In Silico Preliminary Association of Ammonia Metabolism Genes GLS, CPS1, and GLUL with Risk of Alzheimer's Disease, Major Depressive Disorder, and Type 2 Diabetes

Longitudinal Neuropathological Consequences of Extracranial Radiation Therapy in Mice

Cancer-related cognitive impairment (CRCI) is a consequence of chemotherapy and extracranial radiation therapy (ECRT). Our prior work demonstrated gliosis in the brain following ECRT in SKH1 mice. The signals that induce gliosis were unclear. Right hindlimb skin from SKH1 mice was treated with 20 Gy or 30 Gy to induce subclinical or clinical dermatitis, respectively. Mice were euthanized at 6 h, 24 h, 5 days, 12 days, and 25 days post irradiation, and the brain, thoracic spinal cord, and skin were collected. The brains were harvested for spatial proteomics, immunohistochemistry, Nanostring nCounter® glial profiling, and neuroinflammation gene panels. The thoracic spinal cords were evaluated by immunohistochemistry. Radiation injury to the skin was evaluated by histology. The genes associated with neurotransmission, glial cell activation, innate immune signaling, cell signal transduction, and cancer were differentially expressed in the brains from mice treated with ECRT compared to the controls. Dose-dependent increases in neuroinflammatory-associated and neurodegenerative-disease-associated proteins were measured in the brains from ECRT-treated mice. Histologic changes in the ECRT-treated mice included acute dermatitis within the irradiated skin of the hindlimb and astrocyte activation within the thoracic spinal cord. Collectively, these findings highlight indirect neuronal transmission and glial cell activation in the pathogenesis of ECRT-related CRCI, providing possible signaling pathways for mitigation strategies.

Metabolic phenotyping reveals an emerging role of ammonia abnormality in Alzheimer's disease

The metabolic implications in Alzheimer's disease (AD) remain poorly understood. Here, we conducted a metabolomics study on a moderately aging Chinese Han cohort (n = 1397; mean age 66 years). Conjugated bile acids, branch-chain amino acids (BCAAs), and glutamate-related features exhibited strong correlations with cognitive impairment, clinical stage, and brain amyloid-β deposition (n = 421). These features demonstrated synergistic performances across clinical stages and subpopulations and enhanced the differentiation of AD stages beyond demographics and Apolipoprotein E ε4 allele (APOE-ε4). We validated their performances in eight data sets (total n = 7685) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) and Religious Orders Study and Memory and Aging Project (ROSMAP). Importantly, identified features are linked to blood ammonia homeostasis. We further confirmed the elevated ammonia level through AD development (n = 1060). Our findings highlight AD as a metabolic disease and emphasize the metabolite-mediated ammonia disturbance in AD and its potential as a signature and therapeutic target for AD.

Hub genes, key miRNAs and interaction analyses in type 2 diabetes mellitus: an integrative in silico approach

Diabetes is a rising global metabolic disorder and leads to long-term consequences. As a multifactorial disease, the gene-associated mechanisms are important to know. This study applied a bioinformatics approach to explore the molecular underpinning of type 2 diabetes mellitus through differential gene expression analysis. We used microarray datasets GSE16415 and GSE29226 to identify differentially expressed genes between type 2 diabetes and normal samples using R software. Following that, using the STRING database, the protein-protein interaction network was constructed and further analyzed by Cytoscape software. The EnrichR database was used for Gene Ontology and pathway enrichment analysis to explore key pathways and functional annotations of hub genes. We also used miRTarBase and TargetScan databases to predict miRNAs targeting hub genes. We identified 21 hub genes in type 2 diabetes, some showing more significant changes in the PPI network. Our results revealed that GLUL, SLC32A1, PC, MAPK10, MAPT, and POSTN genes are more important in the PPI network and can be experimentally investigated as therapeutic targets. Hsa-miR-492 and hsa-miR-16-5p are suggested for diagnosis and prognosis by targeting GLUL, SLC32A1, PC, MAPK10, and MAPT genes involved in the insulin signaling pathway. Insight: Type 2 diabetes, as a rising global and multifactorial disorder, is important to know the gene-associated mechanisms. In an integrative bioinformatics analysis, we integrated different finding datasets to put together and find valuable diagnostic and prognostic hub genes and miRNAs. In contrast, genes, RNAs, and enzymes interact systematically in pathways. Using multiple databases and software, we identified differential expression between hub genes of diabetes and normal samples. We explored different protein-protein interaction networks, gene ontology, key pathway analysis, and predicted miRNAs that target hub genes. This study reported 21 significant hub genes and some miRNAs in the insulin signaling pathway for innovative and potential diagnostic and therapeutic purposes.

Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism

Introduction

Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

Methods

hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

Results

hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1,061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

Discussion

hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.

Human neural stem cells restore spatial memory in a transgenic Alzheimer's disease mouse model by an immunomodulating mechanism

INTRODUCTION

Stem cells are a promising therapeutic in Alzheimer's disease (AD) given the complex pathophysiologic pathways involved. However, the therapeutic mechanisms of stem cells remain unclear. Here, we used spatial transcriptomics to elucidate therapeutic mechanisms of human neural stem cells (hNSCs) in an animal model of AD.

METHODS

hNSCs were transplanted into the fimbria fornix of the hippocampus using the 5XFAD mouse model. Spatial memory was assessed by Morris water maze. Amyloid plaque burden was quantified. Spatial transcriptomics was performed and differentially expressed genes (DEGs) identified both globally and within the hippocampus. Subsequent pathway enrichment and ligand-receptor network analysis was performed.

RESULTS

hNSC transplantation restored learning curves of 5XFAD mice. However, there were no changes in amyloid plaque burden. Spatial transcriptomics showed 1061 DEGs normalized in hippocampal subregions. Plaque induced genes in microglia, along with populations of stage 1 and stage 2 disease associated microglia (DAM), were normalized upon hNSC transplantation. Pathologic signaling between hippocampus and DAM was also restored.

DISCUSSION

hNSCs normalized many dysregulated genes, although this was not mediated by a change in amyloid plaque levels. Rather, hNSCs appear to exert beneficial effects in part by modulating microglia-mediated neuroinflammation and signaling in AD.

Shared peripheral blood biomarkers for Alzheimer’s disease, major depressive disorder, and type 2 diabetes and cognitive risk factor analysis

Background

Alzheimer's disease (AD), type 2 diabetes mellitus (T2DM), and Major Depressive Disorder (MDD) have a higher incidence rate in modern society. Although increasing evidence supports close associations between the three, the mechanisms underlying their interrelationships remain elucidated.

Objective

The primary purpose is to explore the shared pathogenesis and the potential peripheral blood biomarkers for AD, MDD, and T2DM.

Methods

We downloaded the microarray data of AD, MDD, and T2DM from the Gene Expression Omnibus database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis to identify differentially expressed genes. We took the intersection of differentially expressed genes to obtain co-DEGs. Then, we performed GO and KEGG enrichment analysis on the common genes in the AD, MDD, and T2DM-related modules. Next, we utilized the STRING database to find the hub genes in the protein-protein interaction network. ROC curves were constructed for co-DEGs to obtain the most diagnostic valuable genes and to make drug predictions against the target genes. Finally, we conducted a present condition survey to verify the correlation between T2DM, MDD and AD.

Results

Our findings indicated 127 diff co-DEGs, 19 upregulated co-DEGs, and 25 down-regulated co-DEGs. Functional enrichment analysis showed co-DEGs were mainly enriched in signaling pathways such as metabolic diseases and some neurodegeneration. Protein-protein interaction network construction identified hub genes in AD, MDD and T2DM shared genes. We identified seven hub genes of co-DEGs, namely, SMC4, CDC27, HNF1A, RHOD, CUX1, PDLIM5, and TTR. The current survey results suggest a correlation between T2DM, MDD and dementia. Moreover, logistic regression analysis showed that T2DM and depression increased the risk of dementia.

Conclusion

Our work identified common pathogenesis of AD, T2DM, and MDD. These shared pathways might provide novel ideas for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosing and treating.

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus

Background

Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM.

Methods

The next generation sequencing (NGS) dataset GSE81608 was retrieved from the gene expression omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) between T2DM and normal controls. Then, Gene Ontology (GO) and pathway enrichment analysis, protein-protein interaction (PPI) network, modules, miRNA (micro RNA)-hub gene regulatory network construction and TF (transcription factor)-hub gene regulatory network construction, and topological analysis were performed. Receiver operating characteristic curve (ROC) analysis was also performed to verify the prognostic value of hub genes.

Results

A total of 927 DEGs (461 were up regulated and 466 down regulated genes) were identified in T2DM. GO and REACTOME results showed that DEGs mainly enriched in protein metabolic process, establishment of localization, metabolism of proteins, and metabolism. The top centrality hub genes APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1 were screened out as the critical genes. ROC analysis provides prognostic value of hub genes.

Conclusion

The potential crucial genes, especially APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1, might be linked with risk of T2DM. Our study provided novel insights of T2DM into genetics, molecular pathogenesis, and novel therapeutic targets.

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Glutaminase inhibitory activities of pentacyclic triterpenes isolated from Thymus vulgaris L

Glutaminase is an important target that is often over expressed in neurodegenerative and　lifestyle related diseases but few effective inhibitors of this enzyme have yet reached clinical trials. Ursolic acid (1), betulinic acid (2) and oleanolic acid (3), three pentacyclic triterpene acids, have been isolated from the leaves of Thymus vulgaris L. Enzyme inhibition experiments demonstrated their inhibitory effects against glutaminase activity. Compound 2 significantly inhibited the glutaminase activity with IC₅₀ of 0.31 mM, stronger than the positive control 6-diazo-5-oxo-L-norleucine (DON) with IC₅₀ of 0.57 mM. Compound 2 may serve as a potential lead compound for the prevention and treatment of neurodegenerative diseases and lifestyle related diseases by targeting glutaminase. This is the first report on glutaminase inhibitory activities of 1-3 isolated from T. vulgaris L.

A Comprehensive Analysis of Unique and Recurrent Copy Number Variations in Alzheimer's Disease and its Related Disorders

BACKGROUND

Copy number variations (CNVs) play an important role in the genetic etiology of various neurological disorders, including Alzheimer's disease (AD). Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) were shown to have share mechanisms and signaling pathways with AD.

OBJECTIVE

We aimed to assess CNVs regions that may harbor genes contributing to AD, T2DM, and MDD in 67 Saudi familial and sporadic AD patients, with no alterations in the known genes of AD and genotyped previously for APOE.

METHODS

DNA was analyzed using the CytoScan-HD array. Two layers of filtering criteria were applied. All the identified CNVs were checked in the Database of Genomic Variants (DGV).

RESULTS

A total of 1086 CNVs (565 gains and 521 losses) were identified in our study. We found 73 CNVs harboring genes that may be associated with AD, T2DM or MDD. Nineteen CNVs were novel. Most importantly, 42 CNVs were unique in our studied cohort existing only in one patient. Two large gains on chromosomes 1 and 13 harbored genes implicated in the studied disorders. We identified CNVs in genes that encode proteins involved in the metabolism of amyloid-β peptide (AGRN, APBA2, CR1, CR2, IGF2R, KIAA0125, MBP, RER1, RTN4R, VDR and WISPI) or Tau proteins (CACNAIC, CELF2, DUSP22, HTRA1 and SLC2A14).

CONCLUSION

The present work provided information on the presence of CNVs related to AD, T2DM, and MDD in Saudi Alzheimer's patients.

CPS1: Looking at an ancient enzyme in a modern light

The mammalian urea cycle (UC) is responsible for siphoning catabolic waste nitrogen into urea for excretion. Disruptions of the functions of any of the enzymes or transporters lead to elevated ammonia and neurological injury. Carbamoyl phosphate synthetase 1 (CPS1) is the first and rate-limiting UC enzyme responsible for the direct incorporation of ammonia into UC intermediates. Symptoms in CPS1 deficiency are typically the most severe of all UC disorders, and current clinical management is insufficient to prevent the associated morbidities and high mortality. With recent advances in basic and translational studies of CPS1, appreciation for this enzyme's essential role in the UC has been broadened to include systemic metabolic regulation during homeostasis and disease. Here, we review recent advances in CPS1 biology and contextualize them around the role of CPS1 in health and disease.

A constitutive knockout of murine carbamoyl phosphate synthetase 1 results in death with marked hyperglutaminemia and hyperammonemia

The enzyme carbamoyl phosphate synthetase 1 (CPS1; EC 6.3.4.16) forms carbamoyl phosphate from bicarbonate, ammonia, and adenosine triphosphate (ATP) and is activated allosterically by N-acetylglutamate. The neonatal presentation of bi-allelic mutations of CPS1 results in hyperammonemia with reduced citrulline and is reported as the most challenging nitrogen metabolism disorder to treat. As therapeutic interventions are limited, patients often develop neurological injury or die from hyperammonemia. Survivors remain vulnerable to nitrogen overload, being at risk for repetitive neurological injury. With transgenic technology, our lab developed a constitutive Cps1 mutant mouse and reports its characterization herein. Within 24 hours of birth, all Cps1 ^-/- mice developed hyperammonemia and expired. No CPS1 protein by Western blot or immunostaining was detected in livers nor was Cps1 mRNA present. CPS1 enzymatic activity was markedly decreased in knockout livers and reduced in Cps1^+/- mice. Plasma analysis found markedly reduced citrulline and arginine and markedly increased glutamine and alanine, both intermolecular carriers of nitrogen, along with elevated ammonia, taurine, and lysine. Derangements in multiple other amino acids were also detected. While hepatic amino acids also demonstrated markedly reduced citrulline, arginine, while decreased, was not statistically significant; alanine and lysine were markedly increased while glutamine was trending towards significance. In conclusion we have determined that this constitutive neonatal mouse model of CPS1 deficiency replicates the neonatal human phenotype and demonstrates the key biochemical features of the disorder. These mice will be integral for addressing the challenges of developing new therapeutic approaches for this, at present, poorly treated disorder.

Integrative Analyses of Genes Associated with Subcutaneous Insulin Resistance

Insulin resistance is present in the majority of patients with non-insulin-dependent diabetes mellitus (NIDDM) and obesity. In this study, we aimed to investigate the key genes and potential molecular mechanism in insulin resistance. Expression profiles of the genes were extracted from the Gene Expression Omnibus (GEO) database. Pathway and Gene Ontology (GO) enrichment analyses were conducted at Enrichr. The protein–protein interaction (PPI) network was settled and analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database constructed by Cytoscape software. Modules were extracted and identified by the PEWCC1 plugin. The microRNAs (miRNAs) and transcription factors (TFs) which control the expression of differentially expressed genes (DEGs) were analyzed using the NetworkAnalyst algorithm. A database (GSE73108) was downloaded from the GEO databases. Our results identified 873 DEGs (435 up-regulated and 438 down-regulated) genetically associated with insulin resistance. The pathways which were enriched were pathways in complement and coagulation cascades and complement activation for up-regulated DEGs, while biosynthesis of amino acids and the Notch signaling pathway were among the down-regulated DEGs. Showing GO enrichment were cardiac muscle cell–cardiac muscle cell adhesion and microvillus membrane for up-regulated DEGs and negative regulation of osteoblast differentiation and dendrites for down-regulated DEGs. Subsequently, myosin VB (MYO5B), discs, large homolog 2(DLG2), axin 2 (AXIN2), protein tyrosine kinase 7 (PTK7), Notch homolog 1 (NOTCH1), androgen receptor (AR), cyclin D1 (CCND1) and Rho family GTPase 3 (RND3) were diagnosed as the top hub genes in the up- and down-regulated PPI network and modules. In addition, GATA binding protein 6 (GATA6), ectonucleotide pyrophosphatase/phosphodiesterase 5 (ENPP5), cyclin D1 (CCND1) and tubulin, beta 2A (TUBB2A) were diagnosed as the top hub genes in the up- and down-regulated target gene–miRNA network, while tubulin, beta 2A (TUBB2A), olfactomedin-like 1 (OLFML1), prostate adrogen-regulated mucin-like protein 1 (PARM1) and aldehyde dehydrogenase 4 family, member A1 (ALDH4A1)were diagnosed as the top hub genes in the up- and down-regulated target gene–TF network. The current study based on the GEO database provides a novel understanding regarding the mechanism of insulin resistance and may provide novel therapeutic targets.