Untargeted metabolomics combined with pseudotargeted lipidomics revealed the metabolite profiles of blood-stasis syndrome in type 2 diabetes mellitus.

Objective: Blood-stasis syndrome (BSS), an important syndrome in Type 2 diabetes mellitus(T2DM), is associated with the pathophysiological mechanisms underlying diabetic vascular complications. However, BSS has not been fully characterized as of yet. Due to the strong correlation between BSS and vasculopathy, we hypothesized that the metabolic characteristics of BSS in T2DM (T2DM BSS) are highly specific. By combining untargeted metabolomics and pseudotargeted lipidomics approaches, this study aimed to comprehensively elucidate the metabolic traits of T2DM BSS, thereby providing novel insights into the vascular complications of diabetes and establishing a foundation for precision medicine. Methods: The survey was conducted in Haidian District of Beijing from October 2021 to November 2021, and data collection was completed in January 2022. Liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS) based pseudotargeted lipidomics were performed to detect metabolites and lipids. Multivariate, univariate, and pathway analyses were utilized to investigate metabolic changes. The unique metabolites of BSS were obtained by inter-group comparisons and screening. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of metabolites. Results: A total of 1189 participants completed the survey, of which 120 participants were recruited in this study and further divided into a discovery cohort (n = 90) and a validation cohort (n = 30). Among these, 21 participants were selected for psuedotargeted lipidomics analysis. 81 metabolites, mainly involving glycerophospholipids, were identified as unique metabolites of T2DM BSS, while fatty acyls (FAs) were identified as unique lipids. T2DM BSS was associated with significant dysregulation in glycerophospholipid metabolism and choline metabolism within cancer pathways as major metabolic disturbances. Furthermore, analyses of both the discovery and validation cohorts, indicated that LysoPC (20:5(5Z,8Z,11Z,14Z,17Z)/0:0) and LysoPC (15:0) had the greatest impact on distinguishing BSS. Conclusion: Altered levels of glycerophospholipids and FAs have been associated with T2DM BSS. These results provide valuable mechanistic insights linked with the development of BSS in T2DM subjects.

A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning.

Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR ) is identified as the most crucial feature. While, in the further three-class LLPS-Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.

Mapping current status and emerging trends in NETosis: A bibliometric study.

BACKGROUND: NETosis is a critical innate immune mechanism of neutrophils that contributes to the accelerated progression of autoimmune diseases, thrombosis, cancer, and coronavirus disease 2019 (COVID-19). This study qualitatively and quantitatively analyzed the relevant literature by bibliometric methods in order to provide a more comprehensive and objective view of the knowledge dynamics in the field. METHODS: The literature on NETosis was downloaded from the Web of Science Core Collection, analyzed with VOSviewer, CiteSpace, and Microsoft for co-authorship, co-occurrence, and co-citation analysis. RESULTS: In the field of NETosis, the United States was the most influential countries. Harvard University was the most active institutions. Mariana J. Kaplan and Brinkmann V were, respectively, the most prolific and most co-cited authors. Frontiers in Immunology, Journal of Immunology, Plos One, Blood, Science, Journal of Cell Biology, and Nature Medicine were the most influential journals. The top 15 keywords are associated with immunological and NETosis formation mechanisms. The keywords with the strongest burst detection were mainly related to COVID-19 (coronavirus, ACE2, SARS coronavirus, cytokine storm, pneumonia, neutrophil to lymphocyte ratio), and cancer (circulating tumor cell). CONCLUSION: Research on NETosis is currently booming. The mechanism of NETosis and its role in innate immunity, autoimmune diseases, especially systemic lupus erythematosus and rheumatoid arthritis, and thrombosis are the focus of research in the field of NETosis. A future study will concentrate on the function of NETosis in COVID-19 and recurrent metastasis of cancer.

The PLOD2/succinate axis regulates the epithelial-mesenchymal plasticity and cancer cell stemness.

Aberrant accumulation of succinate has been detected in many cancers. However, the cellular function and regulation of succinate in cancer progression is not completely understood. Using stable isotope-resolved metabolomics analysis, we showed that the epithelial mesenchymal transition (EMT) was associated with profound changes in metabolites, including elevation of cytoplasmic succinate levels. The treatment with cell-permeable succinate induced mesenchymal phenotypes in mammary epithelial cells and enhanced cancer cell stemness. Chromatin immunoprecipitation and sequence analysis showed that elevated cytoplasmic succinate levels were sufficient to reduce global 5-hydroxymethylcytosinene (5hmC) accumulation and induce transcriptional repression of EMT-related genes. We showed that expression of procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 (PLOD2) was associated with elevation of cytoplasmic succinate during the EMT process. Silencing of PLOD2 expression in breast cancer cells reduced succinate levels and inhibited cancer cell mesenchymal phenotypes and stemness, which was accompanied by elevated 5hmC levels in chromatin. Importantly, exogenous succinate rescued cancer cell stemness and 5hmC levels in PLOD2-silenced cells, suggesting that PLOD2 promotes cancer progression at least partially through succinate. These results reveal the previously unidentified function of succinate in enhancing cancer cell plasticity and stemness.

Binding of berberine derivates to G-quadruplex: insight from a computational study.

Human telomerase exhibits significant activity in cancer cells relative to normal cells, which contributes to the immortal proliferation of cancer cells. To counter this, the stabilization of G-quadruplexes formed in the guanine-rich sequence of the cancer cell chromosome has emerged as a promising avenue for anti-cancer therapy. Berberine (BER), an alkaloid that is derived from traditional Chinese medicines, has shown potential for stabilizing G-quadruplexes. To investigate the atomic interactions between G-quadruplexes and BER and its derivatives, molecular dynamics simulations were conducted. Modeling the interactions between G-quadruplexes and ligands accurately is challenging due to the strong negative charge of nucleic acids. Thus, various force fields and charge models for the G-quadruplex and ligands were tested to obtain precise simulation results. The binding energies were calculated by a combination of molecular mechanics/generalized Born surface area and interaction entropy methods, and the calculated results correlated well with experimental results. B-factor and hydrogen bond analyses demonstrated that the G-quadruplex was more stable in the presence of ligands than in the absence of ligands. Calculation of the binding free energy showed that the BER derivatives bind to a G-quadruplex with higher affinity than that of BER. The breakdown of the binding free energy to per-nucleotide energies suggested that the first G-tetrad played a primary role in binding. Additionally, energy and geometric properties analyses indicated that van der Waals interactions were the most favorable interactions between the derivatives and the G-quadruplexes. Overall, these findings provide crucial atomic-level insights into the binding of G-quadruplexes and their inhibitors.

A bibliometric analysis of DNA methylation in cardiovascular diseases from 2001 to 2021.

BACKGROUND: DNA methylation is a dynamically reversible form of epigenetics. Dynamic regulation plays an important role in cardiovascular diseases (CVDs). However, there have been few bibliometric studies in this field. We aimed to visualize the research results and hotspots of DNA methylation in CVDs using a bibliometric analysis to provide a scientific direction for future research. METHODS: Publications related to DNA methylation in CVDs from January 1, 2001, to September 15, 2021, were searched and confirmed from the Web of Science Core Collection. CiteSpace 5.7 and VOSviewer 1.6.15 were used for bibliometric and knowledge-map analyses. RESULTS: A total of 2617 publications were included in 912 academic journals by 15,584 authors from 963 institutions from 85 countries/regions. Among them, the United States of America, China, and England were the top 3 countries contributing to the field of DNA methylation. Harvard University, Columbia University, and University of Cambridge were the top 3 contributing institutions in terms of publications and were closely linked. PLoS One was the most published and co-cited journal. Baccarelli Andrea A published the most content, while Barker DJP had the highest frequency of co-citations. The keyword cluster focused on the mechanism, methyl-containing substance, exposure/risk factor, and biomarker. In terms of research hotspots, references with strong bursts, which are still ongoing, recently included "epigenetic clock" (2017-2021), "obesity, smoking, aging, and DNA methylation" (2017-2021), and "biomarker and epigenome-wide association study" (2019-2021). CONCLUSIONS: We used bibliometric and visual methods to identify research hotspots and trends in DNA methylation in CVDs. Epigenetic clocks, biomarkers, environmental exposure, and lifestyle may become the focus and frontier of future research.

HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models.

The human ether-à-go-go-related gene (hERG) K+ channel plays an important role in cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome (LQTS) and even sudden cardiac death. Due to severe hERG-related cardiotoxicity, many drugs have been withdrawn from the market. Therefore, it is necessary to estimate the chemical blockade of hERG in the early stage of drug discovery. In this study, we collected 12,850 compounds with hERG inhibition data from the literature and trained a series of hERG blocking classification models based on the MACCS and Morgan fingerprints. A consensus model named HergSPred was generated based on the individual models using voting principles. The accuracy of HergSPred is higher than previous models using identical training and test sets. Moreover, we analyzed the contribution of each input fingerprint to the prediction output to obtain intuitive chemical insights into the hERG inhibition, which allows visualization of warning substructures that may cause cardiotoxicity in the input compound. The model is available at http://www.icdrug.com/ICDrug/T.

Discovery of novel inhibitors of CDK2 using docking and physics-based binding free energy calculation.

Cyclin-dependent kinase (CDK) is a serine/threonine protein kinase family that cooperates with cyclin and plays an important role in the regulation of cell cycle. Cyclin-dependent kinase 2 is an important member of the CDK family and holds great promise as an anti-cancer drug target. In this study, we used molecular docking and physics-based binding free energy calculation method AS-IE that explicitly calculated protein-ligand binding entropy to discover novel inhibitors of CDK2. A total of 17 inhibitors were discovered with the best IC50 reaching ~2 µM. Decomposition of the binding free energy using AS-IE reveals key protein-ligand interactions that determines the activity. These results provided a good example of drug design using physics-based free energy calculation method such as AS-IE and the novel compounds offered a good start point for further development of CDK2 inhibitors.

Insights into small molecule inhibitor bindings to PD-L1 with residue-specific binding free energy calculation.

Targeting the immunological checkpoint PD-1/PD-L1 with antibodies has shown opportunities to improve cancer treatment in recent years. However, antibody therapy is a double-edged sword with high cost, low patient tolerance, lack of oral bioavailability, and a reaction to most solid tumors that prevents the adoption of antibodies. Advancement of small-molecule PD-1/PD-L1 inhibitors that could overwhelm these drawbacks is sluggish because of the poor pharmacodynamic properties and shallow pocket of the PD-1/PD-L1 binding interface. Recently, a number of compounds have been discovered to bind the PD-L1/PD-L1 dimer interface, providing an excellent alternative to inhibit the interaction between PD-1/PD-L1 and small molecules. Quantitative characterization of PD-L1 interactions with these inhibitors will advance the design of novel and efficient inhibitors in the future. Here, the binding free energies of 35 PD-L1 dimer inhibitors have been calculated using the alanine-scanning-interaction-entropy (AS-IE) method. Hotspot residues on PD-L1 and potential modification groups on the inhibitors were identified. The experimental results for the AS-IE method were better correlated than the classical MM/GBSA method. These results may set the stage for the design the more powerful PD-L1 inhibitors.Communicated by Ramaswamy H. Sarma.

Analysis of the binding modes of the first- and second-generation antiandrogens with respect to F876L mutation.

Androgen receptor (AR) is an important target for the treatment of prostate cancer, and mutations in the AR have an important impact on the resistance of existing drugs. In this work, we performed molecular dynamics simulations of the existing marketed antiandrogens flutamide, nilutamide, bicalutamide, enzalutamide, apalutamide, darolutamide, and its main metabolite ORM15341 in complex with the wild-type and F876L mutant AR. We calculated the residue-specific binding free energy contribution of the wild-type and mutant ARs with the AS-IE method and analyzed the hotspot residues and the binding free energy contributions of specific residues before and after the mutation. In addition, we analyzed the total binding obtained by adding residue binding energy contributions and compared the results with experimental values. The obtained residue-specific binding information should be very helpful in understanding the mechanism of drug resistance with respect to specific mutations and in the design of new generation drugs against possible new mutations.

Computational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands.

Androgen receptor (AR) is an important therapeutic target for the treatment of diseases such as prostate cancer, hypogonadism, muscle wasting, etc. In this study, the complex structures of the AR ligand-binding domain (LBD) with fifteen ligands were analyzed by molecular dynamics simulations combined with the alanine-scanning-interaction-entropy method (ASIE). The quantitative free energy contributions of the pocket residues were obtained and hotspot residues are quantitatively identified. Our calculation shows that that these hotspot residues are predominantly hydrophobic and their interactions with binding ligands are mainly van der Waals interactions. The total binding free energies obtained by summing over binding contributions by individual residues are in good correlation with the experimental binding data. The current quantitative analysis of binding mechanism of AR to ligands provides important insight on the design of future inhibitors.

Identification of three new compounds that directly target human serine hydroxymethyltransferase 2.

Mitochondrial serine hydroxymethyltransferase 2 (SHMT2) is an important drug target in the one-carbon metabolic pathway, since its activity is critical for purine and pyrimidine biosynthesis. Additionally, it plays a prominent role during metabolic reprogramming of cancer cells, and SHMT2 inhibitors have proven useful as anticancer drugs. Compared to drugs targeting one-carbon metabolic enzymes (mainly dihydrofolate reductase and thymidylate synthase) that have been used for clinical treatment of cancer, efficient SHMT2-specific inhibitors are lacking. Therefore, we established a direct system for virtual screening, protein expression, and identification of inhibitors targeting SHMT2. First, 27 compounds qualifying as potential SHMT2 inhibitors were selected for biological activity verification through virtual screening of the 210 thousand compounds registered in the Specs database. Second, these 27 hits were subjected to quick screening by an in vitro non-competitive kinetic assay of SHMT2 single-enzyme catalysis. This allowed us to identify three compounds featuring medium-strength and non-competitive inhibition of SHMT2: AM-807/42004511 (IC50  = 14.52 ± 4.1665 µM), AM-807/40675298 (IC50  = 12.74 ± 5.8991 µM), and AM-807/42004633 (IC50  = 9.43 ± 0.5646 µM). We describe a quick screening method for the identification of inhibitors targeting SHMT2, providing a basis for subsequent identification and screening of new inhibitors.

Pluripotent anti-inflammatory immunomodulatory effects of papaverine against cerebral ischemic-reperfusion injury.

The mechanism of the papaverine (PV) for the treatment of cerebral ischemia remains unclear. A total of 42 mice induced with focal cerebral ischemia were randomly divided into three groups: PV,baicalin (BA)and vehicle group. Both PV and BA could significantly reduce the ischemic infarct volume (P < 0.05). Pathway enrichment analysis was performed on MetaCore™ to search the molecular pathways associated with the gene expression profile of PV, compared with vehicle and BA. Compared with vehicle, we found that 60% of the top 10 pathways in PV group were related to immune response. The top 10 biological processes of the targeted pathways were mainly related to the multiple immunomodulatory process of neuro-vascular inflammation, including immune_Th17-deried cytokins, regulation of angiogenesis, cell adhesion_Leucocyte chemotaxis, antigen presentation, cell adhesion_synaptic contact, and inflammation related to Amphoterin signaling. Moreover, compared with BA, 17 pathways of PV were identified, and 58.82% (10/17) were also related to immune response, especially, half of the top 10 pathways with the lower p-value. In these top 10 pathways, 4 were the cytokine-mediated signaling pathways, which play key role in inflammation, like IL-17 signaling pathways with the activation of G-CSF,IL-23,RANKL, p38MAPK and NF-κB.These findings indicate that PV may be an efficacious pluripotent anti-inflammatory agent against cerebral ischemic-reperfusion injury by targeting on multiple immunomodulatory process of neuro-vascular inflammation.

Molecular Mechanism of Selective Binding of NMS-P118 to PARP-1 and PARP-2: A Computational Perspective.

The nuclear protein poly (ADP-ribose) polymerase-1 (PARP-1) inhibitors have been proven effective to potentiate both chemotherapeutic agents and radiotherapy. However, a major problem of most current PARP inhibitors is their lack of selectivity for PARP-1 and its closest isoform PARP-2. NMS-P118 is a highly selective PARP inhibitor that binds PARP-1 stronger than PARP-2 and has many advantages such as excellent pharmacokinetic profiles. In this study, molecular dynamics (MD) simulations of NMS-P118 in complex with PARP-1 and PARP-2 were performed to understand the molecular mechanism of its selectivity. Alanine scanning together with free energy calculation using MM/GBSA and interaction entropy reveal key residues that are responsible for the selectivity. Although the conformation of the binding pockets and NMS-P118 are very similar in PARP-1 and PARP-2, most of the hot-spot residues in PARP-1 have stronger binding free energy than the corresponding residues in PARP-2. Detailed analysis of the binding energy shows that the 4'4-difluorocyclohexyl ring on NMS-P118 form favorable hydrophobic interaction with Y889 in PARP-1. In addition, the H862 residue in PARP-1 has stronger binding free energy than H428 in PARP-2, which is due to shorter distance and stronger hydrogen bonds. Moreover, the negatively charged E763 residue in PARP-1 forms stronger electrostatic interaction energy with the positively charged NMS-P118 than the Q332 residue in PARP-2. These results rationalize the selectivity of NMS-P118 and may be useful for designing novel selective PARP inhibitors.

Computational approaches to studying methylated H4K20 recognition by DNA repair factor 53BP1.

Histone lysine methylation regulates the recruitment of mammalian DNA repair factor 53BP1 to the histone H4 lysine 20 (H4K20), through specific recognition of the tandem Tudor domain of 53BP1. The di- and mono-methylated H4K20 bind to 53BP1 with high affinity, but the non- and tri-methylated H4K20 do not. Here, we develop a new approach to carry out computational study to unravel the binding mechanism of methylated H4K20 by 53BP1 and to compute relative binding affinities of different methylations of H4K20 by 53BP1. First, hot spots in 53BP1 were predicted by computational alanine scanning and aromatic cages formed by W1495, Y1500, Y1502, and Y1523 are found to provide the dominant binding to di- and mono-methylated H4K20 in addition to D1521. Secondly, a de-methylation method is proposed to predict relative binding free energies between 53BP1 and different methylated states of H4K20. Finally, the tri-methylated and non-methylated H4K20/53BP1 complexes are found to be dynamically unstable, explaining the experimental finding that neither can bind to 53BP1. The present work provides an important theoretical basis for our understanding of histone methylations of H4K20 and their recognition mechanism by DNA repair factor 53BP1.

Binding Modes of Small-Molecule Inhibitors to the EED Pocket of PRC2.

Polycomb Polycomb repressive complex 2 (PRC2) plays a key role in silencing epigenetic gene through trimethylation of lysine 27 on histone 3 (H3K27). Dysregulations of PRC2 caused by overexpression and mutations of the core subunits of PRC2 have been implicated in many cancers. The core subunits EZH1/2 are histone-lysine N-methyltransferases that function as the enzymatic component of PRC2. While the core subunit EED is a scaffolding protein to support EZH1/2 and binds JARID2K116me3/H3K27me3 to enhance the enzymatic activity of PRC2 through allosteric activation. Recently, several small molecules that compete with JARI2K116me3 and H3K27me3 have been reported. These molecules selectively bind to the JARID2K116me3/H3K27me3-binding pocket of EED, thereby preventing the allosteric regulation of PRC2. These first-in-class PRC2 inhibitors show robust suppression in DLBCL cell lines, demonstrating anticancer drugs that target the EED subunit of PRC2 are viable. In this study, we used the recently developed MM/GBSA_IE and the alanine scanning method to analyze the hot spots in EED/inhibitor interactions. The analysis of these hot and warm spots helps us to understand the fundamental differences between inhibitors. Our results give a quantitative explanation on why the binding affinities of EED/A-395 interactions are stronger than that of EED/EED226 while their binding modes are similar and provide valuable insights for rational design of novel EED inhibitors.

Study of SHMT2 Inhibitors and Their Binding Mechanism by Computational Alanine Scanning.

Mitochondrial serine hydroxymethyl transferase isoform 2 (SHMT2) has attracted increasing attention as a pivotal catalyzing regulator of the serine/glycine pathway in the one-carbon metabolism of cancer cells. However, few inhibitors that target this potential anticancer target have been discovered. Quantitative characterization of the interactions between SHMT2 and its known inhibitors should benefit future discovery of novel inhibitors. In this study, we employed a recently developed alanine-scanning-interaction-entropy method to quantitatively calculate the residue-specific binding free energy of 28 different SHMT2 inhibitors that originate from the same skeleton. Major contributing residues from SHMT2 and chemical groups from the inhibitors were identified, and the binding energy of each residue was quantitatively determined, revealing essential features of the protein-inhibitor interaction. The most important contributing residue is Y105 of the B chain followed by L166 of the A chain. The calculated protein-ligand binding free energies are in good agreement with the experimental results and showed better correlation and smaller errors compared with those obtained using the conventional MM/GBSA with the normal mode method. These results may aid the rational design of more effective SHMT2 inhibitors.

Calculation of hot spots for protein-protein interaction in p53/PMI-MDM2/MDMX complexes.

The recently developed MM/GBSA_IE method is applied to computing hot and warm spots in p53/PMI-MDM2/MDMX protein-protein interaction systems. Comparison of the calculated hot (>2 kcal/mol) and warm spots (>1 kcal/mol) in P53 and PMI proteins interacting with MDM2 and MDMX shows a good quantitative agreement with the available experimental data. Further, our calculation predicted hot spots in MDM2 and MDMX proteins in their interactions with P53 and PMI and they help elucidate the interaction mechanism underlying this important PPI system. In agreement with the experimental result, the present calculation shows that PMI has more hot and warm spots and binds stronger to MDM2/MDMX. The analysis of these hot and warm spots helps elucidate the fundamental difference in binding between P53 and PMI to the MDM2/MDMX systems. Specifically, for p53/PMI-MDM2 systems, p53 and PMI use essentially the same residues (L54, I61, Y67, Q72, V93, H96, and I99) of MDM2 for binding. However, PMI enhanced interactions with residues L54, Y67, and Q72 of MDM2. For the p53/PMI-MDMX system, p53 and PMI use similar residues (M53, I60, Y66, Q71, V92, and Y99) of MDMX for binding. However, PMI exploited three extra residues (M61, K93, and L98) of MDMX for enhanced binding. In addition, PMI enhanced interaction with four residues (M53, Y66, Q71, and Y99) of MDMX. These results gave quantitative explanation on why the binding affinities of PMI-MDM2/MDMX interactions are stronger than that of p53-MDM2/MDMX although their binding modes are similar. © 2018 Wiley Periodicals, Inc.

Membrane associated collagen XIII promotes cancer metastasis and enhances anoikis resistance.

BACKGROUND: Increased collagen expression and deposition are associated with cancer progression and poor prognosis in breast cancer patients. However, function and regulation of membrane-associated collagen in breast cancer have not been determined. Collagen XIII is a type II transmembrane protein within the collagen superfamily. Experiments in tissue culture and knockout mouse models show that collagen XIII is involved in cell adhesion and differentiation of certain cell types. In the present study, we determined roles of collagen XIII in breast cancer progression and metastasis. METHODS: We analyzed the association of collagen XIII expression with breast cancer development and metastasis using published gene expression profiles generated from human breast cancer tissues. Utilizing gain- and loss- of function approaches and 3D culture assays, we investigated roles of collagen XIII in regulating invasive tumor growth. Using the tumorsphere/mammosphere formation assay and the detachment cell culture assay, we determined whether collagen XIII enhances cancer cell stemness and induces anoikis resistance. We also inhibited collagen XIII signaling with ß1 integrin function-blocking antibody. Finally, using the lung colonization assay and the orthotopic mammary tumor model, we investigated roles of collagen XIII in regulating breast cancer colonization and metastasis. Cox proportional hazard (log-rank) test, two-sided Student's t-test (two groups) and one-way ANOVA (three or more groups) analyses were used in this study. RESULTS: Collagen XIII expression is significantly higher in human breast cancer tissue compared with normal mammary gland. Increased collagen XIII mRNA levels in breast cancer tissue correlated with short distant recurrence free survival. We showed that collagen XIII expression promoted invasive tumor growth in 3D culture, enhanced cancer cell stemness, and induced anoikis resistance. Collagen XIII expression induced ß1 integrin activation. Blocking ß1 integrin activation significantly reduced collagen XIII-induced invasion and mammosphere formation. Importantly, silencing collagen XIII in MDA-MB-231 cells reduced lung colonization and metastasis. CONCLUSIONS: Our results demonstrate a novel function of collagen XIII in promoting cancer metastasis, cell invasion, and anoikis resistance.

Roles of PLODs in Collagen Synthesis and Cancer Progression.

Collagen is the major component of extracellular matrix. Collagen cross-link and deposition depend on lysyl hydroxylation, which is catalyzed by procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD). Aberrant lysyl hydroxylation and collagen cross-link contributes to the progression of many collagen-related diseases, such as fibrosis and cancer. Three lysyl hydroxylases (LH1, LH2, and LH3) are identified, encoded by PLOD1, PLOD2, and PLOD3 genes. Expression of PLODs is regulated by multiple cytokines, transcription factors and microRNAs. Dysregulation of PLODs promotes cancer progression and metastasis, suggesting that targeting PLODs is potential strategy for cancer treatment. Here, we summarize the recent progress in the investigation of function and regulation of PLODs in normal tissue development and disease progression, especially in cancer.