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The distribution and abundance of immune cells, particularly T‐cell subsets, play pivotal roles in cancer immunology and therapy. T cells have many subsets with specific function and current methods are limited in estimating them, thus, a method for predicting comprehensive T‐cell subsets is urgently needed in cancer immunology research. Here, Immune Cell Abundance Identifier (ImmuCellAI), a gene set signature‐based method, is introduced for precisely estimating the abundance of 24 immune cell types including 18 T‐cell subsets, from gene expression data. Performance evaluation on both the sequencing data with flow cytometry results and public expression data indicate that ImmuCellAI can estimate the abundance of immune cells with superior accuracy to other methods especially on many T‐cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals that the abundance of dendritic cells, cytotoxic T, and gamma delta T cells is significantly higher both in comparisons of on‐treatment versus pre‐treatment and responders versus non‐responders. Meanwhile, an ImmuCellAI result‐based model is built for predicting the immunotherapy response with high accuracy (area under curve 0.80–0.91). These results demonstrate the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction. Immune Cell Abundance Identifier (ImmuCellAI) is a gene set signature‐based method for precisely estimating the abundance of 24 immune cell types including 18 T‐cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals the dynamic change of immune cell abundance. An ImmuCellAI result‐based model for predicting the immunotherapy response achieves high accuracy with area under curve 0.80–0.91.

Abstract Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.

Plant polysaccharides (PPS) composed of more than 10 monosaccharides show high safety and various pharmacological activities, including immunoregulatory, antitumor, antioxidative, antiaging, and other effects. In recent years, emerging evidence has indicated that many PPS are beneficial for metabolic diseases, such as cardiovascular disease (CVD), diabetes, obesity, and neurological diseases, which are usually caused by the metabolic disorder of fat, sugar, and protein. In this review, we introduce the common characteristics and functional activity of many representative PPS, emphasize the common risks and molecular mechanism of metabolic diseases, and discuss the pharmacological activity and mechanism of action of representative PPS obtained from plants including Aloe vera, Angelica sinensis, pumpkin, Lycium barbarum, Ginseng, Schisandra chinensis, Dioscorea pposite, Poria cocos, and tea in metabolic diseases. Finally, this review will provide directions and a reference for future research and for the development of PPS into potential drugs for the treatment of metabolic diseases.

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular \"omics\" approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

The advent of click chemistry has had a profound impact on many fields and fueled a need for reliable reactions to expand the click chemistry toolkit. However, developing new systems to fulfill the click chemistry criteria remains highly desirable yet challenging. Here, we report the development of light-induced primary amines and o-nitrobenzyl alcohols cyclization (PANAC) as a photoclick reaction via primary amines as direct click handle, to rapid and modular functionalization of diverse small molecules and native biomolecules. With intrinsic advantages of temporal control, good biocompatibility, reliable chemoselectivity, excellent efficiency, readily accessible reactants, operational simplicity and mild conditions, the PANAC photoclick is robust for direct diversification of pharmaceuticals and biorelevant molecules, lysine-specific modifications of unprotected peptides and native proteins in vitro, temporal profiling of endogenous kinases and organelle-targeted labeling in living systems. This strategy provides a versatile platform for organic synthesis, bioconjugation, medicinal chemistry, chemical biology and materials science. Developing new click chemistry reactions for robust molecular assembly remains challenging. Here the authors report a light-induced primary amines and o -nitrobenzyl alcohols photoclick cyclization for rapid and modular functionalization of small molecules and native biomolecules, in vitro and in living systems.

Neuroinflammation, an inflammatory response within the central nervous system (CNS), is a main hallmark of common neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS), among others. The over-activated microglia release pro-inflammatory cytokines, which induces neuronal death and accelerates neurodegeneration. Therefore, inhibition of microglia over-activation and microglia-mediated neuroinflammation has been a promising strategy for the treatment of neurodegenerative diseases. Many drugs have shown promising therapeutic effects on microglia and inflammation. However, the blood–brain barrier (BBB)—a natural barrier preventing brain tissue from contact with harmful plasma components—seriously hinders drug delivery to the microglial cells in CNS. As an emerging useful therapeutic tool in CNS-related diseases, nanoparticles (NPs) have been widely applied in biomedical fields for use in diagnosis, biosensing and drug delivery. Recently, many NPs have been reported to be useful vehicles for anti-inflammatory drugs across the BBB to inhibit the over-activation of microglia and neuroinflammation. Therefore, NPs with good biodegradability and biocompatibility have the potential to be developed as an effective and minimally invasive carrier to help other drugs cross the BBB or as a therapeutic agent for the treatment of neuroinflammation-mediated neurodegenerative diseases. In this review, we summarized various nanoparticles applied in CNS, and their mechanisms and effects in the modulation of inflammation responses in neurodegenerative diseases, providing insights and suggestions for the use of NPs in the treatment of neuroinflammation-related neurodegenerative diseases.

Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD), are characterized by the progressive degeneration of neurons. Although the etiology and pathogenesis of neurodegenerative diseases have been studied intensively, the mechanism is still in its infancy. In general, most neurodegenerative diseases share common molecular mechanisms, and multiple risks interact and promote the pathologic process of neurogenerative diseases. At present, most of the approved drugs only alleviate the clinical symptoms but fail to cure neurodegenerative diseases. Numerous studies indicate that dietary plant polyphenols are safe and exhibit potent neuroprotective effects in various neurodegenerative diseases. However, low bioavailability is the biggest obstacle for polyphenol that largely limits its adoption from evidence into clinical practice. In this review, we summarized the widely recognized mechanisms associated with neurodegenerative diseases, such as misfolded proteins, mitochondrial dysfunction, oxidative damage, and neuroinflammatory responses. In addition, we summarized the research advances about the neuroprotective effect of the most widely reported dietary plant polyphenols. Moreover, we discussed the current clinical study and application of polyphenols and the factors that result in low bioavailability, such as poor stability and low permeability across the blood-brain barrier (BBB). In the future, the improvement of absorption and stability, modification of structure and formulation, and the combination therapy will provide more opportunities from the laboratory into the clinic for polyphenols. Lastly, we hope that the present review will encourage further researches on natural dietary polyphenols in the treatment of neurodegenerative diseases.

Curcumae Longae Rhizoma (CLRh), Curcumae Radix (CRa), and Curcumae Rhizoma (CRh), derived from the different medicinal parts of the Curcuma species, are blood-activating analgesics commonly used for promoting blood circulation and relieving pain. Due to their certain similarities in chemical composition and pharmacological effects, these three herbs exhibit a high risk associated with mixing and indiscriminate use. The diverse methods used for distinguishing the medicinal origins are complex, time-consuming, and limited to intraspecific differentiation, which are not suitable for rapid and systematic identification. We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. Among them, 1D + MSC + 13S emerged as the best pretreatment method. Then, t-distributed stochastic neighbor embedding (t-SNE) was applied to visualize the results, and seven kinds of classification models were constructed. The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. The established method could be employed to rapidly and effectively distinguish the different origins and parts of Curcuma species and thus provides a technique for rapid quality evaluation of affinis species.

Trillium tschonoskii Maxim (TTM), a traditional Chinese medicine, has been demonstrated to have a potent anti-tumor effect. Recently, polyphyllin VI (PPVI), a main saponin isolated from TTM, was reported by us to significantly suppress the proliferation of non-small cell lung cancer (NSCLC) via the induction of apoptosis and autophagy in vitro and in vivo. In this study, we further found that the NLRP3 inflammasome was activated in PPVI administrated A549-bearing athymic nude mice. As is known to us, pyroptosis is an inflammatory form of caspase-1-dependent programmed cell death that plays an important role in cancer. By using A549 and H1299 cells, the in vitro effect and action mechanism by which PPVI induces activation of the NLRP3 inflammasome in NSCLC were investigated. The anti-proliferative effect of PPVI in A549 and H1299 cells was firstly measured and validated by MTT assay. The activation of the NLRP3 inflammasome was detected by using Hoechst33324/PI staining, flow cytometry analysis and real-time live cell imaging methods. We found that PPVI significantly increased the percentage of cells with PI signal in A549 and H1299, and the dynamic change in cell morphology and the process of cell death of A549 cells indicated that PPVI induced an apoptosis-to-pyroptosis switch, and, ultimately, lytic cell death. In addition, belnacasan (VX-765), an inhibitor of caspase-1, could remarkably decrease the pyroptotic cell death of PPVI-treated A549 and H1299 cells. Moreover, by detecting the expression of NLRP3, ASC, caspase-1, IL-1β, IL-18 and GSDMD in A549 and h1299 cells using Western blotting, immunofluorescence imaging and flow cytometric analysis, measuring the caspase-1 activity using colorimetric assay, and quantifying the cytokines level of IL-1β and IL-18 using ELISA, the NLRP3 inflammasome was found to be activated in a dose manner, while VX-765 and necrosulfonamide (NSA), an inhibitor of GSDMD, could inhibit PPVI-induced activation of the NLRP3 inflammasome. Furthermore, the mechanism study found that PPVI could activate the NF-κB signaling pathway via increasing reactive oxygen species (ROS) levels in A549 and H1299 cells, and N-acetyl-L-cysteine (NAC), a scavenger of ROS, remarkably inhibited the cell death, and the activation of NF-κB and the NLRP3 inflammasome in PPVI-treated A549 and H1299 cells. Taken together, these data suggested that PPVI-induced, caspase-1-mediated pyroptosis via the induction of the ROS/NF-κB/NLRP3/GSDMD signal axis in NSCLC, which further clarified the mechanism of PPVI in the inhibition of NSCLC, and thereby provided a possibility for PPVI to serve as a novel therapeutic agent for NSCLC in the future.