YTHDF1 upregulation mediates hypoxia-dependent breast cancer growth and metastasis through regulating PKM2 to affect glycolysis

N6-methyladenosine modification is the most common RNA modification mechanism in mammals. YTHDF1, a m⁶A reader, can recognize the m⁶A of mRNAs to facilitate the interaction with the mRNA ribosome assembly and recruitment of translation initiators to promote translation. From a clinical perspective, YTHDF1 upregulation is frequently observed in breast cancer, but its involvement in those cancer-related events is still unclear. Here we report that YTHDF1 is a cancer driver capable of facilitating the proliferation and invasion of breast cancer cells as well as enhancing tumorigenicity and metastasis through promoting glycolysis. We found that tumor hypoxia can transcriptionally induce HIF1α and post-transcriptionally inhibit the expression of miR-16-5p to promote YTHDF1 expression, which could sequentially enhance tumor glycolysis by upregulating PKM2 and eventually increase the tumorigenesis and metastasis potential of breast cancer cells. Inhibiting YTHDF1 via gene knockdown or miR-16-5p would significantly abolish YTHDF1-dependent tumor growth and metastasis. In summary, we identified the role of the YTHDF1-PKM2 signal axis in the occurrence and development of breast cancer, which can be used as a potential target for breast cancer treatment.

A Transferrin-Conjugated Hollow Nanoplatform for Redox-Controlled and Targeted Chemotherapy of Tumor with Reduced Inflammatory Reactions

Purpose: In this study, we report the design, development and evaluation of a hollow drug delivery nanoplatform for cancer therapy in vitro and in vivo. This composite nanosystem was prepared by modifying hollow mesoporous silica nanoparticles (HMSNs) with transferrin (Tf) targeting moieties via redox-liable linkage, and was capable of delivering therapeutic cargos (doxorubicin) specifically to the tumor site and subsequently releasing them in an on-demand manner. Moreover, the Tf corona could simultaneously reduce the inflammatory response after intravenous administration in vivo. Methods: Nanostructural morphology of the drug delivery system was observed by scanning electron microscope and transmission electron microscope. The preparation process was monitored primarily using Fourier-transform infrared spectroscopy, dynamic light scattering, nitrogen adsorption/desorption isotherm, and thermogravimetric analysis. The release profile in solution was monitored by fluorescence spectroscopy. In vitro drug delivery efficacy was evaluated on MDA-MB-231 breast cancer cell line using confocal laser scanning microscopy, MTT assay and flow cytometry. In vitro inflammatory response was evaluated on RAW264.7 macrophage cells. In vivo therapeutic experiments were carried out using in situ mouse breast cancer models. Results: The experimental results evidently demonstrate that the developed nanocarrier could effectively deliver anticancer drugs to the tumor site in a targeted manner and release them in response to the elevated glutathione level inside tumor cells, resulting in improved anticancer efficacy both in vitro and in vivo. Moreover, the Tf conjugation significantly ameliorated the inflammatory reaction triggered by the administration of the nanocarrier. Conclusions: This manuscript demonstrated that the Tf-conjugated HMSNs could enhance the delivery efficiency of anticancer drugs, while simultaneously alleviating the adverse side effects. The current study presents a promising integrated delivery system toward effective and safe cancer treatment.

Lipidome in colorectal cancer

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths. Understanding its pathophysiology is essential for developing efficient strategies to treat this disease. Lipidome, the sum of total lipids, related enzymes, receptors and signaling pathways, plays crucial roles in multiple cellular processes, such as metabolism, energy storage, proliferation and apoptosis. Dysregulation of lipid metabolism and function contributes to the development of CRC, and can be used towards the evaluation of prognosis. The strategies targeting lipidome have been applied in clinical trails and showed promising results. Here we discuss recent advances in abnormal lipid metabolism in CRC, the mechanisms by which the lipidome regulates tumorigenesis and tumor progression, and suggest potential therapeutic targets for clinical trials.

Sustained raloxifene release from hyaluronan-alendronate-functionalized titanium nanotube arrays capable of enhancing osseointegration in osteoporotic rabbits

To enhance the localized bone remodeling at titanium-based implants under osteoporotic conditions, TiO₂ nanotube arrays (TNT) were used as nanoreserviors for raloxifene (Ral) and then covered with the hybrid multilayered coating of chitosan and alendronate grafted hyaluronic acid (HA-Aln) via a spin-assisted layer-by-layer technique. The fabrication of this system (TNT/Ral/LBL-Aln) was characterized by field emission scanning electron microscopy (SEM), atomic force microscope (AFM) and X-ray photoelectron spectroscopy (XPS), respectively. The release test showed that the composited multilayers onto Ral-loaded TiO₂ nanotube substrate (TNT/Ral) could prevent the burst release of Ral from TiO₂ nanotube arrays and maintain stable Ral concentration at the implant site even after 192h. The TNT/Ral/LBL-Aln system demonstrated higher alkaline phosphatase (ALP) activity, mineralization capability in osteoblasts as well as lower tartrate-resistant acid phosphatase (TRAP) activity in osteoclasts compared to both bare TiO₂ nanotube and TNT/Ral substrate, respectively. Moreover, the in vivo tests of micro-CT, histological staining and push-out testing showed that TNT/Ral/LBL-Aln implant could efficiently enhance the formation of new bone around the implant and promote bone binding in osteoporotic rabbits. The study indicated the potential application of TNT/Ral/LBL-Aln system for bone remodeling under osteoporotic condition.

A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features

Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.

Regulation of local bone remodeling mediated by hybrid multilayer coating embedded with hyaluronan-alendronate/BMP-2 nanoparticles on Ti6Al7Nb implants

Hyaluronate-alendronate/BMP-2 nanoparticles were inserted into Gel/Chi multilayers on Ti6Al7Nb for enhancing BMP-2 stability and promoting local osteogenesis under osteoporosis.

Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction

Mitochondrion, a tiny energy factory, plays an important role in various biological processes of most eukaryotic cells.

Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM

Background

Identification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.

Results

Here we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools.

Conclusions

Comparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots.

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

Involvement of miR-20a in promoting gastric cancer progression by targeting early growth response 2 (EGR2). Int J Mol Sci. 2013;14:16226-16239. [PMID: 23924943 PMCID: PMC3759908 DOI: 10.3390/ijms140816226]

Gastric cancer (GC) is one of the most common cancers, with high incidences in East Asia. microRNAs (miRNAs) play essential roles in the carcinogenesis of GC. miR-20a was elevated in GC, while the potential function of miR-20a was poorly understood. miR-20a expression was examined in GC tissues and cell lines. The effects of miR-20a on the growth, migration, invasion, and chemoresistance of GC cells were examined. Luciferase reporter assay and Western blot were used to screen the target of miR-20a. miR-20a was increased in GC tissues and cell lines. miR-20a promoted the growth, migration and invasion of GC cells, enhanced the chemoresistance of GC cells to cisplatin and docetaxel. Luciferase activity and Western blot confirmed that miR-20a negatively regulated EGR2 expression. Overexpression of EGR2 significantly attenuated the oncogenic effect of miR-20a. miR-20a was involved in the carcinogenesis of GC through modulation of the EGR2 signaling pathway.

Construction of microenvironment onto titanium substrates to regulate the osteoblastic differentiation of bone marrow stromal cells in vitro and osteogenesis in vivo

To mimic the extracellular matrix of natural bone, apatite/gelatin composite was deposited onto nanostructured titanium substrates via a coprecipitation method, which was pretreated by potassium hydroxide and heat treatment to generate an anticorrosive nanostructured layer. The successful formation of the apatite/gelatin nanocomposite onto titanium surfaces was revealed by Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, atomic force microscopy (AFM), and thin film X-ray diffraction (TF-XRD) measurements, respectively. The immunofluorescence staining of vinculin revealed that the apatite/gelatin nanocomposite deposited titanium substrate was favorable for cell adhesion. More importantly, bone marrow stromal cells cultured onto the apatite/gelatin nanocomposite deposited titanium substrates displayed significantly higher (p < 0.05 or p < 0.01) proliferation and differentiation levels of alkaline phosphatase, mRNA expressions of osteocalcin (OC), osteopontin (OPN), and collagen type I (Col I), and OC content after culture for 7, 14, and 21 days, respectively, which was also revealed by the immunofluorescence analysis of OC and OPN expression. The deposition of apatite/gelatin nanocomposite improved bone density (p < 0.05) and bone-implant contact rate (p < 0.05), which was reflected by microcomputed tomography analysis and histological evaluation in vivo using a rabbit model. This work provides an approach to fabricate high-performance titanium-based implants with enhanced bone osseointegration.

Regulation of the differentiation of mesenchymal stem cells in vitro and osteogenesis in vivo by microenvironmental modification of titanium alloy surfaces

To mimic the extracellular microenvironment of bone, a bioactive multilayered structure of gelatin/chitosan pair, containing bone morphogenetic protein 2(BMP2) and fibronectin (FN), was constructed onto Ti6Al4V surface via a layer-by-layer assembly technique. The successful fabrication of multilayered structure was confirmed by contact angle measurement, field emission scanning electron microscopy (FE-SEM) and confocal laser scanning microscopy (CLSM), respectively. Bioactive BMP2 released in a sustained manner along with the degradation of multilayered structure. MSCs grown onto the multilayer coated TC4 substrates displayed significantly higher (p < 0.01 or p < 0.05) production levels of alkaline phosphatase (ALP), mineralization and genes expressions of runt related transcription factor 2 (Runx2), osterix, osteocalcin (OC), osteopontin (OPN), ALP and collagen type Ⅰ(ColⅠ) compared to the controls after culture for 7 days and 21 days, respectively. More importantly, MicroCT analysis and histological observations demonstrated that the multilayer coated Ti6Al4V implants in vivo promoted the bone density and new bone formation around them after implantation for 4 weeks and 12 weeks, respectively. The results indicated that Ti6Al4V coated with biofunctional multilayers was beneficial for osteogenesis and integration of implant/bone. The study therefore presents an alternative to fabricate bio-functionalized Ti6Al4V-based implants for potential application in orthopedics field.

An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity

With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.