Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease

Comparative analysis of the cardiac structure and transcriptome of scallop and snail, perspectives on heart chamber evolution

The evolution of a two-chambered heart, with an atrium and a ventricle, has improved heart function in both deuterostomes (vertebrates) and some protostomes (invertebrates). Although studies have examined the unique structure and function of these two chambers, molecular comparisons are few and limited to vertebrates. Here, we focus on the two-chambered protostome heart of the mollusks, offering data that may provide a better understanding of heart evolution. Specifically, we asked if the atrium and ventricle differ at the molecular level in the mollusk heart. To do so, we examined two very different species, the giant African land snail (Lissachatina fulica) and the relatively small, aquatic yesso scallop (Mizuhopecten yessoensis), with the assumption that if they exhibited commonality these similarities would likely reflect those across the phylum. We found that, although the hearts of these two species differed histologically, their cardiac gene function enrichments were similar, as revealed by transcriptomic analysis. Furthermore, the atrium and ventricle in each species had distinct gene function clusters, suggesting an evolutionary differentiation of cardiac chambers in mollusks. Finally, to explore the relationship between vertebrate and invertebrate two-chambered hearts, we compared our transcriptomic data with published data from the zebrafish, a well-studied vertebrate model with a two-chambered heart. Our analysis indicated a functional similarity of ventricular genes between the mollusks and the zebrafish, suggesting that the ventricle was differentiated to achieve the same functions in invertebrates and vertebrates. As the first such study on protostomes, our findings offered initial insights into how the two-chambered heart arose, including a possible understanding of its occurrence in both protostomes and deuterostomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-023-00202-0.

Transcriptomic analysis reveals upregulated host metabolisms and downregulated immune responses or cell death induced by acute African swine fever virus infection

The African swine fever virus is a virulent and communicable viral disease that can be transmitted by infected swine, contaminated pork products, or soft tick vectors. Nonstructural proteins encoded by ASFV regulate viral replication, transcription, and evasion. However, the mechanisms underlying the host response to ASFV infection remain incompletely understood. In order to enhance comprehension of the biology and molecular mechanisms at distinct time intervals (6, 12, 24 h) post infection, transcriptome analyses were executed to discern differentially expressed genes (DEGs) between ASFV and mock-infected PAMs. The transcriptomic analysis unveiled a total of 1,677, 2,122, and 2,945 upregulated DEGs and 933, 1,148, and 1,422 downregulated DEGs in ASFV- and mock-infected groups at 6, 12, and 24 h.p.i.. The results of the transcriptomic analysis demonstrated that the infection of ASFV significantly stimulated host metabolism pathways while concurrently inhibiting the expression of various immune responses and cell death pathways. Our study offers crucial mechanistic insights into the comprehension of ASFV viral pathogenesis and the multifaceted host immune responses. The genes that were dysregulated may serve as potential candidates for further exploration of anti-ASFV strategies.

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

A computational approach based on weighted gene co-expression network analysis for biomarkers analysis of Parkinson's disease and construction of diagnostic model

Background

Parkinson's disease (PD) is a common age-related chronic neurodegenerative disease. There is currently no affordable, effective, and less invasive test for PD diagnosis. Metabolite profiling in blood and blood-based gene transcripts is thought to be an ideal method for diagnosing PD.

Aim

In this study, the objective is to identify the potential diagnostic biomarkers of PD by analyzing microarray gene expression data of samples from PD patients.

Methods

A computational approach, namely, Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression gene networks and identify the key modules that were highly correlated with PD from the GSE99039 dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the hub genes in the key modules with strong association with PD. The selected hub genes were then used to construct a diagnostic model based on logistic regression analysis, and the Receiver Operating Characteristic (ROC) curves were used to evaluate the efficacy of the model using the GSE99039 dataset. Finally, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was used to validate the hub genes.

Results

WGCNA identified two key modules associated with inflammation and immune response. Seven hub genes, LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3 were identified from the two modules and used to construct diagnostic models. ROC analysis showed that the diagnostic model had a good diagnostic performance for PD in the training and testing datasets. Results of the RT-PCR experiments showed that there were significant differences in the mRNA expression of LILRB1, LSP1, and MBOAT7 among the seven hub genes.

Conclusion

The 7-gene panel (LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3) will serve as a potential diagnostic signature for PD.

Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease

Introduction

The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genesinvolved in biological aging in AD.

Methods

We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers.

Results

WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers.

Conclusion

Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD.

Highlights

Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes.Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging.Among the hub genes of the module, CX3CR1 was downregulated in AD.The CX3CR1 expression level was associated with cognitive performance and brain atrophy.

Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome

Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.

Sensitive and Low-Bias Transcriptome Sequencing Using Agarose PCR

Transcriptome sequencing has emerged as an important research tool for exploring the mysteries of life at the single-cell level. However, its wide application is limited by the bias associated with the amplification reactions which is essential for library building of trace RNA. In this study, low-melting-point agarose was added to the amplification reactions to take advantage of its molecular crowding effect and polymer cross-linked structure to improve the sensitivity of the reactions and reduce bias. To further evaluate the performance of the method, it was applied to transcriptome sequencing of microregion samples from brain tissue sections of mice with Parkinson's disease at the single cell level. The results showed that agarose PCR had better performance than in-tube PCR. Further application of agarose PCR to transcriptome library sequencing could obtain data closer to that of unamplified. With the addition of low melting point agarose, the sensitivity of the amplification reaction was significantly increased, while homogeneity was increased by approximately 2-fold. Not only that, but this work also provides 11% sensitivity improvement for spatial transcriptomic study on Parkinson's disease-associated gene detection. The agarose PCR provides a new tool for efficient and homogeneous amplification of trace samples and can be widely used for spatial transcriptome library sequencing and studies.

Narcolepsy in Parkinson's disease with insulin resistance

Background: Parkinson’s disease (PD) is characterized by its progression of motor-related symptoms such as tremors, rigidity, slowness of movement, and difficulty with walking and balance. Comorbid conditions in PD individuals include insulin resistance (IR) and narcolepsy-like sleep patterns. The intersecting sleep symptoms of both conditions include excessive daytime sleepiness, hallucinations, insomnia, and falling into REM sleep more quickly than an average person. Understanding of the biological basis and relationship of these comorbid disorders with PD may help with early detection and intervention strategies to improve quality of life. Methods: In this study, an integrative genomics and systems biology approach was used to analyze gene expression patterns associated with PD, IR, and narcolepsy in order to identify genes and pathways that may shed light on how these disorders are interrelated. A correlation analysis with known genes associated with these disorders (LRRK2, HLA-DQB1, and HCRT) was used to query microarray data corresponding to brain regions known to be involved in PD and narcolepsy. This includes the hypothalamus, dorsal thalamus, pons, and subcoeruleus nucleus. Risk factor genes for PD, IR, and narcolepsy were also incorporated into the analysis. Results: The PD and narcolepsy signaling networks are connected through insulin and immune system pathways. Important genes and pathways that link PD, narcolepsy, and IR are CACNA1C, CAMK1D, BHLHE41, HMGB1, and AGE-RAGE. Conclusions: We have identified the genetic signatures that link PD with its comorbid disorders, narcolepsy and insulin resistance, from the convergence and intersection of dopaminergic, insulin, and immune system related signaling pathways. These findings may aid in the design of early intervention strategies and treatment regimes for non-motor symptoms in PD patients as well as individuals with diabetes and narcolepsy.

Atremorine in Parkinson's disease: From dopaminergic neuroprotection to pharmacogenomics

Atremorine is a novel bioproduct obtained by nondenaturing biotechnological processes from a genetic species of Vicia faba. Atremorine is a potent dopamine (DA) enhancer with powerful effects on the neuronal dopaminergic system, acting as a neuroprotective agent in Parkinson's disease (PD). Over 97% of PD patients respond to a single dose of Atremorine (5 g, p.o.) 1 h after administration. This response is gender-, time-, dose-, and genotype-dependent, with optimal doses ranging from 5 to 20 g/day, depending upon disease severity and concomitant medication. Drug-free patients show an increase in DA levels from 12.14 ± 0.34 pg/ml to 6463.21 ± 1306.90 pg/ml; and patients chronically treated with anti-PD drugs show an increase in DA levels from 1321.53 ± 389.94 pg/ml to 16,028.54 ± 4783.98 pg/ml, indicating that Atremorine potentiates the dopaminergic effects of conventional anti-PD drugs. Atremorine also influences the levels of other neurotransmitters (adrenaline, noradrenaline) and hormones which are regulated by DA (e.g., prolactin, PRL), with no effect on serotonin or histamine. The variability in Atremorine-induced DA response is highly attributable to pharmacogenetic factors. Polymorphic variants in pathogenic (SNCA, NUCKS1, ITGA8, GPNMB, GCH1, BCKDK, APOE, LRRK2, ACMSD), mechanistic (DRD2), metabolic (CYP2D6, CYP2C9, CYP2C19, CYP3A4/5, NAT2), transporter (ABCB1, SLC6A2, SLC6A3, SLC6A4) and pleiotropic genes (APOE) influence the DA response to Atremorine and its psychomotor and brain effects. Atremorine enhances DNA methylation and displays epigenetic activity via modulation of the pharmacoepigenetic network. Atremorine is a novel neuroprotective agent for dopaminergic neurons with potential prophylactic and therapeutic activity in PD.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

Long non-coding RNA expression profiles and related regulatory networks in areca nut chewing-induced tongue squamous cell carcinoma

Areca nut chewing is an important risk factor for developing tongue squamous cell carcinoma (TSCC), although the underlying molecular mechanism is unknown. To determine the potential molecular mechanisms of areca nut chewing-induced TSCC, the present study performed whole-genome detection with five pairs of TSCC and adjacent normal tissues, via mRNA- and long non-coding (lnc)RNA-gene chip analysis. A total of 3,860 differentially expressed genes were identified, including 2,193 lncRNAs and 1,667 mRNAs. Gene set-enrichment analysis revealed that the differentially expressed mRNAs were enriched in chromosome 22q13, 8p21 and 3p21 regions, and were regulated by nuclear factor kappa B (NF-κB) and interferon regulatory factors (IRFs). The results of ingenuity pathway analysis revealed that these mRNAs were significantly enriched for inflammatory immune-related signaling pathways. A co-expression network of mRNAs and lncRNAs was constructed by performing weighted gene co-expression network analysis. The present study focused on NF-κB-, IRF- and Th cell-signaling pathway-related lncRNAs and the corresponding mRNA-lncRNA regulatory networks. To the best of our knowledge, the present study was the first to investigate differential mRNA- and lncRNA-expression profiles in TSCCs induced by areca nut chewing. Inflammation-related mRNA-lncRNA regulatory networks driven by IRFs and NF-κB were identified, as well as the Th cell-related signaling pathways that play important carcinogenic roles in areca nut chewing-induced TSCC. These differentially expressed mRNAs and lncRNAs, and their regulatory networks provide insight for further analysis on the molecular mechanism of areca nut chewing-induced TSCC, candidate molecular markers and targets for further clinical intervention.