Mucin domains are densely O-glycosylated modular protein domains found in various extracellular and transmembrane proteins. Mucin-domain glycoproteins play important roles in many human diseases, such as cancer and cystic fibrosis, but the scope of the mucinome remains poorly defined. Recently, we characterized a bacterial O-glycoprotease, StcE, and demonstrated that an inactive point mutant retains binding selectivity for mucin-domain glycoproteins. In this work, we leverage inactive StcE to selectively enrich and identify mucin-domain glycoproteins from complex samples like cell lysate and crude ovarian cancer patient ascites fluid. Our enrichment strategy is further aided by an algorithm to assign confidence to mucin-domain glycoprotein identifications. This mucinomics platform facilitates detection of hundreds of glycopeptides from mucin domains and highly overlapping populations of mucin-domain glycoproteins from ovarian cancer patients. Ultimately, we demonstrate our mucinomics approach can reveal key molecular signatures of cancer from in vitro and ex vivo sources. Mucin-domain glycoproteins are densely O-glycosylated proteins with unique secondary structure that imparts a large influence on cellular environments. Here, the authors develop a technique to selectively enrich and characterize mucin-domain glycoproteins from cell lysate and patient biofluids. Mucin-type O-glycans are characterized by an initiating α-N-acetylgalactosamine  monosaccharide that can be further elaborated into several core structures through complex regulation of glycosyltransferases5. As a result, mucin domains serve as highly heterogenous swaths of glycosylation that exert both biophysical and biochemical influence. For instance, this includes the ability to redistribute receptor molecules at the glycocalyx and to drive high avidity binding interactions8. In the canonical mucin (MUC) family, mucin domains often occur as tandem repeats, creating heavily glycosylated superstructures. Canonical mucins are central to many functions in health and disease, and have long been associated with human cancers, e.g., MUC1 and MUC16 12. Dysregulation of mucin domain expression and aberrant mucin domain glycosylation patterns have been implicated in disease pathologies, especially in tumor progression, where mucins modulate immune responses and also promote proliferation through biomechanical mechanisms15.Mucin domains are modular protein domains that adopt rigid and extended bottle-brush-like structures due to a high density of O-glycosylated serine and threonine residues16; mucin domain-containing splice variants of CD44 (CD44v) serve as cancer cell markers relative to the ubiquitously expressed standard isoform17; CD45 mucin domains act as suppressors of T-cell activation18; mucin domain O-glycosylation on PSGL-1 is required for leukocyte-endothelial interactions19; and aberrant regulation of mucin domains in podocalyxin and SynCAM1 are implicated in a variety of cancers21. In all of these cases, shared functional attributes of mucin domains impart structural and biophysical properties relevant to their biology. Thus, instead of the more traditional categorization of the glycoproteome into N- and O-glycoproteins (both of which are represented by mucin-domain glycoproteins), it is logical to parse the glycoproteome into the mucinome, a family of glycoproteins whose mucin domains make them functionally related. However, even as the tools to capture the broadly defined N- and O-glycoproteome continue to improve31, mucin domains remain enigmatic and difficult to characterize. As such, a comprehensive list of all proteins with a mucin domain does not exist. This lack of a well-defined mucinome leaves a critical blind spot in our ability to interrogate mucin domain functions across molecular biology.Mucin domains also exist in proteins outside of the 21 canonical mucins Fig. . For exa38. We recently characterized a panel of such enzymes, termed O-glycoproteases, and showed that each of them harbor a selectivity toward mucins as well as unique peptide- and glycan-based cleavage motifs39. Using catalytic point mutants, we also demonstrated that select O-glycoproteases can retain binding specificity for mucin domains; these were then used as mucin-selective staining reagents for Western blots, immunohistochemistry, and flow cytometry39. One particular enzyme of interest is secreted protease of C1 esterase inhibitor (StcE) from enterohemorrhagic Escherichia coli, which recognizes mucin domains decorated with a variety of O-glycan modifications43. This gives StcE both the selectivity needed to specifically bind mucin domains and the breadth to bind diverse mucin domain subtypes that vary in glycosylation patterns. Indeed, StcE has shown great utility for selective release of mucin fragments from biological samples and for improving mass spectrometry (MS)-based analysis of mucin domains40.Toward this goal, enzymes derived from microorganisms known to colonize mucosal environments have shown promise for developing tools specifically suited to characterize mucin-domain glycoproteinsE447D) could function as a universal mucin enrichment tool for mucin domain discovery, similar to how inactive O-glycosidases and engineered sialidases can enrich broadly for O-glycosylated and sialylated glycoproteins, respectively45. Here we show that StcEE447D-conjugated beads selectively enrich mucin-domain glycoproteins from complex cancer cell lysates and from crude ovarian cancer patient ascites fluid. As part of this workflow, we developed a mucin-domain candidacy algorithm to assign confidence scores to proteins that have a high likelihood of containing a mucin domain. Additionally, we detected hundreds of glycopeptides derived from mucin domains in the StcEE447D-enriched samples. Ultimately, we demonstrate that this mucinomics platform can define key molecular signatures of cancer in both in vitro and ex vivo systems and is a valuable approach to unravel the role of mucin domains in health and disease.We reasoned that the catalytically inactive point mutant of StcE (StcEE447D) retains its binding specificity for mucin domains while leaving them intact for subsequent analysis40. Through a straightforward reductive amination approach, we conjugated StcEE447D to POROS-AL beads to generate a solid phase support material to use for enrichments46. To optimize our enrichment protocol, we added StcEE447D-conjugated beads to OVCAR3 supernatant followed by an anti-MUC16 Western blot for detection. We tuned several parameters of the enrichment, including binding time, bead-to-substrate ratio, wash buffers, and elution conditions (see Methods); a simplified protocol is detailed in Fig. E447D-conjugated beads. Bound proteins were eluted by boiling in protein loading buffer, elutions were separated by one-dimensional gel electrophoresis, and in-gel digestions were performed prior to label-free quantitative shotgun proteomics  values for proteins in the elution relative to lysate, with processing and calculations performed using MaxQuant and Perseus48. The volcano plot in Fig. To calculate the degree of enrichment provided by StcE50, but we wanted to extend our criteria to use protein-level data that includes predicted O-glycosites, subcellular localization information, and previously annotated PTM-sites to annotate putative mucin domains in the human proteome. As summarized graphically in Fig. 51. Predictions from NetOGlyc4.0 were then screened for known phosphosites annotated in Uniprot52 and PhosphoSitePlus53, and any overlap in phosphosites with predicted O-GalNAc sites resulted in removal of the predicted O-GalNAc site from consideration. This was a necessary step because NetOGlyc4.0 often predicted O-GalNAc sites in known phosphodomains of intracellular proteins, resulting in a high number of false positive mucin candidates after downstream processing. Note that O-GalNAc and phosphorylation sites are not known to have a high degree of overlap, as the former is generally extracellular whereas the latter is often intracellular.While these initial results were exciting, it quickly became clear that hand-curating proteins with known mucin domains would be untenable for the mucinome discovery platform. Not only is hand-curation low throughput, but it inherently misses proteins without known mucin domains. Instead, we developed a mucin-domain candidacy algorithm to calculate which proteins have a high probability of bearing a mucin domain. Previous work has mined sequences looking for PTS domains in various non-human organismsMethods. Using a point system based on the answers to these questions, the algorithm ultimately assigned a Mucin Score to each protein in the human proteome. By manually assessing outputs, we determined that a score of >2 was a high confidence mucin-domain glycoprotein, between 2 and 1.5 was a medium confidence mucin-domain glycoprotein, and between 1.5 and 1.2 was a low confidence mucin-domain glycoprotein. Proteins with a score lower than 1.2 were not considered mucin-domain glycoproteins. Levels of confidence also capture the idea that a mucin domain may not be a binary concept; there may be gradients of O-glycosylation density and patterns that contribute to mucin-like attributes. See Supplementary Data 54.Following O-GalNAc site prediction and phosphosite filtering, the algorithm asked four questions of each protein: 1) Was the protein predicted to be extracellular, secreted, and/or transmembrane?; 2) Were there at least 9 predicted O-glycosylation sites within a stretch of 50 residues?; (3) Was the distance between any given pair of O-glycosites less than 12% of the entire mucin domain ?; and (4) Was the ratio of threonine to serine residues skewed toward threonine? Each of these benchmarks were determined through expert curation of known mucin sequences, which are further described in  Was the  Were theE447D elution, some of which are labeled in red with gene names associated with specific proteins. Interestingly, some high confidence and a handful of medium to low confidence mucin-domain glycoproteins are on the left side of the volcano plot, i.e., not enriched in the StcEE447D elution. This could indicate (1) that StcEE447D does not effectively enrich some mucin domains, (2) that mucin domains in these proteins are not heavily glycosylated in HeLa cells, or (3) that the mucin-domain candidacy algorithm has some degree of error. Inherently, the mucin-domain candidacy algorithm is an imperfect predictor of all mucin domains across the proteome. Indeed, no high efficacy mucin domain prediction algorithm exists, nor was that the focus of this work. Instead, our mucin-domain candidacy algorithm indicates degrees of confidence for assigning proteins with a putative mucin domain that can be used to assess mucinome enrichment with StcEE447D-conjugated beads.Using Mucin Scores to reannotate the dataset from Fig. Given that the HeLa lysate enrichment was successful, we decided to expand the approach to other cancer-associated cell lines, including SKBR3 (breast), OVCAR3 (ovarian), K562 (leukemia), and Capan2 . The corresponding volcano plots are shown in Fig. The Upset plot in Fig. 51, which is one of the most comprehensive study on O-glycosites to date . To consider a mucin-domain glycoprotein in this comparison, more than 1 glycopeptide had to be detected from within the assigned mucin domain. Additionally, if the protein was a canonical (e.g. MUC15) or confirmed (e.g. Gp1bα) mucin-domain glycoprotein, these were considered as previously described/known proteins. Several of the proteins (4/7) found in all five cell lines were previously known to have a mucin domain, including: Mucin-1 (MUC1), dystroglycan (DAG1), agrin (AGN), and complement decay factor . However, we discovered that three of the overlapping proteins have previously undescribed mucin domains: low-density lipoprotein receptor 8 (LRP8), major facilitator superfamily domain 6 (MSFD6), and porimin (PORIM). MSFD6 is a multi-pass transmembrane protein that is implicated in antigen processing and presentation of exogenous peptide antigens via MHC class II, whereas porimin is involved in oncotic cell death characterized by vacuolization and increased membrane permeability.To better understand how many of the proteins contained previously undescribed mucin domains, we compared our dataset to the SimpleCell dataset from Clausen and colleagues56. Perhaps unsurprisingly, the most enriched cellular component (CC) GO terms were associated with membranes, cell surfaces, extracellular space, among others  were newly discovered Fig. . Of thesers Fig. .39. BT4244 is a O-glycoprotease of particular interest from Bacteroides thetaiotaomicron that cleaves N-terminally to serine and threonine residues bearing truncated O-glycans, such as the cancer-associated T- and Tn-antigens . We reasoned that a point mutant of BT4244 (BT4244E575A) could also enrich mucin-domain glycoproteins bearing shortened O-glycan structures. Thus, we conjugated BT4244E575A to beads and performed an analogous enrichment using HeLa lysate with and without sialidase pretreatment, with results shown in Supplementary Fig. In another extension of our mucinomics workflow, we performed an enrichment using a different O-glycoprotease. While StcE does not demonstrate drastic glycan specificity, we have characterized several other O-glycoproteases with varying glyco-proteolytic specificitiesE447D-conjugated beads enriched a total of 75 proteins, 28% of which were mucin-domain glycoproteins. Thus, StcEE447D is approximately two-fold more selective for mucin-domain glycoproteins. Further, we detected only 54 non-mucin proteins in this enrichment, compared to the 230 in the Jacalin pulldown, representing a > 4-fold reduction in non-mucin proteins. While Jacalin did enrich more mucin-domain glycoproteins, selectivity is especially important when considering potential goals of characterizing mucin-domain O-glycopeptides; non-mucin proteins, and their associated unmodified peptides, will outcompete the glycopeptides for ionization and detection.We next asked how selective our mucin-domain-centric platform is when compared to lectin  enrichments commonly used for O-glycoproteomics. Jacalin has preference for mucin-type O-glycans including GalNAc and GalNAc-Gal; thus, we conjugated Jacalin to POROS-AL beads and performed enrichments on HeLa cell lysate with and without pretreatment with sialidase. The resulting volcano plots are shown in Supplementary Fig. E447D cell line enrichments to understand if there was an unexpected selectivity for features other than mucin domains or if it was likely due to non-specific binding. We calculated how many of the non-mucins were commonly found between cell lines, as demonstrated by the Upset Plot in Supplementary Fig. We then investigated the non-mucins that were enriched by the StcEAdditionally, we explored which assigned mucin-domain glycoproteins were repeatedly not enriched by our technique. As with the enriched non-mucins, we generated an Upset Plot to determine which of our assigned mucin-domain glycoproteins were not enriched reproducibly . As seen in Fig. Following the establishment of our mucin domain enrichment approach in cell lines, we next wanted to test the mucinomics platform on clinically relevant patient samples. Ovarian cancer ranks fifth in cancer deaths among women and is often diagnosed in stage III or IV, leading to a poor prognosis. This is due, in part, to the fact that the only clinically relevant biomarker is CA-125, a peptide epitope of MUC16, but the exact structural definition of this antigen continues to be elusive. Previously, we showed that StcE could digest MUC16 from crude ovarian cancer patient ascites fluidFigure E447D-enrichment should function as a de facto glycopeptide enrichment by selecting for highly O-glycosylated mucin-domain glycoproteins at the protein  level. We observed a large number of spectra in our ascites enrichments bearing the “HexNAc fingerprint”, that is, oxonium ions specific to glycopeptides, which prompted us to search our data for intact glycopeptides. Generally, electron-driven dissociation is better suited for characterizing O-glycopeptides because it can provide O-glycosite localization58. This is especially true for O-glycopeptides derived from mucin-domain glycoproteins, which will likely have multiply glycosylated sequences61. Even so, collision-based fragmentation can still provide O-glycopeptide identifications that include peptide sequence and the total glycan mass modification, though details about number of glycans or glycosite positions  are usually inaccessible. Previous glycomic work suggests that some of these structures may include large, highly fucosylated and sialylated complex and hybrid N-glycans in addition to highly sialylated core-1 and-2 O-glycans with a smaller amount of sulfated core-2 O-glycan structures64. We collected only higher-energy collision dissociation (HCD) spectra through this study, limiting our ability to thoroughly characterize O-glycopeptides. Additionally, given that we performed in-gel tryptic digestion, it is unlikely that we were able to extract the intact mucin domains from many of our samples, nor were we able to fully characterize mucin domains of interest. Attempts to use StcE for in-gel digests resulted in limited digestion efficiency, and alternative methods to couple StcE proteolysis to this enrichment strategy are currently under investigation. Regardless, we searched our ascites data using O-Pair Search, a recently developed open-modification-centric glycoproteomic search algorithm that is particularly well-suited for the complex searches required of O-glycopeptide searches that consider large protein databases65 , suggesting a significant biological variance in glycopeptide species between patients despite high protein-level overlap observed in Fig. 24 In these networks, unique glycopeptide identifications are arranged vertically as nodes in the middle of the network (black nodes in both panels). Unique glycan masses are then organized as nodes in the semi-circles on either side of the glycopeptide identifications, with each semi-circle representing the same glycan masses. In other words, gray nodes on the left of each network and color nodes on the right show the same glycan masses and are mirror images of each other. If glycan masses map to the same glycopeptide identifications, that means identifications are shared between the ascites  and enriched  conditions. Otherwise, glycopeptide-glycan connections that only appear on one side of the network are unique to that condition. In Fig. Next, we wanted to compare the glycoprotein sources of glycopeptides detected in the elution versus the crude cancer patient ascites fluid. As demonstrated in Fig. A rapidly developing breadth of tools continues to shed light on glycobiology, which is historically understudied relative to other biomolecules. Mucin-domain glycoproteins represent one particularly challenging subset of the glycoproteome that remains poorly defined. Though canonical mucins are recognized as important contributors to health and disease, a “parts list” for the mucinome, i.e., a complete list of mucin-domain glycoproteins, remains elusive, even though the mucinome is poised to address many open questions in glycobiology.E447D, along with a mucin-domain candidacy algorithm to address this problem. We chose to build this candidacy algorithm on the hallmark mucin domain feature of serine and threonine O-glycosylation, as predicted by NetOGlyc4.0, while not focusing on other sequence characteristics such as proline frequency. While the enrichment feature of this mucinome workflow appears robust, we note that the mucin-domain candidacy algorithm is imperfect; yet, it serves a functional purpose for evaluating mucin-domain glycoprotein enrichments. Identification of mucin-domain glycoproteins more abundantly detected in cell lysates rather than the elution could also indicate that certain mucin domains remain under-glycosylated depending on cellular state or cell type, meaning our mucinomics approach could be used to screen the mucin status of proteins under a variety of conditions. Additionally, our mucin-domain candidacy algorithm could improve substantially from enhanced O-glycosite and mucin domain prediction tools. That said, prediction of mucin-type O-glycosites, much less mucin domains, remains challenging due to the complex regulation of O-glycosites by a poorly resolved family of glycosyltransferases. Future iterations could also explore other O-glycosite prediction algorithms beyond NetOGlyc4.0, such as ISOGlyP66.Here, we used a point mutant of our mucin-selective protease, StcE67, meaning that the selectively of StcEE447D enrichments is not purely based on the O-glycosylated TxT motif that dictates its protease activity. This generates interesting new directions to explore complexities of mucin binding harbored by catalytically inactive O-glycoprotease mutants.Though we have identified a subset of putative mucin-domain glycoproteins determined by the candidacy algorithm, we did not detect nearly 300 of these proteins. This can be likely be attributed to a number of reasons: first, we only explored 5 types of epithelial cancer cells; many other cancers and subsets of the same cancers are likely to express a different subset of mucin-domain glycoproteins. Also, we primarily used whole-cell lysates in this study, biasing toward membrane-tethered glycoproteins; given that mucin-domain glycoproteins can also exist as purely secreted biomolecules rather than membrane-tethered, it is possible that we missed a large number of mucin-domain glycoproteins only found in the secretome of cells. Further, it is entirely possible that the dense glycosylation in the mucin-domain glycoproteins renders them inaccessible to the in-gel digestion strategy used here. Current efforts are focused on optimizing the elution of the mucin-domain glycoproteins to enable in-solution digestion approaches. Finally, though previous experiments have suggested otherwise, it is possible that StcE enriches only a certain subset of mucin-domain glycoproteins from the samples. Interestingly, during the review process of this manuscript, Nason, Büll, et al. reported that the C-terminal domain of StcE can confer mucin-binding properties irrespective of the active siteRegardless, with this mucinomics platform, we enriched mucin-domain glycoproteins from several cancer-associated cell lines and crude ovarian cancer patient ascites fluid. We demonstrated high mucin overlap between ovarian cancer patients, and the enrichment strategy allowed us to detect hundreds of glycopeptides from the mucin proteins, with a substantial increase in O- over N-glycopeptides. We also identified many proteins previously unknown to contain a mucin domain, thus demonstrating the utility of this technique in discovering new mucin-domain glycoproteins. Future efforts will be devoted to expanding our patient cohort in order to determine whether the ovarian cancer mucinome can be used as a diagnostic and/or prognostic indicator.Though this work represents a significant step forward in understanding mucin domains, several open questions remain. To begin, mucin domains are known to regulate interactions at cellular peripheries via biophysical effects and cell-to-cell interactions. However, these roles are likely extremely dynamic, and may depend on various glycan structures , expression of the mucin domain, and the overall cellular milieu. Further, the role of an individual mucin domain is unlikely to be identical across all of the mucin-domain glycoproteins. Thus, future studies should be devoted to understanding the role that discrete mucin domains are playing in cellular function. We predict that these mucin domains will fall into subgroups with categorical roles in health and disease.16. Further, while we chose to focus our efforts on the cancer mucinome, several other mucinomes have yet to be studied in diseases known to involve dysregulated mucins. These mucinopathies include, but are not limited to, inflammatory bowel disease, cystic fibrosis, chronic obstructive pulmonary disease (COPD), Sjögren’s syndrome, and dry mouth/eyes. Ultimately, we believe our mucinomics strategy will find utility in several settings and will prove to be an invaluable tool for glycobiologists and biochemists alike.Additionally, while we have identified a large number of mucin-domain glycoproteins from cell lines and ascites fluid, many other mucin-domain glycoproteins are likely present on different cell types and in other indications. In particular, the immune cell mucinome is of incredible interest and may represent a class of new ‘checkpoint inhibitors’ with both glycan and peptide components to investigate40. Briefly, Natalie Strynadka (University of British Columbia) kindly provided the plasmid pET28b-StcE-∆35-NHis43. Robert Hirt (Newcastle University) kindly provided the plasmid pRSETA-BT424433. pET28b-StcEE447D-∆35-NHis and pRSETA-BT4244E575A were generated using the Q5 Site-Directed Mutagenesis Kit (New England Biolabs) with the following primers: StcEE447D_for 5′-TCAGTCATGACGTTGGTCATAATTATG-3’, StcEE447D_rev 5′-ACTCATTCCCCAATGTGG-3′, BT4244E575A_for 5′-CCAGCTCATGCAATTGGCCATG-3′, and BT4244E575A_rev 5′-TCCCCACGCGTTATCTTC-3′.StcE and BT4244 were expressed as previously describedE447D was expressed and purified as previously described40. BT4244E575A was expressed in BL21(DE3) E. coli (New England Biolabs) grown in Luria broth (LB) with 100 μg/mL ampicillin at 37 °C, 225 rpm. The culture was induced at OD 0.6–0.8 with 1 mM IPTG and grown overnight at 20 °C. Cell pellets were lysed in xTractor buffer (Clontech) and lysates were applied to 1 mL HisTrap HP columns (Cytiva Life Sciences) using an ÄKTA Pure FPLC. Columns were washed with 50 column volumes of 20 mM Tris-HCl, 100 mM NaCl, 15 mM imidazole, pH 8, and elution was performed with a linear gradient to 150 mM imidazole. For BT4244, fractions containing pure protein were concentrated using Amicon Ultra 10 kDa MWCO filters (Millipore Sigma), dialyzed into PBS, pH 7.4, and stored at −80 °C. BT4244E575A was further purified by size exclusion chromatography using a Superdex 200 Increase 10/300 GL column (Cytiva Life Sciences) in PBS, pH 7.4, and fractions containing pure protein were stored at −80 °C.StcE2. HeLa cells (ATCC CCL-2) were cultured in DMEM supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (P/S). Capan-2 cells (ATCC HTB-80) were cultured in McCoy’s 5a supplemented with 10% FBS and 1% P/S. K562 and SKBR3 cells  were cultured in RPMI supplemented with 10% FBS and 1% P/S. OVCAR-3 cells (ATCC HTB-161) were cultured in RPMI supplemented with 20% FBS, 0.01 mg/mL bovine insulin, and 1% P/S. To prepare lysate for pulldowns, cells plated in T75 flasks (Thermo Fisher Scientific) were grown until ~70% confluency, washed three times with DPBS, then lysed in 500 µL of RIPA buffer (Thermo Fisher Scientific) supplemented with EDTA-free protease inhibitor cocktail (Roche) and 0.1% benzonase (Millipore Sigma). Lysates were stored at −80 °C prior to pulldown.Cells were maintained at 37 °C and 5% COE447D (1 mL of 1.93 mg/mL) was added to 7–8 mg of POROS-AL beads, along with 1 µL of 80 mg/mL NaCNBH3. The reaction proceeded overnight, with shaking, at 4 °C. After conjugation, the beads were washed three times with 500 µL of ultrapure water, spinning at 8500 rpm for 5 min each time. To cap all excess aldehyde sites on the beads, 200 µL of Tris-HCl with 1 µL of 80 mg/mL NaCNBH3 was added to the beads. The reaction shook at room temperature for 2 h. Excess beads were stored at 4 °C for up to one month and were washed before each enrichment. Jacalin  derivatization was performed identically to the StcEE447D conjugation. For BT4244E575A conjugation, the enzyme concentration was 1.423 mg/mL, so 5.5 mg of POROS-AL beads was used. Otherwise, the conjugation and enrichment steps were identical.An aliquot containing approximately 2 mg of StcEx g, and concentrations were determined using standard BCA assays. As per optimization experiments, the ideal ratio of lysate to beads (w/v) was determined to be 500 µg/100 µL, where 100 µL of the conjugated beads corresponded to 700 µg of beads in solution. The beads were pelleted at 8500 rpm for 5 min and the supernatant was removed. Then, 5 µL of 0.5 M EDTA and 500 µg of cell lysate was added to the beads and incubated at 4 °C overnight, with shaking. The reaction was performed six times, in tandem. After binding, the beads were spun at 8500 rpm for 5 min, and the supernatant was saved (“FT” or flow-through). Then, the beads were washed three times with 250 µL of PBS buffer containing 5 µL of 0.5 M EDTA. After the last wash, 32 µL of 4X protein loading buffer was added to the beads. For unenriched (control) samples, 30 µg of lysate was added to 10 µL of 4x protein loading buffer. All samples were then boiled at 95 °C for 5 min, spun for 2 min at 13,000 × g, and frozen for at least 1 h. The samples were then thawed and loaded onto 4–12% Criterion XT Bis-Tris precast gels (Bio-Rad), and run in 1x MOPS (Bio-Rad) for 90 minutes at 180 V. The total number of lanes for each experiment was 12, which included 6 control and 6 enriched lanes. After running, the lanes were stained using Bulldog Bio SafeStain and destained in ultrapure water. Eight bands were cut from each lane, giving a total of 96 slices per enrichment. The slices were frozen overnight at −80 °C.Cell lysates were clarified by centrifuging for 20 min at 18,000 ® 800CW Goat anti-Mouse IgG  at a dilution of 1:25,000 according to manufacturer recommendations. Images  were generated using an Odyssey CLx Near-Infrared Fluorescence Imaging System (LI-COR Biosciences).For optimization and proof-of-principle purposes, only one replicate was performed, and all steps were run on a gel . Afterward, an anti-MUC16 Western blot was performed using anti-MUC16 antibody [X75]  at a dilution of 1:1000 and IRDyeg for 20 min, and samples were removed from the supernatant. For control experiments, 6 µL of ascites was removed per lane for a total of 36 µL. Otherwise, the procedure was the same as above.Ascites from patients with gynecologic malignancies was collected with patient consent under an approved IRB protocol at from the Dept. of Obstetrics and Gynecology, Stanford Hospital. The study design and conduct complied with all relevant regulations regarding the use of human study participants and was in accordance with the criteria set by the Declaration of Helsinki. Ascites fluid was obtained from O.D. and V.K. and was de-identified prior to our handling. Samples were selected based on the amount of ascites available for the enrichment. BCA analysis revealed the average protein concentration to be 52 mg/mL, with a range of 33–64 mg/mL. In optimization experiments, the ideal ratio of lysate to beads (v/v) was determined to be 100 µL/100 µL, where 100 µL of the conjugated beads corresponded to 700 µg of beads in solution. Ascites was centrifuged at 4 °C at 18,000 × All slices were thawed in 200 µL of ultrapure water (Pierce), followed by a rinse with 200 µL of acetonitrile . Fresh 50 mM ammonium bicarbonate (“AmBic”) was made, and samples were rinsed in 200 µL of AmBic for 20 min at RT. Afterward, samples were reduced using 5 mM dithiothreitol  in AmBic for 35 min at 65 °C, with shaking, followed by alkylation using 50 mM iodoacetamide  in AmBic for 30 min at RT, in the dark. Then, slices were rinsed once in AmBic, followed by two washes in fresh 50:50 AmBic:ACN for 10 min each. Slices were then dried in a vacuum concentrator and rehydrated with 0.1 µg of trypsin in 200 µL of AmBic and reacted overnight at 37 °C. The following day, samples were acidified with 2 µL of formic acid  and held at 37 °C for 45 min. The supernatant was discarded and 100 µL of 0.1% FA in 70% ACN was added to the slices for 30 min at 37 °C. The elution was collected, and the step was repeated once. The elution of adjacent slices was combined, for a total of 48 samples per enrichment. The resultant elution (400 µL) was dried in a vacuum concentrator).g in a Sorvall Legend RT. To begin, wells were wet with 150 µL of ACN followed by equilibration with 150 µL of 0.1% FA in ultrapure water (“solvent A”). Samples were reconstituted in 150 µL of solvent A and added to the plate three times. The wells were then washed three times with 150 µL of solvent A, followed by elution three times using 100 µL of 0.1% FA in 80% ACN (“solvent B”). The combined elution for each sample (48), totaling 300 µL, was taken to dryness in a vacuum concentrator. All samples were reconstituted in 7 µL of solvent A.All samples were subjected to desalting with a 96-well HyperSep C18 plate (Thermo). For all steps, solvent was added to the plate and centrifuged at 2013 × Samples were analyzed by online nanoflow LC-MS/MS using an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific) coupled to a Dionex Ultimate 3000 HPLC (Thermo Fisher Scientific) controlled by Xcalibur4.1 software. A portion of the sample was loaded via autosampler isocratically onto a C18 nano pre-column using 0.1% formic acid in water (“Solvent A”). For all cell lysate samples and enriched ascites fluid, 6.5 µL of sample was injected onto the column; for unenriched ascites fluid, 0.5–6.5 µL of sample was loaded onto the column as determined by peptide BCA (approximately 1 µg per sample). For pre-concentration and desalting, the column was washed with 0.1% formic acid in ACN and 0.1% formic acid in water (“loading pump solvent”). Subsequently, the C18 nano pre-column was switched in line with the C18 nano separation column (75 µm x 250 mm EASYSpray containing 2 µm C18 beads) for gradient elution. The column was held at 45 °C using a column heater in the EASY-Spray ionization source (Thermo Fisher Scientific). The samples were eluted at a constant flow rate of 0.3 µL/min using a 90 min gradient. The gradient profile was as follows  0:3, 3:5, 93:25, 103:35, 104:90, 109:90, 110:3, 140:3. The instrument method used an MS1 resolution of 60,000 at FWHM 200 m/z, an AGC target of 3e5, and a mass range from 350 to 1,500 m/z. Dynamic exclusion was enabled with a repeat count of 3, repeat duration of 10 s, exclusion duration of 10 s. Only charge states 2–6 were selected for fragmentation. MS2s were generated at top speed for 3 s. HCD was performed on all selected precursor masses with the following parameters: isolation window of 2 m/z, 30% collision energy, orbitrap detection , and an AGC target of 1e4 ions.http://www.cbs.dtu.dk/services/NetOGlyc/) for O-glycosite prediction51. NetOglyc4.0 results were saved as.csv files for further processing, with 20,121 entries returning usable output. Those without a NetOGlyc4.0 output received a Mucin Score of NaN in the supplemental datafiles, which differs from a score of 0 that can be calculated through the description below. Cellular component (CC) GO terms for the human proteome were also downloaded from Uniprot, and phosphosite annotations were downloaded from Uniprot and PhosphoSitePlus53. Predictions from NetOGlyc4.0 were then screened for known phosphosites, and any overlap in phosphosites with predicted O-GalNAc sites resulted in removal of the predicted O-GalNAc site from consideration. To annotate proteins as extracellular, secreted, and/or transmembrane, cellular component localization terms from Uniprot were checked for each protein entry. A protein was annotated as “extracellular” if its CC GO terms contained the phrases “Cell Membrane”, “Cell membrane”, “pass membrane protein”, “Secreted”, “extracellular”, or “Extracellular”. Proteins also received the “extracellular” distinction if they contained GO accessions of 0005887, 0016021, or 0005576. Because many proteins have multiple locations, “extracellular” proteins were further denoted as “exclusively extracellular” if their GO term lists did NOT include “Mitochondrion”, “Cyto”, “cyto”, “Nucl”, or “cytoplasmic side”. Next, predicted O-glycosites were iterated over to determine if a given protein would pass our “mucin test”, which consisted of two calculations. First, we required a protein to have at least nine predicted O-glycosites within a 50-residue region. If a protein qualified for this benchmark, we applied our “12% rule” to determine the number of residues that separated any two given O-glycosites within this 50-residue region. The 12% rule applied to a 50-residue region meant that fewer than 6 residues could separate any given pair of O-glycosites. Both the “9 sites within 50 residues” metric and the “12% rule” were derived through hand annotation of known and thoroughly studied mucins mostly curated by the Mucin Biology Group (http://www.medkem.gu.se/mucinbiology/databases/db/Mucin-human-2015.htm)69. Although this could be considered both too stringent or too relaxed depending on perspective, empirical testing showed these rules (in conjunction with the other metrics discussed) to be reasonably reliable in properly annotating known mucin domains. Exploration of these “mucin test” metrics in particular is an interesting area for future studies looking to employ a mucin-domain candidacy algorithm. Finally, a threonine to serine ratio (T/S-ratio) was calculated for the predicted O-glycosites, mainly as a metric to discriminate O-GalNAc sites (slight threonine preference) from phosphosites (slight serine preference) due to the proclivity of NetOGlyc4.0 to predict dense regions of O-GalNAc sites in what are actually intracellular phosphorylation domains. Note, these preferences are based on empirical observations. If the number of serines and threonines were both greater than zero, the T/S-ratio was calculated by taking the number of threonines and dividing by the number of serines. If the number of threonines was > 0 but the number of serines was 0, the T/S-ratio was assigned a value of 2. Otherwise, the T/S-ratio was set at 0. With all of these determinations made, we then generated a Mucin Score. First, an integer score was calculated. If a protein was annotated as “extracellular” and passed the “mucin test”, it received an integer score of 1, while proteins “exclusively extracellular” and passing the mucin test received an integer score of 2. Integer scores were then augmented by 1 point if the predicted number of O-glycosites was greater than the number of annotated phosphosites. Finally, the integer score was multiplied by the T/S-ratio to generate the Mucin Score. This process was completed for all proteins in the human proteome that had predictions returned from NetOGlyc4.0 . Mucin Scores were used to determine confidence that a protein contained a mucin domain, including valuations of high confidence (Mucin Score > 2), medium confidence (2 > Mucin Score > 1.5), low confidence (1.5 > Mucin Score > 1.2), and non-mucin (Mucin Score < 1.2). This annotation was determined manually by assessing all of the factors above. For regions of 50 amino acids identified as putative mucin domains through this analysis, approximately 90% of them mapped to a single exon, which was evaluated manually using the neXtProt knowledgebase70.To build the mucin-domain candidacy algorithm, the entire human proteome was first downloaded from Uniprot  and parsed into FASTA files containing 150 entries each . Each file was individually uploaded to the NetOGlyc4.0 Server (71 with a label-free quantification (LFQ) minimum ratio count of 1. LFQ intensities were calculated using the match between runs feature, and MS/MS spectra were required for LFQ comparisons. For quantitative Article comparisons, protein intensity values were log2-transformed before further analysis, and missing values were imputed from a normal distribution with width 0.3 and downshift value of 1.8  using the Perseus software suite48. A Boolean value “IsAMucin” was also appended to each protein, with the value set as true if the Mucin Score was greater than 1. Mucin Scores and IsAMucin were input manually into MQ ‘protein groups’ txt files for manipulation in Perseus. Significance testing was performed in Perseus using a two-tailed t-test with 250 randomizations to correct for multiple comparisons, an FDR of 0.01, and an S0 value of 2 , or in Microsoft Excel using a two-tailed t-test with heteroscedastic variance. We kept the standard Perseus column headers from these analyses, with “Significant” showing a “+” for proteins calculated as significant based on the t-test performed, -log providing the y-axis value of the volcano plots that shows the log-transformed value of the t-test p-value, and “Difference” indicating the log2 fold change between the conditions . Proteins were sorted by their Mucin Score and highlighted in red if the score was higher than 2 (“high probability mucin”), orange if between 2–1.5 . Upset plots and the 5-sample Venn diagram 72. GO term enrichments were performed using DAVID56, with the human proteome as a background.Raw data were processed using MaxQuant version 1.6.3.4, and tandem mass spectra were searched with the Andromeda search algorithm. Oxidation of methionine and protein N-terminal acetylation were specified as variable modifications, while carbamidomethylation of cysteine was set as a fixed modification. A precursor ion search tolerance of 20 ppm and a product ion mass tolerance of 20 ppm were used for searches, and two missed cleavages were allowed for full trypsin specificity. Peptide spectral matches were made against a target-decoy human reference proteome database downloaded from Uniprot. FBS contamination was not examined for the lysate samples. Peptides were filtered to a 1% FDR and a 1% protein FDR was applied according to the target-decoy method. Proteins were identified and quantified using at least one peptide (razor + unique), where razor peptide is defined as a non-unique peptide assigned to the protein group with the most other peptides (Occam’s razor principle). Proteins were quantified and normalized using MaxLFQ73. The human proteome was loaded into the database , and a “Glyco” search task was selected. For each group of 8 raw files, an N- and an O- glyco search was performed separately. Parameters for the O-Glycopeptide Search were as follows: O-glycan database “Oglycan.gdb” (the default 12-glycan database74), keep top 50 candidates, Dissociation type “HCD” and child scan “null”, 4 maximum Oglycan allowed, with OxoniumIonFit on. For the N-Glycopeptide Search, all parameters were the same except the “NGlycan182.gdb” database was used. These glycan databases are available in Supplementary Data 75.For glycopeptide analysis, samples were loaded into MetaMorpheus in groups of 8, related to one individual replicate (e.g. “Lysate 1” slice 1–8)Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Supplementary Dataset 8Supplementary Dataset 9Supplementary Dataset 10Supplementary Dataset 11Supplementary Dataset 12Supplementary Dataset 13Supplementary SoftwareReporting Summary


Xenopus tropicalis MC1R, a receptor that does not interact with MRAP1, to reveal that EC2 (Extracellular domain 2) is the most likely contact site for hMC2R and mMRAP1 to facilitate activation of the receptor following an ACTH binding event. Prior to activation, mMRAP1 facilitates the trafficking of hMC2R from the ER to the plasma membrane. This process is dependent on the transmembrane domain (TM) of mMRAP1 making contact with one or more TMs of hMC2R. A single alanine substitution paradigm was used to identify residues in TM4 , EC2 (F167), and TM5 (F178) that play a role in the trafficking of hMC2R to the plasma membrane. These results provide further clarification of the activation mechanism for hMC2R.Human melanocortin-2 receptor (hMC2R) co-expressed with the accessory protein mouse (m)MRAP1 in Chinese Hamster Ovary (CHO) cells has been used as a model system to investigate the activation and trafficking of hMC2R. A previous study had shown that the N-terminal domain of mMRAP1 makes contact with one of the extracellular domains of hMC2R to facilitate activation of hMC2R. A chimeric receptor paradigm was used in which the extracellular domains of hMC2R were replaced with the corresponding domains from  The activation of most melanocortin receptors (MCRS) such as MC1R, MC3R, MC4R, and MC5R is fairly uniform and can be achieved via the binding of melanocortin peptides such as ACTH, αMSH, βMSH, or γMSH to the respective receptor with varying degrees of efficacy. The exception is MC2R ,7.MRAP1 gene will result in Type II Familial Glucocorticoid Deficiency (FGD II) [18 D19 Y20 L21) which serves as the activation motif [MRAP1 is a single pass transmembrane protein that forms an antiparallel homodimer with reverse topology ,7. In th(FGD II) . Within (FGD II) ,8 which on motif .18 D19 Y20 L21 motif in the N-terminal of mMRAP1 [Xenopus tropicalis MC1R to identify the extracellular domain on hMC2R that interacts with mMRAP1. The second objective used a single-alanine substitution paradigm of selected residues in TM4 and TM5 of hMC2R, in conjunction with a Cell Surface ELISA assay, to identify the transmembrane domain in hMC2R that interacts with the TM of mMRAP1 to facilitate the trafficking of the receptor to the plasma membrane.A series of studies have used hMC2R and the accessory protein mMRAP1 as a model system to investigate the dynamics of the MC2R/MRAP1 interaction ,8,11,12.Xenopus tropicalis (xt). For example, the replacement of the N-terminal domain of hMC2R with the N-terminal domain of xtMC1R was designated hMC2R/NT xtMC1R. In a similar manner, individual chimer hMC2R receptors were made for EC1, EC2, and EC3 domains of hMC2R, and were designated hMC2R/EC1 xtMC1R, hMC2R/EC2 xtMC1R, and hMC2R/EC3 xtMC1R, respectively. The rationale for using xtMC1R sequences is presented in Results To determine which extracellular domain of hMC2R interacts with mMRAP1, a set of chimeric receptors were made in which an extracellular domain of hMC2R was replaced with the corresponding domain from MC1R of the amphibian Mus musculus (mouse), Mrap1 , Xenopus tropicalis Mc1r , and all of the chimeric receptors  were synthesized by GenScript , and individually inserted into the pcDNA3.1+ expression vector. For cell surface ELISA assays, wild-type hMC2R and single alanine mutants of hMC2R in the TM4 domain , EC2 domain , and the TM5 domain  were synthesized with a N-terminal V-5 epitope tag, and individually inserted into a pcDNA3.1+ expression vector (GenScript). The cAMP reporter cDNA, CRE-Luc [Human MC2R ,  CRE-Luc , was pro−13 M to 10−6 M.The melanocortin peptides used in this study were synthetic hACTH(1-24) and NDP-MSH purchased from Sigma-Aldrich Inc. . These peptides were used in the cAMP CRE-Luciferase reporter gene assay at concentrations ranging from 103 tissue culture flask with vent cap by CELLTREATTM , and maintained in an incubator with 95% air, 5% CO2 at 37 °C. When the CHO cells reached 70–80% confluence, cells were split into new culture flasks using 0.05% trypsin/0.53 mM EDTA purchased from CORNING cellgroTM . CHO cells were selected for this project due to the fact that this cell line does not express endogenous mcr genes [mrap genes [Experiments were done utilizing Chinese Hamster Ovary (CHO) cells . The cells were grown in Kaighn’s Modification of Ham’s F12K media supplied by ATCC. Media was supplemented with 10% fetal bovine serum, 10 unit/mL penicillin, 100 µg/mL streptomycin, and 100 µg/mL normocin (Complete CHO media) The cells were grown in a 25 cmcr genes , or endoap genes .6 cells/reaction were used. Cells were transfected with either hMC2R cDNA, chimeric hMC2R/xtmc1r constructs, or alanine-substituted hMC2R mutants (10 nm/transfection). All receptor constructs were co-transfected with mMrap1 (30 nm/transfection), and the cre-luciferase construct (83 nmoles/transfection) [5 cells per well. After 48 h, the transfected cells were stimulated with hACTH(1-24) in serum-free CHO Media. Serial dilutions were carried out using hACTH(1-24) at concentrations ranging from 10−7 to 10−13 M. Each dose was tested in triplicate. The activation assay for xtMc1r was done in an identical manner, and the transfected cells were stimulated with either hACTH(1-24) or NDP-MSH at concentrations ranging from 10−6 to 10−12 M.For the cAMP Reporter gene Assay, 3.0 × 10fection) . Transfe50 values using Kaleidograph software (www.synergy.com). Data points are expressed as the mean ± SEM (n = 3). To analyze the level of activation, the data sets were analyzed using One-Way ANOVA followed by Tukey’s multi-comparison test using GraphPad Prism 2 software . Significance was set at p ≤ 0.05.Following a 4 h incubation at 37 °C, the stimulating solutions were removed and a luciferase substrate reagent  was added to each well as described in Liang et al. . A Bio-T5 cells/well in a fibronectin-treated 24-well culture dish and grown overnight in in a 37 °C CO2 incubator. Cells were transfected with cDNAs encoding hMC2R-V5 alone (negative control), hMC2R-V5 + mMrap1 (positive control), or hMC2R-V5 alanine-substituted mutant constructs + mMrap1 using jetPRIME transfection reagents  as described in Barney et al. [p < 0.05 . For stimulation with NDP-MSH, the EC50 value for xtMC1R expressed alone was 4.5 × 10−11 M +/− 1.4 × 10−11, and when the receptor was co-expressed with mMRAP1 the EC50 value was 8.6 × 10−11 M +/− 4.1 × 10−11. These EC50 values are also not statistically different . In addition, co-expression of xtMC1R with mMRAP1 had no statistical effect on the trafficking of the receptor to the plasma membrane as compared to CHO cells transfected with xtMc1r alone  of the amphibian, opicalis  fits thehttps://www.bioinformatics.dtu.dk, accessed on 29 October 2018), and the two receptor sequences could be aligned by inserted two gaps into the hMC2Rsequence. While the primary sequence identity of the two receptors was only 40%, identical amino acid motifs were apparent in all seven transmembrane domains and the three intracellular domains, with the highest primary sequence identity observed for IC2 (81%). However, the primary sequence identity in the extracellular domains was more variable .An alignment of the amino acid sequences of hMC2R and xtMc1r is shown in hMC2R, xtmc1r, and the four chimeric receptors are presented in Based on the alignment presented in 21 in the N-terminal domain and has C254 in EC3 that apparently form a disulfide bridge that is essential for the functionality of hMC2R [24  and S269 (EC3) occupy these positions. The absence of the cysteine residues at these positions has no apparent effect on the functional expression of xtMC1R in CHO cells as seen in 26 xtMC1R and chimeric receptor hMC2R/EC3 S269 xtMC1R, when co-expressed with mMRAP1 in CHO cells, could not be stimulated at any of dose of hACTH(1-24) . As a result, the chimeric receptor hMC2R/NT xtMC1R was made with a cysteine residue at position 26, and the chimeric receptor hMC2R/EC3 xtMC1R was made with a cysteine residue at position 253 in the chimeric receptor  and the EC3 chimeric receptor (hMC2R/EC3 xtMC1R) both had ECor hMC2R B. Howeveor hMC2R C.50 value for hMC2R. These data would suggest that EC2 is the most likely contact site between hMC2R and the N-terminal domain of mMRAP1.The most dramatic effect was observed for the EC2 chimeric receptor (hMC2R/EC2 xtMC1R). The EC2 chimeric receptor was nearly three orders of magnitude less sensitive to stimulation by hACTH(1-24) as hMC2R C, and th162, I163, T164, M165, V166, and I167  is presented in n xtMC1R  and co-en xtMC1R .163, M165, and V166 in TM4, F168 in EC2, and F178 in TM5 all decreased trafficking which would suggest that these residues are interacting with the TM domain of the mMRAP1 homodimer to facilitate trafficking of the receptor to the plasma membrane , our analysis of the hypothetical membrane topology for hMC2R  of ACTH (25) is presented in 80 in TM2, D103 and D107 near or in TM3, and F236 and H239 in TM6 all decreased activation of hMC2R expressed in OS3 adrenal tumor cells. All of these residues appear to be part of the HFRW binding site for hMC2R and these residues are shown in the diagrams in Building off of the structure/function analysis done by Pogozheva et al.,  on the H170 in the EC2 domain (MC2R gene and identified a patient with a mutation at H170 in EC2 that interfered with the patient’s ability to produce cortisol. Pharmacological studies indicate that substitution of an alanine residue at H170 results in an altered form of hMC2R that when co-expressed in CHO cells with mMRAP1 has a much lower sensitivity to stimulation by ACTH than wild-type hMC2R [170/A170 mutant did not interfere with trafficking (170 is to stabilize the N-terminal domain of mMRAP1 so that EC2 and a portion of the N-terminal of mMRAP1 can serve as the binding site for the KKRR motif in ACTH. In an earlier study, Fridmanis et al. [In this scenario H2 domain  plays anpe hMC2R . Since ifficking E, perhaps et al.  did an es et al.  but doess et al. .In summary, several studies point to the TM4/EC2/TM5 domain of hMC2R in conjunction with the N-terminal of MRAP1 as the possible location for the binding site for the “address motif” in ACTH ,20,26. T


The rapid evolution of fertilization proteins has generated remarkable diversity in molecular structure and function. Glycoproteins of vertebrate egg coats contain multiple zona pellucida (ZP)-N domains (1–6 copies) that facilitate multiple reproductive functions, including species-specific sperm recognition. In this report, we integrate phylogenetics and machine learning to investigate how ZP-N domains diversify in structure and function. The most C-terminal ZP-N domain of each paralog is associated with another domain type (ZP-C), which together form a “ZP module.” All modular ZP-N domains are phylogenetically distinct from nonmodular or free ZP-N domains. Machine learning–based classification identifies eight residues that form a stabilizing network in modular ZP-N domains that is absent in free domains. Positive selection is identified in some free ZP-N domains. Our findings support that strong purifying selection has conserved an essential structural core in modular ZP-N domains, with the relaxation of this structural constraint allowing free N-terminal domains to functionally diversify. Protein structural domains are a major type of molecular building block that multimerize into higher-order assemblies and provide the architectural foundation for nearly all cellular features, including organelles and extracellular matrices. Within molecular complexes, structural domains function as interlocking modules with specific, well-defined binding surfaces. Consequently, structural proteins commonly experience intense purifying selection to preserve their 3D conformations, which can lead to extreme sequence conservation between diverse taxa  . The modzp1, zp2, zp3, zp4, zpd, and zpax) that experienced clade-specific birth and death events. Consequently, the egg coat of each major vertebrate class has a different composition of ZP module–containing proteins . We do not know of nonreproductive proteins that contain duplicated ZP-N domains. We refer to ZP-N domains in the ZP module as “modular” and the N-terminal repeats as “free” domains. As ZP-N domains can form asymmetric dimers through their β-sandwich edges  and nonreproductive  functions (A). As such, we used a structure-based sequence alignment (B), and this separation was robust to amino acid substitution matrices  (ZP2 and ZPAX (C). Free ZP-Ns have only been identified in proteins associated with the egg coat.We investigated the evolutionary history of vertebrate ZP-N domains by extracting a total of 2,405 ZP-N domain sequences from ZP module–containing genes of 247 species with both reproductive   online. and JTT) . The topand ZPAX C. Free ZA and B). Through this regularization strategy, we identified eight modular-associated and two free-associated residues that were sufficient to predict whether a given ZP-N sequence was modular or free with 100% accuracy (fig. 2B). The greater number of modular-associated residues and their greater probabilistic weight suggest a greater sequence conservation of modular domains (fig. 2B). A further examination of individual clades of modular and free ZP-Ns demonstrates the substantial sequence conservation of our residues identified by machine learning (fig. 2C).The phylogenetic separation of modular and free ZP-N domains using a structure-based alignment suggests important structural differences between the two domain types, but their high sequence divergence has complicated a manual identification of such characteristics. Machine learning methods have been applied to various aspects of protein biology such as function prediction  and the fig. 2D, fig. 1C), along with molecular dynamics, supports the loss of dimerization activity along the free ZP-N lineage, which could have facilitated their evolution of new binding partners  due to both higher genomic assembly quality and to avoid synonymous substitution saturation that may occur when considering greater phylogenetic breadth . Positively selected sites in ZP2-N1 were far from the homodimerization edge and physically closer to the network of modular-biased residues  . Sequence scores . Sets ofe scores  and thene scores . This ape scores , in ordeMaximum likelihood phylogenies were built using RAxML-NG , and mulA basic machine learning algorithm using mean-squared regression and regularization was coded in Python to distinguish the two free and modular groups of ZP-N domains. Logistic regression models are well suited for these classifications, because their outputs are bounded between 0 and 1, which can be interpreted as a probability that a given domain is modular . The muln) features , where n is the alignment length. Each of these features had a parameter associated with it, and the value of the parameter indicated how informative that feature was, and whether it supported a modular ZP-N or free ZP-N classification. There were a large number of possible parameters in this model , which introduced a risk for “overfitting” (For machine learning analysis, aligned ZP-N sequences were one-hot encoded: each position in the sequence was converted into a vector of 20 digits, corresponding to the 20 amino acids. The value was set to 1 for the entry in the vector corresponding to that residue, and all other values were set to 0. Gapped sites were set to a vector of 20 0’s. Thus, the classifier was trained using (1 + 20fitting”  and, thuTo determine the minimal number of highly informative parameters, elastic net regularization was employed to penalize overparameterization and reduce overfitting . In our zp1, zp2, zp3, zp4, umod, tecta, and cuzd1. Boreoeutherian sequences were mined from Ensembl (Our analyses of sequence divergence and positive selection were performed on a set of Boreoeutherian mammals, and we used the mammalian ZP-N domains coming from  Ensembl  and were Ensembl , 2018.2 distribution with one degree of freedom. We also performed a Benjamini–Hochberg P-value correction to account for multiple testing (Evidence of positive selection was measured using PAML analyses , 2007 on testing . Positiv testing  or the a testing  when thiWhen protein structures were not available, Alpha-Fold2 tertiary structure prediction was used , and 3D msac105_Supplementary_DataClick here for additional data file.


Caliciviridae family, which causes acute gastroenteritis in humans and animals. Human sapoviruses (HuSaVs) are genetically and antigenically diverse, but the lack of a viral replication system and structural information has hampered the development of vaccines and therapeutics. Here, we successfully produced a self-assembled virus-like particle (VLP) from the HuSaV GI.6 VP1 protein, and the first atomic structure was determined using single-particle cryo-electron microscopy (cryo-EM) at a 2.9-Å resolution. The atomic model of the VP1 protein revealed a unique capsid protein conformation in caliciviruses. All N-terminal arms in the A, B, and C subunits interacted with adjacent shell domains after extending through their subunits. The roof of the arched VP1 dimer was formed between the P2 subdomains by the interconnected β strands and loops, and its buried surface was minimized compared to those of other caliciviruses. Four hypervariable regions that are potentially involved in the antigenic diversity of SaV formed extensive clusters on top of the P domain. Potential receptor binding regions implied by tissue culture mutants of porcine SaV were also located near these hypervariable clusters. Conserved sequence motifs of the VP1 protein, “PPG” and “GWS,” may stabilize the inner capsid shell and the outer protruding domain, respectively. These findings will provide the structural basis for the medical treatment of HuSaV infections and facilitate the development of vaccines, antivirals, and diagnostic systems.Sapovirus (SaV) is a member of the IMPORTANCE SaV and norovirus, belonging to the Caliciviridae family, are common causes of acute gastroenteritis in humans and animals. SaV and norovirus infections are public health problems in all age groups, which occur explosively and sporadically worldwide. HuSaV is genetically and antigenically diverse and is currently classified into 4 genogroups consisting of 18 genotypes based on the sequence similarity of the VP1 proteins. Despite these detailed genetic analyses, the lack of structural information on viral capsids has become a problem for the development of vaccines or antiviral drugs. The 2.9-Å atomic model of the HuSaV GI.6 VLP presented here not only revealed the location of the amino acid residues involved in immune responses and potential receptor binding sites but also provided essential information for the design of stable constructs needed for the development of vaccines and antivirals. Sapovirus (species Sapporo virus) of the family Caliciviridae, which causes acute gastroenteritis in humans and animals worldwide (Sapovirus (SaV) belongs to the genus orldwide . Outbreaorldwide . To dateorldwide . Of thesorldwide .HuSaV contains a positive-sense single-stranded RNA genome of approximately 7.5 kb in length . The vir5–Caliciviridae family is currently classified into 11 established genera: Bavovirus, Lagovirus, Minovirus, Nacovirus, Nebovirus, Norovirus, Recovirus, Salovirus, Sapovirus, Valovirus, and Vesivirus   was exprThe cryo-EM map allowed atomic modeling of residues 38 to 554 for subunit A, 21 to 554 for subunit B, and 21 to 554 for subunit C, except for one disordered loop (residues 381 to 384) for all subunits . The oveThe icosahedrally independent A/B and C/C dimers showed different conformations primarily between the S domains of each dimer, as reported previously for other T=3 viruses  Fig. 3AFig. 3A. As a novel structure of the calicivirus capsid, all NTAs from the A, B, and C subunits interacted with adjacent shell domains after extending through their subunits . This isA previous study identified two well-conserved motifs of the calicivirus VP1 protein, “PPG” and “GWS,” at residues 136 to 138 and 281 to 283 of HuSaV GI.6, respectively . In our Interactions within the SaV VP1 dimer occurred in the outermost region of the P domain, except for the inner S domain, forming an arched protruding dimer  similar To examine the antigenic diversity and immunogenicity of HuSaVs, the amino acid conservation across human GI, GII, GIV, and GV SaVs listed in in vivo and induced higher levels of serum and mucosal antibody responses  Cowden strain in GIII was the only culturable sapovirus until recently, showing mutations adapted to tissue culture . Compareesponses . We mappesponses . The coresponses , and theesponses . Except esponses . The HuSesponses , but theesponses . These oHere, we successfully generated a VLP of a HuSaV GI.6 strain using a baculovirus expression system, and the atomic structure of the VP1 capsid was determined at a 2.9-Å resolution by single-particle cryo-EM. The structure revealed the unique architecture of the VP1 protein in caliciviruses and provided valuable information on the vast variety of HuSaV genetics and antigenicities.In caliciviruses, the S domains of the VP1 protein were interconnected to each other. They form a stable inner capsid shell in which the NTAs extending from the A, B, and C monomers form various interactions with the adjacent S domains at icosahedral 2-, 3-, and 5-fold axes. We had previously reported that the NTA conformation of caliciviruses is structurally divided into two groups . One gro10–15–In SaV, two conserved motifs, PPG and GWS, in VP1 are known . The PPGSaV shows that the arched P-domain dimer of VP1 consisted of double layers of connections with the antiparallel loops and β strands   . In vesiRecently, dynamic structural changes in capsids involved in viral infection have been reported for some caliciviruses. In murine norovirus, a dynamic rotation of the P domain has been reported to occur in response to aqueous conditions, including bile acids or metal ions, resulting in altered infectivity , 26. In EMD-3281). The map was similar to the current 2.9-Å-resolution cryo-EM map compared at the same 8.5-Å resolution. The homology model built based on the vesivirus Protein Data Bank (PDB) model (PDB accession number 2GH8) showed similar domain and subdomain boundaries for the P1, P2, and S (sub)domains -resolution cryo-EM map (Electron Microscopy Data Bank [EMDB] accession number )domains . This in6 confluent High Five cells  at a multiplicity of infection (MOI) of 5 to 10 in 1.5 mL Express Five serum-free medium (SFM) , and the infected cells were incubated at 26°C. The culture medium was harvested at 5 to 6 days postinfection (dpi), centrifuged for 10 min at 3,000 × g, and further centrifuged for 30 min at 10,000 × g. Finally, the VLPs were concentrated by ultracentrifugation for 2 h at 31,000 rpm at 4°C (Beckman SW-31Ti rotor) and then resuspended in 500 μL of Express Five SFM. Samples were examined for VLP formation by conventional electron microscopy after purifying VLPs through CsCl, as described previously . All the following processes were conducted using RELION 2.1 software  at 4°C and with 100% humidity and then plunge-frozen into liquid ethane using a Vitrobot Mark IV system . The cryo-EM grids were examined at liquid nitrogen temperature using a cryo-electron microscope , incorporating a field emission gun and a Cs corrector . The microscope was operated at 300 kV and a nominal magnification of ×75,000. Movie frames were recorded using a Falcon II direct electron detector , applied with a nominal underfocus value ranging from −1.0 to −2.5 μm. An accumulated dose of 20 electrons per Åsoftware . A totalsoftware  and subjsoftware  (Fig. 11software . Local rsoftware . Data code novo atomic model construction of the VP1 protein in O (The 2.9-Å map was used for ein in O . The iniein in O  and manuein in O . The finein in O . SaV VP1ein in O . Identicein in O  and wereein in O , 42.EMD-30793. Atomic coordinates for the atomic model of the VLP have been deposited in the Protein Data Bank under accession number 7DOD.The cryo-EM map of the HuSaV VLP of the GI.6 strain has been deposited in the Electron Microscopy Data Bank under accession number


The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction of protein domain boundaries  from sequence remains a significant challenge. In this work, we develop a deep learning method based on multi-head U-Nets  to predict protein domain boundaries utilizing 1D sequence features and predicted 2D inter-residue distance map as input. The 1D features contain the evolutionary and physicochemical information of protein sequences, whereas the 2D distance map includes the structural information of proteins that was rarely used in domain boundary prediction before. The 1D and 2D features are processed by the 1D and 2D U-Nets respectively to generate hidden features. The hidden features are then used by the multi-head attention to predict the probability of each residue of a protein being in a domain boundary, leveraging both local and global information in the features. The residue-level domain boundary predictions can be used to classify proteins as single-domain or multi-domain proteins. It classifies the CASP14 single-domain and multi-domain targets at the accuracy of 75.9%, 13.28% more accurate than the state-of-the-art method. Tested on the CASP14 multi-domain protein targets with expert annotated domain boundaries, the average per-target F1 measure score of the domain boundary prediction by DistDom is 0.263, 29.56% higher than the state-of-the-art method. Protein domains are cohesive structural regions of a protein chain, which usually assume a compact structure. Domains are the structural, functional or evolutionary units of proteins . They alThere are two types of approaches for predicting protein domain boundaries from sequences without known experimental structures, i.e., template-based and ab initio prediction. In the case of template-based prediction, the goal is to find homologous template sequences that have known domain information for a target protein. These alignments between the templates and a target are used to transfer the domain boundary information from the homologous sequences to the target protein. Some template-based methods are CHOP , DomPredWe use 4072 protein targets from the Topdomain dataset  to creatWe generate two kinds of input features including two-dimensional (2D) residue-residue distance maps and one-dimensional (1D) sequential information for each protein, which are described below.Residue-residue distance map is a concise representation for protein conformation, which can be used to reconstruct 3D protein structure. The distance map can be rather accurately predicted for many proteins from sequences , 38. TheThe domain boundary prediction is a 1D prediction problem that has some similarity with other 1D structural feature prediction problems such as secondary structure prediction, in which sequence profile and residue conservation information are useful. Therefore, we use the input generation module of an accurate secondary structure predictor - DNSS2  to generThe overall workflow of DistDom is depicted in Fig. A customized 2D U-Net architecture  Fig. 3)3) is useWe generalize the U-Net typically applied to 2D input to a customized 1D U-Net architecture for processing the 1D input. The key difference between 1D U-Net and 2D U-Net is that it uses 1D convolution instead of the 2D convolution, and for upsampling, it uses 1D transposed convolution. The detailed structure of the 1D U-Net is depicted in Fig. Q, K and V values are the same as the values of the concatenated hidden feature map from the two U-Nets. That is, the concatenated output of the 2D U-Net branch and 1D U-Net branch are each used as the query, key and value for each of their attention heads. The multi-head attention is applied to the concatenated The output feature map of the 2D U-Net branch and 1D U-Net branch above are concatenated to generate a feature map of on layer , 39 to pWe use the GPU computing resource on the Summit supercomputer provided by Oak Ridge National Laboratory (ORNL) to train the deep learning network above. The Summit cluster , 41 provDistDom is trained on Topdomain  trainingDistDom is trained with three different distance threshold values  commonly used to reconstruct tertiary structures from distance maps to select predicted residue-residue distances as input for domain prediction. The average per target F1 scores on the validation and test datasets on multi-domain targets for the three thresholds are shown in Table An interesting observation is that the performance of our method is substantially lower on CASP7-13 and CASP14 dataset than the Topdomain validation dataset. A main reason of the difference is because the distribution of the CASP7-13 and CASP14 datasets is different from that of the Topdomain training and validation data. The targets in the CASP datasets were hand picked by the experts and tend to be more challenging than those in the Topdomain dataset.To decide a cut-off probability threshold needed to convert the predicted domain boundary probabilities to binary predictions (in a domain boundary or not), we test different cut-off thresholds between 0 and 1, with an interval of 0.01 on the Topdomain validation dataset to find the threshold yielding the highest F1 score. We plot the average per target precision, recall and F1 scores against the cut off values to choose a cut-off threshold Fig. . Based oTo study the impact of the distance normalization, we compare the performance of DistDom trained with the distance normalization at 12 Å distance threshold and without distance normalization. The results on the validation dataset are reported in Table We perform an ablation study to measure the effectiveness of 1D and 2D inputs. We conduct three experiments: one with 1D input only, one with only the 2D input, and one combining both inputs together. The results of the three experiments on the multi-domain proteins in the Topdomain validation dataset, CASP7-13 test dataset, and CASP14 test dataset are reported in Table Because there are many more negative examples than positive examples, we test a weighted cross-entropy loss function. The 1:10 weight ratio is applied to negative (non-boundary) class and positive (boundary) class, respectively. Table We compare DistDom with two other most recent methods—DeepDom and FUPred on the multi-domain targets in the CASP14 dataset. DeepDom  is a metIn this section, we analyzed the domain boundary prediction performance on easy and hard multi-domain targets in the CASP14 dataset. The easy and hard targets are defined as follows: if the average TM-score of the first models of a target predicted by the best 50% of CASP14 servers is higher than 0.65 then the target is considered an easy target, otherwise a hard one. The CASP14 dataset contains 4 easy targets and 16 hard targets. Tables A classification problem related to the protein domain boundary prediction is to classify if a protein is a multi-domain protein or a single-domain one. To classify the protein, DistDom is applied to predict the boundary residues of the protein. If there are ts as in , which aThe results of DistDom, FUPred, and DeepDom are reported in Table Figure We develop an end-to-end deep learning based method based on the U-Nets and multi-head attention to improve the domain boundary prediction. It is the first method of combining traditional 1D sequence features and new 2D distance maps to predict protein domain boundaries. The 1D and 2D input features are processed by a customized 1D and 2D U-Net simultaneously to generate hidden features for the multi-head attention to predict the domain boundary. The experiment shows that, with the appropriate preprocessing , the 2D distance map can be effectively used to predict protein domain boundaries. Combining 1D and 2D features via the multi-head attention can further improve the prediction accuracy.Moreover, the domain boundary predictions can be effectively used to classify if a protein is a single-domain protein or multi-domain one.


The most consumed cereal-based product worldwide is bread. “Caaveiro”, an autochthonous variety with a recent growing interest, is one of the wheat varieties that fulfill the 25% local flour requirement in the PGI “Pan Galego” bread baking industry. The element content of the refined wheat flours used to make “Pan Galego”  was evaluated in ICP-MS. In addition, wholegrain flour (FWM) was included in the analysis. Loaves of bread were made with these flours ; and c, FM: 75% FC + 25% FCv) and their element content was analyzed. Wholegrain flour ranked the highest in almost all elements, highlighting the P (494.80 mg/100 g), while the FM and the FC presented the opposite behavior, with the highest Se values . FCv was situated in an intermediate position regarding P, K, Mg, Mn, Zn, Fe and Na content, standing closer to FWM, although it presents the highest values for Cu (1076.3 µg/100 g). The differences observed in flour were maintained in bread. Hence, the local cultivar ‘‘Caaveiro’’ has an interesting nutritional profile from the point of view of the element content. Triticum aestivum) is one of the most cultivated and consumed cereals in the world [Wheat . K levels are also high in wheat, but cereal grains (before processing) are not considered to be a high dietary source of sodium. Wheat, rye, barley, and oats are also classified as moderate sources of Ca (100–200 mg/100 g), Mg (100–200 mg/100 g), Fe (1–5 mg per 100 g), Zn (1–5 mg/100 g) and Cu (0.1–1 mg/100 g). In addition to these elements, a large number of other elements are present in trace quantities.Elements are essential micronutrients for human health  and wheaSourdough, as a leavening agent, is the ancestor of modern yeast, although it is currently still used in the preparation of traditional breads such as PGI “Pan Galego”, originally from NW-Spain , and enrSince wheat bread is a widely consumed product and is part of the culinary culture in various regions, knowing its element contribution and how different wheat flours (because of the difference in variety and the milling process) influence the element content of bread is of interest to both the healthy population and also patients with certain pathologies. Thus, the aim of this work is to establish whether the element content of the flour is affected by flour variety or use of wholemeal flour, both in flour as a raw material, and in a manufactured product such as bread made with different leavening agents (sourdough and/or yeast) and fermentation times (2 and 12 h), usual conditions in Galician bakeries order to evaluate if these different conditions can cause differences in the content of mineral elements.The wheat flours used in the analysis and in the bread baking process were the Castilla-Spanish commercial variety  and the ‘Caaveiro’ variety ; they have used alone and mixed together. Therefore, the wheat flour samples used were: (a) 100% Castilla variety (FC), (b) 100% ‘Caaveiro’ variety (FCv), (c) mixed flour (75% Castilla + 25% ‘Caaveiro’) (FM). Finally, a sample of wholegrain flour was also included: (d) homologous mixed wholegrain flour (75% Castilla + 25% ‘Caaveiro’) (FWM). Flour samples were provided by the DaCunha group.Yeast, sourdough, and a mixture of both were used as leavening agents. Two sourdough types were used: sourdough, made from refined wheat flour (pH = 4.0) and sourdough made from wholegrain flour (pH = 4.2). The latter was used to make wholegrain breads.Common commercial salt was used.The breads were made with the 4 types of flour described above, water, yeast (yeast breads), sourdough (sourdough breads) or sourdough and yeast (mixed leavening breads), and salt. One loaf of each type of bread has been made.The dough formulation for yeast breads was flour (280.0 g), water (210.0 g), yeast (5.0 g) and salt (5.0 g). For sourdough breads, it was flour (225.7 g), water (169.3 g), sourdough (99.8 g) and salt (5.2 g). For the mixed leavening batches, the dough formulation included flour (239.5 g), water (179.6 g), sourdough (74.7 g), yeast (1.2 g) and commercial NaCl Hacendado (5.0 g). The total amount of dough was 500 g. For each type of bread, the total amount of dough was 500 g.The sourdough was made mixing the corresponding flour, hydrated to 75% and leaving it at 25 °C for 3 days. From the 3rd day, it was renewed every day, taking half the weight, and completing the same weight with new water and flour. We waited for it to double in volume and kept it refrigerated until the following day.® KH 2232). The short fermentation (2 h) was carried out at a controlled temperature of 23 °C ± 2 and humidity of 45 ± 5%. The long one (12 h) was carried out at 12 °C ± 2. Loaves were manually round-shaped and baked at 180 °C for 30 min. in a domestic oven (Carrefour®).The ingredients were mixed and kneaded for 30 min using the kneading program of a domestic bakery  and 2 mL of deionized H2O (Milli-Q) were added. Microwave digestion  was performed at 240 °C, and 40 bars for 40 min at 1500 W. Once digested, they were filled to a final volume of 50 mL with H2O (Milli-Q). The macro-  and microelements  were determined.The wheat flour and bread (crust and crumb) samples were dried at 130 ± 3 °C according to the official AOAC method 925.10 . Each saThe equipment used was an ICP-MS (Agilent 7700×) with a sample introduction system consisting of a Micromist glass low-flow nebulizer, a double-pass glass spray chamber with a Peltier system (2 °C) and a quartz torch. Measurements were taken five times.3-H2O, in the same proportion as in the samples. As calibration standards, the multi-elemental standard Multi IV (Merck®) in concentrations between 0.2–10,000 µg·L−1, P and Ca standard (Panreac®) in concentrations between 0.1–10 mg·L−1 and Selenium (Merck®) in concentrations between 0.2–100 µg·L−1 were used. The correlation coefficients of the calibration lines for each element were equal to or greater than 0.999.The calibration standards were prepared in HNOA multivariate approach using Principal Component Analysis (PCA) was applied in order to carry out a graphical representation in low dimension space. The distances between samples of flour or breads were analyzed with 90% confidence ellipses on the first two dimensions resulting from a PCA of the element content dataset.Computing the mean on the element content dataset is not a good location measure to characterize the samples because of the influence of outliers or highly skewed distributional shapes. In this case, one strategy is to use more robust measures such as the trimmed mean and perform tests based on the corresponding sampling distribution of such robust measures. The 20% trimmed means and standard error were computed and used in robust variants of one-way ANOVA. An appealing feature of a 20% trimmed mean is that it achieves nearly the same amount of power as the mean when sampling from a normal distribution, and when there are outliers, a 20% trimmed mean can present a substantially smaller standard error .Hierarchical clustering was carried out to classify samples of breads according to the element content. Hierarchical clustering is a cluster analysis method which aims at identifying a series of clusters within a nested structure. It produces a tree-based representation  of sorted data. Objects in the dendrogram are linked together based on their similarity. The Euclidean distance of scaled data was used to calculate the degree of similarity between each pair of samples and Ward’s minimum variance method was the agglomeration (linkage) method used to compute the distance between clusters. It minimizes the total within-cluster variance. In the dendrogram displayed. The height of the fusion into branches, provided on the vertical axis, indicates the (dis)similarity between the two clusters. The higher the height of the fusion, the less similar the clusters are. This height is known as the cophenetic distance between the two clusters, one way to measure how well the cluster tree generated reflects the original data is to compute the correlation between the cophenetic distances and the original distance data.The description of the clusters was made using a v-test  calculated for each element so that the list of significant elements was provided for each cluster. Test Values are measurements of the distance between the within-class value and the overall value . The cluThe R package ecosystem  was usedThe element content of the flours analyzed is shown in Principal Components Analysis was performed with the data obtained after analyzing the 4 flours used in this study in order to provide a data structure in a reduced dimension, covering the maximum amount of information present in the data . The firWhen examining the score plot of the samples in the space defined by the two principal components considered , a separFCv was situated in an intermediate position between FWM and the refined flours (FC and FM) in dimension 1 and located in the negative part of dimension 2 due to the strong influence of Cu, and to a lesser extent Ca, since FCv was the flour with the highest Cu content and the lowest Ca content.In order to compare the means, a robust variant of one-way ANOVA based on trimmed means (20% trimming level) was applied .The most abundant element in the flours tested was P, followed by K. Regarding microelements, Mn, Zn and Fe were clearly the ones observed in higher proportions. These results concurred with those obtained in previous studies in both refined and wholegrain wheat flours ,27.When the literature was reviewed, a great diversity of results was found. The differences observed when comparing the results of the present study with the results reported by the different researchers might be due to the differences in the wheat varieties analyzed, the edaphoclimatic variables associated with different locations, harvest years, etc. ,29,30,31Thus, regarding the content of element in wholegrain flours, element values lower than those obtained for FWM were found by different researchers ,32,  for all elements. According to the results, wholegrain mixed flour (FWM) showed a significantly higher content for all elements, except Cu and Se. All in all, the array of the high element content in flours followed this order, FWM was first, followed by FCv and then, FM and FC with statistically significant differences as shown in The results of a post-hoc Yuen’s test when comparing the 4 flour samples were statistically significant  ; in someFollowing the same trend as that observed in the present study, some previous investigations revealed pronounced differences between refined and whole wheat flour regarding other elements. Thus, Mn values showed six times as high in whole wheat as compared to refined flour ,31  . In addiIn the case of Cu, a significantly higher value has been observed in FCv than in the rest of the flours; this is true even in FWM, the wholegrain flour. In the same way, different studies showed higher Cu values in wholegrain flours than in refined ones; in these cases, the degree of difference found depended on the variety and harvest: 161/295 vs. 215/320 µg/100 g  and 140/Finally, in the observation of the remaining elements analyzed, a clear distinction was found for Se, which had a noticeably higher proportion in FC and FM than in FCv and FWM. However, in this case, there was no marked difference between the FC and FM or between FCv and FWM, respectively. In contrast to these results, other researchers reported higher Se contents in a whole wheat flour (3.9 µg/100 g d.w.) than in two refined flours  .The results obtained for the breads analyzed are shown in Cluster analysis was used as an exploratory analysis method to determine element content. Breads were clustered to suggest connections between samples. To assess the clustering tendency of the dataset, the Hopkins statistic (H) was used. It is based on measuring the probability that the dataset of element content in breads was generated by a uniform data distribution . This dataset formed by 10 variables, one for each metal analyzed, is highly clusterable. The H value = 0.18 and was far below the H threshold of 0.5.In the agglomerative hierarchical cluster  each breThe correlation between the cophenetic distances and the original distance data was computed to measure how well the cluster tree generated reflected the original data. When the clustering is valid, the linking of objects in the cluster tree should have a strong correlation with the distances between objects in the original distance matrix. The closer the value of the correlation coefficient is to 1, the more accurately the clustering solution reflects the data. Values above 0.75 are considered to be good. For the element dataset of the present study, the value of the correlation coefficient was 0.89. These results show that the variability in terms of the total element content was due to the type and origin of the flour used in the bread preparation rather than to the leavening agent or the fermentation time.p-value from the test that compared the mean with the samples of the cluster to the overall mean. The elements were organized using the descending order provided by the v-test by only considering p-values less than 5% to be significant.The v-test was used to describe each group of breads. In p-values provide an indication of the “significance” of a given deviation of the general average and in this cluster and in this group the p-values associated with all elements were particularly small compared to the one associated with Cu.Cluster 1 was composed of wholegrain flour breads , which displayed a high value in most of the elements except Se. The high values found here were somewhat higher than the global average for each element, as reflected by the positive sign of the v-test . MoreoveA research where two wholegrain breads were analyzed, one made with a spontaneously fermenting sourdough and the other made with commercial yeast, and obtained results that showed mean Zn (sourdough: 0.68 mg/100 g and yeast: 1.14 mg/100 g d.w.), Mg (sourdough: 28.88 mg/100 g and yeast: 24.78 mg/100 g d.w.), K (sourdough: 85.87 mg/100 g and yeast: 84.78 mg/100 g d.w.) and Ca (sourdough: 4.11 mg/100 g and yeast: 8.06 mg/100 g d.w.) values lower than BWM breads .In addition, others examined the range values for 12 wholegrain and sourdough breads  and obseCluster 2 is composed of ‘Caaveiro’ flour breads that can be described as a sort of average bread as regards all elements, except Cu, Ca, and Se. ‘Caaveiro’ flour breads show higher than average values for Cu and below average values for Ca and Se. As previously observed for the different types of flour, bread made from ‘Caaveiro’ flour showed characteristics that appear between those found for whole wheat flour and refined flours because the flour that is used to make PGI bread “Pan Galego” is traditionally ground in stone mills and retains part of the bran.p-value.In cluster 3, composed of breads made with refined flours , the behavior of the elements was opposite that of the wholegrain breads. This category was characterized by breads having a below-average element content as evidenced in the negative v-test for all of them except Se. In these breads, Ca presented the least significant In the element contents obtained in 16 pre-baked breads made with refined flour using a mixture of sourdough and yeast or only yeast as a leavening agent  was obseIn addition, when comparing the results obtained in the present study with those obtained in previous research, who also determined elements in Portuguese refined wheat breads , the valTo visualize the behavior of the different bread samples in terms of element content, a PCA analysis was performed . The varIn a previous study, was observed that bread made with wholegrain flour contained more Ca (36.0 vs. 88.0 mg/100 g d.w.), Mg (102.0 vs. 32.0 mg/100 g d.w.), Fe (4.4 vs. 1.7 mg/100 g d.w.), Cu (400 vs. 200 µg/100 g d.w.) and Zn (2.7 vs. 0.11 mg/100 g d.w.) than bread made with refined flour .Other investigation also reported a higher content of Mg (48 vs. 13 mg/100 g d.w.), K (386 vs. 205 mg/100 g d.w.), Ca (75 vs. 19 mg/100 g d.w.), Mn (1.26 vs. 0.62 mg/100 g d.w.), Fe (2.25 vs. 1.05 mg/100 g d.w.) and Zn (1.53 vs. 0.78 mg/100 g d.w.) in wholegrain bread than in refined bread . These rThe study performed by  confirmeThe type and origin of the flour had an important influence on the total element content, both in the flour itself and in the breads.Wholegrain flour (FWM) showed the highest value for 7 of the 10 elements analyzed  and the lowest value for Se.The refined flours (FC and FM) have the opposite behavior, they showed the lowest values for the 7 elements mentioned and the highest value for Se.The Galician local cultivar flour (FCv) used in this study stands out as having the lowest Ca and the highest Cu content. The element content of Mg, P, K, Ca, Na, Mn, Fe and Zn in FCv was found to be between the values displayed for wholegrain (FWM) and refined (FC and FM) flours; however, the FCv values were closer to those found for FWM.In the analysis of the presence of these elements in breads made with the different flours and methods, the same behavior observed for flours was observed in the bread itself, with the exception of the appearance of Na, which happened because NaCl was added during the bread making process. This finding endorses the inclusion of the ‘Caaveiro’ flour variety, an autochthonous Galician cultivar (Northwest Spain) in the PGI “Pan Galego” bread baking industry since it provides strong evidence of its nutritional quality.