The C. elegans Taste Receptor Homolog LITE-1 Is a Photoreceptor.

Many animal tissues/cells are photosensitive, yet only two types of photoreceptors (i.e., opsins and cryptochromes) have been discovered in metazoans. The question arises as to whether unknown types of photoreceptors exist in the animal kingdom. LITE-1, a seven-transmembrane gustatory receptor (GR) homolog, mediates UV-light-induced avoidance behavior in C. elegans. However, it is not known whether LITE-1 functions as a chemoreceptor or photoreceptor. Here, we show that LITE-1 directly absorbs both UVA and UVB light with an extinction coefficient 10-100 times that of opsins and cryptochromes, indicating that LITE-1 is highly efficient in capturing photons. Unlike typical photoreceptors employing a prosthetic chromophore to capture photons, LITE-1 strictly depends on its protein conformation for photon absorption. We have further identified two tryptophan residues critical for LITE-1 function. Interestingly, unlike GPCRs, LITE-1 adopts a reversed membrane topology. Thus, LITE-1, a taste receptor homolog, represents a distinct type of photoreceptor in the animal kingdom.

Targeting DCAF5 suppresses SMARCB1-mutant cancer by stabilizing SWI/SNF.

Whereas oncogenes can potentially be inhibited with small molecules, the loss of tumour suppressors is more common and is problematic because the tumour-suppressor proteins are no longer present to be targeted. Notable examples include SMARCB1-mutant cancers, which are highly lethal malignancies driven by the inactivation of a subunit of SWI/SNF (also known as BAF) chromatin-remodelling complexes. Here, to generate mechanistic insights into the consequences of SMARCB1 mutation and to identify vulnerabilities, we contributed 14 SMARCB1-mutant cell lines to a near genome-wide CRISPR screen as part of the Cancer Dependency Map Project1-3. We report that the little-studied gene DDB1-CUL4-associated factor 5 (DCAF5) is required for the survival of SMARCB1-mutant cancers. We show that DCAF5 has a quality-control function for SWI/SNF complexes and promotes the degradation of incompletely assembled SWI/SNF complexes in the absence of SMARCB1. After depletion of DCAF5, SMARCB1-deficient SWI/SNF complexes reaccumulate, bind to target loci and restore SWI/SNF-mediated gene expression to levels that are sufficient to reverse the cancer state, including in vivo. Consequently, cancer results not from the loss of SMARCB1 function per se, but rather from DCAF5-mediated degradation of SWI/SNF complexes. These data indicate that therapeutic targeting of ubiquitin-mediated quality-control factors may effectively reverse the malignant state of some cancers driven by disruption of tumour suppressor complexes.

Ligand-induced monoubiquitination of BIK1 regulates plant immunity.

Recognition of microbe-associated molecular patterns (MAMPs) by pattern recognition receptors (PRRs) triggers the first line of inducible defence against invading pathogens1-3. Receptor-like cytoplasmic kinases (RLCKs) are convergent regulators that associate with multiple PRRs in plants4. The mechanisms that underlie the activation of RLCKs are unclear. Here we show that when MAMPs are detected, the RLCK BOTRYTIS-INDUCED KINASE 1 (BIK1) is monoubiquitinated following phosphorylation, then released from the flagellin receptor FLAGELLIN SENSING 2 (FLS2)-BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1 (BAK1) complex, and internalized dynamically into endocytic compartments. The Arabidopsis E3 ubiquitin ligases RING-H2 FINGER A3A (RHA3A) and RHA3B mediate the monoubiquitination of BIK1, which is essential for the subsequent release of BIK1 from the FLS2-BAK1 complex and activation of immune signalling. Ligand-induced monoubiquitination and endosomal puncta of BIK1 exhibit spatial and temporal dynamics that are distinct from those of the PRR FLS2. Our study reveals the intertwined regulation of PRR-RLCK complex activation by protein phosphorylation and ubiquitination, and shows that ligand-induced monoubiquitination contributes to the release of BIK1 family RLCKs from the PRR complex and activation of PRR signalling.

Developmental programming and lineage branching of early human telencephalon.

The dorsal and ventral human telencephalons contain different neuronal subtypes, including glutamatergic, GABAergic, and cholinergic neurons, and how these neurons are generated during early development is not well understood. Using scRNA-seq and stringent validations, we reveal here a developmental roadmap for human telencephalic neurons. Both dorsal and ventral telencephalic radial glial cells (RGs) differentiate into neurons via dividing intermediate progenitor cells (IPCs_div) and early postmitotic neuroblasts (eNBs). The transcription factor ASCL1 plays a key role in promoting fate transition from RGs to IPCs_div in both regions. RGs from the regionalized neuroectoderm show heterogeneity, with restricted glutamatergic, GABAergic, and cholinergic differentiation potencies. During neurogenesis, IPCs_div gradually exit the cell cycle and branch into sister eNBs to generate distinct neuronal subtypes. Our findings highlight a general RGs-IPCs_div-eNBs developmental scheme for human telencephalic progenitors and support that the major neuronal fates of human telencephalon are predetermined during dorsoventral regionalization with neuronal diversity being further shaped during neurogenesis and neural circuit integration.

Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

Dissecting Detergent-Insoluble Proteome in Alzheimer's Disease by TMTc-Corrected Quantitative Mass Spectrometry.

Protein aggregation of amyloid-ß peptides and tau are pathological hallmarks of Alzheimer's disease (AD), which are often resistant to detergent extraction and thus enriched in the insoluble proteome. However, additional proteins that coaccumulate in the detergent-insoluble AD brain proteome remain understudied. Here, we comprehensively characterized key proteins and pathways in the detergent-insoluble proteome from human AD brain samples using differential extraction, tandem mass tag (TMT) labeling, and two-dimensional LC-tandem mass spectrometry. To improve quantification accuracy of the TMT method, we developed a complement TMT-based strategy to correct for ratio compression. Through the meta-analysis of two independent detergent-insoluble AD proteome datasets (8914 and 8917 proteins), we identified 190 differentially expressed proteins in AD compared with control brains, highlighting the pathways of amyloid cascade, RNA splicing, endocytosis/exocytosis, protein degradation, and synaptic activity. To differentiate the truly detergent-insoluble proteins from copurified background during protein extraction, we analyzed the fold of enrichment for each protein by comparing the detergent-insoluble proteome with the whole proteome from the same AD samples. Among the 190 differentially expressed proteins, 84 (51%) proteins of the upregulated proteins (n = 165) were enriched in the insoluble proteome, whereas all downregulated proteins (n = 25) were not enriched, indicating that they were copurified components. The vast majority of these enriched 84 proteins harbor low-complexity regions in their sequences, including amyloid-ß, Tau, TARDBP/TAR DNA-binding protein 43, SNRNP70/U1-70K, MDK, PTN, NTN1, NTN3, and SMOC1. Moreover, many of the enriched proteins in AD were validated in the detergent-insoluble proteome by five steps of differential extraction, proteomic analysis, or immunoblotting. Our study reveals a resource list of proteins and pathways that are exclusively present in the detergent-insoluble proteome, providing novel molecular insights to the formation of protein pathology in AD.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

PPIH acts as a potential predictive biomarker for patients with common solid tumors.

BACKGROUND: Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker. METHODS: We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers. RESULTS: Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis. CONCLUSION: Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

Transforming primary human hepatocytes into hepatocellular carcinoma with genetically defined factors.

Our understanding of human hepatocellular carcinoma (HCC) development and progression has been hampered by the lack of in vivo models. We performed a genetic screen of 10 oncogenes and genetic mutations in Fah-ablated immunodeficient mice in which primary human hepatocytes (PHHs) are used to reconstitute a functional human liver. We identified that MYC, TP53R249S , and KRASG12D are highly expressed in induced HCC (iHCC) samples. The overexpression of MYC and TP53R249S transform PHHs into iHCC in situ, though the addition of KRASG12D significantly increases the tumorigenic efficiency. iHCC, which recapitulate the histological architecture and gene expression characteristics of clinical HCC samples, reconstituted HCC after serial transplantations. Transcriptomic analysis of iHCC and PHHs showed that MUC1 and FAP are expressed in iHCC but not in normal livers. Chimeric antigen receptor (CAR) T cells against these two surface markers efficiently lyse iHCC cells. The properties of iHCC model provide a biological basis for several clinical hallmarks of HCC, and iHCC may serve as a model to study HCC initiation and to identify diagnostic biomarkers and targets for cellular immunotherapy.

Intrauterine adhesions treated with hysteroscopic adhesiolysis and subsequent obstetric outcome: A retrospective matched cohort study.

OBJECTIVE: To examine whether a history of hysteroscopic adhesiolysis (HA)-treated intrauterine adhesions (IUAs) was associated with an increased risk of adverse obstetrical outcomes in subsequent pregnancies. DESIGN: Retrospective cohort study. SETTING: A tertiary-care hospital in Shanghai, China. POPULATION: A cohort of 114 142 pregnant women who were issued an antenatal card and received routine antenatal care in Shanghai First Maternity and Infant Hospital, between January 2016 and October 2021. METHODS: From the cohort of 114 142 pregnant women, each woman with a history of HA-treated IUA prior to the current pregnancy (n = 780) was matched with four women without a history of IUAs (n = 3010) using propensity score matching. The matching variables were maternal age and parity, mode of conception, pre-pregnancy body mass index and prior history of abortion. MAIN OUTCOME MEASURES: Pregnancy complications, placental abnormalities, postpartum haemorrhage and adverse birth outcomes. RESULTS: Compared with women with no history of IUAs, women with a history of HA-treated IUAs were at higher risk of pre-eclampsia (RR 1.69, 95% CI 1.23-2.33), placenta accreta spectrum (RR 4.72, 95% CI 3.9-5.73), placenta praevia (RR 4.23, 95% CI 2.85-6.30), postpartum haemorrhage (RR 2.86, 95% CI 1.94-4.23), preterm premature rupture of membranes (RR 3.02, 95% CI 1.97-4.64) and iatrogenic preterm birth (RR 2.86, 95% CI 2.14-3.81). Those women were also more likely to receive cervical cerclage (RR 5.63, 95% CI 3.95-8.02) during pregnancy and haemostatic therapies after delivery (RR 2.17, 95% CI 1.75-2.69). Moreover, we observed that the RRs of those adverse obstetrical outcomes increased with the increasing number of hysteroscopic surgeries. CONCLUSIONS: This study found that a history of HA-treated IUAs, especially a history of repeated HAs, was associated with an increased risk of adverse obstetrical outcomes.

Tetraspanin Tspan8 restrains interferon signaling to stabilize intestinal epithelium by directing endocytosis of interferon receptor.

Inflammation can impair intestinal barrier, while increased epithelial permeability can lead to inflammation. In this study, we found that the expression of Tspan8, a tetraspanin expressed specifically in epithelial cells, is downregulated in mouse model of ulcerative disease (UC) but correlated with those of cell-cell junction components, such as claudins and E-cadherin, suggesting that Tspan8 supports intestinal epithelial barrier. Tspan8 removal increases intestinal epithelial permeability and upregulates IFN-γ-Stat1 signaling. We also demonstrated that Tspan8 coalesces with lipid rafts and facilitates IFNγ-R1 localization at or near lipid rafts. As IFN-γ induces its receptor undergoing clathrin- or lipid raft-dependent endocytosis and IFN-γR endocytosis plays an important role in Jak-Stat1 signaling, our analysis on IFN-γR endocytosis revealed that Tspan8 silencing impairs lipid raft-mediated but promotes clathrin-mediated endocytosis of IFN-γR1, leading to increased Stat1 signaling. These changes in IFN-γR1 endocytosis upon Tspan8 silencing correlates with fewer lipid raft component GM1 at the cell surface and more clathrin heavy chain in the cells. Our findings indicate that Tspan8 determines the IFN-γR1 endocytosis route, to restrain Stat1 signaling, stabilize intestine epithelium, and subsequently prevent intestine from inflammation. Our finding also implies that Tspan8 is needed for proper endocytosis through lipid rafts.

Structural and sensory characteristics of ultrasonic assisted wet-heating Maillard reaction products of Giant salamander protein hydrolysates.

BACKGROUND: Chinese giant salamander protein hydrolysates (CGSPH) are beneficial to human health as a result of their high content of amino acids and peptides. However, the formation of bitter peptides in protein hydrolysates (PHs) would hinder their application in food industry. The ultrasound assisted wet-heating Maillard reaction (MR) is an effective way to improve the flavor of PHs. Thus, the effect of ultrasonic assisted wet-heating MR on the structure and flavor of CGSPH was investigated in the present study. RESULTS: The results indicated that the ultrasound assisted wet-heating MR products (MRPs) exhibited a higher degree of graft and more significant changes in the secondary and tertiary structures of CGSPH compared to traditional wet-heating MRPs. Moreover, ultrasound assisted wet-heating MR could significantly increase the content of small molecule peptides and reduce the content of free amino acids of CGSPH, which resulted in more significant changes in flavor characteristics. The changed in flavor properties after MR (especially ultrasound assisted wet-heating MRPs) were mainly manifested by a significant reduction in bitterness, as well as a significant increase in the content of aromatic aldehyde ester compounds such as furan-2-carbaldehyde, butanal, benzaldehyde, furfural, etc. CONCLUSIONS: Ultrasound assisted wet-heating MR between CGSPH and xylose could be a promising way to improve the sensory characteristics of CGSPH. © 2024 Society of Chemical Industry.

JUMPptm: Integrated software for sensitive identification of post-translational modifications and its application in Alzheimer's disease study.

BACKGROUND: Mass spectrometry (MS)-based proteomic analysis of posttranslational modifications (PTMs) usually requires the pre-enrichment of modified proteins or peptides. However, recent ultra-deep whole proteome profiling generates millions of spectra in a single experiment, leaving many unassigned spectra, some of which may be derived from PTM peptides. METHODS: Here we present JUMPptm, an integrative computational pipeline, to extract PTMs from unenriched whole proteome. JUMPptm combines the advantages of JUMP, MSFragger and Comet search engines, and includes de novo tags, customized database search and peptide filtering, which iteratively analyzes each PTM by a multi-stage strategy to improve sensitivity and specificity. RESULTS: We applied JUMPptm to the deep brain proteome of Alzheimer's disease (AD), and identified 34,954 unique peptides with phosphorylation, methylation, acetylation, ubiquitination, and others. The phosphorylated peptides were validated by enriched phosphoproteome from the same sample. TMT-based quantification revealed 482 PTM peptides dysregulated at different stages during AD progression. For example, the acetylation of numerous mitochondrial proteins is significantly decreased in AD. A total of 60 PTM sites are found in the pan-PTM map of the Tau protein. CONCLUSION: The JUMPptm program is an effective tool for pan-PTM analysis and the resulting AD pan-PTM profile serves as a valuable resource for AD research.

Evaluation of a Pooling Chemoproteomics Strategy with an FDA-Approved Drug Library.

Chemoproteomics is a key platform for characterizing the mode of action for compounds, especially for targeted protein degraders such as proteolysis targeting chimeras (PROTACs) and molecular glues. With deep proteome coverage, multiplexed tandem mass tag-mass spectrometry (TMT-MS) can tackle up to 18 samples in a single experiment. Here, we present a pooling strategy for further enhancing the throughput and apply the strategy to an FDA-approved drug library (95 best-in-class compounds). The TMT-MS-based pooling strategy was evaluated in the following steps. First, we demonstrated the capability of TMT-MS by analyzing more than 15 000 unique proteins (> 12 000 gene products) in HEK293 cells treated with five PROTACs (two BRD/BET degraders and three degraders for FAK, ALK, and BTK kinases). We then introduced a rationalized pooling strategy to separate structurally similar compounds in different pools and identified the proteomic response to 14 pools from the drug library. Finally, we validated the proteomic response from one pool by reprofiling the cells via treatment with individual drugs with sufficient replicates. Interestingly, numerous proteins were found to change upon drug treatment, including AMD1, ODC1, PRKX, PRKY, EXO1, AEN, and LRRC58 with 7-hydroxystaurosporine; C6orf64, HMGCR, and RRM2 with Sorafenib; SYS1 and ALAS1 with Venetoclax; and ATF3, CLK1, and CLK4 with Palbocilib. Thus, pooling chemoproteomics screening provides an efficient method for dissecting the molecular targets of compound libraries.

Identifying Sex-Specific Serum Patterns of Alzheimer's Mice through Deep TMT Profiling and a Concentration-Dependent Concatenation Strategy.

Alzheimer's disease (AD) is the most prevalent form of dementia, disproportionately affecting women in disease prevalence and progression. Comprehensive analysis of the serum proteome in a common AD mouse model offers potential in identifying possible AD pathology- and gender-associated biomarkers. Here, we introduce a multiplexed, nondepleted mouse serum proteome profiling via tandem mass-tag (TMTpro) labeling. The labeled sample was separated into 475 fractions using basic reversed-phase liquid chromatography (RPLC), which were categorized into low-, medium-, and high-concentration fractions for concatenation. This concentration-dependent concatenation strategy resulted in 128 fractions for acidic RPLC-tandem mass spectrometry (MS/MS) analysis, collecting ∼5 million MS/MS scans and identifying 3972 unique proteins (3413 genes) that cover a dynamic range spanning at least 6 orders of magnitude. The differential expression analysis between wild type and the commonly used AD model (5xFAD) mice exhibited minimal significant protein alterations. However, we detected 60 statistically significant (FDR < 0.05), sex-specific proteins, including complement components, serpins, carboxylesterases, major urinary proteins, cysteine-rich secretory protein 1, pregnancy-associated murine protein 1, prolactin, amyloid P component, epidermal growth factor receptor, fibrinogen-like protein 1, and hepcidin. The results suggest that our platform possesses the sensitivity and reproducibility required to detect sex-specific differentially expressed proteins in mouse serum samples.

ULBP2 is a biomarker related to prognosis and immunity in colon cancer.

The study aimed to determine whether ULBP2 was associated with prognosis and immune infiltration in colon cancer (CC) and provided important molecular basis in order to early non-invasive diagnosis and immunotherapy of CC. Using The Cancer Genome Atlas database (TCGA) and ImmPort database, we extracted messenger RNA (mRNA) data of CC and immune-related genes, then we used "limma" package, "survival" package, and Venn overlap analysis to obtain the differentially expressed mRNA (DEmRNA) associated with prognosis and immunity of CC patients. "pROC" package was used to analyze receiver operating characteristics (ROC) of target gene. We used chi-square test and two-class logistics model to identify clinicopathological parameters that correlated with target gene expression. In order to determine the effects of target gene expression and clinicopathological parameters on survival, univariate and multivariate cox regression analyses were performed. We analyzed the related functions and signaling pathways of target gene by enrichment analysis. Finally, the correlation between target gene and tumor immune infiltrating was explored by ssGSEA and spearman correlation analysis. Results showed that ULBP2 was a target gene associated with immunity and prognosis in CC patients. CC patients with higher ULBP2 expression had poor outcomes. In terms of ROC, ULBP2 had an area under the curve (AUC) of 0.984. ULBP2 was associated with T stage, N stage, and pathologic stage of CC patients, and served as an independent predictor of overall survival in CC patients. Functional enrichment analysis revealed ULBP2 was obviously enriched in pathways connected with carcinogenesis and immunosuppression. The expression of ULBP2 was significantly associated with tumor immune cells and immune checkpoints according to ssGSEA and spearman correlation analysis. To conclude, our study suggested that ULBP2 was associated with tumor immunity, and might be a biomarker associated with the diagnosis and prognosis of CC patients, and a potential target of CC immunotherapy.

Constructed the ceRNA network and predicted a FEZF1-AS1/miR-92b-3p/ZIC5 axis in colon cancer.

The purpose of this study was to identify the role of FEZF1-AS1 in colon cancer and predicted the underlying mechanism. We extracted sequencing data of colon cancer patients from The Cancer Genome Atlas database, identified the differential expression of long noncoding RNA, microRNA, and messenger RNA, constructed a competitive endogenous RNA network, and then analyzed prognosis. Then, we used the enrichment analysis databases for functional analysis. Finally, we studied the FEZF1-AS1/miR-92b-3p/ZIC5 axis. We detected the expression of FEZF1-AS1, miR-92b-3p, and ZIC5 via quantitative reverse transcription-PCR, transfected colon cancer cell RKO with lentivirus and conducted FEZF1-AS1 knockdown, and performed cancer-related functional assays. It indicated that many RNA in the competitive endogenous RNA network, such as ZIC5, were predicted to be related to overall survival of colon cancer patients, and enrichment analysis showed cancer-related signaling pathways, such as PI3K/AKT signaling pathway. The expression of FEZF1-AS1 and ZIC5 was significantly higher and that of miR-92b-3p was lower in the colon cancer than in the normal colon tissues. FEZF1-AS1 promoted the migration, proliferation, as well as invasion of RKO. According to the prediction, FEZF1-AS1 and ZIC5 might competitively bind to miR-92b-3p, leading to the weakening of the inhibitory impact of miR-92b-3p on ZIC5 and increasing expression of ZIC5, thus further activating the PI3K/AKT signaling pathway, which led to the occurrence and development of colon cancer. The study suggested that FEZF1-AS1 might be an effective diagnosis biomarker for colon cancer.

Deep Single-Cell-Type Proteome Profiling of Mouse Brain by Nonsurgical AAV-Mediated Proximity Labeling.

Proteome profiling is a powerful tool in biological and biomedical studies, starting with samples at bulk, single-cell, or single-cell-type levels. Reliable methods for extracting specific cell-type proteomes are in need, especially for the cells (e.g., neurons) that cannot be readily isolated. Here, we present an innovative proximity labeling (PL) strategy for single-cell-type proteomics of mouse brain, in which TurboID (an engineered biotin ligase) is used to label almost all proteins in a specific cell type. This strategy bypasses the requirement of cell isolation and includes five major steps: (i) constructing recombinant adeno-associated viruses (AAVs) to express TurboID driven by cell-type-specific promoters, (ii) delivering the AAV to mouse brains by direct intravenous injection, (iii) enhancing PL labeling by biotin administration, (iv) purifying biotinylated proteins, followed by on-bead protein digestion, and (v) quantitative tandem-mass-tag (TMT) labeling. We first confirmed that TurboID can label a wide range of cellular proteins in human HEK293 cells and optimized the single-cell-type proteomic pipeline. To analyze specific brain cell types, we generated recombinant AAVs to coexpress TurboID and mCherry proteins, driven by neuron- or astrocyte-specific promoters and validated the expected cell expression by coimmunostaining of mCherry and cellular markers. Subsequent biotin purification and TMT analysis identified ∼10,000 unique proteins from a few micrograms of protein samples with excellent reproducibility. Comparative and statistical analyses indicated that these PL proteomes contain cell-type-specific cellular pathways. Although PL was originally developed for studying protein-protein interactions and subcellular proteomes, we extended it to efficiently tag the entire proteomes of specific cell types in the mouse brain using TurboID biotin ligase. This simple, effective in vivo approach should be broadly applicable to single-cell-type proteomics.