Cultured meat with enriched organoleptic properties by regulating cell differentiation.

Research on cultured meat has primarily focused on the mass proliferation or differentiation of muscle cells; thus, the food characteristics of cultured meat remain relatively underexplored. As the quality of meat is determined by its organoleptic properties, cultured meat with similar sensory characteristics to animal-derived meat is highly desirable. In this study, we control the organoleptic and nutritional properties of cultured meat by tailoring the 2D differentiation of primary bovine myoblasts and primary bovine adipose-derived mesenchymal stem cells on gelatin/alginate scaffolds with varying stiffness. We assess the effect of muscle and adipose differentiation quality on the sensory properties of cultured meat. Thereafter, we fabricate cultured meat with similar sensory profiles to that of conventional beef by assembling the muscle and adipose constructs composed of highly differentiated cells. We introduce a strategy to produce cultured meat with enriched food characteristics by regulating cell differentiation with scaffold engineering.

New Function Annotation of PROSER2 in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis due to the absence of diagnostic markers and molecular targets. Here, we took an unconventional approach to identify new molecular targets for pancreatic cancer. We chose uncharacterized protein evidence level 1 without function annotation from extensive proteomic research on pancreatic cancer and focused on proline and serine-rich 2 (PROSER2), which ranked high in the cell membrane and cytoplasm. In our study using cell lines and patient-derived orthotopic xenograft cells, PROSER2 exhibited a higher expression in cells derived from primary tumors than in those from metastatic tissues. PROSER2 was localized in the cell membrane and cytosol by immunocytochemistry. PROSER2 overexpression significantly reduced the metastatic ability of cancer cells, whereas its suppression had the opposite effect. Proteomic analysis revealed that PROSER2 interacts with STK25 and PDCD10, and their binding was confirmed by immunoprecipitation and immunocytochemistry. STK25 knockdown enhanced metastasis by decreasing p-AMPK levels, whereas PROSER2-overexpressing cells increased the level of p-AMPK, indicating that PROSER2 suppresses invasion via the AMPK pathway by interacting with STK25. This is the first demonstration of the novel role of PROSER2 in antagonizing tumor progression via the STK25-AMPK pathway in PDAC. LC-MS/MS data are available at MassIVE (MSV000092953) and ProteomeXchange (PXD045646).

High protein-containing new food by cell powder meat.

Demand for a new protein source to replace meat is increasing to solve various issues such as limited resources and food shortages. Diverse protein sources are being developed, but alternative proteins such as plants or insects need to improve people's perceptions and organoleptic properties. Therefore, cell-based meat research is intensively conducted, and most studies are aimed at scale-up and cost-down via the research of scaffolds and culture media. Here, we proposed a new food by cell powder meat (CPM), which has a high protein content and a meaty flavor. The powder was manufactured 76% more cost-effectively with less serum than the conventional culture medium and without 3D scaffold. Due to its comprehensive characteristics, the potential applicability of CPM in the cell-based meat industry could be expected.

Refining Classification of Cholangiocarcinoma Subtypes via Proteogenomic Integration Reveals New Therapeutic Prospects.

BACKGROUND & AIMS: Intrahepatic cholangiocarcinomas (iCCs) are characterized by their rarity, difficult diagnosis, and overall poor prognosis. The iCC molecular classification for developing precision medicine strategies was investigated. METHODS: Comprehensive genomic, transcriptomic, proteomic, and phosphoproteomic analyses were performed on treatment-naïve tumor samples from 102 patients with iCC who underwent surgical resection with curative intent. An organoid model was constructed for testing therapeutic potential. RESULTS: Three clinically supported subtypes (stem-like, poorly immunogenic, and metabolism) were identified. NCT-501 (aldehyde dehydrogenase 1 family member A1 [ALDH1A1] inhibitor) exhibited synergism with nanoparticle albumin-bound-paclitaxel in the organoid model for the stem-like subtype. The oncometabolite dysregulations were associated with different clinical outcomes in the stem-like and metabolism subtypes. The poorly immunogenic subtype harbors the non-T-cell tumor infiltration. Integrated multiomics analysis not only reproduced the 3 subtypes but also showed heterogeneity in iCC. CONCLUSIONS: This large-scale proteogenomic analysis provides information beyond that obtained with genomic analysis, allowing the functional impact of genomic alterations to be discerned. These findings may assist in the stratification of patients with iCC and in developing rational therapeutic strategies.

Advanced assessment through intact glycopeptide analysis of Infliximab's biologics and biosimilar.

Characterization of therapeutic monoclonal antibodies (mAbs) represents a major challenge for analytical sciences due to their heterogeneity associated with post-translational modifications (PTMs). The protein glycosylation requires comprehensive identification, which could influence on the mAbs' structure and their function. Here, we demonstrated high-resolution tandem mass spectrometry with an ultra-high-performance liquid chromatography for characterization and comparison between biologics and biosimilar of infliximab at an advanced level. Comparing the N- and O-glycopeptides profiles, a total of 49 and 54 glycopeptides was identified for each product of the biologics and biosimilar, respectively. We also discovered one novel N-glycosylation site at the light chain from both biopharmaceuticals and one novel O-glycopeptide at the heavy chain from only biosimilar. Site-specific glycopeptide analysis process will be a robust and useful technique for evaluating therapeutic mAbs and complex glycoprotein products.

Transcriptome and Proteome Co-Profiling Offers an Understanding of Pre-Harvest Sprouting (PHS) Molecular Mechanisms in Wheat (Triticum aestivum).

While wheat (Triticum aestivum L.) is a widely grown and enjoyed crop, the diverse and complex global situation and climate are exacerbating the instability of its supply. In particular, pre-harvest sprouting (PHS) is one of the major abiotic stresses that frequently occurs due to irregular climate conditions, causing serious damage to wheat and its quality. In this study, transcriptomic analysis with RNA-seq and proteomic analysis with LC-MS/MS were performed in PHS-treated spikes from two wheat cultivars presenting PHS sensitivity and tolerance, respectively. A total of 13,154 differentially expressed genes (DEGs) and 706 differentially expressed proteins (DEPs) were identified in four comparison groups between the susceptible/tolerant cultivars. Gene function and correlation analysis were performed to determine the co-profiled genes and proteins affected by PHS treatment. In the functional annotation of each comparative group, similar functions were confirmed in each cultivar under PHS treatment; however, in Keumgang PHS+7 (K7) vs. Woori PHS+7 (W7), functional annotations presented clear differences in the "spliceosome" and "proteasome" pathways. In addition, our results indicate that alternative splicing and ubiquitin-proteasome support the regulation of germination and seed dormancy. This study provides an advanced understanding of the functions involved in transcription and translation related to PHS mechanisms, thus enabling specific proposals for the further analysis of germination and seed dormancy mechanisms and pathways in wheat.

Defect in cytosolic Neu2 sialidase abrogates lipid metabolism and impairs muscle function in vivo.

Sialic acid (SA) is present in glycoconjugates and important in cell-cell recognition, cell adhesion, and cell growth and as a receptor. Among the four mammalian sialidases, cytosolic NEU2 has a pivotal role in muscle and neuronal differentiation in vitro. However, its biological functions in vivo remain unclear due to its very low expression in humans. However, the presence of cytoplasmic glycoproteins, gangliosides, and lectins involved in cellular metabolism and glycan recognition has suggested the functional importance of cytosolic Neu2 sialidases. We generated a Neu2 knockout mouse model via CRISPR/Cas9-mediated genome engineering and analyzed the offspring littermates at different ages to investigate the in vivo function of cytosolic Neu2 sialidase. Surprisingly, knocking out the Neu2 gene in vivo abrogated overall lipid metabolism, impairing motor function and leading to diabetes. Consistent with these results, Neu2 knockout led to alterations in sialylated glycoproteins involved in lipid metabolism and muscle function, as shown by glycoproteomics analysis.

Plasma complement C7 as a target in non-small cell lung cancer patients to implement 3P medicine strategies.

BACKGROUND: Programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) immune checkpoint inhibitors (ICIs) significantly affect outcomes in non-small cell lung cancer (NSCLC) patients. However, differences in reactions toward PD-1/PD-L1 ICI among patients impose inefficient treatment. Therefore, developing a reliable biomarker to predict PD-1/PD-L1 ICI reaction is highly necessary for predictive, preventive, and personalized (3P) medicine. MATERIALS AND METHODS: We recruited 63 patients from the National Cancer Center (NCC) and classified them into the training and validation sets. Next, 99 patients were recruited for inclusion into the external validation set at the Samsung Medical Center (SMC). Proteomic analysis enabled us to identify plasma C7 levels, which were significantly different among groups classified by their overall response to the RECIST V 1.1-based assessment. Analytical performance was evaluated to predict the PD-1/PD-L1 ICI response for each type of immunotherapy, and NSCLC histology was evaluated by determining the C7 levels via ELISA. RESULTS: Plasma C7 levels were significantly different between patients with and without clinical benefits (PFS ≥ 6 months). Among the groups sorted by histology and PD-1/PD-L1 immunotherapy type, only the predicted accuracy for pembrolizumab-treated patients from both NCC and SMC was greater than 73%. In patients treated with pembrolizumab, C7 levels were superior to those of the companion diagnostics 22C3 (70.3%) and SP263 (62.1%). Moreover, for pembrolizumab-treated patients for whom the PD-L1 tumor proportion score (TPS) was < 50%, the predictive accuracy of C7 was nearly 20% higher than that of 22C3 and SP263. CONCLUSION: Evaluation of plasma C7 levels shows an accurate prediction of NSCLC patient reactions on pembrolizumab. It demonstrates plasma C7 is an alternative and supportive biomarker to overcome the predictive limitation of previous 22C3 and SP263. Thus, it is clear that clinical use of plasma C7 allows predictive diagnosis on lung cancer patients who have not been successfully treated with current CDx and targeted prevention on metastatic diseases in secondary care caused by a misdiagnosis of current CDx. Reduction of patients' financial burden and increased efficacy of cancer treatment would also enable prediction, prevention, and personalization of medical service on NSCLC patients. In other words, plasma C7 provides efficient medical service and an optimized medical economy followed which finally promotes the prosperity of 3P medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-021-00266-x.

Flashlight into the Function of Unannotated C11orf52 using Affinity Purification Mass Spectrometry.

For an enhanced understanding of the biological mechanisms of human disease, it is essential to investigate protein functions. In a previous study, we developed a prediction method of gene ontology (GO) terms by the I-TASSER/COFACTOR result, and we applied this to uPE1 in chromosome 11. Here, to validate the bioinformatics prediction of C11orf52, we utilized affinity purification and mass spectrometry to identify interacting partners of C11orf52. Using immunoprecipitation methods with three different peptide tags (Myc, Flag, and 2B8) in HEK 293T cell lines, we identified 79 candidate proteins that are expected to interact with C11orf52. The results of a pathway analysis of the GO and STRING database with candidate proteins showed that C11orf52 could be related to signaling receptor binding, cell-cell adhesion, and ribosome biogenesis. Then, we selected three partner candidates of DSG1, JUP, and PTPN11 for verification of the interaction with C11orf52 and confirmed them by colocalization at the cell-cell junctions by coimmunofluorescence experiments. On the basis of this study, we expect that C11orf52 is related to the Wnt signaling pathway via DSG1 from the protein-protein interactions, given the results of a comprehensive analysis of the bioinformatic predictions. The data set is available at the ProteomeXchange consortium via PRIDE repository (PXD026986).

Electrochemical microgap immunosensors for selective detection of pathogenic Aspergillus niger.

Aspergillus niger (A. niger) is a well-known allergenic, harmful fungus in the indoor environment that can cause asthmatic symptoms and atopy. Previous immunosensing approach suffers from an insufficient detection limit, mainly because there are no techniques for target amplification. We report an electrochemical immunosensor that selectively quantifies the A. niger based on the detection of extracellular proteins by using a specific interaction with antibody. The sensor was designed to show a decrease in redox current upon binding of the antigens secreted from A. niger onto an antibody-immobilized surface between the interdigitated electrodes. The extracellular proteins were profiled by LC-MS/MS to identify the antigens existing in the A. niger solution. Since the targets of the sensor are the proteins, its sensitivity and selectivity remain almost intact even after filtration of the spores. It was also found that the use of secretion promoter in the sampling stage greatly improved the sensor's limit of detection (LOD) for the spores. By this, the LOD was lowered by a few orders of magnitude so as to reach the value as low as ~101 spores/mL.

Spatial and temporal diversity of glycome expression in mammalian brain.

Mammalian brain glycome remains a relatively poorly understood area compared to other large-scale "omics" studies, such as genomics and transcriptomics due to the inherent complexity and heterogeneity of glycan structure and properties. Here, we first performed spatial and temporal analysis of glycome expression patterns in the mammalian brain using a cutting-edge experimental tool based on liquid chromatography-mass spectrometry, with the ultimate aim to yield valuable implications on molecular events regarding brain functions and development. We observed an apparent diversity in the glycome expression patterns, which is spatially well-preserved among nine different brain regions in mouse. Next, we explored whether the glycome expression pattern changes temporally during postnatal brain development by examining the prefrontal cortex (PFC) at different time point across six postnatal stages in mouse. We found that glycan expression profiles were dynamically regulated during postnatal developments. A similar result was obtained in PFC samples from humans ranging in age from 39 d to 49 y. Novel glycans unique to the brain were also identified. Interestingly, changes primarily attributed to sialylated and fucosylated glycans were extensively observed during PFC development. Finally, based on the vast heterogeneity of glycans, we constructed a core glyco-synthesis map to delineate the glycosylation pathway responsible for the glycan diversity during the PFC development. Our findings reveal high levels of diversity in a glycosylation program underlying brain region specificity and age dependency, and may lead to new studies exploring the role of glycans in spatiotemporally diverse brain functions.

Bioinformatic Prediction of Gene Ontology Terms of Uncharacterized Proteins from Chromosome 11.

In chromosome 11, 71 out of its 1254 proteins remain functionally uncharacterized on the basis of their existence evidence (uPE1s) following the latest version of neXtProt (release 2020-01-17). Because in vivo and in vitro experimental strategies are often time-consuming and labor-intensive, there is a need for a bioinformatics tool to predict the function annotation. Here, we used I-TASSER/COFACTOR provided on the neXtProt web site, which predicts gene ontology (GO) terms based on the 3D structure of the protein. I-TASSER/COFACTOR predicted 2413 GO terms with a benchmark dataset of the 22 proteins belonging to PE1 of chromosome 11. In this study, we developed a filtering algorithm in order to select specific GO terms using the GO map generated by I-TASSER/COFACTOR. As a result, 187 specific GO terms showed a higher average precision-recall score at the least cellular component term compared to 2413 predicted GO terms. Next, we applied 65 proteins belonging to uPE1s of chromosome 11, and then 409 out of 6684 GO terms survived, where 103 and 142 GO terms of molecular function and biological process, respectively, were included. Representatively, the cellular component GO terms of CCDC90B, C11orf52, and the SMAP were predicted and validated using the overexpression system into 293T cells and immunofluorescence staining. We will further study their biological and molecular functions toward the goal of the neXt-CP50 project as a part of C-HPP. We shared all results and programs in Github (https://github.com/heeyounh/I-TASSER-COFACTOR-filtering.git).

Computational classification of core and outer fucosylation of N-glycoproteins in human plasma using collision-induced dissociation in mass spectrometry.

RATIONALE: Glycoprotein fucosylation, one of the major posttranslational modifications, is known to be highly involved in proteins related to various cancers. Fucosylation occurs in the core and/or outer sites of N-glycopeptides. Elucidation of the fucosylation type of N-glycoproteins is therefore important. However, it has remained a challenge to classify the fucosylation types of N-glycopeptides using collision-induced dissociation (CID) tandem mass (MS/MS) spectra. METHODS: The relative intensities of the Y1 F, Y2 F, Y3 F, and Y4 F product ions in the CID-MS/MS spectra of the IgG N-glycopeptides were measured for core fucosylation. The Core Fucose Index (CFI) was then calculated by multiplication of the relative intensities with a weight factor from logistic regression to differentiate between the core and none fucosylation. From the relative intensities of the B2 F and B3 SF ions of the MS/MS spectra of the AGP N-glycopeptides for outer fucosylation, the Outer Fucose Index (OFI) was calculated to differentiate between the outer and none fucosylation. RESULTS: In order to classify core and/or outer fucosylation of N-glycoproteins, we defined the fucosylation score (F-score) by a sigmoidal equation using a combination of the CFI and the OFI. For application, we classified the fucosylation types of N-glycoproteins in human plasma with 99.7% accuracy from the F-score. Human plasma samples showed 54.4%, 33.3%, 10.3%, and 1.6% for none, core, outer, and dual fucosylated N-glycopeptides, respectively. Core fucosylation was abundant at mono- and bi-antennary N-glycopeptides. Outer fucosylation was abundant at tri- and tetra-antennary N-glycopeptides. In total, 113 N-glycopeptides of 29 glycoproteins from 3365 glycopeptide spectral matches (GPSMs) were classified for different types of fucosylation. CONCLUSIONS: We established an F-score to classify three different fucosylation types: core, outer, and dual types of N-glycopeptides. The fucosylation types of 20 new N-glycopeptides from 11 glycoproteins in human plasma were classified using the F-score. Therefore, the F-score can be useful for the automatic classification of different types of fucosylation in N-glycoproteins of biological fluids including plasma, serum, and urine.

Author Correction: Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

Comprehensive Profiling of Surface Gangliosides Extracted from Various Cell Lines by LC-MS/MS.

Gangliosides act as a surface marker at the outer cellular membrane and play key roles in cancer cell invasion and metastasis. Despite the biological importance of gangliosides, they have been still poorly characterized due to the lack of effective analytical tools. Herein, we performed molecular profiling and structural elucidation of intact gangliosides in various cell lines including CFPAC1, A549, NCI-H358, MCF7, and Caski. We identified and quantified a total of 76 gangliosides on cell membrane using C18 LC-MS/MS. Gangliosides found in each cell line exhibited high complexity and diversity both qualitatively and quantitatively. The most abundant species was GM3(d34:1) in CFPAC1, NCI-H358, and MCF7, while GM2(d34:1) and GM1(d34:1) were major components in A549 and Caski, respectively. Notably, glycan moieties showed more diversity between cancer cell lines than ceramide moieties. In addition, noncancerous pancreatic cell line (hTERT/HPNE) could be distinguished by gangliosides containing different levels of sialic acid compared with cancerous pancreatic cell line (CFPAC1). These results clearly demonstrated the feasibility of our analytical platform to comprehensive profile of cell surface gangliosides for identifying cell types and subgrouping cancer cell types.

SAAVpedia: Identification, Functional Annotation, and Retrieval of Single Amino Acid Variants for Proteogenomic Interpretation.

Next-generation genome sequencing has enabled the discovery of numerous disease- or drug-response-associated nonsynonymous single nucleotide variants (nsSNVs) that alter the amino acid sequences of a protein. Although several studies have attempted to characterize pathogenic nsSNVs, few have been confirmed as single amino acid variants (SAAVs) at the protein level. Here we developed the SAAVpedia platform to identify, annotate, and retrieve pathogenic SAAV candidates from proteomic and genomic data. The platform consists of four modules: SAAVidentifier, SAAVannotator, SNV/SAAVretriever, and SAAVvisualizer. The SAAVidentifier provides a reference database containing 18 206 090 SAAVs and performs the identification and quality assessment of SAAVs. The SAAVannotator provides functional annotation with biological, clinical, and pharmacological information for the interpretation of condition-specific SAAVs. The SNV/SAAVretriever module enables bidirectional navigation between relevant SAAVs and nsSNVs with diverse genomic and proteomic data. SAAVvisualizer provides various statistical plots based on functional annotations of detected SAAVs. To demonstrate the utility of SAAVpedia, the proteogenomic pipeline with protein-protein interaction network analysis was applied to proteomic data from breast cancer and glioblastoma patients. We identified 1326 and 12 breast-cancer- and glioblastoma-related genes that contained one or more SAAVs, including BRCA2 and FAM49B, respectively. SAAVpedia is a suitable platform for confirming whether a genomic variant is maintained in an amino acid sequence. Furthermore, as a result of the SAAV discovery of these positive controls, the SAAVpedia could play a key role in the protein functional study for the Human Proteome Project (HPP).

Identification of Missing Proteins in Human Olfactory Epithelial Tissue by Liquid Chromatography-Tandem Mass Spectrometry.

We performed proteomic analyses of human olfactory epithelial tissue to identify missing proteins using liquid chromatography-tandem mass spectrometry. Using a next-generation proteomic pipeline with a < 1.0% false discovery rate at the peptide and protein levels, we identified 3731 proteins, among which five were missing proteins (P0C7M7, P46721, P59826, Q658L1, and Q8N434). We validated the identified missing proteins using the corresponding synthetic peptides. No olfactory receptor (OR) proteins were detected in olfactory tissue, suggesting that detection of ORs would be very difficult. We also identified 49 and 50 alternative splicing variants mapped at the neXtProt and GENCODE databases, respectively, and 2000 additional single amino acid variants. This data set is available at the ProteomeXchange consortium via PRIDE repository (PXD010025).

Direct Monitoring of Fucosylated Glycopeptides of Alpha-Fetoprotein in Human Serum for Early Hepatocellular Carcinoma by Liquid Chromatography-Tandem Mass Spectrometry with Immunoprecipitation.

PURPOSE: Alpha-fetoprotein (AFP) is a widely used serological marker that is associated with hepatocellular carcinoma (HCC). Although the level of AFP is increased in HCC, its sensitivity for diagnosis is poor because AFP levels are also increased in liver diseases. Changes in glycoform, especially fucosylation, have been reported to be associated with the development of HCC. EXPERIMENTAL DESIGN: The authors introduce the monitoring of fucosylated glycopeptides by liquid chromatography (LC)-mass spectrometry (MS) combined with immunoprecipitation, where glycan-cleaved fragments with an amino acid sequence are used as transitions. Furthermore, neuraminidase for desialylation is useful to improve the MS detection limit (limit of detection [LOD] <2 ng mL-1 ) in 0.1 µL of serum. RESULTS: The performance of the relative percentage of fucosylated AFP (AFP-fuc%) for differentiating between early HCC and cirrhosis is better than that of serum AFP levels as indicated by a greater area under the receiver operator characteristic curve (area under the curve = 0.962 vs. 0.628) and sensitivity (92.3% vs. 53.9%), respectively. Furthermore, the inter- and intraday repeatability of AFP-fuc% in serum is less than 2.1%. CONCLUSIONS AND CLINICAL RELEVANCE: These findings suggest that glycopeptide-based LC-MS/MS is a promising method and that AFP-fuc% is a clinically useful parameter for differentiating between early HCC and liver cirrhosis.