ABSTRACT
Cancer immunotherapy represents a paradigm shift in oncology, offering a superior anti-tumor efficacy and the potential for durable remission. The success of personalized vaccines and cell therapies hinges on the identification of immunogenic epitopes capable of eliciting an effective immune response. Current limitations in the availability of immunogenic epitopes restrict the broader application of such therapies. A critical criterion for serving as potential cancer antigens is their ability to stably bind to the major histocompatibility complex (MHC) for presentation on the surface of tumor cells. To address this, we have developed a comprehensive database of MHC epitopes, experimentally validated for their MHC binding and cell surface presentation. Our database catalogs 451 065 MHC peptide epitopes, each with experimental evidence for MHC binding, along with detailed information on human leukocyte antigen allele specificity, source peptides, and references to original studies. We also provide the grand average of hydropathy scores and predicted immunogenicity for the epitopes. The database (MHCepitopes) has been made available on the web and can be accessed at https://github.com/jcm1201/MHCepitopes.git. By consolidating empirical data from various sources coupled with calculated immunogenicity and hydropathy values, our database offers a robust resource for selecting actionable tumor antigens and advancing the design of antigen-specific cancer immunotherapies. It streamlines the process of identifying promising immunotherapeutic targets, potentially expediting the development of effective antigen-based cancer immunotherapies.
ABSTRACT
Sequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.e. viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. DeepMicroClass achieved high performance for all sequence classes at various tested sequence lengths ranging from 500 bp to 100 kbps. By benchmarking on a synthetic dataset with variable sequence class composition, we showed that DeepMicroClass obtained better performance for eukaryotic, plasmid and viral contig classification than other state-of-the-art predictors. DeepMicroClass achieved comparable performance on viral sequence classification with geNomad and VirSorter2 when benchmarked on the CAMI II marine dataset. Using a coastal daily time-series metagenomic dataset as a case study, we showed that microbial eukaryotes and prokaryotic viruses are integral to microbial communities. By analyzing monthly metagenomes collected at HOT and BATS, we found relatively higher viral read proportions in the subsurface layer in late summer, consistent with the seasonal viral infection patterns prevalent in these areas. We expect DeepMicroClass will promote metagenomic studies of under-appreciated sequence types.
ABSTRACT
The purpose of this study was to use liquid chromatography-mass spectrometry (LC-MS) with XCMS for a quantitative metabolomic analysis of UM1 and UM2 oral cancer cells after knockdown of metabolic enzyme adenylate kinase 2 (AK2) or phosphorylate glycerol kinase 1 (PGK1). UM1 and UM2 cells were initially transfected with AK2 siRNA, PGK1 siRNA or scrambled control siRNA, and then analyzed with LC-MS for metabolic profiles. XCMS analysis of the untargeted metabolomics data revealed a total of 3200-4700 metabolite features from the transfected UM1 or UM2 cancer cells and 369-585 significantly changed metabolites due to AK2 or PGK1 suppression. In addition, cluster analysis showed that a common group of metabolites were altered by AK2 knockdown or by PGK1 knockdown between the UM1 and UM2 cells. However, the set of significantly changed metabolites due to AK2 knockdown was found to be distinct from those significantly changed by PGK1 knockdown. Our study has demonstrated that LC-MS with XCMS is an efficient tool for metabolomic analysis of oral cancer cells, and knockdown of different genes results in distinct changes in metabolic phenotypes in oral cancer cells.
ABSTRACT
Metastasis represents a key factor associated with poor prognosis of head and neck squamous cell carcinoma (HNSC). However, the underlying molecular mechanisms remain largely unknown. In this study, our liquid chromatography with tandem mass spectrometry analysis revealed a number of significantly differentially expressed membrane/membrane-associated proteins between high invasive UM1 and low invasive UM2 cells. One of the identified membrane proteins, Syntenin-1, was remarkably up-regulated in HNSC tissues and cell lines when compared to the controls, and also over-expressed in recurrent HNSC and high invasive UM1 cells. Syntenin-1 over-expression was found to be significantly associated with lymph node metastasis and disease recurrence. HNSC patients with higher syntenin-1 expression had significantly poorer long term overall survival and similar results were found in many other types of cancers based on analysis of The Cancer Genome Atlas data. Finally, knockdown of syntenin-1 inhibited the proliferation, migration and invasion of HNSC cells, and opposite findings were observed when syntenin-1 was over-expressed. Collectively, our studies indicate that syntenin-1 promotes invasion and progression of HNSC. It may serve as a valuable biomarker for lymph node metastasis or a potential target for therapeutic intervention in HNSC.
