The bioaccumulation and biotransformation of tetrabromobisphenol A bis (allyl ether) in common carp (Cyprinus carpio).

Tetrabromobisphenol A bis (allyl ether) (TBBPA-BAE) is an extensively used brominated flame retardant, which has raised considerable concern because of its neurotoxic and endocrine disruption effects on aquatic organisms. However, previous studies mainly focused on the parent compound before modification, tetrabromobisphenol A (TBBPA), and little information is available about the bioconcentration and biotransformation of TBBPA derivatives in fish. In this study, we investigated the tissue-specific uptake, elimination kinetic, and biotransformation of TBBPA-BAE in common carp (Cyprinus carpio). The fish were exposed to TBBPA-BAE at environmentally relevant concentrations (20 µg·L-1) for 28 days, followed by 14 days of depuration. The results showed TBBPA-BAE could rapidly accumulate in common carp. Among the seven tissues studied, the highest concentrations of TBBPA-BAE were observed in the liver (6.00 µg·g-1 wet weight [ww]) on day 24, while the longest residence time was observed in the kidney (t1/2 values of 18.7 days). Biotransformation of TBBPA-BAE was documented in the in vivo experiments, and 14 different phase I and phase II metabolites were identified in the liver. These findings suggest the biotransformation products of TBBPA-BAE should be considered for a comprehensive risk evaluation.

Construction and Validation of Convenient Clinicopathologic Signatures for Predicting the Prognosis of Stage I-III Gastric Cancer.

Background: Patients with stage I-III gastric cancer (GC) undergoing R0 radical resection display extremely different prognoses. How to discriminate high-risk patients with poor survival conveniently is a clinical conundrum to be solved urgently. Methods: Patients with stage I-III GC from 2010 to 2016 were included in our study. The associations of clinicopathological features with disease-free survival (DFS) and overall survival (OS) were examined via Cox proportional hazard model. Nomograms were developed which systematically integrated prognosis-related features. Kaplan-Meier survival analysis was performed to compare DFS and OS among groups. The results were then externally validated by The Sixth Affiliated Hospital, Sun Yat-sen University. Results: A total of 585 and 410 patients were included in the discovery cohort and the validation cohort, respectively. T stage, N stage, lymphatic/vascular/nerve infiltration, preoperative CEA, and CA19-9 were independent prognostic factors (P < 0.05). Two prognostic signatures with a concordance index (C-index) of 0.7502 for DFS and 0.7341 for OS were developed based on the nomograms. The 3-year and 5-year calibration curves showed a perfect correlation between predicted and observed outcomes. Patients were divided into three risk groups (low, intermediate, high), and distinct differences were noticed (p < 0.001). Similar results were achieved in the validation cohort. Notably, a free website was constructed based on our signatures to predict the recurrence risk and survival time of patients with stage I-III GC. Conclusions: The signatures demonstrate the powerful ability to conveniently identify distinct subpopulations, which may provide significant suggestions for individual follow-up and adjuvant therapy.

INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features.

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

[Application of immune cell infiltration in the diagnosis and prognosis of non-small cell lung cancer].

Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.

dbPepNeo: a manually curated database for human tumor neoantigen peptides.

Neoantigens can function as actual antigens to facilitate tumor rejection, which play a crucial role in cancer immunology and immunotherapy. Emerging evidence revealed that neoantigens can be used to develop personalized, cancer-specific vaccines. To date, large numbers of immunogenomic peptides have been computationally predicted to be potential neoantigens. However, experimental validation remains the gold standard for potential clinical application. Experimentally validated neoantigens are rare and mostly appear scattered among scientific papers and various databases. Here, we constructed dbPepNeo, a specific database for human leukocyte antigen class I (HLA-I) binding neoantigen peptides based on mass spectrometry (MS) validation or immunoassay in human tumors. According to the verification methods of these neoantigens, the collection of peptides was classified as 295 high confidence, 247 medium confidence and 407 794 low confidence neoantigens, respectively. This can serve as a valuable resource to aid further screening for effective neoantigens, optimize a neoantigen prediction pipeline and study T-cell receptor (TCR) recognition. Three applications of dbPepNeo are shown. In summary, this work resulted in a platform to promote the screening and confirmation of potential neoantigens in cancer immunotherapy. Database URL: www.biostatistics.online/dbPepNeo/.

Selective enrichment of sialylated glycopeptides with mesoporous poly-melamine-formaldehyde (mPMF) material.

Analysis of glycoprotein sialylation is challenging due to the relatively low abundance of sialylated glycopeptides (SGPs) in complex biosamples and low signals of SGPs in mass spectrometry. In this study, a mesoporous poly-melamine-formaldehyde (mPMF) polymer was prepared and utilized as the high-efficiency sorbent for SGPs. The mPMF polymer featured high surface area (755.4 m2 g-1) and high density of amine and triazine functional groups. This polymer demonstrated high enrichment selectivity (resistant to 100 molar fold interference of BSA) and superior adsorption capacity (560 mg g-1) for SGPs. The high performance of mPMF toward SGPs ascribes to the unique physicochemical properties of mPMF and high density of accessible binding sites for glycopeptides. Further application of mPMF to HeLa S3 cell lysate resulted in 576 characterized glycopeptides with 218 unique glycosylation sites. This finding provides a new choice of promising extraction approach for characterization of protein glycosylation. Graphical abstract A mesoporous poly-melamine-formaldehyde (mPMF) polymer was prepared and utilized as the high-efficiency enrichment sorbent for sialylated glycopeptides (SGPs).

Antibiotic ivermectin selectively induces apoptosis in chronic myeloid leukemia through inducing mitochondrial dysfunction and oxidative stress.

Mitochondria has been a promising target in blood cancer given their unique dependencies on mitochondrial functions compared to normal hematopoietic cells. In line with this concept, we show that an anthelminthic drug ivermectin selectively kills chronic myeloid leukemia (CML) cells via inducing mitochondrial dysfunctions and oxidative stress. Ivermectin is significantly more effective in inducing caspase-dependent apoptosis in CML cell line K562 and primary CML CD34 than normal bone marrow (NBM) CD34 cells. Ivermectin also augments in vitro and in vivo efficacy of standard CML tyrosine kinase inhibitors. Mechanistically, ivermectin inhibits respiratory complex I activity and suppresses mitochondrial respiration in K562 and CML CD34 cells. Interestingly, we demonstrate that mitochondrial respiration are lower in NBM CD34 compared to malignant CD34 cells. In addition, ivermectin also induces mitochondrial dysfunctions in NBM CD34 cells in a similar manner as in CML CD34 cells whereas NBM CD34 cells are significantly less sensitive to ivermectin than CML CD34 cells. These suggest that NBM CD34 cells are more tolerable to mitochondrial dysfunctions than CML CD34 cells. Consistently, ivermectin induces higher levels of oxidative stress and damage in CML than normal counterparts. Antioxidant NAC rescues ivermectin's effects, confirming oxidative stress as the mechanism of its action in CML. Our work provides the fundamental evidence to repurpose ivermectin for CML treatment. Our work also highlights the therapeutic value of targeting mitochondria respiration in CML.

The sensitivity of chronic myeloid leukemia CD34 cells to Bcr-Abl tyrosine kinase inhibitors is modulated by ceramide levels.

Despite BCR-ABL tyrosine kinase inhibitors (TKIs) improved outcome of patients with chronic myeloid leukemia (CML), resistance still develops when progresses to blast phase (BP). The mechanisms underlying resistance to TKIs are not well understood. In this study, we analyzed ceramide levels in CD34 cells derived from BP-CML patients and healthy donor bone marrow (BM) using liquid chromatography mass spectrometry. We found that ceramide level was significantly lower in BP-CML CD34 compared with normal BM counterparts. BP-CML CD34 ceramide(low) were more resistant to BCR-ABL TKIs compared to BP-CML CD34 ceramide(normal). Both mRNA and proteins levels of sphingomyelin synthase 1 and 2 are lower in BP-CML CD34 ceramide(low) compared to normal BM CD34 cells, suggesting that these two ceramide synthesis enzymes maybe the mechanism of how ceramide level is suppressed. Importantly, up-regulation of cellular ceramide level induces apoptosis of multiple CML cell lines and BP-CML CD34 progenitors. Combination of BCR-ABL TKIs with ceramide analog is synergistic in targeting BP-CML 34 progenitors. Collectively, our work provides evidence that down-regulation of ceramide level is involved in the resistance of BP-CML CD34 progenitors to TKIs treatment. Targeting ceramide metabolism together with BCR-ABL inhibition makes it an attractive addition to the armamentarium in BP-CML treatment.

Highly selective separation of aminoglycoside antibiotics on a zwitterionic Click TE-Cys column.

Hydrophilic interaction liquid chromatography has emerged as a valuable alternative approach to ion-pair chromatography for the separation of aminoglycoside antibiotics in recent years. However, the resolution of structurally related aminoglycosides is a great challenge owing to the limited selectivity. In this work, a cysteine-based zwitterionic stationary phase (named Click TE-Cys) was utilized and compared with five commonly used hydrophilic interaction liquid chromatography columns. Click TE-Cys displayed much better selectivity for structurally similar aminoglycosides. The retention behaviors of aminoglycosides were investigated in detail, revealing that low pH (2.7 or 3.0) and high buffer concentration (≥50 mM) were preferable for achieving good peak shape and selectivity. Effective resolution of ten aminoglycosides including spectinomycin, dihydrostreptomycin, streptomycin, gentamicin C1, gentamicin C2/C2a, gentamicin C1a, kanamycin, paromonycin, tobramycin, and neomycin was realized at optimized conditions. Additionally, spectinomycin and its related impurities were successfully resolved. The results indicated the great potential of the Click TE-Cys column in the separation of aminoglycoside mixtures and related impurities.