Epigenetic profiles guide improved CRISPR/Cas9-mediated gene knockout in human T cells.

Genetic modification of specific genes is emerging as a useful tool to enhance the functions of antitumor T cells in adoptive immunotherapy. Current advances in CRISPR/Cas9 technology enable gene knockout during in vitro preparation of infused T-cell products through transient transfection of a Cas9-guide RNA (gRNA) ribonucleoprotein complex. However, selecting optimal gRNAs remains a major challenge for efficient gene ablation. Although multiple in silico tools to predict the targeting efficiency have been developed, their performance has not been validated in cultured human T cells. Here, we explored a strategy to select optimal gRNAs using our pooled data on CRISPR/Cas9-mediated gene knockout in human T cells. The currently available prediction tools alone were insufficient to accurately predict the indel percentage in T cells. We used data on the epigenetic profiles of cultured T cells obtained from transposase-accessible chromatin with high-throughput sequencing (ATAC-seq). Combining the epigenetic information with sequence-based prediction tools significantly improved the gene-editing efficiency. We further demonstrate that epigenetically closed regions can be targeted by designing two gRNAs in adjacent regions. Finally, we demonstrate that the gene-editing efficiency of unstimulated T cells can be enhanced through pretreatment with IL-7. These findings enable more efficient gene editing in human T cells.

High CD62L expression predicts the generation of chimeric antigen receptor T cells with potent effector functions.

The efficient generation of chimeric antigen receptor (CAR) T cells is highly influenced by the quality of apheresed T cells. Healthy donor-derived T cells usually proliferate better than patients-derived T cells and are precious resources to generate off-the-shelf CAR-T cells. However, relatively little is known about the determinants that affect the efficient generation of CAR-T cells from healthy donor-derived peripheral blood mononuclear cells (PBMCs) compared with those from the patients' own PBMCs. We here examined the efficiency of CAR-T cell generation from multiple healthy donor samples and analyzed its association with the phenotypic features of the starting peripheral blood T cells. We found that CD62L expression levels within CD8+ T cells were significantly correlated with CAR-T cell expansion. Moreover, high CD62L expression within naïve T cells was associated with the efficient expansion of T cells with a stem cell-like memory phenotype, an indicator of high-quality infusion products. Intriguingly, genetic disruption of CD62L significantly impaired CAR-T cell proliferation and cytokine production upon antigen stimulation. Conversely, ectopic expression of a shedding-resistant CD62L mutant augmented CAR-T cell effector functions compared to unmodified CAR-T cells, resulting in improved antitumor activity in vivo. Collectively, we identified the surface expression of CD62L as a concise indicator of potent T-cell proliferation. CD62L expression is also associated with the functional properties of CAR-T cells. These findings are potentially applicable to selecting optimal donors to massively generate CAR-T cell products.

AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations.

The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

The top 100 most cited articles in the treatment of basal cell carcinoma over the last decade: A bibliometric analysis and review.

Basal cell carcinoma (BCC) represents the most prevalent cancer globally. The past decade has witnessed significant advancements in BCC treatment, primarily through bibliometric studies. Aiming to perform a comprehensive bibliometric analysis of BCC treatments to comprehend the research landscape and identify trends within this domain, a dataset comprising 100 scientific publications from the Web of Science Core Collection was analyzed. Country co-operation, journal co-citation, theme bursts, keyword co-occurrence, author co-operation, literature co-citation, and field-specific references were examined using VOSviewer and CiteSpace visualization tools. These articles, published between 2013 and 2020, originated predominantly from 30 countries/regions and 159 institutions, with the USA and Germany at the forefront, involving a total of 1118 authors. The keyword analysis revealed significant emphasis on the hedgehog pathway, Mohs micrographic surgery, and photodynamic therapy. The research shows developed nations are at the forefront in advancing BCC therapies, with significant focus on drugs targeting the hedgehog pathway. This treatment avenue has emerged as a crucial area, meriting considerable attention in BCC therapeutic strategies.

Safety of tildrakizumab: a disproportionality analysis based on the FDA adverse event reporting system (FAERS) database from 2018-2023.

Background: Tildrakizumab, the IL-23 inhibitor, is used to treat plaque psoriasis and psoriatic arthritis. Many studies have reported adverse drug reactions (ADRs) associated with Tildrakizumab. Objective: The aim of this study was to describe ADRs associated with Tildrakizumab monotherapy by mining data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: The signals of Tildrakizumab-associated ADRs were quantified using disproportionality analyses such as the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multiitem gamma Poisson shrinker (MGPS) algorithms. Results: A total of 10,530,937 reports of ADRs were collected from the FAERS database, of which 1,177 reports were identified with tildrakizumab as the "primary suspect (PS)". Tildrakizumab-induced ADRs occurred against 27 system organ classes (SOCs). A total of 32 significant disproportionality Preferred Terms (PTs) conformed to the algorithms. Unexpected significant ADRs such as coronavirus infection, herpes simplex, diverticulitis, atrial fibrillation and aortic valve incompetence were also possible. The median time to onset of Tildrakizumab-associated ADRs was 194 days (interquartile range [IQR] 84-329 days), with the majority occurring, within the first 1 and 3 months after initiation of Tildrakizumab. Conclusion: This study identified a potential signal for new ADRs with Tildrakizumab, which might provide important support for clinical monitoring and risk prediction.

Protocol to measure human IL-6 secretion from CAR T cell-primed macrophage and monocyte lineage cells in vitro and in vivo using humanized mice.

Chimeric antigen receptor (CAR) T cell therapy often causes serious toxicities, such as cytokine release syndrome (CRS), mainly due to interleukin-6 (IL-6) secreted by monocyte lineage cells. Here, we describe a protocol to generate anti-CD19 CAR T cells and quantify human monocyte-derived IL-6 cocultured with CAR T cells and target tumor cells in vitro. We further describe a protocol to generate a humanized mouse model and evaluate CAR T cell-associated plasma IL-6 levels in vivo. For complete details on the use and execution of this protocol, please refer to Yoshikawa et al.1.

Development of a chimeric cytokine receptor that captures IL-6 and enhances the antitumor response of CAR-T cells.

The efficacy of chimeric antigen receptor (CAR)-engineered T cell therapy is suboptimal in most cancers, necessitating further improvement in their therapeutic actions. However, enhancing antitumor T cell response inevitably confers an increased risk of cytokine release syndrome associated with monocyte-derived interleukin-6 (IL-6). Thus, an approach to simultaneously enhance therapeutic efficacy and safety is warranted. Here, we develop a chimeric cytokine receptor composed of the extracellular domains of GP130 and IL6RA linked to the transmembrane and cytoplasmic domain of IL-7R mutant that constitutively activates the JAK-STAT pathway (G6/7R or G6/7R-M452L). CAR-T cells with G6/7R efficiently absorb and degrade monocyte-derived IL-6 in vitro. The G6/7R-expressing CAR-T cells show superior expansion and persistence in vivo, resulting in durable antitumor response in both liquid and solid tumor mouse models. Our strategy can be widely applicable to CAR-T cell therapy to enhance its efficacy and safety, irrespective of the target antigen.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics.

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

CD83 expression characterizes precursor exhausted T cell population.

T cell exhaustion is a main obstacle against effective cancer immunotherapy. Exhausted T cells include a subpopulation that maintains proliferative capacity, referred to as precursor exhausted T cells (TPEX). While functionally distinct and important for antitumor immunity, TPEX possess some overlapping phenotypic features with the other T-cell subsets within the heterogeneous tumor-infiltrating T-lymphocytes (TIL). Here we explore surface marker profiles unique to TPEX using the tumor models treated by chimeric antigen receptor (CAR)-engineered T cells. We find that CD83 is predominantly expressed in the CCR7+PD1+ intratumoral CAR-T cells compared with the CCR7-PD1+ (terminally differentiated) and CAR-negative (bystander) T cells. The CD83+CCR7+ CAR-T cells exhibit superior antigen-induced proliferation and IL-2 production compared with the CD83- T cells. Moreover, we confirm selective expression of CD83 in the CCR7+PD1+ T-cell population in primary TIL samples. Our findings identify CD83 as a marker to discriminate TPEX from terminally exhausted and bystander TIL.

Exhaustion-associated cholesterol deficiency dampens the cytotoxic arm of antitumor immunity.

The concept of targeting cholesterol metabolism to treat cancer has been widely tested in clinics, but the benefits are modest, calling for a complete understanding of cholesterol metabolism in intratumoral cells. We analyze the cholesterol atlas in the tumor microenvironment and find that intratumoral T cells have cholesterol deficiency, while immunosuppressive myeloid cells and tumor cells display cholesterol abundance. Low cholesterol levels inhibit T cell proliferation and cause autophagy-mediated apoptosis, particularly for cytotoxic T cells. In the tumor microenvironment, oxysterols mediate reciprocal alterations in the LXR and SREBP2 pathways to cause cholesterol deficiency of T cells, subsequently leading to aberrant metabolic and signaling pathways that drive T cell exhaustion/dysfunction. LXRß depletion in chimeric antigen receptor T (CAR-T) cells leads to improved antitumor function against solid tumors. Since T cell cholesterol metabolism and oxysterols are generally linked to other diseases, the new mechanism and cholesterol-normalization strategy might have potential applications elsewhere.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.