Identification of pyruvic and maleic acid as potential markers for disease activity and prognosis in chronic urticaria.

BACKGROUND: Population-based studies have highlighted the link between chronic urticaria (CU) and metabolic syndrome, and metabolic alterations have been revealed in CU. However, to our knowledge, a comprehensive metabolomics study on a large cohort of patients with CU has not been reported. OBJECTIVE: We sought to explore the underlying metabolic subtypes and novel metabolite biomarkers for CU diagnosis and therapy. METHODS: Plasma samples from 80 patients with CU and 82 healthy controls were collected for metabolomics quantification and bioinformatics analysis. Another independent cohort consisting of 144 patients with CU was studied to validate the findings. Bone marrow-derived mast cells and mice with IgE-induced passive cutaneous anaphylaxis were used for in vitro and in vivo experiments, respectively. RESULTS: We observed clear metabolome differences between CU patients and healthy controls. Meanwhile, differential metabolites N6-acetyl-l-lysine, l-aspartate, maleic acid, and pyruvic acid were used to construct random forest classifiers and achieved area under receiver operating characteristic curve values greater than 0.85, suggesting their potential as diagnostic biomarkers of CU. More importantly, by exploring the underlying metabolic subtypes of CU, we found that the low abundance of pyruvic acid and maleic acid was significantly related to the activity of CU, poor efficacy of second-generation H1 antihistamines, and short relapse-free time. The results were validated in the independent cohort. Moreover, supplementation with pyruvate or maleate could significantly attenuate IgE-mediated mast cell activation in vitro and in vivo. CONCLUSIONS: Plasma pyruvic acid and maleic acid may be effective biomarkers for predicting disease activity, therapeutic efficacy, and prognosis for patients with CU.

Genomic alterations of dermatofibrosarcoma protuberans revealed by whole-genome sequencing.

BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a rare and marginal cutaneous sarcoma of intermediate-grade malignancy, for which the genomic landscape remains unclear. Understanding the landscape of DFSP will help to further classify the genomic pathway of malignant development in soft tissue. OBJECTIVES: To identify the comprehensive molecular pathogenesis of DFSP. METHODS: In this study, the comprehensive genomic features, with 53 tumour-normal pairs of DFSP, were revealed by whole-genome sequencing. RESULTS: The mutational signature 1 (C > T mutation at CpG dinucleotides) is featured in DFSP, resulting in higher mutations in DNA replication. Interestingly, the recurrence of DFSP is correlated with low tumour mutation burden. Novel mutation genes in DFSP were identified, including MUC4/6, KMT2C and BRCA1, and subsequently, three molecular subtypes of DFSP were classified on the basis of MUC4 and MUC6 mutations. Various structural aberrations including genomic rearrangements were identified in DSFPs, particularly in 17q and 22q, which cause oncogene amplification (AKT1, SPHK1, COL1A1, PDGFß) or tumour suppressor deletion (CDKN2A/B). In addition to gene fusion of COL1A1-PDGFß [t(17;22)], we identified gene fusion of SLC2A5-BTBD7 [t(1;14)] in DFSP through whole-genome sequencing, and verified it experimentally. Enrichment analysis of altered molecules revealed that DNA repair, cell cycle, phosphoinositide 3-kinase and Janus kinase pathways were primarily involved in DFSP. CONCLUSIONS: This is the first large-scale whole-genome sequencing for DFSP, and our findings describe the comprehensive genomic landscape, highlighting the molecular complexity and genomic aberrations of DFSP. Our findings also provide novel potential diagnostic and therapeutic targets for this disease. What is already known about this topic? Chromosomal translocation between chromosome 17 and chromosome 22 is the main feature in the pathogenesis of dermatofibrosarcoma protuberans (DFSP). What does this study add? We describe the comprehensive genomic landscape of DFSP, highlighting the molecular complexity and genomic aberrations. Our findings provide novel potential diagnostic and therapeutic targets for this disease. What is the translational message? Our study revealed novel molecular subtypes of DFSP based on genetic mutations, which benefits precision diagnosis. We also found oncogene amplification, including AKT1 and SPHK1, which provides novel potential target molecules for further DFSP treatment. In addition to gene fusion of COL1A1-PDGFß, we identified a novel gene fusion of SLC2A5-BTBD7 in DFSP, which is a novel potential diagnostic and therapeutic target for this disease.

High COX-2 expression in cancer-associated fibiroblasts contributes to poor survival and promotes migration and invasiveness in nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) has the highest rate of metastasis among head and neck cancers, and distant metastasis is the major reason for treatment failure. We have previously shown that high cyclooxygenase-2 (COX-2) expression is associated with a poor prognosis of patients with NPC and inhibits chemotherapy-induced senescence in NPC cells. In this study, we found that COX-2 was upregulated in cancer-associated fibroblasts (CAFs) derived from NPC by RNA-Seq. Furthermore, elevated COX-2 expression in CAF was detected in NPC patients with poor survival and distant metastasis by using immunohistochemistry. Then, we identified that COX-2 is highly expressed in CAF at the distant metastasis site in seven paired NPC patients. High expression of COX-2 and secretion of prostaglandin E2, a major product catalyzed by COX-2 in fibroblasts, promotes migration and invasiveness of NPC cells in vitro. On the contrary, inhibition of COX-2 has the opposite effect in vitro as well as in the COX-2-/- mouse with the lung metastasis model in vivo. Mechanistically, we discovered that COX-2 elevates tumor necrosis factor-α expression in CAF to promote NPC cell migration and invasiveness. Overall, our results identified a novel target in CAF promoting NPC metastasis. Our findings suggested that high expression of COX-2 in CAF may serve as a new prognostic indicator for NPC metastasis and provide the possibility of targeting CAF for treating advanced NPC.

Gut microbiota facilitate chronic spontaneous urticaria.

Chronic spontaneous urticaria (CSU) comes with gut dysbiosis, but its relevance remains elusive. Here we use metagenomics sequencing and short-chain fatty acids metabolomics and assess the effects of human CSU fecal microbial transplantation, Klebsiella pneumoniae, Roseburia hominis, and metabolites in vivo. CSU gut microbiota displays low diversity and short-chain fatty acids production, but high gut Klebsiella pneumoniae levels, negatively correlates with blood short-chain fatty acids levels and links to high disease activity. Blood lipopolysaccharide levels are elevated, link to rapid disease relapse, and high gut levels of conditional pathogenic bacteria. CSU microbiome transfer and Klebsiella pneumoniae transplantation facilitate IgE-mediated mast cell(MC)-driven skin inflammatory responses and increase intestinal permeability and blood lipopolysaccharide accumulation in recipient mice. Transplantation of Roseburia hominis and caproate administration protect recipient mice from MC-driven skin inflammation. Here, we show gut microbiome alterations, in CSU, may reduce short-chain fatty acids and increase lipopolysaccharide levels, respectively, and facilitate MC-driven skin inflammation.

PTGS2: A potential immune regulator and therapeutic target for chronic spontaneous urticaria.

AIMS: Chronic spontaneous urticaria (CSU) is a common and debilitating skin disease that is difficult to control with existing treatments, and the pathogenesis of CSU has not been fully revealed. The aim of this study was to explore the underlying mechanisms of CSU and identify potential treatments. MATERIALS AND METHODS: Microarray datasets of CSU were obtained from Gene Expression Omnibus database. Differentially expressed genes between skin lesions of CSU and normal controls (LNS-DEGs) were identified, and the enrichment analyses of LNS-DEGs were performed. Hub genes of LNS-DEGs were selected by protein-protein interaction analysis. The co-expression and transcriptional regulatory networks of hub genes were conducted using GeneMANIA and TRRUST database, respectively. CIBERSORT was utilized for immune cell infiltration analysis. Experimental validation was performed by ß-hexosaminidase release examination and passive cutaneous anaphylaxis (PCA) mouse model. KEY FINDINGS: A total of 247 LNS-DEGs were identified, which were enriched in cell migration, cell chemotaxis, and inflammatory pathways such as TNF and interleukin (IL) -17 signaling pathway. Among LNS-DEGs, seven upregulated (PTGS2, CCL2, IL1B, CXCL1, IL6, VCAM1, ICAM1) and one downregulated hub gene (PECAM1) were selected. Immune infiltration analysis identified eight different immune cells, such as activated/resting mast cells and neutrophils. Furthermore, PTGS2, encoding cyclooxygenase 2 (COX2), was selected for further validation. COX2 inhibitor, celecoxib, significantly inhibited mast cell degranulation, and reduced vascular permeability and inflammatory cytokine expression in PCA mouse model. SIGNIFICANCE: PTGS2 may be a potential regulator of immunity and inflammation in CSU. Targeting PTGS2 is a new perspective for CSU treatment.

Targeting gut microbial nitrogen recycling and cellular uptake of ammonium to improve bortezomib resistance in multiple myeloma.

The gut microbiome has been found to play a crucial role in the treatment of multiple myeloma (MM), which is still considered incurable due to drug resistance. In previous studies, we demonstrated that intestinal nitrogen-recycling bacteria are enriched in patients with MM. However, their role in MM relapse remains unclear. This study highlights the specific enrichment of Citrobacter freundii (C. freundii) in patients with relapsed MM. Through fecal microbial transplantation experiments, we demonstrate that C. freundii plays a critical role in inducing drug resistance in MM by increasing levels of circulating ammonium. The ammonium enters MM cells through the transmembrane channel protein SLC12A2, promoting chromosomal instability and drug resistance by stabilizing the NEK2 protein. We show that furosemide sodium, a loop diuretic, downregulates SLC12A2, thereby inhibiting ammonium uptake by MM cells and improving progression-free survival and curative effect scores. These findings provide new therapeutic targets and strategies for the intervention of MM progression and drug resistance.

CoV2-TCR: A web server for screening TCR CDR3 from TCR immune repertoire of COVID-19 patients and their recognized SARS-CoV-2 epitopes.

Although multiple vaccines have been developed and widely administered, several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been reported to evade immune responses and spread diffusely. Here, 108 RNA-seq files from coronavirus disease 2019 (COVID-19) patients and healthy donors (HD) were downloaded to extract their TCR immune repertoire by MiXCR. Those extracted TCR repertoire were compared and it was found that disease progression was related negatively with diversity and positively with clonality. Specifically, greater proportions of high-abundance clonotypes were observed in active and severe COVID-19 samples, probably resulting from strong stimulation of SARS-CoV-2 epitopes and a continued immune response in host. To investigate the specific recognition between TCR CDR3 and SARS-CoV-2 epitopes, we constructed an accurate classifier CoV2-TCR with an AUC of 0.967 in an independent dataset, which outperformed several similar tools. Based on this model, we observed a huge range in the number of those TCR CDR3 recognizing those different peptides, including 28 MHC-I epitopes from SARS-CoV-2 and 22 immunogenic peptides from SARS-CoV-2 variants. Interestingly, their proportions of high-abundance, low-abundance and rare clonotypes were close for each peptide. To expand the potential application of this model, we established the webserver, CoV2-TCR, in which users can obtain those recognizing CDR3 sequences from the TCR repertoire of COVID-19 patients based on the 9-mer peptides containing mutation site(s) on the four main proteins of SARS-CoV-2 variants. Overall, this study provides preliminary screening for candidate antigen epitopes and the TCR CDR3 that recognizes them, and should be helpful for vaccine design on SARS-CoV-2 variants.

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features.

Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.

PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions.

The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI.

ToPP: Tumor online prognostic analysis platform for prognostic feature selection and clinical patient subgroup selection.

Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php.

NAIRscore as a biomarker for the quality of immune response to neoantigens is related with an increased overall survival in multiple myeloma.

Neoantigen provides a promising breakthrough in tumor immunotherapy, although only a subset of patients responds well due to the quality of their immune response. However, few biomarkers have been reported to measure the quality of immune response to neoantigens and to predict prognosis of patients with multiple myeloma (MM). Here, we first developed a neoantigen-prediction pipeline starting from outcomes of somatic mutations and gene-expression profiles. Given the expression of some specific marker genes, the human leukocyte antigen (HLA)-I score and the cytolytic score were evaluated respectively to reflect HLA-I molecular expression and CD8+ T/natural killer (NK) cell abundance. According to the process of the immune response to neoantigens, we comprehensively took neoantigen load, cytolytic score, and HLA-I score to construct a neoantigen immune response score (NAIRscore), in which the HLA-I score presented a hazard ratio (HR) of less than 1, while the cytolytic score and neoantigen load presented a HR of greater than 1. Meanwhile, NAIRscore presented a competitive advantage to stratify MM samples. Especially, those exhibiting high NAIRscore correlated with an increased overall survival (OS), echoing the underlying molecular signatures of lower driver-gene mutations and down-regulated immune response. Notably, an online tool based on this study is provided to identify neoantigens and predict OS.

Revealing a novel contributing landscape of ferroptosis-related genes in Parkinson's disease.

Transcriptomics studies have yielded great insights into disease processes by detecting differentially expressed genes (DEGs). In this study, due to the high heritability of Parkinson's disease (PD), we performed bioinformatics analyses on nine transcriptomic datasets regarding substantia nigra from Gene Expression Omnibus database, including seven microarray datasets and two next-generation sequencing datasets. As a result, between age-matched PD patients and normal control, we identified 630 DEGs, of which 22 hub DEGs involved in PD or ferroptosis were found to be associated with each other at the transcriptional level and protein-protein interaction network, suggesting their high correlations among these hub genes. Moreover, 16 DEGs were singled out due to their comparable AUC (>0.6) in random forest classifiers, including seven PD-related genes (MAP4K4, LRP10, UCHL1, PAM, RIT2, SNCA, GCH1) and nine ferroptosis-related genes (GCH1, DDIT4, RGS4, MAPK9, CAV1, RELA, DUSP1, ATP6V1G2, ATF4 and ISCU). Furthermore, to probe the potential of those hub genes in predicting the PD progression and survival, we constructed a Cox model featured by an eight-gene signature, including four PD-related genes (SNCA, UCHL1, LRP10, and GCH1) and four ferroptosis-related genes (DDIT4, RGS4, RELA, and CAV1), and validated it successful in an independent dataset, indicating that it would be an effective tool for clinical research to predict PD progression. In conclusion, ferroptosis-related DEGs identified in this study were closely correlated with the known PD-related genes, revealing the involvement of ferroptosis in the development of PD. This study presented the potential of several ferroptosis-related genes as novel clinical biomarkers for PD.

dbPepNeo2.0: A Database for Human Tumor Neoantigen Peptides From Mass Spectrometry and TCR Recognition.

Neoantigens are widely reported to induce T-cell response and lead to tumor regression, indicating a promising potential to immunotherapy. Previously, we constructed an open-access database, i.e., dbPepNeo, providing a systematic resource for human tumor neoantigens to storage and query. In order to expand data volume and application scope, we updated dbPepNeo to version 2.0 (http://www.biostatistics.online/dbPepNeo2). Here, we provide about 801 high-confidence (HC) neoantigens (increased by 170%) and 842,289 low-confidence (LC) HLA immunopeptidomes (increased by 107%). Notably, 55 class II HC neoantigens and 630 neoantigen-reactive T-cell receptor-ß (TCRß) sequences were firstly included. Besides, two new analytical tools are developed, DeepCNN-Ineo and BLASTdb. DeepCNN-Ineo predicts the immunogenicity of class I neoantigens, and BLASTdb performs local alignments to look for sequence similarities in dbPepNeo2.0. Meanwhile, the web features and interface have been greatly improved and enhanced.

ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy.

A proteogenomics-based neoantigen prediction pipeline, namely ProGeo-neo, was previously developed by our team to predict neoantigens, allowing the identification of class-I major histocompatibility complex (MHC) binding peptides based on single-nucleotide variation (SNV) mutations. To improve it, we here present an updated pipeline, i.e., ProGeo-neo v2.0, in which a one-stop software solution was proposed to identify neoantigens based on the paired tumor-normal whole genome sequencing (WGS)/whole exome sequencing (WES) data in FASTQ format. Preferably, in ProGeo-neo v2.0, several new features are provided. In addition to the identification of MHC-I neoantigens, the new version supports the prediction of MHC class II-restricted neoantigens, i.e., peptides up to 30-mer in length. Moreover, the source of neoantigens has been expanded, allowing more candidate neoantigens to be identified, such as in-frame insertion-deletion (indels) mutations, frameshift mutations, and gene fusion analysis. In addition, we propose two more efficient screening approaches, including an in-group authentic neoantigen peptides database and two more stringent thresholds. The range of candidate peptides was effectively narrowed down to those that are more likely to elicit an immune response, providing a more meaningful reference for subsequent experimental validation. Compared to ProGeo-neo, the ProGeo-neo v2.0 performed well based on the same dataset, including updated functionality and improved accuracy.

Intestinal Klebsiella pneumoniae Contributes to Pneumonia by Synthesizing Glutamine in Multiple Myeloma.

Pneumonia accounts for a significant cause of morbidity and mortality in multiple myeloma (MM) patients. It has been previously shown that intestinal Klebsiella pneumonia (K. pneumonia) enriches in MM and promotes MM progression. However, what role the altered gut microbiota plays in MM with pneumonia remains unknown. Here, we show that intestinal K. pneumonia is significantly enriched in MM with pneumonia. This enriched intestinal K. pneumonia links to the incidence of pneumonia in MM, and intestinal colonization of K. pneumonia contributes to pneumonia in a 5TGM1 MM mice model. Further targeted metabolomic assays reveal the elevated level of glutamine, which is consistently increased with the enrichment of K. pneumonia in MM mice and patients, is synthesized by K. pneumonia, and leads to the elevated secretion of TNF-α in the lung normal fibroblast cells for the higher incidence of pneumonia. Inhibiting glutamine synthesis by establishing glnA-mutated K. pneumonia alleviates the incidence of pneumonia in the 5TGM1 MM mice model. Overall, our work proposes that intestinal K. pneumonia indirectly contributes to pneumonia in MM by synthesizing glutamine. Altogether, we unveil a gut-lung axis in MM with pneumonia and establish a novel mechanism and a possible intervention strategy for MM with pneumonia.

Construction and Validation of an Immune Cell Signature Score to Evaluate Prognosis and Therapeutic Efficacy in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with high morbidity and mortality worldwide. Tumor immune microenvironment (TIME) plays a pivotal role in the outcome and treatment of HCC. However, the effect of immune cell signatures (ICSs) representing the characteristics of TIME on the prognosis and therapeutic benefit of HCC patients remains to be further studied. Materials and methods: In total, the gene expression profiles of 1,447 HCC patients from several databases, i.e., The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, and Gene Expression Omnibus, were obtained and applied. Based on a comprehensive collection of marker genes, 182 ICSs were evaluated by single sample gene set enrichment analysis. Then, by performing univariate and multivariate Cox analysis and random forest modeling, four significant signatures were selected to fit an immune cell signature score (ICSscore). Results: In this study, an ICSscore-based prognostic model was constructed to stratify HCC patients into high-risk and low-risk groups in the TCGA-LIHC cohort, which was successfully validated in two independent cohorts. Moreover, the ICSscore values were found to positively correlate with the current American Joint Committee on Cancer staging system, indicating that ICSscore could act as a comparable biomarker for HCC risk stratification. In addition, when setting the four ICSs and ICSscores as features, the classifiers can significantly distinguish treatment-responding and non-responding samples in HCC. Also, in melanoma and breast cancer, the unified ICSscore could verify samples with therapeutic benefits. Conclusion: Overall, we simplified the tedious ICS to develop the ICSscore, which can be applied successfully for prognostic stratification and therapeutic evaluation in HCC. This study provides an insight into the therapeutic predictive efficacy of prognostic ICS, and a novel ICSscore was constructed to allow future expanded application.

Lung microbiome alterations in NSCLC patients.

Lung is colonized by a diverse array of microbes and the lung microbiota is profoundly involved in the development of respiratory diseases. There is little knowledge about the role of lung microbiota dysbiosis in lung cancer. In this study, we performed metagenomic sequencing on bronchoalveolar lavage (BAL) from two different sampling methods in non-small cell lung cancer (NSCLC) patients and non-cancer controls. We found the obvious variation between bronchoscopy samples and lobectomy samples. Oral taxa can be found in both bronchoscopy and lobectomy samples and higher abundance of oral taxa can be found in bronchoscopy samples. Although the NSCLC patients had similar microbial communities with non-cancer controls, rare species such as Lactobacillus rossiae, Bacteroides pyogenes, Paenibacillus odorifer, Pseudomonas entomophila, Magnetospirillum gryphiswaldense, fungus Chaetomium globosum et al. showed obvious difference between NSCLC patients and non-cancer controls. Age-, gender-, and smoking-specific species and EGFR expression-related species in NSCLC patients were detected. There results implicated that different lung segments have differential lung microbiome composition. The oral taxa are found in the lobectomy samples suggesting that oral microbiota are the true members of lung microbiota, rather than contamination during bronchoscopy. Lung cancer does not obviously alter the global microbial composition, while rare species are altered more than common species. Certain microbes may be associated with lung cancer progression.

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/.

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.

Alterations of gut microbiome accelerate multiple myeloma progression by increasing the relative abundances of nitrogen-recycling bacteria.

BACKGROUND: Gut microbiome alterations are closely related to human health and linked to a variety of diseases. Although great efforts have been made to understand the risk factors for multiple myeloma (MM), little is known about the role of the gut microbiome and alterations of its metabolic functions in the development of MM. RESULTS: Here, in a cohort of newly diagnosed patients with MM and healthy controls (HCs), significant differences in metagenomic composition were discovered, for the first time, with higher bacterial diversity in MM. Specifically, nitrogen-recycling bacteria such as Klebsiella and Streptococcus were significantly enriched in MM. Also, the bacteria enriched in MM were significantly correlated with the host metabolome, suggesting strong metabolic interactions between microbes and the host. In addition, the MM-enriched bacteria likely result from the regulation of urea nitrogen accumulated during MM progression. Furthermore, by performing fecal microbiota transplantation (FMT) into 5TGM1 mice, we proposed a mechanistic explanation for the interaction between MM-enriched bacteria and MM progression via recycling urea nitrogen. Further experiments validated that Klebsiella pneumoniae promoted MM progression via de novo synthesis of glutamine in mice and that the mice fed with glutamine-deficient diet exhibited slower MM progression. CONCLUSIONS: Overall, our findings unveil a novel function of the altered gut microbiome in accelerating the malignant progression of MM and open new avenues for novel treatment strategies via manipulation of the intestinal microbiota of MM patients. Video abstract.