Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

Associations of Intrapancreatic Fat Deposition With Incident Diseases of the Exocrine and Endocrine Pancreas: A UK Biobank Prospective Cohort Study.

INTRODUCTION: To investigate whether increased intrapancreatic fat deposition (IPFD) heightens the risk of diseases of the exocrine and endocrine pancreas. METHODS: A prospective cohort study was conducted using data from the UK Biobank. IPFD was quantified using MRI and a deep learning-based framework called nnUNet. The prevalence of fatty change of the pancreas (FP) was determined using sex- and age-specific thresholds. Associations between IPFD and pancreatic diseases were assessed with multivariate Cox-proportional hazard model adjusted for age, sex, ethnicity, body mass index, smoking and drinking status, central obesity, hypertension, dyslipidemia, liver fat content, and spleen fat content. RESULTS: Of the 42,599 participants included in the analysis, the prevalence of FP was 17.86%. Elevated IPFD levels were associated with an increased risk of acute pancreatitis (hazard ratio [HR] per 1 quintile change 1.513, 95% confidence interval [CI] 1.179-1.941), pancreatic cancer (HR per 1 quintile change 1.365, 95% CI 1.058-1.762) and diabetes mellitus (HR per 1 quintile change 1.221, 95% CI 1.132-1.318). FP was also associated with a higher risk of acute pancreatitis (HR 3.982, 95% CI 2.192-7.234), pancreatic cancer (HR 1.976, 95% CI 1.054-3.704), and diabetes mellitus (HR 1.337, 95% CI 1.122-1.593, P = 0.001). DISCUSSION: FP is a common pancreatic disorder. Fat in the pancreas is an independent risk factor for diseases of both the exocrine pancreas and endocrine pancreas.

Tumor Microenvironment-Activatable Nanosystem Capable of Overcoming Multiple Therapeutic Obstacles for Augmenting Immuno/Metal-Ion Therapy.

Abnormal tumor microenvironment (TME) imposes barriers to nanomedicine penetration into tumors and evolves tumor-supportive nature to provide tumor cell protection, seriously weakening the action of antitumor nanomedicines and posing significant challenges to their development. Here, we engineer a TME-activatable size-switchable core-satellite nanosystem (Mn-TI-Ag@HA) capable of increasing the effective dose of therapeutic agents in deep-seated tumors while reversing tumor-supportive microenvironment for augmenting immuno/metal-ion therapy. When activated by TME, the nanosystem disintegrates, allowing ultrasmall-sized Ag nanoparticles to become unbound and penetrate deep into solid tumors. Simultaneously, the nanosystem produces O2 and releases TGF-ß inhibitors in situ to drive macrophage M2-to-M1 polarization, increasing intratumoral H2O2 concentration, and ultimately augmenting metal-ion therapy by accelerating hypertoxic Ag+ production. The nanosystem can overcome multiple obstacles that aid in tumor resistance to nanomedicine, demonstrating effective tumor penetration, TME regulation, and tumor inhibition effects. It can provoke long-term immunological memory effects against tumor rechallenge when combined with immune checkpoint inhibitor anti-PD-1. This work provides a paradigm for designing efficient antitumor nanomedicines.

Perioperative and Post-Hospital Whole-Course nutrition management in patients with pancreatoduodenectomy - a Single-Center prospective randomized controlled trial.

OBJECTIVE: Whole-course nutrition management (WNM) has been proven to improve outcomes and reduce complications. We conducted this randomized controlled trial to validate its effectiveness in patients undergoing pancreatoduodenectomy. METHODS: From December 1, 2020, to November 30, 2023, this single-center randomized clinical trial was conducted at the Department of Hepatobiliopancreatic Surgery in a major hospital in Beijing, China. Participants who were undergoing pancreatoduodenectomy were enrolled and randomly allocated to either the WNM group or the control group. The primary outcome was the incidence of postoperative complications. Subgroup analysis in patients who were at nutritional risk was performed. Finally, a six-month follow-up was conducted and the economic benefit was evaluated using an incremental cost-effectiveness ratio (ICER). RESULTS: A total of 84 patients were randomly assigned (1:1) into the WNM group and the control group. The incidences of total complications (47.6% vs. 69.0%, P=0.046), total infections (14.3% vs. 33.3%, P= 0.040) and abdominal infection (11.9% vs. 31.0%, P= 0.033) were significantly lower in the WNM group. In the subgroup analysis of patients at nutritional risk, 66 cases were included (35 cases in the WNM group and 31 cases in the control group). The rate of abdominal infection (11.4% vs. 32.3%, P= 0.039) and postoperative length of stay (23.1±10.3 vs. 30.4±17.2, P= 0.046) were statistically different between the two subgroups. In the six-month follow-up, more patients reached the energy target in the WNM group (97.0% vs. 79.4%, P=0.049) and got a higher daily energy intake (1761.3±339.5 vs. 1599.6±321.5, P=0.045). The ICER suggested that WNM saved 31,511 Chinese Yuan (CNY) while reducing the rate of total infections by 1% in the ITT population and saved 117,490 CNY in patients at nutritional risk, while WNM saved 31,511 CNY while reducing the rate of abdominal infections by 1% in the ITT population and saved 101,359 CNY in patients at nutritional risk. CONCLUSION: In this trial, whole-course nutrition management was associated with fewer total postoperative complications, total and abdominal infections, and was cost-effective, especially in patients at nutritional risk. It seems to be a favorable strategy for patients undergoing PD.

Current Advances on Nanomaterials Interfering with Lactate Metabolism for Tumor Therapy.

Increasing numbers of studies have shown that tumor cells prefer fermentative glycolysis over oxidative phosphorylation to provide a vast amount of energy for fast proliferation even under oxygen-sufficient conditions. This metabolic alteration not only favors tumor cell progression and metastasis but also increases lactate accumulation in solid tumors. In addition to serving as a byproduct of glycolytic tumor cells, lactate also plays a central role in the construction of acidic and immunosuppressive tumor microenvironment, resulting in therapeutic tolerance. Recently, targeted drug delivery and inherent therapeutic properties of nanomaterials have attracted great attention, and research on modulating lactate metabolism based on nanomaterials to enhance antitumor therapy has exploded. In this review, the advanced tumor therapy strategies based on nanomaterials that interfere with lactate metabolism are discussed, including inhibiting lactate anabolism, promoting lactate catabolism, and disrupting the "lactate shuttle". Furthermore, recent advances in combining lactate metabolism modulation with other therapies, including chemotherapy, immunotherapy, photothermal therapy, and reactive oxygen species-related therapies, etc., which have achieved cooperatively enhanced therapeutic outcomes, are summarized. Finally, foreseeable challenges and prospective developments are also reviewed for the future development of this field.

Temporal and structural patterns of hepatitis B virus integrations in hepatocellular carcinoma.

Chronic infection of hepatitis B virus (HBV) is the major cause of hepatocellular carcinoma (HCC). Notably, 90% of HBV-positive HCC cases exhibit detectable HBV integrations, hinting at the potential early entanglement of these viral integrations in tumorigenesis and their subsequent oncogenic implications. Nevertheless, the precise chronology of integration events during HCC tumorigenesis, alongside their sequential structural patterns, has remained elusive thus far. In this study, we applied whole-genome sequencing to multiple biopsies extracted from six HBV-positive HCC cases. Through this approach, we identified point mutations and viral integrations, offering a blueprint for the intricate tumor phylogeny of these samples. The emergent narrative paints a rich tapestry of diverse evolutionary trajectories characterizing the analyzed tumors. We uncovered oncogenic integration events in some samples that appear to happen before and during the initiation stage of tumor development based on their locations in reconstituted trajectories. Furthermore, we conducted additional long-read sequencing of selected samples and unveiled integration-bridged chromosome rearrangements and tandem repeats of the HBV sequence within integrations. In summary, this study revealed premalignant oncogenic and sequential complex integrations and highlighted the contributions of HBV integrations to HCC development and genome instability.

Excavation of diagnostic biomarkers and construction of prognostic model for clear cell renal cell carcinoma based on urine proteomics.

Purpose: Clear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients' prognosis. Materials and methods: Urine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data. Results: 133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FC<0.5). Top 10 proteins including S100A14, PKHD1L1, FABP4, ITIH2, C3, C8G, C2, ATF6, ANGPTL6, F13B were performed ELISA to verify. The results showed that PKHD1L1, ANGPTL6, FABP4 and C3 were statistically significant (P<0.05). We performed multivariate logistic regression analysis and plotted a nomogram. Receiver operating characteristic (ROC) curve indicted that the diagnostic efficiency of combined indicators is satisfactory (Aare under curve, AUC=0.835). Furthermore, the prognostic value of the urine proteomics was explored through the intersection between urine proteomics and TCGA RNA-seq data. Thus, COX regression analysis showed that VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 were statistically significant (P<0.05). Conclusion: Our study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.

Integrative transcriptome and proteome analyses of clear cell renal cell carcinoma develop a prognostic classifier associated with thrombus.

Clear cell renal cell carcinoma (ccRCC) with venous tumor thrombus (VTT) is associated with poor prognosis. Our integrative analyses of transcriptome and proteome reveal distinctive molecular features of ccRCC with VTT, and yield the development of a prognostic classifier to facilitate ccRCC molecular subtyping and treatment. The RNA sequencing and mass spectrometry were performed in normal-tumor-thrombus tissue triples of five ccRCC patients. Statistical analysis, GO and KEGG enrichment analysis, and protein-protein interaction network construction were used to interpret the transcriptomic and proteomic data. A six-gene-based classifier was developed to predict patients' survival using Cox regression, which was validated in an independent cohort. Transcriptomic analysis identified 1131 tumorigenesis-associated differentially expressed genes (DEGs) and 856 invasion-associated DEGs. Overexpression of transcription factor EGR2 in VTT indicated its important role in tumor invasion. Furthermore, proteomic analysis showed 597 tumorigenesis-associated differentially expressed proteins (DEPs) and 452 invasion-associated DEPs. The invasion-associated DEPs showed unique enrichment in DNA replication, lysine degradation, and PPAR signaling pathway. Integration of transcriptome and proteome reveals 142 tumorigenesis-associated proteins and 84 invasion-associated proteins displaying changes consistent with corresponding genes in transcriptomic profiling. Based on their different expression patterns among normal-tumor-thrombus triples, RAB25 and GGT5 were supposed to play a consistent role in both tumorigenesis and invasion processes, while SHMT2 and CADM4 might play the opposite roles in tumorigenesis and thrombus invasion. A prognostic classifier consisting of six DEGs (DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5, SUSD2) performed satisfactorily in predicting survival of ccRCC patients (HR = 4.41, P < 0.001), which was further validated in an independent cohort of 40 cases (HR = 5.52, P = 0.026). Our study revealed the transcriptomic and proteomic profiles of ccRCC patients with VTT, and identified the distinctive molecular features associated with VTT. The six-gene-based prognostic classifier developed by integrative analyses may facilitate ccRCC molecular subtyping and treatment.

Metastatic Prostate Cancers with BRCA2 versus ATM Mutations Exhibit Divergent Molecular Features and Clinical Outcomes.

PURPOSE: In patients with metastatic prostate cancer (mPC), ATM and BRCA2 mutations dictate differences in PARPi inhibitor response and other therapies. We interrogated the molecular features of ATM- and BRCA2-mutated mPC to explain the divergent clinical outcomes and inform future treatment decisions. EXPERIMENTAL DESIGN: We examined a novel set of 1,187 mPCs after excluding microsatellite-instable (MSI) tumors. We stratified these based on ATM (n = 88) or BRCA2 (n = 98) mutations. As control groups, mPCs with mutations in 12 other homologous recombination repair (HRR) genes were considered non-BRCA2/ATM HRR-deficient (HRDother, n = 193), whereas lack of any HRR mutations were considered HRR-proficient (HRP; n = 808). Gene expression analyses were performed using Limma. Real-world overall survival was determined from insurance claims data. RESULTS: In noncastrate mPCs, only BRCA2-mutated mPCs exhibited worse clinical outcomes to AR-targeted therapies. In castrate mPCs, both ATM and BRCA2 mutations exhibited worse clinical outcomes to AR-targeted therapies. ATM-mutated mPCs had reduced TP53 mutations and harbored coamplification of 11q13 genes, including CCND1 and genes in the FGF family. BRCA2-mutated tumors showed elevated genomic loss-of-heterozygosity scores and were often tumor mutational burden high. BRCA2-mutated mPCs had upregulation of cell-cycle genes and were enriched in cell-cycle signaling programs. This was distinct from ATM-mutated tumors. CONCLUSIONS: Tumoral ATM and BRCA2 mutations are associated with differential clinical outcomes when patients are stratified by treatments, including hormonal or taxane therapies. ATM- and BRCA2-mutated tumors exhibited differences in co-occurring molecular features. These unique molecular features may inform therapeutic decisions and development of novel therapies.

Chiral Skeletons of Mesoporous Silica Nanospheres to Mitigate Alzheimer's ß-Amyloid Aggregation.

Chiral mesoporous silica (mSiO2) nanomaterials have gained significant attention during the past two decades. Most of them show a topologically characteristic helix; however, little attention has been paid to the molecular-scale chirality of mSiO2 frameworks. Herein, we report a chiral amide-gel-directed synthesis strategy for the fabrication of chiral mSiO2 nanospheres with molecular-scale-like chirality in the silicate skeletons. The functionalization of micelles with the chiral amide gels via electrostatic interactions realizes the growth of molecular configuration chiral silica sols. Subsequent modular self-assembly results in the formation of dendritic large mesoporous silica nanospheres with molecular chirality of the silica frameworks. As a result, the resultant chiral mSiO2 nanospheres show abundant large mesopores (∼10.1 nm), high pore volumes (∼1.8 cm3·g-1), high surface areas (∼525 m2·g-1), and evident CD activity. The successful transfer of the chirality from the chiral amide gels to composited micelles and further to asymmetric silica polymeric frameworks based on modular self-assembly leads to the presence of molecular chirality in the final products. The chiral mSiO2 frameworks display a good chiral stability after a high-temperature calcination (even up to 1000 °C). The chiral mSiO2 can impart a notable decline in ß-amyloid protein (Aß42) aggregation formation up to 79%, leading to significant mitigation of Aß42-induced cytotoxicity on the human neuroblastoma line SH-ST5Y cells in vitro. This finding opens a new avenue to construct the molecular chirality configuration in nanomaterials for optical and biomedical applications.

Integrative analysis of transcriptomic landscape and urinary signature reveals prognostic biomarkers for clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) patients with venous tumor thrombus (VTT) have poor prognosis. We aimed to reveal features of ccRCC with VTT and develop a urine-based prognostic classifier to predict ccRCC prognosis through integrative analyses of transcriptomic landscape and urinary signature. Methods: RNA sequencing was performed in five patients with ccRCC thrombus-tumor-normal tissue triples, while mass spectrometry was performed for urine samples from 12 ccRCC and 11 healthy controls. A urine-based classifier consisting of three proteins was developed to predict patients' survival and validated in an independent cohort. Results: Transcriptomic analysis identified 856 invasion-associated differentially expressed genes (DEGs). Furthermore, proteomic analysis showed 133 differentially expressed proteins (DEPs). Integration of transcriptomic landscape and urinary signature reveals 6 urinary detectable proteins (VSIG4, C3, GAL3ST1, TGFBI, AKR1C3, P4HB) displaying abundance changes consistent with corresponding genes in transcriptomic profiling. According to TCGA database, VSIG4, TGFBI, and P4HB were significantly overexpressed in patients with shorter survival and might be independent prognostic factors for ccRCC (all p<0.05). A prognostic classifier consisting of the three DEPs highly associated with survival performed satisfactorily in predicting overall survival (HR=2.0, p<0.01) and disease-free survival (HR=1.6, p<0.001) of ccRCC patients. The ELISA analysis of urine samples from an independent cohort confirmed the satisfied predictive power of the classifier for pathological grade (AUC=0.795, p<0.001) and stage (AUC=0.894, p<0.001). Conclusion: Based on integrative analyses of transcriptomic landscape and urinary signature, the urine-based prognostic classifier consisting of VSIG4, TGFBI, and P4HB has satisfied predictive power of ccRCC prognosis and may facilitate ccRCC molecular subtyping and treatment.

ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems.

Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Is a Promising Signature to Predict Prognosis and Therapies for Hepatocellular Carcinoma (HCC).

BACKGROUND: The roles of mitochondria and the endoplasmic reticulum (ER) in the progression of hepatocellular carcinoma (HCC) are well established. However, a special domain that regulates the close contact between the ER and mitochondria, known as the mitochondria-associated endoplasmic reticulum membrane (MAM), has not yet been investigated in detail in HCC. METHODS: The TCGA-LIHC dataset was only used as a training set. In addition, the ICGC and several GEO datasets were used for validation. Consensus clustering was applied to test the prognostic value of the MAM-associated genes. Then, the MAM score was constructed using the lasso algorithm. In addition, uncertainty of clustering in single-cell RNA-seq data using a gene co-expression network (AUCell) was used for the detection of the MAM scores in various cell types. Then, CellChat analysis was applied for comparing the interaction strength between the different MAM score groups. Further, the tumor microenvironment score (TME score) was calculated to compare the prognostic values, the correlation with the other HCC subtypes, tumor immune infiltration landscape, genomic mutations, and copy number variations (CNV) of different subgroups. Finally, the response to immune therapy and sensitivity to chemotherapy were also determined. RESULTS: First, it was observed that the MAM-associated genes could differentiate the survival rates of HCC. Then, the MAM score was constructed and validated using the TCGA and ICGC datasets, respectively. The AUCell analysis indicated that the MAM score was higher in the malignant cells. In addition, enrichment analysis demonstrated that malignant cells with a high MAM score were positively correlated with energy metabolism pathways. Furthermore, the CellChat analysis indicated that the interaction strength was reinforced between the high-MAM-score malignant cells and T cells. Finally, the TME score was constructed, which demonstrated that the HCC patients with high MAM scores/low TME scores tend to have a worse prognosis and high frequency of genomic mutations, while those with low MAM scores/high TME scores were more likely to have a better response to immune therapy. CONCLUSIONS: MAM score is a promising index for determining the need for chemotherapy, which reflects the energy metabolic pathways. A combination of the MAM score and TME score could be a better indicator to predict prognosis and response to immune therapy.

Long non-coding RNA ENST00000503625 is a potential prognostic biomarker and metastasis suppressor gene in prostate cancer.

BACKGROUND: Dysregulation of Long Non-coding RNAs (lncRNAs) emerges to be a hallmark of cancers. Metastatic prostate cancer and localized disease that recurs after treatment are clinical challenges, it remains unclear how lncRNA plays a role in those processes. METHODS: From previous RNA-Seq data on 65 prostate cancer and adjacent normal tissues. We identified a novel lncRNA ENST00000503625 down-regulated in prostate cancer and correlated with tumor progression characteristics. Public datasets were examined for associations between ENST00000503625 expression and clinical parameters and prognoses. Subsequently, we constructed and externally validated a nomogram for predicting biochemical recurrence (BCR). Finally, in vitro experiments were carried out to determine how ENST00000503625 functions biologically in prostate cancer. RESULTS: Low ENST00000503625 in tumor was associated with poor clinical features and prognoses. TCGA pan-cancer analysis found that ENST00000503625 was deregulated in a variety of tumors and correlated with overall survival, disease-specific survival, and progression-free survival. The nomogram for predicting BCR was constructed using TCGA data, which exhibited excellent accuracy in external validation with Chinese Prostate Cancer Genome and Epigenome Atlas data. Gene Ontology and KEGG pathway analysis found that genes related to ENST00000503625 were enriched in multiple tumor progression related pathways. When ENST00000503625 was knocked down in vitro, the epithelial-mesenchymal transition was induced, by which cancer cells migrated and invaded more readily. CONCLUSION: Our data suggested that ENST00000503625 may serve as a potential prognostic marker or a therapeutic target for prostate cancer metastases.

C-Type Lectin Receptors-Triggered Antifungal Immunity May Synergize with and Optimize the Effects of Immunotherapy in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system worldwide, and there is a lack of effective treatment for late-stage HCC. Recent experimental studies have demonstrated that dysfunction of the intestinal flora has a significant impact on hepatocarcinogenesis. The pathophysiological link between the intestine, its microbiota, and the liver has been described as the "gut-liver axis". Dysbiosis of the intestinal flora and increased permeability of the intestinal wall are closely associated with liver pathology through the immune response. The "gut-liver axis" theory has been applied to the clinical study of the pathogenesis and treatment of HCC. The intestinal fungal community, as part of the gut microbiome, has a significant impact on human health and disease, while relatively little research has been done in HCC. In this study, we performed a comprehensive analysis of the expression and potential biological functions of the fungal recognition receptors C-type lectin receptors (CLRs) (Dectin-1, Dectin-2, Dectin-3, and Mincle) in HCC. We found that CLRs were downregulated in HCC, and their expressions were correlated with the clinical prognosis of HCC patients. Further studies suggested that the expression of CLRs were significantly correlated with immune infiltration and immunotherapy efficacy in HCC. Based on previous studies and our findings, we hypothesize that intestinal fungal communities and CLRs-triggered antifungal immunity have a key role in the pathogenesis of HCC, and these findings may provide new perspectives and targets for HCC immunotherapy.

Human endoderm stem cells reverse inflammation-related acute liver failure through cystatin SN-mediated inhibition of interferon signaling.

Acute liver failure (ALF) is a life-threatening disease that occurs secondary to drug toxicity, infection or a devastating immune response. Orthotopic liver transplantation is an effective treatment but limited by the shortage of donor organs, the requirement for life-long immune suppression and surgical challenges. Stem cell transplantation is a promising alternative therapy for fulminant liver failure owing to the immunomodulatory abilities of stem cells. Here, we report that when transplanted into the liver, human endoderm stem cells (hEnSCs) that are germ layer-specific and nontumorigenic cells derived from pluripotent stem cells are able to effectively ameliorate hepatic injury in multiple rodent and swine drug-induced ALF models. We demonstrate that hEnSCs tune the local immune microenvironment by skewing macrophages/Kupffer cells towards an anti-inflammatory state and by reducing the infiltrating monocytes/macrophages and inflammatory T helper cells. Single-cell transcriptomic analyses of infiltrating and resident monocytes/macrophages isolated from animal livers revealed dramatic changes, including changes in gene expression that correlated with the change of activation states, and dynamic population heterogeneity among these cells after hEnSC transplantation. We further demonstrate that hEnSCs modulate the activation state of macrophages/Kupffer cells via cystatin SN (CST1)-mediated inhibition of interferon signaling and therefore highlight CST1 as a candidate therapeutic agent for diseases that involve over-activation of interferons. We propose that hEnSC transplantation represents a novel and powerful cell therapeutic treatment for ALF.

Nanozyme-like single-atom catalyst combined with artesunate achieves photothermal-enhanced nanocatalytic therapy in the near-infrared biowindow.

Selectively generating active free radical (AFR) in tumor microenvironment (TME) can promote irreversible oxidation of biomolecules and damage tumor cells, resulting in effective tumor inhibition. However, therapeutic efficacy of AFR-based tumor suppression approaches is often limited by insufficient amount of H2O2 or O2 within TME. To overcome this obstacle, we design a pH/photothermal dual responsive nanosystem (PFeSA@AS) for combined photothermal and nanocatalytic therapy in the near-infrared biowindow. Here the Fe single-atom dispersed N, S-doped carbon nanosheets (FeSA) nanozyme is dispersed by phospholipid-polyethylene glycol-amine (DSPE-PEG-NH2), and further loads artesunate (AS) via an amide reaction. Upon 808-nm laser irradiation in TME, the AS is released and further be catalyzed by the FeSA nanozyme to produce cytotoxic C-centered AFRs, and further be accelerated due to the photothermal conversion performance of FeSA (23.35%). The nanocatalytic process of FeSA nanozyme is realized by density functional theory (DFT). The tumor inhibition rates of a CT26 xenograft model is 92% through a photothermal-enhanced nanocatalytic synergistic therapy, and negligible systematic toxicity is observed. This work offers a potential paradigm of multifunctional single atomic catalysts (SACs) for enhancing tumor nanocatalytic therapy. STATEMENT OF SIGNIFICANCE: We designed a pH/photothermal dual responsive nanosystem (PFeSA@AS) for nanocatalytic therapy: (1) the nanosystem responsively releases AS under 808-nm laser irradiation in TME; (2) FeSA in the nanosystem can act as heme mimetic to convert AS into high cytotoxic C-centered free radicals for nanocatalytic therapy; (3) the photothermal conversion performance of FeSA further enhances the catalytic process to yield abundant AFR. Both in vitro and in vivo results demonstrate that this nanosystem can efficiently inhibit tumor growth through a photothermal-enhanced nanocatalytic synergistic therapy.

Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content.

Introduction: Genomic selection (GS) is a potential breeding approach for soybean improvement. Methods: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. Results: Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log10(P) greater than 1 were included. Discussion: This information will help improve GS efficiency and facilitate the application of GS.