Identify Binding Proteins Research Articles

Galectin-3 induces pathogenic immunosuppressive macrophages through interaction with TREM2 in lung cancer

BackgroundHigh infiltration of tumor-associated macrophages (TAMs) is associated with tumor promotion and immunosuppression. The triggering receptor expressed on myeloid cells 2 (TREM2) is emerged as a key immunosuppressive regulator for TAMs, however, how TREM2-expressing TAMs are recruited and what ligands TREM2 interacts with to mediate immunosuppression is unknown.MethodsFlow cytometry and single-cell RNA sequencing were used to analyze TREM2 expression. Mechanistically, mass spectrometry and immunoprecipitation were employed to identify proteins binding to TREM2. Phagocytosis and co-culture experiments were used to explore the in vitro functions of galectin3-TREM2 pair. Establishment of TREM2f/f-Lyz2-cre mice to validate the role of TREM2 signaling pathway in lung carcinogenesis. GB1107 were further supplemented to validate the therapeutic effect of Galectin3 based on TREM2 signaling regulation.ResultsThis study identified that abundant TREM2+ macrophages were recruited at the intra-tumor site through the CCL2-CCR2 chemotactic axis. Galectin-3 impaired TREM2-mediated phagocytosis and promoted the conversion of TREM2+ macrophages to immunosuppressive TAMs with attenuated antigen presentation and co-stimulatory functions both in vitro both in vivo, and galectin-3 is a potential ligand for TREM2. Genetic and pharmacological blockade of TREM2 and galectin-3 significantly inhibited lung cancer progression in subcutaneous and orthotopic cancer models by remodeling the tumor immune microenvironment.ConclusionOur findings revealed a previously unknown association between galectin-3 and TREM2 in TAMs of lung cancer, and suggested simultaneous inhibition of galectin3 and TREM2 as potent therapeutic approach for lung cancer therapy.Graphical

Identification of the binding proteins of organic acid metabolites by matrix thermal shift assay

Organic acid metabolites exhibit acidic properties. These metabolites serve as intermediates in major carbon metabolic pathways and are involved in several biochemical pathways, including the tricarboxylic acid (TCA) cycle and glycolysis. They also regulate cellular activity and play crucial roles in epigenetics, tumorigenesis, and cellular signal transduction. Knowledge of the binding proteins of organic acid metabolites is crucial for understanding their biological functions. However, identifying the binding proteins of these metabolites has long been a challenging task owing to the transient and weak nature of their interactions. Moreover, traditional methods are unsuitable for the structural modification of the ligands of organic acid metabolites because these metabolites have simple and similar structures. Even minor structural modifications can significantly affect protein interactions. Thermal proteome profiling (TPP) provides a promising avenue for identifying binding proteins without the need for structural modifications. This approach has been successfully applied to the identification of the binding proteins of several metabolites. In this study, we investigated the binding proteins of two TCA cycle intermediates, i.e., succinate and fumarate, and lactate, an end-product of glycolysis, using the matrix thermal shift assay (mTSA) technique. This technique involves combining single-temperature (52 ℃) TPP and dose-response curve analysis to identify ligand-binding proteins with high levels of confidence and determine the binding affinity between ligands and proteins. To this end, HeLa cells were lysed, followed by protein desalting to remove endogenous metabolites from the cell lysates. The desalted cell lysates were treated with fumarate or succinate at final concentrations of 0.004, 0.04, 0.4, and 2 mmol/L in the experimental groups or 2 mmol/L sodium chloride in the control group. Considering that the cellular concentration of lactate can be as high as 2-30 mmol/L, we then applied lactate at final concentrations of 0.2, 1, 5, 10, and 25 mmol/L in the experimental groups or 25 mmol/L sodium chloride in the control group. Using high-sensitivity mass spectrometry coupled with data-independent acquisition (DIA) quantification, we quantified 5870, 5744, and 5816 proteins in succinate, fumarate, and lactate mTSA experiments, respectively. By setting stringent cut-off values (i.e., significance of changes in protein thermal stability (p-value)<0.001 and quality of the dose-response curve fitting (square of Pearson's correlation coefficient, R2)>0.95), multiple binding proteins for these organic acid metabolites from background proteins were confidently determined. Several known binding proteins were identified, notably fumarate hydratase (FH) as a binding protein for fumarate, and α-ketoglutarate-dependent dioxygenase (FTO) as a binding protein for both fumarate and succinate. Additionally, the affinity data for the interactions between these metabolites and their binding proteins were obtained, which closely matched those reported in the literature. Interestingly, ornithine aminotransferase (OAT), which is involved in amino acid biosynthesis, and 3-mercaptopyruvate sulfurtransferase (MPST), which acts as an antioxidant in cells, were identified as lactate-binding proteins. Subsequently, an orthogonal assay technique developed in our laboratory, the solvent-induced precipitation (SIP) technique, was used to validate the mTSA results. SIP identified OAT as the top target candidate, validating the mTSA-based finding that OAT is a novel lactate-binding protein. Although MPST was not identified as a lactate-binding protein by SIP, statistical analysis of MPST in the mTSA experiments with 10 or 25 mmol/L lactate revealed that MPST is a lactate-binding protein with a high level of confidence. Peptide-level empirical Bayes t-tests combined with Fisher's exact test also supported the conclusion that MPST is a lactate-binding protein. Lactate is structurally similar to pyruvate, the known binding protein of MPST. Therefore, assuming that lactate could potentially occupy the binding site of pyruvate on MPST. Overall, the novel binding proteins identified for lactate suggest their potential involvement in amino acid synthesis and redox balance regulation.

Exploring the Antiangiogenic and Anti-Inflammatory Potential of Homoisoflavonoids: Target Identification Using Biotin Probes.

Chemical proteomics using biotin probes of natural products have significantly advanced our understanding of molecular targets and therapeutic potential. This review highlights recent progress in the application of biotin probes of homoisoflavonoids for identifying binding proteins and elucidating mechanisms of action. Notably, homoisoflavonoids exhibit antiangiogenic, anti-inflammatory, and antidiabetic effects. A combination of biotin probes, pull-down assays, mass spectrometry, and molecular modeling has revealed how natural products and their derivatives interact with several proteins such as ferrochelatase (FECH), soluble epoxide hydrolase (sEH), inosine monophosphate dehydrogenase 2 (IMPDH2), phosphodiesterase 4 (PDE4), and deoxyhypusine hydroxylase (DOHH). These target identification approaches pave the way for new therapeutic avenues, especially in the fields of oncology and ophthalmology. Future research aimed at expanding the repertoire of target identification using biotin probes of homoisoflavonoids promises to further elucidate the complex mechanisms and develop new drug candidates.

CRISPR-Cas13d screens identify KILR, a breast cancer risk-associated lncRNA that regulates DNA replication and repair

BackgroundLong noncoding RNAs (lncRNAs) have surpassed the number of protein-coding genes, yet the majority have no known function. We previously discovered 844 lncRNAs that were genetically linked to breast cancer through genome-wide association studies (GWAS). Here, we show that a subset of these lncRNAs alter breast cancer risk by modulating cell proliferation, and provide evidence that a reduced expression on one lncRNA increases breast cancer risk through aberrant DNA replication and repair.MethodsWe performed pooled CRISPR-Cas13d-based knockdown screens in breast cells to identify which of the 844 breast cancer-associated lncRNAs alter cell proliferation. We selected one of the lncRNAs that increased cell proliferation, KILR, for follow-up functional studies. KILR pull-down followed by mass spectrometry was used to identify binding proteins. Knockdown and overexpression studies were performed to assess the mechanism by which KILR regulates proliferation.ResultsWe show that KILR functions as a tumor suppressor, safeguarding breast cells against uncontrolled proliferation. The half-life of KILR is significantly reduced by the risk haplotype, revealing an alternative mechanism by which variants alter cancer risk. Mechanistically, KILR sequesters RPA1, a subunit of the RPA complex required for DNA replication and repair. Reduced KILR expression promotes breast cancer cell proliferation by increasing the available pool of RPA1 and speed of DNA replication. Conversely, KILR overexpression promotes apoptosis in breast cancer cells, but not normal breast cells.ConclusionsOur results confirm lncRNAs as mediators of breast cancer risk, emphasize the need to annotate noncoding transcripts in relevant cell types when investigating GWAS variants and provide a scalable platform for mapping phenotypes associated with lncRNAs.

Association of TRAIL receptor with phosphatase SHP-1 enables repressing T cell receptor signaling and T cell activation through inactivating Lck

BackgroundT cell receptor (TCR) signaling and T cell activation are tightly regulated by gatekeepers to maintain immune tolerance and avoid autoimmunity. The TRAIL receptor (TRAIL-R) is a TNF-family death receptor that transduces apoptotic signals to induce cell death. Recent studies have indicated that TRAIL-R regulates T cell-mediated immune responses by directly inhibiting T cell activation without inducing apoptosis; however, the distinct signaling pathway that regulates T cell activation remains unclear. In this study, we screened for intracellular TRAIL-R-binding proteins within T cells to explore the novel signaling pathway transduced by TRAIL-R that directly inhibits T cell activation.MethodsWhole-transcriptome RNA sequencing was used to identify gene expression signatures associated with TRAIL-R signaling during T cell activation. High-throughput screening with mass spectrometry was used to identify the novel TRAIL-R binding proteins within T cells. Co-immunoprecipitation, lipid raft isolation, and confocal microscopic analyses were conducted to verify the association between TRAIL-R and the identified binding proteins within T cells.ResultsTRAIL engagement downregulated gene signatures in TCR signaling pathways and profoundly suppressed phosphorylation of TCR proximal tyrosine kinases without inducing cell death. The tyrosine phosphatase SHP-1 was identified as the major TRAIL-R binding protein within T cells, using high throughput mass spectrometry-based proteomics analysis. Furthermore, Lck was co-immunoprecipitated with the TRAIL-R/SHP-1 complex in the activated T cells. TRAIL engagement profoundly inhibited phosphorylation of Lck (Y394) and suppressed the recruitment of Lck into lipid rafts in the activated T cells, leading to the interruption of proximal TCR signaling and subsequent T cell activation.ConclusionsTRAIL-R associates with phosphatase SHP-1 and transduces a unique and distinct immune gatekeeper signal to repress TCR signaling and T cell activation via inactivating Lck. Thus, our results define TRAIL-R as a new class of immune checkpoint receptors for restraining T cell activation, and TRAIL-R/SHP-1 axis can serve as a potential therapeutic target for immune-mediated diseases.

CAMP-phosphodiesterase 4D7 (PDE4D7) forms a cAMP signalosome complex with DHX9 and is implicated in prostate cancer progression.

A robust body of work has demonstrated that a reduction in cAMP-specific 3',5'-cyclic phosphodiesterase 4D isoform 7 (PDE4D7) is linked with negative prostate cancer outcomes; however, the exact molecular mechanism that underpins this relationship is unknown. Epigenetic profiling has shown that the PDE4D gene can be hyper-methylated in transmembrane serine protease 2 (TMPRSS2)-ETS transcriptional regulator ERG (ERG) gene-fusion-positive prostate cancer (PCa) tumours, and this inhibits messenger RNA (mRNA) expression, leading to a paucity of cellular PDE4D7 protein. In an attempt to understand how the resulting aberrant cAMP signalling drives PCa growth, we immunopurified PDE4D7 and identified binding proteins by mass spectrometry. We used peptide array technology and proximity ligation assay to confirm binding between PDE4D7 and ATP-dependent RNA helicase A (DHX9), and in the design of a novel cell-permeable disruptor peptide that mimics the DHX9-binding region on PDE4D7. We discovered that PDE4D7 forms a signalling complex with the DExD/H-box RNA helicase DHX9. Importantly, disruption of the PDE4D7-DHX9 complex reduced proliferation of LNCaP cells, suggesting the complex is pro-tumorigenic. Additionally, we have identified a novel protein kinase A (PKA) phosphorylation site on DHX9 that is regulated by PDE4D7 association. In summary, we report the existence of a newly identified PDE4D7-DHX9 signalling complex that may be crucial in PCa pathogenesis and could represent a potential therapeutic target.

DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model.

Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information. In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite. The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.

Optimized Tensor Decomposition and Principal Component Analysis Outperforming State-of-the-Art Methods When Analyzing Histone Modification Chromatin Immunoprecipitation Profiles

It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification but are general methods that aim to identify protein binding to the genome. In this study, tensor decomposition (TD) and principal component analysis (PCA)-based unsupervised feature extraction with optimized standard deviation were successfully applied to gene expression and DNA methylation. The proposed method was used to identify histone modification. Histone modification along the genome is binned within the region of length L. Considering principal components (PCs) or singular value vectors (SVVs) that PCA or TD attributes to samples, we can select PCs or SVVs attributed to regions. The selected PCs and SVVs further attribute p-values to regions, and adjusted p-values are used to select regions. The proposed method identified various histone modifications successfully and outperformed various state-of-the-art methods. This method is expected to serve as a de facto standard method to identify histone modification. For reproducibility and to ensure the systematic analysis of our study is applicable to datasets from different gene expression experiments, we have made our tools publicly available for download from gitHub.

CircMMD reduction following tumor treating fields inhibits glioblastoma progression through FUBP1/FIR/DVL1 and miR-15b-5p/FZD6 signaling

BackgroundTumor treating fields (TTF) is the latest treatment for GBM. Circular RNA (circRNA) has been demonstrated to play critical roles in tumorigenesis. However, the molecular mechanism of TTF remained largely unknown and the role of circRNA in TTF was not reported. The aim of this study was to elucidate the role and mechanism of circMMD in TTF treatment of GBM.MethodsDivergent primer was designed to verify the existence of circMMD in GBM cells. The prognostic role of circMMD was explored in glioma specimens. The knockdown and overexpressed plasmids were used to evaluate the effect of circMMD on GBM cell proliferation and TTF efficacy. RNA pull-down and RNA immunoprecipitation were performed to identify binding proteins of circMMD. Subcutaneous and intracranial tumor models were established to validate findings in vivo.ResultsThe expression of circMMD was elevated in GBM and its high expression indicated poor prognoses. TTF intervention could reduce circMMD synthesis, which suppressed GBM proliferation and increased TTF-mediated apoptosis. The reduction of circMMD promoted the interaction between FUBP1 and FIR, which decreased DVL1 transcription. Meanwhile, decreased circMMD would promote the activity of miR-15b-5p to degrade FZD6. Finally, the diminished expression of DVL1 and FZD6 expression suppressed the activation of Wnt/β-catenin pathway.ConclusionsOur study revealed a novel mechanism of TTF that TTF-mediated reduction of circMMD could inhibit Wnt/β-catenin pathway to suppress GBM proliferation.

Generation of nucleotide-linked resins for identification of novel binding proteins.

Organisms use numerous nucleotide-containing compounds as intracellular signals to control behavior. Identifying the biomolecules responsible to sensing and responding to changes in signaling molecule concentration is an important area of research. However, identifying the binding proteins can be challenging when there is no prior information available about binding motifs. In this chapter, we describe a straightforward method to generate nucleotide-linked resins for use in pull-down experiments to identify binding proteins. The protocol outlined in this chapter also can be adapted to generate custom resins linked to other molecules of interest.

Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins.

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (>99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

Integrated structure-based protein interface prediction

BackgroundIdentifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model.ResultsWe describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces.ConclusionsThe integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone.

A Photo-Crosslinking Approach to Identify Class II SUMO-1 Binders.

The small ubiquitin-like modifier (SUMO) is involved in various cellular processes and mediates known non-covalent protein-protein interactions by three distinct binding surfaces, whose interactions are termed class I to class III. While interactors for the class I interaction, which involves binding of a SUMO-interacting motif (SIM) to a hydrophobic groove in SUMO-1 and SUMO-2/3, are widely abundant, only a couple of examples have been reported for the other two types of interactions. Class II binding is conveyed by the E67 loop region on SUMO-1. Many previous studies to identify SUMO binders using pull-down or microarray approaches did not strategize on the SUMO binding mode. Identification of SUMO binding partners is further complicated due to the typically transient and low affinity interactions with the modifier. Here we aimed to identify SUMO-1 binders selectively enriched for class II binding. Using a genetically encoded photo-crosslinker approach, we have designed SUMO-1 probes to covalently capture class II SUMO-1 interactors by strategically positioning the photo-crosslinking moiety on the SUMO-1 surface. The probes were validated using known class II and class I binding partners. We utilized the probe with p-benzoyl-phenylalanine (BzF, also termed BpF or Bpa) at the position of Gln69 to identify binding proteins from mammalian cell extracts using mass spectrometry. By comparison with results obtained with a similarly designed SUMO-1 probe to target SIM-mediated binders of the class I type, we identified 192 and 96 proteins specifically enriched by either probe, respectively. The implicated preferential class I or class II binding modes of these proteins will further contribute to unveiling the complex interplay of SUMO-1-mediated interactions.

Structure‐function relationships for the lncRNA SChLAP1 in aggressive prostate cancer

Advances in deep sequencing technologies have facilitated the identification and annotation of thousands of long noncoding RNAs (lncRNAs) across the transcriptome. LncRNAs are documented to play critical housekeeping roles within the cell and are implicated in a wide variety of diseases, including cancer. While studies into lncRNA function in cancer abound, there are limited examples to date of detailed lncRNA structural analyses to enable understanding of structure‐function relationships. Understanding structure‐function relationships would increase our insight into the noncoding transcriptome and yield potential avenues for targeting lncRNAs implicated in disease. The lncRNA Second Chromosome Locus Associated with Prostate 1 (SChLAP1) has been identified in multiple clinical studies as a predictive biomarker and molecular driver of aggressive prostate cancer. While several protein interactors have been identified for SChLAP1 to date, structural insight into SChLAP1:protein recognition has not yet been explored. We believe that structural analysis of SChLAP1 will assist in designing specific therapeutic strategies to inhibit SChLAP1:protein interactions implicated in prostate cancer. To this end, we performed Selective 2′‐Hydroxyl Acylation analyzed by Primer Extension with Mutational Profiling (SHAPE‐MaP) and dimethyl sulfate (DMS)‐MaP in vitroand in cellulo. This approach yielded the first secondary structure model of SChLAP1, which revealed a complex architecture with a wide variety of secondary structures throughout the length of the transcript. Analyzing our in‐cell probing data with the ΔSHAPE algorithm, we identified protein binding regions within SChLAP1 and mapped them to non‐human primate‐conserved exons within the transcript. Finally, we identified a smaller, highly structured fragment of SChLAP1 that houses multiple putative protein binding sites implicated in prostate cancer. We believe that this region of SChLAP1 is amenable to therapeutic targeting and may be used as a smaller construct for in vitrodevelopment of SChLAP1‐targeting therapeutics. We also believe this fragment may be amenable to 3D biophysical analyses, such as X‐ray crystallography or cryo‐EM, to further enhance structural understanding of SChLAP1:protein complexation. Ongoing work is focused on determining the sufficiency of this fragment for protein recognition and characterizing potential magnesium‐dependent tertiary structures. We believe this work will facilitate the development of specific therapeutic strategies for SChLAP1 and contribute to the growing need of characterizing structure‐function relationships within lncRNAs.

DCLK1 promotes colorectal cancer stemness and aggressiveness via the XRCC5/COX2 axis

Rationale: Doublecortin-like kinase 1 (DCLK1) is a serine/threonine kinase that selectively marks cancer stem-like cells (CSCs) and promotes malignant progression in colorectal cancer (CRC). However, the exact molecular mechanism by which DCLK1 drives the aggressive phenotype of cancer cells is incompletely determined.Methods: Here, we performed comprehensive genomics and proteomics analyses to identify binding proteins of DCLK1 and discovered X-ray repair cross-complementing 5 (XRCC5). Thus, we explored the biological role and downstream events of the DCLK1/XRCC5 axis in human CRC cells and CRC mouse models.Results: The results of comprehensive bioinformatics analyses suggested that DCLK1-driven CRC aggressiveness is linked to inflammation. Mechanistically, DCLK1 bound and phosphorylated XRCC5, which in turn transcriptionally activated cyclooxygenase-2 expression and enhanced prostaglandin E2 production; these events collectively generated the inflammatory tumor microenvironment and enhanced the aggressive behavior of CRC cells. Consistent with the discovered mechanism, inhibition of DCLK1 kinase activity strongly impaired the tumor seeding and growth capabilities in CRC mouse models.Conclusion: Our study illuminates a novel mechanism that mediates the pro-inflammatory function of CSCs in driving the aggressive phenotype of CRC, broadening the biological function of DCLK1 in CRC.

Overexpression of ARHGAP30 suppresses growth of cervical cancer cells by downregulating ribosome biogenesis.

We aimed to identify whether Rho GTPase activating proteins (RhoGAPs) were downregulated in cervical cancers and might be targeted to reduce the growth of cervical cancer using the GEO database and immunohistochemical analysis to identified changes in transcription and protein levels. We analyzed their proliferation, clone formation ability, and their growth as subcutaneous tumors in mice. To detect ARHGAP30 localization in cells, immunofluorescence assays were conducted. Mass spectrometry combined with immunoprecipitation experiments were used to identify binding proteins. Protein interactions were validated with co‐immunoprecipitation assays. Western‐blot and q‐PCR were applied to analyze candidate binding proteins that were associated with ribosome biogenesis. Puromycin incorporation assay was used to detect the global protein synthesis rate. We identified that ARHGAP30 was the only downregulated RhoGAP and was related to the survival of cervical cancer patients. Overexpression of ARHGAP30 in cervical cancer cells inhibited cell proliferation and migration. ARHGAP30 immunoprecipitated proteins were enriched in the ribosome biogenesis process. ARHGAP30 was located in the nucleous and interacted with nucleolin (NCL). Overexpression of ARHGAP30 inhibited rRNA synthesis and global protein synthesis. ARHGAP30 overexpression induced the ubiquitination of NCL and decreased its protein level in Hela cells. The function of ARHGAP30 on cervical cancer cell ribosome biogenesis and proliferation was independent of its RhoGAP activity as assessed with a RhoGAP‐deficient plasmid of ARHGAP30R55A. Overall, the findings revealed that ARHGAP30 was frequently downregulated and associated with shorter survival of cervical cancer patients. ARHGAP30 may suppress growth of cervical cancer by reducing ribosome biogenesis and protein synthesis through promoting ubiquitination of NCL.

Systematic Identification of Proteins Binding with Cisplatin in Blood by Affinity Chromatography and a Four-Dimensional Proteomic Method.

Cisplatin is widely used for the treatment of various solid tumors. It is mainly administered by intravenous injection, and a substantial amount of the drug will bind to plasma proteins, a feature that is closely related to its pharmacokinetics, activity, toxicity, and side effects. However, due to the unique properties of platinum complexes and the complexity of the blood proteome, existing methods cannot systematically identify the binding proteome of cisplatin in blood. In this study, high-abundance protein separation and an ion mobility mass spectrometry-based 4D proteomic method were combined to systematically and comprehensively identify the binding proteins of cisplatin in blood. The characteristic isotope patterns of platinated peptides and a similarity algorithm were utilized to eliminate false-positive identification. Finally, 39 proteins were found to be platinated. Bioinformatics analysis showed that the identified proteins were mainly involved in the complement and coagulation cascade pathways. The binding ratio of some peptides with cisplatin was measured based on the area ratio of the free peptide using the parallel reaction monitoring method. This study provides a new method for systematically identifying binding proteins of metal drugs in blood, and the identified proteins might be helpful for understanding the toxicity of platinum anticancer drugs.

FOXQ1 promotes the osteogenic differentiation of bone mesenchymal stem cells via Wnt/\u03b2-catenin signaling by binding with ANXA2

BackgroundThis study investigated the role of Forkhead box Q1 (FOXQ1) in the osteogenic differentiation of bone mesenchymal stem cells.MethodsMouse bone mesenchymal stem cells (mBMSCs) were transfected with lentivirus to generate Foxq1-overexpressing mBMSCs, Foxq1-suppressed mBMSCs, and mBMSC controls. The activity of osteogenic differentiation was evaluated with alizarin red staining, alkaline phosphatase activity assay, and RT-qPCR. Wnt/β-catenin signaling activities were compared among groups by TOPFlash/FOPFlash assay, immunofluorescence staining, and western blot assay of beta-catenin (CTNNB1). Coimmunoprecipitation mass spectrometry was also carried out to identify proteins binding with FOXQ1.ResultsOur data showed that FOXQ1 expression was positively correlated with the osteogenic differentiation of the mBMSCs. FOXQ1 also promoted the nuclear translocation of CTNNB1 in the mBMSCs, enhancing Wnt/β-catenin signaling, which was also shown to be essential for the osteogenic differentiation-promoting effect of FOXQ1 in the mBMSCs. Annexin A2 (ANXA2) was bound with FOXQ1, and its depletion reversed the promoting effect of FOXQ1 on Wnt/β-catenin signaling.ConclusionThese results showed that FOXQ1 binds with ANXA2, promoting Wnt/β-catenin signaling in bone mesenchymal stem cells, which subsequently promotes osteogenic differentiation.

CRISPR-assisted detection of RNA-protein interactions in living cells.

We have developed CRISPR-assisted RNA-protein interaction detection method (CARPID), which leverages CRISPR-CasRx-based RNA targeting and proximity labeling to identify binding proteins of specific long non-coding RNAs (lncRNAs) in the native cellular context. We applied CARPID to the nuclear lncRNA XIST, and it captured a list of known interacting proteins and multiple previously uncharacterized binding proteins. We generalized CARPID to explore binders of the lncRNAs DANCR and MALAT1, revealing the method's wide applicability in identifying RNA-binding proteins.

DDX39A interacts with LGP2 to inhibit RLR responses

Abstract RIG-I-like receptors (RLRs) are DEAD-box helicases and cytoplasmic pathogen recognition receptors that recognize and bind to nonself/viral RNA and trigger innate antiviral immunity. RLRs are essential for the detection of RNA virus infection and include RIG-I, MDA5, and LGP2. LGP2 has been implicated as RIG-I or MDA5 co-factor to regulate their activity. We conducted a yeast 2-hybrid (Y2H) screen (using LGP2 as bait) of a human hepatic cDNA library to identify protein binding partners of LGP2. Identified proteins were validated as LGP2 interactors through overexpression and co-immunoprecipitation assays of LGP2 binding. DDX39A, a DEAD Box helicase protein with known roles in RNA splicing, was identified as a binding partner. DDX39A overexpression was found to inhibit interferon-beta production from RLR-signaling in a cell-based ligand-free model in a dose-dependent manner. Further, in a cell-based interferon beta-promoter-luciferase assay model of Sendai virus (SenV) infection, DDX39A and LGP2 synergistically inhibited promoter activity. Similar results were seen in a cell-based NFkB-luciferase assay of the same system. Endogenous expression of DDX39A was assayed through the Huh7 human hepatoma cell line system with various stimuli including viral infection with SenV and Interferon-beta treatment showing that DDX39A is constitutively expressed in those cells. Structure-function mutants of DDX39A and LGP2 were used to determine the protein domains and functions required for interaction via co-immunoprecipitation assays. Through these studies, we will define the interactions between LGP2 and DDX39A, and reveal the roles these proteins have as co-factors in RLR signaling.