The barley pan-genome reveals the hidden legacy of mutation breeding.

Genetic diversity is key to crop improvement. Owing to pervasive genomic structural variation, a single reference genome assembly cannot capture the full complement of sequence diversity of a crop species (known as the 'pan-genome'1). Multiple high-quality sequence assemblies are an indispensable component of a pan-genome infrastructure. Barley (Hordeum vulgare L.) is an important cereal crop with a long history of cultivation that is adapted to a wide range of agro-climatic conditions2. Here we report the construction of chromosome-scale sequence assemblies for the genotypes of 20 varieties of barley-comprising landraces, cultivars and a wild barley-that were selected as representatives of global barley diversity. We catalogued genomic presence/absence variants and explored the use of structural variants for quantitative genetic analysis through whole-genome shotgun sequencing of 300 gene bank accessions. We discovered abundant large inversion polymorphisms and analysed in detail two inversions that are frequently found in current elite barley germplasm; one is probably the product of mutation breeding and the other is tightly linked to a locus that is involved in the expansion of geographical range. This first-generation barley pan-genome makes previously hidden genetic variation accessible to genetic studies and breeding.

CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops.

The explosive amount of multi-omics data has brought a paradigm shift both in academic research and further application in life science. However, managing and reusing the growing resources of genomic and phenotype data points presents considerable challenges for the research community. There is an urgent need for an integrated database that combines genome-wide association studies (GWAS) with genomic selection (GS). Here, we present CropGS-Hub, a comprehensive database comprising genotype, phenotype, and GWAS signals, as well as a one-stop platform with built-in algorithms for genomic prediction and crossing design. This database encompasses a comprehensive collection of over 224 billion genotype data and 434 thousand phenotype data generated from >30 000 individuals in 14 representative populations belonging to 7 major crop species. Moreover, the platform implemented three complete functional genomic selection related modules including phenotype prediction, user model training and crossing design, as well as a fast SNP genotyper plugin-in called SNPGT specifically built for CropGS-Hub, aiming to assist crop scientists and breeders without necessitating coding skills. CropGS-Hub can be accessed at https://iagr.genomics.cn/CropGS/.

TEDD: a database of temporal gene expression patterns during multiple developmental periods in human and model organisms.

Characterization of the specific expression and chromatin profiles of genes enables understanding how they contribute to tissue/organ development and the mechanisms leading to diseases. Whilst the number of single-cell sequencing studies is increasing dramatically; however, data mining and reanalysis remains challenging. Herein, we systematically curated the up-to-date and most comprehensive datasets of sequencing data originating from 2760 bulk samples and over 5.1 million single-cells from multiple developmental periods from humans and multiple model organisms. With unified and systematic analysis, we profiled the gene expression and chromatin accessibility among 481 cell-types, 79 tissue-types and 92 timepoints, and pinpointed cells with the co-expression of target genes. We also enabled the detection of gene(s) with a temporal and cell-type specific expression profile that is similar to or distinct from that of a target gene. Additionally, we illustrated the potential upstream and downstream gene-gene regulation interactions, particularly under the same biological process(es) or KEGG pathway(s). Thus, TEDD (Temporal Expression during Development Database), a value-added database with a user-friendly interface, not only enables researchers to identify cell-type/tissue-type specific and temporal gene expression and chromatin profiles but also facilitates the association of genes with undefined biological functions in development and diseases. The database URL is https://TEDD.obg.cuhk.edu.hk/.

VThunter: a database for single-cell screening of virus target cells in the animal kingdom.

Viral infectious diseases are a devastating and continuing threat to human and animal health. Receptor binding is the key step for viral entry into host cells. Therefore, recognizing viral receptors is fundamental for understanding the potential tissue tropism or host range of these pathogens. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology has paved the way for studying the expression of viral receptors in different tissues of animal species at single-cell resolution, resulting in huge scRNA-seq datasets. However, effectively integrating or sharing these datasets among the research community is challenging, especially for laboratory scientists. In this study, we manually curated up-to-date datasets generated in animal scRNA-seq studies, analyzed them using a unified processing pipeline, and comprehensively annotated 107 viral receptors in 142 viruses and obtained accurate expression signatures in 2 100 962 cells from 47 animal species. Thus, the VThunter database provides a user-friendly interface for the research community to explore the expression signatures of viral receptors. VThunter offers an informative and convenient resource for scientists to better understand the interactions between viral receptors and animal viruses and to assess viral pathogenesis and transmission in species. Database URL: https://db.cngb.org/VThunter/.

Comprehensive analysis of immune subtypes reveals the prognostic value of cytotoxicity and FAP+ fibroblasts in stomach adenocarcinoma.

BACKGROUND: The heterogeneity limits the effective application of immune checkpoint inhibitors for patients with stomach adenocarcinoma (STAD). Precise immunotyping can help select people who may benefit from immunotherapy and guide postoperative management by describing the characteristics of tumor microenvironment. METHODS: Gene expression profiles and clinical information of patients were collected from ACRG and TCGA-STAD datasets. The immune subtypes (ISs) were identified by consensus clustering analysis. The tumor immune microenvironments (TIME) of each IS were characterized using a series of immunogenomics methods and further confirmed by multiplex immunohistochemistry (mIHC) staining in clinical samples. Two online datasets and one in-house dataset were utilized to construct and validate a prognostic immune-related gene (IRG) signature. RESULTS: STAD patients were stratified into five reproducible ISs. IS1 (immune deserve subtype) had low immune infiltration and the highest degree of HER2 gene mutation. With abundant CD8+ T cells infiltration and activated cytotoxicity reaction, patients in the IS2 (immune-activated subtype) had the best overall survival (OS). IS3 and IS4 subtypes were both in the reactive stroma state and indicated the worst prognosis. However, IS3 (immune-inhibited subtype) was characterized by enrichment of FAP+ fibroblasts and upregulated TGF-ß signaling pathway, while IS4 (activated stroma subtype) was characterized by enrichment of ACTA2+ fibroblasts. In addition, mIHC staining confirmed that TGF-ß upregulated FAP+ fibroblasts were independent risk factor of OS. IS5 (chronic inflammation subtype) displayed moderate immune cells infiltration and had a relatively good survival. Lastly, we developed a nine-IRG signature model with a robust performance on overall survival prognostication. CONCLUSIONS: The immunotyping is indicative for characterize the TIME heterogeneity and the prediction of tumor prognosis for STADs, which may provide valuable stratification for the design of future immunotherapy.

Genome architecture and diverged selection shaping pattern of genomic differentiation in wild barley.

Divergent selection of populations in contrasting environments leads to functional genomic divergence. However, the genomic architecture underlying heterogeneous genomic differentiation remains poorly understood. Here, we de novo assembled two high-quality wild barley (Hordeum spontaneum K. Koch) genomes and examined genomic differentiation and gene expression patterns under abiotic stress in two populations. These two populations had a shared ancestry and originated in close geographic proximity but experienced different selective pressures due to their contrasting micro-environments. We identified structural variants that may have played significant roles in affecting genes potentially associated with well-differentiated phenotypes such as flowering time and drought response between two wild barley genomes. Among them, a 29-bp insertion into the promoter region formed a cis-regulatory element in the HvWRKY45 gene, which may contribute to enhanced tolerance to drought. A single SNP mutation in the promoter region may influence HvCO5 expression and be putatively linked to local flowering time adaptation. We also revealed significant genomic differentiation between the two populations with ongoing gene flow. Our results indicate that SNPs and small SVs link to genetic differentiation at the gene level through local adaptation and are maintained through divergent selection. In contrast, large chromosome inversions may have shaped the heterogeneous pattern of genomic differentiation along the chromosomes by suppressing chromosome recombination and gene flow. Our research offers novel insights into the genomic basis underlying local adaptation and provides valuable resources for the genetic improvement of cultivated barley.

Digital Image Analysis of Ki67 Heterogeneity Improves the Diagnosis and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms.

Ki67 is a reliable grading and prognostic biomarker of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). The intratumor heterogeneity of Ki67, correlated with tumor progression, is a valuable factor that requires image analysis. The application of digital image analysis (DIA) enables new approaches for the assessment of Ki67 heterogeneity distribution. We investigated the diagnostic utility of Ki67 heterogeneity parameters in the classification and grading of GEP-NENs and explored their clinical values with regard to their prognostic relevance. The DIA algorithm was performed on whole-slide images of 102 resection samples with Ki67 staining. Good agreement was observed between the manual and DIA methods in the hotspot evaluation (R2 = 0.94, P < .01). Using the grid-based region of interest approach, score-based heat maps provided a distinctive overview of the intratumoral distribution of Ki67 between neuroendocrine carcinomas and neuroendocrine tumors. The computation of heterogeneity parameters related to DIA-determined Ki67 showed that the coefficient of variation and Morisita-Horn index were directly related to the classification and grading of GEP-NENs and provided insights into distinguishing high-grade neuroendocrine neoplasms (grade 3 neuroendocrine tumor vs neuroendocrine carcinoma, P < .01). Our study showed that a high Morisita-Horn index correlated with poor disease-free survival (multivariate analysis: hazard ratio, 56.69), which was found to be the only independent predictor of disease-free survival in patients with GEP-NEN. These spatial biomarkers have an impact on the classification and grading of tumors and highlight the prognostic associations of tumor heterogeneity. Digitization of Ki67 variations provides a direct and objective measurement of tumor heterogeneity and better predicts the biological behavior of GEP-NENs.

A chromosome conformation capture ordered sequence of the barley genome.

Cereal grasses of the Triticeae tribe have been the major food source in temperate regions since the dawn of agriculture. Their large genomes are characterized by a high content of repetitive elements and large pericentromeric regions that are virtually devoid of meiotic recombination. Here we present a high-quality reference genome assembly for barley (Hordeum vulgare L.). We use chromosome conformation capture mapping to derive the linear order of sequences across the pericentromeric space and to investigate the spatial organization of chromatin in the nucleus at megabase resolution. The composition of genes and repetitive elements differs between distal and proximal regions. Gene family analyses reveal lineage-specific duplications of genes involved in the transport of nutrients to developing seeds and the mobilization of carbohydrates in grains. We demonstrate the importance of the barley reference sequence for breeding by inspecting the genomic partitioning of sequence variation in modern elite germplasm, highlighting regions vulnerable to genetic erosion.

Calcipotriol abrogates cancer-associated fibroblast-derived IL-8-mediated oxaliplatin resistance in gastric cancer cells via blocking PI3K/Akt signaling.

Activation of vitamin D receptor (VDR) in cancer-associated fibroblasts (CAFs) has been implicated in hesitating tumor progression and chemoresistance of several human malignancies. Yet, the role of VDR in CAF-induced chemotherapy resistance of gastric cancer (GC) cells remains elusive. In this study we first conducted immunohistochemistry analysis on tissue microarrays including 88 pairs of GC and normal mucosa samples, and provided clinical evidence that VDR was mainly expressed in gastric mucous cells but almost invisible in CAFs, and VDR expression was negatively correlated with malignant clinical phenotype and advanced stages, low VDR expression confers to poor overall survival rate of patients with GC. In a co-culture system of primary CAFs and cancer cells, we showed that treatment of HGC-27 and AGS GC cells with VDR ligand calcipotriol (Cal, 500 nM) significantly inhibited CAF-induced oxaliplatin resistance. By using RNA-sequencing and Human Cytokine Antibody Array, we demonstrated that IL-8 secretion from CAFs induced oxaliplatin resistance via activating the PI3K/AKT pathway in GC, whereas Cal treatment greatly attenuated the tumor-supportive effect of CAF-derived IL-8 on GC cells. Taken together, this study verifies the specific localization of VDR in GC tissues and demonstrates that activation of VDR abrogates CAF-derived IL-8-mediated oxaliplatin resistance in GC via blocking PI3K/Akt signaling, suggesting vitamin D supplementation as a potential strategy of enhancing the anti-tumor effect of chemotherapy in GC.

Effect of fracture risk in inhaled corticosteroids in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.

BACKGROUND: The fracture risk of patients with chronic obstructive pulmonary disease (COPD) treated with inhaled corticosteroids is controversial. And some large-scale randomized controlled trials have not solved this problem. The purpose of our systematic review and meta-analysis including 44 RCTs is to reveal the effect of inhaled corticosteroids on the fracture risk of COPD patients. METHODS: Two reviewers independently retrieved randomized controlled trials of inhaled corticosteroids or combinations of inhaled corticosteroids in the treatment of COPD from PubMed, Embase, Medline, Cochrane Library, and Web of Science. The primary outcome was a fracture event. This study was registered at PROSPERO (CRD42022366778). RESULTS: Forty-four RCTs were performed in 87,594 patients. Inhaled therapy containing ICSs (RR, 1.19; 95%CI, 1.04-1.37; P = 0.010), especially ICS/LABA (RR, 1.30; 95%CI, 1.10-1.53; P = 0.002) and triple therapy (RR, 1.49; 95%CI, 1.03-2.17; P = 0.04) were significantly associated with the increased risk of fracture in COPD patients when compared with inhaled therapy without ICSs. Subgroup analyses showed that treatment duration ≥ 12 months (RR, 1.19; 95%CI, 1.04-1.38; P = 0.01), budesonide therapy (RR, 1.64; 95%CI., 1.07-2.51; P = 0.02), fluticasone furoate therapy (RR, 1.37; 95%CI, 1.05-1.78; P = 0.02), mean age of study participants ≥ 65 (RR, 1.27; 95%CI, 1.01-1.61; P = 0.04), and GOLD stage III(RR, 1.18; 95%CI, 1.00-1.38; P = 0.04) were significantly associated with an increased risk of fracture. In addition, budesonide ≥ 320 ug bid via MDI (RR, 1.75; 95%CI, 1.07-2.87; P = 0.03) was significantly associated with the increased risk of fracture. CONCLUSION: Inhalation therapy with ICSs, especially ICS/LABA or triple therapy, increased the risk of fracture in patients with COPD compared with inhaled therapy without ICS. Treatment duration, mean age of participants, GOLD stage, drug dosage form, and drug dose participated in this association. Moreover, different inhalation devices of the same drug also had differences in risk of fracture.

Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis.

BACKGROUND: Invasive detection methods such as liver biopsy are currently the gold standard for diagnosing liver cirrhosis and can be used to determine the degree of liver fibrosis and cirrhosis. In contrast, non-invasive diagnostic methods, such as ultrasonography, elastography, and clinical prediction scores, can prevent patients from invasiveness-related discomfort and risks and are often chosen as alternative or supplementary diagnostic methods for liver fibrosis or cirrhosis. However, these non-invasive methods cannot specify the pathological grading and early diagnosis of the lesions. Recent studies have revealed that gut microbiome-based machine learning can be utilized as a non-invasive diagnostic technique for liver cirrhosis or fibrosis, but there is no evidence-based support. Therefore, this study conducted a systematic review and meta-analysis for the first time to investigate the accuracy of machine learning based on the gut microbiota in the prediction of liver fibrosis and cirrhosis. METHODS: A comprehensive and systematic search of publications published before April 2th, 2023 in PubMed, Cochrane Library, Embase, and Web of Science was conducted for relevant studies on the application of gut microbiome-based metagenomic sequencing modeling technology to the diagnostic prediction of liver cirrhosis or fibrosis. A bivariate mixed-effects model and Stata software 15.0 were adopted for the meta-analysis. RESULTS: Ten studies were included in the present study, involving 11 prediction trials and 838 participants, 403 of whom were fibrotic and cirrhotic patients. Meta-analysis showed the pooled sensitivity (SEN) = 0.81 [0.75, 0.85], specificity (SEP) = 0.85 [0.77, 0.91], positive likelihood ratio (PLR) = 5.5 [3.6, 8.7], negative likelihood ratio (NLR) = 0.23 [0.18, 0.29], diagnostic odds ratio (DOR) = 24 [14, 41], and area under curve (AUC) = 0.86 [0.83-0.89]. The results demonstrated that machine learning methods had excellent potential to analyze gut microbiome data and could effectively predict liver cirrhosis or fibrosis. Machine learning provides a powerful tool for non-invasive prediction and diagnosis of liver cirrhosis or liver fibrosis, with broad clinical application prospects. However, these results need to be interpreted with caution due to limited clinical data. CONCLUSION: Gut microbiome-based machine learning can be utilized as a practical, non-invasive technique for the diagnostic prediction of liver cirrhosis or fibrosis. However, most of the included studies applied the random forest algorithm in modeling, so a diversified prediction system based on microorganisms is needed to improve the non-invasive detection of liver cirrhosis or fibrosis.

Support of deep learning to classify vocal fold images in flexible laryngoscopy.

PURPOSE: To collect a dataset with adequate laryngoscopy images and identify the appearance of vocal folds and their lesions in flexible laryngoscopy images by objective deep learning models. METHODS: We adopted a number of novel deep learning models to train and classify 4549 flexible laryngoscopy images as no vocal fold, normal vocal folds, and abnormal vocal folds. This could help these models recognize vocal folds and their lesions within these images. Ultimately, we made a comparison between the results of the state-of-the-art deep learning models, and another comparison of the results between the computer-aided classification system and ENT doctors. RESULTS: This study exhibited the performance of the deep learning models by evaluating laryngoscopy images collected from 876 patients. The efficiency of the Xception model was higher and steadier than almost the rest of the models. The accuracy of no vocal fold, normal vocal folds, and vocal fold abnormalities on this model were 98.90 %, 97.36 %, and 96.26 %, respectively. Compared to our ENT doctors, the Xception model produced better results than a junior doctor and was near an expert. CONCLUSION: Our results show that current deep learning models can classify vocal fold images well and effectively assist physicians in vocal fold identification and classification of normal or abnormal vocal folds.

A global barley panel revealing genomic signatures of breeding in modern Australian cultivars.

The future of plant cultivar improvement lies in the evaluation of genetic resources from currently available germplasm. Today's gene pool of crop genetic diversity has been shaped during domestication and more recently by breeding. Recent efforts in plant breeding have been aimed at developing new and improved varieties from poorly adapted crops to suit local environments. However, the impact of these breeding efforts is poorly understood. Here, we assess the contributions of both historical and recent breeding efforts to local adaptation and crop improvement in a global barley panel by analysing the distribution of genetic variants with respect to geographic region or historical breeding category. By tracing the impact that breeding had on the genetic diversity of Hordeum vulgare (barley) released in Australia, where the history of barley production is relatively young, we identify 69 candidate regions within 922 genes that were under selection pressure. We also show that modern Australian barley varieties exhibit 12% higher genetic diversity than historical cultivars. Finally, field-trialling and phenotyping for agriculturally relevant traits across a diverse range of Australian environments suggests that genomic regions under strong breeding selection and their candidate genes are closely associated with key agronomic traits. In conclusion, our combined data set and germplasm collection provide a rich source of genetic diversity that can be applied to understanding and improving environmental adaptation and enhanced yields.

The immune subtypes and landscape of sarcomas.

BACKGROUND: Considering the molecular heterogeneity of sarcomas and their immunologically quiet character, immunotherapy (e.g., immune checkpoint inhibitors) plays a viable role in only a subset of these tumors. This study aimed to determine the immune subtypes (IMSs) of sarcomas for selecting suitable patients from an extremely heterogeneous population. RESULTS: By performing consensus clustering analysis of the gene expression profiles of 538 patients with sarcomas in online databases, we stratified sarcomas into three IMSs characterized by different immune cell features, tumor mutational burdens (TMBs), gene mutations, and clinical outcomes. IMS1 showed an immune "hot" and immunosuppressive phenotype, the highest frequencies of CSMD3 mutation but the lowest frequencies of HMCN1 and LAMA2 mutations; these patients had the worst progression-free survival (PFS). IMS2 was defined by a high TMB and more gene mutations, but had the lowest frequency of MND1 mutations. IMS3 displayed the highest MDN1 expression level and an immune "cold" phenotype, these patients had the worst PFS. Each subtype was associated with different expression levels of immunogenic cell death modulators and immune checkpoints. Moreover, we applied graph learning-based dimensionality reduction to the immune landscape and identified significant intra-cluster heterogeneity within each IMS. Finally, we developed and validated an immune gene signature with good prognostic performance. CONCLUSIONS: Our results provide a conceptual framework for understanding the immunological heterogeneity of sarcomas. The identification of immune-related subtypes may facilitate optimal selection of sarcoma patients who will respond to appropriate therapeutic strategies.

Molecular signatures of tumor progression in pancreatic adenocarcinoma identified by energy metabolism characteristics.

BACKGROUND: In this study, we performed a molecular evaluation of primary pancreatic adenocarcinoma (PAAD) based on the comprehensive analysis of energy metabolism-related gene (EMRG) expression profiles. METHODS: Molecular subtypes were identified by nonnegative matrix clustering of 565 EMRGs. An overall survival (OS) predictive gene signature was developed and internally and externally validated based on three online PAAD datasets. Hub genes were identified in molecular subtypes by weighted gene correlation network analysis (WGCNA) coexpression algorithm analysis and considered as prognostic genes. LASSO cox regression was conducted to establish a robust prognostic gene model, a four-gene signature, which performed better in survival prediction than four previously reported models. In addition, a novel nomogram constructed by combining clinical features and the 4-gene signature showed high-confidence clinical utility. According to gene set enrichment analysis (GSEA), gene sets related to the high-risk group participate in the neuroactive ligand receptor interaction pathway. CONCLUSIONS: In summary, EMRG-based molecular subtypes and prognostic gene models may provide a novel research direction for patient stratification and trials of targeted therapies.

Multiplexed immunofluorescence analysis of CAF-markers, EZH2 and FOXM1 in gastric tissue: associations with clinicopathological parameters and clinical outcomes.

BACKGROUND: The aim of this study is to explore the expression and clinical relevance of CAF-associated markers, EZH2 and FOXM1 in gastric samples. METHODS: Protein expression were detected and evaluated by multi-plex immunofluorescence (mIF) in 93 cases of gastric cancer (GC) and 31 cases of gastric intraepithelial neoplasia (GIN). The correlation among their expression, and the relationship of them with clinicopathological parameters and prognosis in GC were then analyzed. RESULTS: FAP was specific expressed in the CAFs of GC samples, and thus be utilized as a CAF-associated marker in our subsequently analysis. The immunostaining of EZH2, FOXM1 and FAP were significantly upregulated in patients with GC tissues than in those normal gastric mucosa or GIN tissues. The average fluorescence intensity of FAP was slightly positively correlated with EZH2 in GC, GIN and normal samples, whereas the percentage of FAP positive cells has no correlation with that of EZH2. Both the percentage of positive cells and the average fluorescence intensity of FOXM1 were positively correlated with that of FAP and EZH2 in GC, GIN and normal samples, except for FOXM1 and EZH2 expression in normal tissue samples. No significant association was observed between FAP expression and any clinicopathological parameters, whereas the positive frequency of EZH2 and FOXM1 were correlated with tumor location significantly and tumor invasion depth, respectively. In addition, there was strong positive correlations between FAP protein expression and overall survival (OS) and disease-free survival (DFS), and EZH2 expression was positively associated with OS in patients with GC. Furthermore, EZH2 and FAP protein expression was an independent prognostic factor for OS and DFS, respectively. CONCLUSIONS: These results suggest that both EZH2 and FOXM1 expression was positively associated with CAFs abundance in GC. They may be potential cellular target for therapeutic intervention, especially in patients with a large number of CAFs.

A study protocol of a randomized phase II trial of perioperative chemoimmunotherapy verses perioperative chemoimmunotherapy plus preoperative chemoradiation for locally advanced gastric (G) or gastroesophageal junction (GEJ) adenocarcinoma: the NeoRacing study.

BACKGROUND: Perioperative chemotherapy (ChT) and preoperative chemoradiation (CRT) are both the standard treatments for locally advanced gastric cancer (LAGC). CRT can achieve a higher pathological complete regression (pCR) rate, but whether this higher pCR rate can be transformed into a long-term survival benefit remains inconclusive. Therefore, relevant studies are in progress. On the other hand, immunotherapy has been established for the first-line treatment of advanced gastric cancer (AGC) and has been widely explored in the perioperative setting. The combination of chemotherapy/radiotherapy and immunotherapy may have a synergistic effect, which will lead to a better antitumor effect. The preliminary reports of ongoing studies show promising results, including a further improved pCR rate. However, the preferred treatment combination for LAGC is still not established. To solve this problem, we are carrying out this randomized phase II trial, which aims to evaluate the efficacy and safety of perioperative chemotherapy plus the use of PD-1 antibody with or without preoperative chemoradiation for LAGC. METHODS: Eligible patients with LAGC or gastroesophageal junction (GEJ) adenocarcinoma were randomized to receive perioperative ChT, PD-1 antibody, surgery with (Arm A) or without preoperative CRT (Arm B), and PD-1 antibody maintenance until one year after surgery. The primary endpoint of this study is that the pCR rate of Arm A will be significantly higher than that of Arm B. The secondary endpoints include the pathological partial regression (pPR) rate, R0 resection rate, objective response rate (ORR), event-free survival (EFS), overall survival (OS), safety and surgical complications. Moreover, several explorative endpoints will be evaluated to find and validate the predictive biomarkers of immunotherapy. DISCUSSION: The results of the NeoRacing study will provide important information concerning the application of PD-1 antibody in LAGC patients during the perioperative setting. Meanwhile, the two treatment protocols will be compared in terms of efficacy and safety. TRIAL REGISTRATION: ClinicalTrials.gov , NCT05161572 . Registered 17 December 2021 - Retrospectively registered.

Stanniocalcin-2 promotes cell EMT and glycolysis via activating ITGB2/FAK/SOX6 signaling pathway in nasopharyngeal carcinoma.

Stanniocalcin-2 (STC2) has been proved to regulate a variety of signaling pathways including cell growth, metastasis, and therapeutic resistance. However, the role of STC2 in the regulation of nasopharyngeal carcinoma (NPC) remains poorly understood. In this study, we investigated the regulatory function of STC2 on epithelial-mesenchymal transition (EMT) and glycolysis traits in NPC and revealed the underlying molecular mechanisms. We found that STC2 was highly expressed in primary nasopharyngeal carcinoma tissues and lymph node metastatic tissues. Silencing of STC2 inhibited cell proliferation, invasion, and glycolysis. Further analyses for the clinical samples demonstrated that STC2 expression was associated with the poor clinical progression. Moreover, we demonstrated the interaction of ITGB2 with STC2 and its involvement in STC2-mediated ITGB2/FAK/SOX6 axis. Collectively, our results provide new insights into understanding the regulatory mechanism of STC2 and suggest that the STC2/ITGB2/FAK/SOX6 signaling axis may be a potential therapeutic target for NPC.

Uncovering the evolutionary origin of blue anthocyanins in cereal grains.

Functional divergence after gene duplication plays a central role in plant evolution. Among cereals, only Hordeum vulgare (barley), Triticum aestivum (wheat) and Secale cereale (rye) accumulate delphinidin-derived (blue) anthocyanins in the aleurone layer of grains, whereas Oryza sativa (rice), Zea mays (maize) and Sorghum bicolor (sorghum) do not. The underlying genetic basis for this natural occurrence remains elusive. Here, we mapped the barley Blx1 locus involved in blue aleurone to an approximately 1.13 Mb genetic interval on chromosome 4HL, thus identifying a trigenic cluster named MbHF35 (containing HvMYB4H, HvMYC4H and HvF35H). Sequence and expression data supported the role of these genes in conferring blue-coloured (blue aleurone) grains. Synteny analyses across monocot species showed that MbHF35 has only evolved within distinct Triticeae lineages, as a result of dispersed gene duplication. Phylogeny analyses revealed a shared evolution pattern for MbHF35 in Triticeae, suggesting that these genes have co-evolved together. We also identified a Pooideae-specific flavonoid 3',5'-hydroxylase (F3'5'H) lineage, termed here Mo_F35H2, which has a higher amino acid similarity with eudicot F3'5'Hs, demonstrating a scenario of convergent evolution. Indeed, selection tests identified 13 amino acid residues in Mo_F35H2 that underwent positive selection, possibly driven by protein thermostablility selection. Furthermore, through the interrogation of barley germplasm there is evidence that HvMYB4H and HvMYC4H have undergone human selection. Collectively, our study favours blue aleurone as a recently evolved trait resulting from environmental adaptation. Our findings provide an evolutionary explanation for the absence of blue anthocyanins in other cereals and highlight the importance of gene functional divergence for plant diversity and environmental adaptation.

Genomic structural equation modelling provides a whole-system approach for the future crop breeding.

KEY MESSAGE: Using genomic structural equation modelling, this research demonstrates an efficient way to identify genetically correlating traits and provides an effective proxy for multi-trait selection to consider the joint genetic architecture of multiple interacting traits in crop breeding. Breeding crop cultivars with optimal value across multiple traits has been a challenge, as traits may negatively correlate due to pleiotropy or genetic linkage. For example, grain yield and grain protein content correlate negatively with each other in cereal crops. Future crop breeding needs to be based on practical yet accurate evaluation and effective selection of beneficial trait to retain genes with the best agronomic score for multiple traits. Here, we test the framework of whole-system-based approach using structural equation modelling (SEM) to investigate how one trait affects others to guide the optimal selection of a combination of agronomically important traits. Using ten traits and genome-wide SNP profiles from a worldwide barley panel and SEM analysis, we revealed a network of interacting traits, in which tiller number contributes positively to both grain yield and protein content; we further identified common genetic factors affecting multiple traits in the network of interaction. Our method demonstrates an efficient way to identify genetically correlating traits and underlying pleiotropic genetic factors and provides an effective proxy for multi-trait selection within a whole-system framework that considers the joint genetic architecture of multiple interacting traits in crop breeding. Our findings suggest the promise of a whole-system approach to overcome challenges such as the negative correlation of grain yield and protein content to facilitating quantitative and objective breeding decisions in future crop breeding.