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).

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.

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.

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).

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.

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).

Next Generation Proteomic Pipeline for Chromosome-Based Proteomic Research Using NeXtProt and GENCODE Databases.

Human Proteome Project aims to map all human proteins including missing proteins as well as proteoforms with post translational modifications, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). neXtProt and Ensemble databases are usually used to provide curated information on human coding genes. However, to find these proteoforms, we (Chr #11 team) first introduce a streamlined pipeline using customized and concatenated neXtProt and GENCODE originated from Ensemble, with controlled false discovery rate (FDR). Because of large sized databases used in this pipeline, we found more stringent FDR filtering (0.1% at the peptide level and 1% at the protein level) to claim novel findings, such as GENCODE ASVs and missing proteins, from human hippocampus data set (MSV000081385) and ProteomeXchange (PXD007166). Using our next generation proteomic pipeline (nextPP) with neXtProt and GENCODE databases, two missing proteins such as activity-regulated cytoskeleton-associated protein (ARC, Chr 8) and glutamate receptor ionotropic, kainite 5 (GRIK5, Chr 19) were additionally identified with two or more unique peptides from human brain tissues. Additionally, by applying the pipeline to human brain related data sets such as cortex (PXD000067 and PXD000561), spinal cord, and fetal brain (PXD000561), seven GENCODE ASVs such as ACTN4-012 (Chr.19), DPYSL2-005 (Chr.8), MPRIP-003 (Chr.17), NCAM1-013 (Chr.11), EPB41L1-017 (Chr.20), AGAP1-004 (Chr.2), and CPNE5-005 (Chr.6) were identified from two or more data sets. The identified peptides of GENCODE ASVs were mapped onto novel exon insertions, alternative translations at 5'-untranslated region, or novel protein coding sequence. Applying the pipeline to male reproductive organ related data sets, 52 GENCODE ASVs were identified from two testis (PXD000561 and PXD002179) and a spermatozoa (PXD003947) data sets. Four out of 52 GENCODE ASVs such as RAB11FIP5-008 (Chr. 2), RP13-347D8.7-001 (Chr. X), PRDX4-002 (Chr. X), and RP11-666A8.13-001 (Chr. 17) were identified in all of the three samples.

Direct analysis of site-specific N-glycopeptides of serological proteins in dried blood spot samples.

Dried blood spot (DBS) samples have a number of advantages, especially with respect to ease of collection, transportation, and storage and to reduce biohazard risk. N-glycosylation is a major post-translational modification of proteins in human blood that is related to a variety of biological functions, including metastasis, cell-cell interactions, inflammation, and immunization. Here, we directly analyzed tryptic N-glycopeptides from glycoproteins in DBS samples using liquid chromatography-tandem mass spectrometry (LC-MS/MS) without centrifugation of blood samples, depletion of major proteins, desalting of tryptic peptides, and enrichment of N-glycopeptides. Using this simple method, we identified a total of 41 site-specific N-glycopeptides from 16 glycoproteins in the DBS samples, from immunoglobulin gamma 1 (IgG-1, 10 mg/mL) down to complement component C7 (50 µg/mL). Of these, 32 N-glycopeptides from 14 glycoproteins were consistently quantified over 180 days stored at room temperature. The major abundant glycoproteins in the DBS samples were IgG-1 and IgG-2, which contain nine asialo-fucosylated complex types of 16 different N-glycopeptide isoforms. Sialo-non-fucosylated complex types were primarily detected in the other glycoproteins such as alpha-1-acid glycoprotein 1, 2, alpha-1-antitypsin, alpha-2-macroglobulin, haptoglobin, hemopexin, Ig alpha 1, 2 chain C region, kininogen-1, prothrombin, and serotransferrin. We first report the characterization of site-specific N-glycoproteins in DBS samples by LC-MS/MS with minimal sample preparation.

gFinder: A Web-Based Bioinformatics Tool for the Analysis of N-Glycopeptides.

Glycoproteins influence numerous indispensable biological functions, and changes in protein glycosylation have been observed in various diseases. The identification and characterization of glycoprotein and glycosylation sites by mass spectrometry (MS) remain challenging tasks, and great efforts have been devoted to the development of proteome informatics tools that facilitate the MS analysis of glycans and glycopeptides. Here we report on the development of gFinder, a web-based bioinformatics tool that analyzes mixtures of native N-glycopeptides that have been profiled by tandem MS. gFinder not only enables the simultaneous integration of collision-induced dissociation (CID) and high-energy collisional dissociation (HCD) fragmentation but also merges the spectra for high-throughput analysis. These merged spectra expedite the identification of both glycans and N-glycopeptide backbones in tandem MS data using the glycan database and a proteomic search tool (e.g., Mascot). These data can be used to simultaneously characterize peptide backbone sequences and possible N-glycan structures using assigned scores. gFinder also provides many convenient functions that make it easy to perform manual calculations while viewing the spectrum on-screen. We used gFinder to detect an additional protein (Q8N9B8) that was missed from the previously published data set containing N-linked glycosylation. For N-glycan analysis, we used the GlycomeDB glycan structure database, which integrates the structural and taxonomic data from all of the major carbohydrate databases available in the public domain. Thus, gFinder is a convenient, high-throughput analytical tool for interpreting the tandem mass spectra of N-glycopeptides, which can then be used for identification of potential missing proteins having glycans. gFinder is available publicly at http://gFinder.proteomix.org/ .

Integrated Proteomic Pipeline Using Multiple Search Engines for a Proteogenomic Study with a Controlled Protein False Discovery Rate.

In the Chromosome-Centric Human Proteome Project (C-HPP), false-positive identification by peptide spectrum matches (PSMs) after database searches is a major issue for proteogenomic studies using liquid-chromatography and mass-spectrometry-based large proteomic profiling. Here we developed a simple strategy for protein identification, with a controlled false discovery rate (FDR) at the protein level, using an integrated proteomic pipeline (IPP) that consists of four engrailed steps as follows. First, using three different search engines, SEQUEST, MASCOT, and MS-GF+, individual proteomic searches were performed against the neXtProt database. Second, the search results from the PSMs were combined using statistical evaluation tools including DTASelect and Percolator. Third, the peptide search scores were converted into E-scores normalized using an in-house program. Last, ProteinInferencer was used to filter the proteins containing two or more peptides with a controlled FDR of 1.0% at the protein level. Finally, we compared the performance of the IPP to a conventional proteomic pipeline (CPP) for protein identification using a controlled FDR of <1% at the protein level. Using the IPP, a total of 5756 proteins (vs 4453 using the CPP) including 477 alternative splicing variants (vs 182 using the CPP) were identified from human hippocampal tissue. In addition, a total of 10 missing proteins (vs 7 using the CPP) were identified with two or more unique peptides, and their tryptic peptides were validated using MS/MS spectral pattern from a repository database or their corresponding synthetic peptides. This study shows that the IPP effectively improved the identification of proteins, including alternative splicing variants and missing proteins, in human hippocampal tissues for the C-HPP. All RAW files used in this study were deposited in ProteomeXchange (PXD000395).

Characterization of Site-Specific N-Glycopeptide Isoforms of α-1-Acid Glycoprotein from an Interlaboratory Study Using LC-MS/MS.

Glycoprotein conformations are complex and heterogeneous. Currently, site-specific characterization of glycopeptides is a challenge. We sought to establish an efficient method of N-glycoprotein characterization using mass spectrometry (MS). Using alpha-1-acid glycoprotein (AGP) as a model N-glycoprotein, we identified its tryptic N-glycopeptides and examined the data reproducibility in seven laboratories running different LC-MS/MS platforms. We used three test samples and one blind sample to evaluate instrument performance with entire sample preparation workflow. 165 site-specific N-glycopeptides representative of all N-glycosylation sites were identified from AGP 1 and AGP 2 isoforms. The glycopeptide fragmentations by collision-induced dissociation or higher-energy collisional dissociation (HCD) varied based on the MS analyzer. Orbitrap Elite identified the greatest number of AGP N-glycopeptides, followed by Triple TOF and Q-Exactive Plus. Reproducible generation of oxonium ions, glycan-cleaved glycopeptide fragment ions, and peptide backbone fragment ions was essential for successful identification. Laboratory proficiency affected the number of identified N-glycopeptides. The relative quantities of the 10 major N-glycopeptide isoforms of AGP detected in four laboratories were compared to assess reproducibility. Quantitative analysis showed that the coefficient of variation was <25% for all test samples. Our analytical protocol yielded identification and quantification of site-specific N-glycopeptide isoforms of AGP from control and disease plasma sample.

Analysis of fucosylation in liver-secreted N-glycoproteins from human hepatocellular carcinoma plasma using liquid chromatography with tandem mass spectrometry.

Fucosylation of N-glycoproteins has been implicated in various diseases, such as hepatocellular carcinoma (HCC). However, few studies have performed site-specific analysis of fucosylation in liver-secreted proteins. In this study, we characterized the fucosylation patterns of liver-secreted proteins in HCC plasma using a workflow to identify site-specific N-glycoproteins, where characteristic B- and/or Y-ion series with and without fucose in collision-induced dissociation were used in tandem mass spectrometry. In total, 71 fucosylated N-glycopeptides from 13 major liver-secreted proteins in human plasma were globally identified by LC-MS/MS. Additionally, 37 fucosylated N-glycopeptides were newly identified from nine liver-secreted proteins, including alpha-1-antichymotrypsin, alpha-1-antitrypsin, alpha-2-HS-glycoprotein, ceruloplasmin, alpha-1-acid glycoprotein 1/2, alpha-2-macroglobulin, serotransferrin, and beta-2-glycoprotein 1. Of the fucosylated N-glycopeptides, bi- and tri-antennary glycoforms were the most common ones identified in liver-secreted proteins from HCC plasma. Therefore, we suggest that this analytical method is effective for characterizing fucosylation in liver-secreted proteins. Graphical abstract A global map of fucosylated and non-fucosylated glycopeptides from 13 liver-secreted glycoproteins in hepatocellular carcinoma plasma.

Chromosome-Based Proteomic Study for Identifying Novel Protein Variants from Human Hippocampal Tissue Using Customized neXtProt and GENCODE Databases.

The goal of the Chromosome-Centric Human Proteome Project (C-HPP) is to fully provide proteomic information from each human chromosome, including novel proteoforms, such as novel protein-coding variants expressed from noncoding genomic regions, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). In the 144 LC/MS/MS raw files from human hippocampal tissues of control, epilepsy, and Alzheimer's disease, we identified the novel proteoforms with a workflow including integrated proteomic pipeline using three different search engines, MASCOT, SEQUEST, and MS-GF+. With a <1% false discovery rate (FDR) at the protein level, the 11 detected peptides mapped to four translated long noncoding RNA variants against the customized databases of GENCODE lncRNA, which also mapped to coding-proteins at different chromosomal sites. We also identified four novel ASVs against the customized databases of GENCODE transcript. The target peptides from the variants were validated by tandem MS fragmentation pattern from their corresponding synthetic peptides. Additionally, a total of 128 SAAVs paired with their wild-type peptides were identified with FDR <1% at the peptide level using a customized database from neXtProt including nonsynonymous single nucleotide polymorphism (nsSNP) information. Among these results, several novel variants related in neuro-degenerative disease were identified using the workflow that could be applicable to C-HPP studies. All raw files used in this study were deposited in ProteomeXchange (PXD000395).

In-depth analysis of site-specific N-glycosylation in vitronectin from human plasma by tandem mass spectrometry with immunoprecipitation.

The characterization of site-specific microheterogeneity in glycoprotein is very important for understanding cell biology and disease processes. Vitronectin is well known to be a multifunctional glycoprotein in the blood and the extracellular matrix, which is related to hepatocellular carcinoma (HCC). Here, we systematically analyzed the site-specific N-glycopeptides of vitronectin in human plasma by tandem mass spectrometry combined with immunoprecipitation and hydrophilic interaction liquid chromatography (HILIC) enrichment. Vitronectin was purified with immunoprecipitation by monoclonal antibody from plasma and digested to tryptic N-glycopeptides.Then, enrichment with HILIC materials was used and followed by analysis with nano-LC/MS/MS. The sequences of N-glycopeptides were identified from the mass spectra by high-energy C-trap dissociation (HCD) and collision-induced dissociation (CID). In HCD mode, oxonium ions were used for recognizing glycopeptides and y ions for sequencing the peptide backbone. In CID mode, Y ions were used for characterizing their glycoforms. As a result, a total of 17 site-specific N-glycopeptides were completely identified in all of the three N-glycosylation sites of vitronectin in human plasma, including 12 N-glycopeptides first reported. Finally, we specifically found that three hybrid and four complex glycopeptides of triantennary forms with outer fucosylation increased in HCC human plasma.

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.

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.

Proteomic identification of rhythmic proteins in rice seedlings.

Many aspects of plant metabolism that are involved in plant growth and development are influenced by light-regulated diurnal rhythms as well as endogenous clock-regulated circadian rhythms. To identify the rhythmic proteins in rice, periodically grown (12h light/12h dark cycle) seedlings were harvested for three days at six-hour intervals. Continuous dark-adapted plants were also harvested for two days. Among approximately 3000 reproducible protein spots on each gel, proteomic analysis ascertained 354 spots (~12%) as light-regulated rhythmic proteins, in which 53 spots showed prolonged rhythm under continuous dark conditions. Of these 354 ascertained rhythmic protein spots, 74 diurnal spots and 10 prolonged rhythmic spots under continuous dark were identified by MALDI-TOF MS analysis. The rhythmic proteins were functionally classified into photosynthesis, central metabolism, protein synthesis, nitrogen metabolism, stress resistance, signal transduction and unknown. Comparative analysis of our proteomic data with the public microarray database (the Plant DIURNAL Project) and RT-PCR analysis of rhythmic proteins showed differences in rhythmic expression phases between mRNA and protein, suggesting that the clock-regulated proteins in rice are modulated by not only transcriptional but also post-transcriptional, translational, and/or post-translational processes.