MACC: a visual interactive knowledgebase of metabolite-associated cell communications.

Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/.

Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope.

Neoantigen vaccines are one of the most effective immunotherapies for personalized tumour treatment. The current immunogen design of neoantigen vaccines is usually based on whole-genome sequencing (WGS) and bioinformatics prediction that focuses on the prediction of binding affinity between peptide and MHC molecules, ignoring other peptide-presenting related steps. This may result in a gap between high prediction accuracy and relatively low clinical effectiveness. In this study, we designed an integrated in-silico pipeline, Neo-intline, which started from the SNPs and indels of the tumour samples to simulate the presentation process of peptides in-vivo through an integrated calculation model. Validation on the benchmark dataset of TESLA and clinically validated neoantigens illustrated that neo-intline could outperform current state-of-the-art tools on both sample level and melanoma level. Furthermore, by taking the mouse melanoma model as an example, we verified the effectiveness of 20 neoantigens, including 10 MHC-I and 10 MHC-II peptides. The in-vitro and in-vivo experiments showed that both peptides predicted by Neo-intline could recruit corresponding CD4+ T cells and CD8+ T cells to induce a T-cell-mediated cellular immune response. Moreover, although the therapeutic effect of neoantigen vaccines alone is not sufficient, combinations with other specific therapies, such as broad-spectrum immune-enhanced adjuvants of granulocyte-macrophage colony-stimulating factor (GM-CSF) and polyinosinic-polycytidylic acid (poly(I:C)), or immune checkpoint inhibitors, such as PD-1/PD-L1 antibodies, can illustrate significant anticancer effects on melanoma. Neo-intline can be used as a benchmark process for the design and screening of immunogenic targets for neoantigen vaccines.

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

Co-Expression of miR155 or LSD1 shRNA Increases the Anti-Tumor Functions of CD19 CAR-T Cells.

Chimeric antigen receptor (CAR) T cells targeting CD19 antigen have produced remarkable clinical outcomes for cancer patients. However, identifying measures to enhance effector function remains one of the most challenging issues in CD19-targeted immunotherapy. Here, we report a novel approach in which a microRNA (miRNA) or short-hairpin RNA (shRNA) cassette was integrated into CAR-expressing retroviral vectors. Using this system, we generated anti-CD19 CAR-T cells co-expressing miR155 or LSD1 shRNA and found that anti-CD19 CAR-T cells with miR155 upregulation or LSD1 downregulation exhibited increased anti-tumor functions in vitro and in vivo. Transcriptional profiling analysis by RNA sequencing revealed the targets of miR155 and LSD1 in anti-CD19 CAR-T cells. Our experiments indicated that introduction of miRNA or shRNA expression into anti-CD19 CAR T-cells might be an effective strategy to improve the anti-tumor effects of CAR-T cell therapy.