Spatial resolved transcriptomics reveals distinct cross-talk between cancer cells and tumor-associated macrophages in intrahepatic cholangiocarcinoma.

BACKGROUND: Multiple studies have shown that tumor-associated macrophages (TAMs) promote cancer initiation and progression. However, the reprogramming of macrophages in the tumor microenvironment (TME) and the cross-talk between TAMs and malignant subclones in intrahepatic cholangiocarcinoma (iCCA) has not been fully characterized, especially in a spatially resolved manner. Deciphering the spatial architecture of variable tissue cellular components in iCCA could contribute to the positional context of gene expression containing information pathological changes and cellular variability. METHODS: Here, we applied spatial transcriptomics (ST) and digital spatial profiler (DSP) technologies with tumor sections from patients with iCCA. RESULTS: The results reveal that spatial inter- and intra-tumor heterogeneities feature iCCA malignancy, and tumor subclones are mainly driven by physical proximity. Tumor cells with TME components shaped the intra-sectional heterogenetic spatial architecture. Macrophages are the most infiltrated TME component in iCCA. The protein trefoil factor 3 (TFF3) secreted by the malignant subclone can induce macrophages to reprogram to a tumor-promoting state, which in turn contributes to an immune-suppressive environment and boosts tumor progression. CONCLUSIONS: In conclusion, our description of the iCCA ecosystem in a spatially resolved manner provides novel insights into the spatial features and the immune suppressive landscapes of TME for iCCA.

Dimerized M-series Acceptors with Low Diffusion Coefficients for Efficient and Stable Polymer Solar Cells.

As the simplest oligomeric acceptors, dimerized acceptors (DAs) are easier to synthesize, and more importantly, they can retain good intermolecular interaction and photovoltaic properties of their parent small-molecule acceptors (SMAs). Nevertheless, currently most efficient DAs are derived from banana-shaped acceptors and they might suffer from inferior device stability with high diffusion coefficients. Herein, we design and synthesize two planar DAs (DMT-FH and DMT-HF) by bridging two linear-shaped M-series SMAs with a thiophene unit. The effects of fluorination position on the diffusion coefficients, power conversion efficiencies (PCEs) and stability of the DAs are systematically studied. Our results suggest that DMT-HF with fluorination on the ending indanone groups shows enhanced intermolecular interactions, improved PCE and stability compared with the counterpart (DMT-FH) with fluorination on the central indanone groups. Further optimization on the DMT-HF-based devices yields an outstanding PCE of 17.17%, which is the highest among all linear-shaped SMA-based DAs. Notably, with the low diffusion coefficient (3.36×10-24 cm2 s-1) of DMT-HF, the resulting device retains over 93% of the initial PCE after 5000 h of continuous heating at 85 oC, suggesting its excellent thermal stability. The results highlight the importance of intermolecular interaction and fluorination for achieving efficient and stable polymer solar cells.

Biomimetic Porous Ti6Al4V Implants: A Novel Interbody Fusion Cage via Gel-Casting Technique to Promote Spine Fusion.

An interbody fusion cage (Cage) is crucial in spinal decompression and fusion procedures for restoring normal vertebral curvature and rebuilding spinal stability. Currently, these Cages suffer from issues related to mismatched elastic modulus and insufficient bone integration capability. Therefore, a gel-casting technique is utilized to fabricate a biomimetic porous titanium alloy material from Ti6Al4V powder. The biomimetic porous Ti6Al4V is compared with polyetheretherketone (PEEK) and 3D-printed Ti6Al4V materials and their respective Cages. Systematic validation is performed through mechanical testing, in vitro cell, in vivo rabbit bone defect implantation, and ovine anterior cervical discectomy and fusion experiments to evaluate the mechanical and biological performance of the materials. Although all three materials demonstrate good biocompatibility and osseointegration properties, the biomimetic porous Ti6Al4V, with its excellent mechanical properties and a structure closely resembling bone trabecular tissue, exhibited superior bone ingrowth and osseointegration performance. Compared to the PEEK and 3D-printed Ti6Al4V Cages, the biomimetic porous Ti6Al4V Cage outperforms in terms of intervertebral fusion performance, achieving excellent intervertebral fusion without the need for bone grafting, thereby enhancing cervical vertebra stability. This biomimetic porous Ti6Al4V Cage offers cost-effectiveness, presenting significant potential for clinical applications in spinal surgery.

Genetic basis of phenotypic convergence in pig terminal sires using pathway-based selection signature detection methods.

The primary purpose of genetic improvement in lean pig breeds is to enhance production performance. Owing to their similar breeding directions, Duroc and Pietrain pigs are ideal models for investigating the phenotypic convergence underlying artificial selection. However, most important economic traits are controlled by a polygenic basis, so traditional strategies for detecting selection signatures may not fully reveal the genetic basis of complex traits. The pathway-based gene network analysis method utilizes each pathway as a unit, overcoming the limitations of traditional strategies for detecting selection signatures by revealing the selection of complex biological processes. Here, we utilized 13 122 398 high-quality SNPs from whole-genome sequencing data of 48 Pietrain pigs, 156 Duroc pigs and 36 European wild boars to detect selective signatures. After calculating FST and iHS scores, we integrated the pathway information and utilized the r/bioconductor graphite and signet packages to construct gene networks, identify subnets and uncover candidate genes underlying selection. Using the traditional strategy, a total of 47 genomic regions exhibiting parallel selection were identified. The enriched genes, including INO80, FZR1, LEPR and FAF1, may be associated with reproduction, fat deposition and skeletal development. Using the pathway-based selection signatures detection method, we identified two significant biological pathways and eight potential candidate genes underlying parallel selection, such as VTN, FN1 and ITGAV. This study presents a novel strategy for investigating the genetic basis of complex traits and elucidating the phenotypic convergence underlying artificial selection, by integrating traditional selection signature methods with pathway-based gene network analysis.

Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population.

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.

Cistanche phenylethanoid glycosides induce apoptosis and pyroptosis in T-cell lymphoma.

Cistanche deserticola, known for its extensive history in Traditional Chinese Medicine (TCM), is valued for its therapeutic properties. Recent studies have identified its anticancer capabilities, yet the mechanisms underlying these properties remain to be fully elucidated. In this study, we determined that a mixture of four cistanche-derived phenylethanoid glycosides (CPhGs), echinacoside, acteoside, 2-acetylacteoside, and cistanoside A, which are among the main bioactive compounds in C. deserticola, eliminated T-cell lymphoma (TCL) cells by inducing apoptosis and pyroptosis in vitro and attenuated tumor growth in vivo in a xenograft mouse model. At the molecular level, these CPhGs elevated P53 by inhibiting the SIRT2-MDM2/P300 and PI3K/AKT carcinogenic axes and activating PTEN-Bax tumor-suppressing signaling. Moreover, CPhGs activated noncanonical and alternative pathways to trigger pyroptosis. Interestingly, CPhGs did not activate canonical NLRP3-caspase-1 pyroptotic signaling pathway; instead, CPhGs suppressed the inflammasome factor NLRP3 and the maturation of IL-1ß. Treatment with a caspase-1/4 inhibitor and silencing of Gasdermin D (GSDMD) or Gasdermin E (GSDME) partially rescued CPhG-induced cell death. Conversely, forced expression of NLRP3 restored cell proliferation. In summary, our results indicate that CPhGs modulate multiple signaling pathways to achieve their anticancer properties and perform dual roles in pyroptosis and NLRP3-driven proliferation. This study offers experimental support for the potential application of CPhGs in the treatment of TCL.

Potential global distribution of Setaria italica, an important species for dryland agriculture in the context of climate change.

Setaria italica (S. italica, Linnaeus, 1753) is a drought-resistant, barren-tolerant, and widely adapted C-4 crop that plays a vital role in maintaining agricultural and economic stability in arid and barren regions of the world. However, the potential habitat of S. italica under current and future climate scenarios remains to be explored. Predicting the potential global geographic distribution of S. italica and clarifying its ecological requirements can help promote sustainable agriculture, which is crucial for addressing the global food crisis. In this study, we predicted the potential global geographic distribution of S. italica based on 3,154 global distribution records using the Maxent model and ArcGIS software. We assessed the constraints on its potential distribution based on the contribution of environmental factors variables. The predictive accuracy of the Maxent model was evaluated using AUC values, TSS values, and Kappa statistics, respectively. The results showed that the Maxent model had a high prediction accuracy, and the simulation results were also reliable; the total suitable habitats of S. italica is 5.54×107 km2, which mainly included the United States (North America), Brazil (South America), Australia (Oceania), China, India (Asia), and the Russian Federation (Europe). The most suitable habitat of S. italica was 0.52×107 km2, accounting for 9.44% of the total areas, mainly in the United States, India, the Russian Federation, and China. Soil and precipitation (driest monthly precipitation, hottest seasonal precipitation) are the most critical factors limiting the potential distribution of S. italica. Compared with the modern potential distribution, we predict that the four future climate change scenarios will result in varying reductions in the possible geographic ranges of S. italica. Overall, climate change may significantly affect the global distribution of S. italica, altering its worldwide production and trade patterns.

Endogenous mutant Huntingtin alters the corticogenesis via lowering Golgi recruiting ARF1 in cortical organoid.

Pathogenic mutant huntingtin (mHTT) infiltrates the adult Huntington's disease (HD) brain and impairs fetal corticogenesis. However, most HD animal models rarely recapitulate neuroanatomical alterations in adult HD and developing brains. Thus, the human cortical organoid (hCO) is an alternative approach to decode mHTT pathogenesis precisely during human corticogenesis. Here, we replicated the altered corticogenesis in the HD fetal brain using HD patient-derived hCOs. Our HD-hCOs had pathological phenotypes, including deficient junctional complexes in the neural tubes, delayed postmitotic neuronal maturation, dysregulated fate specification of cortical neuron subtypes, and abnormalities in early HD subcortical projections during corticogenesis, revealing a causal link between impaired progenitor cells and chaotic cortical neuronal layering in the HD brain. We identified novel long, oriented, and enriched polyQ assemblies of HTTs that hold large flat Golgi stacks and scaffold clathrin+ vesicles in the neural tubes of hCOs. Flat Golgi stacks conjugated polyQ assemblies by ADP-ribosylation factor 1 (ARF1). Inhibiting ARF1 activation with Brefeldin A (BFA) disassociated polyQ assemblies from Golgi. PolyQ assembles with mHTT scaffolded fewer ARF1 and formed shorter polyQ assembles with fewer and shorter Golgi and clathrin vesicles in neural tubes of HD-hCOs compared with those in hCOs. Inhibiting the activation of ARF1 by BFA in healthy hCOs replicated impaired junctional complexes in the neural tubes. Together, endogenous polyQ assemblies with mHTT reduced the Golgi recruiting ARF1 in the neuroepithelium, impaired the Golgi structure and activities, and altered the corticogenesis in HD-hCO.

Mendelian randomization analysis reveals a causal effect of Streptococcus salivarius on diabetic retinopathy through regulating host fasting glucose.

Diabetic retinopathy (DR) is one of leading causes of vision loss in adults with increasing prevalence worldwide. Increasing evidence has emphasized the importance of gut microbiome in the aetiology and development of DR. However, the causal relationship between gut microbes and DR remains largely unknown. To investigate the causal associations of DR with gut microbes and DR risk factors, we employed two-sample Mendelian Randomization (MR) analyses to estimate the causal effects of 207 gut microbes on DR outcomes. Inputs for MR included Genome-wide Association Study (GWAS) summary statistics of 207 taxa of gut microbes (the Dutch Microbiome Project) and 21 risk factors for DR. The GWAS summary statistics data of DR was from the FinnGen Research Project. Data analysis was performed in May 2023. We identified eight bacterial taxa that exhibited significant causal associations with DR (FDR < 0.05). Among them, genus Collinsella and species Collinsella aerofaciens were associated with increased risk of DR, while the species Bacteroides faecis, Burkholderiales bacterium_1_1_47, Ruminococcus torques, Streptococcus salivarius, genus Burkholderiales_noname and family Burkholderiales_noname showed protective effects against DR. Notably, we found that the causal effect of species Streptococcus salivarius on DR was mediated through the level of host fasting glucose, a well-established risk factor for DR. Our results reveal that specific gut microbes may be causally linked to DR via mediating host metabolic risk factors, highlighting potential novel therapeutic or preventive targets for DR.

Polymerizing M-Series Acceptors for Efficient Polymer Solar Cells: Effect of the Molecular Shape.

Currently, high-performance polymerized small-molecule acceptors (PSMAs) based on ADA-type SMAs are still rare and greatly demanded for polymer solar cells (PSCs). Herein, two novel regioregular PSMAs (PW-Se and PS-Se) are designed and synthesized by using centrosymmetric (linear-shaped) and axisymmetric (banana-shaped) ADA-type SMAs as the main building blocks, respectively. It is demonstrated that photovoltaic performance of the PSMAs can be significantly improved by optimizing the configuration of ADA-type SMAs. Compared to the axisymmetric SMA-based polymer (PS-Se), PW-Se using a centrosymmetric SMA as the main building block exhibits better backbone coplanarity thereby resulting in bathochromically shifted absorption with a higher absorption coefficient, tighter interchain π-π stacking, and more favorable blend film morphology. As a result, enhanced and more-balanced charge transport, better exciton dissociation, and reduced charge recombination are achieved for PW-Se-based devices with PM6 as polymer donor. Benefiting from these positive factors, the optimal PM6:PW-Se-based device exhibits a higher power conversion efficiency (PCE) of 15.65% compared to the PM6:PS-Se-based device (8.90%). Furthermore, incorporation of PW-Se as a third component in the binary active layer of PM6:M36 yields ternary devices with an outstanding PCE of 18.0%, which is the highest value for PSCs based on ADA-type SMAs, to the best of the knowledge.

A high-resolution time-frequency analysis technique based on bi-directional squeezing and its application in fault diagnosis of rotating machinery.

Most of the signals in the field of industrial engineering are nonstationary signals, and the accurate description of the time-frequency (TF) characteristics of nonstationary signals is important for the advancement of industrial engineering. Instantaneous frequency (IF) and group delay (GD) are common TF features used to describe nonstationary signals. Time-reassigned synchrosqueezing transform and synchrosqueezing transform are two TF analysis tools that can accurately characterize the GD and IF of nonstationary signals by squeezing the TF coefficients in the time direction and frequency direction, respectively. However, it is difficult for such two techniques to accurately characterize IF and GD simultaneously. A novel method called time-frequency squeezing transform is introduced in this paper to conquer this drawback. The technique first uses the short-time Fourier transform to calculate the time-frequency representation (TFR) of a signal, and then divides the TFR into two parts according to a chirp rate estimator. The subdivided TFR parts are then squeezed in the frequency and time directions to accurately characterize the IF and GD, respectively, and the two squeezed results are added to form a high-resolution result. The effectiveness of the proposed technique is demonstrated with numerical and experimental signals.

A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps.

The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data.

Long-term ecological restoration increased plant diversity and soil total phosphorus content of the alpine flowing sand land in northwest Sichuan, China.

The ecological restoration techniques that combine grazing, sand barriers with willows, fertilization, artificial planting, and continuous management are increasingly adopted in the management of flowing sandy land in high-altitude and cold regions. However, few studies have focused on the long-term ecological restoration effects of such technologies. This study systematically compared the vegetation and soil characteristics under different ecological restoration durations (0 (CK), 3 (F1), 14 (F2), 26 (F3), and 46 (F4) years) in the alpine sandy land of northwest Sichuan. The results showed that, with the increase of ecological restoration durations, (1) the aboveground and underground biomass of plants, and species number significantly increased, while the shannon-wiener index, margalef index, and simpson index dramatically decreased; (2) in the early stage of ecological restoration (0-3 yr), Cyperaceae accounted for the main groups, while in the late stage of ecological restoration (14-46 yr), Leguminosae and Forb groups predominated; (3) ecological restoration durations significantly influenced the total phosphorus (TP) content at a soil depth of 0-60 cm, but soil organic carbon and C/P ratio were only significantly impacted at 40-60 cm; (4) the plant and soil characteristics of F1, F2, and F3 treatments were more similar, and CK and F4 treatments were clearly distinguished on PC1 of principal component analysis; (5) there was no significant correlation between Leguminosae groups and environmental factors. Instead, a correlation between total nitrogen (TN) and Forb groups, Gramineae groups, and Cyperaceae groups was revealed. TN was very significantly positively correlated with species diversity and TP. Long-term ecological restoration improved plants biomass, plant species diversity, functional plant groups, and increased soil TP content in the alpine sandy land of northwest Sichuan.

AGIDB: a versatile database for genotype imputation and variant decoding across species.

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

Alterations in gut microbiota and host transcriptome of patients with coronary artery disease.

BACKGROUND: Coronary artery disease (CAD) is a widespread heart condition caused by atherosclerosis and influences millions of people worldwide. Early detection of CAD is challenging due to the lack of specific biomarkers. The gut microbiota and host-microbiota interactions have been well documented to affect human health. However, investigation that reveals the role of gut microbes in CAD is still limited. This study aims to uncover the synergistic effects of host genes and gut microbes associated with CAD through integrative genomic analyses. RESULTS: Herein, we collected 52 fecal and 50 blood samples from CAD patients and matched controls, and performed amplicon and transcriptomic sequencing on these samples, respectively. By comparing CAD patients with health controls, we found that dysregulated gut microbes were significantly associated with CAD. By leveraging the Random Forest method, we found that combining 20 bacteria and 30 gene biomarkers could distinguish CAD patients from health controls with a high performance (AUC = 0.92). We observed that there existed prominent associations of gut microbes with several clinical indices relevant to heart functions. Integration analysis revealed that CAD-relevant gut microbe genus Fusicatenibacter was associated with expression of CAD-risk genes, such as GBP2, MLKL, and CPR65, which is in line with previous evidence (Tang et al., Nat Rev Cardiol 16:137-154, 2019; Kummen et al., J Am Coll Cardiol 71:1184-1186, 2018). In addition, the upregulation of immune-related pathways in CAD patients were identified to be primarily associated with higher abundance of genus Blautia, Eubacterium, Fusicatenibacter, and Monoglobus. CONCLUSIONS: Our results highlight that dysregulated gut microbes contribute risk to CAD by interacting with host genes. These identified microbes and interacted risk genes may have high potentials as biomarkers for CAD.

Uncovering nutritional metabolites and candidate genes involved in flavonoid metabolism in Houttuynia cordata through combined metabolomic and transcriptomic analyses.

The perennial herb Houttuynia cordata has long been cultivated and used as medicinal and edible plant in Asia. Nowadays, increasing attention is attracted due to its numerous health benefits. Flavonoids are the main chemical constituents exerting pharmacological activities. In the present study, we investigated both metabolome and transcriptome of two H. cordata accessions (6# and 7#) with distinct flavonoids contents. In total 397 metabolites, i.e., 220 flavonoids, 92 amino acids and derivatives, 20 vitamins, and 65 saccharides were abundant in aboveground part. Cyanidin-3-O-rutinoside and quercetin-3-O-galactoside were the most abundant flavonoids, which can be categorized into seven classes, namely anthocyanidins, chalcones, flavanols, flavanones, flavanonols, flavones, and flavonols. Flavonols was the most abundant group. Contents of 112 flavonoids differed significantly between the two accessions, with catechin-(7,8-bc)-4α-(3,4-dihydroxyphenyl)-dihydro-2-(3H)-one, cinchonain Id, and cinchonain Ic being the dominant flavonoid metabolites among them. Pinocembrin-7-O-neohesperidoside, pinocembrin-7-O-rutinoside, and kaempferol-3-O-galactoside-4'-O-glucoside were uniquely abundant in accession 7. Transcriptome data revealed a total of 110 different expressed genes related to flavonoid metabolism, with more highly expressed genes observed in 7#. We annotated a total of 19 differential flavonoid metabolites and 34 differentially expressed genes that are associated with the flavonoid metabolic network. Based on the transcriptome and qPCR data a total of 8 key candidate genes involved in flavonoid metabolism were identified. The ANS gene were found to play an important role in the synthesis of cyanidin-3-O-glucoside, while the CHI, F3'H and FLS genes were mainly responsible for controlling the levels of flavanones, flavones, and flavonols, respectively. Collectively, the present study provides important insights into the molecular mechanism underlying flavonoid metabolism in H. cordata.

Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma.

Neoadjuvant immune checkpoint blockade (ICB) demonstrates promise in operable esophageal squamous cell carcinoma (ESCC), but lacks available efficacy biomarkers. Here, we perform single-cell RNA-sequencing of tumors from patients with ESCC undergoing neoadjuvant ICB, revealing a subset of exhausted CD8+ T cells expressing SPRY1 (CD8+ Tex-SPRY1) that displays a progenitor exhausted T cell (Tpex) phenotype and correlates with complete response to ICB. We validate CD8+ Tex-SPRY1 cells as an ICB-specific predictor of improved response and survival using independent ICB-/non-ICB cohorts and demonstrate that expression of SPRY1 in CD8+ T cells enforces Tpex phenotype and enhances ICB efficacy. Additionally, CD8+ Tex-SPRY1 cells contribute to proinflammatory phenotype of macrophages and functional state of B cells, which thereby promotes antitumor immunity by enhancing CD8+ T cell effector functions. Overall, our findings unravel progenitor-like CD8+ Tex-SPRY1 cells' role in effective responses to ICB for ESCC and inform mechanistic biomarkers for future individualized immunotherapy.

The recent progress in the research of extraction and functional applications of basil seed gum.

Basil seed gum (BSG) is a new hydrophilic colloid of natural plant origin. Extracted from basil seeds, it possesses excellent functional characteristics in terms of emulsification, rheology, gelation, stability, and adsorption, which are just as favorable as those of certain commercial gums. Besides, BSG has been widely used in food, medicine, industry, and many other fields for its physiological functions of weight reduction, detoxification, and control of blood sugar and cholesterol as a good dietary fiber. In this paper, we analyzed and discussed the extraction procedures, composition structures, functional characteristics, and modification strategies of BSG. In addition, we summarized the latest research on the applications of BSG in different industries to provide theoretical references for the high-value processing and utilization of BSG.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data.

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.