Integrating multi-omics data of childhood asthma using a deep association model.

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases.

DNA Methylation Effects on Van der Woude Syndrome Phenotypic Variability.

OBJECTIVE: Van der Woude Syndrome (VWS) presents with combinations of lip pits (LP) and cleft lip and/or cleft palate (CL/P, CPO). VWS phenotypic heterogeneity even amongst relatives, suggests that epigenetic factors may act as modifiers. IRF6, causal for 70% of VWS cases, and TP63 interact in a regulatory loop coordinating epithelial proliferation and differentiation in palatogenesis. We hypothesize that differential DNA methylation within IRF6 and TP63 regulatory regions underlie VWS phenotypic discordance. METHODS: DNA methylation of CpG sites in IRF6 and TP63 promoters and in an IRF6 enhancer element was compared amongst blood or saliva DNA samples of 78 unrelated cases. Analyses were done separately for blood and saliva, within each sex and in combination, and to address cleft type (CL/P ± LP vs. CPO ± LP) and phenotypic severity (any cleft + LP vs. any cleft only). RESULTS: For cleft type, blood samples showed higher IRF6 and TP63 promoter methylation on males with CPO ± LP compared to CL/P ± LP and on individuals with CPO ± LP compared to those with CL/P ± LP, respectively. Saliva samples showed higher IRF6 enhancer methylation on individuals with CPO ± LP compared to CL/P ± LP and contrary to above, lower TP63 promoter methylation on CPO ± LP compared to CL/P ± LP. For phenotypic severity, blood samples showed no differences; however, saliva samples showed higher IRF6 promoter methylation in individuals with any cleft + LP compared to those without lip pits. CONCLUSION: We observed differential methylation in IRF6 and TP63 regulatory regions associated with cleft type and phenotypic severity, indicating that epigenetic changes in IRF6 and TP63 can contribute to phenotypic heterogeneity in VWS.

The multivariate Bernoulli detector: change point estimation in discrete survival analysis.

Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.

Lipid analysis of breast milk and formula for preterm infants and the application and prospects of novel structural lipids - a comprehensive review.

Preterm infants, often characterized by lower birth weights and underdeveloped physiologies, necessitate specialized nutritional care. While breast milk stands as the ideal nutritional source, offering substantial energy through its fatty acid content to support the infants' growth and developmental needs, its usage might not always be feasible. Fatty acids in breast milk are critical for the development of these infants. In scenarios where breast milk is not an option, formula feeding becomes a necessary alternative. Thus, a comprehensive understanding of the fatty acid profiles in both breast milk and formulas is crucial for addressing the distinct nutritional requirements of preterm infants. This paper aims to summarize the effects of lipid composition, structure, and positioning in breast milk and formula on the growth and development of preterm infants. Furthermore, it explores recent advancements in the use of novel structural lipids in formulas, laying the groundwork for future innovations in formula design specifically catered to the needs of preterm infants.

Study on the interaction mechanism of whey protein isolate with phosphatidylcholine: By multispectral methods and molecular docking.

The elucidation of the interaction mechanism between phospholipids and milk proteins within emulsions is pivotal for comprehending the properties of infant formula fat globules. In this study, multispectral methods and molecular docking were employed to explore the relationship between phosphatidylcholine (PC) and whey protein isolate (WPI). Observations indicate that the binding constant, alongside thermodynamic parameters, diminishes as temperature ascends, hinting at a predominantly static quenching mechanism. Predominantly, van der Waals forces and hydrogen bonds constitute the core interactions between WPI and PC. This assertion is further substantiated by Fourier transform infrared spectroscopy, which verifies PC's influence on WPI's secondary structure. A detailed assessment of thermodynamic parameters coupled with molecular docking reveals that PC predominantly adheres to specific sites within α-lactalbumin, ß-lactoglobulin, and bovine serum albumin, propelled by a synergy of hydrophobic interactions, hydrogen bonding, and van der Waals forces, with binding energies noted at -5.59, -6.71, and -7.85 kcal/mol, respectively. An increment in PC concentration is observed to amplify the emulsification properties of WPI whilst concurrently diminishing the zeta potential. This study establishes a theoretical foundation for applying the PC-WPI interaction mechanism in food.

Establishment of a logistic regression model nomogram for clinicopathological characteristics and risk factors with axillary lymph node metastasis in T1 locally advanced breast cancer: a retrospective study.

Background: Although the research reports on locally advanced breast cancer (LABC) are increasing year by year, there are few reports on T1 LABC axillary lymph node metastasis (ALNM). By establishing a prediction model for T1 LABC ALNM, this study provides a reference value for the probability of ALNM of related patients, which helps clinicians to develop a more effective and individualized treatment plan for LABC. Methods: Cases with pathologically confirmed T1 breast cancer (BC) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were identified. Logistic regression was used to analyze the correlation between LABC lymph node metastasis and every factor, and the odds ratio (OR) and 95% confidence interval (CI) were used to identify any influencing factors. A nomogram was drawn after incorporating meaningful factors identified in multivariate logistic regression into the model. The receiver operating characteristic (ROC) curve of the model was drawn, and the area under the curve (AUC) and its 95% CI were calculated. Hosmer-Lemeshow goodness-of-fit test and clinical decision curve analysis (DCA) were performed. The results were validated in the validation group. Results: A total of 200,933 female T1 BC patients were included in this study. Univariate and multivariate logistic regression analysis of T1 BC showed that progesterone receptor (PR)-negative, race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and larger tumor diameter increased the probability of ALNM in T1 LABC. A predictive nomogram was established using the above predictors, the AUC of the modeling group was 0.739 (95% CI: 0.732-0.747), and when the AUC cut-off value was 0.026, the specificity and sensitivity of the model were 65.78% and 69.99%, respectively. Validation of the model showed that the AUC of the validation group (n=60,280) was 0.741. When all the risk factors were met, the predicted probability of N2-N3 was 50.40%. Conclusions: In this study, it was found that PR-negative, Black race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and tumor diameter increased the probability of large lymph node metastasis in T1 LABC small tumors.

A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

BACKGROUND: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement. METHODS: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. CONCLUSION: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.

Comprehensive genome-wide identification of Snf2 gene family and their expression profile under salt stress in six Brassica species of U's triangle model.

MAIN CONCLUSION: We comprehensively identified and analyzed the Snf2 gene family. Some Snf2 genes were involved in responding to salt stress based on the RNA-seq and qRT-PCR analysis. Sucrose nonfermenting 2 (Snf2) proteins are core components of chromatin remodeling complexes that not only alter DNA accessibility using the energy of ATP hydrolysis, but also play a critical regulatory role in growth, development, and stress response in eukaryotes. However, the comparative study of Snf2 gene family in the six Brassica species in U's triangle model remains unclear. Here, a total of 405 Snf2 genes were identified, comprising 53, 50, and 46 in the diploid progenitors: Brassica rapa (AA, 2n = 20), Brassica nigra (BB, 2n = 16), and Brassica oleracea (CC, 2n = 18), and 93, 91, and 72 in the allotetraploid: Brassica juncea (AABB, 2n = 36), Brassica napus (AACC, 2n = 38), and Brassica carinata (BBCC, 2n = 34), respectively. These genes were classified into six clades and further divided into 18 subfamilies based on their conserved motifs and domains. Intriguingly, these genes showed highly conserved chromosomal distributions and gene structures, indicating that few dynamic changes occurred during the polyploidization. The duplication modes of the six Brassica species were diverse, and the expansion of most Snf2 in Brassica occurred primarily through dispersed duplication (DSD) events. Additionally, the majority of Snf2 genes were under purifying selection during polyploidization, and some Snf2 genes were associated with various abiotic stresses. Both RNA-seq and qRT-PCR analysis showed that the expression of BnaSnf2 genes was significantly induced under salt stress, implying their involvement in salt tolerance response in Brassica species. The results provide a comprehensive understanding of the Snf2 genes in U's triangle model species, which will facilitate further functional analysis of the Snf2 genes in Brassica plants.

Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.

Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high heterogeneity among radiologists make it difficult to diagnose and stage pneumoconiosis accurately. Here, based on DR images collected from two centers, a novel deep learning model, namely Multi-scale Lesion-aware Attention Networks (MLANet), is proposed for diagnosis of pneumoconiosis, staging of pneumoconiosis, and screening of stage I pneumoconiosis. A series of indicators including area under the receiver operating characteristic curve, accuracy, recall, precision, and F1 score were used to comprehensively evaluate the performance of the model. The results show that the MLANet model can effectively improve the consistency and efficiency of pneumoconiosis diagnosis. The accuracy of the MLANet model for pneumoconiosis diagnosis on the internal test set, external validation set, and prospective test set reached 97.87%, 98.03%, and 95.40%, respectively, which was close to the level of qualified radiologists. Moreover, the model can effectively screen stage I pneumoconiosis with an accuracy of 97.16%, a recall of 98.25, a precision of 93.42%, and an F1 score of 95.59%, respectively. The built model performs better than the other four classification models. It is expected to be applied in clinical work to realize the automated diagnosis of pneumoconiosis digital chest radiographs, which is of great significance for individualized early prevention and treatment.

Identification of DNA methylation characteristics associated with metastasis and prognosis in colorectal cancer.

Colorectal cancer (CRC) is prone to metastasis and recurrence after surgery, which is one of the main causes for its poor treatment and prognosis. Therefore, it is essential to identify biomarkers associated with metastasis and recurrence in CRC. DNA methylation has a regulatory role in cancer metastasis, tumor immune microenvironment (TME), and prognosis and may be one of the most valuable biomarkers for predicting CRC metastasis and prognosis. We constructed a diagnostic model and nomogram that can effectively predict CRC metastasis based on the differential methylation CpG sites (DMCs) between metastatic and non-metastatic CRC patients. Then, we identified 17 DMCs associated with progression free survival (PFS) of CRC and constructed a prognostic model. The prognosis model based on 17 DMCs can predict the PFS of CRC with medium to high accuracy. The results of immunohistochemical analysis indicated that the protein expression levels of the genes involved in prognostic DMCs were different between normal and colorectal cancer tissues. According to the results of immune-related analysis, we found that the low-risk patients had better immunotherapy response. In addition, high risk scores were negatively correlated with high tumor mutation burden (TMB) levels, and patients with low TMB levels in the high-risk group had the worst PFS. Our work shows the clinical value of DNA methylation in predicting CRC metastasis and PFS, as well as their correlation with TME, immunotherapy, and TMB, which helps understand the changes of DNA methylation in CRC metastasis and improving the treatment and prognosis of CRC.

Effects of the Lactiplantibacillus plantarum YB-106 and Leuconostoc mesenteroides YB-23 strains on the quality and microbial diversity of spicy cabbage.

Spicy cabbage is a popular fermented vegetable food. The study aimed to determine the physicochemical properties, volatile flavor components, sensory evaluation, and microbial diversity of spicy cabbage prepared using different methods. Three methods were used: single-bacteria fermentation with Lactiplantibacillus plantarum YB-106 and Leuconostoc mesenteroides YB-23, mixed fermentation (LMP) using both strains, and natural fermentation as the blank control (CON). The LMP group has the best quality of spicy cabbage and the highest sensory score. Esters and alkenes were the main volatile flavor components of the spicy cabbage by GC-MS. The fermentation time of LMP group was shorter, and the nitrite degradation rate was >60 %, which was significantly higher than that of other groups (p < 0.05). From the perspective of microbial diversity, the dominant bacteria genera in each group were Lactobacillus, Pantoea, Enterococcus and Pseudomonas. However, mixed fermentation decreased the abundance of pathogenic bacteria, of which the abundance of Serratia was <0.1 %. In conclusion, mixed fermentation can significantly improve the quality of spicy cabbage and shorten the fermentation time. These findings laid the theoretical foundation for the industrial production of high-quality spicy cabbage.

Characteristic analysis and mitigation strategy for SSCI in series-compensated DFIG-based wind farm controlled by a virtual synchronous generator.

The proliferation of virtual synchronous generator (VSG) technology within series-compensated double-fed induction generator (DFIG)-based wind farms is substantially hampered by the attendant risk of subsynchronous control interaction (SSCI), resulting in a significant research deficiency on systematic control interaction analysis and the development of mitigation strategies. The paper proposes an advanced active disturbance rejection control (ADRC) framework, incorporating real-time compensation mechanisms to mitigate the inadequate suppression efficacy attributable to the VSG's diminished output impedance. Initially, the mathematical expression for the VSG output impedance is rigorously deduced, and the positive damping attributes of the VSG in relation to SSCI are elucidated from the perspective of underlying mechanistic principles. Subsequently, the suppressive mechanism of SSCI by the ADRC is revealed in the context of VSG involvement, and the consequent augmentation of SSCI attributed to PI control is systematically derived. In immediate succession, the quanta of oscillation and inductive cross-coupling are encapsulated as the system's aggregate disturbance, thereby streamlining the ADRC to its primary order configuration, permitting the utilization of an extended state observer (ESO) for the dynamic estimation of said disturbance. Furthermore, a fractional-order filter function is instituted to engineer an augmented ESO, which refines the output voltage of the grid-side converter. Concurrently, a meticulous discourse on the rectification strategy for the proposed ESO parameters and its stability ensues. Ultimately, the efficacy of the mechanism analysis, alongside the robustness of the proffered control strategy for SSCI mitigation under diverse perturbation conditions, is corroborated via impedance evaluation and time-domain simulation.

[Analysis and reflection on current situation of Chinese patent medicine for children in China].

This study took Chinese patent medicine for children included in Chinese Pharmacopoeia(2020 edition and the first supplement), Medicine Catalogue for National Basic Medical Insurance, Work Injury Insurance, and Maternity Insurance(2022 edition), and National Essential Medicines List(2018 edition) as the research objects, so as to sort out the distribution situation, characteristics, and the problems of Chinese patent medicine for children(including child-specific medicines, common medicines for children and adults, and discretionary medicines for children). According to statistics and summary, Chinese patent medicine for children is mainly administered orally, and the dosage forms are mostly traditional dosage forms, such as tablets, granules, capsules, and oral liquids, with mostly bitter or sweet taste. Diseases are mainly classified into pulmonary diseases and spleen and stomach diseases, and varieties of medication without Children's medication safety information or "still unclear" account for a relatively large proportion of the medicines. There are relatively few varieties of Chinese patent medicines for children, poor compliance of child-specific medication, lack of refinement of Chinese patent medicines for children dosage, and lack of information about safe use of medication. It is recommended to update and improve the instruction manuals in a timely manner, develop new varieties of Chinese patent medicine for children, and actively carry out post-marketing evaluation and clinical comprehensive evaluation of Chinese patent medicine for children, so as to provide a reference for the supplement and improvement of the instructions, the comprehensive improvement, the formulation of the catalogue, and the research and development of new Chinese patent medicine for children and ensure the use of medicines for children.

Comparisons between referred and non-referred patients to a university paediatric dental clinic.

BACKGROUND: Referrals of paediatric patients to a university clinic have been increasing over the last several years. AIM: To evaluate characteristics of referred and non-referred patients at the University of Iowa's Pediatric Dental Clinic (UIPDC). DESIGN: A retrospective chart review included dental records of 340 referred and 383 non-referred patients from July 1, 2015, to May 31, 2016 (n = 723). Age, distance to the clinic, size of the patient's community, insurance, number of teeth with decay, treatment needs, educational level of the provider, and presence of patient special health care needs were obtained. Descriptive statistics, bivariate analysis, and multivariable logistic regression were performed to analyze the outcomes (alpha = .05). RESULTS: Referred patients were more likely to live >60 miles away, live in a community of >75 000 people, have special health care needs, have caries/greater number of teeth with decay, need endodontic treatment, and were less likely to remain patients at the clinic (p < .0001). Referred patients were also more likely to need extractions (p = .0104), but less likely to need space maintenance/comprehensive orthodontic treatment (p = .0002). CONCLUSION: There was a difference in the complexity of patient treatment needs between referred and non-referred patients.

Genome-wide identification of the ICS family genes and its role in resistance to Plasmodiophora brassicae in Brassica napus L.

The isochorismate synthase (ICS) proteins are essential regulators of salicylic acid (SA) synthesis, which has been reported to regulate resistance to biotic and abiotic stresses in plants. Clubroot caused by Plasmodiophora brassicae is a common disease that threatens the yield and quality of Oilseed rape (Brassica napus L.). Exogenous application of salicylic acid reduced the incidence of clubroot in oilseed rape. However, the potential importance of the ICS genes family in B. napus and its diploid progenitors has been unclear. Here, we identified 16, 9, and 10 ICS genes in the allotetraploid B. napus, diploid ancestor Brassica rapa and Brassica oleracea, respectively. These ICS genes were classified into three subfamilies (I-III), and member of the same subfamilies showed relatively conserved gene structures, motifs, and protein domains. Furthermore, many hormone-response and stress-related promoter cis-acting elements were observed in the BnaICS genes. Exogenous application of SA delayed the growth of clubroot galls, and the expression of BnaICS genes was significantly different compared to the control groups. Protein-protein interaction analysis identified 58 proteins involved in the regulation of ICS in response to P. brassicae in B. napus. These results provide new clues for understanding the resistance mechanism to P. brassicae.

Exploring lipid digestion discrepancies between preterm formula and human milk: Insights from in vitro gastrointestinal digestion and the impact of added milk fat.

Lipids play a pivotal role in the nutrition of preterm infants, acting as a primary energy source. Due to their underdeveloped gastrointestinal systems, lipid malabsorption is common, leading to insufficient energy intake and slowed growth. Therefore, it is critical to explore the reasons behind the low lipid absorption rate in formulas for preterm infants. This study utilized a simulated in intro gastrointestinal digestion model to assess the differences in lipid digestion between preterm human milk and various infant formulas. Results showed that the fatty acid release rates for formulas IF3, IF5, and IF7 were 58.90 %, 56.58 %, and 66.71 %, respectively, lower than human milk's 72.31 %. The primary free fatty acids (FFA) and 2-monoacylglycerol (2-MAG) released during digestion were C14:0, C16:0, C18:0, C18:1n-9, and C18:2n-6, in both human milk and formulas. Notably, the higher release of C16:0 in formulas may disrupt fatty acid balance, impacting lipid absorption. Further investigations are necessary to elucidate lipid absorption differences, which will inform the optimization of lipid content in preterm infant formulas.

A Guanidinobenzol-Rich Polymer Overcoming Cascade Delivery Barriers for CRISPR-Cas9 Genome Editing.

The efficient cytosolic delivery of the CRISPR-Cas9 machinery remains a challenge for genome editing. Herein, we performed ligand screening and identified a guanidinobenzol-rich polymer to overcome the cascade delivery barriers of CRISPR-Cas9 ribonucleoproteins (RNPs) for genome editing. RNPs were stably loaded into the polymeric nanoparticles (PGBA NPs) by their inherent affinity. The polymer facilitated rapid endosomal escape of RNPs via a dynamic multiple-step cascade process. Importantly, the incorporation of fluorescence in the polymer helps to identify the correlation between cellular uptake and editing efficiency, increasing the efficiency up to 70% from the initial 30% for the enrichment of edited cells. The PGBA NPs efficiently deliver RNPs for in vivo gene editing via both local and systemic injections and dramatically reduce PCSK9 level. These results indicate that PGBA NPs enable the cascade delivery of RNPs for genome editing, showing great promise in broadening the therapeutic potential of the CRISPR-Cas9 technique.

Identification, Evolutionary Dynamics, and Gene Expression Patterns of the ACP Gene Family in Responding to Salt Stress in Brassica Genus.

Acyl carrier proteins (ACPs) have been reported to play a crucial role in responding to biotic and abiotic stresses, regulating growth and development. However, the biological function of the ACP gene family in the Brassica genus has been limited until now. In this study, we conducted a comprehensive analysis and identified a total of 120 ACP genes across six species in the Brassica genus. Among these, there were 27, 26, and 30 ACP genes in the allotetraploid B. napus, B. juncea, and B. carinata, respectively, and 14, 13, and 10 ACP genes in the diploid B. rapa, B. oleracea, and B. nigra, respectively. These ACP genes were further classified into six subclades, each containing conserved motifs and domains. Interestingly, the majority of ACP genes exhibited high conservation among the six species, suggesting that the genome evolution and polyploidization processes had relatively minor effects on the ACP gene family. The duplication modes of the six Brassica species were diverse, and the expansion of most ACPs in Brassica occurred primarily through dispersed duplication (DSD) events. Furthermore, most of the ACP genes were under purifying selection during the process of evolution. Subcellular localization experiments demonstrated that ACP genes in Brassica species are localized in chloroplasts and mitochondria. Cis-acting element analysis revealed that most of the ACP genes were associated with various abiotic stresses. Additionally, RNA-seq data revealed differential expression levels of BnaACP genes across various tissues in B. napus, with particularly high expression in seeds and buds. qRT-PCR analysis further indicated that BnaACP genes play a significant role in salt stress tolerance. These findings provide a comprehensive understanding of ACP genes in Brassica plants and will facilitate further functional analysis of these genes.

Genome-wide identification and stress response analysis of BcaCPK gene family in amphidiploid Brassica carinata.

BACKGROUND: Calcium-dependent protein kinases (CPKs) are crucial for recognizing and transmitting Ca2+ signals in plant cells, playing a vital role in growth, development, and stress response. This study aimed to identify and detect the potential roles of the CPK gene family in the amphidiploid Brassica carinata (BBCC, 2n = 34) using bioinformatics methods. RESULTS: Based on the published genomic information of B. carinata, a total of 123 CPK genes were identified, comprising 70 CPK genes on the B subgenome and 53 on the C subgenome. To further investigate the homologous evolutionary relationship between B. carinata and other plants, the phylogenetic tree was constructed using CPKs in B. carinata and Arabidopsis thaliana. The phylogenetic analysis classified 123 family members into four subfamilies, where gene members within the same subfamily exhibited similar conserved motifs. Each BcaCPK member possesses a core protein kinase domain and four EF-hand domains. Most of the BcaCPK genes contain 5 to 8 introns, and these 123 BcaCPK genes are unevenly distributed across 17 chromosomes. Among these BcaCPK genes, 120 replicated gene pairs were found, whereas only 8 genes were tandem duplication, suggesting that dispersed duplication mainly drove the family amplification. The results of the Ka/Ks analysis indicated that the CPK gene family of B. carinata was primarily underwent purification selection in evolutionary selection. The promoter region of most BcaCPK genes contained various stress-related cis-acting elements. qRT-PCR analysis of 12 selected CPK genes conducted under cadmium and salt stress at various points revealed distinct expression patterns among different family members in response to different stresses. Specifically, the expression levels of BcaCPK2.B01a, BcaCPK16.B02b, and BcaCPK26.B02 were down-regulated under both stresses, whereas the expression levels of other members were significantly up-regulated under at least one stress. CONCLUSION: This study systematically identified the BcaCPK gene family in B. carinata, which contributes to a better understanding the CPK genes in this species. The findings also serve as a reference for analyzing stress responses, particularly in relation to cadmium and salt stress in B. carinata.