Gene Expression Analysis Reveals Novel Gene Signatures Between Young and Old Adults in Human Prefrontal Cortex

Comprehensive transcriptome analysis reveals altered mRNA splicing and post-transcriptional changes in the aged mouse brain

A comprehensive understanding of molecular changes during brain aging is essential to mitigate cognitive decline and delay neurodegenerative diseases. The interpretation of mRNA alterations during brain aging is influenced by the health and age of the animal cohorts studied. Here, we carefully consider these factors and provide an in-depth investigation of mRNA splicing and dynamics in the aging mouse brain, combining short- and long-read sequencing technologies with extensive bioinformatic analyses. Our findings encompass a spectrum of age-related changes, including differences in isoform usage, decreased mRNA dynamics and a module showing increased expression of neuronal genes. Notably, our results indicate a reduced abundance of mRNA isoforms leading to nonsense-mediated RNA decay and suggest a regulatory role for RNA-binding proteins, indicating that their regulation may be altered leading to the reshaping of the aged brain transcriptome. Collectively, our study highlights the importance of studying mRNA splicing events during brain aging.

Genome-wide transcriptome profiling and development of age prediction models in the human brain

Aging-related transcriptome changes in various regions of the healthy human brain have been explored in previous works, however, a study to develop prediction models for age based on the expression levels of specific panels of transcripts is lacking. Moreover, studies that have assessed sexually dimorphic gene activities in the aging brain have reported discrepant results, suggesting that additional studies would be advantageous. The prefrontal cortex (PFC) region was previously shown to have a particularly large number of significant transcriptome alterations during healthy aging in a study that compared different regions in the human brain. We harmonized neuropathologically normal PFC transcriptome datasets obtained from the Gene Expression Omnibus (GEO) repository, ranging in age from 21 to 105 years, and found a large number of differentially regulated transcripts in the old and elderly, compared to young samples overall, and compared female and male-specific expression alterations. We assessed the genes that were associated with age by employing ontology, pathway, and network analyses. Furthermore, we applied various established (least absolute shrinkage and selection operator (Lasso) and Elastic Net (EN)) and recent (eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)) machine learning algorithms to develop accurate prediction models for chronological age and validated them. Studies to further validate these models in other large populations and molecular studies to elucidate the potential mechanisms by which the transcripts identified may be related to aging phenotypes would be advantageous.

Identifying possible hub genes and biological mechanisms shared between bladder cancer and inflammatory bowel disease using machine learning and integrated bioinformatics

BACKGROUND

Recent studies have shown that inflammatory bowel disease (IBD) is associated with bladder cancer (BC) incidence. But there is still a lack of understanding regarding its pathogenesis. Thus, this study aimed to identify potential hub genes and their important pathways and pathological mechanisms of interactions between IBD and BC using bioinformatics methods.

METHODS

The data from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) were analyzed to screen common differentially expressed genes (DEGs) between IBD and BC. The "clusterProfiler" package was used to analyze GO term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment in DEGs. After that, we conducted a weighted gene co-expression network analysis (WGCNA) on these DEGs to determine the vital modules and genes significantly related to BC. Protein-protein interaction (PPI) networks was used to identify hub genes. Further, the hub genes were used to develop a prognostic signature by Cox analysis. The validity of the ten hub DEGs was tested using three classification algorithms. Finally, we analyzed the microRNAs (miRNA)-mRNA, transcription factors (TFs)-mRNA regulatory network.

RESULTS

Positive regulation of organelle fission, chromosomal region, tubulin binding, and cell cycle signaling pathway were the major enriched pathways for the common DEGs. PPI networks identified three hub proteins (AURKB, CDK1, and CCNA2) with high connectivity. Three machine-learning classification algorithms based on ten hub genes performed well for IBD and BC (accuracy > 0.80). The robust predictive model based on the ten hub genes could accurately classify BC cases with various clinical outcomes. Based on the gene-TFs and gene-miRNAs network construction, 9 TFs and 6 miRNAs were identified as potential critical TFs and miRNAs. There are 13 drugs that interact with the hub gene based on gene-drug interaction analysis.

CONCLUSIONS

This study explored common gene signatures and the potential pathogenesis of IBD and BC. We revealed that an unbalanced immune response, cell cycle pathway, and neutrophil infiltration might be the common pathogenesis of IBD and BC. Molecular mechanisms for the treatment of IBD and CC still require further investigation.

Prioritization of risk genes for Alzheimer's disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine

Background: Alzheimer's disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed. Methods: We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD. Results: The recursive feature elimination (RFE) method was utilized for feature selection. Data for 64 tissue-age features were obtained before feature selection, and this number was reduced to 19 after RFE was performed. The SVM models were built and evaluated using 19 selected and full features. The area under curve (AUC) values for the SVM model based on 19 selected features (0.740 [0.690-0.790]) and full feature sets (0.730 [0.678-0.769]) were very similar. Fifteen genes predicted to be risk genes for AD with a probability greater than 90% were obtained. Conclusion: The newly proposed framework performed comparably to previous prediction methods based on protein-protein interaction (PPI) network properties. A list of 15 candidate genes for AD risk was also generated to provide data support for further studies on the genetic etiology of AD.

Longitudinal APOE4- and amyloid-dependent changes in the blood transcriptome in cognitively intact older adults

BACKGROUND

Gene expression is dysregulated in Alzheimer's disease (AD) patients, both in peripheral blood and post mortem brain. We investigated peripheral whole-blood gene (co)expression to determine molecular changes prior to symptom onset.

METHODS

RNA was extracted and sequenced for 65 cognitively healthy F-PACK participants (65 (56-80) years, 34 APOE4 non-carriers, 31 APOE4 carriers), at baseline and follow-up (interval: 5.0 (3.4-8.6) years). Participants received amyloid PET at both time points and amyloid rate of change derived. Accumulators were defined with rate of change ≥ 2.19 Centiloids. We performed differential gene expression and weighted gene co-expression network analysis to identify differentially expressed genes and networks of co-expressed genes, respectively, with respect to traits of interest (APOE4 status, amyloid accumulation (binary/continuous)), and amyloid positivity status, followed by Gene Ontology annotation.

RESULTS

There were 166 significant differentially expressed genes at follow-up compared to baseline in APOE4 carriers only, whereas 12 significant differentially expressed genes were found only in APOE4 non-carriers, over time. Among the significant genes in APOE4 carriers, several had strong evidence for a pathogenic role in AD based on direct association scores generated from the DISQOVER platform: NGRN, IGF2, GMPR, CLDN5, SMIM24. Top enrichment terms showed upregulated mitochondrial and metabolic pathways, and an exacerbated upregulation of ribosomal pathways in APOE4 carriers compared to non-carriers. Similarly, there were 33 unique significant differentially expressed genes at follow-up compared to baseline in individuals classified as amyloid negative at baseline and positive at follow-up or amyloid positive at both time points and 32 unique significant differentially expressed genes over time in individuals amyloid negative at both time points. Among the significant genes in the first group, the top five with the highest direct association scores were as follows: RPL17-C18orf32, HSP90AA1, MBP, SIRPB1, and GRINA. Top enrichment terms included upregulated metabolism and focal adhesion pathways. Baseline and follow-up gene co-expression networks were separately built. Seventeen baseline co-expression modules were derived, with one significantly negatively associated with amyloid accumulator status (r² =  - 0.25, p = 0.046). This was enriched for proteasomal protein catabolic process and myeloid cell development. Thirty-two follow-up modules were derived, with two significantly associated with APOE4 status: one downregulated (r² =  - 0.27, p = 0.035) and one upregulated (r² = 0.26, p = 0.039) module. Top enrichment processes for the downregulated module included proteasomal protein catabolic process and myeloid cell homeostasis. Top enrichment processes for the upregulated module included cytoplasmic translation and rRNA processing.

CONCLUSIONS

We show that there are longitudinal gene expression changes that implicate a disrupted immune system, protein removal, and metabolism in cognitively intact individuals who carry APOE4 or who accumulate in cortical amyloid. This provides insight into the pathophysiology of AD, whilst providing novel targets for drug and therapeutic development.

Decoding the role of transcriptomic clocks in the human prefrontal cortex

Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.

Proteome-Wide Discovery of Cortical Proteins That May Provide Motor Resilience to Offset the Negative Effects of Pathologies in Older Adults

BACKGROUND

Motor resilience proteins have not been identified. This proteome-wide discovery study sought to identify proteins that may provide motor resilience.

METHODS

We studied the brains of older decedents with annual motor testing, postmortem brain pathologies, and proteome-wide data. Parkinsonism was assessed using 26 items of a modified United Parkinson Disease Rating Scale. We used linear mixed-effect models to isolate motor resilience, defined as the person-specific estimate of progressive parkinsonism after controlling for age, sex, and 10 brain pathologies. A total of 8 356 high-abundance proteins were quantified from dorsal lateral prefrontal cortex using tandem mass tag and liquid chromatography-mass spectrometry.

RESULTS

There were 391 older adults (70% female), mean age 80 years at baseline and 89 years at death. Five proteins were associated with motor resilience: A higher level of AP1B1 (Estimate -0.504, SE 0.121, p = 3.12 × 10-5) and GNG3 (Estimate -0.276, SE 0.068, p = 4.82 × 10-5) was associated with slower progressive parkinsonism. By contrast, a higher level of TTC38 (Estimate 0.140, SE 0.029, p = 1.87 × 10-6), CARKD (Estimate 0.413, SE 0.100, p = 3.50 × 10-5), and ABHD14B (Estimate 0.175, SE 0.044, p = 6.48 × 10-5) was associated with faster progressive parkinsonism. Together, these 5 proteins accounted for almost 25% of the variance of progressive parkinsonism above the 17% accounted for by 10 indices of brain pathologies.

DISCUSSION

Cortical proteins may provide more or less motor resilience in older adults. These proteins are high-value therapeutic targets for drug discovery that may lead to interventions that maintain motor function despite the accumulation of as yet untreatable brain pathologies.

Whole-Genome Transcriptomics of PBMC to Identify Biomarkers of Early Metabolic Risk in Apparently Healthy People with Overweight-Obesity and in Normal-Weight Subjects

SCOPE

Peripheral blood mononuclear cells (PBMC) provide a useful and minimally invasive source of biomarkers. Here to identify PBMC transcriptomic biomarkers predictive of metabolic impairment related to increased adiposity is aimed.

METHODS AND RESULTS

The study analyzed the global PBMC transcriptome in metabolically healthy (normoglycemic) volunteers with overweight-obesity (OW-OB, n = 12), and in subjects with metabolically obese normal-weight (MONW, n = 5) phenotype, in comparison to normal-weight (NW, n = 12) controls. The study identifies 1072 differentially expressed genes (DEGs) in OW-OB versus NW and 992 in MONW versus NW. Hierarchical clustering of the top 100 DEGs clearly distinguishes OW-OB and MONW from NW. Remarkably, the OW-OB and MONW phenotypes share 257 DEGs regulated in the same direction. The top up-regulated gene CXCL8, coding for interleukin 8, with a role in obesity-related pathologies, is of special interest as a potential marker for predicting increased metabolic risk. CXCL8 expression is increased mainly in the MONW group and correlated directly with C-reactive protein levels.

CONCLUSIONS

PBMC gene expression analysis of CXCL8 or a pool of DEGs may be used to identify early metabolic risk in an apparently healthy population regardless of their BMI, i.e., subjects with OW-OB or MONW phenotype and to apply adequate and personalized nutritional preventive strategies.

Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea

The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany's application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 μM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea.

Comprehensive analysis of omics data identifies relevant gene networks for Attention-Deficit/Hyperactivity Disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder that results from the interaction of both genetic and environmental risk factors. Genome-wide association studies have started to identify multiple genetic risk loci associated with ADHD, however, the exact causal genes and biological mechanisms remain largely unknown. We performed a multi-step analysis to identify and characterize modules of co-expressed genes associated with ADHD using data from peripheral blood mononuclear cells of 270 ADHD cases and 279 controls. We identified seven ADHD-associated modules of co-expressed genes, some of them enriched in both genetic and epigenetic signatures for ADHD and in biological pathways relevant for psychiatric disorders, such as the regulation of gene expression, epigenetics and immune system. In addition, for some of the modules, we found evidence of potential regulatory mechanisms, including microRNAs and common genetic variants. In conclusion, our results point to promising genes and pathways for ADHD, supporting the use of peripheral blood to assess gene expression signatures in psychiatric disorders. Furthermore, they highlight that the combination of multi-omics signals provides deeper and broader insights into the biological mechanisms underlying ADHD.

Bacterial community response to chronic heavy metal contamination in marine sediments of the East China Sea

Marine sediments act as a sink for various heavy metals, which may have profound impact on sedimentary microbiota. However, our knowledge about the collaborative response of bacterial community to chronic heavy metal contamination remains little. In this study, concentrations of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in sediments collected from the East China Sea were analyzed and Illumina Miseq 16 S rRNA sequencing was applied to characterize the structure of bacterial community. Microbiota inhabiting sediments in the East China Sea polluted with heavy metals showed different community composition from relatively pristine sites. The response of bacterial community to heavy metal stress was further interrogated with weighted correlation network analysis (WGCNA). WGCNA revealed ten bacterial modules exhibiting distinct co-occurrence patterns and among them, five modules were related to heavy metal pollution. Three of them were positively correlated with an increase in at least one heavy metal concentration, hubs (more influential bacterial taxa) of which were previously reported to be involved in the geochemical cycling of heavy metals or possess tolerance to heavy metals, while another two modules showed opposite patterns. Our research suggested that ecological functional transition might have occurred in East China Sea sediments by shifts of community composition with sensitive modules majorly involved in the meaningful global biogeochemical cycling of carbon, sulfur, and nitrogen replaced by more tolerant groups of bacteria due to long-term exposure to low-concentration heavy metals. Hubs may serve as indicators of perturbations of benthic bacterial community caused by heavy metal pollution and support monitoring remediation of polluted sites in marine environments.

Brain Epitranscriptomic Analysis Revealed Altered A-to-I RNA Editing in Septic Patients

Recent studies suggest that RNA editing is associated with impaired brain function and neurological and psychiatric disorders. However, the role of A-to-I RNA editing during sepsis-associated encephalopathy (SAE) remains unclear. In this study, we analyzed adenosine-to-inosine (A-to-I) RNA editing in postmortem brain tissues from septic patients and controls. A total of 3024 high-confidence A-to-I RNA editing sites were identified. In sepsis, there were fewer A-to-I RNA editing genes and editing sites than in controls. Among all A-to-I RNA editing sites, 42 genes showed significantly differential RNA editing, with 23 downregulated and 19 upregulated in sepsis compared to controls. Notably, more than 50% of these genes were highly expressed in the brain and potentially related to neurological diseases. Notably, cis-regulatory analysis showed that the level of RNA editing in six differentially edited genes was significantly correlated with the gene expression, including HAUS augmin-like complex subunit 2 (HAUS2), protein phosphatase 3 catalytic subunit beta (PPP3CB), hook microtubule tethering protein 3 (HOOK3), CUB and Sushi multiple domains 1 (CSMD1), methyltransferase-like 7A (METTL7A), and kinesin light chain 2 (KLC2). Furthermore, enrichment analysis showed that fewer gene functions and KEGG pathways were enriched by edited genes in sepsis compared to controls. These results revealed alteration of A-to-I RNA editing in the human brain associated with sepsis, thus providing an important basis for understanding its role in neuropathology in SAE.

Poly(A) RNA sequencing reveals age-related differences in the prefrontal cortex of dogs

Dogs may possess a unique translational potential to investigate neural aging and dementia because they are prone to age-related cognitive decline, including an Alzheimer’s disease–like pathological condition. Yet very little is known about the molecular mechanisms underlying canine cognitive decline. The goal of the current study was to explore the transcriptomic differences between young and old dogs’ frontal cortex, which is a brain region often affected by various forms of age-related dementia in humans. RNA isolates from the frontal cortical brain area of 13 pet dogs, which represented 7 different breeds and crossbreds, were analyzed. The dogs were euthanized for medical reasons, and their bodies had been donated by their owners for scientific purposes. The poly(A) tail RNA subfraction of the total transcriptome was targeted in the sequencing analysis. Cluster analyses, differential gene expression analyses, and gene ontology analyses were carried out to assess which genes and genetic regulatory mechanisms were mostly affected by aging. Age was the most prominent factor in the clustering of the animals, indicating the presence of distinct gene expression patterns related to aging in a genetically variable population. A total of 3436 genes were found to be differentially expressed between the age groups, many of which were linked to neural function, immune system, and protein synthesis. These findings are in accordance with previous human brain aging RNA sequencing studies. Some genes were found to behave more similarly to humans than to rodents, further supporting the applicability of dogs in translational aging research.

Gene Expression Analysis Reveals Age and Ethnicity Signatures Between Young and Old Adults in Human PBMC

Human immune system functions over an entire lifetime, yet how and why the immune system becomes less effective with age are not well understood. Here, we characterize peripheral blood mononuclear cell transcriptome from 132 healthy adults with 21–90 years of age using the weighted gene correlation network analyses. In our study, 113 Caucasian from the 10KIP database and RNA-seq data of 19 Asian (Chinese) are used to explore the differential co-expression genes in PBMC aging. These two dataset reveal a set of insightful gene expression modules and representative gene biomarkers for human immune system aging from Asian and Caucasian ancestry, respectively. Among them, the aging-specific modules may show an age-related gene expression variation spike around early-seventies. In addition, we find the top hub genes including NUDT7, CLPB, OXNAD1, and MLLT3 are shared between Asian and Caucasian aging related modules and further validated in human PBMCs from different age groups. Overall, the impact of age and race on transcriptional variation elucidated from this study may provide insights into the transcriptional driver of immune aging.

Identification of differentially expressed genomic repeats in primary hepatocellular carcinoma and their potential links to biological processes and survival

Hepatocellular carcinoma (HCC) is one of the deadliest cancers. Research on HCC so far primarily focused on genes and provided limited information on genomic repeats, which constitute more than half of the human genome and contribute to genomic stability. In line with this, repeat dysregulation was significantly shown to be pathological in various cancers and other diseases. In this study, we aimed to determine the full repeat expression profile of HCC for the first time. We utilised two independent RNA-seq datasets obtained from primary HCC tumours with matched normal tissues of 20 and 17 HCC patients, respectively. We quantified repeat expressions and analysed their differential expression. We also identified repeats that are cooperatively expressed with genes by constructing a gene coexpression network. Our results indicated that HCC tumours in both datasets harbour 24 differentially expressed repeats and even more elements were coexpressed with genes involved in various metabolic pathways. We discovered that two L1 elements (L1M3b, L1M3de) were downregulated and a handful of HERV subfamily repeats (HERV-Fc1-int, HERV3-int, HERVE_a-int, HERVK11D-int, HERVK14C-int, HERVL18-int) were upregulated with the exception of HERV1_LTRc, which was downregulated. Various LTR elements (LTR32, LTR9, LTR4, LTR52-int, LTR70) and MER elements (MER11C, MER11D, MER57C1, MER9a1, MER74C) were implicated along with few other subtypes including Charlie12, MLT2A2, Tigger15a, Tigger 17b. The only satellite repeat differentially expressed in both datasets was GSATII, whose expression was upregulated in 33 (>90%) out of 37 patients. Notably, GSATII expression correlated with HCC survival genes. Elements discovered here promise future studies to be considered for biomarker and HCC therapy research. The coexpression pattern of the GSATII satellite with HCC survival genes and the fact that it has been upregulated in the vast majority of patients make this repeat particularly stand out for HCC.

An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein-protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2, NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer.

Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

Gene expression alterations occurring with aging have been described for a multitude of species, organs, and cell types. However, most of the underlying studies rely on static comparisons of mean gene expression levels between age groups and do not account for the dynamics of gene expression throughout the lifespan. These studies also tend to disregard the pairwise relationships between gene expression profiles, which may underlie commonly altered pathways and regulatory mechanisms with age. To overcome these limitations, we have combined segmented regression analysis with weighted gene correlation network analysis (WGCNA) to identify high-confidence signatures of aging in the brain, heart, liver, skeletal muscle, and pancreas of C57BL/6 mice in a publicly available RNA-Seq dataset (GSE132040). Functional enrichment analysis of the overlap of genes identified in both approaches showed that immune- and inflammation-related responses are prominently altered in the brain and the liver, while in the heart and the muscle, aging affects amino and fatty acid metabolism, and tissue regeneration, respectively, which reflects an age-related global loss of tissue function. We also explored sexual dimorphism in the aging mouse transcriptome and found the liver and the muscle to have the most pronounced gender differences in gene expression throughout the lifespan, particularly in proteostasis-related pathways. While the data showed little overlap among the age-dysregulated genes between tissues, aging triggered common biological processes in distinct tissues, which we highlight as important features of murine tissue physiological aging.

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).

Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach

Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies.

Widespread Dysregulation of Long Noncoding Genes Associated With Fatty Acid Metabolism, Cell Division, and Immune Response Gene Networks in Xenobiotic-exposed Rat Liver

Xenobiotic exposure dysregulates hundreds of protein-coding genes in mammalian liver, impacting many physiological processes and inducing diverse toxicological responses. Little is known about xenobiotic effects on long noncoding RNAs (lncRNAs), many of which have important regulatory functions. Here, we present a computational framework to discover liver-expressed, xenobiotic-responsive lncRNAs (xeno-lncs) with strong functional, gene regulatory potential and elucidate the impact of xenobiotic exposure on their gene regulatory networks. We assembled the long noncoding transcriptome of xenobiotic-exposed rat liver using RNA-seq datasets from male rats treated with 27 individual chemicals, representing 7 mechanisms of action (MOAs). Ortholog analysis was combined with coexpression data and causal inference methods to infer lncRNA function and deduce gene regulatory networks, including causal effects of lncRNAs on protein-coding gene expression and biological pathways. We discovered > 1400 liver-expressed xeno-lncs, many with human and/or mouse orthologs. Xenobiotics representing different MOAs often regulated common xeno-lnc targets: 123 xeno-lncs were dysregulated by ≥ 10 chemicals, and 5 xeno-lncs responded to ≥ 20 of the 27 chemicals investigated; 81 other xeno-lncs served as MOA-selective markers of xenobiotic exposure. Xeno-lnc-protein-coding gene coexpression regulatory network analysis identified xeno-lncs closely associated with exposure-induced perturbations of hepatic fatty acid metabolism, cell division, or immune response pathways, and with apoptosis or cirrhosis. We also identified hub and bottleneck lncRNAs, which are expected to be key regulators of gene expression. This work elucidates extensive networks of xeno-lnc-protein-coding gene interactions and provides a framework for understanding the widespread transcriptome-altering actions of foreign chemicals in a key-responsive mammalian tissue.

Skeletal muscle transcriptional networks linked to type I myofiber grouping in Parkinson's disease

Parkinson's disease (PD) is a common neurodegenerative disorder impacting cognition, movement, and quality of life in >10 million individuals worldwide. We recently characterized and quantified a skeletal muscle pathology in PD represented by exaggerated type I myofiber grouping presumed to result from denervation-reinnervation processes. Our previous findings indicated that impaired neuromuscular junction integrity may be involved in type I grouping, which is associated with excessive motor unit activation during weight-bearing tasks. In this study, we performed transcriptional profiling to test the hypothesis that type I grouping severity would link to distinct gene expression networks. We generated transcriptome-wide poly(A) RNA-Seq data from skeletal muscle of individuals with PD [n = 12 (9 men, 3 women); 67 ± 2 yr], age- and sex-matched older adults (n = 12; 68 ± 2 yr), and sex-matched young adults (n = 12; 30 ± 1 yr). Differentially expressed genes were evaluated across cohorts. Weighted gene correlation network analysis (WGCNA) was performed to identify gene networks most correlated with indicators of abnormal type I grouping. Among coexpression networks mapping to phenotypes pathologically increased in PD muscle, one network was highly significantly correlated to type I myofiber group size and another to percentage of type I myofibers found in groups. Annotation of coexpressed networks revealed that type I grouping is associated with altered expression of genes involved in neural development, postsynaptic signaling, cell cycle regulation and cell survival, protein and energy metabolism, inflammation/immunity, and posttranscriptional regulation (microRNAs). These transcriptomic findings suggest that skeletal muscle may play an active role in signaling to promote myofiber survival, reinnervation, and remodeling, perhaps to an extreme in PD.NEW & NOTEWORTHY Despite our awareness of the impact of Parkinson's disease (PD) on motor function for over two centuries, limited attention has focused on skeletal muscle. We previously identified type I myofiber grouping, a novel indicator of muscle dysfunction in PD, presumably a result of heightened rates of denervation/reinnervation. Using transcriptional profiling to identify networks associated with this phenotype, we provide insight into potential mechanistic roles of skeletal muscle in signaling to promote its survival in PD.