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This paper attempts to investigate the impact of economic growth, population, and energy consumption on consumption-based CO2 emissions for a global panel of China and U.S. with the comparison of two dynamic models, pooled OLS and LSDV, for the period 1990–2016. The empirical evidence indicates significant positive impacts of economic growth and energy consumption and negative effect of population on CO2 emissions for global panels. Besides, it shows the different responsibilities on reducing energy consumption and CO2 emissions between developing and developed countries.

The international regional economic integration is developing to a broader and higher standard. The BRICS countries have been cooperating for a long time. As the representatives of the emerging economies of the world, the establishment of a free trade zone by the BRICS can not only promote economic and trade growth among the BRICS members, but also help the developing countries to speak in the new round of international economic and trade rules reshaping. Based on the above background, this paper uses the GTAP-E model to simulate the economic effects of the BRICS free trade area with the zero tariff and exception sectors scenarios to simulate the expected economic effects of the BRICS free trade area. The construction of the BRICS free trade zone has brought different impacts on different industries of member countries, and the overall optimization and adjustment in the direction of their respective comparative advantages. Resource integration in the industrial sector can increase productivity in BRICS countries.

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell–cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.

SUMMARYPD-1 blockade unleashes the potent antitumor activity of CD8 cells but can also promote immunosuppressive T regulatory (Treg) cells, which may worsen response to immunotherapy. Tumor Treg inhibition is a promising strategy to overcome therapeutic resistance; however, the mechanisms supporting tumor Tregs during PD-1 immunotherapy are largely unexplored. Here, we report that PD-1 blockade increases tumor Tregs in mouse models of immunogenic tumors, including melanoma, and metastatic melanoma patients. Unexpectedly, Treg accumulation was not caused by Treg-intrinsic inhibition of PD-1 signaling but instead depended on an indirect effect of activated CD8 cells. CD8 cells colocalized with Tregs within tumors and produced IL-2, especially after PD-1 immunotherapy. IL-2 upregulated the anti-apoptotic protein ICOS on tumor Tregs, causing their accumulation. ICOS signaling inhibition before PD-1 immunotherapy resulted in increased control of immunogenic melanoma. Thus, interrupting the intratumor CD8:Treg crosstalk is a novel strategy that may enhance the efficacy of immunotherapy in patients.

The monocytic/macrophage system is essential for skeletal muscle homeostasis, but its dysregulation contributes to the pathogenesis of muscle degenerative disorders. Despite our increasing knowledge of the role of macrophages in degenerative disease, it still remains unclear how macrophages contribute to muscle fibrosis. Here, we used single-cell transcriptomics to determine the molecular attributes of dystrophic and healthy muscle macrophages. We identified six novel clusters. Unexpectedly, none corresponded to traditional definitions of M1 or M2 macrophage activation. Rather, the predominant macrophage signature in dystrophic muscle was characterized by high expression of fibrotic factors, galectin-3 and spp1. Spatial transcriptomics and computational inferences of intercellular communication indicated that spp1 regulates stromal progenitor and macrophage interactions during muscular dystrophy. Galectin-3+macrophages were chronically activated in dystrophic muscle and adoptive transfer assays showed that the galectin-3+phenotype was the dominant molecular program induced within the dystrophic milieu. Histological examination of human muscle biopsies revealed that galectin-3+macrophages were also elevated in multiple myopathies. These studies advance our understanding of macrophages in muscular dystrophy by defining the transcriptional programs induced in muscle macrophages, and reveal spp1 as a major regulator of macrophage and stromal progenitor interactions.

Diet can impact on gut health and disease by modulating intestinal stem cells (ISCs). However, it is largely unknown if and how the ISC niche responds to diet and influences ISC function. Here, we demonstrate that Lepr(+) mesenchymal cells (MCs) surrounding intestinal crypts sense diet change and provide a novel niche signal to maintain ISC and progenitor cell proliferation. The abundance of these MCs increases upon administration of a high-fat diet (HFD) but dramatically decreases upon fasting. Depletion of Lepr(+) MCs resulted in fewer intestinal stem/progenitor cells, compromised the architecture of crypt–villus axis and impaired intestinal regeneration. Furthermore, we showed that IGF1 secreted by Lepr(+) MCs is an important effector that promotes proliferation of ISCs and progenitor cells in the intestinal crypt. We conclude that Lepr(+) MCs sense diet alterations and, in turn, modulate intestinal stem/progenitor cell function via a stromal IGF1–epithelial IGF1R axis. These findings reveal that Lepr(+) MCs are important mediators linking systemic diet changes to local ISC function and might serve as a novel therapeutic target for gut diseases.

At present, more than one cell death pathways have been found, one of which is ferroptosis. Ferroptosis was discovered in 2012 and described as an iron-dependent and lipid peroxidation-driven regulated cell death pathway. In the past few years, ferroptosis has been shown to induce tumor cell death, providing new ideas for tumor treatment. In this article, we summarize the latest advances in ferroptosis-induced tumor therapy at the intersection of tumor biology, molecular biology, redox biology, and materials chemistry. First, we state the characteristics of ferroptosis in cells, then introduce the key molecular mechanism of ferroptosis, and describes the relationship between ferroptosis and oxidative stress signaling pathways. Finally, we focused on several types of ferroptosis inducers discovered by scholars, and the application of ferroptosis in systemic chemotherapy, radiotherapy, immunotherapy and nanomedicine, in the hope that ferroptosis can exert its potential in the treatment of tumors.

Classification of GO terms. Based on the biological processes and key words, the GO pathways are classified into 20 classes.

Mutation rate of the 20 biological processes.

Supporting Tables includes: Table S4: Cancer risks obtained from model simulation with inflammation score from 0 to 15 and duration from 1 to 30 years after inflammation. Table S5: Differentially expression genes from chronic inflammation to cancer. Table S6: Effects of gene mutation to the biological processes included in the model based on literature study. Table S7: High mutation genes associated with differentially expression genes. Table S8: GO term mutation rate of high mutation genes.

Cancer risks from 1 to 30 years obtained from simulation under different protocols of periodic acute inflammation.

This Supplementary file includes: (1) Model description, Parameter values, Model implementation, and Experimental data analysis. (2) Supporting Tables S1-S3. Table S1: The influence between gene mutation type and cell function; Table S2: Parameters used model simulation; and Table S3: Parameters for simplifying success DNA repair probability. (3) Supporting Figures S1-S19. Figure S1: Illustration of the model; Figure S2: The inflammatory score divided into mild, moderate, and severe; Figure S3: Illustration of the DNA damage/repair module; Figure S4: Cell fate decision after DNA damage; Figure S5: The functions for the probability of successful repair and the number of un-repaired damage locus obtained from stochastic simulation; Figure S6: Flow chart of the numerical scheme of the simulation process; Figure S7: Population dynamics with three stages; Figure S8: Major pathways responsible to the process from inflammation to tumorigenesis; Figure S9: Mutations rates of the 20 classes biological processes; Figure S10: Effects of inflammation on population size and DNA damage; Figure S11: Population dynamics with different inflammation conditions; Figure S12: Evolution dynamics from 2 sample runs; Figure S13: Evolution dynamics from under various inflammation conditions; Figure S14: Evolution dynamics from 1 sample runs occurs to a single stem cell; Figure S15: Evolution dynamics from 1 sample runs for a group of stem cells; Figure S16: Summary of the double mutations present at the three time points. Figure S17: Evolution dynamics from 4 sample runs; Figure S18: Evolution dynamics under 5-15 protocols, periodic acute inflammation attacks; and Figure S19: Evolution dynamics under 0-15 protocols, periodic acute inflammation attacks.

Chronic inflammation is a serious risk factor for cancer; however, the routes from inflammation to cancer are poorly understood. On the basis of the processes implicated by frequently mutated genes associated with inflammation and cancer in three organs (stomach, colon, and liver) extracted from the Gene Expression Omnibus, The Cancer Genome Atlas, and Gene Ontology databases, we present a multiscale model of the long-term evolutionary dynamics leading from inflammation to tumorigenesis. The model incorporates cross-talk among interactions on several scales, including responses to DNA damage, gene mutation, cell-cycle behavior, population dynamics, inflammation, and metabolism-immune balance. Model simulations revealed two stages of inflammation-induced tumorigenesis: a precancerous state and tumorigenesis. The precancerous state was mainly caused by mutations in the cell proliferation pathway; the transition from the precancerous to tumorigenic states was induced by mutations in pathways associated with apoptosis, differentiation, and metabolism-immune balance. We identified opposing effects of inflammation on tumorigenesis. Mild inflammation removed cells with DNA damage through DNA damage-induced cell death, whereas severe inflammation accelerated accumulation of mutations and hence promoted tumorigenesis. These results provide insight into the evolutionary dynamics of inflammation-induced tumorigenesis and highlight the combinatorial effects of inflammation and metabolism-immune balance. This approach establishes methods for quantifying cancer risk, for the discovery of driver pathways in inflammation-induced tumorigenesis, and has direct relevance for early detection and prevention and development of new treatment regimes. Cancer Res; 77(22); 6429–41. ©2017 AACR.

Mutation rate of the 6 biological processes.

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell–cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

Many approaches have been developed to overcome technical noise in single cell (and single nucleus) RNA-sequencing (scRNAseq). As researchers dig deeper into data— looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and a minimum ofad hocparameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in the event that ground truth about biological variation is unknown (i.e., most of the time). Here we approach this problem analytically, based on the assumption that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We then analyze scRNAseq data without normalization—a step that can skew distributions, particular for sparse data—and calculatep-values associated with key statistics. We develop an improved method for the selection of features for cell clustering and the identification of gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics andGeneStatistics fromUnnormalizedReads), accurately captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify tens of thousands of correlations that, when clustered without supervision into gene communities, both align with cellular components and biological processes, and point toward potentially novel cell biological relationships.

Aging and metabolic disorders feedback and promote each other and are closely related to the occurrence and development of cardiovascular disease, type 2 diabetes, neurodegeneration and other degenerative diseases. Liupao tea is a geographical indication product of Chinese dark tea, with a “red, concentrated, aged and mellow” flavor quality. In this study, the aqueous extract of aged Liupao tea (ALPT) administered by continuous gavage significantly inhibited the increase of visceral fat and damage to the intestinal–liver–microbial axis in high-fat modeling of SAMP8 (P8+HFD) mice. Its potential mechanism is that ALPT significantly inhibited the inflammation and aggregation formation pathway caused by P8+HFD, increased the abundance of short-chain fatty acid producing bacteria Alistipes, Alloprevotella and Bacteroides, and had a calorie restriction effect. The results of the whole target metabolome network pharmacological analysis showed that there were 139 potential active components in the ALPT aqueous extract, and the core targets of their actions were SRC, TP53, AKT1, MAPK3, VEGFA, EP300, EGFR, HSP90AA1, CASP3, etc. These target genes were mainly enriched in cancer, neurodegenerative diseases, glucose and lipid metabolism and other pathways of degenerative changes. Molecular docking further verified the reliability of network pharmacology. The above results indicate that Liupao tea can effectively delay the body’s degenerative changes through various mechanisms and multi-target effects. This study revealed that dark tea such as Liupao tea has significant drinking value in a modern and aging society.

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.

Spatial transcriptomic technologies and spatially annotated single cell RNA-sequencing (scRNA-seq) datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). How to incorporate the spatial information and complex biochemical processes in reconstructing CCC remains a major challenge. Here we present COMMOT to infer CCC in spatial transcriptomics, which accounts for the competition among different ligand and receptor species as well as spatial distances between cells. A novel collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. We introduce downstream analysis tools on spatial directionality of signalings and genes regulated by such signalings using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies, showing its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT reveals new CCCs during skin morphogenesis in a case study of human epidermal development. Both the method and the computational package have broad applications in inferring cell-cell interactions within spatial genomics datasets.

Palmoplantar skin is structurally and functionally unique, but the transcriptional programs driving this specialization are unclear. Here, we use bulk and single-cell RNA sequencing of human palm, sole, and hip skin to describe the distinguishing characteristics of palmoplantar and non-palmoplantar skin while also uncovering differences between palmar and plantar sites. Our approach reveals an altered immune environment in palmoplantar skin, with downregulation of diverse immunological processes and decreased immune cell populations. Further, we identify specific fibroblast populations that appear to orchestrate key differences in cell-cell communication in palm, sole, and hip. Dedicated keratinocyte analysis highlights major differences in basal cell fraction among the three sites and demonstrates the existence of two spinous keratinocyte populations constituting parallel, site-selective epidermal differentiation trajectories. In summary, this deep characterization of highly adapted palmoplantar skin contributes key insights into the fundamental biology of human skin and provides a valuable data resource for further investigation.

DNA methylation is a well-characterized epigenetic modification involved in numerous molecular and cellular functions. Methylation patterns have also been associated with aging mechanisms. However, how DNA methylation patterns change within key brain regions involved in memory formation in an age- and sex-specific manner remains unclear. Here, we performed reduced representation bisulfite sequencing (RRBS) from mouse dorsal hippocampus - which is necessary for the formation and consolidation of specific types of memories - in young and aging mice of both sexes. Overall, our findings demonstrate that methylation levels within the dorsal hippocampus are divergent between sexes during aging in genomic features correlating to mRNA functionality, transcription factor binding sites, and gene regulatory elements. These results define age-related changes in the methylome across genomic features and build a foundation for investigating potential target genes regulated by DNA methylation in an age- and sex-specific manner.

Animal pigment patterns are excellent models to elucidate mechanisms of biological organization. Although theoretical simulations, such as Turing reaction-diffusion systems, recapitulate many animal patterns, they are insufficient to account for those showing a high degree of spatial organization and reproducibility. Here, we compare the coats of the African striped mouse (Rhabdomys pumilio) and the laboratory mouse (Mus musculus) to study the molecular mechanisms controlling stripe pattern formation. By combining transcriptomics, mathematical modeling, and mouse transgenics, we show thatSfrp2regulates the distribution of hair follicles and establishes an embryonic prepattern that foreshadows pigment stripes. Moreover, by developing and employingin vivogene editing experiments in striped mice, we find thatSfrp2knockout is sufficient to alter the stripe pattern. Strikingly, mutants also exhibit changes in coat color, revealing an additional function ofSfrp2in regulating hair color. Thus, a single factor controls coat pattern formation by acting both as an orienting signaling mechanism and a modulator of pigmentation. By uncovering a multifunctional regulator of stripe formation, our work provides insights into the mechanisms by which spatial patterns are established in developing embryos and the molecular basis of phenotypic novelty.

Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell-cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip-Stalk cell patterning can undergo an order-disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understating of cell plasticity underlying angiogenesis and other complex morphogenic processes.Significance StatementWe investigate the spatial and temporal patterns of Tip/Stalk specification and the ensuing angiogenic sprouting by using a novel controlled micro-engineered experimental model of angiogenesis and a set of mathematical models of the spatially resolved, cell population-level VEGF-NOTCH signaling. Our analysis provides a dynamic view of the initial step of angiogenesis, revealing fluctuations in its onset, and features suggesting transitions between order and disorder in cell organization. These findings suggest how a potentially very restrictive patterning mechanism can become sensitive to a variety of environmental cues. This sensitivity can be crucial for proper vascularization of a damaged organ, and may suggest new ways of analyzing angiogenesis in the context of cancer and other pathologies. This analysis also suggests a framework for understanding of other instances of NOTCH-mediated patterning processes.

Extracting dynamical information from single-cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory-oriented, bottom-up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single-cell RNA sequencing (scRNA-seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state-specific gene-gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial-mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre-existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.

Cancer-associated fibroblasts (CAFs), the most abundant cells in the tumor microenvironment, play an indispensable role in cancer initiation, progression, metastasis, and metabolism. The limitations of traditional treatments can be partly attributed to the lack of understanding of the role of the tumor stroma. For this reason, CAF targeting is gradually gaining attention, and many studies are trying to overcome the limitations of tumor treatment with CAF as a breakthrough. Glutamine (GLN) has been called a “nitrogen reservoir” for cancer cells because of its role in supporting anabolic processes such as fuel proliferation and nucleotide synthesis, but ammonia is a byproduct of the metabolism of GLN and other nitrogenous compounds. Moreover, in some studies, GLN has been reported as a fundamental nitrogen source that can support tumor biomass. In this review, we discuss the latest findings on the role of GLN and ammonia in the crosstalk between CAFs and cancer cells as well as the potential therapeutic implications of nitrogen metabolism.

Motivation Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modeling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies and high sensitivity to confounding factors from various sources. Results Here, we propose a new deep generative model framework, named SAILER, for analyzing scATAC-seq data. SAILER aims to learn a low-dimensional nonlinear latent representation of each cell that defines its intrinsic chromatin state, invariant to extrinsic confounding factors like read depth and batch effects. SAILER adopts the conventional encoder-decoder framework to learn the latent representation but imposes additional constraints to ensure the independence of the learned representations from the confounding factors. Experimental results on both simulated and real scATAC-seq datasets demonstrate that SAILER learns better and biologically more meaningful representations of cells than other methods. Its noise-free cell embeddings bring in significant benefits in downstream analyses: clustering and imputation based on SAILER result in 6.9% and 18.5% improvements over existing methods, respectively. Moreover, because no matrix factorization is involved, SAILER can easily scale to process millions of cells. We implemented SAILER into a software package, freely available to all for large-scale scATAC-seq data analysis. Availability and implementation The software is publicly available at https://github.com/uci-cbcl/SAILER. Supplementary information Supplementary data are available at Bioinformatics online.

Paraneoplastic neurological syndromes (PNS) are rare immune-mediated disorders, and the detection of onconeural antibodies is helpful for PNS diagnosis. The aim of this study was to investigate the clinical characteristics of patients with PNS with positive onconeural antibodies in a single center in Hubei, China. We retrospectively analyzed the clinical characteristics of 54 patients with positive onconeural antibodies from January 2016 to September 2020. Among 780 patients with suspected PNS, 54 (6.9%) had positive onconeural antibodies. Of those 54 patients, 28 (51.8%) were diagnosed with definite PNS and 13 (24.1%) with possible PNS. Eighteen (33.3%) patients were confirmed with cancer. Ten PNS syndromes were detected among the 28 patients with definite PNS, and they had either classical (12/28, 42.8%) or non-classical syndromes (17/28, 60.7%). Peripheral neuropathy (9/28, 32.1%), subacute cerebellar degeneration (4/28, 14.3%), and limbic encephalitis (4/28, 14.3%) were the most common PNS syndromes. The anti-CV2/CRMP5-antibody was observed most frequently. Lung cancer was the most common tumor type. For patients with possible PNS, peripheral neuropathy was the most common PNS syndrome, and the anti-Tr-antibody was the most frequent onconeural antibody. Immunotherapy was effective in treating PNS. The anti-CV2/CRMP5-antibody was the most subsequently observed antibody. The manifestations of PNS are diverse and include peripheral neuropathy, subacute cerebellar degeneration, and limbic encephalitis. In patients with PNS, lung cancer was the most common tumor.

Integration of functional micro-/nano-frameworks with various analytical techniques is favored to produce advanced and practical methods for identification, inspection, and regulation of hazards in agro-food products. Herein, two novel synthetic strategies were developed for preparation of core-shell structured ZIF-8@COF by using metal halides as the catalyst, and further construction of hollow-structured covalent organic frameworks (HCOFs) with a sacrificial template method. The HCOFs adsorbent exhibited high stability, universality and adsorption affinity for advanced glycation end products (AGEs) and aflatoxins (AFTs) than pristine COF. Combined with HPLC and HPLC-MS/MS, the applicability of this method was verified by infant formula samples with satisfying recoveries of 87.3%-98.9%. Furthermore, HCOF showed potential ability of adsorbing and removing AGEs from plasma without causing other risk. This work opens up the road for further research on the simple method to synthesis HCOF, besides, provides a technological basis for monitoring and controlling AGEs and AFTs in milk powder.

Several methods for generating human skin equivalent (HSE) organoid cultures are regularly used to study skin biology and test pharmaceuticals, however few studies have thoroughly characterized these systems. To fill this gap, we used single cell-RNA sequencing to compare the cellular states of in vitro HSEs generated from distinct culture methods, HSEs xenografted onto mice, and in vivo epidermis. By combining differential gene expression, pseudotime analyses, splicing kinetics, and spatial localization, we reconstructed HSE keratinocyte differentiation trajectories that recapitulated known in vivo epidermal differentiation pathways and show that HSEs contain many of the major in vivo cellular states. However, HSEs also develop several unique keratinocyte states, an expanded basal stem cell program, and disrupted terminal differentiation. In addition, cell-cell communication modeling showed the presence of EMT-associated signaling pathways not normally active in homeostatic skin and we show that EGF supplementation influences the EMT signature. Lastly, xenografted HSEs at early timepoints post-transplantation significantly rescued many of the observed in vitro deficits, while undergoing a hypoxic response that drove an alternative differentiation lineage. This study highlights the strengths and limitations of organoid cultures and identifies areas for potential innovation.

One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing methods on several spatial transcriptomic datasets at both spot and single-cell resolutions, SpaceFlow is shown to produce a robust domain segmentation and identify biologically meaningful spatiotemporal patterns. Applications of SpaceFlow reveal evolving lineage in heart developmental data and tumor-immune interactions in human breast cancer data. Our study provides a flexible deep learning framework to incorporate spatiotemporal information in analyzing spatial transcriptomic data.

Yue Zhou,1 Ai-Qing Nie,1 Shang Chen,2 Meng-Meng Wang,1 Rui Yin,1 Bo-Hao Tang,1 Yue-E Wu,1 Fan Yang,1 Bin Du,1 Hai-Yan Shi,3 Xin-Mei Yang,3 Guo-Xiang Hao,1 Xiu-Li Guo,4 Qiu-Ju Han,5 Yi Zheng,1,* Wei Zhao1,3,* 1Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 2Institute of Biochemical and Biotechnological Drug, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 3Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People’s Republic of China; 4Department of Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 5Institute of Immunopharmaceutical Sciences, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi Zheng; Wei ZhaoDepartment of Clinical Pharmacy, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, No. 44, Wenhua West Road, Jinan, Shandong Province, People’s Republic of ChinaTel/Fax +86 531 8838 3308Email zhengyi@sdu.edu.cn; zhao4wei2@hotmail.comPurpose: Considering prior investigations on reductions of renal multidrug resistance-associated protein (MRP) 2 and 4 transporters in mice with acute lymphoblastic leukemia (ALL), we sought to characterize the underlying mechanisms responsible for IL-6/STAT3/PXR-mediated changes in the expression of MRP2 and MRP4 in ALL.Subjects and Methods: ALL xenograft models were established and intravenously injected with methotrexate (MTX) of MRPs substrate in NOD/SCID mice. Protein expression of MRPs and associated mechanisms were detected by Western blotting and immunocytochemistry. Plasma concentrations of MTX were determined using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS).Results: Plasma IL-6 levels in patients with newly diagnosed ALL were increased compared to children with pneumonia. Similarly, plasma IL-6 levels in ALL, ALL-tocilizumab (TCZ, an IL-6 receptor inhibitor) and ALL-S3I-201 (a selective inhibitor of STAT3) mice were increased compared to the control group. The MRP2, MRP4, and PXR expression in HK-2 cells treated with IL-6 were decreased, whereas the p-STAT3 expression was significantly increased compared to the control group results. These results are consistent with clearance of MRPs-mediated MTX in the ALL group. These effects were attenuated by blocking IL-6/STAT3/PXR signaling pathway.Conclusion: Inflammation-mediated changes in pharmacokinetics are thought to be executed through pathways IL-6-activated pathways, which can facilitate a better understanding of the potential for the use of IL-6 to predict the severity of adverse outcomes and the major implications on potential ALL treatments.Keywords: disease-induced pharmacokinetic change, renal transporters, inflammatory cytokines, clearance

AIM: To detect the visual dysfunction, and investigate the changes of Tau and its phosphorylated Ser396/Ser404 forms in retinas and optic nerves in traumatic optic neuropathy(TON)model rats by using FVEP technique.METHODS: Totally 30 SD rats were conducted FVEP electrode implantation. One week later, all rates were implemented TON operation with the optic nerve of left eye crushed and the optic nerve of right eye exposed(sham-operated). FVEP detections were performed respectively in these TON model rats at 1, 3, 7, 14, and 28d post crush, with 5 rats tested at each time point. After FVEP tests were taken, rats were sacrificed and then retinas and optic nerves of left eyes were separated for detecting the expression levels of Tau and pTau-Ser396/404 by Western Blot.RESULTS: Typical FVEP waves were observed in the sham-operated eyes. Compared to the sham group, the N2 waves were significantly delayed and the amplitude of N2-P2 were greatly reduced at each time point in the operation eyes. However, the differences of N2 wave and the amplitude reduction of N2-P2 were not significant at each time point after crush. The contents of total Tau protein in retinas of TON rats sharply decreased at 1d post crush, briefly recovered at 7d post crush, and remained a slightly lower level than normal condition till 28d. The changes of pTau-Ser396/404 were consistent with the changes of total Tau in retains and the Ser396 was the main phosphorylation site. However, the total Tau contents in optic nerves of TON rats increased gradually, and peaked at the 14d post crush and remained till 28d. The changes of pTau-Ser396/404 were similar to the changes of total Tau in optic nerves, which peaked at 7d post crush. However, Ser404 was the main phosphorylation site of Tau in optic nerves.CONCLUSION: The related indexes of N2 and P2 waves in FVEP can be used to detect the visual dysfunction in TON rats. After TON, the content changes of total Tau in retinas and optic nerves were much different while the changes of pTau-Ser396/404 followed the alterations of total Tau in the two locations. However, the main phosphorylation site of Tau was differnet according to the locations.

Economists and policymakers believe that households’ and firms’ expectations of future inflation are key determinants of actual inflation. This paper applies the ARDL model and nonlinear ARDL model to long-term inflation-targeting policy mechanisms in the United States and China to assess the impact of oil price dynamics and asymmetries on inflation expectations, as well as the difference of this impact before and after the COVID-19 pandemic. In order to show the significant role of the COVID-19 outbreak, this paper includes the data from 2010 to 2021 and takes the pandemic period as a structural break. Taking oil price changes as a variable of interest, and introducing some other significant variables, we find that during the pandemic, the positive impact of oil price shock on U.S. inflation expectations has enhanced, whereas the positive impact on Chinese inflation expectations has weakened. There is also sufficient evidence of the existence of the asymmetric effects of oil price changes on inflation expectations in both countries, but the positive oil price change in the United States has always played a larger role than the negative oil price shock. In China, the impact of positive oil price shock was greater than that of negative oil prices before the epidemic and the effect of negative oil price shocks has increased significantly in the COVID-19 regime.

Multicellular organisms require intercellular and intracellular signaling to coordinately regulate different cell functions. Although many methods of cell-cell communication (CCC) inference have been developed, they seldom account for both the intracellular signaling responses and global spatial information. The recent advancement of spatial transcriptomics (ST) provides unprecedented opportunities to better decipher CCC signaling and functioning. In this paper, we propose anST-basedmultilayernetwork method, stMLnet, for inferring spatial intercellular communication and multilayer signaling regulations by quantifying distance-weighted ligand–receptor signaling activity based on diffusion and mass action models and mapping it to intracellular targets. We benchmark stMLnet with existing methods using simulation data and 8 real datasets of cell type-specific perturbations. Furthermore, we demonstrate the applicability of stMLnet on six ST datasets acquired with four different technologies (e.g., seqFISH+, Slide-seq v2, MERFIS and Visium), showing its effectiveness and reliability on ST data with varying spatial resolutions and gene coverages. Finally, stMLnet identifies positive feedback circuits between alveolar epithelial cells, macrophages, and monocytes via multilayer signaling pathways within a COVID-19 microenvironment. Our proposed method provides an effective tool for predicting multilayer signaling regulations between interacting cells, which can advance the mechanistic and functional understanding of spatial CCCs.

How basal cell carcinoma (BCC) interacts with its tumor microenvironment to promote growth is unclear. We use singe-cell RNA sequencing to define the human BCC ecosystem and discriminate between normal and malignant epithelial cells. We identify spatial biomarkers of tumors and their surrounding stroma that reinforce the heterogeneity of each tissue type. Combining pseudotime, RNA velocity–PAGA, cellular entropy, and regulon analysis in stromal cells reveals a cancer-specific rewiring of fibroblasts, where STAT1, TGF-β, and inflammatory signals induce a noncanonical WNT5A program that maintains the stromal inflammatory state. Cell-cell communication modeling suggests that tumors respond to the sudden burst of fibroblast-specific inflammatory signaling pathways by producing heat shock proteins, whose expression we validated in situ. Last, dose-dependent treatment with an HSP70 inhibitor suppresses in vitro vismodegib-resistant BCC cell growth, Hedgehog signaling, and in vivo tumor growth in a BCC mouse model, validating HSP70’s essential role in tumor growth and reinforcing the critical nature of tumor microenvironment cross-talk in BCC progression.

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

The effects of adjuvants for increasing the immunogenicity of influenza vaccines are well known. However, the effect of adjuvants on increasing the breadth of cross-reactivity is less well understood. In this study we have performed a systematic screen of different toll-like receptor (TLR) agonists, with and without a squalene-in-water emulsion on the immunogenicity of a recombinant trimerized hemagglutinin (HA) vaccine in mice after single-dose administration. Antibody (Ab) cross-reactivity for other variants within and outside the immunizing subtype (homosubtypic and heterosubtypic cross-reactivity, respectively) was assessed using a protein microarray approach. Most adjuvants induced broad IgG profiles, although the response to a combination of CpG, MPLA and AddaVax (termed ‘IVAX-1’) appeared more quickly and reached a greater magnitude than the other formulations tested. Antigen-specific plasma cell labeling experiments show the components of IVAX-1 are synergistic. This adjuvant preferentially stimulates CD4 T cells to produce Th1>Th2 type (IgG2c>IgG1) antibodies and cytokine responses. Moreover, IVAX-1 induces identical homo- and heterosubtypic IgG and IgA cross-reactivity profiles when administered intranasally. Consistent with these observations, a single-cell transcriptomics analysis demonstrated significant increases in expression of IgG1, IgG2b and IgG2c genes of B cells in H5/IVAX-1 immunized mice relative to naïve mice, as well as significant increases in expression of the IFNγ gene of both CD4 and CD8 T cells. These data support the use of adjuvants for enhancing the breath and durability of antibody responses of influenza virus vaccines.

During oocyte growth, various epigenetic modifications are gradually established, accompanied by accumulation of large amounts of mRNAs and proteins. However, little is known about the relationship between epigenetic modifications and meiotic progression. Here, by using