% IACS ,2,3,4,5.he alloy ,10,11. L 1050 °C . Since t−1. Moreover, the constitutive relationship of hot deformation and thermal processing maps were established to reveal the microstructure evolution and deformation mechanism in the process of hot deformation, and to provide guidance for the establishment of a hot working process for the Cu–Ni–Co–Si alloy.Therefore, the high-temperature deformation behavior of the Cu-1.7 wt% Ni-1.4 wt% Co-0.65 wt% Si alloy (hereinafter referred to as Cu-1.7Ni-1.4Co-0.65Si) was studied under the conditions of a deformation temperature of 760–970 °C and a strain rate of 0.01–10 sThe raw materials were pure Cu (99.95 wt%), pure Ni (99.99 wt%), pure Co (99.95 wt%) and pure Si (99.90 wt%). The ingot was prepared by using an intermediate-frequency induction melting furnace with the dimensions of 640 mm length, 210 mm width, and 6000 mm height. The melting temperature was about 1300 °C and the casting temperature was about 1250 °C.−1, respectively. The samples were heated with a rate of 20 °C/s and held for 3 min. Before the experiment, lubricant (75% graphite + 20% oil + 5% trimethylbenzene nitrate) was applied to both ends of the cylinder sample, and tantalum pieces were affixed to the end of the hydraulic shaft of the equipment to reduce the influence of friction in the deformation process. The deformed specimens were quickly cooled by water to room temperature to obtain the deformed structure. The microstructure was observed with a LV150 Optical Microscope . The samples for OM observation were mechanically polished and corroded in a solution of 5 g FeCl3 + 10 mL HCl + 90 mL H2O. The samples for transmission electron microscope  observations were first cut to a thickness of around 500 µm and then ground to 60 µm, after which these samples were punched into a disc of 3 mm in diameter followed by electro-polishing using a mixed solution of CH3OH:HNO3 = 4:1 under a temperature of −35–30 °C. The microstructures of different states were observed under a JEM 2100 LaB6 transmission electron microscope .The high temperature compression test was conducted on the Gleeble1500D material thermal simulation test machine . The samples were taken from alloy ingots with a diameter of 10 mm and a length of 15 mm. The hot deformation treatments were 760 °C, 790 °C, 820 °C, 850 °C, 880 °C, 910 °C, 940 °C and 970 °C, and the strain rates were 0.01, 0.1, 1 and 10 s−1 at various temperatures. When the stress exceeds the yield strength of those alloys, plastic deformation is observed and the stress continues to enhance as the true strain rises. The softening effect derived from the appearance of dynamic recovery and recrystallization is less than the hardening effect derived from the increase in dislocation density during the deformation process. Compared to the Cu–Ni–Si alloy [−1. The hot deformed 760 °C shows smaller grains compared to the reference Cu–Ni–Si alloy [Si alloy , the addSi alloy . This ca−1, the true stress–true strain curve shows an obvious serrated feature, as shown in When the strain rate is 10 sAs a basic parameter to characterize the deformation of alloys, flow stress can be used to determine the load capacity and energy consumption during hot deformed processes. The hot process of alloys is also controlled by the thermal activation process . The conThe relationship between deformation parameters can be shown as Equation (2) for low stress and Equation (3) for high stress, respectively:−1; A1, A2, A, α, n1 and β are material constants; R is the gas constant, 8.314 J/mol·K; Q is the activation energy, kJ/mol; T is the thermodynamic temperature, K; α = β/n1.For all stress levels, the hyperbolic sine Equation (4) can be used to express:Combining Equations (1) and (3) can obtain:Taking the logarithm of both sides of Equations (2) and (3) can obtain:1 = 8.57, and the mean slope of the three lines at high stress (750–850 °C) is β = 0.127, α can be obtained by β/n1, which is 0.015.Taking the logarithm of both sides of Equation (5) can obtain:Combining Equations (1) and (5), taking the logarithm of both sides and putting Q into it can obtain:20 and n = 7.55 can be obtained from the slope and intercept, respectively, where the value of n is very close to the previous 7.62. In this work, 7.62 is taken as the value of n for subsequent calculations.nexp ,18,19,20K is determined by the microstructure and temperature of the deformed alloy; m is the strain rate sensitivity coefficient. Under the determined strain and temperature, the value of m can be obtained from Equations (12) and (13):The deformation behavior of alloys is controlled by their internal microstructure and the rate of strain hardening. The flow stress is sensitive to the strain rate and can be expressed in the form of the flow rate:J and G, the value of m reflects the hot deformation characteristics of alloys. Prasad thought [G and J correspond to the generation and consumption of dislocations, respectively. In the stable flow process, the dislocation consumption rate cannot be greater than the generation rate, so J is less than or equal to G. According to Equation (12):According to the physical meaning of  thought ,24 that m, deformation temperature and strain rate. According to Equation (15), the change of J is also nonlinear. The ideal linear dissipation unit, m = 1, where the value of J is Max, maxJ. For the nonlinear dissipation unit, the power dissipation efficiency can be expressed by the ratio of J to maxJ:According to Equation (13), there is a nonlinear relationship between η is a dimensionless parameter and a function of temperature, strain and strain rate. η reflects the energy dissipation efficiency of the material.Power dissipation efficiency ξ and (17) can be expressed as:−1, respectively.In this paper, the hot deformation behavior of Cu-1.7Ni-1.4Co-0.65Si alloy is analyzed. The deformation temperature and strain rate in different deformation conditions can be put into Equation (20) to obtain the power dissipation diagram and instability diagram, so as to obtain the hot processing map. −1, as shown in To further analyze the hot compression deformation behavior of the Cu-1.7Ni-1.4Co-0.65Si alloy, the microstructure of different regions is observed. −1 are shown in 2Si, Co2Si or 2Si according to the select area diffraction pattern, as shown in 2Si in the Cu-1.7Ni-1.4Co-0.65Si alloy.The TEM images of a hot specimen at 760 °C with a strain rate of 10 sWhen the hot deformation temperature increases to 910 °C, the density of dislocation clearly decreases and dynamic recrystallization occurs, as shown in −1. Compared to the microstructure in the instability zone as shown in (1)2Si phase are observed and have a strong effect on dislocation movement, which is a response to the increase in strain and stress.Deformation temperature and strain rate have significant effects on the flow stress of the Cu-1.7Ni-1.4Co-0.65Si alloy. The flow stress increases with the extension of strain rate and decreases with the increase in deformation temperature. The dynamic recovery, recrystallization and work hardening coexist during the hot compression deformation process. The precipitates of the (2)The hot deformation activation energy Q of the Cu-1.7Ni-1.4Co-0.65Si alloy can be calculated as 468.5 kJ/mol, and the high temperature deformation constitutive equation is confirmed as:(3)−1, the fine equiaxed grain and no holes are found in the matrix.The hot processing map of the Cu-1.7Ni-1.4Co-0.65Si alloy is established on the basis of hot deformation behavior and hot working characteristics. With optimal hot deformation conditions of 940 to 970 °C and 0.01 to 10 s


The synthesis of phosphine‐based functional covalent organic frameworks (COFs) has attracted great attention recently. Herein, we present two examples of triphenylphosphine‐based COFs (termed P‐COFs) with well‐defined crystalline structures, high specific surface areas, and good thermal stability. Furthermore, rhodium catalysts with these P‐COFs as support material show high turnover frequency for the hydroformylation of olefins, as well as excellent recycling performance. This work not only extends the phosphine‐based COF family, but also demonstrates their application in immobilizing homogeneous metal‐based  catalysts for application in heterogeneous catalysis. A turn up for turnover: Two triphenylphosphine‐based COFs (P‐COFs) that stack in an eclipsed AA manner and have high crystallinity, high specific surface areas and good thermal stability have been synthesized. The application of these P‐COFs in phosphorus coordination catalysis was demonstrated by the hydroformylation of olefins over supported Rh/P‐COFs catalysts, which showed high turnover frequencies and excellent recycling. This interaction forces the unit to deviate from planarity and twist to a smaller angle (<180°) between phenyl propellers when forming a two‐dimensional structure, resulting in POP materials.Covalent organic frameworks (COFs), materials with porous crystalline structure assembled by covalent bonds,r angle <0° betweeHerein, we report an improved synthesis of two examples of triphenylphosphine‐based COFs  or benzidine (CAS no. 92‐87‐5), denoted P‐COF‐1 and P‐COF‐2, respectively  and P‐COF‐2  yielding unit cell parameters of a=b=32.55 Å, c=7.9 Å for P‐COF‐1 (Tables S1) and a=b=40.35 Å, c=7.9 Å for P‐COF‐2 (Tables S4). The corresponding pore diameters for P‐COF‐1 and P‐COF‐2 were calculated to be 37 and 46 Å, respectively, with an interlayer separation of 7.9 Å.Two imine‐linked P‐COFs were synthesized (Section 2 in the Supporting Information) through a Schiff base reaction of tris‐(4‐formylphenyl)phosphane p‐phenylenediamine or benzidine,−1 for p‐phenylenediamine  and benzidine . A relatively weak C=O stretching band at 1694 cm−1 was still present in the FTIR spectra of P‐COFs . 31P static solid‐state NMR spectra of the P‐COFs . There is a presence of a little collar around δ=−0.6 ppm in P‐COF‐2, which may represent P‐COF‐2 with different polymerization degree from that of δ=−4.8 ppm.The C=N bonds in the P‐COFs are formed by the condensation of aldehyde groups of TFP with the amino groups of s Figure  a, probas Figure  b show c2  show the combined characteristics of type I and type IV,P/P0<0.1) and mesoporous (0.4<P/P0<1) structure. The pore size distributions (PSDs) of the P‐COFs 2 g−1 (P‐COF‐2); as well as the corresponding total pore volumes are 0.69 cm3 g−1 (P‐COF‐1) and 4.22 cm3 g−1 (P‐COF‐2). Notably, these surface areas of triphenylphosphine‐based COFs are higher than those for amorphous triphenylphosphine‐based POPs.2 atmosphere during thermogravimetric analysis  and tetrahydrofuran (THF). To examine the chemical stability of P‐COFs,m), NaOH (aq) (pH 14), and concentrated NaOH (4 m). P‐COF‐1 and P‐COF‐2 retain their original skeleton and crystalline structure after treatment in THF, DMF, and ethanol, as indicated by well‐kept PXRD patterns . Comparatively, P‐COF‐1 has better stability in boiling water and NaOH (aq) than P‐COF‐2. Both P‐COF‐1 and P‐COF‐2 lost their crystallinity after acidic medium treatment, likely related to the hydrolytic nature of the imine bonds, which was also observed for other imine‐linked COFs.Scanning electron microscopy ) was performed. After Rh loading, the morphology of P‐COFs was maintained, as shown by SEM  were not observed on both Rh‐P‐COFs. However, the lattice fringes of Rh (111) plane with approximately 0.26 nm spacing can be observed on Rh‐P‐COF‐1 treated by NaBH4 . These results indicated that the Rh(CO)2(acac) is uniformly distributed on Rh‐P‐COFs, in accordance with the SEM‐EDS mapping  of the Rh‐P‐COFs. FTIR spectra of Rh‐P‐COFs  at 1972 cm−1,2(acac)  showing the symmetric and asymmetric ν(CO) at 2062 cm−1 and 1993 cm−1, respectively.−1 was observed for Rh‐P‐COFs  (309.3 eV). The binding energies of the P 2p3/2 band in Rh‐P‐COFs (131.8 eV) are higher than that in P‐COFs  of two P‐COFs with (acetylacetonato)dicarbonylrhodium (I) (Rh(CO)M Figure . In addi2(acac) and Rh(CO)2(acac) in combination with P(Ph)3. It was found (Table −1) and aldehyde selectivity (96 %) than the only Rh(CO)2(acac) . As expected, the heterogeneous Rh‐P‐COFs catalysts show higher TOFs  catalyst. When prolonging the reaction time to 6 h, the olefin conversion over Rh‐P‐COFs reaches 95 % . Such promising catalytic conversion and product selectivity were also obtained for the hydroformylation of other olefins, for example, hex‐1‐ene, oct‐1‐ene, 4‐methoxystyrene, and 4‐chlorostyrene (Table S7). Furthermore, the reusability of the heterogeneous Rh‐P‐COFs catalysts were investigated by filtrating the used Rh‐P‐COFs catalysts and recycling several times. Similar olefin conversions after 6 h reaction was achieved over five cycles , with a small drop in aldehyde selectivity  catalysts (Rh‐P‐COFs) formed through Rh‐P coordination bonds have high turnover frequencies (>2000 h−1), high aldehyde selectivity (ca. 99 %), and excellent catalyst reusability (>5 cycles) for the hydroformylation of olefins. We believe that the novel triphenyl phosphine‐based COFs are not only suitable for immobilizing homogeneous metal‐based catalysts, but also have important potential applications in other fields such as adsorption–separation, electrocatalysis and photocatalysis.In summary, we have demonstrated the synthesis of two examples of high crystalline and porous triphenylphosphine‐based covalent organic frameworks (P‐COFs) through a Schiff base reaction. Both P‐COF‐1 and P‐COF‐2 adopt AA stacking to form accessible open channels of 37 and 46 Å, respectively. The P‐COF‐supported Rh(CO)The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.