Improved biomarker discovery through a plot twist in transcriptomic data analysis

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

JOURNAL/nrgr/04.03/01300535-202412000-00028/figure1/v/2024-04-08T165401Z/r/image-tiff Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spinal cord injury. In this study, we obtained microRNA expression profiles from the peripheral blood of patients with spinal cord injury using high-throughput sequencing. We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus (GEO) database (GSE151371). We identified 54 differentially expressed microRNAs and 1656 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation pathway, T cell receptor signaling pathway, and nuclear factor-κB signal pathway, were abnormally activated or inhibited in spinal cord injury patient samples. We applied an integrated strategy that combines weighted gene co-expression network analysis, LASSO logistic regression, and SVM-RFE algorithm and identified three biomarkers associated with spinal cord injury: ANO10, BST1, and ZFP36L2. We verified the expression levels and diagnostic performance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve. Quantitative polymerase chain reaction results showed that ANO10 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients. We also constructed a small RNA-mRNA interaction network using Cytoscape. Additionally, we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal cord injury patients using the CIBERSORT tool. The proportions of naïve B cells, plasma cells, monocytes, and neutrophils were increased while the proportions of memory B cells, CD8+ T cells, resting natural killer cells, resting dendritic cells, and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects, and ANO10, BST1 and ZFP26L2 were closely related to the proportion of certain immune cell types. The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal cord injury and suggest that ANO10, BST1, and ZFP36L2 are potential biomarkers for spinal cord injury. The study was registered in the Chinese Clinical Trial Registry (registration No. ChiCTR2200066985, December 12, 2022).

Co-Expression Network and Machine Learning Analysis of Transcriptomics Data Identifies Distinct Gene Signatures and Pathways in Lesional and Non-Lesional Atopic Dermatitis

BACKGROUND

Atopic dermatitis (AD) is a common inflammatory skin condition with complex origins. Current treatments often yield suboptimal results due to an incomplete understanding of its underlying mechanisms. This study aimed to identify pathway and gene signatures that distinguish between lesional AD, non-lesional AD, and healthy skin.

METHOD

We conducted differential gene expression and co-expression network analyses to identify differentially co-expressed genes (DCEGs) in lesional AD vs. healthy skin, lesional vs. non-lesional AD, and non-lesional AD vs. healthy skin. Modules associated with lesional and non-lesional AD were identified based on the correlation coefficients between module eigengenes and clinical phenotypes (|R| ≥ 0.5, p-value < 0.05). Subsequently, we employed Ingenuity Pathway Analysis (IPA) on the identified DCEGs, followed by machine learning (ML) analysis within the pathway expression framework. The ML analysis of pathway expressions, selected by IPA and derived from gene expression data, identified relevant pathway signatures, which were validated using an independent dataset and correlated with AD severity measures (EASI and SCORAD).

RESULTS

We identified 975, 441, and 40 DCEGs in lesional vs. healthy skin, lesional vs. non-lesional, and non-lesional vs. healthy skin, respectively. IPA and ML analyses revealed 25 relevant pathway signatures, including wound healing, glucocorticoid receptor signaling, and S100 gene family signaling pathways. Validation confirmed the significance of 10 pathway signatures, which were correlated with the AD severity measures. DCEGs such as MMP12 and S100A8 demonstrated high diagnostic efficacy (AUC > 0.70) in both the discovery and validation datasets.

CONCLUSIONS

Differential gene expression, co-expression networks and ML analyses of pathway expression have unveiled relevant pathways and gene signatures that distinguish between lesional, non-lesional, and healthy skin, providing valuable insights into AD pathogenesis.

Day-night gene expression reveals circadian gene disco as a candidate for diel-niche evolution in moths

Temporal ecological niche partitioning is an underappreciated driver of speciation. While insects have long been models for circadian biology, the genes and circuits that allow adaptive changes in diel-niches remain poorly understood. We compared gene expression in closely related day- and night-active non-model wild silk moths, with otherwise similar ecologies. Using an ortholog-based pipeline to compare RNA-Seq patterns across two moth species, we find over 25 pairs of gene orthologs showing differential expression. Notably, the gene disco, involved in circadian control, optic lobe and clock neuron development in Drosophila, shows robust adult circadian mRNA cycling in moth heads. Disco is highly conserved in moths and has additional zinc-finger domains with specific nocturnal and diurnal mutations. We propose disco as a candidate gene for the diversification of temporal diel-niche in moths.

Shallow water seeding cultivation enhances cold tolerance in tobacco seedlings

Cold stress can impact plant biology at both the molecular and morphological levels. We cultivated two different types of tobacco seedlings using distinct seeding methods, observing significant differences in their cold tolerance at 4 °C. After 12 h cold stress, shallow water seeding cultivation treatment demonstrates a relatively good growth state with slight wilting of the leaves. Tobacco grown using the float system exhibited short, thick roots, while those cultivated through shallow water seeding had elongated roots with more tips and forks. After cold stress, the shallow water seeding cultivation treatment demonstrated higher antioxidant enzyme activity, and lower malondialdehyde (MDA) content.Transcriptome analysis was performed on the leaves of these tobacco seedlings at three stages of cold treatment (before cold stress, after cold stress, and after 3 days of recovery). Upon analyzing the raw data, we found that the shallow water seeding cultivation treatment was associated with significant functional enrichment of nicotinamide adenine dinucleotide (NAD) biosynthesis and NAD metabolism before cold stress, enrichment of functions related to the maintenance of cellular structure after cold stress, and substantial functional enrichment related to photosynthesis during the recovery period. Weighted gene co-expression network analysis (WGCNA) was conducted, identifying several hub genes that may contribute to the differences in cold tolerance between the two tobacco seedlings. Hub genes related to energy conversion were predominantly identified in shallow water seeding cultivation treatment during our analysis, surpassing findings in other areas. These include the AS gene, which controls the synthesis of NAD precursors, the PED1 gene, closely associated with fatty acid β-oxidation, and the RROP1 gene, related to ATP production.Overall, our study provides a valuable theoretical basis for exploring improved methods of cultivating tobacco seedlings. Through transcriptome sequencing technology, we have elucidated the differences in gene expression in different tobacco seedlings at three time points, identifying key genes affecting cold tolerance in tobacco and providing possibilities for future gene editing.

Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms

Spinal cord injury (SCI) is a severe neurological complication following spinal fracture, which has long posed a challenge for clinicians. Microglia play a dual role in the pathophysiological process after SCI, both beneficial and detrimental. The underlying mechanisms of microglial actions following SCI require further exploration. The present study combined three different machine learning algorithms, namely weighted gene co-expression network analysis, random forest analysis and least absolute shrinkage and selection operator analysis, to screen for differentially expressed genes in the GSE96055 microglia dataset after SCI. It then used protein-protein interaction networks and gene set enrichment analysis with single genes to investigate the key genes and signaling pathways involved in microglial function following SCI. The results indicated that microglia not only participate in neuroinflammation but also serve a significant role in the clearance mechanism of apoptotic cells following SCI. Notably, bioinformatics analysis and lipopolysaccharide + UNC569 (a MerTK-specific inhibitor) stimulation of BV2 cell experiments showed that the expression levels of Anxa2, Myo1e and Spp1 in microglia were significantly upregulated following SCI, thus potentially involved in regulating the clearance mechanism of apoptotic cells. The present study suggested that Anxa2, Myo1e and Spp1 may serve as potential targets for the future treatment of SCI and provided a theoretical basis for the development of new methods and drugs for treating SCI.

Effect of Nutrient Solution Flow on Lettuce Root Morphology in Hydroponics: A Multi-Omics Analysis of Hormone Synthesis and Signal Transduction

This study examined how the nutrient flow environment affects lettuce root morphology in hydroponics using multi-omics analysis. The results indicate that increasing the nutrient flow rate initially increased indicators such as fresh root weight, root length, surface area, volume, and average diameter before declining, which mirrors the trend observed for shoot fresh weight. Furthermore, a high-flow environment significantly increased root tissue density. Further analysis using Weighted Gene Co-expression Network Analysis (WGCNA) and Weighted Protein Co-expression Network Analysis (WPCNA) identified modules that were highly correlated with phenotypes and hormones. The analysis revealed a significant enrichment of hormone signal transduction pathways. Differences in the expression of genes and proteins related to hormone synthesis and transduction pathways were observed among the different flow conditions. These findings suggest that nutrient flow may regulate hormone levels and signal transmission by modulating the genes and proteins associated with hormone biosynthesis and signaling pathways, thereby influencing root morphology. These findings should support the development of effective methods for regulating the flow of nutrients in hydroponic contexts.

Water deficit response in nodulated soybean roots: a comprehensive transcriptome and translatome network analysis

BACKGROUND

Soybean establishes a mutualistic interaction with nitrogen-fixing rhizobacteria, acquiring most of its nitrogen requirements through symbiotic nitrogen fixation. This crop is susceptible to water deficit; evidence suggests that its nodulation status-whether it is nodulated or not-can influence how it responds to water deficit. The translational control step of gene expression has proven relevant in plants subjected to water deficit.

RESULTS

Here, we analyzed soybean roots' differential responses to water deficit at transcriptional, translational, and mixed (transcriptional + translational) levels. Thus, the transcriptome and translatome of four combined-treated soybean roots were analyzed. We found hormone metabolism-related genes among the differentially expressed genes (DEGs) at the translatome level in nodulated and water-restricted plants. Also, weighted gene co-expression network analysis followed by differential expression analysis identified gene modules associated with nodulation and water deficit conditions. Protein-protein interaction network analysis was performed for subsets of mixed DEGs of the modules associated with the plant responses to nodulation, water deficit, or their combination.

CONCLUSIONS

Our research reveals that the stand-out processes and pathways in the before-mentioned plant responses partially differ; terms related to glutathione metabolism and hormone signal transduction (2 C protein phosphatases) were associated with the response to water deficit, terms related to transmembrane transport, response to abscisic acid, pigment metabolic process were associated with the response to nodulation plus water deficit. Still, two processes were common: galactose metabolism and branched-chain amino acid catabolism. A comprehensive analysis of these processes could lead to identifying new sources of tolerance to drought in soybean.

Identifying molecular subgroups of patients with preeclampsia through bioinformatics

Preeclampsia (PE) is a pregnancy-related disorder associated with serious complications. Its molecular mechanisms remain undefined; hence, we aimed to identify molecular subgroups of patients with PE using bioinformatics to aid treatment strategies. R software was used to analyze gene expression data of 130 patients with PE and 138 healthy individuals from the Gene Expression Omnibus database. Patients with PE were divided into two molecular subgroups using the unsupervised clustering learning method. Clinical feature analysis of subgroups using weighted gene co-expression network analysis showed that the patients in subgroup I were primarily characterized by early onset of PE, severe symptoms at disease onset, and induced labor as the main delivery method. Patients in subgroup II primarily exhibited late PE onset, relatively mild symptoms, and natural delivery as the main delivery method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the significant enrichment of calcium ion channels in subgroup II indicated the potential efficacy of calcium antagonists and magnesium sulfate therapy. In conclusion, the establishment of PE molecular subgroups can aid in diagnosing and treating PE.

Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases

Gynecological diseases are triggered by aberrant molecular pathways that alter gene expression, hormonal balance, and cellular signaling pathways, which may lead to long-term physiological consequences. This study was able to identify highly preserved modules and key hub genes that are mainly associated with gynecological diseases, represented by endometriosis (EM), ovarian cancer (OC), cervical cancer (CC), and endometrial cancer (EC), through the weighted gene co-expression network analysis (WGCNA) of microarray datasets sourced from the Gene Expression Omnibus (GEO) database. Five highly preserved modules were observed across the EM (GSE51981), OC (GSE63885), CC (GSE63514), and EC (GSE17025) datasets. The functional annotation and pathway enrichment analysis revealed that the highly preserved modules were heavily involved in several inflammatory pathways that are associated with transcription dysregulation, such as NF-kB signaling, JAK-STAT signaling, MAPK-ERK signaling, and mTOR signaling pathways. Furthermore, the results also include pathways that are relevant in gynecological disease prognosis through viral infections. Mutations in the ESR1 gene that encodes for ERα, which were shown to also affect signaling pathways involved in inflammation, further indicate its importance in gynecological disease prognosis. Potential drugs were screened through the Drug Repurposing Encyclopedia (DRE) based on the up-and downregulated hub genes, wherein a bacterial ribosomal subunit inhibitor and a benzodiazepine receptor agonist were the top candidates. Other drug candidates include a dihydrofolate reductase inhibitor, glucocorticoid receptor agonists, cholinergic receptor agonists, selective serotonin reuptake inhibitors, sterol demethylase inhibitors, a bacterial antifolate, and serotonin receptor antagonist drugs which have known anti-inflammatory effects, demonstrating that the gene network highlights specific inflammatory pathways as a therapeutic avenue in designing drug candidates for gynecological diseases.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Extraordinary variability in gene activation and repression programs during gonadal sex differentiation across vertebrates

Genes involved in gonadal sex differentiation have been traditionally thought to be fairly conserved across vertebrates, but this has been lately questioned. Here, we performed the first comparative analysis of gonadal transcriptomes across vertebrates, from fish to mammals. Our results unambiguously show an extraordinary overall variability in gene activation and repression programs without a phylogenetic pattern. During sex differentiation, genes such as dmrt1, sox9, amh, cyp19a and foxl2 were consistently either male- or female-enriched across species while many genes with the greatest expression change within each sex were not. We also found that downregulation in the opposite sex, which had only been quantified in the mouse model, was also prominent in the rest of vertebrates. Finally, we report 16 novel conserved markers (e.g., fshr and dazl) and 11 signaling pathways. We propose viewing vertebrate gonadal sex differentiation as a hierarchical network, with conserved hub genes such as sox9 and amh alongside less connected and less conserved nodes. This proposed framework implies that evolutionary pressures may impact genes based on their level of connectivity.

Gene coexpression network analysis reveals perirenal adipose tissue as an important target of prenatal malnutrition in sheep

We have previously demonstrated that pre- and early postnatal malnutrition in sheep induced depot- and sex-specific changes in adipose morphological features, metabolic outcomes, and transcriptome in adulthood, with perirenal (PER) as the major target followed by subcutaneous (SUB) adipose tissue. We aimed to identify coexpressed and hub genes in SUB and PER to identify the underlying molecular mechanisms contributing to the early nutritional programming of adipose-related phenotypic outcomes. Transcriptomes of SUB and PER of male and female adult sheep with different pre- and early postnatal nutrition histories were used to construct networks of coexpressed genes likely to be functionally associated with pre- and early postnatal nutrition histories and phenotypic traits using weighted gene coexpression network analysis. The modules from PER showed enrichment of cell cycle regulation, gene expression, transmembrane transport, and metabolic processes associated with both sexes' prenatal nutrition. In SUB (only males), a module of enriched adenosine diphosphate metabolism and development correlated with prenatal nutrition. Sex-specific module enrichments were found in PER, such as chromatin modification in the male network but histone modification and mitochondria- and oxidative phosphorylation-related functions in the female network. These sex-specific modules correlated with prenatal nutrition and adipocyte size distribution patterns. Our results point to PER as a primary target of prenatal malnutrition compared to SUB, which played only a minor role. The prenatal programming of gene expression and cell cycle, potentially through epigenetic modifications, might be underlying mechanisms responsible for observed changes in PER expandability and adipocyte-size distribution patterns in adulthood in both sexes.

Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential genes correlated with NAFLD, which may serve as diagnostic biomarkers and even potential treatment targets.

METHODS

The weighted gene co-expression network analysis (WGCNA) was constructed based on dataset E-MEXP-3291. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to evaluate the function of genes.

RESULTS

Blue module was positively correlated, and turquoise module negatively correlated with the severity of NAFLD. Furthermore, 8 driving genes (ANXA9, FBXO2, ORAI3, NAGS, C/EBPα, CRYAA, GOLM1, TRIM14) were identified from the overlap of genes in blue module and GSE89632. And another 8 driving genes were identified from the overlap of turquoise module and GSE89632. Among these driving genes, C/EBPα (CCAAT/enhancer binding protein α) was the most notable. By validating the expression of C/EBPα in the liver of NAFLD mice using immunohistochemistry, we discovered a significant upregulation of C/EBPα protein in NAFLD.

CONCLUSION

we identified two modules and 16 driving genes associated with the progression of NAFLD, and confirmed the protein expression of C/EBPα, which had been paid little attention to in the context of NAFLD, in the present study. Our study will advance the understanding of NAFLD. Moreover, these driving genes may serve as biomarkers and therapeutic targets of NAFLD.

Key Pathways and Genes of Arabidopsis thaliana and Arabidopsis halleri Roots under Cadmium Stress Responses: Differences and Similarities

Cadmium (Cd) is among the world's major health concerns, as it renders soils unsuitable and unsafe for food and feed production. Phytoremediation has the potential to remediate Cd-polluted soils, but efforts are still needed to develop a deep understanding of the processes underlying it. In this study, we performed a comprehensive analysis of the root response to Cd stress in A. thaliana, which can phytostabilize Cd, and in A. halleri, which is a Cd hyperaccumulator. Suitable RNA-seq data were analyzed by WGCNA to identify modules of co-expressed genes specifically associated with Cd presence. The results evidenced that the genes of the hyperaccumulator A. halleri mostly associated with the Cd presence are finely regulated (up- and downregulated) and related to a general response to chemical and other stimuli. Additionally, in the case of A. thaliana, which can phytostabilize metals, the genes upregulated during Cd stress are related to a general response to chemical and other stimuli, while downregulated genes are associated with functions which, affecting root growth and development, determine a deep modification of the organ both at the cellular and physiological levels. Furthermore, key genes of the Cd-associated modules were identified and confirmed by differentially expressed gene (DEG) detection and external knowledge. Together, key functions and genes shed light on differences and similarities among the strategies that the plants use to cope with Cd and may be considered as possible targets for future research.

Genome-Wide Analysis of MYB Transcription Factors and Screening of MYBs Involved in the Red Color Formation in Rhododendron delavayi

Flower color is one of the crucial traits of ornamental plants. Rhododendron delavayi Franch. is a famous ornamental plant species distributed in the mountain areas of Southwest China. This plant has red inflorescence and young branchlets. However, the molecular basis of the color formation of R. delavayi is unclear. In this study, 184 MYB genes were identified based on the released genome of R. delavayi. These genes included 78 1R-MYB, 101 R2R3-MYB, 4 3R-MYB, and 1 4R-MYB. The MYBs were divided into 35 subgroups using phylogenetic analysis of the MYBs of Arabidopsis thaliana. The members of the same subgroup in R. delavayi had similar conserved domains and motifs, gene structures, and promoter cis-acting elements, which indicate their relatively conserved function. In addition, transcriptome based on unique molecular identifier strategy and color difference of the spotted petals, unspotted petals, spotted throat, unspotted throat, and branchlet cortex were detected. Results showed significant differences in the expression levels of R2R3-MYB genes. Weighted co-expression network analysis between transcriptome and chromatic aberration values of five types of red samples showed that the MYBs were the most important TFs involved in the color formation, of which seven were R2R3-MYB, and three were 1R-MYB. Two R2R3-MYB (DUH019226.1 and DUH019400.1) had the highest connectivity in the whole regulation network, and they were identified as hub genes for red color formation. These two MYB hub genes provide references for the study of transcriptional regulation of the red color formation of R. delavayi.

Dynamic transcriptome analysis reveals the gene network of gonadal development from the early history life stages in dwarf surfclam Mulinia lateralis

BACKGROUND

Gonadal development is driven by a complex genetic cascade in vertebrates. However, related information remains limited in molluscs owing to the long generation time and the difficulty in maintaining whole life cycle in the lab. The dwarf surfclam Mulinia lateralis is considered an ideal bivalve model due to the short generation time and ease to breed in the lab.

RESULTS

To gain a comprehensive understanding of gonadal development in M. lateralis, we conducted a combined morphological and molecular analysis on the gonads of 30 to 60 dpf. Morphological analysis showed that gonad formation and sex differentiation occur at 35 and 40-45 dpf, respectively; then the gonads go through gametogenic cycle. Gene co-expression network analysis on 40 transcriptomes of 35-60 dpf gonads identifies seven gonadal development-related modules, including two gonad-forming modules (M6, M7), three sex-specific modules (M14, M12, M11), and two sexually shared modules (M15, M13). The modules participate in different biological processes, such as cell communication, glycan biosynthesis, cell cycle, and ribosome biogenesis. Several hub transcription factors including SOX2, FOXZ, HSFY, FOXL2 and HES1 are identified. The expression of top hub genes from sex-specific modules suggests molecular sex differentiation (35 dpf) occurs earlier than morphological sex differentiation (40-45 dpf).

CONCLUSION

This study provides a deep insight into the molecular basis of gonad formation, sex differentiation and gametogenesis in M. lateralis, which will contribute to a comprehensive understanding of the reproductive regulation network in molluscs.