Differential allelopathic effects of mangrove plants Kandelia obovata and Aegiceras corniculatum on harmful algal species: Potential applications in algal bloom control.

This study examined effects of mangrove plants Kandelia obovata and Aegiceras corniculatum on harmful algal species. While A. corniculatum leaf extract had no inhibitory effect, K. obovata leaf extract significantly inhibited the growth of two harmful algal species Alexandrium tamarense and Karenia mikimotoi. The inhibitory effect was concentration-dependent, with over 90 % inhibition at the highest concentration. Morphological changes and cell size reduction were observed in both microalgae. Excessive production of reactive oxygen species and damage to algal photosynthetic system were found. The allelopathic effect of K. obovata on K. mikimotoi with low-concentration repeated exposure was more effective than high-concentration single exposure. The EC50 of K. obovata (0.33 g L-1) was lower than reported values on other coastal plants. Higher inhibitory effects of K. obovata were found on naked algal species than the armoured ones. These findings suggest potential applications of K. obovata leaf extract in controlling harmful algal blooms.

Identifying key circulating tumor DNA parameters for predicting clinical outcomes in metastatic non-squamous non-small cell lung cancer after first-line chemoimmunotherapy.

Circulating tumor DNA (ctDNA) provides valuable tumor-related information without invasive biopsies, yet consensus is lacking on optimal parameters for predicting clinical outcomes. Utilizing longitudinal ctDNA data from the large phase 3 IMpower150 study (NCT02366143) of atezolizumab in combination with chemotherapy with or without bevacizumab in patients with stage IV non-squamous Non-Small Cell Lung Cancer (NSCLC), here we report that post-treatment ctDNA response correlates significantly with radiographic response. However, only modest concordance is identified, revealing that ctDNA response is likely not a surrogate for radiographic response; both provide distinct information. Various ctDNA metrics, especially early ctDNA nadirs, emerge as primary predictors for progression-free survival and overall survival, potentially better assessing long-term benefits for chemoimmunotherapy in NSCLC. Integrating radiographic and ctDNA assessments enhances prediction of survival outcomes. We also identify optimal cutoff values for risk stratification and key assessment timepoints, notably Weeks 6-9, for insights into clinical outcomes. Overall, our identified optimal ctDNA parameters can enhance the prediction of clinical outcomes, refine trial designs, and inform therapeutic decisions for first-line NSCLC patients.

Genome-wide association studies on resistance to powdery mildew in cultivated emmer wheat.

Powdery mildew, caused by the fungal pathogen Blumeria graminis (DC.) E. O. Speer f. sp. tritici Em. Marchal (Bgt), is a constant threat to global wheat (Triticum aestivum L.) production. Although ∼100 powdery mildew (Pm) resistance genes and alleles have been identified in wheat and its relatives, more is needed to minimize Bgt's fast evolving virulence. In tetraploid wheat (Triticum turgidum L.), wild emmer wheat [T. turgidum ssp. dicoccoides (Körn. ex Asch. & Graebn.) Thell.] accessions from Israel have contributed many Pm resistance genes. However, the diverse genetic reservoirs of cultivated emmer wheat [T. turgidum ssp. dicoccum (Schrank ex Schübl.) Thell.] have not been fully exploited. In the present study, we evaluated a diverse panel of 174 cultivated emmer accessions for their reaction to Bgt isolate OKS(14)-B-3-1 and found that 66% of accessions, particularly those of Ethiopian (30.5%) and Indian (6.3%) origins, exhibited high resistance. To determine the genetic basis of Bgt resistance in the panel, genome-wide association studies were performed using 46,383 single nucleotide polymorphisms (SNPs) from genotype-by-sequencing and 4331 SNPs from the 9K SNP Infinium array. Twenty-five significant SNP markers were identified to be associated with Bgt resistance, of which 21 SNPs are likely novel loci, whereas four possibly represent emmer derived Pm4a, Pm5a, PmG16, and Pm64. Most novel loci exhibited minor effects, whereas three novel loci on chromosome arms 2AS, 3BS, and 5AL had major effect on the phenotypic variance. This study demonstrates cultivated emmer as a rich source of powdery mildew resistance, and the resistant accessions and novel loci found herein can be utilized in wheat breeding programs to enhance Bgt resistance in wheat.

Association mapping of tan spot and septoria nodorum blotch resistance in cultivated emmer wheat.

KEY MESSAGE: A total of 65 SNPs associated with resistance to tan spot and septoria nodorum blotch were identified in a panel of 180 cultivated emmer accessions through association mapping Tan spot and septoria nodorum blotch (SNB) are foliar diseases caused by the respective fungal pathogens Pyrenophora tritici-repentis and Parastagonospora nodorum that affect global wheat production. To find new sources of resistance, we evaluated a panel of 180 cultivated emmer wheat (Triticum turgidum ssp. dicoccum) accessions for reactions to four P. tritici-repentis isolates Pti2, 86-124, 331-9 and DW5, two P. nodorum isolate, Sn4 and Sn2000, and four necrotrophic effectors (NEs) produced by the pathogens. About 8-36% of the accessions exhibited resistance to the four P. tritici-repentis isolates, with five accessions demonstrating resistance to all isolates. For SNB, 64% accessions showed resistance to Sn4, 43% to Sn2000 and 36% to both isolates, with Spain (11% accessions) as the most common origin of resistance. To understand the genetic basis of resistance, association mapping was performed using SNP (single nucleotide polymorphism) markers generated by genotype-by-sequencing and the 9 K SNP Infinium array. A total of 46 SNPs were significantly associated with tan spot and 19 SNPs with SNB resistance or susceptibility. Six trait loci on chromosome arms 1BL, 3BL, 4AL (2), 6BL and 7AL conferred resistance to two or more isolates. Known NE sensitivity genes for disease development were undetected except Snn5 for Sn2000, suggesting novel genetic factors are controlling host-pathogen interaction in cultivated emmer. The emmer accessions with the highest levels of resistance to the six pathogen isolates (e.g., CItr 14133-1, PI 94634-1 and PI 377672) could serve as donors for tan spot and SNB resistance in wheat breeding programs.

Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA.

PURPOSE: To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and treatment strategies for patients with 1L non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: Using IMpower150 trial data on patients with untreated metastatic NSCLC treated with atezolizumab and chemotherapies, we developed a machine learning algorithm to extract predictive features from ctDNA kinetics, improving survival and progression prediction. We analyzed kinetic data from 17 ctDNA summary markers, including cell-free DNA concentration, allele frequency, tumor molecules in plasma, and mutation counts. RESULTS: Three hundred and ninety-eight patients with ctDNA data (206 in training and 192 in validation) were analyzed. Our models outperformed existing workflow using conventional temporal ctDNA features, raising overall survival (OS) concordance index to 0.72 and 0.71 from 0.67 and 0.63 for C3D1 and C4D1, respectively, and substantially improving progression-free survival (PFS) to approximately 0.65 from the previous 0.54-0.58, a 12%-20% increase. Additionally, they enhanced risk stratification for patients with NSCLC, achieving clear OS and PFS separation. Distinct patterns of ctDNA kinetic characteristics (eg, baseline ctDNA markers, depth of ctDNA responses, and timing of ctDNA clearance, etc) were revealed across the risk groups. Rapid and complete ctDNA clearance appears essential for long-term clinical benefit. CONCLUSION: Our machine learning approach offers a novel tool for analyzing ctDNA kinetics, extracting critical features from longitudinal data, improving our understanding of the link between ctDNA kinetics and progression/mortality risks, and optimizing personalized immunotherapies for 1L NSCLC.

Organ-specific tumor dynamics predict survival of patients with metastatic colorectal cancer.

BACKGROUND: We aim to compare the prognostic value of organ-specific dynamics with the sum of the longest diameter (SLD) dynamics in patients with metastatic colorectal cancer (mCRC). METHODS: All datasets are accessible in Project Data Sphere, an open-access platform. The tumor growth inhibition models developed based on organ-level SLD and SLD were used to estimate the organ-specific tumor growth rates (KGs) and SLD KG. The early tumor shrinkage (ETS) from baseline to the first measurement after treatment was also evaluated. The relationship between organ-specific dynamics, SLD dynamics, and survival outcomes (overall survival, OS; progression-free survival, PFS) was quantified using Kaplan-Meier analysis and Cox regression. RESULTS: This study included 3687 patients from 6 phase III mCRC trials. The liver emerged as the most frequent metastatic site (2901, 78.7 %), with variable KGs across different organs in individual patients (liver 0.0243 > lung 0.0202 > lymph node 0.0127 > other 0.0118 [week-1]). Notably, the dynamics for different organs did not equally contribute to predicting survival outcomes. In liver metastasis cases, liver KG proved to be a superior prognostic indicator for OS and surpasses the predictive performance of SLD, (C-index, liver KG 0.610 vs SLD KG 0.606). A similar result can be found for PFS. Moreover, liver ETS also outperforms SLD ETS in predicting survival. Cox regression analysis confirmed liver KG is the most significant variable in survival prediction. CONCLUSIONS: In mCRC patients with liver metastasis, liver dynamics is the primary prognostic indicator for both PFS and OS. In future drug development for mCRC, greater emphasis should be directed towards understanding the dynamics of liver metastasis development.

Evolution, diversity, and function of the disease susceptibility gene Snn1 in wheat.

Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a disease of durum and common wheat initiated by the recognition of pathogen-produced necrotrophic effectors (NEs) by specific wheat genes. The wheat gene Snn1 was previously cloned, and it encodes a wall-associated kinase that directly interacts with the NE SnTox1 leading to programmed cell death and ultimately the development of SNB. Here, sequence analysis of Snn1 from 114 accessions including diploid, tetraploid, and hexaploid wheat species revealed that some wheat lines possess two copies of Snn1 (designated Snn1-B1 and Snn1-B2) approximately 120 kb apart. Snn1-B2 evolved relatively recently as a paralog of Snn1-B1, and both genes have undergone diversifying selection. Three point mutations associated with the formation of the first SnTox1-sensitive Snn1-B1 allele from a primitive wild wheat were identified. Four subsequent and independent SNPs, three in Snn1-B1 and one in Snn1-B2, converted the sensitive alleles to insensitive forms. Protein modeling indicated these four mutations could abolish Snn1-SnTox1 compatibility either through destabilization of the Snn1 protein or direct disruption of the protein-protein interaction. A high-throughput marker was developed for the absent allele of Snn1, and it was 100% accurate at predicting SnTox1-insensitive lines in both durum and spring wheat. Results of this study increase our understanding of the evolution, diversity, and function of Snn1-B1 and Snn1-B2 genes and will be useful for marker-assisted elimination of these genes for better host resistance.

The Hessian fly resistance gene HvRHF1 is localized in an NBS-LRR gene cluster in barley.

Hessian fly (Mayetiola destructor Say) is a significant pest in cereal crops, causing substantial yield losses worldwide. While host resistance is the most efficient method for pest control, research on genetic characterization of Hessian fly resistance in barley (Hordeum vulgare L.) has been limited, and the underlying resistance mechanism remains largely unknown. In this study, we conducted fine mapping of a crucial Hessian fly resistance locus, known as HvRHF1, using a biparental population. Assisted with genetic markers and robust phenotyping assay, we pinpointed the HvRHF1 gene to an ~ 82 kb region on chromosome 4H. Gene prediction and annotation revealed that the HvRHF1 locus comprises three complete NBS-LRR genes, which are characteristic of disease resistance genes. As a result, our study not only provides valuable resources for resistance in barley and genetic tools for breeding, but also identifies candidate genes that lay the foundation for cloning HvRHF1. This endeavor will significantly contribute to our understanding of the molecular mechanisms underlying cereal resistance to Hessian fly.

Revealing the algicidal characteristics of Maribacter dokdonensis: An investigation into bacterial strain P4 isolated from Karenia mikimotoi bloom water.

Harmful algal blooms (HABs) are a global environmental concern, causing significant economic losses in fisheries and posing risks to human health. Algicidal bacteria have been suggested as a potential solution to control HABs, but their algicidal efficacy is influenced by various factors. This study aimed to characterize a novel algicidal bacterium, Maribacter dokdonensis (P4), isolated from a Karenia mikimotoi (Hong Kong strain, KMHK) HAB and assess the impact of P4 and KMHK's doses, growth phase, and algicidal mode and the axenicity of KMHK on P4's algicidal effect. Our results demonstrated that the algicidal effect of P4 was dose-dependent, with the highest efficacy at a dose of 25% v/v. The study also determined that P4's algicidal effect was indirect, with the P4 culture and the supernatant, but not the bacterial cells, showing significant effects. The algicidal efficacy was higher when both P4 and KMHK were in the stationary phase. Furthermore, the P4 culture at the log phase could effectively kill KMHK cells at the stationary phase, with higher algicidal efficacy in the bacterial culture than that of the supernatant alone. Interestingly, P4's algicidal efficacy was significantly higher when co-culturing with xenic KMHK (~90% efficacy at day 1) than that with the axenic KMHK (~50% efficacy at day 1), suggesting the presence of other bacteria could regulate P4's algicidal effect. The bacterial strain P4 also exhibited remarkable algicidal efficacy on four other dinoflagellate species, particularly the armored species. These results provide valuable insights into the algicidal effect of M. dokdonensis on K. mikimotoi and on their interactions.

Identification of Sr67, a new gene for stem rust resistance in KU168-2 located close to the Sr13 locus in wheat.

KEY MESSAGE: Sr67 is a new stem rust resistance gene that represents a new resource for breeding stem rust resistant wheat cultivars Re-appearance of stem rust disease, caused by the fungal pathogen Puccinia graminis f. sp. tritici (Pgt), in different parts of Europe emphasized the need to develop wheat varieties with effective resistance to local Pgt populations and exotic threats. A Kyoto University wheat (Triticum aestivum L.) accession KU168-2 was reported to carry good resistance to leaf and stem rust. To identify the genomic region associated with the KU168-2 stem rust resistance, a genetic study was conducted using a doubled haploid (DH) population from the cross RL6071 × KU168-2. The DH population was phenotyped with three Pgt races (TTKSK, TPMKC, and QTHSF) and genotyped using the Illumina 90 K wheat SNP array. Linkage mapping showed the resistance to all three Pgt races was conferred by a single stem rust resistance (Sr) gene on chromosome arm 6AL, associated with Sr13. Presently, four Sr13 resistance alleles have been reported. Sr13 allele-specific KASP and STARP markers, and sequencing markers all showed null alleles in KU168-2. KU168-2 showed a unique combination of seedling infection types for five Pgt races (TTKSK, QTHSF, RCRSF, TMRTF, and TPMKC) compared to Sr13 alleles. The phenotypic uniqueness of the stem rust resistance gene in KU168-2 and null alleles for Sr13 allele-specific markers showed the resistance was conferred by a new gene, designated Sr67. Since Sr13 is less effective in hexaploid background, Sr67 will be a good source of stem rust resistance in bread wheat breeding programs.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping.

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance.

PURPOSE: To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. MATERIALS AND METHODS: Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. RESULTS: Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. CONCLUSION: Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.

Evaluating Prognostic Value of Dynamics of Circulating Lactate Dehydrogenase in Colorectal Cancer Using Modeling and Machine Learning.

Pretreatment serum lactate dehydrogenase (LDH) levels have been associated with poor prognosis in several types of cancer, including metastatic colorectal cancer (mCRC). However, very few models link survival to longitudinal LDH measured repeatedly over time during treatment. We investigated the prognostic value of on-treatment LDH dynamics in mCRC. Using data from two large phase III studies (2L and 3L+ mCRC settings, n = 824 and 210, respectively), we found that integrating longitudinal LDH data with baseline risk factors significantly improved survival prediction. Current LDH values performed best, enhancing discrimination ability (area under the receiver operating characteristic curve) by 4.5~15.4% and prediction accuracy (Brier score) by 3.9~15.0% compared with baseline variables. Combining all longitudinal LDH markers further improved predictive performance. After controlling for baseline covariates and other longitudinal LDH indicators, current LDH levels remained a significant risk factor in mCRC, increasing mortality risk by over 90% (P < 0.001) in 2L patients and 60-70% (P < 0.01) in 3L+ patients per unit increment in current log (LDH). Machine-learning techniques, like functional principal component analysis (FPCA), extracted informative features from longitudinal LDH data, capturing over 99% of variability and allowing prediction of survival. Unsupervised clustering based on the extracted FPCA features stratified patients into three groups with distinct LDH dynamics and survival outcomes. Hence, our approaches offer a valuable and cost-effective way for risk stratification and improves survival prediction in mCRC using LDH trajectories.

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response.

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

Identification of robust yield quantitative trait loci derived from cultivated emmer for durum wheat improvement.

Durum wheat (Triticum turgidum ssp. durum L.) is an important world food crop used to make pasta products. Compared to bread wheat (Triticum aestivum L.), fewer studies have been conducted to identify genetic loci governing yield-component traits in durum wheat. A potential source of diversity for durum is its immediate progenitor, cultivated emmer (T. turgidum ssp. dicoccum). We evaluated two biparental populations of recombinant inbred lines (RILs) derived from crosses between the durum lines Ben and Rusty and the cultivated emmer wheat accessions PI 41025 and PI 193883, referred to as the Ben × PI 41025 (BP025) and Rusty × PI 193883 (RP883) RIL populations, respectively. Both populations were evaluated under field conditions in three seasons with an aim to identify quantitative trait loci (QTLs) associated with yield components and seed morphology that were expressed in multiple environments. A total of 44 and 34 multi-environment QTLs were identified in the BP025 and RP883 populations, respectively. As expected, genetic loci known to govern domestication and development were associated with some of the QTLs, but novel QTLs derived from the cultivated emmer parents and associated with yield components including spikelet number, grain weight, and grain size were identified. These QTLs offer new target loci for durum wheat improvement, and toward that goal, we identified five RILs with increased grain weight and size compared to the durum parents. These materials along with the knowledge of stable QTLs and associated markers can help to expedite the development of superior durum varieties.

A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers Using Sequencer Architecture.

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. This study developed a novel and efficient digital pathology classifier called DPSeq to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizontal and vertical bidirectional long short-term memory networks. Using hematoxylin and eosin-stained histopathologic images of colorectal cancer from two international data sets (The Cancer Genome Atlas and Molecular and Cellular Oncology), the predictive performance of DPSeq was evaluated in a series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in colorectal cancer (microsatellite instability status, hypermutation, CpG island methylator phenotype status, BRAF mutation, TP53 mutation, and chromosomal instability), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. In addition, under the same experimental conditions using the same set of training and testing data sets, DPSeq surpassed four CNNs (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and two transformer (Vision Transformer and Swin Transformer) models, achieving the highest area under the receiver operating characteristic curve and area under the precision-recall curve values in predicting microsatellite instability status, BRAF mutation, and CpG island methylator phenotype status. Furthermore, DPSeq required less time for both training and prediction because of its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.

Linkage drag analysis in three Aegilops speltoides introgressions carrying Sr47 in modern durum and hard red spring wheat germplasm.

KEY MESSAGE: Yield and quality tests of wheat lines derived from RWG35 show they carry little, or no linkage drag and are the preferred source of Sr47 for stem rust resistance. Three durum wheat (Triticum turgidum L. subsp. durum) lines, RWG35, RWG36, and RWG37 carrying slightly different Aegilops speltoides introgressions, but each carrying the Sr47 stem rust resistance gene, were backcrossed to three durum and three hard red spring (HRS) wheat (Triticum aestivum L.) cultivars to produce 18 backcross populations. Each population was backcrossed to the recurrent parent six times and prepared for yield trials to test for linkage drag. Lines carrying the introgression (S-lines) were compared to euploid sibling lines (W-lines) and their parent. Yield trials were conducted from 2018 to 2021 at three locations. Three agronomic and several quality traits were studied. In durum, lines derived from RWG35 had little or no linkage drag. Lines derived from RWG36 and RWG37 still retained linkage drag, most notably involving yield and thousand kernel weight, but also test weight, falling number, kernel hardness index, semolina extract, semolina protein content, semolina brightness, and peak height. In HRS wheat, the results were more complex, though the general result of RWG35 lines having little or no linkage drag and RWG36 and RWG37 lines retaining linkage drag still applied. But there was heterogeneity in the Glenn35S lines, and Linkert lines had problems combining with the Ae. speltoides introgressions. We concluded that introgressions derived from RWG35 either had eliminated linkage drag or any negative effects were minor in nature. We recommend that breeders who wish to incorporate Sr47 into their cultivars should work exclusively with germplasm derived from RWG35.

Waist-to-Height Ratio Is a Stronger Mediator in the Association between DASH Diet and Hypertension: Potential Micro/Macro Nutrients Intake Pathways.

Several studies have demonstrated that adhering to the Dietary Approaches to Stop Hypertension (DASH) diet may result in decreased blood pressure levels and hypertension risk. This may be an effect of a reduction in central obesity. In the current study, we explored the mediation role of multiple anthropometric measurements in association with DASH score and hypertension risk, and we investigated potential common micro/macro nutrients that react with the obesity-reduction mechanism. Our study used data from the National Health and Nutrition Examination Survey (NHANES). Important demographic variables, such as gender, race, age, marital status, education attainment, poverty income ratio, and lifestyle habits such as smoking, alcohol drinking, and physical activity were collected. Various anthropometric measurements, including weight, waist circumference, body mass index (BMI), and waist-to-height ratio (WHtR) were also obtained from the official website. The nutrient intake of 8224 adults was quantified through a combination of interviews and laboratory tests. We conducted stepwise regression to filter the most important anthropometric measurements and performed a multiple mediation analysis to test whether the selected anthropometric measurements had mediation effects on the total effect of the DASH diet on hypertension. Random forest models were conducted to identify nutrient subsets associated with the DASH score and anthropometric measurements. Finally, associations between common nutrients and DASH score, anthropometric measurements, and risk of hypertension were respectively evaluated by a logistic regression model adjusting for possible confounders. Our study revealed that BMI and WHtR acted as full mediators between DASH score and high blood pressure levels. Together, they accounted for more than 45% of the variation in hypertension. Interestingly, WHtR was found to be the strongest mediator, explaining approximate 80% of the mediating effect. Furthermore, we identified a group of three commonly consumed nutrients (sodium, potassium, and octadecatrienoic acid) that had opposing effects on DASH score and anthropometric measurements. These nutrients were also found to be associated with hypertension in the same way as BMI and WHtR in univariate regression models. The most important among these nutrients was sodium, which was negatively correlated with the DASH score (ß = -0.53, 95% CI = -0.56~-0.50, p < 0.001) and had a positive association with BMI (ß = 0.04, 95% CI = 0.01~0.07, p = 0.02), WHtR (ß = 0.06, 95% CI = 0.03~0.09, p < 0.001), and hypertension (OR = 1.09, 95% CI = 1.01~1.19, p = 0.037). Our investigation revealed that the WHtR exerts a greater mediating effect than BMI on the correlation between the DASH diet and hypertension. Notably, we identified a plausible nutrient intake pathway involving sodium, potassium, and octadecatrienoic acid. Our findings suggested that lifestyle modifications that emphasize the reduction of central obesity and the attainment of a well-balanced micro/macro nutrient profile, such as the DASH diet, could potentially be efficacious in managing hypertension.

CD142 Identifies Neoplastic Desmoid Tumor Cells, Uncovering Interactions Between Neoplastic and Stromal Cells That Drive Proliferation.

The interaction between neoplastic and stromal cells within a tumor mass plays an important role in cancer biology. However, it is challenging to distinguish between tumor and stromal cells in mesenchymal tumors because lineage-specific cell surface markers typically used in other cancers do not distinguish between the different cell subpopulations. Desmoid tumors consist of mesenchymal fibroblast-like cells driven by mutations stabilizing beta-catenin. Here we aimed to identify surface markers that can distinguish mutant cells from stromal cells to study tumor-stroma interactions. We analyzed colonies derived from single cells from human desmoid tumors using a high-throughput surface antigen screen, to characterize the mutant and nonmutant cells. We found that CD142 is highly expressed by the mutant cell populations and correlates with beta-catenin activity. CD142-based cell sorting isolated the mutant population from heterogeneous samples, including one where no mutation was previously detected by traditional Sanger sequencing. We then studied the secretome of mutant and nonmutant fibroblastic cells. PTX3 is one stroma-derived secreted factor that increases mutant cell proliferation via STAT6 activation. These data demonstrate a sensitive method to quantify and distinguish neoplastic from stromal cells in mesenchymal tumors. It identifies proteins secreted by nonmutant cells that regulate mutant cell proliferation that could be therapeutically. Significance: Distinguishing between neoplastic (tumor) and non-neoplastic (stromal) cells within mesenchymal tumors is particularly challenging, because lineage-specific cell surface markers typically used in other cancers do not differentiate between the different cell subpopulations. Here, we developed a strategy combining clonal expansion with surface proteome profiling to identify markers for quantifying and isolating mutant and nonmutant cell subpopulations in desmoid tumors, and to study their interactions via soluble factors.

Intra- and inter-habitat variation in sediment heavy metals, antibiotics and ecological risks in Mai Po RAMSAR, China.

Distribution of heavy metals (HMs) and antibiotics (ABs) in surface sediments of three habitats: mudflat, mangrove and gei wai (inter-tidal shrimp ponds), at Mai Po RAMSAR were determined with inductively coupled plasma and liquid chromatograph tandem - mass spectrometry, respectively. Eight HMs (Cr, As, Pb, Cd, Mn, Ni, Cu and Zn), and ten ABs (tetracyclines, quinolones, macrolides and sulphonamides) were detected in all habitats, with relatively lower concentration in gei wai. Ecological risk assessment based on PNEC revealed that HMs posed a higher ecological risk to microorganisms than ABs. All metals except Mn were above their respective threshold effect levels according to sediment quality guidelines, indicating their potential toxicity to benthos. The enrichment factor and geo-accumulation index on background values suggested sediments were moderately polluted by Zn, Cu and Cd, possibly from anthropogenic inputs. This study implies that HMs pollution must be prevented through proper regulation of agricultural and industrial discharge.