Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges. METHODS: This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data. RESULTS: Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes. CONCLUSIONS: MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.

Identifying the key hub genes linked with lung squamous cell carcinoma by examining the differentially expressed and survival genes.

Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC.

Network preservation analysis to identify transcriptional biomarkers related to flowering in Crocus sativus.

Crocus sativus L. is known as an ornamental geophyte and a source of valuable spice and secondary metabolites. Network preservation module analysis is one of the best approaches to revealing special features of different conditions. It can determine patterns of divergence and conservation between transcriptome data. Herein, we explored the regulatory genes of the flowering process by RNA-Seq data containing flowering and non-flowering samples in gene expression profiles. Persevered module analysis revealed three significant non-persevered modules related to the flowering process, namely pink, green, and blue. Several hub genes associated with non-preserved modules such as PIA1, NAC90, ALY3, Sus3, MYB31, ARF5/MP, MYB31, HD-ZIP, SEP3d, OR_B, AGL6a, bZIP(TGA1) and GRAS were identified. These candidate genes can be considered key diagnostic biomarkers for the flowering process. Here, we also compared two approaches, WGCNA and NetRep for module preservation analysis. The results of these methods were consistent with non-preserved modules. NetRep was a faster (11 times) and more efficient (run more than 10000 permutations for each comparison) method than WGCNA module preservation. Differential expression genes (DEGs) screening showed that many hub genes were downregulated in non-flowering than flowering samples. Our finding revealed regulatory mechanisms of the flowering process in C. sativus as can be developed transcriptional biomarkers which could pave the way for promoting saffron yield via flowering induction.

Association of XRCC2 with breast cancer, a multi-omics analysis at genomic, transcriptomic, and epigenomic level.

One of the main causes of cancer-related mortality for women worldwide is breast cancer (BC). The XRCC2 gene, essential for DNA repair, has been implicated in cancer susceptibility. This study aims to evaluate the association between XRCC2 and BC risk. The study was conducted at Zheen International Hospital in Erbil, Iraq, between 2021 and 2024 with a total of 88 samples, including 44 paired normal and cancer tissue samples. Mutation analysis was performed using Next-Generation Sequencing, coupled with in silico tools for variant impact prediction. Expression levels were assessed through RT-PCR, and methylation status was determined using methylation-sensitive restriction enzyme digestion PCR. The study identified seven inherited germline variants in the XRCC2 gene, with five of these mutations being Uncertain Significance, one being Likely Pathogenic, and one being Likely benign. RNA purity was found high with mean A260/280 ratios of 1.986 ± 0.097 in normal (N) and 1.963 ± 0.092 in tumor (T) samples. Tumor samples exhibited a higher RNA concentration (78.56 ± 40.87 ng/µL) than normal samples (71.44 ± 40.79 ng/µL). XRCC2 gene expression was significantly upregulated in tumor tissue, with marked increases in patients aged 40-55 and >56 years and in higher cancer grades (II and III) and invasive ductal carcinoma (p-values ranging from <0.0001 to 0.0392). DNA methylation rates in tumor tissues were low (7%), suggesting limited regulation by methylation. The study suggests that XRCC2 can be classified as an oncogene and that its structural investigation by targeted NGS and expression evaluation can be used as a potential biomarker in BC.

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating single-cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell-related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell-to-cell communication using CellChat, assessed immune-related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA-seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG-based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.

Identification of the Key Gene DfCCoAOMT1 through Comparative Analysis of Lignification in Dendrocalamus farinosus XK4 and ZPX Bamboo Shoots during Cold Storage.

Dendrocalamus farinosus bamboo shoots, a species with rich nutritional value, are important in Southwest China. Lignin is an important factor affecting the postharvest flavor quality of bamboo shoots; however, the underlying mechanism of lignin deposition in D. farinosus bamboo shoots during cold storage is still not fully understood. In this study, the mutant D. farinosus XK4 with low lignin content at 3.11% and the cultivated variety ZPX at 4.47% were used as experimental materials. The lignin content of D. farinosus XK4 and ZPX, as well as the gene expression differences between them, were compared and analyzed during cold storage using transcriptomic and physiological methods. Our analysis revealed several key genes and found that D. farinosus CCoAOMT1 plays a key role in the regulatory network of bamboo shoots during cold storage. Tobacco heterologous transformation experiments demonstrated that overexpression of DfCCoAOMT1 significantly increases lignin content. This study provides a novel foundation for future research aimed at improving the postharvest quality and flavor of D. farinosus bamboo shoots through targeted genetic manipulation during cold storage.

Multi-omic Analyses Shed Light on The Genetic Control of High-altitude Adaptation in Sheep.

Sheep were domesticated in the Fertile Crescent and then spread globally, where they have been encountering various environmental conditions. The Tibetan sheep has adapted to high altitudes on the Qinghai-Tibet Plateau over the past 3000 years. To explore genomic variants associated with high-altitude adaptation in Tibetan sheep, we analyzed Illumina short-reads of 994 whole genomes representing ∼ 60 sheep breeds/populations at varied altitudes, PacBio High fidelity (HiFi) reads of 13 breeds, and 96 transcriptomes from 12 sheep organs. Association testing between the inhabited altitudes and 34,298,967 variants was conducted to investigate the genetic architecture of altitude adaptation. Highly accurate HiFi reads were used to complement the current ovine reference assembly at the most significantly associated ß-globin locus and to validate the presence of two haplotypes A and B among 13 sheep breeds. The haplotype A carried two homologous gene clusters: (1) HBE1, HBE2, HBB-like, and HBBC, and (2) HBE1-like, HBE2-like, HBB-like, and HBB; while the haplotype B lacked the first cluster. The high-altitude sheep showed highly frequent or nearly fixed haplotype A, while the low-altitude sheep dominated by haplotype B. We further demonstrated that sheep with haplotype A had an increased hemoglobin-O2 affinity compared with those carrying haplotype B. Another highly associated genomic region contained the EGLN1 gene which showed varied expression between high-altitude and low-altitude sheep. Our results provide evidence that the rapid adaptive evolution of advantageous alleles play an important role in facilitating the environmental adaptation of Tibetan sheep.

Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer.

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

Construction of a panoramic mRNA map of adult noncystic fibrosis bronchiectasis and a preliminary study of the underlying molecular mechanisms.

BACKGROUND: The pathogenesis of noncystic fibrosis bronchiectasis in adults is complex, and the relevant molecular mechanisms remain unclear. In this study, we constructed a panoramic map of bronchiectasis mRNA, explored the potential molecular mechanisms, and identified potential therapeutic targets, thus providing a new clinical perspective for the preventive management of bronchiectasis and its acute exacerbation. METHODS: The mRNA profiles of peripheral blood and bronchiectasis tissues were obtained through transcriptome sequencing and public databases, and bioinformatics methods were used to screen for differentially expressed genes (DEGs). The DEGs were then subjected to biological function and pathway analyses. Some DEGs were validated using a real-time quantitative polymerase chain reaction (RT-qPCR) in peripheral blood. Spearman's correlation analysis was used to analyse the correlation between DEGs and clinical indicators. RESULTS: Based on transcriptome sequencing and public databases, the mRNA profile of bronchiectasis was determined. DEGs were obtained from the peripheral blood sequencing dataset (985 DEGs), tissue sequencing dataset (2919 DEGs), and GSE97258 dataset (1083 DEGs). Bioinformatics analysis showed that upregulated DEGs had enriched neutrophil-related pathways, and downregulated DEGs had enriched ribosome-related pathways. RT-qPCR testing confirmed the upregulated expression of VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 in bronchiectasis. These genes were related to many clinical parameters, such as neutrophils, C-reactive protein, and procalcitonin (P < 0.05). CONCLUSIONS: Transcriptomic methods were used to construct a panoramic map of bronchiectasis mRNA expression. The findings showed that neutrophil activation, chronic inflammation, immune regulation, impaired ribosomal function, oxidative phosphorylation, and energy metabolism disorders are important factors in the development of bronchiectasis. VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 may play important roles in the pathogenesis of bronchiectasis and are potential therapeutic targets.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. MATERIALS AND METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. CONCLUSION: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Combinatorial Genomic Biomarkers Associated with High Response in IgE-Dependent Degranulation in Human Mast Cells.

Mast cells are the major effector cells that mediate IgE-dependent allergic reactions. We sought to use integrated network analysis to identify genomic biomarkers associated with high response in IgE-mediated activation of primary human mast cells. Primary human mast cell cultures derived from 262 normal donors were categorized into High, Average and Low responder groups according to their activation response profiles. Transcriptome analysis was used to identify genes that were differentially expressed in different responder cultures in their baseline conditions, and the data were analyzed by constructing a personalized perturbed profile (PEEP). For upregulated genes, the construction of PEEP for each individual sample of all three responder groups revealed that High responders exhibited a higher percentage of "perturbed" samples whose PEEP values lay outside the normal range of expression. Moreover, the integration of PEEP of four selected upregulated genes into distinct sets of combinatorial profiles demonstrated that the specific pattern of upregulated expression of these four genes, in a tandem combination, was observed exclusively among the High responders. In conclusion, this combinatorial approach was useful in identifying a set of genomic biomarkers that are associated with high degranulation response in human mast cell cultures derived from the blood of a cohort of normal donors.

Paired Transcriptomic Analyses of Atheromatous and Control Vessels Reveal Novel Autophagy and Immunoregulatory Genes in Peripheral Artery Disease.

Peripheral artery disease (PAD), a significant health burden worldwide, affects lower extremities due to atherosclerosis in peripheral vessels. Although the mechanisms of PAD have been well studied, the molecular milieu of the plaques localized within peripheral arteries are not well understood. Thus, to identify PAD-lesion-specific gene expression profiles precluding genetic, environmental, and dietary biases, we studied the transcriptomic profile of nine plaque tissues normalized to non-plaque tissues from the same donors. A total of 296 upregulated genes, 274 downregulated genes, and 186 non-coding RNAs were identified. STAG1, SPCC3, FOXQ1, and E2F3 were key downregulated genes, and CD93 was the top upregulated gene. Autophagosome assembly, cellular response to UV, cytoskeletal organization, TCR signaling, and phosphatase activity were the key dysregulated pathways identified. Telomerase regulation and autophagy were identified as novel interacting pathways using network analysis. The plaque tissue was predominantly composed of immune cells and dedifferentiated cell populations indicated by cell-specific marker-imputed gene expression analysis. This study identifies novel genes, non-coding RNAs, associated regulatory pathways, and the cell composition of the plaque tissue in PAD patients. The autophagy and immunoregulatory genes may drive novel mechanisms, resulting in atheroma. These novel interacting networks and genes have potential for PAD-specific therapeutic applications.

Comparative transcriptome database for Camellia sinensis reveals genes related to the cold sensitivity and albino mechanism of 'Anji Baicha'.

Tea, a globally popular beverage, contains various beneficial secondary metabolites. Tea plants (Camellia sinensis) exhibit diverse genetic traits across cultivars, impacting yield, adaptability, morphology, and secondary metabolite composition. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 tea cultivars were assembled, and comparative analysis was conducted on the coding sequences of 13 cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform a cross-cultivar comparison of co-expressed gene clusters and tissue-specific gene expression. We observed that 'Anji Baicha' possesses the highest number of cultivar-specific genes and the second-highest number of expanded genes. These genes in 'Anji Baicha' tend to be enriched in functions associated with cold stress response, chloroplast thylakoid structure, and nitrogen metabolism. Notably, we identified three significantly expanded homologous genes in 'Anji Baicha' encoding the ICE1, SIZ1, and MAPKK2, which are closely associated with the cold sensitivity of 'Anji Baicha'. Additionally, one significantly expanded homologous gene in 'Anji Baicha' encoding regulatory factor RIQ may play a crucial role in the abnormal chloroplast structure and absence of thylakoid membranes in 'Anji Baicha'.

Unveiling the association between HMG-CoA reductase inhibitors and bladder cancer: a comprehensive analysis using Mendelian randomization, animal models, and transcriptomics.

This study utilized Mendelian randomization (MR) analysis and genome-wide association study (GWAS) data to investigate the association between commonly prescribed drugs and bladder cancer (BLCA) risk. Our results revealed that HMG CoA reductase (HMGCR) inhibitors, specifically simvastatin, are significantly associated with reduced BLCA risk. We further showed that simvastatin could significantly inhibit BLCA proliferation and epithelial-mesenchymal transition in animal models, with transcriptomic data identifying several pathways associated with these processes. Higher expression of HMGCR were linked with BLCA development and progression, and certain blood lipids, such as lipoprotein particles and very low density lipoprotein (VLDL) cholesterol, might influence BLCA risk. These findings suggested that HMGCR inhibitors, particularly simvastatin, could be potential treatment options or adjuvant therapies for BLCA.

Transcriptomic changes in oligodendrocytes and precursor cells associate with clinical outcomes of Parkinson's disease.

Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson's disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from a small cohort of post-mortem control and PD brain tissue. We found a significant association of oligodendrocytes (ODCs) and oligodendrocyte precursor cells (OPCs) with PD-linked risk loci and report several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria. In an independent PD cohort with clinical measures (681 cases and 549 controls), polygenic risk scores derived from the dysregulated genes significantly predicted Montreal Cognitive Assessment (MoCA)-, and Beck Depression Inventory-II (BDI-II)-scores but not motor impairment (UPDRS-III). We extended our analysis of clinical outcome prediction by incorporating differentially expressed genes from three separate datasets that were previously published by different laboratories. In the first dataset from the anterior cingulate cortex, we identified an association between ODCs and BDI-II. In the second dataset obtained from the substantia nigra (SN), OPCs displayed an association with UPDRS-III. In the third dataset from the SN region, a distinct subtype of OPCs, labeled OPC_ADM, exhibited an association with UPDRS-III. Intriguingly, the OPC_ADM cluster also demonstrated a significant increase in PD samples. These results suggest that by expanding our focus to glial cells, we can uncover region-specific molecular pathways associated with PD symptoms.

Identification of key genes involved in lignin and flavonoid accumulation during Tilia tuan seed maturation.

KEY MESSAGE: Transcriptomics and phenotypic data analysis identified 24 transcription factors (TFs) that play key roles in regulating the competitive accumulation of lignin and flavonoids. Tilia tuan Szyszyl. (T. tuan) is a timber tree species with important ecological and commercial value. However, its highly lignified pericarp results in a low seed germination rate and a long dormancy period. In addition, it is unknown whether there is an interaction between the biosynthesis of flavonoids and lignin as products of the phenylpropanoid pathway during seed development. To explore the molecular regulatory mechanism of lignin and flavonoid biosynthesis, T. tuan seeds were harvested at five stages (30, 60, 90, 120, and 150 days after pollination) for lignin and flavonoid analyses. The results showed that lignin accumulated rapidly in the early and middle stages (S1, S3, and S4), and rapid accumulation of flavonoids during the early and late stages (S1 and S5). High-throughput RNA sequencing analysis of developing seeds identified 50,553 transcripts, including 223 phenylpropanoid biosynthetic pathway genes involved in lignin accumulation grouped into 3 clusters, and 106 flavonoid biosynthetic pathway genes (FBPGs) grouped into 2 clusters. Subsequent WGCNA and time-ordered gene co-expression network (TO-GCN) analysis revealed that 24 TFs (e.g., TtARF2 and TtWRKY15) were involved in flavonoids and lignin biosynthesis regulation. The transcriptome data were validated by qRT-PCR to analyze the expression profiles of key enzyme-coding genes. This study revealed that there existed a competitive relationship between flavonoid and lignin biosynthesis pathway during the development of T. tuan seeds, that provide a foundation for the further exploration of molecular mechanisms underlying lignin and flavonoid accumulation in T. tuan seeds.

Integrative single-cell RNA-seq and spatial transcriptomics analyses reveal diverse apoptosis-related gene expression profiles in EGFR-mutated lung cancer.

In EGFR-mutated lung cancer, the duration of response to tyrosine kinase inhibitors (TKIs) is limited by the development of acquired drug resistance. Despite the crucial role played by apoptosis-related genes in tumor cell survival, how their expression changes as resistance to EGFR-TKIs emerges remains unclear. Here, we conduct a comprehensive analysis of apoptosis-related genes, including BCL-2 and IAP family members, using single-cell RNA sequence (scRNA-seq) and spatial transcriptomics (ST). scRNA-seq of EGFR-mutated lung cancer cell lines captures changes in apoptosis-related gene expression following EGFR-TKI treatment, most notably BCL2L1 upregulation. scRNA-seq of EGFR-mutated lung cancer patient samples also reveals high BCL2L1 expression, specifically in tumor cells, while MCL1 expression is lower in tumors compared to non-tumor cells. ST analysis of specimens from transgenic mice with EGFR-driven lung cancer indicates spatial heterogeneity of tumors and corroborates scRNA-seq findings. Genetic ablation and pharmacological inhibition of BCL2L1/BCL-XL overcome or delay EGFR-TKI resistance. Overall, our findings indicate that BCL2L1/BCL-XL expression is important for tumor cell survival as EGFR-TKI resistance emerges.

Genome-Wide Association and RNA-Seq Analyses Reveal a Potential Candidate Gene Related to Oil Content in Soybean Seeds.

Soybean is a crucial crop globally, serving as a significant source of unsaturated fatty acids and protein in the human diet. However, further enhancements are required for the related genes that regulate soybean oil synthesis. In this study, 155 soybean germplasms were cultivated under three different environmental conditions, followed by phenotypic identification and genome-wide association analysis using simplified sequencing data. Genome-wide association analysis was performed using SLAF-seq data. A total of 36 QTLs were significantly associated with oil content (-log10(p) > 3). Out of the 36 QTLs associated with oil content, 27 exhibited genetic overlap with previously reported QTLs related to oil traits. Further transcriptome sequencing was performed on extreme high-low oil soybean varieties. Combined with transcriptome expression data, 22 candidate genes were identified (|log2FC| ≥ 3). Further haplotype analysis of the potential candidate genes showed that three potential candidate genes had excellent haplotypes, including Glyma.03G186200, Glyma.09G099500, and Glyma.18G248900. The identified loci harboring beneficial alleles and candidate genes likely contribute significantly to the molecular network's underlying marker-assisted selection (MAS) and oil content.