Weighted gene co-expression network analysis of the salt-responsive transcriptomes reveals novel hub genes in green halophytic microalgae Dunaliella salina

SGCP: a spectral self-learning method for clustering genes in co-expression networks

BACKGROUND

A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules.

RESULTS

We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines.

CONCLUSION

Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Trancriptome data mining in combination with co-expression network analysis identifies the functional modules and critical regulators in Hordeum vulgare L. in response to cold stress

Cold stress, as an abiotic stress, is one of the most limiting factors which pose a great threat to the plant's productivity. To understand the transcriptional regulation and connectivity pattern of genes involved in barley cold stress responses, co-expression network analysis was performed based on the global transcriptome profiling. The microarray datasets related to cold stress treatments were retrieved from the Gene Expression Omnibus (GEO) and Array express databases. Four microarray datasets related to cold stress-responsive transcriptome in barley were included in our study. Gene co-expression analysis was constructed using WGCNA method. Module-Trait Relationships (MTR) analysis and hub genes determination and validation were carried out. Finally, transcription factor and kinase regulatory networks were Inferred using machine learning algorithm. The co-expression modules were determined using beta index = 10. In total 13 co-expressed modules were identified with an average size of 153 genes. Functional enrichment based on gene ontology (GO) showed that each of the stress related significant modules were enriched in different biological processes. Annotation of significant modules identifies some TFs and Kinases such as ethylene-responsive transcription factor 1-like, transcription factor PCL1-like, transcription factor MYC2, WRKY, serine/threonine-protein kinase PBL7, and receptor-like protein kinase At2g42960 were contributed in barley cold stress response. Our analysis highlighted the functional importance of ABA signaling pathway, ROS signaling, defensive and protective proteins, degrading protein, Ca2+ related signaling, ribosome-mediated translation and etc. in responding of barley to cold stress condition. The current findings add substantially to our understanding of the cold responsive underlying mechanism of barley which can serve in future studies and breeding programs.

Meta-analysis of transcriptomic profiles in Dunaliella tertiolecta reveals molecular pathway responses to different abiotic stresses

Microalgae are photosynthetic organisms and a potential source of sustainable metabolite production. However, different stress conditions might affect the production of various metabolites. In this study, a meta-analysis of RNA-seq experiments in Dunaliella tertiolecta was evaluated to compare metabolite biosynthesis pathways in response to abiotic stress conditions such as high light, nitrogen deficiency and high salinity. Results showed downregulation of light reaction, photorespiration, tetrapyrrole and lipid-related pathways occurred under salt stress. Nitrogen deficiency mostly induced the microalgal responses of light reaction and photorespiration metabolism. Phosphoenol pyruvate carboxylase, phosphoglucose isomerase, bisphosphoglycerate mutase and glucose-6-phosphate-1-dehydrogenase (involved in central carbon metabolism) were commonly upregulated under salt, light and nitrogen stresses. Interestingly, the results indicated that the meta-genes (modules of genes strongly correlated) were located in a hub of stress-specific protein-protein interaction (PPI) network. Module enrichment of meta-genes PPI networks highlighted the cross-talk between photosynthesis, fatty acids, starch and sucrose metabolism under multiple stress conditions. Moreover, it was observed that the coordinated expression of the tetrapyrrole intermediated with meta-genes was involved in starch biosynthesis. Our results also showed that the pathways of vitamin B6 metabolism, methane metabolism, ribosome biogenesis and folate biosynthesis responded specifically to different stress factors. Since the results of this study revealed the main pathways underlying the abiotic stress, they might be applied in optimised metabolite production by the microalga Dunaliella in future studies. PRISMA check list was also included in the study.

The RNA landscape of Dunaliella salina in response to short-term salt stress

Using the halotolerant green microalgae Dunaliella salina as a model organism has special merits, such as a wide range of salt tolerance, unicellular organism, and simple life cycle and growth conditions. These unique characteristics make it suitable for salt stress study. In order to provide an overview of the response of Dunaliella salina to salt stress and hopefully to reveal evolutionarily conserved mechanisms of photosynthetic organisms in response to salt stress, the transcriptomes and the genome of the algae were sequenced by the second and the third-generation sequencing technologies, then the transcriptomes under salt stress were compared to the transcriptomes under non-salt stress with the newly sequenced genome as the reference genome. The major cellular biological processes that being regulated in response to salt stress, include transcription, protein synthesis, protein degradation, protein folding, protein modification, protein transport, cellular component organization, cell redox homeostasis, DNA repair, glycerol synthesis, energy metabolism, lipid metabolism, and ion homeostasis. This study gives a comprehensive overview of how Dunaliella salina responses to salt stress at transcriptomic level, especially characterized by the nearly ubiquitous up-regulation of the genes involving in protein folding, DNA repair, and cell redox homeostasis, which may confer the algae important mechanisms to survive under salt stress. The three fundamental biological processes, which face huge challenges under salt stress, are ignored by most scientists and are worth further deep study to provide useful information for breeding economic important plants competent in tolerating salt stress, other than only depending on the commonly acknowledged osmotic balance and ion homeostasis.

Identification of genes associated with abiotic stress tolerance in sweetpotato using weighted gene co-expression network analysis

Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange-fleshed sweetpotato cultivar "Beauregard" was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress-specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with "cell population proliferation" suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co-expression networks under multiple abiotic stress conditions, weighted gene co-expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated 'Beauregard' leaves yielding 18 co-expression modules. One module was enriched for "response to water deprivation," "response to abscisic acid," and "nitrate transport" indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought-associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato.

Effects of flaxseed powder in improving non-alcoholic fatty liver by regulating gut microbiota-bile acids metabolic pathway through FXR/TGR5 mediating

Non-alcoholic fatty liver disease (NAFLD) is gradually becoming one of the most common and health-endangering diseases. Flaxseed powder (FLA) is rich in α-linolenic acid, dietary fiber, lignans, and other active ingredients, which have lipid-lowering and anti-inflammatory effects. Here, we investigated whether the FLA improves host metabolism by gut bacteria modulation and further bile acid modulation in mice fed a high-fat diet. At the end of the experiment, we found that FLA can significantly reduce the body weight, body fat content, and serum TG, LDL-C, and TNF-α levels of mice, and improve liver steatosis. FLA intervention has a significant effect on preventing and regulating the gut flora disturbance caused by HFD. FLA intervention affects bile acid metabolism in the intestine and causes significant changes in functional bile acids, which can play a lipid-lowering and anti-inflammatory role by activating the intestinal Fxr- Fgfr4-Cyp7a1 and Tgr5-Tlr4-Tnfα pathways.

Weighted gene co-expression network analysis revealed the key pathways and hub genes of potassium regulating cotton root adaptation to salt stress

Soil salinization is one of the main abiotic stresses affecting cotton yield and planting area. Potassium application has been proven to be an important strategy to reduce salt damage in agricultural production. However, the mechanism of potassium regulating the salt adaptability of cotton has not been fully elucidated. In the present research, the appropriate potassium application rate for alleviating salt damage of cotton based on different K⁺/Na⁺ ratios we screened, and a gene co-expression network based on weighted gene co-expression network analysis (WGCNA) using the transcriptome data sets treated with CK (0 mM NaCl), S (150 mM NaCl), and SK8 (150 mM NaCl + 9.38 mM K₂SO₄) was constructed. In this study, four key modules that are highly related to potassium regulation of cotton salt tolerance were identified, and the mitogen-activated protein kinase (MAPK) signaling pathway, tricarboxylic acid (TCA) cycle and glutathione metabolism pathway were identified as the key biological processes and metabolic pathways for potassium to improve cotton root salt adaptability. In addition, 21 hub genes and 120 key candidate genes were identified in this study, suggesting that they may play an important role in the enhancement of salt adaptability of cotton by potassium. The key modules, key biological pathways and hub genes discovered in this study will provide a new understanding of the molecular mechanism of potassium enhancing salinity adaptability in cotton, and lay a theoretical foundation for the improvement and innovation of high-quality cotton germplasm.

Combined analysis of chromatin accessibility and gene expression profiles provide insight into Fucoxanthin biosynthesis in Isochrysis galbana under green light

Isochrysis galbana, as a potential accumulator of fucoxanthin, has become a valuable material to develop functional foods for humans. Our previous research revealed that green light effectively promotes the accumulation of fucoxanthin in I. galbana, but there is little research on chromatin accessibility in the process of transcriptional regulation. This study was conducted to reveal the mechanism of fucoxanthin biosynthesis in I. galbana under green light by analyzing promoter accessibility and gene expression profiles. Differentially accessible chromatin regions (DARs)-associated genes were enriched in carotenoid biosynthesis and photosynthesis-antenna protein formation, including IgLHCA1, IgLHCA4, IgPDS, IgZ-ISO, IglcyB, IgZEP, and IgVDE. The motifs for the MYB family were also identified as candidates controlling metabolic regulation responses to green light culture of I. galbana, including IgMYB1, IgMYB2, IgMYB33, IgMYB42, IgMYB98, IgMYB118, and IgMYB119. The results of differential expression analysis and WGCNA showed that several genes or transcription factors (TFs) related to carotenoid metabolism and photosynthesis exhibited a higher expression level and were significantly upregulated in A-G5d compared with A-0d and A-W5d, including IgMYB98, IgLHCA1, IgLHCX2, IgLHCB4, and IgLHCB5. This suggests that upregulation of these genes by green light may be the key factor leading to fucoxanthin accumulation by regulating the photosynthesis-antenna protein pathway. An integrated analysis of ATAC-seq and RNA-seq showed that 3 (IgphoA, IgPKN1, IgOTC) of 34 DARs-associated genes displayed obvious changes in their chromatin regions in ATAC-seq data, suggesting that these genes specific for green light may play a key role in fucoxanthin biosynthesis in I. galbana through a complex regulatory network of multiple metabolic pathways interacting with each other. These findings will facilitate in-depth understanding the molecular regulation mechanisms of fucoxanthin in I. galbana and its role in response to green light regulation, providing technical support for the construction of high fucoxanthin content strains.

Identification of potentially functional modules and diagnostic genes related to amyotrophic lateral sclerosis based on the WGCNA and LASSO algorithms

Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases', 'amyotrophic lateral sclerosis', and 'endocytosis' KEGG terms. Further, some of other modules related to ALS were enriched in 'autophagy' and 'amyotrophic lateral sclerosis'. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value.

Global transcriptome and coexpression network analyses reveal cultivar-specific molecular signatures associated with different rooting depth responses to drought stress in potato

Potato is one of the most important vegetable crops worldwide. Its growth, development and ultimately yield is hindered by drought stress condition. Breeding and selection of deep-rooted and drought-tolerant potato varieties has become a prime approach for improving the yield and quality of potato (Solanum tuberosum L.) in arid and semiarid areas. A comprehensive understanding of root development-related genes has enabled scientists to formulate strategies to incorporate them into breeding to improve complex agronomic traits and provide opportunities for the development of stress tolerant germplasm. Root response to drought stress is an intricate process regulated through complex transcriptional regulatory network. To understand the rooting depth and molecular mechanism, regulating root response to drought stress in potato, transcriptome dynamics of roots at different stages of drought stress were analyzed in deep (C119) and shallow-rooted (C16) cultivars. Stage-specific expression was observed for a significant proportion of genes in each cultivar and it was inferred that as compared to C16 (shallow-rooted), approximately half of the genes were differentially expressed in deep-rooted cultivar (C119). In C16 and C119, 11 and 14 coexpressed gene modules, respectively, were significantly associated with physiological traits under drought stress. In a comparative analysis, some modules were different between the two cultivars and were associated with differential response to specific drought stress stage. Transcriptional regulatory networks were constructed, and key components determining rooting depth were identified. Through the results, we found that rooting depth (shallow vs deep) was largely determined by plant-type, cell wall organization or biogenesis, hemicellulose metabolic process, and polysaccharide metabolic process. In addition, candidate genes responding to drought stress were identified in deep (C119) and shallow (C16) rooted potato varieties. The results of this study will be a valuable source for further investigations on the role of candidate gene(s) that affect rooting depth and drought tolerance mechanisms in potato.

Transcriptome analysis reveals the proline metabolic pathway and its potential regulation TF-hub genes in salt-stressed potato

Potato (Solanum tuberosum) is currently the third most important food crop in the world. However, the production of potato is seriously threatened by salt stress, which often occurs in the facility cultivation environment, and the mining of salt tolerance genes in potato remains to be further studied. In this study, test-tube plantlets of DM potato were treated with 200-mM NaCl to simulate salt stress, and 15 cDNA libraries were constructed for RNA-seq analysis. A total of 8383 DEGs were identified, of which 3961 DEGs were shared among all the salt treatments, and 264 (7.15%) TF-coding genes were identified from these shared DEGs. KEGG enrichment analysis showed that most DEGs identified from the "arginine and proline metabolism" (ko00330) were enriched in the proline metabolic pathway, and their functions almost covered the whole proline metabolic process. Further analysis showed that expression levels of all the 13 structural DEGs in the pathway were significantly up-regulated and proline accumulation was also significantly increased under salt stress, and 13 TF-hub genes were discovered by WGCNA in the lightcyan and tan modules which were highly positively correlated with the proline contents. Correlation analysis revealed that the four TF-hub genes of the lightcyan module and seven structural DEGs of the proline metabolic pathway might be the potential candidate genes, especially the potential and novel regulatory gene StGLK014720. Furthermore, the dual-luciferase reporter assay confirmed that the key protein StGLK014720 could activate the promoters of both structural genes StAST021010 and StAST017480. In conclusion, these results lay the foundation for further study on the salt tolerance mechanism of potato, and provide a theoretical basis and new genetic resources for salt tolerance breeding of potato.

Root and shoot biology of Arabidopsis halleri dissected by WGCNA: an insight into the organ pivotal pathways and genes of an hyperaccumulator

Arabidopsis halleri is a hyperaccumulating pseudo-metallophyte and an emerging model to explore molecular basis of metal tolerance and hyperaccumulation. In this regard, understanding of interacting genes can be a crucial aspect as these interactions regulate several biological functions at molecular level in response to multiple signals. In this current study, we applied a weighted gene co-expression network analysis (WGCNA) on root and shoot RNA-seq data of A. halleri to predict the related scale-free organ specific co-expression networks, for the first time. A total of 19,653 genes of root and 18,081 genes of shoot were grouped into 14 modules and subjected to GO and KEGG enrichment analysis. "Photosynthesis" and "photosynthesis-antenna proteins" were identified as the most enriched and common pathway to both root and shoot. Whereas "glucosinolate biosynthesis," "autophagy," and "SNARE interactions in vesicular transport" were specific to root, and "circadian rhythm" was found to be enriched only in shoot. Later, hub and bottleneck genes were identified in each module by using cytoHubba plugin based on Cytoscape and scoring the relevance of each gene to the topology of the network. The modules with the most significant differential expression pattern across control and treatment (Cd-Zn treatment) were selected and their hub and bottleneck genes were screened to validate their possible involvement in heavy metal stress. Moreover, we combined the analysis of co-expression modules together with protein-protein interactions (PPIs), confirming some genes as potential candidates in plant heavy metal stress and as biomarkers. The results from this analysis shed the light on the pivotal functions to the hyperaccumulative trait of A. halleri, giving perspective to new paths for future research on this species.

Identification of hub genes related to CD4+ memory T cell infiltration with gene co-expression network predicts prognosis and immunotherapy effect in colon adenocarcinoma

Background: CD4+ memory T cells (CD4+ MTCs), as an important part of the microenvironment affecting tumorigenesis and progression, have rarely been systematically analyzed. Our purpose was to comprehensively analyze the effect of CD4+ MTC infiltration on the prognosis of colon adenocarcinoma (COAD). Methods: Based on RNA-Seq data, weighted gene co-expression network analysis (WGCNA) was used to screen the CD4+ MTC infiltration genes most associated with colon cancer and then identify hub genes and construct a prognostic model using the least absolute shrinkage and selection operator algorithm (LASSO). Finally, survival analysis, immune efficacy analysis, and drug sensitivity analysis were performed to evaluate the role of the prognostic model in COAD. Results: We identified 929 differentially expressed genes (DEGs) associated with CD4+ MTCs and constructed a prognosis model based on five hub genes (F2RL2, TGFB2, DTNA, S1PR5, and MPP2) to predict overall survival (OS) in COAD. Kaplan-Meier analysis showed poor prognosis in the high-risk group, and the analysis of the hub gene showed that overexpression of TGFB2, DTNA, S1PR5, or MPP2 was associated with poor prognosis. Clinical prediction nomograms combining CD4+ MTC-related DEGs and clinical features were constructed to accurately predict OS and had high clinical application value. Immune efficacy and drug sensitivity analysis provide new insights for individualized treatment. Conclusion: We constructed a prognostic risk model to predict OS in COAD and analyzed the effects of risk score on immunotherapy efficacy or drug sensitivity. These studies have important clinical significance for individualized targeted therapy and prognosis.

Identification of stress-related genes by co-expression network analysis based on the improved turbot genome

Turbot (Scophthalmus maximus), commercially important flatfish species, is widely cultivated in Europe and China. With the continuous expansion of the intensive breeding scale, turbot is exposed to various stresses, which greatly impedes the healthy development of turbot industry. Here, we present an improved high-quality chromosome-scale genome assembly of turbot using a combination of PacBio long-read and Illumina short-read sequencing technologies. The genome assembly spans 538.22 Mb comprising 27 contigs with a contig N50 size of 25.76 Mb. Annotation of the genome assembly identified 104.45 Mb repetitive sequences, 22,442 protein-coding genes and 3,345 ncRNAs. Moreover, a total of 345 stress responsive candidate genes were identified by gene co-expression network analysis based on 14 published stress-related RNA-seq datasets consisting of 165 samples. Significantly improved genome assembly and stress-related candidate gene pool will provide valuable resources for further research on turbot functional genome and stress response mechanism, as well as theoretical support for the development of molecular breeding technology for resistant turbot varieties.

Measurement(s)	whole genome sequencing
Technology Type(s)	PacBio long-read and Illumina short-read sequencing technologies

Comprehensive Transcriptome Analysis Uncovers Distinct Expression Patterns Associated with Early Salinity Stress in Annual Ryegrass (Lolium Multiflorum L.)

Soil salination is likely to reduce crop production worldwide. Annual ryegrass (Lolium multiflorum L.) is one of the most important forages cultivated in temperate and subtropical regions. We performed a time-course comparative transcriptome for salinity-sensitive (SS) and salinity-insensitive (SI) genotypes of the annual ryegrass at six intervals post-stress to describe the transcriptional changes and identify the core genes involved in the early responses to salt stress. Our study generated 215.18 Gb of clean data and identified 7642 DEGs in six pairwise comparisons between the SS and SI genotypes of annual ryegrass. Function enrichment of the DEGs indicated that the differences in lipid, vitamins, and carbohydrate metabolism are responsible for variation in salt tolerance of the SS and SI genotypes. Stage-specific profiles revealed novel regulation mechanisms in salinity stress sensing, phytohormones signaling transduction, and transcriptional regulation of the early salinity responses. High-affinity K⁺ (HAKs) and high-affinity K1 transporter (HKT1) play different roles in the ionic homeostasis of the two genotypes. Moreover, our results also revealed that transcription factors (TFs), such as WRKYs, ERFs, and MYBs, may have different functions during the early signaling sensing of salt stress, such as WRKYs, ERFs, and MYBs. Generally, our study provides insights into the mechanisms of the early salinity response in the annual ryegrass and accelerates the breeding of salt-tolerant forage.

The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination, which leads to the formation of white matter lesions (WMLs) and gray matter lesions (GMLs). Recently, a large amount of transcriptomics or proteomics research works explored MS, but few studies focused on the differences and similarities between GMLs and WMLs in transcriptomics. Furthermore, there are astonishing pathological differences between WMLs and GMLs, for example, there are differences in the type and abundance of infiltrating immune cells between WMLs and GMLs. Here, we used consensus weighted gene co-expression network analysis (WGCNA), single-sample gene set enrichment analysis (ssGSEA), and machine learning methods to identify the transcriptomic differences and similarities of the MS between GMLs and WMLs, and to find the co-expression modules with significant differences or similarities between them. Through weighted co-expression network analysis and ssGSEA analysis, CD56 bright natural killer cell was identified as the key immune infiltration factor in MS, whether in GM or WM. We also found that the co-expression networks between the two groups are quite similar (density = 0.79), and 28 differentially expressed genes (DEGs) are distributed in the midnightblue module, which is most related to CD56 bright natural killer cell in GM. Simultaneously, we also found that there are huge disparities between the modules, such as divergences between darkred module and lightyellow module, and these divergences may be relevant to the functions of the genes in the modules.

Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC.

A novel prognostic 6-gene signature for osteoporosis

INTRODUCTION

The incidence of osteoporosis (OP) keeps increasing due to global aging of the population. Therefore, identifying the diagnostic and prognostic biomarkers of OP is of great significance.

METHODS

mRNA data from OP and non-OP samples were obtained from GEO database, which were divided into training set (GSE35959) and testing sets (GSE7158, GSE62402, GSE7429 and GSE56815). Gene modules most significantly related to OP were revealed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) between OP and normal samples in training set were identified using limma R package. Thereafter, above two gene sets were intersected to obtain the genes potentially related to OP. Protein-protein interaction (PPI) pairs were screened by STRING database and visualized using Cytoscape, while the plug-in cytoHubba was used to screen hub genes by determining their topological parameters. Afterwards, a diagnostic model was constructed using those hub genes, whose creditability was further evaluated by testing sets.

RESULTS

The results of WGCNA analysis found the Black module was most significantly related to OP, which included altogether 1286 genes. Meanwhile, 2771 DEGs were discovered between OP patients and the normal controls. After taking the intersection, 479 genes were identified potentially correlated with the development of OP. Subsequently, six hub genes were discovered through PPI network construction and node topological analysis. Finally, we constructed a support vector machine model based on these six genes, which can accurately classified training and testing set samples into OP and normal groups.

CONCLUSION

Our current study constructed a six hub genes-based diagnostic model for OP. Our findings may shed some light on the research of the early diagnosis for OP and had certain practical significance.

Transcriptome Analysis Revealed the Molecular Response Mechanism of Non-heading Chinese Cabbage to Iron Deficiency Stress

Iron is a trace metal that is found in animals, plants, and the human body. Human iron absorption is hampered by plant iron shortage, which leads to anemia. Leafy vegetables are one of the most direct and efficient sources of iron for humans. Despite the fact that ferrotrophic disorder is common in calcareous soil, however, non-heading Chinese cabbage performs a series of reactions in response to iron deficiency stress that help to preserve iron homeostasis in vivo. In this study, we discovered that iron deficiency stress caused leaf yellowing and impeded plant development in both iron-deficient and control treatments by viewing or measuring phenotypic, chlorophyll content, and Fe²⁺ content in both iron-deficient and control treatments. We found a total of 9213 differentially expressed genes (DEGs) in non-heading Chinese cabbage by comparing root and leaf transcriptome data with iron deficiency and control treatments. For instance, 1927 DEGs co-expressed in root and leaf, including 897 up-regulated and 1030 down-regulated genes, respectively. We selected some key antioxidant genes, hormone signal transduction, iron absorption and transport, chlorophyll metabolism, and transcription factors involved in the regulation of iron deficiency stress utilizing GO enrichment, KEGG enrichment, multiple types of functional annotation, and Weighted Gene Co-expression Network Analysis (WGCNA). This study identifies prospective genes for maintaining iron homeostasis under iron-deficient stress, offering a theoretical foundation for further research into the molecular mechanisms of greater adaptation to iron-deficient stress, and perhaps guiding the development of iron-tolerant varieties.

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

Background

The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT.

Methods

Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database.

Results

In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT.

Conclusions

These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02409-4.

Global Transcriptome and Coexpression Network Analyses Reveal New Insights Into Somatic Embryogenesis in Hybrid Sweetgum (Liquidambar styraciflua × Liquidambar formosana)

Somatic embryogenesis (SE) is a process of somatic cells that dedifferentiate to totipotent embryonic stem cells and generate embryos in vitro. Despite recent scientific headway in deciphering the difficulties of somatic embryogenesis, the overall picture of key genes, pathways, and co-expression networks regulating SE is still fragmented. Therefore, deciphering the molecular basis of somatic embryogenesis of hybrid sweetgum remains pertinent. In the present study, we analyzed the transcriptome profiles and gene expression regulation changes via RNA sequencing from three distinct developmental stages of hybrid sweetgum: non-embryogenic callus (NEC), embryogenic callus (EC), and redifferentiation. Comparative transcriptome analysis showed that 19,957 genes were differentially expressed in ten pairwise comparisons of SE. Among these, plant hormone signaling-related genes, especially the auxin and cytokinin signaling components, were significantly enriched in NEC and EC early. The K-means method was used to identify multiple transcription factors, including HB-WOX, B3-ARF, AP2/ERF, and GRFs (growth regulating factors). These transcription factors showed distinct stage- or tissue-specific expression patterns mirroring each of the 12 superclusters to which they belonged. For example, the WOX transcription factor family was expressed only at NEC and EC stages, ARF transcription factor was expressed in EC early, and GRFs was expressed in late SE. It was noteworthy that the AP2/ERF transcription factor family was expressed during the whole SE process, but almost not in roots, stems and leaves. A weighted gene co-expression network analysis (WGCNA) was used in conjunction with the gene expression profiles to recognize the genes and modules that may associate with specific tissues and stages. We constructed co-expression networks and revealed 22 gene modules. Four of these modules with properties relating to embryonic potential, early somatic embryogenesis, and somatic embryo development, as well as some hub genes, were identified for further functional studied. Through a combination analysis of WGCNA and K-means, SE-related genes including AUX22, ABI3, ARF3, ARF5, AIL1, AIL5, AGL15, WOX11, WOX9, IAA29, BBM1, MYB36, LEA6, SMR4 and others were obtained, indicating that these genes play an important role in the processes underlying the progression from EC to somatic embryos (SEs) morphogenesis. The transcriptome information provided here will form the foundation for future research on genetic transformation and epigenetic control of plant embryogenesis at a molecular level. In follow-up studies, these data could be used to construct a regulatory network for SE; Key genes obtained from coexpression network analysis at each critical stage of somatic embryo can be considered as potential candidate genes to verify these networks.

Integrative Systems Biology Analysis Elucidates Mastitis Disease Underlying Functional Modules in Dairy Cattle

Background: Mastitis is the most prevalent disease in dairy cattle and one of the most significant bovine pathologies affecting milk production, animal health, and reproduction. In addition, mastitis is the most common, expensive, and contagious infection in the dairy industry. Methods: A meta-analysis of microarray and RNA-seq data was conducted to identify candidate genes and functional modules associated with mastitis disease. The results were then applied to systems biology analysis via weighted gene coexpression network analysis (WGCNA), Gene Ontology, enrichment analysis for the Kyoto Encyclopedia of Genes and Genomes (KEGG), and modeling using machine-learning algorithms. Results: Microarray and RNA-seq datasets were generated for 2,089 and 2,794 meta-genes, respectively. Between microarray and RNA-seq datasets, a total of 360 meta-genes were found that were significantly enriched as "peroxisome," "NOD-like receptor signaling pathway," "IL-17 signaling pathway," and "TNF signaling pathway" KEGG pathways. The turquoise module (n = 214 genes) and the brown module (n = 57 genes) were identified as critical functional modules associated with mastitis through WGCNA. PRDX5, RAB5C, ACTN4, SLC25A16, MAPK6, CD53, NCKAP1L, ARHGEF2, COL9A1, and PTPRC genes were detected as hub genes in identified functional modules. Finally, using attribute weighting and machine-learning methods, hub genes that are sufficiently informative in Escherichia coli mastitis were used to optimize predictive models. The constructed model proposed the optimal approach for the meta-genes and validated several high-ranked genes as biomarkers for E. coli mastitis using the decision tree (DT) method. Conclusion: The candidate genes and pathways proposed in this study may shed new light on the underlying molecular mechanisms of mastitis disease and suggest new approaches for diagnosing and treating E. coli mastitis in dairy cattle.

Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

Background: Pancreatic adenocarcinoma (PAAD) has a considerably bad prognosis, and its pathophysiologic mechanism remains unclear. Thus, we aimed to identify real hub genes to better explore the pathophysiology of PAAD and construct a prognostic panel to better predict the prognosis of PAAD using the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithms. Methods: WGCNA identified the modules most closely related to the PAAD stage and grade based on the Gene Expression Omnibus. The module genes significantly associated with PAAD progression and prognosis were considered as the real hub genes. Eligible genes in the most significant module were selected for construction and validation of a multigene prognostic signature based on the LASSO-Cox regression analysis in The Cancer Genome Atlas and the International Cancer Genome Consortium databases, respectively. Results: The brown module identified by WGCNA was most closely associated with the clinical characteristics of PAAD. Scaffold attachment factor B (SAFB) was significantly associated with PAAD progression and prognosis, and was identified as the real hub gene of PAAD. Moreover, both transcriptional and translational levels of SAFB were significantly lower in PAAD tissues compared with normal pancreatic tissues. In addition, a novel multigene-independent prognostic signature consisting of SAFB, SP1, and SERTAD3 was identified and verified. The predictive accuracy of our signature was superior to that of previous studies, especially for predicting 3- and 5-year survival probabilities. Furthermore, a prognostic nomogram based on independent prognostic variables was developed and validated using calibration curves. The predictive ability of this nomogram was also superior to the well-established AJCC stage and histological grade. The potential mechanisms of different prognoses between the high- and low-risk subgroups were also investigated using functional enrichment analysis, GSEA, ssGSEA, immune checkpoint analysis, and mutation profile analysis. Conclusion: SAFB was identified as the real hub gene of PAAD. A novel multigene-independent prognostic signature was successfully identified and validated to better predict PAAD prognosis. An accurate nomogram was also developed and verified to aid in the accurate treatment of tumors, as well as in early intervention.

Molecular Characteristics of Choledochal Cysts in Children: Transcriptome Sequencing

A choledochal cyst (CC) is a common congenital biliary disease in children, yet the underlying molecular bases for the cystic and fusiform clinical subtypes are unknown. RNA sequencing (RNA-seq) has been performed on 22 high-quality CC samples, including 12 cystic CC and 10 fusiform CC samples, to search for molecular features. Weighted gene co-expression network analysis (WGCNA) was performed to identify key modules associated with clinical subtypes. Bioinformatic analyses were conducted to elucidate potential mechanisms. Then, we constructed protein–protein interaction (PPI) networks to identify candidate hub genes related to CC. Finally, we used the support vector machine (SVM) to eliminate redundant features and screen out the hub genes. The selected gene expression was determined in CC patients through quantitative real-time polymerase chain reaction (PCR). A total of 6,463 genes were found to be aberrantly expressed between cystic CC and fusiform CC. Twelve co-expression modules that correlated with clinical subtypes of CC were identified and assigned representative colors. Among the 12 modules, the blue module was considered the key module. Two functionally distinct sets of dysregulated genes have been identified in two major subtypes, metabolism-related genes in cystic CC and immune-related genes in fusiform CC. A total of 20 candidate hub genes that were correlated with clinical subtypes were found in the blue module. In addition, we found ERBB2 and WNT11 that have not been studied in CC and verified their differential expression in CC through quantitative real-time PCR experiments. For the first time, we have described the transcriptome characteristics of CC. These results suggest that cystic CC and fusiform CC have different molecular mechanisms. The bi-omics-identified novel candidate genes and pathways might be helpful for personalized treatment and are of great clinical significance for CC.

Expression of 5-methylcytosine regulators is highly associated with the clinical phenotypes of prostate cancer and DNMTs expression predicts biochemical recurrence

In patients with prostate cancer (PCa), there is a high rate of overdiagnosis and frequent overtreatment. Therefore, there is an urgent need for more accurate prediction of biochemical recurrence (BCR). DNA methylation regulation patterns play crucial roles in tumorigenicity, progression, and treatment efficacy in PCa. However, the global relationship between epigenetic alterations, changes in mRNA levels, and pathologic phenotypes of PCa remain largely undefined. Here, we conducted a systematic analysis to identify global coexpression and comethylation modules in PCa. We identified coregulated methylation and expression modules and the relationships between epigenetic modifications, tumor progression, and the corresponding immune microenvironment in PCa. Our results show that DNA methyltransferases (DNMTs) are strongly associated with pathologic phenotypes and immune infiltration patterns in PCa. We built a two-factor predictive model using the expression features of DNMT3B and DNMT1. The model was used to predict the BCR status of patients with PCa and achieved area under the receiver operating characteristic curve values of 0.70 and 0.88 in the training and independent testing datasets, respectively.

Integrative Identification of Crucial Genes Associated With Plant Hormone-Mediated Bud Dormancy in Prunus mume

Prunus mume is an important ornamental woody plant with winter-flowering property, which is closely related to bud dormancy. Despite recent scientific headway in deciphering the mechanism of bud dormancy in P. mume, the overall picture of gene co-expression regulating P. mume bud dormancy is still unclear. Here a total of 23 modules were screened by weighted gene co-expression network analysis (WGCNA), of which 12 modules were significantly associated with heteroauxin, abscisic acid (ABA), and gibberellin (GA), including GA1, GA3, and GA4. The yellow module, which was positively correlated with the content of ABA and negatively correlated with the content of GA, was composed of 1,426 genes, among which 156 transcription factors (TFs) were annotated with transcriptional regulation function. An enrichment analysis revealed that these genes are related to the dormancy process and plant hormone signal transduction. Interestingly, the expression trends of PmABF2 and PmABF4 genes, the core members of ABA signal transduction, were positively correlated with P. mume bud dormancy. Additionally, the PmSVP gene had attracted lots of attention because of its co-expression, function enrichment, and expression level. PmABF2, PmABF4, and PmSVP were the genes with a high degree of expression in the co-expression network, which was upregulated by ABA treatment. Our results provide insights into the underlying molecular mechanism of plant hormone-regulated dormancy and screen the hub genes involved in bud dormancy in P. mume.

Application of machine learning in bacteriophage research

Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.