Integrated Analysis Research Articles

The Influence of Sodium Humate on the Biosynthesis and Contents of Flavonoid Constituents in Lemons

Sodium humate (SH) is the sodium salt of humic acid. Our previous research has demonstrated that SH has the ability to enhance the levels of total flavonoids in various parts of lemons, including the leaves, peels, pulps, and seeds, thereby improving the quality of lemons. In the current study, the regulation effect of SH on the biosynthesis and content of lemon flavonoid compounds was examined using transcriptome sequencing technology and flavonoid metabolomic analysis. Following SH treatment, the transcriptome sequencing analysis revealed 320 differentially expressed genes (DEGs) between samples treated with SH and control (CK) samples, some of which were associated with the phenylalanine pathway by KEGG annotation analysis. The levels of seven flavonoid compounds identified in lemon peels were observed to increase, and eriocitrin and isoorientin were identified as differential metabolites (DMs, VIP > 1) using OPLS-DA analysis. The integrated analysis of transcriptomics and flavonoid metabolomics indicates that SH treatment induces alterations in gene expression and metabolite levels related to flavonoid synthesis. Specifically, SH influences flavonoid biosynthesis by modulating the activity of key enzymes in the phenylalanine pathway, including HCT (O-hydroxycinnamoyltransferase) and F5H (ferulate-5-hydroxylase).

Evaluation of ecological health of Hainan Island inshore waters in the South China Sea based on preliminary fish biotic integrity index (Actinopterygii and Elasmobranchii)

The well-being of marine ecosystems significantly influences biodiversity and ecological equilibrium amidst pressing challenges such as overfishing, water pollution, and climate change. Drawing upon data from two fishery stock surveys conducted in the inshore waters of Hainan Island in 2022, this study undertook a comprehensive evaluation of the marine ecosystem’s health status in the region. Employing the Fish Index of Biological Integrity (F-IBI) analysis method, coupled with an examination of the structural and functional aspects of the fish community, our research sheds light on the prevailing conditions. Our study revealed a spatial dichotomy within the fish communities of the study area and delineated them into two distinct groups: the northwestern and southeastern ones, with evident disparities in community structure between the two. By employing indicator screening and calculation, we segmented the fish health index in the inshore waters of Hainan Island into five tiers. Despite discernible anthropogenic influences, the ecological health of these waters remained generally robust. Notably, the mean F-IBI of fall (56.30) significantly exceeded that of spring (48.16) (P < 0.01). Furthermore, regarding spatial distribution, the ecological well-being of the southeastern waters surpassed that of the northwestern and Qionghzhou Strait waters. This study represents a pioneer endeavor to apply ecological health assessment methodologies towards informing resource management and conservation strategies for the inshore fisheries of Hainan Island. By furnishing a scientific foundation, our research contributes to the pursuit of sustainable marine ecological development within this locale.

Identification of potential biomarkers of papillary thyroid carcinoma.

This study aimed to identify candidate biomarkers for papillary thyroid carcinoma using an integrative analysis of bioinformatics and machine learning (ML). The PTC datasets GSE6004, GSE3467, and GSE33630 (species: Homo sapiens) were downloaded from NCBI and analyzed using the limma package to obtain DEGs. Once DEGs were identified, GO and KEGG enrichment analyses were performed as the first step in the bioinformatics process. Subsequently, a protein-protein interaction (PPI) network was constructed according to the common genes in bioinformatics and machine learning using STRING to elucidate the important genes involved in PTC pathogenesis. In machine learning, finding genes entails feature selection to identify the key genes that distinguish biological states. Hybrid feature selection will be used for this. In the second step, the original data sets were preprocessed to detect and correct missing and noisy data; after that, all data were merged. Following performing Linear and Discriminative Hybrid Feature Selection (LDHFS) on the processed dataset, machine learning algorithms such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machines (SVM) are utilized. Bioinformatics and machine learning analyses indicate that the genes RXRG, CDH2, ETV5, QPCT, LRP4, FN1, and LPAR5 are integral to the progression of thyroid cancer. This study attained the highest accuracy utilizing the RF algorithm, achieving an accuracy rate of 94.62%, a Kappa value of 91.36%, and an AUC value of 96.13%. These results offer additional evidence and confirmation for the genetic alterations of these genes. These findings may accelerate the development of prospective therapeutic and diagnostic methods in future research. Bioinformatics and machine learning techniques identified the common genes "RXRG, CDH2, ETV5, QPCT, LRP4, FN1, and LPAR5" as PTC biomarkers, providing novel reference markers for the diagnosis and treatment of PTC patients. The model is anticipated to possess significant predictive value and assist in the early diagnosis and screening of clinical PTC. These insights enhance the field of PTC management and offer guidance for future research.

CD74+ fibroblasts proliferate upon mechanical stretching to promote angiogenesis in keloids.

The healing of human skin wounds is susceptible to perturbation caused by excessive mechanical stretching, resulting in enlarged scars, hypertrophic scars, or even keloids in predisposed individuals. Keloids are fibro-proliferative scar tissues that extend beyond the initial wound boundary, consisting of the actively progressing periphery and the quiescent center. The stretch-associated outgrowth and enhanced angiogenesis are two features of the periphery of keloids. However, which cell population is responsible for transducing the mechanical stimulation to the progression of keloids remains unclear. Herein, through integrative analysis of single-cell RNA sequencing of keloids, we identified CD74+ fibroblasts, a previously unappreciated subset of fibroblasts with pro-angiogenic and stretch-induced proliferative capacities, as a key player in stretch-induced progression of keloids. Immunostaining of keloid cryosections depicted a predominant distribution of CD74+ fibroblasts in the periphery, interacting with the vasculature. Invitro tube formation assays on purified CD74+ fibroblasts ascertained their pro-angiogenic function. BrdU assays revealed that these cells proliferate upon stretching, through PIEZO1-mediated calcium influx and the downstream ERK and AKT signaling. Collectively, our findings propose a model wherein CD74+ fibroblasts serve as pivotal drivers of stretch-induced keloid progression, fueled by their proliferative and pro-angiogenic activities. Targeting the attributes of CD74+ fibroblasts holds promise as a therapeutic strategy for the management of keloids.

MicrobiomeKG: Bridging Microbiome Research and Host Health Through Knowledge Graphs.

The microbiome represents a complex community of trillions of microorganisms residing in various body parts, and plays critical roles in maintaining host health and well-being. Understanding the interactions between microbiota and host offers valuable insights into potential strategies to promote health, including microbiome-targeted interventions. We have created MicrobiomeKG, a Knowledge Graph for microbiome research, bridging various taxa and microbial pathways with host health. This novel knowledge graph derives algorithmically-generated knowledge assertions from the supplemental tables supporting published microbiome papers, and is deployed for integrative analysis and hypothesis generation via the Biomedical Data Translator ecosystem.

Equitable stormwater utility fees: an integrated analysis of environmental, socioeconomic and infrastructure factors at the community scale

Abstract Municipalities worldwide implement stormwater management programs to mitigate the hydrological and water quality impacts of stormwater runoff. Stormwater utility fees (SWUF) are often used to fund such important programs by collecting revenue from residential and commercial properties. However, existing SWUFs often solely rely on the estimate of impervious surfaces and do not consider other environmental, infrastructure, and socioeconomic factors in the generation and effects of stormwater runoff. This study is the first attempt to propose a reconstruction of SWUFs from the perspectives of social equity and environmental justice. The method aims to address disparities in fee rates among residential parcels, focusing on helping economically disadvantaged communities. It integrates drainage service, potential contribution to non-point source pollution, and socioeconomic status through two alternative schemes. The two schemes allocate fees based on combined rankings of the three factors at the level of census block groups. The proposed method was applied to 88 180 residential parcels in Corpus Christi, Texas, a mid-sized coastal community. The results suggest that over 70% of the disadvantaged communities would benefit from the reconstructed SWUFs without affecting the targeted funding for stormwater infrastructure. This method builds on publicly available datasets and offers an adaptive framework for other municipalities to incorporate additional factors or datasets, representing an exploratory step toward achieving more equitable stormwater management practices.

Non‐Typical Supershear Rupture: Fault Heterogeneity and Segmentation Govern Unilateral Supershear and Cascading Multi‐Fault Rupture in the 2021 Mw ${M}_{w}$7.4 Maduo Earthquake

AbstractPrevious geodetic and teleseismic observations of the 2021 7.4 Maduo earthquake imply surprising but difficult‐to‐constrain complexity, including rupture across multiple fault segments and supershear rupture. Here, we present an integrated analysis of multi‐fault 3D dynamic rupture models, high‐resolution optical correlation analysis, and joint optical‐InSAR slip inversion. Our preferred model, validated by the teleseismic multi‐peak moment rate release, includes unilateral eastward double‐onset supershear speeds and cascading rupture dynamically triggering two adjacent fault branches. We propose that pronounced along‐strike variation in fracture energy, complex fault geometries, and multi‐scale variable prestress drives this event's complex rupture dynamics. We illustrate how supershear transition has signatures in modeled and observed off‐fault deformation. Our study opens new avenues to combine observations and models to better understand complex earthquake dynamics, including local and potentially repeating supershear episodes across immature faults or under heterogeneous stress and strength conditions, which are potentially not unusual.

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpaTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. This algorithm integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. The model employs an efficient collapsed Gibbs sampling algorithm for both statistical and computational inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. Our findings demonstrate that SpaTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer.

Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases. This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa. Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines. 7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells. LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.

Multi-omics characterization of lymphedema-induced adipose tissue resulting from breast cancer-related surgery.

Secondary lymphedema (LE) following breast cancer-related surgery is a life-long complication, which currently has no cure. LE induces significant regional adipose tissue deposition, requiring liposuction as a treatment. Here, we aimed to elucidate the transcriptional, metabolomic, and lipidomic signature of the adipose tissue developed due to the surgery-induced LE in short- and long-term LE patients and compared the transcriptomic landscape of LE adipose tissue to the obesity-induced adipose tissue. Adipose tissue biopsies were obtained from breast cancer-operated females with LE from the affected and non-affected arms (n = 20 patients). To decipher the molecular properties of the LE adipose tissue, we performed RNA sequencing, metabolomics, and lipidomics combined with bioinformatics analyses. Differential gene expression data from a cohort of lean and obese patients without LE was used for comparisons. Integrative analysis of functional genomics revealed that inflammatory response, cell chemotaxis, and angiogenesis were upregulated biological processes in the LE arm, indicating a sustained inflammation in the edematous adipose tissue; whereas, epidermal differentiation, cell-cell junction organization, water homeostasis, and neurogenesis were downregulated in the LE arm. Surprisingly, only a few genes were found to be the same in the LE-induced and the obesity-induced adipose tissue expansion, indicating a different type of adipose tissue development in these two conditions. In metabolomics analysis, we found reduced levels of a branched-chain amino acid valine in the LE arm and downregulation of the mRNA levels of its transporter SLC6A15. Lipidomics analyses did not show any significant differences between the LE and non-LE arms, suggesting that other factors affect the lipid composition of the adipose tissue more than the LE in these patients. Our results provide a detailed molecular characterization of adipose tissue in secondary LE after breast cancer-related surgery. We also show distinct differences in transcriptomic signatures between LE-induced adipose tissue and obesity-induced adipose tissue, but only minor differences in metabolome and lipidome between the LE and the non-LE arm.

Integrative analysis of Text-to-Image AI systems in architectural design education: pedagogical innovations and creative design implications

Integrative analysis of Text-to-Image AI systems in architectural design education: pedagogical innovations and creative design implications

Integrative multiomics analysis identifies key genes regulating intramuscular fat deposition during development

Intramuscular fat (IMF) content is an important indicator of livestock and poultry meat quality. Enhancing IMF deposition can significantly improve meat quality. Focusing on the core process of IMF deposition, this study used the Jingxing Yellow (JXY) chickens as a model organism and employed multi-omics approaches, including RNA-sequencing (RNA-seq), Whole-genome bisulfite sequencing (WGBS), and metabolomics, to identify the key genes influencing IMF deposition in chickens during development. The results indicated that the contents of triglycerides (TG) and phospholipids (PLIP) exhibited an upward trend. The TG content did not differ significantly between day 1 (D1) and day 7 (D7), but increased significantly after 35 days (D35) of age. The WGBS results revealed that CpG methylation was the predominant methylation type in the breast muscle tissue of JXY chickens. Integrative analysis of RNA-seq and WGBS identified 50 genes, including PLA2G4F, PALMD, PLSCR5, ARHGEF26, LUM, DCN, TNRC6B, CACNA1C, ROBO1, and MBTPS2, whose methylation levels were significantly negatively correlated with their expression levels. In addition, the combined Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially-expressed metabolites (DEM) and differentially-expressed genes (DEG) converged on the glycerophospholipid metabolism pathway, which was significantly enriched in DEGs such as PLA2G4F, PLA2G15, LPIN1, MBOAT2, DGKH, AGPAT2, and CHKA, as well as DEM like glycerophosphocholine and phosphocholine. Notably, PLA2G4F was identified as a DEG by DNA methylation, suggesting that PLA2G4F could be a key candidate gene influencing IMF deposition during chicken development. These findings are expected to provide a solid theoretical foundation for improving meat quality through targeted genetic and epigenetic interventions.

Integrative multi-omic analysis identifies genes associated with cuticular wax biogenesis in adult maize leaves.

Studying the genetic basis of leaf wax composition and its correlation with leaf cuticular conductance (gc) is crucial for improving crop productivity. The leaf cuticle, which comprises a cutin matrix and various waxes, functions as an extracellular hydrophobic layer, protecting against water loss upon stomatal closure. To address the limited understanding of genes associated with the natural variation of adult leaf cuticular waxes and their connection to gc, we conducted statistical genetic analyses using leaf transcriptomic, metabolomic, and physiological data sets collected from a maize (Zea mays L.) panel of ∼300 inbred lines. Through a random forest analysis with 60 cuticular wax traits, it was shown that high molecular weight wax esters play an important role in predicting gc. Integrating results from genome-wide and transcriptome-wide studies (GWAS and TWAS) via a Fisher's combined test revealed 231 candidate genes detected by all three association tests. Among these, 11 genes exhibit known or predicted roles in cuticle-related processes. Throughout the genome, multiple hotspots consisting of GWAS signals for several traits from one or more wax classes were discovered, identifying four additional plausible candidate genes and providing insights into the genetic basis of correlated wax traits. Establishing a partially shared genetic architecture, we identified 35 genes for both gc and at least one wax trait, with four considered plausible candidates. Our study enhances the understanding of how adult leaf cuticle wax composition relates to gc and implicates both known and novel candidate genes as potential targets for optimizing productivity in maize.

Integrative multi-omics analysis unveils the connection between transcriptomic characteristics associated with mitochondria and the tumor immune microenvironment in lower-grade gliomas

Lower-grade gliomas (LGGs) exhibit diverse clinical behaviors and varying immune infiltration levels. Mitochondria have been implicated in numerous cancer pathogenesis and development, including LGGs. However, the precise biological functions of mitochondrial genes in shaping the immune landscape and the prognostic significance of LGGs remain elusive. Utilizing the Mito-Carta3.0 database, we curated a total of 1136 genes implicated in mitochondrial functions. By leveraging the expression profiles of 1136 genes related to mitochondria, we successfully categorized LGGs into four distinctive mitochondria-related transcriptome (MRT) subtypes. Our thorough analysis conclusively demonstrated that these subtypes exhibited marked disparities. To enable a personalized and integrated evaluation of LGG patients, we developed a prognostic signature known as MRT-related prognostic signature (MTRS). MTRS demonstrated correlation with mitochondria-related transcriptome (MRT) subtypes, allowing the assessment of patients’ prognosis and immune microenvironment. We conducted a detailed exploration of the single-cell distribution of MTRS in lower-grade gliomas and verified the core genes of MTRS within the spatial transcriptome of these tumors. Furthermore, our study pinpointed MGME1 as the pivotal gene in the model, functioning as an oncogene that exerts influence on cell proliferation and migration capabilities. Our research highlights the importance of mitochondrial transcriptomic features in LGGs, offering paths for tailored therapies.

Integrated analysis of microbiome and metabolome reveals signatures in PDAC tumorigenesis and prognosis.

We conducted a large sample-size pancreatic adenocarcinoma microbiome study using a novel microbiome sequencing method and two metabolomic assays. Two significant outcomes of our analysis are: (i) commensal opportunistic pathogens Staphylococcus aureus, Cutibacterium acnes, and Cutibacterium granulosum were enriched in pancreatic ductal adenocarcinoma (PDAC) tumors compared with normal adjacent tissues, and (ii) worse overall survival was found related to the presence of Ralstonia pickettii_B. Microbial species affect the tumorigenesis, metastasis, and prognosis of PDAC via unique microbe-enzyme-metabolite interaction. Thus, our study highlights the need for further investigation of the potential associations between pancreatic microbiota-derived omics signatures, which may drive the clinical transformation of microbiome-derived strategies toward therapy-targeted bacteria.

Integrated analysis of gene expressions and targeted mirnas for explaining crosstalk between oral and esophageal squamous cell carcinomas through an interpretable machine learning approach.

This study explores the bidirectional relation of esophageal squamous cell carcinoma (ESCC) and oral squamous cell carcinoma (OSCC), examining shared risk factors and underlying molecular mechanisms. By employing random forest (RF) classifier, enhanced with interpretable machine learning (IML) through SHapley Additive exPlanations (SHAP), we analyzed gene expression from two GEO datasets (GSE30784 and GSE44021). The GSE30784 dataset comprises 167 OSCC samples and 45 control group, whereas the GSE44021 dataset encompasses 113 ESCC samples and 113 control group. Our analysis led to identification of 20 key genes, such as XBP1, VGLL1, and RAD1, which are significantly associated with development of ESCC and OSCC. Further investigations were conducted using tools like NetworkAnalyst 3.0, Single Cell Portal, and miRNET 2.0, which highlighted complex interactions between these genes and specific miRNA targets including hsa-mir-124-3p and hsa-mir-1-3p. Our model achieved high precision in identifying genes linked to crucial processes like programmed cell death and cancer pathways, suggesting new avenues for diagnosis and treatment. This study confirms the bidirectional relationship between OSCC and ESCC, laying groundwork for targeted therapeutic approaches. This study helps to identify shared biological pathways and genetic factors of these conditions for designing personalized medicine strategies and to improve disease management.

An integrated analysis of the Passifloraceae virome using public-domain data.

Passifloraceae is a plant family that includes several species of interest in the food, medicinal, and ornamental industries. The most relevant species are the purple and yellow varieties of P. edulis, which are among the most highly prized tropical fruits in the international markets. Unfortunately, the rapid expansion of this crop worldwide has resulted in the emergence of several viral diseases that endangered the productivity of this crop. In this work, we performed an integrated analysis of the Passifloraceae virome using public data. We investigated Pubmed and Genbank records and analyzed all the transcriptome data available for members of this plant family. This analysis resulted in the identification of six novel virus associations and six putative new viral species. We also used RNAseq to inspect virus accumulation levels and mixed infections. Using network analysis, we also examined the global distribution of Passiflora viruses and their associations with alternative hosts, which is valuable information in implementing viral disease management strategies. Our data suggest that a large diversity of viruses remains to be discovered. Finally, we used the information gathered in this work to estimate the cross-transmission risk of viruses in Colombian Passiflora fields.

Integrated analysis and experiments uncover the function of disulfidptosis in predicting immunotherapy effectiveness and delineating immune landscapes in uterine corpus endometrial carcinoma.

Recently, a novel type of metabolic-regulated cell demise titled disulfidptosis has been discovered. Studies have demonstrated its importance in immune responses against cancer and its impact on the proliferation of cancer cells. Nonetheless, the precise mechanism and roles of disulfidptosis are not fully understood, particularly regarding the prognosis for individuals with uterine corpus endometrial carcinoma (UCEC). In this research, a distinctive disulfidptosis pattern was developed in UCEC, and by utilizing Non-negative Matrix Factorization (NMF) on 23 disulfidptosis related genes within the TCGA database, 3 distinct subgroups were distinguished. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA and GEO database, specifically from UCEC patients. Cell line experiments and immunohistochemical (IHC) staining were conducted to validate the role of the LRPPRC in proliferation, migration and invasion. The genetic features and immune microenvironment of these subgroups were examined. It is worth mentioning that these subgroups offer important insights into comprehending the tumor microenvironment (TME) and the response of patients to immunotherapy and chemotherapy. Moreover, a disulfidptosis model was developed and validated, demonstrating a high level of accuracy in predicting the prognosis and outcomes of immunotherapy in UCEC patients. Additionally, a novel biomarker, LRPPRC, was identified, which can server as a promising predictor for forecasting prognosis in UCEC patients, with validation through tissue microarray staining and cell line experiments. This study has designed a classification system and a disulfidptosis model for UCEC, in addition to identifying a new biomarker, LRPPRC, for UCEC. These advancements serve as reliable and positive indicators for predicting outcomes and the efficacy of immunotherapy for each UCEC patient.

Interoperable and scalable echosounder data processing with Echopype

Abstract Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype’s modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies.

Integrated Metabolome and Transcriptome Analyses Reveal the Mechanisms Regulating Flavonoid Biosynthesis in Blueberry Leaves under Salt Stress

The flavonoids play important roles in plant salt tolerance. Blueberries (Vaccinium spp.) are extremely sensitive to soil salt increases. Therefore, improving the salt resistance of blueberries by increasing the flavonoid content is crucial for the development of the blueberry industry. To explore the underlying molecular mechanism, we performed an integrated analysis of the metabolome and transcriptome of blueberry leaves under salt stress. We identified 525 differentially accumulated metabolites (DAMs) under salt stress vs. control treatment, primarily including members of the flavonoid class. We also identified 20,920 differentially expressed genes (DEGs) based on transcriptome data; of these, 568 differentially expressed transcription factors (TFs) were annotated, and bHLH123, OsHSP20, and HSP20 TFs might be responsible for blueberry leaf salt tolerance. DEGs involved in the flavonoid biosynthesis pathway were significantly enriched at almost all stages of salt stress. Salt treatment upregulated the expression of most flavonoid biosynthetic pathway genes and promoted the accumulation of flavonols, flavonol glycosides, flavans, proanthocyanidins, and anthocyanins. Correlation analysis suggested that 4-coumarate CoA ligases (4CL5 and 4CL1) play important roles in the accumulation of flavonols (quercetin and pinoquercetin) and flavan-3-ol (epicatechin and prodelphinidin C2) under salt stress, respectively. The flavonoid 3′5′-hydroxylases (F3′5′H) regulate anthocyanin (cyanidin 3-O-beta-D-sambubioside and delphinidin-3-O-glucoside chloride) biosynthesis, and leucoanthocyanidin reductases (LAR) are crucial for the biosynthesis of epicatechin and prodelphinidin C2 during salt stress. Taken together, it is one of the future breeding goals to cultivate salt-resistant blueberry varieties by increasing the expression of flavonoid biosynthetic genes, especially 4CL, F3′5′H, and LAR genes, to promote flavonoid content in blueberry leaves.