Study on the potential hypoglycemic flavonoids in Abrus precatorius leaves: purification process, quality profile and activity mechanisms by transcriptomics and network pharmacology.

The aim of the study was to investigate the relationship between flavonoids in Abrus precatorius leaves (APL) and their hypoglycaemic effects, which have not been studied before. An efficient purification process, transcriptomics and network pharmacology analysis were applied for the first time. High-performance liquid chromatography (HPLC) was used to determine the content of total flavonoids. The results showed that D101 resin was most suitable for purification of flavonoids of APL, which could increase its purity from 25.2% to 85.2% and achieve a recovery rate of 86.9%. The analysis of transcriptomics and network pharmacology revealed that flavonoids of APL could play a hypoglycaemic role by regulating 31 targets through AGE-RAGE and other signal pathways. Flavonoids of APL could exert hydroglycaemic effects by inhibiting AGEs, α-glucosidase and DPPH. This study provides a solid basis for hypoglycaemic product development and in-depth research of flavonoids in APL.

Ontological approach for competency-based curriculum analysis.

This article is dedicated to the development of a model for competencies within an educational program and its implementation through the use of semantic technologies. The model proposed by the authors is distinctive in that competencies are organized into a hierarchical data structure with arbitrary levels of nesting. Furthermore, the article presents an original solution for modelling the input requirements for studying a course, which is defined in the form of dependencies between the competencies generated by the course and the competencies of other courses. The outcome of this work is an ontological model of a competency-based curriculum, for which the authors have developed and implemented algorithms for data addition and retrieval, as well as for analyzing the consistency of the curriculum in terms of the input requirements for studying a discipline and the learning outcomes from previous periods. The findings presented in the article will prove to be valuable in the development of educational process management information systems and educational program constructors. They will also be instrumental in aligning diverse educational programs within the context of academic mobility.

Towards a social harm approach in drug policy.

In this paper, we explore how the social harm approach can be adapted within drug policy scholarship. Since the mid-2000s, a group of critical criminologists have moved beyond the concept of crime and criminology, towards the study of social harm. This turn proceeds decades of research that highlights the inequities within the criminal legal system, the formation of laws that protect the privileged and punish the disadvantaged, and the systemic challenge of the effectiveness of retribution and punishment at addressing harm in the community. The purpose of this paper is to first identify parallels between the social harm approach and critical drug scholarship, and second to advocate for the adoption of a social harm lens in drug policy scholarship. In the paper, we draw out the similarities between social harm and drug policy literatures, as well as outline what the study of social harm can bring to an analysis of drug policy. This includes a discussion on the ontology of drug crime, the myth of drug crime and the ineffective use of the crime control system in response to drug use. The paper then discusses how these conversations in critical criminology and critical drugs scholarship can be brought together to inform future drug policy research. This reflection details the link between social harm and the impingement of human flourishing, explores the role of decolonizing drug policy, advocates for the centralization of lived experience within the research process and outlines how this might align with harm reduction approaches. We conclude by arguing that the social harm approach challenges the idea that neutrality is the goal in drug policy and explicitly seeks to expand new avenues in activist research and social justice approaches to policymaking.

AlphaFun: Structural-Alignment-Based Proteome Annotation Reveals why the Functionally Unknown Proteins (uPE1) Are So Understudied.

With the rapid expansion of sequencing of genomes, the functional annotation of proteins becomes a bottleneck in understanding proteomes. The Chromosome-centric Human Proteome Project (C-HPP) aims to identify all proteins encoded by the human genome and find functional annotations for them. However, until now there are still 1137 identified human proteins without functional annotation, called uPE1 proteins. Sequence alignment was insufficient to predict their functions, and the crystal structures of most proteins were unavailable. In this study, we demonstrated a new functional annotation strategy, AlphaFun, based on structural alignment using deep-learning-predicted protein structures. Using this strategy, we functionally annotated 99% of the human proteome, including the uPE1 proteins and missing proteins, which have not been identified yet. The accuracy of the functional annotations was validated using the known-function proteins. The uPE1 proteins shared similar functions to the known-function PE1 proteins and tend to express only in very limited tissues. They are evolutionally young genes and thus should conduct functions only in specific tissues and conditions, limiting their occurrence in commonly studied biological models. Such functional annotations provide hints for functional investigations on the uPE1 proteins. This proteome-wide-scale functional annotation strategy is also applicable to any other species.

Dissection of quantitative trait nucleotides and candidate genes associated with agronomic and yield-related traits under drought stress in rapeseed varieties: integration of genome-wide association study and transcriptomic analysis.

Introduction: An important strategy to combat yield loss challenge is the development of varieties with increased tolerance to drought to maintain production. Improvement of crop yield under drought stress is critical to global food security. Methods: In this study, we performed multiomics analysis in a collection of 119 diverse rapeseed (Brassica napus L.) varieties to dissect the genetic control of agronomic traits in two watering regimes [well-watered (WW) and drought stress (DS)] for 3 years. In the DS treatment, irrigation continued till the 50% pod development stage, whereas in the WW condition, it was performed throughout the whole growing season. Results: The results of the genome-wide association study (GWAS) using 52,157 single-nucleotide polymorphisms (SNPs) revealed 1,281 SNPs associated with traits. Six stable SNPs showed sequence variation for flowering time between the two irrigation conditions across years. Three novel SNPs on chromosome C04 for plant weight were located within drought tolerance-related gene ABCG16, and their pleiotropically effects on seed weight per plant and seed yield were characterized. We identified the C02 peak as a novel signal for flowering time, harboring 52.77% of the associated SNPs. The 288-kbps LD decay distance analysis revealed 2,232 candidate genes (CGs) associated with traits. The CGs BIG1-D, CAND1, DRG3, PUP10, and PUP21 were involved in phytohormone signaling and pollen development with significant effects on seed number, seed weight, and grain yield in drought conditions. By integrating GWAS and RNA-seq, 215 promising CGs were associated with developmental process, reproductive processes, cell wall organization, and response to stress. GWAS and differentially expressed genes (DEGs) of leaf and seed in the yield contrasting accessions identified BIG1-D, CAND1, and DRG3 genes for yield variation. Discussion: The results of our study provide insights into the genetic control of drought tolerance and the improvement of marker-assisted selection (MAS) for breeding high-yield and drought-tolerant varieties.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

Functional associations of evolutionarily recent human genes exhibit sensitivity to the 3D genome landscape and disease.

Genome organization is intricately tied to regulating genes and associated cell fate decisions. In this study, we examine the positioning and functional significance of human genes, grouped by their evolutionary age, within the 3D organization of the genome. We reveal that genes of different evolutionary origin have distinct positioning relationships with both domains and loop anchors, and remarkably consistent relationships with boundaries across cell types. While the functional associations of each group of genes are primarily cell type-specific, such associations of conserved genes maintain greater stability across 3D genomic features and disease than recently evolved genes. Furthermore, the expression of these genes across various tissues follows an evolutionary progression, such that RNA levels increase from young genes to ancient genes. Thus, the distinct relationships of gene evolutionary age, function, and positioning within 3D genomic features contribute to tissue-specific gene regulation in development and disease.

Identification of chemoresistance associated key genes-miRNAs-TFs in docetaxel resistant breast cancer by bioinformatics analysis.

The present study aimed to identify the differentially expressed genes (DEGs) and enriched pathways in docetaxel (DTX) resistant breast cancer cell lines by bioinformatics analysis. The microarray dataset GSE28784 was obtained from gene expression omnibus (GEO) database. The differentially expressed genes (DEGs), gene ontology (GO), and Kyoto Encyclopedia of gene and genome (KEGG) pathway analyses were performed with the help of GEO2R and DAVID tools. Furthermore, the protein-protein interaction (PPI) and hub-gene network of DEGs were constructed using STRING and Cytohubba tools. The prognostic values of hub genes were calculated with the help of the Kaplan-Meier plotter database. From the GEO2R analysis, 222 DEGs were identified of which 120 are upregulated and 102 are downregulated genes. In the PPIs network, five up-regulated genes including CCL2, SPARC, CYR61, F3, and MFGE8 were identified as hub genes. It was observed that low expression of six hub genes CXCL8, CYR61, F3, ICAM1, PLAT, and THBD were significantly correlated with poor overall survival of BC patients in survival analysis. miRNA analysis identified that hsa-mir-16-5p, hsa-mir-335-5p, hsa-mir-124-3p, hsa-mir-20a-5p, and hsa-mir-155-5p are the top 5 interactive miRNAs that are commonly interacting with more hub genes with degree score of greater than five. Additionally, drug-gene interaction analysis was performed to identify drugs which are could potentially elevate/lower the expression levels of hub genes. In summary, the gene-miRNAs-TFs network and subsequent correlation of candidate drugs with hub genes may improve individualized diagnosis and help select appropriate combination therapy for DTX-resistant BC in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-03971-2.

Unveiling the Core Functional Networks of Cognition: An Ontology-Guided Machine Learning Approach.

Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application.

BACKGROUND: The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. MATERIALS AND METHODS: The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. RESULTS: WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. CONCLUSIONS: Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Gene ontology functional annotation datasets for the ITAG3.2 and ITAG4.0 tomato (Solanum lycopersicum) genome annotations.

Functional annotation based on Gene Ontology has provided a structured and comprehensive system to access the current knowledge about the function of genes. For model plants such as Arabidopsis thaliana, there is a constant updating and restructuring of the functional annotation that increases the reliability of the analyses that use it. For tomato (Solanum lycopersicum), a crop widely used as a model plant for the study of fleshy fruits, there is no functional annotation, at least not freely accessible, even though its genome has long been sequenced and annotated. In this work, we generated, using a simplified version of the maize GAMER pipeline, a tomato Gene Ontology functional annotation with 72.42% (ITAG3.2) and 74.2% (ITAG4.0) of protein-coding genes with at least one GO term association. With this dataset, we share a reliable and easy-to-use tool with the tomato community.

A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules.

According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.

Exploration of the Key Genes Involved in Non-alcoholic Fatty Liver Disease and Possible MicroRNA Therapeutic Targets.

Background: MicroRNAs (miRNAs) are promising therapeutic agents for non-alcoholic fatty liver disease (NAFLD). This study aimed to identify key genes/proteins involved in NAFLD pathogenesis and progression and to evaluate miRNAs influencing their expression. Methods: Gene expression profiles from datasets GSE151158, GSE163211, GSE135251, GSE167523, GSE46300, and online databases were analyzed to identify significant NAFLD-related genes. Then, protein-protein interaction networks and module analysis identified hub genes/proteins, which were validated using real-time PCR in oleic acid-treated HepG2 cells. Functional enrichment analysis evaluated signaling pathways and biological processes. Gene-miRNA interaction networks identified miRNAs targeting critical NAFLD genes. Results: The most critical overexpressed hub genes/proteins included: TNF, VEGFA, TLR4, CYP2E1, ACE, SCD, FASN, SREBF2, and TGFB1 based on PPI network analysis, of which TNF, TLR4, SCD, FASN, SREBF2, and TGFB1 were up-regulated in oleic acid-treated HepG2 cells. Functional enrichment analysis for biological processes highlighted programmed necrotic cell death, lipid metabolic process response to reactive oxygen species, and inflammation. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the highest adjusted P-value signaling pathways encompassed AGE-RAGE in diabetic complications, TNF, and HIF-1 signaling pathways. In gene-miRNA network analysis, miR-16 and miR-124 were highlighted as the miRNAs exerting the most influence on important NAFLD-related genes. Conclusion: In silico analyses identified NAFLD therapeutic targets and miRNA candidates to guide further experimental investigation.

The Assemblage and Dismantling of Access Barriers in Administrative Bureaucracies: Constructing the Problem of Diversity in the German Welfare State.

The article extends the literature on the construction of "diversity management" by personnel managers in corporate America. Such research has highlighted that Human Resource (HR) specialists draw heavily on social-scientific thinking in implementing various remedies against discrimination. However, it has paid less attention to how such esoteric views of reality, comprising such "things" as "structural barriers" impeding occupational advancement and "diversity sensitivity," have been successfully established as a self-evident reality in the workplace. In order to more thoroughly investigate how the world of diversity management is established outside the circle of academic specialists, the article employs perspectives from science and technology studies on the ways in which sociotechnical assemblages, i.e., networks of human actors and material devices, enact scientific ontologies. It applies such perspectives to a German case of diversity management, a program of "intercultural opening" that seeks to make bureaucracies of the welfare state more accessible to immigrants. The article delineates the specific ontology behind this version of diversity management, rooted in sociological perspectives on social mobility, and explores the various techniques and instruments through which officers of intercultural opening establish this ontology as a visible reality in municipal administrations.

A Semantic Approach to Describe Social and Economic Characteristics That Impact Health Outcomes (Social Determinants of Health): Ontology Development Study.

BACKGROUND: Social determinants of health (SDoH) have been described by the World Health Organization as the conditions in which individuals are born, live, work, and age. These conditions can be grouped into 3 interrelated levels known as macrolevel (societal), mesolevel (community), and microlevel (individual) determinants. The scope of SDoH expands beyond the biomedical level, and there remains a need to connect other areas such as economics, public policy, and social factors. OBJECTIVE: Providing a computable artifact that can link health data to concepts involving the different levels of determinants may improve our understanding of the impact SDoH have on human populations. Modeling SDoH may help to reduce existing gaps in the literature through explicit links between the determinants and biological factors. This in turn can allow researchers and clinicians to make better sense of data and discover new knowledge through the use of semantic links. METHODS: An experimental ontology was developed to represent knowledge of the social and economic characteristics of SDoH. Information from 27 literature sources was analyzed to gather concepts and encoded using Web Ontology Language, version 2 (OWL2) and Protégé. Four evaluators independently reviewed the ontology axioms using natural language translation. The analyses from the evaluations and selected terminologies from the Basic Formal Ontology were used to create a revised ontology with a broad spectrum of knowledge concepts ranging from the macrolevel to the microlevel determinants. RESULTS: The literature search identified several topics of discussion for each determinant level. Publications for the macrolevel determinants centered around health policy, income inequality, welfare, and the environment. Articles relating to the mesolevel determinants discussed work, work conditions, psychosocial factors, socioeconomic position, outcomes, food, poverty, housing, and crime. Finally, sources found for the microlevel determinants examined gender, ethnicity, race, and behavior. Concepts were gathered from the literature and used to produce an ontology consisting of 383 classes, 109 object properties, and 748 logical axioms. A reasoning test revealed no inconsistent axioms. CONCLUSIONS: This ontology models heterogeneous social and economic concepts to represent aspects of SDoH. The scope of SDoH is expansive, and although the ontology is broad, it is still in its early stages. To our current understanding, this ontology represents the first attempt to concentrate on knowledge concepts that are currently not covered by existing ontologies. Future direction will include further expanding the ontology to link with other biomedical ontologies, including alignment for granular semantics.

Simple method for isolation and culture of primary buffalo (Bubalus bubalis) endometrial epithelial cells (pBuEECs) and its characterization using high throughput proteomics approach.

Early embryonic mortality resulting from insufficient interaction between the embryo and the uterus leads to the failure of pregnancy in livestock animals. Thus, it is imperative to comprehend the multifaceted process of implantation at molecular levels, which requires synchronized feto-maternal interaction. The in-vitro models serve as valuable tools to investigate the specific stages of implantation. The present study was undertaken to develop a simple method to isolate and culture the primary buffalo endometrial epithelial cells (pBuEECs), followed by proteome profiling of the proliferating cells. Collagenase I was used to separate uterine epithelial cells (UECs) from the ipsilateral uterine horn, and then the cells were separated using a cell strainer. After being seeded on culture plates, UECs developed colonies with characteristic epithelial shape and expressed important markers such as cytokeratin 18 (KRT18), progesterone receptor (PGR), ß-estrogen receptor (ESR1), and leukemia inhibitory factor (LIF), which were confirmed by PCR. The purity of epithelial cells was assessed using cytokeratin 18 immunostaining, which indicated approximately 99% purity in cultured cells. The proteome profiling of pBuEECs via high-throughput tandem mass spectrometry (MS), identified a total of 3383 proteins. Bioinformatics analysis revealed enrichment in various biological processes, including cellular processes, metabolic processes, biological regulation, localization, signaling, and developmental processes. Moreover, the KEGG pathway analysis highlighted associations with the ribosome, proteosome, oxidative phosphorylation, spliceosome, and cytoskeleton regulation pathways. In conclusion, these well characterized cells offer valuable in-vitro model to enhance the understanding of implantation and uterine pathophysiology in livestock animals, particularly buffaloes.

m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach.

BACKGROUND: N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level. RESULTS: We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively. CONCLUSION: Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .

Identification and analysis of the molecular targets of statins in colorectal cancer.

Colorectal cancer (CRC) is the third most common cancer in the world. According to several types of research, statins may impact the development and treatment of CRC. This work aimed to use bioinformatics to discover the relationship between statin targets and differentially expressed genes (DEGs) in CRC patients and determine the possible molecular effect of statins on CRC suppression. We used CRC datasets from the GEO database to select CRC-related DEGs. DGIdb and STITCH databases were used to identify gene targets of subtypes of statin. Further, we identified the statin target of CRC DEGs hub genes by using a Venn diagram of CRC DEGs and statin targets. Funrich and enrichr databases were carried out for the KEGG pathway and gene ontology (GO) enrichment analysis, respectively. GSE74604 and GSE10950 were used to identify CRC DEGs. After analyzing datasets,1370 genes were identified as CRC DEGs, and 345 targets were found for statins. We found that 35 genes are CRC DEGs statin targets. We found that statin targets in CRC were enriched in the receptor and metallopeptidase activity for molecular function, cytoplasm and plasma membrane for cellular component, signal transduction, and cell communication for biological process genes were substantially enriched based on FunRich enrichment. Analysis of the KEGG pathways revealed that the overexpressed DEGs were enriched in the IL-17, PPAR, and Toll-like receptor signaling pathways. Finally, CCNB1, DNMT1, AURKB, RAC1, PPARGC1A, CDKN1A, CAV1, IL1B, and HSPD1 were identified as hub CRC DEGs statin targets. The genetic and molecular aspects of our findings reveal that statins might have a therapeutic effect on CRC.

Differentially Expressed Genes Related to Isoflavone Biosynthesis in a Soybean Mutant Revealed by a Comparative Transcriptomic Analysis.

Soybean [Glycine max (L.) Merr.] isoflavones, which are secondary metabolites with various functions, are included in food, cosmetics, and medicine. However, the molecular mechanisms regulating the glycosylation and malonylation of isoflavone glycoconjugates remain unclear. In this study, we conducted an RNA-seq analysis to compare soybean genotypes with different isoflavone contents, including Danbaek and Hwanggeum (low-isoflavone cultivars) as well as DB-088 (high-isoflavone mutant). The transcriptome analysis yielded over 278 million clean reads, representing 39,156 transcripts. The analysis of differentially expressed genes (DEGs) detected 2654 up-regulated and 1805 down-regulated genes between the low- and high-isoflavone genotypes. The putative functions of these 4459 DEGs were annotated on the basis of GO and KEGG pathway enrichment analyses. These DEGs were further analyzed to compare the expression patterns of the genes involved in the biosynthesis of secondary metabolites and the genes encoding transcription factors. The examination of the relative expression levels of 70 isoflavone biosynthetic genes revealed the HID, IFS, UGT, and MAT expression levels were significantly up/down-regulated depending on the genotype and seed developmental stage. These expression patterns were confirmed by quantitative real-time PCR. Moreover, a gene co-expression analysis detected potential protein-protein interactions, suggestive of common functions. The study findings provide valuable insights into the structural genes responsible for isoflavone biosynthesis and accumulation in soybean seeds.

Comparative genomics and bioinformatics approaches revealed the role of CC-NBS-LRR genes under multiple stresses in passion fruit.

Passion fruit is widely cultivated in tropical, subtropical regions of the world. The attack of bacterial and fungal diseases, and environmental factors heavily affect the yield and productivity of the passion fruit. The CC-NBS-LRR (CNL) gene family being a subclass of R-genes protects the plant against the attack of pathogens and plays a major role in effector-triggered immunity (ETI). However, no information is available regarding this gene family in passion fruit. To address the underlying problem a total of 25 and 21 CNL genes have been identified in the genome of purple (Passiflora edulis Sims.) and yellow (Passiflora edulis f. flavicarpa) passion fruit respectively. Phylogenetic tree was divided into four groups with PeCNLs present in 3 groups only. Gene structure analysis revealed that number of exons ranged from 1 to 9 with 1 being most common. Most of the PeCNL genes were clustered at the chromosome 3 and underwent strong purifying selection, expanded through segmental (17 gene pairs) and tandem duplications (17 gene pairs). PeCNL genes contained cis-elements involved in plant growth, hormones, and stress response. Transcriptome data indicated that PeCNL3, PeCNL13, and PeCNL14 were found to be differentially expressed under Cucumber mosaic virus and cold stress. Three genes were validated to be multi-stress responsive by applying Random Forest model of machine learning. To comprehend the biological functions of PeCNL proteins, their 3D structure and gene ontology (GO) enrichment analysis were done. Our research analyzed the CNL gene family in passion fruit to understand stress regulation and improve resilience. This study lays the groundwork for future investigations aimed at enhancing the genetic composition of passion fruit to ensure robust growth and productivity in challenging environments.