Discover the network underlying the connections between aging and age-related diseases

Impact of comorbidity on health-related quality of life in newly diagnosed patients with lymphoma or multiple myeloma: results from the PROFILES-registry

With the increasing prevalence of comorbidity in an ageing population, it is crucial to better understand the impact of comorbidity on health-related quality of life (HRQoL) after lymphoma or multiple myeloma (MM) diagnosis. We included 261 newly diagnosed patients (67% response rate) diagnosed with lymphoma or MM between October 2020 and March 2023 in a longitudinal survey. The European Organisation for Research and Treatment of Cancer (EORTC) questionnaires were used to measure generic and disease-specific HRQoL. Evidence-based guidelines for interpretation of the EORTC questionnaires were used to identify clinical importance. Patients were classified as having 'no comorbidity', 'mild comorbidity' (e.g. arthrosis or rheumatism), or 'moderate-severe comorbidity' (e.g. heart or lung disease), using the adapted self-administered comorbidity questionnaire. At diagnosis, the mean age was 64 years, 63% were male and 38% reported no comorbidity, 33% mild comorbidity, and 29% moderate-severe comorbidity. Patients with mild or moderate-severe comorbidity reported clinically relevant worse HRQoL at diagnosis than patients without comorbidity. One year post-diagnosis most outcomes showed clinically relevant improvement, irrespective of comorbidity. However, outcomes of physical functioning (β=-7.9, p < 0.05), global health status (β=-7.6, p < 0.05), bone pain (β = 8.1 to 9.1, p < 0.05), muscle/joint pain (β = 14.5 to 18.8, p < 0.01) and muscle weakness (β = 10.4 to 15.6, p < 0.05) improved less among those with comorbidity, and clinically relevant differences between comorbidity groups persisted over time. With clinically relevant worse HRQoL at diagnosis and less recovery of HRQoL during the first year after diagnosis in patients with comorbidity, consideration of both prognosis and HRQoL is important when making treatment decisions.

Pathogenesis of gout: Exploring more therapeutic target

Gout is a chronic metabolic and immune disease, and its specific pathogenesis is still unclear. When the serum uric acid exceeds its saturation in the blood or tissue fluid, it is converted to monosodium urate crystals, which lead to acute arthritis of varying degrees, urinary stones, or irreversible peripheral joint damage, and in severe cases, impairment of vital organ function. Gout flare is a clinically significant state of acute inflammation in gout. The current treatment is mostly anti-inflammatory analgesics, which have numerous side effects with limited treatment methods. Gout pathogenesis involves many aspects. Therefore, exploring gout pathogenesis from multiple perspectives is conducive to identifying more therapeutic targets and providing safer and more effective alternative treatment options for patients with gout flare. Thus, this article is of great significance for further exploring the pathogenesis of gout. The author summarizes the pathogenesis of gout from four aspects: signaling pathways, inflammatory factors, intestinal flora, and programmed cell death, focusing on exploring more new therapeutic targets.

Whole Exome Sequencing Identified Two Single Nucleotide Polymorphisms of Human Leukocyte Antigen-DRB5 in Familial Sarcoidosis in China

BACKGROUND

Sarcoidosis is a multisystem granulomatous disorder whose etiology is related to genetic and immunological factors. Familial aggregation and ethnic prevalence suggest a genetic predisposition and inherited susceptibility to sarcoidosis.

OBJECTIVE

This study aimed to identify suspected risk loci for familial sarcoidosis patients.

METHODS

We conducted whole exome sequencing on two sarcoidosis patients and five healthy family members in a Chinese family for a case-control study. The two sarcoidosis patients were siblings who showed chronic disease.

RESULTS

The Gene Ontology results showed single nucleotide polymorphisms in three genes, including human leukocyte antigen (HLA)-DRB1, HLA-DRB5, and KIR2DL4, associated with both 'antigen processing and presentation' and 'regulation of immune response.' Sanger sequencing verified two nonsynonymous mutations in HLA-DRB5 (rs696318 and rs115817940) located on 6p21.3 in the major histocompatibility complex (MHC) class II beta 1 region. The structural model simulated on Prot- Param protein analysis by the Expert Protein Analysis System predicted that the hydropathy index changed at two mutation sites (rs696318: p.F96L, -1.844 to -1.656 and rs115817940: p.T106N, -0.322 to -0.633), which indicated the probability of changes in peptide-binding selectivity.

CONCLUSION

Our results indicated that two nonsynonymous mutations of HLA-DRB5 have been identified in two sarcoidosis siblings, while their healthy family members do not have the mutations. The two HLA-DRB5 alleles may influence genetic susceptibility and chronic disease progression through peptide mutations on the MHC class II molecule among the two affected family members.

An Autophagy-Associated Prognostic Gene Signature for Breast Cancer

Autophagy is closely related to breast cancer and has the dual role of promoting and inhibiting the progression of breast cancer. In this study, we aimed to establish an autophagy-related gene signature for the prognosis of breast cancer. A gene signature composed of the eight most survival-relevant autophagy-associated genes was identified by least absolute shrinkage and selection operator (LASSO) regression analysis. A risk score was calculated based on the gene signature, which divided breast cancer patients into low- or high-risk groups and showed good and poor prognosis, respectively. The risk score displayed good prognostic performance in both the training cohort (TCGA, 1-10-year AUC > 0.63) and the validation cohort (GEO, 1-10-year AUC > 0.66). The multivariate Cox regression and stratified analysis revealed that the risk score was an independent prognostic factor for breast cancer patients. Moreover, the high-risk score was associated with higher infiltration of neutrophils and M2-polarized macrophages, and lower infiltration of resting memory CD4⁺ T cells, CD8⁺ T cells, and NK cells. Finally, the high-risk score was associated with myc target, glycolysis, and mTORC1 signaling. The risk score developed based on the autophagy-associated gene signature was an independent prognostic biomarker for breast cancer.

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.

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.

Persistent symptoms of fatigue, neuropathy and role-functioning impairment among indolent non-Hodgkin lymphoma survivors: A longitudinal PROFILES registry study

Little is known about the long-term health-related quality of life (HRQoL) and persistence of symptoms among patients with indolent non-Hodgkin lymphoma (iNHL). This large population-based longitudinal study therefore investigated the long-term HRQoL and persistence of symptoms and identified associated sociodemographic, clinical and psychological factors. Patients diagnosed between 1999 and 2014 and four or more months after diagnosis were invited to participate in a longitudinal survey. Sociodemographic and clinical data were obtained from the Netherlands Cancer Registry. The EORTC QLQ-C30 and CLL-16 were completed by 669 patients (74% response rate). Patients completed on average four questionnaires. Primary treatment was active surveillance (52%), systemic therapy (31%) or radiotherapy (13%). Respectively, 36% reported persistent fatigue, 33% persistent neuropathy and 25% persistent role-functioning impairment. This was 2-3 times higher than in the age- and sex-matched normative population. Up to 10 years after diagnosis, scores remained relatively stable without clinically relevant changes. Comorbidities, psychological distress, shorter time since diagnosis, systemic therapy, younger age, education level and having no partner were associated with worse outcomes (all ps < 0.05). Up to a third of patients with iNHL experience long-term persistent symptoms which do not improve over time. Early recognition of symptoms will help in providing tailored supportive care for those in need.

Biological mechanisms of aging predict age-related disease co-occurrence in patients

Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity.

Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

Transcriptomic and Proteomic Profiling of Human Stable and Unstable Carotid Atherosclerotic Plaques

Atherosclerosis is a chronic inflammatory disease with high prevalence and mortality. The rupture of atherosclerotic plaque is the main reason for the clinical events caused by atherosclerosis. Making clear the transcriptomic and proteomic profiles between the stabe and unstable atherosclerotic plaques is crucial to prevent the clinical manifestations. In the present study, 5 stable and 5 unstable human carotid atherosclerotic plaques were obtained by carotid endarterectomy. The samples were used for the whole transcriptome sequencing (RNA-Seq) by the Next-Generation Sequencing using the Illumina HiSeq, and for proteome analysis by HPLC-MS/MS. The lncRNA-targeted genes and circRNA-originated genes were identified by analyzing their location and sequence. Gene Ontology and KEGG enrichment was carried out to analyze the functions of differentially expressed RNAs and proteins. The protein-protein interactions (PPI) network was constructed by the online tool STRING. The consistency of transcriptome and proteome were analyzed, and the lncRNA/circRNA-miRNA-mRNA interactions were predicted. As a result, 202 mRNAs, 488 lncRNAs, 91 circRNAs, and 293 proteins were identified to be differentially expressed between stable and unstable atherosclerotic plaques. The 488 lncRNAs might target 381 protein-coding genes by cis-acting mechanisms. Sequence analysis indicated the 91 differentially expressed circRNAs were originated from 97 protein-coding genes. These differentially expressed RNAs and proteins were mainly enriched in the terms of the cellular response to stress or stimulus, the regulation of gene transcription, the immune response, the nervous system functions, the hematologic activities, and the endocrine system. These results were consistent with the previous reported data in the dataset GSE41571. Further analysis identified CD5L, S100A12, CKB (target gene of lncRNA MSTRG.11455.17), CEMIP (target gene of lncRNA MSTRG.12845), and SH3GLB1 (originated gene of hsacirc_000411) to be critical genes in regulating the stability of atherosclerotic plaques. Our results provided a comprehensive transcriptomic and proteomic knowledge on the stability of atherosclerotic plaques.

Evaluating the Risk of Breast Cancer Recurrence and Metastasis After Adjuvant Tamoxifen Therapy by Integrating Polymorphisms in Cytochrome P450 Genes and Clinicopathological Characteristics

Tamoxifen (TAM) is the most commonly used adjuvant endocrine drug for hormone receptor-positive (HR+) breast cancer patients. However, how to accurately evaluate the risk of breast cancer recurrence and metastasis after adjuvant TAM therapy is still a major concern. In recent years, many studies have shown that the clinical outcomes of TAM-treated breast cancer patients are influenced by the activity of some cytochrome P450 (CYP) enzymes that catalyze the formation of active TAM metabolites like endoxifen and 4-hydroxytamoxifen. In this study, we aimed to first develop and validate an algorithm combining polymorphisms in CYP genes and clinicopathological signatures to identify a subpopulation of breast cancer patients who might benefit most from TAM adjuvant therapy and meanwhile evaluate major risk factors related to TAM resistance. Specifically, a total of 256 patients with invasive breast cancer who received adjuvant endocrine therapy were selected. The genotypes at 10 loci from three TAM metabolism-related CYP genes were detected by time-of-flight mass spectrometry and multiplex long PCR. Combining the 10 loci with nine clinicopathological characteristics, we obtained 19 important features whose association with cancer recurrence was assessed by importance score via random forests. After that, a logistic regression model was trained to calculate TAM risk-of-recurrence score (TAM RORs), which is adopted to assess a patient's risk of recurrence after TAM treatment. The sensitivity and specificity of the model in an independent test cohort were 86.67% and 64.56%, respectively. This study showed that breast cancer patients with high TAM RORs were less sensitive to TAM treatment and manifested more invasive characteristics, whereas those with low TAM RORs were highly sensitive to TAM treatment, and their conditions were stable during the follow-up period. There were some risk factors that had a significant effect on the efficacy of TAM. They were tissue classification (tumor Grade < 2 vs. Grade ≥ 2, p = 2.2e-16), the number of lymph node metastases (Node-Negative vs. Node < 4, p = 5.3e-07; Node < 4 vs. Node ≥ 4, p = 0.003; Node-Negative vs. Node ≥ 4, p = 7.2e-15), and the expression levels of estrogen receptor (ER) and progesterone receptor (PR) (ER < 50% vs. ER ≥ 50%, p = 1.3e-12; PR < 50% vs. PR ≥ 50%, p = 2.6e-08). The really remarkable thing is that different genotypes of CYP2D6*10(C188T) show significant differences in prediction function (CYP2D6*10 CC vs. TT, p < 0.019; CYP2D6*10 CT vs. TT, p < 0.037). There are more than 50% Chinese who have CYP2D6*10 mutation. So the genotype of CYP2D6*10(C188T) should be tested before TAM therapy.

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 Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including α-adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further in vitro and in vivo experimental validation.

Potential Drug Prediction of Glioblastoma Based on Drug Perturbation-Induced Gene Expression Signatures

OBJECTIVES

Glioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures.

METHODS

We first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings.

RESULTS

We identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset.

CONCLUSIONS

In this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.

Proteodynamics and aging of eukaryotic cells

All aspects of each protein existence in the eukaryotic cells, starting from the pre-translation events, through translation, multiple different post-translational modifications, functional life and eventual proteostatic removal after loss of functionality and changes in physico-chemical properties, can be collectively called the proteodynamics. With aging, passing of time as well as accumulating effects of exposures, interactions and wearing-off lead to problems at each of the above mentioned stages, eventually leading to general malfunction of the proteome. This work briefly reviews and summarizes current knowledge concerning this important topic.

A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them

Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes. Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers. Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit β2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas. Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas.

Human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases

A key goal of geroscience research is to identify effective interventions to extend human healthspan, the years of healthy life. Currently, majority of the geroprotectors are found by screening compounds in model organisms; whether they will be effective in humans is largely unknown. Here we present a new strategy called ANDRU (aging network based drug discovery) to help the discovery of human geroprotectors. It first identifies human aging subnetworks that putatively function at the interface between aging and age-related diseases; it then screens for pharmacological interventions that may "reverse" the age-associated transcriptional changes occurred in these subnetworks. We applied ANDRU to human adipose gene expression data from the Genotype Tissue Expression (GTEx) project. For the top 31 identified compounds, 19 of them showed at least some evidence supporting their function in improving metabolic traits or lifespan, which include type 2 diabetes drugs such as pioglitazone. As the query aging genes were refined to the ones with more intimate links to diseases, ANDRU identified more meaningful drug hits than the general approach without considering the underlying network structures. In summary, ANDRU represents a promising human data-driven strategy that may speed up the discovery of interventions to extend human healthspan.

Revealing the Interactions Between Diabetes, Diabetes-Related Diseases, and Cancers Based on the Network Connectivity of Their Related Genes

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein-protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov-Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, "respiratory system development" and "regulation of mRNA metabolic process" are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.

DNA methylation profile is a quantitative measure of biological aging in children

DNA methylation changes within the genome can be used to predict human age. However, the existing biological age prediction models based on DNA methylation are predominantly adult-oriented. We established a methylation-based age prediction model for children (9-212 months old) using data from 716 blood samples in 11 DNA methylation datasets. Our elastic net model includes 111 CpG sites, mostly in genes associated with development and aging. The model performed well and exhibited high precision, yielding a 98% correlation between the DNA methylation age and the chronological age, with an error of only 6.7 months. When we used the model to assess age acceleration in children based on their methylation data, we observed the following: first, the aging rate appears to be fastest in mid-childhood, and this acceleration is more pronounced in autistic children; second, lead exposure early in life increases the aging rate in boys, but not in girls; third, short-term recombinant human growth hormone treatment has little effect on the aging rate of children. Our child-specific methylation-based age prediction model can effectively detect epigenetic changes and health imbalances early in life. This may thus be a useful model for future studies of epigenetic interventions for age-related diseases.

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features

To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.

Exercise-induced AMPK activation is involved in delay of skeletal muscle senescence

Accumulation of senescent cells leads to aging related phenotypes in various organs. Sarcopenia is a frequently observed aging-related disease, which is associated with the loss of muscle mass and functional disability. Physical activity represents the most critical treatment method for preventing decreased muscle size, mass and strength. However, the underlying mechanism as to how physical activity provides this beneficial effect on muscle function has not yet been fully understood. In particular, one unresolved question about aging is how the boost in catabolism induced by aerobic exercise affects skeletal muscle atrophy and other senescence phenotypes. Here we show that pre-activation of AMPK with the AMPK activator, AICAR can mitigate the diminished cellular viability of skeletal muscle cells induced by doxorubicin, which accelerates senescence through free radical production. Pre-incubation for 3 h with AICAR decreased doxorubicin-induced phosphorylation of AMPK in a differentiated skeletal muscle cell line. Accordingly, cellular viability of skeletal muscle cells was recovered in the cells pre-treated with AICAR then administered doxorubicin as compared to that of doxorubicin-only treatment. In accordance with the results of cellular experiments, we verified that 4 weeks of treadmill exercise decreased the senescence marker, p16 and p21 in 19-month-old mice compared to sedentary mice. In this study, we provide new evidence that prior activation of AMPK can reduce doxorubicin induced cell senescence phenotypes. The evidence in this paper suggest that aerobic exercise-activated catabolism in the skeletal muscle may prevent cellular senescence, partially through the cell cycle regulation.

Roles of Phenolic Compounds in the Reduction of Risk Factors of Cardiovascular Diseases

The population is now living longer during the period classified as "elderly" (60 years and older), exhibiting multimorbidity associated to the lengthening of the average life span. The dietary intake of phenolic compounds (PC) may affect the physiology, disease development and progression during the aging process, reducing risk factors of age related diseases. The aim of this review is to briefly describe some of the possible effects of a series of PC on the reduction of risk factors of the onset of cardiovascular diseases, considering their potential mechanisms of action. The main actions described for PC are associated with reduced platelet activity, anti-inflammatory effects, and the protection from oxidation to reduce LDL and the generation of advanced glycation end products. Preclinical and clinical evidence of the physiological effects of various PC is presented, as well as the health claims approved by regulatory agencies.

Integrative approach to sporadic Alzheimer's disease: deficiency of TYROBP in cerebral Aβ amyloidosis mouse normalizes clinical phenotype and complement subnetwork molecular pathology without reducing Aβ burden

Integrative gene network approaches enable new avenues of exploration that implicate causal genes in sporadic late-onset Alzheimer's disease (LOAD) pathogenesis, thereby offering novel insights for drug-discovery programs. We previously constructed a probabilistic causal network model of sporadic LOAD and identified TYROBP/DAP12, encoding a microglial transmembrane signaling polypeptide and direct adapter of TREM2, as the most robust key driver gene in the network. Here, we show that absence of TYROBP/DAP12 in a mouse model of AD-type cerebral Aβ amyloidosis (APP^KM670/671NL/PSEN1^Δexon9) recapitulates the expected network characteristics by normalizing the transcriptome of APP/PSEN1 mice and repressing the induction of genes involved in the switch from homeostatic microglia to disease-associated microglia (DAM), including Trem2, complement (C1qa, C1qb, C1qc, and Itgax), Clec7a and Cst7. Importantly, we show that constitutive absence of TYROBP/DAP12 in the amyloidosis mouse model prevented appearance of the electrophysiological and learning behavior alterations associated with the phenotype of APP^KM670/671NL/PSEN1^Δexon9 mice. Our results suggest that TYROBP/DAP12 could represent a novel therapeutic target to slow, arrest, or prevent the development of sporadic LOAD. These data establish that the network pathology observed in postmortem human LOAD brain can be faithfully recapitulated in the brain of a genetically manipulated mouse. These data also validate our multiscale gene networks by demonstrating how the networks intersect with the standard neuropathological features of LOAD.

An Efficient Classifier for Alzheimer's Disease Genes Identification

Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.

Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer

Colorectal cancer is a severe cancer associated with a high prevalence and fatality rate. There are three major mechanisms for colorectal cancer: (1) Chromosome instability (CIN), (2) CpG island methylator phenotype (CIMP) and (3) mismatch repair (MMR), of which CIN is the most common type. However, these subtypes are not exclusive and overlap. To investigate their biological mechanisms and cross talk, the gene expression profiles of 585 colorectal cancer patients with CIN, CIMP and MMR status records were collected. By comparing the CIN+ and CIN-samples, CIMP+ and CIMP-samples, MMR+ and MMR-samples with minimal redundancy maximal relevance (mRMR) and incremental feature selection (IFS) methods, the CIN, CIMP and MMR associated genes were selected. Unfortunately, there was little direct overlap among them. To investigate their indirect interactions, downstream genes of CIN, CIMP and MMR were identified using the random walk with restart (RWR) method and a greater overlap of downstream genes was indicated. The common downstream genes were involved in biosynthetic and metabolic pathways. These findings were consistent with the clinical observation of wide range metabolite aberrations in colorectal cancer. To conclude, the present study gave a gene level explanation of CIN, CIMP and MMR, but also showed the network level cross talk of CIN, CIMP and MMR. The common genes of CIN, CIMP and MMR may be useful for cross-subtype general colorectal cancer drug development.

Identification of human age-associated gene co-expressions in functional modules using liquid association

Aging is a major risk factor for age-related diseases such as certain cancers. In this study, we developed Age Associated Gene Co-expression Identifier (AAGCI), a liquid association based method to infer age-associated gene co-expressions at thousands of biological processes and pathways across 9 human tissues. Several hundred to thousands of gene pairs were inferred to be age co-expressed across different tissues, the genes involved in which are significantly enriched in functions like immunity, ATP binding, DNA damage, and many cancer pathways. The age co-expressed genes are significantly overlapped with aging genes curated in the GenAge database across all 9 tissues, suggesting a tissue-wide correlation between age-associated genes and co-expressions. Interestingly, age-associated gene co-expressions are significantly different from gene co-expressions identified through correlation analysis, indicating that aging might only contribute to a small portion of gene co-expressions. Moreover, the key driver analysis identified biologically meaningful genes in important function modules. For example, IGF1, ERBB2, TP53 and STAT5A were inferred to be key genes driving age co-expressed genes in the network module associated with function “T cell proliferation”. Finally, we prioritized a few anti-aging drugs such as metformin based on an enrichment analysis between age co-expressed genes and drug signatures from a recent study. The predicted drugs were partially validated by literature mining and can be readily used to generate hypothesis for further experimental validations.

Using molecular functional networks to manifest connections between obesity and obesity-related diseases

Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example, TGFB1 and VEGFA are inferred to be the top two key genes mediating obesity-breast cancer connection in modules associated with brain development. Finally, the top modules identified by OBNet in breast cancer significantly overlap with modules identified from TCGA breast cancer gene expression study, revealing the power of OBNet in identifying biological processes involved in the disease.

Bioactive Nutrients and Nutrigenomics in Age-Related Diseases

The increased life expectancy and the expansion of the elderly population are stimulating research into aging. Aging may be viewed as a multifactorial process that results from the interaction of genetic and environmental factors, which include lifestyle. Human molecular processes are influenced by physiological pathways as well as exogenous factors, which include the diet. Dietary components have substantive effects on metabolic health; for instance, bioactive molecules capable of selectively modulating specific metabolic pathways affect the development/progression of cardiovascular and neoplastic disease. As bioactive nutrients are increasingly identified, their clinical and molecular chemopreventive effects are being characterized and systematic analyses encompassing the "omics" technologies (transcriptomics, proteomics and metabolomics) are being conducted to explore their action. The evolving field of molecular pathological epidemiology has unique strength to investigate the effects of dietary and lifestyle exposure on clinical outcomes. The mounting body of knowledge regarding diet-related health status and disease risk is expected to lead in the near future to the development of improved diagnostic procedures and therapeutic strategies targeting processes relevant to nutrition. The state of the art of aging and nutrigenomics research and the molecular mechanisms underlying the beneficial effects of bioactive nutrients on the main aging-related disorders are reviewed herein.