Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases

Evaluating transcriptional alterations associated with ageing and developing age prediction models based on the human blood transcriptome

Ageing-related DNA methylome and proteome changes and machine-learned ageing clock models have been described previously; however, there is a dearth of ageing clock prediction models based on human blood transcript information. Applying various machine learning algorithms is expected to aid in the development of age prediction models. Using blood transcriptome data from healthy subjects ranging in age from 21 to 90 in the 10 K Immunomes repository, we evaluated differentially regulated transcripts, assessed enriched gene ontology, pathway and disease ontology analysis to characterize biological functions associated with the genes associated with age. Furthermore, we constructed and compared age prediction models developed by applying the Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), eXtreme Gradient Boosting (XGBoost) and Light Gradient-Boosting Machine (LightGBM) algorithms. Compared to LASSO (7 genes) and EN (9 genes) regularized regression, XGBoost (142 genes) and LightGBM (149 genes) Gradient Boosted Decision Tree methods performed better in this dataset (training set r = 0.836 (LASSO), 0.837 (EN), 1.000 (XGBoost) and 0.995 (LightGBM); test set: r = 0.883 (LASSO), 0.876 (EN), 0.931 (XGBoost) and 0.915 (LightGBM); external validation set: r = 0.535 (LASSO), 0.534 (EN), 0.591 (XGBoost) and 0.645 (LightGBM)). Blood transcriptome-based age prediction models may provide a simple method to monitor biological ageing, and provide additional molecular insight. Future studies to externally validate these models in various diverse large populations and molecular studies to elucidate the underlying mechanisms by which the gene expression levels may be related to ageing phenotypes would be advantageous.

A computational framework for detecting inter-tissue gene-expression coordination changes with aging

Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging.

Histologic and Quality Assessment of Genotype-Tissue Expression (GTEx) Research Samples: A Large Postmortem Tissue Collection

CONTEXT.—

The National Institutes of Health Genotype-Tissue Expression (GTEx) project was developed to elucidate how genetic variation influences gene expression in multiple normal tissues procured from postmortem donors.

OBJECTIVE.—

To provide critical insight into a biospecimen's suitability for subsequent analysis, each biospecimen underwent quality assessment measures that included evaluation for underlying disease and potential effects introduced by preanalytic factors.

DESIGN.—

Electronic images of each tissue collected from nearly 1000 postmortem donors were evaluated by board-certified pathologists for the extent of autolysis, tissue purity, and the type and abundance of any extraneous tissue. Tissue-specific differences in the severity of autolysis and RNA integrity were evaluated, as were potential relationships between these markers and the duration of postmortem interval and rapidity of death.

RESULTS.—

Tissue-specific challenges in the procurement and preservation of the nearly 30 000 tissue specimens collected during the GTEx project are summarized. Differences in the degree of autolysis and RNA integrity number were observed among the 40 tissue types evaluated, and tissue-specific susceptibilities to the duration of postmortem interval and rapidity of death were observed.

CONCLUSIONS.—

Ninety-five percent of tissues were of sufficient quality to support RNA sequencing analysis. Biospecimens, annotated whole slide images, de-identified clinical data, and genomic data generated for GTEx represent a high-quality and comprehensive resource for the scientific community that has contributed to its use in approximately 1695 articles. Biospecimens and data collected under the GTEx project are available via the GTEx portal and authorized access to the Database of Genotypes and Phenotypes; procedures and whole slide images are available from the National Cancer Institute.

Increased expression of ER stress, inflammasome activation, and mitochondrial biogenesis-related genes in peripheral blood mononuclear cells in major depressive disorder

A subset of major depressive disorder (MDD) is characterized by immune system dysfunction, but the intracellular origin of these immune changes remains unclear. Here we tested the hypothesis that abnormalities in endoplasmic reticulum (ER) stress, inflammasome activity and mitochondrial biogenesis contribute to the development of systemic inflammation in MDD. RT-qPCR was used to measure mRNA expression of key organellar genes from peripheral blood mononuclear cells (PBMCs) isolated from 186 MDD and 67 healthy control (HC) subjects. The comparative CT (2-ΔΔCT) method was applied to quantify mRNA expression using GAPDH as the reference gene. After controlling for age, sex, BMI, and medication status using linear regression models, expression of the inflammasome (NLRC4 and NLRP3) and the ER stress (XBP1u, XBP1s, and ATF4) genes was found to be significantly increased in the MDD versus the HC group. Sensitivity analyses excluding covariates yielded similar results. After excluding outliers, expression of the inflammasome genes was no longer statistically significant but expression of the ER stress genes (XBP1u, XBP1s, and ATF4) remained significant and the mitochondrial biogenesis gene, MFN2, was significantly increased in the MDD group. NLRC4 and MFN2 were positively correlated with serum C-reactive protein concentrations, while ASC trended significant. The altered expression of inflammasome activation, ER stress, and mitochondrial biogenesis pathway components suggest that dysfunction of these organelles may play a role in the pathogenesis of MDD.

Response eQTLs, chromatin accessibility, and 3D chromatin structure in chondrocytes provide mechanistic insight into osteoarthritis risk

Osteoarthritis (OA) poses a significant healthcare burden with limited treatment options. While genome-wide association studies (GWAS) have identified over 100 OA-associated loci, translating these findings into therapeutic targets remains challenging. Integrating expression quantitative trait loci (eQTL), 3D chromatin structure, and other genomic approaches with OA GWAS data offers a promising approach to elucidate disease mechanisms; however, comprehensive eQTL maps in OA-relevant tissues and conditions remain scarce. We mapped gene expression, chromatin accessibility, and 3D chromatin structure in primary human articular chondrocytes in both resting and OA-mimicking conditions. We identified thousands of differentially expressed genes, including those associated with differences in sex and age. RNA-seq in chondrocytes from 101 donors across two conditions uncovered 3782 unique eGenes, including 420 that exhibited strong and significant condition-specific effects. Colocalization with OA GWAS signals revealed 13 putative OA risk genes, 10 of which have not been previously identified. Chromatin accessibility and 3D chromatin structure provided insights into the mechanisms and conditional specificity of these variants. Our findings shed light on OA pathogenesis and highlight potential targets for therapeutic development.

An atlas of the aging mouse proteome reveals the features of age-related post-transcriptional dysregulation

To what extent and how post-transcriptional dysregulation affects aging proteome remains unclear. Here, we provide proteomic data of whole-tissue lysates (WTL) and low-solubility protein-enriched fractions (LSF) of major tissues collected from mice of 6, 15, 24, and 30 months of age. Low-solubility proteins are preferentially affected by age and the analysis of LSF doubles the number of proteins identified to be differentially expressed with age. Simultaneous analysis of proteome and transcriptome using the same tissue homogenates reveals the features of age-related post-transcriptional dysregulation. Post-transcriptional dysregulation becomes evident especially after 24 months of age and age-related post-transcriptional dysregulation leads to accumulation of core matrisome proteins and reduction of mitochondrial membrane proteins in multiple tissues. Based on our in-depth proteomic data and sample-matched transcriptome data of adult, middle-aged, old, and geriatric mice, we construct the Mouse aging proteomic atlas ( https://aging-proteomics.info/ ), which provides a thorough and integrative view of age-related gene expression changes.

Analysis of Human Papillomavirus-Associated Cervical Cancer Differentially Expressed Genes and Identification of Prognostic Factors using Integrated Bioinformatics Approaches

Background

Human papillomavirus (HPV)-induced cervical cancer progresses through a series of steps. Despite our limited understanding of the mechanisms driving this progression, identifying the key genes involved could significantly improve early detection and treatment.

Materials and Methods

Two gene expression profiles of GSE9750 and GSE6791, which included cervical cancer HPV-positive and -negative samples, were evaluated using the R limma package with established cut-off criteria of P value < 0.05 and | fold change| ≥ 1. KEGG pathway enrichment was performed to identify potential pathways. Weighted gene co-expression network analysis (WGCNA) was used to discover co-expressed gene modules and trait-module connections.

Results

Considering the defined criteria, 115 differentially expressed genes (DEGs) were identified. The DEG's KEGG pathway enrichment analysis revealed enrichment in highly relevant pathways to the HPV infection, including cell cycle, viral carcinogenesis, autophagy-animal, Epstein-Barr virus infection, human T-cell leukemia virus 1 infection, and microRNAs in cancer. WGCNA results in 13 co-expression modules, and the magenta module is identified with significant relations to HPV, cervical cancer stage, and metastasis traits. The survival analysis identified BEX1 and CDC45 as potential prognostic factors in HPV-associated cervical cancer.

Conclusion

The innovation of our work lies in identifying essential genes associated with the multi-step process of cervical carcinogenesis. In fact, the current study has the potential to give a distinct viewpoint on the molecular pathways linked to cervical cancer. Considering the potential importance of the hub genes, we recommend conducting in-depth wet lab research to determine their impact on the biological mechanisms of cervical cancer.

Targeted co-expression networks for the study of traits

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer's disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .

The downregulation of genes encoding muscle proteins have a potential role in the development of scrotal hernia in pigs

BACKGROUND

Testicular descent is a physiological process regulated by many factors. Eventually, disturbances in the embryological/fetal development path facilitate the occurrence of scrotal hernia, a congenital malformation characterized by the presence of intestinal portions within the scrotal sac due to the abnormal expansion of the inguinal ring. In pigs, some genes have been related to this anomaly, but the genetic mechanisms involved remain unclear. This study aimed to investigate the expression profile of a set of genes potentially involved with the manifestation of scrotal hernia in the inguinal ring tissue.

METHODS AND RESULTS

Tissue samples from the inguinal ring/canal of normal and scrotal hernia-affected male pigs with approximately 30 days of age were used. Relative expression analysis was performed using qPCR to confirm the expression profile of 17 candidate genes previously identified in an RNA-Seq study. Among them, the Myosin heavy chain 1 (MYH1), Desmin (DES), and Troponin 1 (TNNI1) genes were differentially expressed between groups and had reduced levels of expression in the affected animals. These genes encode proteins involved in the formation of muscle tissue, which seems to be important for increasing the resistance of the inguinal ring to the abdominal pressure, which is essential to avoid the occurrence of scrotal hernia.

CONCLUSIONS

The downregulation of muscular candidate genes in the inguinal tissue clarifies the genetic mechanisms involved with this anomaly in its primary site, providing useful information for developing strategies to control this malformation in pigs and other mammals.

Open Genes-a new comprehensive database of human genes associated with aging and longevity

The Open Genes database was created to enhance and simplify the search for potential aging therapy targets. We collected data on 2402 genes associated with aging and developed convenient tools for searching and comparing gene features. A comprehensive description of genes has been provided, including lifespan-extending interventions, age-related changes, longevity associations, gene evolution, associations with diseases and hallmarks of aging, and functions of gene products. For each experiment, we presented the necessary structured data for evaluating the experiment's quality and interpreting the study's findings. Our goal was to stay objective and precise while connecting a particular gene to human aging. We distinguished six types of studies and 12 criteria for adding genes to our database. Genes were classified according to the confidence level of the link between the gene and aging. All the data collected in a database are provided both by an API and a user interface. The database is publicly available on a website at https://open-genes.org/.

Improving liver transplant outcomes with transplant-omics and network biology

PURPOSE OF REVIEW

Molecular omics data is increasingly ubiquitous throughout medicine. In organ transplantation, recent large-scale research efforts are generating the 'transplant-ome' - the entire set of molecular omics data, including the genome, transcriptome, proteome, and metabolome. Importantly, early studies in anesthesiology have demonstrated how perioperative interventions alter molecular profiles in various patient populations. The next step for anesthesiologists and intensivists will be to tailor perioperative care to the transplant-ome of individual liver transplant patients.

RECENT FINDINGS

In liver transplant patients, elements of the transplant-ome predict complications and point to novel interventions. Importantly, molecular profiles of both the donor organ and recipient contribute to this risk, and interventions like normothermic machine perfusion influence these profiles. As we can now measure various omics molecules simultaneously, we can begin to understand how these molecules interact to form molecular networks and emerging technologies offer noninvasive and continuous ways to measure these networks throughout the perioperative period. Molecules that regulate these networks are likely mediators of complications and actionable clinical targets throughout the perioperative period.

SUMMARY

The transplant-ome can be used to tailor perioperative care to the individual liver transplant patient. Monitoring molecular networks continuously and noninvasively would provide new opportunities to optimize perioperative management.

The biology of aging in a social world: Insights from free-ranging rhesus macaques

Social adversity can increase the age-associated risk of disease and death, yet the biological mechanisms that link social adversities to aging remain poorly understood. Long-term naturalistic studies of nonhuman animals are crucial for integrating observations of social behavior throughout an individual's life with detailed anatomical, physiological, and molecular measurements. Here, we synthesize the body of research from one such naturalistic study system, Cayo Santiago, which is home to the world's longest continuously monitored free-ranging population of rhesus macaques (Macaca mulatta). We review recent studies of age-related variation in morphology, gene regulation, microbiome composition, and immune function. We also discuss ecological and social modifiers of age-markers in this population. In particular, we summarize how a major natural disaster, Hurricane Maria, affected rhesus macaque physiology and social structure and highlight the context-dependent and domain-specific nature of aging modifiers. Finally, we conclude by providing directions for future study, on Cayo Santiago and elsewhere, that will further our understanding of aging across different domains and how social adversity modifies aging processes.

Molecular basis and genetics of hypohidrotic ectodermal dysplasias

Ectodermal dysplasia (ED) is a heterogeneous group of hereditary diseases of the skin and its appendages, which are characterized by impaired development and/or homeostasis of two or more ectoderm derivatives, including: hair, teeth, nails, sweat glands and their modifications (mammary glands, for instance). The overall prevalence of ectodermal dysplasia remains precisely unknown not only in Russia, but also in the world, nor is known the contribution of individual genes to its structure. This complicates the DNA diagnosis establishment of this disease due to the lack of an accurate diagnostic algorithm and a universal cost-effective method of analysis. To date, the most highly-researched genes involved in the development of anhydrous or hypohidrotic forms of ED are EDA, EDAR, EDARADD and WNT10A. The ectodysplasin A (EDA) gene is the cause of the most common X-linked form of ED, a gene from the Wnt family (WNT10A) is responsible for the autosomal recessive form of the disease, and two other genes (EDAR and EDARADD) can cause both autosomal recessive and autosomal dominant forms. This review provides the characteristics of the genes involved in ED, their mutation spectra, the level of their expression in human tissues, as well as the interrelation of the aforementioned genes. The domain structures of the corresponding proteins are considered, as well as the molecular genetic pathways in which they are involved. Animal models for studying this disorder are also taken into consideration. Due to the cross-species genes conservation, their mutations cause the disruption of the development of ectoderm derivatives not only in humans, but also in mice, cows, dogs, and even fish. It can be exploited for a better understanding of the etiopathogenesis of ectodermal dysplasias. Moreover, this article brings up the possibility of recurrent mutations in the EDA and WNT10A genes. The review also presents data on promising approaches for intrauterine ED treatment.

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

Senolytic and senomorphic interventions to defy senescence-associated mitochondrial dysfunction

The accumulation of senescent cells in the aging individual is associated with an increase in the occurrence of age-associated pathologies that contribute to poor health, frailty, and mortality. The number and type of senescent cells is viewed as a contributor to the body's senescence burden. Cellular models of senescence are based on induction of senescence in cultured cells in the laboratory. One type of senescence is triggered by mitochondrial dysfunction. There are several indications that mitochondria defects contribute to body aging. Senotherapeutics, targeting senescent cells, have been shown to induce their lysis by means of senolytics, or repress expression of their secretome, by means of senomorphics, senostatics or gerosuppressors. An outline of the mechanism of action of various senotherapeutics targeting mitochondria and senescence-associated mitochondria dysfunction will be here addressed. The combination of geroprotective interventions together with senotherapeutics will help to strengthen mitochondrial energy metabolism, biogenesis and turnover, and lengthen the mitochondria healthspan, minimizing one of several molecular pathways contributing to the aging phenotype.

A Multi-omic Analysis of the Human Lung Reveals Distinct Cell Specific Aging and Senescence Molecular Programs

Age is a major risk factor for lung disease. To understand the mechanisms underlying this association, we characterized the changing cellular, genomic, transcriptional, and epigenetic landscape of lung aging using bulk and single-cell RNAseq (scRNAseq) data. Our analysis revealed age-associated gene networks that reflected hallmarks of aging, including mitochondrial dysfunction, inflammation, and cellular senescence. Cell type deconvolution revealed age-associated changes in the cellular composition of the lung: decreased alveolar epithelial cells and increased fibroblasts and endothelial cells. In the alveolar microenvironment, aging is characterized by decreased AT2B cells and reduced surfactant production, a finding that was validated by scRNAseq and IHC. We showed that a previously reported senescence signature, SenMayo, captures cells expressing canonical senescence markers. SenMayo signature also identified cell-type specific senescence-associated co-expression modules that have distinct molecular functions, including ECM regulation, cell signaling, and damage response pathways. Analysis of somatic mutations showed that burden was highest in lymphocytes and endothelial cells and was associated with high expression of senescence signature. Finally, aging and senescence gene expression modules were associated with differentially methylated regions, with inflammatory markers such as IL1B, IL6R, and TNF being significantly regulated with age. Our findings provide new insights into the mechanisms underlying lung aging and may have implications for the development of interventions to prevent or treat age-related lung diseases.

The biology of aging in a social world:insights from free-ranging rhesus macaques

Social adversity can increase the age-associated risk of disease and death, yet the biological mechanisms that link social adversities to aging remain poorly understood. Long-term naturalistic studies of nonhuman animals are crucial for integrating observations of social behavior throughout an individual's life with detailed anatomical, physiological, and molecular measurements. Here, we synthesize the body of research from one such naturalistic study system, Cayo Santiago Island, which is home to the world's longest continuously monitored free-ranging population of rhesus macaques. We review recent studies of age-related variation in morphology, gene regulation, microbiome composition, and immune function. We also discuss ecological and social modifiers of age-markers in this population. In particular, we summarize how a major natural disaster, Hurricane Maria, affected rhesus macaque physiology and social structure and highlight the context-dependent and domain-specific nature of aging modifiers. Finally, we conclude by providing directions for future study, on Cayo Santiago and elsewhere, that will further our understanding of aging across different domains and how social adversity modifies aging processes.

The Role of SOX Transcription Factors in Ageing and Age-Related Diseases

The quest for eternal youth and immortality is as old as humankind. Ageing is an inevitable physiological process accompanied by many functional declines that are driving factors for age-related diseases. Stem cell exhaustion is one of the major hallmarks of ageing. The SOX transcription factors play well-known roles in self-renewal and differentiation of both embryonic and adult stem cells. As a consequence of ageing, the repertoire of adult stem cells present in various organs steadily declines, and their dysfunction/death could lead to reduced regenerative potential and development of age-related diseases. Thus, restoring the function of aged stem cells, inducing their regenerative potential, and slowing down the ageing process are critical for improving the health span and, consequently, the lifespan of humans. Reprograming factors, including SOX family members, emerge as crucial players in rejuvenation. This review focuses on the roles of SOX transcription factors in stem cell exhaustion and age-related diseases, including neurodegenerative diseases, visual deterioration, chronic obstructive pulmonary disease, osteoporosis, and age-related cancers. A better understanding of the molecular mechanisms of ageing and the roles of SOX transcription factors in this process could open new avenues for developing novel strategies that will delay ageing and prevent age-related diseases.

A multi-omics longitudinal aging dataset in primary human fibroblasts with mitochondrial perturbations

Aging is a process of progressive change. To develop biological models of aging, longitudinal datasets with high temporal resolution are needed. Here we report a multi-omics longitudinal dataset for cultured primary human fibroblasts measured across their replicative lifespans. Fibroblasts were sourced from both healthy donors (n = 6) and individuals with lifespan-shortening mitochondrial disease (n = 3). The dataset includes cytological, bioenergetic, DNA methylation, gene expression, secreted proteins, mitochondrial DNA copy number and mutations, cell-free DNA, telomere length, and whole-genome sequencing data. This dataset enables the bridging of mechanistic processes of aging as outlined by the "hallmarks of aging", with the descriptive characterization of aging such as epigenetic age clocks. Here we focus on bridging the gap for the hallmark mitochondrial metabolism. Our dataset includes measurement of healthy cells, and cells subjected to over a dozen experimental manipulations targeting oxidative phosphorylation (OxPhos), glycolysis, and glucocorticoid signaling, among others. These experiments provide opportunities to test how cellular energetics affect the biology of cellular aging. All data are publicly available at our webtool: https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study/.

Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop

Biologic aging reflects the genetic, molecular, and cellular changes underlying the development of morbidity and mortality with advancing chronological age. As several potential mechanisms have been identified, there is a growing interest in developing robust measures of biologic age that can better reflect the underlying biology of aging and predict age-related outcomes. To support this endeavor, the Research Centers Collaborative Network (RCCN) conducted a workshop in January 2022 to discuss emerging concepts in the field and identify opportunities to move the science forward. This paper presents workshop proceedings and summarizes the identified research needs, priorities, and recommendations for measuring biologic age. The highest priorities identified were the need for more robust measures, longitudinal studies, multidisciplinary collaborations, and translational approaches.

DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.

Two different aging paths in human blood revealed by integrated analysis of gene Expression, mutation and alternative splicing

Aging is a complex life process that human organs and tissues steadily and continuously decline. Aging has huge heterogeneity, which shows different aging rates among different individuals and in different tissues of the same individual. Many studies of aging are often contradictory and show little common signature. The integrated analysis of these transcriptome datasets will provide an unbiased global view of the aging process. Here, we integrated 8 transcriptome datasets including 757 samples from healthy human blood to study aging from three aspects of gene expression, mutations, and alternative splicing. Surprisingly, we found that transcriptome changes in blood are relatively independent of the chronological age. Further pseudotime analysis revealed two different aging paths (AgingPath1 and AgingPath2) in human blood. The differentially expressed genes (DEGs) along the two paths showed a limited overlap and are enriched in different biological processes. The mutations of DEGs in AgingPath1 are significantly increased in the aging process, while the opposite trend was observed in AgingPath2. Expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) analysis identified 304 important mutations that can affect both gene expression and alternative splicing during aging. Finally, by comparison between aging and Alzheimer's disease, we identified 37 common DEGs in AgingPath1, AgingPath2 and Alzheimer's disease. These genes may contribute to the shift from aging state to Alzheimer's disease. In summary, this study revealed the two aging paths and the related genes and mutations, which provides a new insight into aging and aging-related disease.

Informative SNP Selection Based on a Fuzzy Clustering and Improved Binary Particle Swarm Optimization Algorithm

Single-nucleotide polymorphism (SNP) involves the replacement of a single nucleotide in a deoxyribonucleic acid (DNA) sequence and is often linked to the development of specific diseases. Although current genotyping methods can tag SNP loci within biological samples to provide accurate genetic information for a disease associated, they have limited prediction accuracy. Furthermore, they are complex to perform and may result in the prediction of an excessive number of tag SNP loci, which may not always be associated with the disease. Therefore in this manuscript, we aimed to evaluate the impact of a newly optimized fuzzy clustering and binary particle swarm optimization algorithm (FCBPSO) on the accuracy and running time of informative SNP selection. Fuzzy clustering and FCBPSO were first applied to identify the equivalence relation and the candidate tag SNP set to reduce the redundancy between loci. The FCBPSO algorithm was then optimized and used to obtain the final tag SNP set. The prediction performance and running time of the newly developed model were compared with other traditional methods, including NMC, SPSO, and MCMR. The prediction accuracy of the FCBPSO algorithm was always higher than that of the other algorithms especially as the number of tag SNPs increased. However, when the number of tag SNPs was low, the prediction accuracy of FCBPSO was slightly lower than that of MCMR (add prediction accuracy values for each algorithm). However, the running time of the FCBPSO algorithm was always lower than that of MCMR. FCBPSO not only reduced the size and dimension of the optimization problem but also simplified the training of the prediction model. This improved the prediction accuracy of the model and reduced the running time when compared with other traditional methods.

Transcriptomic Characteristics Associated With Aging in the Thyroid Gland

The aging thyroid is associated with a plethora of morphological and functional changes. Limited studies have addressed the gene expression signature in the aging thyroid, except for sporadic reports using data from postmortem samples in the Genotype-Tissue Expression (GTEx) project. In this investigation, we analyzed the RNA sequencing data of 58 samples of normal-appearing counterpart thyroid tissues from The Cancer Genome Atlas. Aging-correlated genes were identified by determining the Spearman rank-order correlation between patient age and gene expression level. Additionally, we performed gene set enrichment analysis and conducted a weighted correlation network analysis. The results were compared with those analyzed using the GTEx data. The over-represented protein class of aging-correlated genes is mainly metabolite interconversion enzymes. Our analyses identified alterations in immune and inflammatory responses, mitochondrial functions, cytoskeletal proteins, as well as amino acid and cytochrome P450 metabolism. There was no significant association between thyroid differentiation and age. Our findings may shed molecular light on thyroid disorders in the geriatric population.

A Systematic Framework for Identifying Prognostic Genes in the Tumor Microenvironment of Colon Cancer

As one of the most common cancers of the digestive system, colon cancer is a predominant cause of cancer-related deaths worldwide. To investigate prognostic genes in the tumor microenvironment of colon cancer, we collected 461 colon adenocarcinoma (COAD) and 172 rectal adenocarcinoma (READ) samples from The Cancer Genome Atlas (TCGA) database, and calculated the stromal and immune scores of each sample. We demonstrated that stromal and immune scores were significantly associated with colon cancer stages. By analyzing differentially expressed genes (DEGs) between two stromal and immune score groups, we identified 952 common DEGs. The significantly enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for these DEGs were associated with T-cell activation, immune receptor activity, and cytokine–cytokine receptor interaction. Through univariate Cox regression analysis, we identified 22 prognostic genes. Furthermore, nine key prognostic genes, namely, HOXC8, SRPX, CCL22, CD72, IGLON5, SERPING1, PCOLCE2, FABP4, and ARL4C, were identified using the LASSO Cox regression analysis. The risk score of each sample was calculated using the gene expression of the nine genes. Patients with high-risk scores had a poorer prognosis than those with low-risk scores. The prognostic model established with the nine-gene signature was able to effectively predict the outcome of colon cancer patients. Our findings may help in the clinical decisions and improve the prognosis for colon cancer.

Exploring the Association between Glutathione Metabolism and Ferroptosis in Osteoblasts with Disuse Osteoporosis and the Key Genes Connecting them

Disused osteoporosis is a kind of osteoporosis, a common age-related disease. Neurological disorders are major risk factors for osteoporosis. Though there are many studies on disuse osteoporosis, the genetic mechanisms for the association between glutathione metabolism and ferroptosis in osteoblasts with disuse osteoporosis are still unclear. The purpose of this study is to explore the key genes and other related mechanism of ferroptosis and glutathione metabolism in osteoblast differentiation and disuse osteoporosis. By weighted gene coexpression network analysis (WGCNA), the process of osteoblast differentiation-related genes was studied in GSE30393. And the related functional pathways were found through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. By combining GSE1367 and GSE100933 together, key genes which were separately bound up with glutathione metabolism and ferroptosis were located. The correlation of these key genes was analyzed by the Pearson correlation coefficient. GSTM1 targeted agonist glutathione (GSH) selected by connectivity map (CMap) analysis was used to interfere with the molding disused osteoporosis process in MC3T3-E1 cells. RT-PCR and intracellular reactive oxygen species (ROS) were performed. Two important pathways, glutathione metabolism and ferroptosis pathways, were found. GSTM1 and TFRC were thought as key genes in disuse osteoporosis osteoblasts with the two mechanisms. The two genes have a strong negative correlation. Our experiment results showed that the expression of TFRC was consistent with the negative correlation with the activation process of GSTM1. The strong relationship between the two genes was proved. Glutathione metabolism and ferroptosis are important in the normal differentiation of osteoblasts and the process of disuse osteoporosis. GSTM1 and TFRC were the key genes. The two genes interact with each other, which can be seen as a bridge between the two pathways. The two genes participate in the process of reducing ROS in disuse osteoporosis osteoblasts.

The Evaluation of FGFR1, FGFR2 and FOXO1 in Orofacial Cleft Tissue

Although cleft lip with or without cleft palate (CL/P) is one of the most common congenital anomalies worldwide, the morphopathogenesis of non-syndromic orofacial clefts is still unclear. Many candidate genes have been proposed to play a causal role; however, only a few have been confirmed, leaving many still to be assessed. Taking into account the significance of FGFR1, FGFR2 and FOXO1 in embryogenesis, the aim of this work was to detect and compare the three candidate genes in cleft-affected lip and palatine tissue. Ten soft tissue samples were taken during cheiloplasty and veloplasty. The signals of the candidate genes were visualized using chromogenic in situ hybridization and analyzed using a semi-quantitative method. No statistically important difference in the distribution of FGFR1, FGFR2 and FOXO1 between neither the patients’ lip and vomer mucosa nor the control group was observed. Statistically significant very strong and strong correlations were found between genes in the lip and palatine tissue. The expression of FGFR1, FGFR2 and FOXO1 in cleft-affected lip and palatine tissue seems to be highly individual. Numerous intercorrelations between the genes do not exclude their role in the possible complex morphopathogenesis of orofacial clefts.

Reduced expression of mitochondrial complex I subunit Ndufs2 does not impact healthspan in mice

Aging in mammals leads to reduction in genes encoding the 45-subunit mitochondrial electron transport chain complex I. It has been hypothesized that normal aging and age-related diseases such as Parkinson’s disease are in part due to modest decrease in expression of mitochondrial complex I subunits. By contrast, diminishing expression of mitochondrial complex I genes in lower organisms increases lifespan. Furthermore, metformin, a putative complex I inhibitor, increases healthspan in mice and humans. In the present study, we investigated whether loss of one allele of Ndufs2, the catalytic subunit of mitochondrial complex I, impacts healthspan and lifespan in mice. Our results indicate that Ndufs2 hemizygous mice (Ndufs2^+/−) show no overt impairment in aging-related motor function, learning, tissue histology, organismal metabolism, or sensitivity to metformin in a C57BL6/J background. Despite a significant reduction of Ndufs2 mRNA, the mice do not demonstrate a significant decrease in complex I function. However, there are detectable transcriptomic changes in individual cell types and tissues due to loss of one allele of Ndufs2. Our data indicate that a 50% decline in mRNA of the core mitochondrial complex I subunit Ndufs2 is neither beneficial nor detrimental to healthspan.

Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity

Developmental trajectories of gene expression may reverse in their direction during ageing, a phenomenon previously linked to cellular identity loss. Our analysis of cerebral cortex, lung, liver and muscle transcriptomes of 16 mice, covering development and ageing intervals, revealed widespread but tissue-specific ageing-associated expression reversals. Cumulatively, these reversals create a unique phenomenon: mammalian tissue transcriptomes diverge from each other during postnatal development, but during ageing, they tend to converge towards similar expression levels, a process we term Divergence followed by Convergence, or DiCo. We found that DiCo was most prevalent among tissue-specific genes and associated with loss of tissue identity, which is confirmed using data from independent mouse and human datasets. Further, using publicly available single-cell transcriptome data, we showed that DiCo could be driven both by alterations in tissue cell type composition and also by cell-autonomous expression changes within particular cell types.

Ectodysplasin A/Ectodysplasin A Receptor System and Their Roles in Multiple Diseases

Ectodysplasin A (EDA) is a member of the tumor necrosis factor (TNF) family of ligands that was initially reported to induce the formation of various ectodermal derivatives during normal prenatal development. EDA exerts its biological activity as two splice variants, namely, EDA-A1 and EDA-A2. The former binds to the EDA receptor (EDAR), resulting in the recruitment of the intracellular EDAR-associated death domain (EDARADD) adapter protein and the activation of the NF-κB signaling pathway, while the latter binds to a different receptor, EDA2R, also known as X-linked ectodermal dysplasia receptor (XEDAR). Inactivation mutation of the EDA gene or the genes coding for its receptors can result in hypohidrosis ectodermal dysplasia (HED), a condition that is characterized by oligotrichosis, edentulosis or oligodontia, and oligohidrosis or anhidrosis. Recently, as a new liver factor, EDA is gradually known and endowed with some new functions. EDA levels were observed to be upregulated in several metabolic diseases, such as non-alcoholic fatty liver disease (NAFLD), obesity, and insulin resistance. In addition, EDA and its receptors have been implicated in tumor pathogenesis through the regulation of tumor cell proliferation, apoptosis, differentiation, and migration. Here, we first review the role of EDA and its two-receptor system in various signaling pathways and then discuss the physiological and pathological roles of EDA and its receptors.

Research on the Computational Prediction of Essential Genes

Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

Transcriptomic Changes Highly Similar to Alzheimer's Disease Are Observed in a Subpopulation of Individuals During Normal Brain Aging

Aging is a major risk factor for late-onset Alzheimer’s disease (LOAD). How aging contributes to the development of LOAD remains elusive. In this study, we examined multiple large-scale transcriptomic datasets from both normal aging and LOAD brains to understand the molecular interconnection between aging and LOAD. We found that shared gene expression changes between aging and LOAD are mostly seen in the hippocampal and several cortical regions. In the hippocampus, the expression of phosphoprotein, alternative splicing and cytoskeleton genes are commonly changed in both aging and AD, while synapse, ion transport, and synaptic vesicle genes are commonly down-regulated. Aging-specific changes are associated with acetylation and methylation, while LOAD-specific changes are more related to glycoprotein (both up- and down-regulations), inflammatory response (up-regulation), myelin sheath and lipoprotein (down-regulation). We also found that normal aging brain transcriptomes from relatively young donors (45–70 years old) clustered into several subgroups and some subgroups showed gene expression changes highly similar to those seen in LOAD brains. Using brain transcriptomic datasets from another cohort of older individuals (>70 years), we found that samples from cognitively normal older individuals clustered with the “healthy aging” subgroup while AD samples mainly clustered with the “AD similar” subgroups. This may imply that individuals in the healthy aging subgroup will likely remain cognitively normal when they become older and vice versa. In summary, our results suggest that on the transcriptome level, aging and LOAD have strong interconnections in some brain regions in a subpopulation of cognitively normal aging individuals. This supports the theory that the initiation of LOAD occurs decades earlier than the manifestation of clinical phenotype and it may be essential to closely study the “normal brain aging” to identify the very early molecular events that may lead to LOAD development.

Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci

Late-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, genetically regulated expression (GReX) analysis is an emerging approach to investigate the genetic mechanisms of complex diseases. Here, we conducted GReX analysis within and across 51 tissues on 39 LOAD GWAS data sets comprising 58,713 cases and controls from the Alzheimer's Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer's Project (IGAP). Meta-analysis across studies identified 216 unique significant genes, including 72 with no previously reported LOAD GWAS associations. Cross-brain-tissue and cross-GTEx models revealed eight additional genes significantly associated with LOAD. Conditional analysis of previously reported loci using established LOAD-risk variants identified eight genes reaching genome-wide significance independent of known signals. Moreover, the proportion of SNP-based heritability is highly enriched in genes identified by GReX analysis. In summary, GReX-based meta-analysis in LOAD identifies 216 genes (including 72 novel genes), illuminating the role of gene regulatory models in LOAD.

Regulation of Gene Expression by Telomere Position Effect

Many diseases that involve malignant tumors in the elderly affect the quality of human life; therefore, the relationship between aging and pathogenesis in geriatric diseases must be under-stood to develop appropriate treatments for these diseases. Recent reports have shown that epigenetic regulation caused by changes in the local chromatin structure plays an essential role in aging. This review provides an overview of the roles of telomere shortening on genomic structural changes during an age-dependent shift in gene expression. Telomere shortening is one of the most prominent events that is involved in cellular aging and it affects global gene expression through genome rearrangement. This review provides novel insights into the roles of telomere shortening in disease-affected cells during pathogenesis and suggests novel therapeutic approaches.

Evaluation of the MGISEQ-2000 Sequencing Platform for Illumina Target Capture Sequencing Libraries

Illumina is the leading sequencing platform in the next-generation sequencing (NGS) market globally. In recent years, MGI Tech has presented a series of new sequencers, including DNBSEQ-T7, MGISEQ-2000 and MGISEQ-200. As a complex application of NGS, cancer-detecting panels pose increasing demands for the high accuracy and sensitivity of sequencing and data analysis. In this study, we used the same capture DNA libraries constructed based on the Illumina protocol to evaluate the performance of the Illumina Nextseq500 and MGISEQ-2000 sequencing platforms. We found that the two platforms had high consistency in the results of hotspot mutation analysis; more importantly, we found that there was a significant loss of fragments in the 101-133 bp size range on the MGISEQ-2000 sequencing platform for Illumina libraries, but not for the capture DNA libraries prepared based on the MGISEQ protocol. This phenomenon may indicate fragment selection or low fragment ligation efficiency during the DNA circularization step, which is a unique step of the MGISEQ-2000 sequence platform. In conclusion, these different sequencing libraries and corresponding sequencing platforms are compatible with each other, but protocol and platform selection need to be carefully evaluated in combination with research purpose.

Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.

A trans-omic Mendelian randomization study of parental lifespan uncovers novel aging biology and therapeutic candidates for chronic diseases

The study of parental lifespan has emerged as an innovative tool to advance aging biology and our understanding of the genetic architecture of human longevity and aging-associated diseases. Here, we leveraged summary statistics of a genome-wide association study including over one million parental lifespans to identify genetically regulated genes from the Genotype-Tissue Expression project. Through a combination of multi-tissue transcriptome-wide association analyses and genetic colocalization, we identified novel genes that may be associated with parental lifespan. Mendelian randomization (MR) analyses also identified circulating proteins and metabolites causally associated with parental lifespan and chronic diseases offering new drug repositioning opportunities such as those targeting apolipoprotein-B-containing lipoproteins. Liver expression of HP, the gene encoding haptoglobin, and plasma haptoglobin levels were causally linked with parental lifespan. Phenome-wide MR analyses were used to map genetically regulated genes, proteins and metabolites with other human traits as well as the disease-related phenome in the FinnGen cohorts (n = 135,638). Altogether, this study identified new candidate genes, circulating proteins and metabolites that may influence human aging as well as potential therapeutic targets for chronic diseases that warrant further investigation.

DNA Repair and Immune Response Pathways Are Deregulated in Melanocyte-Keratinocyte Co-cultures Derived From the Healthy Skin of Familial Melanoma Patients

Familial melanoma accounts for 10% of cases, being CDKN2A the main high-risk gene. However, the mechanisms underlying melanomagenesis in these cases remain poorly understood. Our aim was to analyze the transcriptome of melanocyte-keratinocyte co-cultures derived from healthy skin from familial melanoma patients vs. controls, to unveil pathways involved in melanoma development in at-risk individuals. Accordingly, primary melanocyte-keratinocyte co-cultures were established from the healthy skin biopsies of 16 unrelated familial melanoma patients (8 CDKN2A mutant, 8 CDKN2A wild-type) and 7 healthy controls. Whole transcriptome was captured using the SurePrint G3 Human Microarray. Transcriptome analyses included: differential gene expression, functional enrichment, and protein-protein interaction (PPI) networks. We identified a gene profile associated with familial melanoma independently of CDKN2A germline status. Functional enrichment analysis of this profile showed a downregulation of pathways related to DNA repair and immune response in familial melanoma (P < 0.05). In addition, the PPI network analysis revealed a network that consisted of double-stranded DNA repair genes (including BRCA1, BRCA2, BRIP1, and FANCA), immune response genes, and regulation of chromosome segregation. The hub gene was BRCA1. In conclusion, the constitutive deregulation of BRCA1 pathway genes and the immune response in healthy skin could be a mechanism related to melanoma risk.

Bioinformatics Analysis Reveals MCM3 as an Important Prognostic Marker in Cervical Cancer

The minichromosome maintenance complex 3 (MCM3) is essential for the regulation of DNA replication and cell cycle progression. However, the expression and prognostic values of MCM3 in cervical cancer (CC) have not been well-studied. Herein, we investigated the expression patterns and survival data of MCM3 in cervical cancer patients from the ONCOMINE, GEPIA, Human Protein Atlas, UALCAN, Kaplan-Meier Plotter, and LinkedOmics databases. The expression level of MCM3 is negatively correlated with advanced tumor stage and metastatic status. Specifically, MCM3 is significantly differentially expressed between patients in stage 1 and stage 3 cervical cancer with p value 0.0138. Similarly, the p values between stage 1 and stage 4 cervical cancer, between stage 2 and stage 3, and between stage 2 and stage 4 are 0.00089, 0.0244, and 0.00197, respectively. Not only that, cervical cancer patients with high mRNA expression of MCM3 may indicate longer overall survival but indicate shorter relapse-free survival. PRIM2 and MCM6 are positively correlated genes of MCM3. Bioinformatics analysis revealed that MCM3 might be considered a biological indicator for prognostic evaluation of cervical cancer. However, it is currently limited to bioinformatics analysis, and more clinical tissue specimens and cell experiments are needed to further explore the role of MCM3 in the occurrence and progression of cervical cancer.

Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study

Background

Hospital mortality is high for patients with encephalopathy caused by microbial infection. Microbial infections often induce sepsis. The damage to the central nervous system (CNS) is defined as sepsis-associated encephalopathy (SAE). However, the relationship between pathogenic microorganisms and the prognosis of SAE patients is still unclear, especially gut microbiota, and there is no clinical tool to predict hospital mortality for SAE patients. The study aimed to explore the relationship between pathogenic microorganisms and the hospital mortality of SAE patients and develop a nomogram for the prediction of hospital mortality in SAE patients.

Methods

The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of SAE patients was developed by multivariable Cox regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) to evaluate the clinical utility of the model.

Results

Unfortunately, the results of our study did not find intestinal infection and microorganisms of the gastrointestinal (such as: Escherichia coli) that are related to the prognosis of SAE. Lasso regression and multivariate Cox regression indicated that factors including respiratory failure, lactate, international normalized ratio (INR), albumin, SpO₂, temperature, and renal replacement therapy were significantly correlated with hospital mortality. The AUC of 0.812 under the nomogram was more than that of the Simplified Acute Physiology Score (0.745), indicating excellent discrimination. DCA demonstrated that using the nomogram or including the prognostic signature score status was better than without the nomogram or using the SAPS II at predicting hospital mortality.

Conclusion

The prognosis of SAE patients has nothing to do with intestinal and microbial infections. We developed a nomogram that predicts hospital mortality in patients with SAE according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

Downregulation of exhausted cytotoxic T cells in gene expression networks of multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56dimCD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C.

Gonadotropin-releasing hormone receptor pathway affects the function of human EBV-transformed B lymphocytes in an age-independent way

Immune system function changes during aging, but the molecular mechanisms of this phenomenon are not fully understood. The present study identified pathways that are associated with age-associated changes in human B lymphocytes. Initial in silico analysis of 1355 genes involved in aging revealed the strongest association (p = 4.36E-21) with the gonadotropin-releasing hormone receptor (GnRHR) pathway. Extended analysis of 2736 aging-related genes using updated databases confirmed such association (p = 2.41E-16). Genes involved in both aging and the GnRHR pathway were significantly involved in lymphocyte B and T activation and aging-related phenotypes, including hyperinsulinemia and diabetes, arthritis, cerebrovascular disease, and cancers. We, therefore, examined non-tumorigenic Epstein-Barr virus (EBV)-transformed B-lymphocyte cell lines that originated from 12 young subjects (20-31 years old) and 10 centenarians (100-102 years old). Gonadotropin-releasing hormone I (GnRH-I) and GnRHR levels did not depend on the age of the cell donors. Inhibition of the GnRHR pathway age-independently decreased cell proliferation (p < 0.001) and increased apoptosis (p < 0.001). However, the decrease in immunoglobulin G synthesis (p < 0.01) was twice as high in centenarian cells than in young cells. In conclusion, the GnRHR pathway regulated essential properties of B lymphocytes. However, upon EBV transformation, memory class-switched B cells became the dominant cell subpopulation. Therefore, the observed effects of GnRHR inhibition were attributable to this subpopulation.

Age-dependent transcriptional alterations in cardiac endothelial cells

Aging is a significant risk factor for cardiovascular disease. Despite the fact that endothelial cells play critical roles in cardiovascular function and disease, the molecular impact of aging on this cell population in many organ systems remains unknown. In this study, we sought to determine age-associated transcriptional alterations in cardiac endothelial cells. Highly enriched populations of endothelial cells (ECs) isolated from the heart, brain, and kidney of young (3 mo) and aged (24 mo) C57/BL6 mice were profiled for RNA expression via bulk RNA sequencing. Approximately 700 cardiac endothelial transcripts significantly differ by age. Gene set enrichment analysis indicated similar patterns for cellular pathway perturbations. Receptor-ligand comparisons indicated parallel alterations in age-affected circulating factors and cardiac endothelial-expressed receptors. Gene and pathway enrichment analyses show that age-related transcriptional response of cardiac endothelial cells is distinct from that of endothelial cells derived from the brain or kidney vascular bed. Furthermore, single-cell analysis identified nine distinct EC subtypes and shows that the Apelin Receptor-enriched subtype is reduced with age in mouse heart. Finally, we identify age-dysregulated genes in specific aged cardiac endothelial subtypes.

Chronological and biological aging of the human left ventricular myocardium: Analysis of microRNAs contribution

Aging is the main risk factor for cardiovascular diseases. In humans, cardiac aging remains poorly characterized. Most studies are based on chronological age (CA) and disregard biological age (BA), the actual physiological age (result of the aging rate on the organ structure and function), thus yielding potentially imperfect outcomes. Deciphering the molecular basis of ventricular aging, especially by BA, could lead to major progresses in cardiac research. We aim to describe the transcriptome dynamics of the aging left ventricle (LV) in humans according to both CA and BA and characterize the contribution of microRNAs, key transcriptional regulators. BA is measured using two CA-associated transcriptional markers: CDKN2A expression, a cell senescence marker, and apparent age (AppAge), a highly complex transcriptional index. Bioinformatics analysis of 132 LV samples shows that CDKN2A expression and AppAge represent transcriptomic changes better than CA. Both BA markers are biologically validated in relation to an aging phenotype associated with heart dysfunction, the amount of cardiac fibrosis. BA-based analyses uncover depleted cardiac-specific processes, among other relevant functions, that are undetected by CA. Twenty BA-related microRNAs are identified, and two of them highly heart-enriched that are present in plasma. We describe a microRNA-gene regulatory network related to cardiac processes that are partially validated in vitro and in LV samples from living donors. We prove the higher sensitivity of BA over CA to explain transcriptomic changes in the aging myocardium and report novel molecular insights into human LV biological aging. Our results can find application in future therapeutic and biomarker research.

Genome-Wide Transcriptomic Analysis of Non-Tumorigenic Tissues Reveals Aging-Related Prognostic Markers and Drug Targets in Renal Cell Carcinoma

The relationship between expression of aging-related genes in normal tissues and cancer patient survival has not been assessed. We developed a genome-wide transcriptomic analysis approach for normal tissues adjacent to the tumor to identify aging-related transcripts associated with survival outcome, and applied it to 12 cancer types. As a result, five aging-related genes (DUSP22, MAPK14, MAPKAPK3, STAT1, and VCP) in normal tissues were found to be significantly associated with a worse survival outcome in patients with renal cell carcinoma (RCC). This computational approach was investigated using nontumorigenic immune cells purified from young and aged mice. Aged immune cells showed upregulated expression of all five aging-related genes and promoted RCC invasion compared to young immune cells. Further studies revealed DUSP22 as a regulator and druggable target of metastasis. DUSP22 gene knockdown reduced RCC invasion and the small molecule inhibitor BML-260 prevented RCC dissemination in a tumor/immune cell xenograft model. Overall, these results demonstrate that deciphering the relationship between aging-related gene expression in normal tissues and cancer patient survival can provide new prognostic markers, regulators of tumorigenesis and novel targets for drug development.

A Common Polymorphism in the FADS1 Locus Links miR1908 to Low-Density Lipoprotein Cholesterol Through BMP1

[Figure: see text].

Shifting epigenetic contexts influence regulatory variation and disease risk

Epigenetic shifts are a hallmark of aging that impact transcriptional networks at regulatory level. These shifts may modify the effects of genetic regulatory variants during aging and contribute to disease pathomechanism. However, these shifts occur on the backdrop of epigenetic changes experienced throughout an individual's development into adulthood; thus, the phenotypic, and ultimately fitness, effects of regulatory variants subject to developmental- versus aging-related epigenetic shifts may differ considerably. Natural selection therefore may act differently on variants depending on their changing epigenetic context, which we propose as a novel lens through which to consider regulatory sequence evolution and phenotypic effects. Here, we define genomic regions subjected to altered chromatin accessibility as tissues transition from their fetal to adult forms, and subsequently from early to late adulthood. Based on these epigenomic datasets, we examine patterns of evolutionary constraint and potential functional impacts of sequence variation (e.g., genetic disease risk associations). We find that while the signals observed with developmental epigenetic changes are consistent with stronger fitness consequences (i.e., negative selection pressures), they tend to have weaker effects on genetic risk associations for aging-related diseases. Conversely, we see stronger effects of variants with increased local accessibility in adult tissues, strongest in young adult when compared to old. We propose a model for how epigenetic status of a region may influence the effects of evolutionary relevant sequence variation, and suggest that such a perspective on gene regulatory networks may elucidate our understanding of aging biology.

Proteomics and Epidemiological Models of Human Aging

Human aging is associated with a decline of physical and cognitive function and high susceptibility to chronic diseases, which is influenced by genetics, epigenetics, environmental, and socio-economic status. In order to identify the factors that modulate the aging process, established measures of aging mechanisms are required, that are both robust and feasible in humans. It is also necessary to connect these measures to the phenotypes of aging and their functional consequences. In this review, we focus on how this has been addressed from an epidemiologic perspective using proteomics. The key aspects of epidemiological models of aging can be incorporated into proteomics and other omics which can provide critical detailed information on the molecular and biological processes that change with age, thus unveiling underlying mechanisms that drive multiple chronic conditions and frailty, and ideally facilitating the identification of new effective approaches for prevention and treatment.

Molecular evolution and the decline of purifying selection with age

Life history theory predicts that the intensity of selection declines with age, and this trend should impact how genes expressed at different ages evolve. Here we find consistent relationships between a gene’s age of expression and patterns of molecular evolution in two mammals (the human Homo sapiens and the mouse Mus musculus) and two insects (the malaria mosquito Anopheles gambiae and the fruit fly Drosophila melanogaster). When expressed later in life, genes fix nonsynonymous mutations more frequently, are more polymorphic for nonsynonymous mutations, and have shorter evolutionary lifespans, relative to those expressed early. The latter pattern is explained by a simple evolutionary model. Further, early-expressed genes tend to be enriched in similar gene ontology terms across species, while late-expressed genes show no such consistency. In humans, late-expressed genes are more likely to be linked to cancer and to segregate for dominant disease-causing mutations. Last, the effective strength of selection (N_es) decreases and the fraction of beneficial mutations increases with a gene’s age of expression. These results are consistent with the diminishing efficacy of purifying selection with age, as proposed by Medawar’s classic hypothesis for the evolution of senescence, and provide links between life history theory and molecular evolution.

A fundamental principle of evolutionary theory is that the force of natural selection is weaker on traits expressed late in life relative to traits expressed early. Here, the authors find strong and consistent patterns of molecular evolution reflecting this principle in four species of animals, including humans.