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B cell-activating factor (BAFF) binds the three receptors BAFF-R, BCMA, and TACI, predominantly expressed on mature B cells. Almost all B cell cancers are reported to express at least one of these receptors. Here we develop a BAFF ligand-based chimeric antigen receptor (CAR) and generate BAFF CAR-T cells using a non-viral gene delivery method. We show that BAFF CAR-T cells bind specifically to each of the three BAFF receptors and are effective at killing multiple B cell cancers, including mantle cell lymphoma (MCL), multiple myeloma (MM), and acute lymphoblastic leukemia (ALL), in vitro and in vivo using different xenograft models. Co-culture of BAFF CAR-T cells with these tumor cells results in induction of activation marker CD69, degranulation marker CD107a, and multiple proinflammatory cytokines. In summary, we report a ligand-based BAFF CAR-T capable of binding three different receptors, minimizing the potential for antigen escape in the treatment of B cell cancers. Antigen escape represents a potential drawback of chimeric antigen receptor T cell (CAR-T) therapy targeting a single tumor-associated antigen. To reduce the risk of antigen escape, here the authors report the design and characterization of a BAFF ligand CAR-T that can recognize three different receptors (BAFF-R, BCMA and TACI), demonstrating in vitro and in vivo cytotoxicity against multiple B cell cancer models.

Emissions from the transportation sector are significant contributors to climate change and health problems because of the common use of gasoline vehicles. Countries in the world are attempting to transition away from gasoline vehicles and to electric vehicles (EVs), in order to reduce emissions. However, there are several practical limitations with EVs, one of which is the “range anxiety” issue, due to the lack of charging infrastructure, the high cost of long-ranged EVs, and the limited range of affordable EVs. One potential solution to the range anxiety problem is the use of range extenders, to extend the driving range of EVs while optimizing the costs and performance of the vehicles. This paper provides a comprehensive review of different types of EV range extending technologies, including internal combustion engines, free-piston linear generators, fuel cells, micro gas turbines, and zinc-air batteries, outlining their definitions, working mechanisms, and some recent developments of each range extending technology. A comparison between the different technologies, highlighting the advantages and disadvantages of each, is also presented to help address future research needs. Since EVs will be a significant part of the automotive industry future, range extenders will be an important concept to be explored to provide a cost-effective, reliable, efficient, and dynamic solution to combat the range anxiety issue that consumers currently have.

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

Natural killer (NK) cells mediate killing of malignant and virus-infected cells, a property that is explored as a cell therapy approach in the clinic. Various cell intrinsic and extrinsic factors affect NK cell cytotoxic function, and an improved understanding of the mechanism regulating NK cell function is necessary to accomplish better success with NK cell therapeutics. Here, we explored the role of O-GlcNAcylation, a previously unexplored molecular mechanism regulating NK cell function. O-GlcNAcylation is a post-translational modification mediated by O-GlcNAc transferase (OGT) that adds the monosaccharide N-acetylglucosamine to serine and threonine residues on intracellular proteins and O-GlcNAcase (OGA) that removes the sugar. We found that stimulation of NK cells with the cytokines interleukin-2 (IL-2) and IL-15 results in enhanced O-GlcNAcylation of several cellular proteins. Chemical inhibition of O-GlcNAcylation using OSMI-1 was associated with a decreased expression of NK cell receptors (NKG2D, NKG2A, NKp44), cytokines [tumor necrosis factor (TNF)-α, interferon (IFN-γ)], granulysin, soluble Fas ligand, perforin, and granzyme B in NK cells. Importantly, inhibition of O-GlcNAcylation inhibited NK cell cytotoxicity against cancer cells. However, increases in O-GlcNAcylation following OGA inhibition using an OGA inhibitor or shRNA-mediated suppression did not alter NK cell cytotoxicity. Finally, we found that NK cells pretreated with OSMI-1 to inhibit O-GlcNAcylation showed compromised cytotoxic activity against tumor cells in vivo in a lymphoma xenograft mouse model. Overall, this study provides the seminal insight into the role of O-GlcNAcylation in regulating NK cell cytotoxic function.

Post-translational modifications (PTMs) are important for the stability and function of many therapeutic proteins and peptides. Current methods for studying and engineering PTMs are often limited by low-throughput experimental techniques. Here we describe a generalizable, in vitro workflow coupling cell-free gene expression (CFE) with AlphaLISA for the rapid expression and testing of PTM installing proteins. We apply our workflow to two representative classes of peptide and protein therapeutics: ribosomally synthesized and post-translationally modified peptides (RiPPs) and glycoproteins. First, we demonstrate how our workflow can be used to characterize the binding activity of RiPP recognition elements, an important first step in RiPP biosynthesis, and be integrated into a biodiscovery pipeline for computationally predicted RiPP products. Then, we adapt our workflow to study and engineer oligosaccharyltransferases (OSTs) involved in protein glycan coupling technology, leading to the identification of mutant OSTs and sites within a model vaccine carrier protein that enable high efficiency production of glycosylated proteins. We expect that our workflow will accelerate design-build-test-learn cycles for engineering PTMs. Post-translational modifications (PTMs) are important for the stability and function of many therapeutic proteins. Here, the authors develop a high-throughput workflow combining cell-free gene expression with AlphaLISA to rapidly characterize and engineer PTMs on both proteins and peptides.

The phenomenon of zero pronoun (ZP) has attracted increasing interest in the machine translation community due to its importance and difficulty. However, previous studies generally evaluate the quality of translating ZPs with BLEU score on MT testsets, which is not expressive or sensitive enough for accurate assessment. To bridge the data and evaluation gaps, we propose a benchmark testset and evaluation metric for target evaluation on Chinese ZP translation. The human-annotated testset covers five challenging genres, which reveal different characteristics of ZPs for comprehensive evaluation. We systematically revisit advanced models on ZP translation and identify current challenges for future exploration. We release data, code, and trained models, which we hope can significantly promote research in this field.

Natural Killer (NK) cells are cytotoxic lymphocytes critical to the innate immune system. We found that germline deficiency of NF-κB c-Rel results in a marked decrease in cytotoxic function of NK cells, both in vitro and in vivo , with no significant differences in the stages of NK cell development. We found that c-Rel binds to the promoters of perforin and granzyme B, two key proteins required for NK cytotoxicity, and controls their expression. We generated a NK cell specific c-Rel conditional knockout to study NK cell intrinsic role of c- Rel and found that both global and conditional c-Rel deficiency leads to decreased perforin and granzyme B expression and thereby cytotoxic function. We also confirmed the role of c-Rel in perforin and granzyme B expression in human NK cells. c-Rel reconstitution rescued perforin and granzyme B expressions in c-Rel deficient NK cells and restored their cytotoxic function. Our results show a previously unknown role of c-Rel in transcriptional regulation of perforin and granzyme B expressions and control of NK cell cytotoxic function.

Aberrations in Capicua ( CIC ) have recently been implicated as a negative prognostic factor in a multitude of cancer types through activation of the MAPK signalling cascade and derepression of oncogenic ETS transcription factors. The Ataxin-family protein ATXN1L has previously been reported to interact with CIC in developmental and disease contexts to facilitate the repression of CIC target genes. To further investigate this relationship, we performed functional in vitro studies utilizing ATXN1L KO and CIC KO human cell lines and characterized a reciprocal functional relationship between CIC and ATXN1L. Transcriptomic interrogation of the CIC–ATXN1–ATXN1L axis in low-grade glioma, prostate adenocarcinoma and stomach adenocarcinoma TCGA cohorts revealed context-dependent convergence of gene sets and pathways related to mitotic cell cycle and division. This study highlights the CIC–ATXN1–ATXN1L axis as a more potent regulator of the cell cycle than previously appreciated.

Germline pathogenic TP53 variants predispose individuals to a high lifetime risk of developing multiple cancers and are the hallmark feature of Li-Fraumeni syndrome (LFS). Our group has previously shown that LFS patients harbor shorter plasma cell-free DNA fragmentation; independent of cancer status. To understand the functional underpinning of cfDNA fragmentation in LFS, we conducted a fragmentomic analysis of 199 cfDNA samples from 82 TP53 mutation carriers and 30 healthy TP53 -wildtype controls. We find that LFS individuals exhibit an increased prevalence of A/T nucleotides at fragment ends, dysregulated nucleosome positioning at p53 binding sites, and loci-specific changes in chromatin accessibility at development-associated transcription factor binding sites and at cancer-associated open chromatin regions. Machine learning classification resulted in robust differentiation between TP53 mutant versus wildtype cfDNA samples (AUC-ROC = 0.710–1.000) and intra-patient longitudinal analysis of ctDNA fragmentation signal enabled early cancer detection. These results suggest that cfDNA fragmentation may be a useful diagnostic tool in LFS patients and provides an important baseline for cancer early detection. Here, Wong et al investigate the cell-free DNA landscape of individuals with Li-Fraumeni syndrome (LFS), a cancer predisposition, and find altered composition compared to non-LFS individuals which can be used to detect and track cancer development.

Signaling pathways depend on regulatory protein-protein interactions; controlling these interactions in cells has important applications for reengineering biological functions. As many regulatory proteins are modular, considerable progress in engineering signaling circuits has been made by recombining commonly occurring domains. Our ability to predictably engineer cellular functions, however, is constrained by complex crosstalk observed in naturally occurring domains. Here we demonstrate a strategy for improving and simplifying protein network engineering: using computational design to create orthogonal (non-crossreacting) protein-protein interfaces. We validated the design of the interface between a key signaling protein, the GTPase Cdc42, and its activator, Intersectin, biochemically and by solving the crystal structure of the engineered complex. The designed GTPase (orthoCdc42) is activated exclusively by its engineered cognate partner (orthoIntersectin), but maintains the ability to interface with other GTPase signaling circuit components in vitro. In mammalian cells, orthoCdc42 activity can be regulated by orthoIntersectin, but not wild-type Intersectin, showing that the designed interaction can trigger complex processes. Computational design of protein interfaces thus promises to provide specific components that facilitate the predictable engineering of cellular functions.