Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by the formation of amyloid plaques implicated in neuronal death. Genetics, age, and sex are the risk factors attributed to AD. Though omics studies have helped to identify pathways associated with AD, an integrated systems analysis with the available data could help to understand mechanisms, potential biomarkers, and therapeutic targets. Analysis of transcriptomic data sets from the GEO database, and proteomic and metabolomic data sets from literature was performed to identify deregulated pathways and commonality analysis identified overlapping pathways among the data sets. The deregulated pathways included those of neurotransmitter synapses, oxidative stress, inflammation, vitamins, complement, and coagulation pathways. Cell type analysis of GEO data sets showed microglia, endothelial, myeloid, and lymphoid cells are affected. Microglia are associated with inflammation and pruning of synapses with implications for memory and cognition. Analysis of the protein-cofactor network of B2, B6, and pantothenate shows metabolic pathways modulated by these vitamins which overlap with the deregulated pathways from the multi-omics analysis. Overall, the integrated analysis identified the molecular signature associated with AD. Treatment with anti-oxidants, B2, B6, and pantothenate in genetically susceptible individuals in the pre-symptomatic stage might help in better management of the disease.
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Introduction
Alzheimer’s disease (AD) is a progressive neurological disorder characterized by the formation of amyloid plaques, tau tangles, and synaptic and neuronal dysfunction1. The major risk factors of AD include age, sex, and genetic variants2. Rare coding variants in APP(Amyloid precursor protein), PSEN1 (Presenilin 1), and PSEN2 (Presenilin 2) increase the risk for late-onset of AD3. Genetic variants of SLC9C1, CSN1S1, and LOXL4 though recessive, delay the age of onset of AD4. The ε4 allele of apolipoprotein E (ApoE), involved in cholesterol metabolism, which is secreted by astrocytes is strongly associated with the late onset of AD5. ApoE isoforms have a role in controlling brain glucose and lipid metabolism, neuronal signaling, inflammation, and mitochondrial function and regulate Aβ aggregation and clearance6,7. ApoE4 is associated with gene expression changes in all cell types of the human brain and aberrant deposition of cholesterol in oligodendrocytes leads to reduced myelination8. ApoE-expressing microglia promote neurodegeneration by disrupting the phagocytosis of Aβ aggregates9. Mutations in APP, PSEN1 & 2, ADAM10, and ADAM1J cause the formation of Aβ plaques and are sufficient to cause the whole biochemical and morphological characteristic of AD2. However, studies have also shown that the toxicity is caused due to soluble monomers or oligomers10. AD is associated with changes in brain volume, and atrophy in the hippocampus and entorhinal cortex at the early stages of disease progression11. The disease manifestations include motor, behavioral, cognitive, and memory dysfunction5. Current medications are restricted to the management of symptoms and disease-modifying therapies with antibodies such as aducanumab and lecanumab is shown to reduce the amyloid plaques in AD patients which influence the course of disease12,13. Hence understanding the factors that contribute to amyloid formation will help to achieve mechanistic insights and also help to identify potential biomarkers and therapeutic targets which will aid in the better management of the disease14. Studies have shown impaired energy metabolism in AD which could act both as biomarkers and therapeutic target in disease15. Previous studies have also shown that brain volume and metabolic changes precede the onset of symptoms in neurodegenerative diseases like AD and Huntington’s disease (HD)16,17.
Mitochondrial impairment, cellular energy deficiencies, and oxidative damage are found to be important in the pathogenesis of AD18,19. Studies have reported the presence of Aβ in the cells and mitochondria. In mitochondria, Aβ binds to the heme group leading to the production of free radicals, thus causing oxidative damage and cell death20. Bioenergetic deficits and oxidative stress are the key contributors to cognitive decline in AD21. The mitochondrial enzymes (cytochrome c oxidase, COX) that are important for energy metabolism is deficient in AD brains22.
Consistent with mitochondrial dysfunction, AD is associated with metabolic remodeling23. Glycolysis was found to be upregulated in AD24. Levels of lactate and lactate transporters are altered in AD mouse models resulting in deregulated astrocyte-neuron lactate shuttle25. AD brain was shown to exhibit hypometabolism decades before the manifestation of symptoms suggesting the role of metabolic dysfunction in AD26. Fluorodeoxyglucose-positron emission tomography (FDG-PET) studies have shown a reduction in glucose metabolism at the mild cognitive impairment (MCI) stage of AD progression27. Magnetic resonance imaging studies have shown decreased levels of glutamate in the AD brain which is crucial for the functioning of the central nervous system (CNS). The astrocyte-neuron glutamine-glutamate shuttle was also found to be affected in AD28. A decrease in metabolite levels of N-acetyl aspartate/Creatinine (NAA/Cr) ratio is also observed in AD29. The data from the literature are suggestive of metabolic pathways as potential modifiers of AD. Consistent with metabolic deregulation, changes in the levels of various vitamins like B2, B5, B6, B12 and B9 are reported in AD30,31,32,33. The vitamins act as cofactors in various enzymatic reactions and low levels of these vitamins are associated with many diseases including neurodegenerative diseases34. For many genetic diseases with non-synonymous single nucleotide polymorphisms (SNPs) in the genes encoding enzymes, vitamin/cofactor supplementation is used as the standard of care treatment35. Previous studies have shown that supplementation of B2, B5 and B6 was shown to improve symptoms in AD model system36,37,38. The vitamins might potentially act by helping to achieve metabolic homeostasis though this remains to be answered39. Previous studies have shown that metabolic deregulation precedes onset of symptoms in AD40.
Earlier omics analysis carried out on the samples obtained from AD patients and model systems has shown the associated deregulated pathways with diseases41. Transcriptomics profiling of late-onset Alzheimer’s affected brain tissue revealed pathways associated with neural communication, cerebral vasculature, and amyloid-beta clearance are deregulated42. Blood transcriptomic analysis has shown an association of AD at different stages with type 2 diabetes43. Single-cell transcriptomics analysis revealed myelination-related processes were deregulated in multiple cell types like neurons, astrocytes, oligodendrocytes and microglia44. An individual’s metabolome reflects the culmination of the cumulative alterations in genomics, transcriptomic and proteomic profile. In addition, it is also influenced by the environment and lifestyle of an individual45. Metabolomic analysis of different kinds of samples of AD patients also indicates deregulation of the Krebs cycle, mitochondrial function, neurotransmitter and amino acid metabolism, and lipid biosynthesis pathways46,47,48. Multi-omic analysis and validation has helped to understand mechanisms, biomarkers and potential therapeutic targets in osteonecrosis, rheumatoid arthritis, glaucoma49,50,51.
Although the multi-omics approach enabled the comprehensive understanding of deregulated pathways associated with the disease. An integrated systems analysis using the available multi-omics data sets can potentially shed light on the interactions of the different molecular processes with further implications for the disease and could also help identify markers, early prognosis, and new therapeutic targets. In the present study an integrative analysis of transcriptomics, proteomics, and metabolomics of blood, brain, and cerebrospinal fluid samples obtained from patient datasets was performed. In addition, proteomics datasets of the brain and blood, and metabolomic datasets of the brain, blood, and cerebrospinal fluid of mice were analysed. The overall analysis from this approach shows the deregulation of inflammation, complement and coagulation, signalling, and metabolic pathways as well as vitamin B2, B6, and pantothenate pathways. The cell type analysis showed endothelial, microglial, myeloid, and lymphoid cells. Since vitamins act as cofactors, their implications for enzyme activity associated with the deregulation of metabolic pathways have been analysed using a cofactor-protein interaction network using Cytoscape. The results are discussed in light of the implications of deregulated pathways for disease, symptoms and therapeutic targets.
Results
Analysis of transcriptomic data sets from the GEO database shows deregulated pathways and cell types with potential implications for disease
The GEO (Gene Expression Omnibus) data sets GSE528152, GSE3698053, GSE4477054, GSE4835055, and GSE14082956 from Humans were used for analysis. All the data sets were from the brain except for GSE140829 which is from the peripheral blood data set. The data sets were analysed using Enrichr57,58 as given in methods and genes are binned into pathways. To corroborate the gene expression profile to different cell types that are associated with the disease, we carried out cell type analysis using Enrichr as given in the methods. The kinases and transcription factors were predicted using X2K59 on all the GEO datasets as described in the methods. The significant differential genes with an adjusted p-value of 0.05 were used for all analyses. Pathway and transcription factor analysis was performed for up-regulated and downregulated genes separately and provided in Supplementary file 12.
GSE5281
Represent the pooled data sets from the brain regions: Entorhinal cortex, hippocampus, medial temporal gyrus, posterior cingulate, superior frontal gyrus, and Primary visual cortex. Analysis of the differentially expressed significant genes using Enrichr binned them into pathways. The top significant pathways include pathways involved in the protein life cycle, neurodegenerative diseases like spinocerebellar ataxia, amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), citric acid cycle, and pathways involved in infection (Fig. 1a and Supplementary 1). Pathway Analysis of upregulated genes showed AGE-RAGE and p53 signalling pathways, different viral infection and cancer pathways. The pathways obtained using the down regulated genes involved include various neurodegenerative diseases like ALS, HD, PD, prions, spinocerebellar ataxia, proteasome and oxidative phosphorylation (Supplementary 2) The transcription factors involved include TAF1, BRCA, CREB, MYC, etc. (Supplementary 3 and 5). Further, analysis of kinases involved shows CDKs, MAPKs, ERK, DNAPK ATR/ATM, etc. (Supplementary 4 and 5). The cell types involve astrocytes, glutamatergic and GABAergic neurons, oligodendrocytes and, capillary intermediate neurons (Fig. 1b and Supplementary 6).
Gene expression data analysis of GSE5281, GSE36980, and GSE44770 GEO datasets. Pathway enrichment analysis of differentially expressed genes by Enrichr tool. Top significant pathways from datasets of (a) GSE5281 (c). GSE36980 (e). GSE44770. Cell type analysis and genes associated with each cell type (b). GSE5281 (d). GSE36980 (f) GSE44770. Figure generated using, Microsoft powerpoint 2019 MSO (Version 2212 Build 16.0.15928.20196) 64-bit.
GSE36980
Dataset represents the pooled datasets from brain regions frontal cortex, temporal cortex, and hippocampus. Differentially expressed genes are binned into pathways. The top significant pathways involved Synapse based pathways like Synaptic vesicle cycle, Dopaminergic synapse, neurodegenerative diseases such as (Huntington’s disease, Parkinson’s disease, spinocerebellar ataxia, pathways of neurodegeneration), proteasome, endocytosis, and signaling pathways (Fig. 1c and Supplementary 1). Pathway analysis of upregulated genes showed cytokine and complement and coagulation cascades, bacterial and viral infection and immune pathways. The pathways obtained using the downregulated genes involved include neurodegenerative diseases such as HD, ALS, PD, spinocerebellar ataxia and pathways of neurodegeneration (Supplementary 2). The transcription factors involved include REST, RCOR1, ZNF384, SMAD4, etc. (Supplementary 3 and 5). Further, analysis of kinases showed MAPKs, CDKs, JNK2, GSK3B, HIPK2, etc. (Supplementary 4 and 5). The cell types involve astrocytes, glutamatergic and GABAergic neurons (Fig. 1d and Supplementary 6).
GSE44770
Dataset represents the pooled datasets from brain regions dorsolateral prefrontal cortex, visual cortex, and cerebellum. Differentially expressed genes are binned into pathways. The top significant pathways involved signaling pathways like retrograde endocannabinoid signalling, AGE-RAGE signaling pathway in diabetic complications, C-type lectin receptor signaling pathway, long-term potentiation, axon guidance, gastric acid secretion, citric acid cycle, pathways involved in infection, and DNA repair (Fig. 1e and Supplementary 1). Pathway analysis of upregulated genes showed viral, bacteria and protozoan infection pathways, AGE-RAGE signalling and osteoclast differentiation. Pathways obtained from downregulated genes include neurodegenerative diseases like PD, HD, pathways of neurodegeneration, nicotine addiction, citrate cycle and retrograde endocannabinoid signalling. (Supplementary 2) The transcription factors involved include SUZ12, AR, GATA1, SOX2, etc. (Supplementary 3 and 5). Further analysis of kinases involved MAPKs, CSNK2A1, GSK3BETA, CDC2, etc. (Supplementary 4 and 5). The cell types involve glutamatergic and GABAergic neurons, monocytes, and microglia (Fig. 1f and Supplementary 6).
GSE48350
Represent the pooled data sets from the brain regions Entorhinal cortex, hippocampus, superior frontal gyrus, and post-central gyrus. Analysis of the differentially expressed significant genes using Enrichr binned them into pathways. The top significant pathways involved riboflavin metabolism, neurodegenerative diseases such as Huntington’s disease, Amyotrophic lateral sclerosis, Parkinson’s disease, pathways of neurodegeneration, prion disease, and proteasome, endocytosis, and pathways involved in infection (Fig. 2a and Supplementary 1). Pathway analysis using upregulated genes showed allograft rejection, signalling, inflammatory, protozoan and cancer related pathways. Pathways obtained from downregulated genes include neurodegenerative diseases like HD, PD, AD, spinocerebellar ataxia, ALS and pathways associated with neurodegeneration. (Supplementary 2) The transcription factors involved include RUNX1, SMAD4, EGR1, GATAs, etc. (Supplementary 1 and 5). Further analysis of kinases involved CREB1, USF1, MYC, ATF2, YY1, etc. (Supplementary 4 and 5). The cell types involve glutamatergic and GABAergic neurons, CD4 + T-cells, microglia, and dendritic cell (Fig. 2b and Supplementary 6).
Gene expression data analysis of GSE48350, and GSE140829 GEO datasets. Pathway enrichment analysis of differentially expressed genes by Enrichr tool Top significant pathways from dataset (a). GSE48350 (c). GSE3140829. Cell type analysis and genes associated with each cell type (b). GSE48350 (d). GSE140829. Commonality analysis for (e). Pathways (f). Kinases (g). Transcription factors (h). Cell types. Figure generated using, Microsoft powerpoint 2019 MSO (Version 2212 Build 16.0.15928.20196) 64-bit. Multiple list comparator (https://molbiotools.com/listcompare.php).
GSE140829
Dataset was obtained from peripheral blood. Analysis of the differentially expressed significant genes using Enrichr binned them into pathways. The top significant pathways involved infection-based pathways like salmonella infection, Yersinia infection, Shigellosis, pathogenic Escherichia coli infection, signaling pathways such as NOD-like receptor signaling pathway, carbohydrate metabolism pathways like pentose phosphate pathway, and osteoclast differentiation (Fig. 2c and Supplementary 1). Pathway analysis using upregulated genes showed signalling, infection and osteoclast differentiation pathways. Pathways obtained from downregulated genes include ribosome, spliceosome, ubiquitin mediated proteolysis, circardian rhythm and metabolic pathways. (Supplementary 2) The transcription factors involved E2F1, USF2, RCOR1, SPI1, etc. (Supplementary 3 and 5). Kinase analysis showed the involvement of CDKs, MAPKs, CSNK2A1, and CK2ALPHA (Supplementary 4 and 5). The cell types affected include CD4 + T-cells, microglia (perivascular macrophage), CD4 + cytotoxic T-cell, and natural killer cells (Fig. 2d and Supplementary 6).
Overlap of pathways, kinases, transcription factors, and cell types shows deregulated pathways and dysfunction of cell type with implications for disease
Further, we studied if the pathways obtained from different data sets across populations, samples, and study settings exhibit a common trend. The overlapping pathways include retrograde endocannabinoid signaling and salmonella infection are found in 4 datasets and Alzheimer’s disease, TCA cycle, endocytosis, pathways of neurodegeneration and proteasome occurred in 3 datasets while dopaminergic synapse and axon guidance are found in two of the datasets (Fig. 2e).
For understanding common transcription factors, we looked at the overlap of transcription factors from 5 datasets. Our study showed that 6 transcription factors are common among the different transcriptomic datasets. These transcription factors include CREB1, MYC, RCOR1, TCF3, UBTF, and ZNF384. Functional annotation of these TFs shows that they belong to signaling pathways, pathways related to infection, and protein synthesis pathways (Fig. 2f).
Further, we studied the kinases that are common among the different transcriptomic data sets. Analysis using the top 15 kinases from the 5 transcriptomic data sets conjured 10 kinases to be common among them. These kinases include AKT1, CDC2, CDK1, CDK2, CDK4, CK2Alpha, CSNK2A1, GSK3B, MAPK1, and MAPK14. Functional annotation of these kinases shows that they largely fall into those regulating cell proliferation, inflammation, etc. However, commonality analysis of kinases among the 5 kinases data sets using all the significant kinases conjured 69 kinases (Fig. 2g).
To corroborate the gene expression profile to different cell types that are associated with the disease we carried out a cell type analysis. We further looked at the overlap of the cell types obtained from the 5 transcriptomic data sets showed GABAergic neurons and Glutamatergic neurons to be common to 4 data sets, microglia to 3 of the data sets while T cells and Monocytes to two of the data sets (Fig. 2h). Corroborating the pathways, TFs, Kinases, and Cell type from commonality studies might shed light on the mechanisms associated with disease and factors that might modulate disease progression.
Analysis of proteomic data sets from the literature shows pathways with potential implications for inflammation and disease progression
Proteomic analysis of brain60 data set from literature was performed using Enrichr using significantly differential proteins. Our analysis shows Fluid sheer stress and atherosclerosis, complement and coagulation pathway, various inflammatory diseases like systemic lupus erythematosus, inflammatory bowel disease, Rheumatoid arthritis, and infectious diseases like viral myocarditis, malaria, and tuberculosis (Fig. 3a and Supplementary 7). Proteomic analysis of the blood61 data set shows focal adhesion, ECM Receptor interaction, complement and coagulation pathway, infection disease pathways like staphylococcus aureus infection and coronavirus disease. Metabolic pathways like fat and vitamin digestion and absorption, Cholesterol metabolism, inflammatory process like neutrophil extracellular trap formation, and platelet activation were enriched (Fig. 3b and Supplementary 7). Proteomic analysis of the CSF62 data set shows enrichment of proteins belonging to Long Term Potentiation and infectious disease like glutathione metabolism. In particular, enrichment of metabolic pathways was observed in CSF. These major pathways include Glutathione and pyruvate metabolism, glycolysis/gluconeogenesis, Metabolism of amino acids (Methionine and cysteine, phenylalanine tyrosine, and tryptophan metabolism), and Vitamin B6 metabolism (Fig. 3c and Supplementary 7).
Pathway analysis of differentially expressed proteins (DEPs) between healthy controls and Alzheimer’s patients using Enrichr tool. Top significant pathways from datasets of (a). Brain (b). Blood serum (c). Cerebrospinal fluid (CSF) (d). Common proteomics pathways in humans. Proteomics analysis of mice datasets (e). Brain (f). Blood (g). Common proteomics pathways between humans and mice. Figure generated using Venny (bioinfogp.cnb.csic.es/tools/venny/), Microsoft power point 2019 MSO (Version 2212 Build 16.0.15928.20196) 64-bit.
The commonality of the pathways among the different proteomic data sets shows complement and coagulation pathways, pathways enriched for various infectious diseases, phagosomes as well as signalling pathways. These pathways might have potential implications for inflammation and disease (Fig. 3d).
Further, we analysed the proteomic data sets from the brain and blood of mice models of Alzheimer’s disease63,64. Proteomic analysis of the brain63 data set showed pathways like Prion, Huntington’s and Parkinson’s disease, salmonella, pathogenic E. coli infection, phagosome and glycolysis, and gluconeogenesis (Fig. 3e and Supplementary 8). Proteomic analysis of the blood64 data set showed pathways like lipid and atherosclerosis, fat and vitamin digestion and absorption, Complement and coagulation pathway, and PPAR signalling (Fig. 3f and Supplementary 8).
We further looked for the pathways that are common to Alzheimer’s disease patients and mice models of disease. For the commonality studies, we pooled the respective patient and mice model pathway data sets and looked for overlapping pathways. Our analysis showed that a total of 8 pathways are common between the mice and human pathway data sets. The common pathways include complement and coagulation cascade, lipid and atherosclerosis, phagosome and HIF1 signalling, African trypanosomiasis, cholesterol metabolism and vitamin digestion and absorption are also found. The common pathways might be more important and representative of the disease as they are found to be conserved across taxa, populations, and study settings (Fig. 3g).
Analysis of metabolomic data sets from the literature shows enrichment of pathways for immune-metabolism and disease progression
Analysis of metabolomic data sets from the brain, blood, and cerebrospinal fluid was essentially performed using Metaboanalyst65 with significant differential metabolites obtained from the literature. Metabolomic analysis of brain66 data sets from the literature shows that Pantothenate and CoA biosynthesis, pyruvate metabolism, TCA Cycle, Glyoxylate and dicarboxylate metabolism, Purine metabolism, aminoacyl tRNA synthase, and metabolism of various amino acids (cysteine and methionine, histidine, arginine and proline, alanine aspartate and glutamate), as well as unsaturated fatty acids and arginine biosynthesis, are deregulated (Fig. 4a and Supplementary 9). Many of these metabolic pathways represent immune metabolism which is consistent with inflammation in Alzheimer’s disease. Analysis of blood67 metabolomic data sets showed deregulated pathways like pyruvate metabolism, TCA Cycle, Glyoxylate and dicarboxylate metabolism, Purine metabolism, aminoacyl tRNA synthase, and metabolism of various amino acids (cysteine and methionine, histidine, arginine and proline, alanine aspartate and glutamate) as well as arginine metabolism (Fig. 4b and Supplementary 9). Further, analysis of CSF68 metabolomic data sets showed pathways like aminoacyl tRNA biosynthesis, Valine, leucine and isoleucine biosynthesis, glycine, serine, and threonine metabolism as well as glyoxylate and dicarboxylate metabolism (Fig. 4c and Supplementary 9).
Pathway analysis of differential metabolites between healthy controls and Alzheimer’s patients using MetaboAnalyst tool (a). Brain (b). Blood serum (c). Cerebrospinal fluid (CSF) (d). Common metabolomic pathways in humans. Metabolomics analysis of mice datasets. (e). Brain (f). Blood (g). Cerebrospinal fluid (h). Common metabolomic pathways between humans and mice. Figure generated using Venny (bioinfogp.cnb.csic.es/tools/venny/), Microsoft powerpoint 2019 MSO(Version 2212 Build 16.0.15928.20196) 64-bit.
Commonality analysis of pathways obtained for the different human metabolomic data sets showed aminoacyl-tRNA biosynthesis to be common among them. The brain and CSF showed glyoxylate and dicarboxylate pathway to be common between them. The brain and blood data sets showed 5 common pathways which include alanine, aspartate and glutamate metabolism, glutamine and glutamate metabolism, histidine, and purine metabolism as well as arginine biosynthesis (Fig. 4d).
Similarly, we carried out the pathway analysis of metabolic data sets from the brain, blood, and cerebrospinal fluid from mice using metaboanalyst. The analysis for brain69 sample data sets showed deregulation of pathways like aminoacyl-tRNA and arginine biosynthesis, metabolism of alanine, aspartate and glutamate, Beta-alanine, glutamine and glutamate, nitrogen, glycine, serine and threonine, glyoxylate and dicarboxylate, glutathione as well as arginine and proline (Fig. 4e and Supplementary 10). Analysis of metabolic data sets from blood70 shows deregulation of linolenic acid, purine, and glycerophospholipid metabolism as well as biosynthesis of unsaturated fatty acid (Fig. 4f and Supplementary 9). Analysis of metabolic data sets from CSF71 also showed deregulation of Alanine, aspartate, glutamate, and purine metabolism (Fig. 4g and Supplementary 9).
Commonality studies was performed on the pooled pathways obtained for the human and mice metabolomic data sets. Our analysis showed that 8 pathways were common to the two data sets (Fig. 4h). The commonly deregulated pathways include Biosynthesis of aminoacyl-tRNA, arginine, unsaturated fatty acids and metabolism of nitrogen, Arginine and proline, glutamine and glutamate, purine as well as alanine, aspartate, and glutamate. The results suggest considerable concordance in metabolism between Alzheimer’s patients and mice models of disease despite differences in population, taxa, samples, and study settings.
Further, we looked at the levels of metabolites in the pathways that are common to brain, blood and CSF. Previous studies have shown that the levels of different metabolites in brain bears an inverse correlation with those in the blood72. Consistent with this our analysis recapitulates these findings despite these are different data sets (Fig. 5c). In addition, from pathway obtained from transcriptomic data sets, we have also provided the expression profiles of these genes along with the metabolites in blood and brain (Fig. 5c).
(a) Common pathways between transcriptomics proteomics and metabolomics from human datasets. (b) Common pathways between mice multi-omics data and human multi-omics data. (c) Metabolite expression levels of common metabolic pathways in humans. Metabolite and gene expression levels pathways common between transcriptomics and metabolomics in humans. Figure generated using Venny (bioinfogp.cnb.csic.es/tools/venny/), Microsoft powerpoint 2019 MSO (Version 2212 Build 16.0.15928.20196) 64-bit.
Commonality studies of multi-omics data sets shows the considerable overlap of pathways among the different data sets across taxa, populations, study settings, and platforms
The pooled transcriptomic human data sets show 100 pathways while the pooled proteomic data sets show 60 pathways and the pooled metabolomic data sets had 28 pathways. A total of 12 pathways were common to transcriptomics and proteomics (Fig. 5a) while 4 pathways were common between proteomics and metabolomics (Fig. 5a). The transcriptomic data set and metabolomic data set had two common pathways (Fig. 5a). Overall, the data sets show deregulation of inflammation and cytokine signalling pathways, metabolic pathways, neuronal signalling, etc. The cell type analysis also corroborates with the deregulated pathways observed in our multi-omic analysis. The transcription factor and kinase analysis shows critical regulators of these deregulated pathways.
The commonality studies of the pooled human and mice proteomic data sets showed 8 pathways were common to them (Fig. 3g). The commonality studies of the pooled human and mice metabolomic data sets showed 8 pathways between the data sets (Fig. 4h). The commonality studies of pooled human and mice pathways together showed 28 pathways are common (Fig. 5b). Taken together, our analysis captures the common pathways which are deregulated across taxa, populations, study settings, and different platforms used. These conserved pathways might have major ramifications for the observed symptoms, disease progression, and prognosis.
Vitamin B2, B5, and B6 cofactor-protein interaction network pathway analysis showed considerable overlap with deregulated metabolic pathways in Alzheimer’s disease patients and mice models
Integrated multi-omics analysis showed the deregulation of Vitamin B2, B5, and B6 pathways. Hence, we constructed the Vitamin B2, B5, and B6 protein interactions network and binned the genes into pathways (Fig. 6a–c and Supplementary 11). Commonality studies of human (Fig. 6d) and mice metabolic pathways (Fig. 6e) with significant pathways from vitamin cofactor analysis showed considerable overlap. The transcriptomic data was further imported into the network. Vitamin B2 is a cofactor for succinate dehydrogenase complex flavoprotein subunit A (SDHA), and succinate dehydrogenase complex iron sulphur subunit B (SDHB) that are involved in the citric acid cycle and oxidative phosphorylation (Fig. 6f). Pantothenate is a precursor for Coenzyme A. The Valine, leucine, and isoleucine degradation pathway is deregulated in Alzheimer’s disease. Various genes associated with this pathway such as Acetyl-Coenzyme A Acetyltransferase 2 (ACAT2), acetyl-CoA acetyltransferase 1 (ACAT1), 3-Hydroxy-3-Methylglutaryl-CoA Synthase 1 (HMGCS1), acetyl-Coenzyme A acyltransferase 1 (ACAA1), dihydrolipoamide branched chain transacylase E2 (DBT), Acetyl-CoA Acyltransferase 2 (ACAA2) are down-regulated while Aldehyde Dehydrogenase 6 Family Member A1 (ALDH6A1) is upregulated. The enzyme which utilizes vitamin B2 as a cofactor, Interleukin-4-Induced Protein 1 (IL4I1) an L-amino acid oxidase, and monoamine oxidases (MAOA and MAOB) are upregulated while SDHA and SDHB are downregulated (Fig. 6g).
Pathway analysis of vitamin co-factor network. Top significant pathways from (a). Vitamin-B2 (b). Vitamin-B5 (c). Vitamin-B6. Commonality analysis (d). Pathways common between human metabolomics and pooled vitamin-cofactor analysis (e). Pathways are common between mice metabolomics and pooled vitamin co-factor analysis. Network representing cofactor network of (f). B2 with metabolomic pathways. (g). B5 with metabolic pathways (h). B6 with metabolic pathways. Figure generated using Microsoft powerpoint 2019 MSO(Version 2212 Build 16.0.15928.20196) 64-bit, cytoscape (cytoscape 3.8.2) cytoscape/cytoscape/3.8.2/. Venny (bioinfogp.cnb.csic.es/tools/venny/).
B6-dependent enzyme Dopa decarboxylase (DDC) in the dopaminergic synapse, Glutamate decarboxylases (GAD1, GAD2) in GABAergic synapse, ABAT and BCAT1 in valine, leucine, and isoleucine degradation pathway are down-regulated. The enzymes Glutamine synthetase (GLUL) in glutamatergic synapse and GABAergic synapse is upregulated while the BCAT2 is upregulated in the valine, leucine, and isoleucine degradation pathway (Fig. 6h).
Overall, our multi-omics study results are well correlated with the signs and symptoms of AD. AD is associated with memory loss, cognition, inflammation, oxidative stress, synaptic dysfunction and neuronal dysfunction. Figure 7 and Supplementary file 14 depicts the involvement of pathways, transcription factors, kinases and cell types and pathways associated with each cell type in AD from our multi-omics analysis.
Summary of overall findings from multi-omics studies. Role of vitamin B2, B5 and B6 modulated pathways with overlap with deregulated metabolic pathways in AD and its implications for observed symptoms. Cell types affected and their role in symptoms associated with AD. Deregulated energy metabolism and their effect on AD symptoms. Figure generated using Microsoft power point 2019 MSO (Version 2212 Build 16.0.15928.20196) 64-bit.
Discussion
Implications of deregulated pathways from transcriptomic datasets to AD
Our integrated multi-omics analysis showed pathways related to neuronal signalling. The deregulated pathways include dopaminergic, cholinergic synapses, nicotine, and morphine addiction, and choline metabolism in cancer which are all downregulated. We also find that the B6 pathway is deregulated in the proteomic data sets. Previous studies have shown low levels of B6, B12, or folate lead to elevated levels of Aβ aggregates in neurons of transgenic mice models of Alzheimer’s disease30,31,73. B6 is a cofactor that is involved in dopaminergic, and GABAergic synapse function74. The cholinergic synapses are also deregulated in Alzheimer’s disease75. The choline pathway is dependent on the biosynthesis of choline which in turn is dependent on the methionine pathway31. In addition, acetylcholine biosynthesis is also dependent on the levels of acetyl CoA. However, our transcriptomic and metabolomic analysis also shows that pantothenate pathway is deregulated. Pantothenate (B5) is an important substrate in CoA biosynthesis. The levels of CoA decide the availability of acetyl CoA for the biosynthesis of acetylcholine76. Our analysis clearly captures the role of B6 and pantothenate in the deregulation of neuronal signalling and function. In addition, proteomic and metabolomic analysis shows deregulation of cysteine and methionine metabolism. Methionine is an important component involved in the biosynthesis of choline32. In the methionine cycle the by-product homocysteine is either converted into methionine in a pathway involving B12 or folate or into cysteine through the trans-sulphuration pathway involving B630. Cysteine is an important substrate used in the biosynthesis of cellular antioxidants like glutathione, taurine, coenzyme A and lanthionine77. Reduced B6 will lead to reduced levels of cysteine. Consistent with these observations, glutathione pathway was found to be deregulated in proteomic analysis. Elevated oxidative stress is associated with AD, which contributes to disease progression78,79,80.
B2, B5 and B6 modulate deregulated metabolic pathways in AD
Our analysis also showed impaired riboflavin (B2) metabolism. Previous studies have shown that B2 metabolism is affected in AD37. Using a yeast model it was demonstrated that riboflavin improved mitochondrial function and reduced amyloid load in AD81. To further delineate the role of vitamins/cofactors like riboflavin, B6, and pantothenate in metabolic deregulation and AD we generated a vitamin/cofactor-protein interaction network. The interacting proteins were further binned into pathways. The pathways so obtained were then analysed for overlap with deregulated pathways obtained from the multi-omics analysis of AD data sets. Considerable concordance in pathways was observed between the two data sets. Taken together, our results show good concordance between neuronal dysfunction with metabolic pathways. In particular, our results show a potential role for riboflavin (B2), B6, and pantothenate in the disease process.
Water-soluble vitamins are coenzymes for enzymes involved in metabolic pathways involved in cellular functions. Poor vitamin B6 status is commonly observed among older people and higher amounts of homocysteine levels have been recommended as the cause of this deficiency82. Supplementation with B vitamins including B6 has been shown to decrease blood homocysteine levels and slow down the atrophy of specific brain regions38. Severe cerebral deficiency of vitamin B5 is observed in the AD brain. Vitamin B5 is the obligate precursor of acetyl-coenzyme-A which has a wide range of functions, it is the precursor of the neurotransmitter acetylcholine76. CoA is also essential for many metabolic process like TCA cycle83. Mice studies indicate vitamin B2 protects AD-affected brain from ROS-induced damage, probably due to its anti-oxidant property37. So as a new disease-modifying strategy vitamin supplementation is widely being explored70.
Deregulated arginine pathway and its implications for AD
Previous studies have shown that AD is associated with metabolic remodelling. Analysis of metabolomic data sets of AD brain and blood from literature84 shows deregulation of arginine biosynthesis. Arginine is an important metabolite for the biosynthesis of Nitric oxide by NOS85. Further, increased arginine is also indicative of arginosuccinate a precursor in arginine biosynthesis which is associated with Guinidilation of GABA to give guanidinobutanoic acid86. The production of guanidinobutanoic acid further reduces the availability of GABA, which is already low due to low B6 levels leading to GABAergic neuron dysfunction31. Indeed, cell type analysis from transcriptomic data sets showed the involvement of GABAergic neurons.
Mitochondrial dysfunction, metabolic remodelling and ROS: implications for AD
Our multi-omic analysis revealed AD is associated with mitochondrial dysfunction as well as deregulation of pathways like pyruvate metabolism, TCA cycle and nucleotide metabolism, and glutamine and glutamate metabolism. Previous studies in yeast model of ALS has shown that either inhibition or knock out (KO) of genes in complex III and IV lead to clearance of protein aggregates87. mice model of AD has shown that KO of complex III gene led to reduce a-beta aggregation88. Similarly, KO of genes involved in mitochondrial fission helped to clear protein aggregates in yeast model of HD89. Activation of immune cells leads to metabolic remodelling90. The mitochondrial function is impaired and the cells rely on activation of glycolysis, TCA cycle, glutaminolysis, and nucleotide metabolism91. Studies have shown that the increased intermediates in glycolytic and glutaminolysis pathways are pooled for the growth and proliferation of immune cells92. In addition, changes in phenylalanine which is associated with inflammation are also associate with AD47.
Mitochondrial dysfunction leads to elevated levels of ROS and oxidative stress93. In addition, the inflammatory response also leads to elevated levels of ROS. ROS is shown to mediate signalling processes leading to the expression of inflammatory cytokines94,95. Scavenging of ROS is shown to attenuate inflammation. Consistent with inflammation analysis of transcriptomic data sets from the GEO database shows enrichment of TNF signalling pathway in AD. The results of this analysis suggest elevated levels of cytokines, ROS, oxidative stress, immune metabolism and involvement of immune cells in AD.
The role of cell types in AD progression
Our cell type analysis also shows changes in immune cell types like T cells, microglia, astroglia, etc. Renin-angiotensin system regulates the blood pressure and fluid balance and in brain it is involved in memory acquisition and consolidation96. In CNS astrocytes synthesis and secrete both renin and Angiotensinogen97. The renin-angiotensin system is involved in the functioning of glymphatic system and helps to clear toxic metabolites and Aβ peptides98. Hypertension decreases CSF flow in the glymphatic system thus adversely affecting clearance of toxic metabolites or Aβ peptides resulting in it aggregation, contributing to neurodegeneration99. Further, studies have shown that inhibition of immune metabolism lead, to impaired synthesis and secretion of inflammatory cytokines by microglia100. Taken together our analysis shows activation of immuno-metabolism in AD. The glutamatergic dysfunction is associated with AD101. In brain, the glutamate from the synapse is taken through the excitatory amino acid transporters (EAAT) by astrocytes. Glutamate is further converted to glutamine and it is released by astrocytes which is taken up by the neurons102. AD is shown to have impaired glutamate-glutamine cycle resulting in excitotoxicity and death of neurons103. The GABAergic neurons are considerably reduced in AD compared to controls104. Excess amounts of Aβ downregulate GABA inhibitory interneuron activity that cause loss of inhibitory interneurons104. Further, our analysis shows the deregulation of complementation and coagulation pathway. Interestingly studies have shown the role for complement pathway in the pruning of synapses by microglia and memory loss105. The complement protein was shown to label the synapse to be pruned which is subsequently acted upon by the microglial cells106. The upregulation of complement pathway concomitant with microglial activation due to the inflammatory milieu in AD might prove to be detrimental to the disease process107. The impinging evidence thus accounts for the observed memory loss during disease progression in AD.
Implications of the findings for AD pathology and management
The study shows an important role for metabolic deregulation of pathways like TCA, Arginine, glutathione and energy metabolism, cell types like microglia, astroglia and neuronal cell types, Vitamin B2, B5 and B6 ROS and oxidative stress in AD. Previous studies using MRI have shown that atrophy of affected regions in the brain of genetically susceptible individuals precedes the onset of symptoms in AD17. Neuronal protection early during the pre-symptomatic stage in genetically susceptible individuals could help to prolong the onset of symptoms in AD. Further, incorporation of N-acetyl cysteine or other anti-oxidants in the diet might also help to mitigate oxidative stress and neuronal cell death in AD108. Dietary intervention with B2, B5 and B6 will help to achieve favourable prognosis in AD. Overall, our results show that a combination of antioxidants, B2, B6, and pantothenate (B5) during the pre-symptomatic stage in genetically susceptible individuals might help to postpone the onset of symptoms and potentially help in better management of AD. Above all this study will help to appreciate the role of deregulated pathways in the biology of AD and as potential therapeutic targets to manage the disease. Summary of the findings from this study are shown in Fig. 7 and detailed information is provided in Supplementary 14.
Methods
Transcriptomics data analysis
Transcriptomics data from previously published literature curated in Gene Expression Omnibus (GEO) database was searched for Alzheimer’s disease and only those GEO datasets were selected where the differential gene expression analysis could be done by the GEO2R tool for gene expression data. We have analysed the gene expression datasets with GSE IDs—GSE528152, GSE3698053, GSE4477054, GSE4835055, GSE14082956. Single cell transcriptomics examines the gene expression levels of individual cell types in a given population. The transcriptomic data sets used in this study are from those which represent only controls and AD patients. We have also ensured that the data sets have enough subjects for analysis and comparison. Detailed information of age, number, and gender of subjects is provided in Supplementary file 12.
GEO2R analysis
The GEO2R tool was used to compare two or more sample groups in a GEO dataset to find differentially expressed genes (DEGs) in various experimental or clinical conditions. The samples to be evaluated are listed in the GEO2R109 analysis tool, and the samples are divided into two or more experimental or clinical groups. When comparing test samples to controls, the fold change between the groups follows the convention of positive for genes upregulated and negative for genes downregulated. Running the GEO2R analysis with default parameters after the samples have been assigned to the groups yields fold change in the top DEGs between the test and control groups based on their p-value. The DEGs were sorted based on the adjusted p-value for further analysis when the findings were downloaded. For subsequent analysis, differentially expressed genes having an adjusted p-value ≤ 0.05 were chosen. Upregulated and downregulated genes were segregated based on fold change value and pathway and transcription factor analysis was performed separately.
Gene enrichment analysis by Enrichr
For the list of differentially expressed genes (with an adjusted p-value ≤ 0.05) that were found during GEO2R analysis, gene enrichment analysis was performed using Enrichr maayanlab.cloud/Enrichr/57,58. Enrichr examines these genes using databases such as the KEGG110, Wiki, and Reactome pathway databases, as well as the DisGeNet database and the Gene Ontology (GO) database for biological processes, to determine which enriched pathways, biological processes, and diseases do they belong. Enrichr uses the combined score to identify pathways and cell types. Multiplying the Z-score of the deviation from the expected rank by the log of p-value from Fisher's exact test, the combined score is calculated58. The results are presented as a bar graph containing pathways, biological processes, and diseases based on the combined score. Cell type analysis was performed for all datasets using the Azimuth database.
iLINCS server (kinase and transcription factor analysis)
iLINCS111 is a web tool that allows studying gene expression data and signatures from GEO Datasets. From the input of gene expression data, this platform allows for the analysis of gene enrichment networks, associated transcription factors, and kinases. We used the iLINCS X2K maayanlab.cloud/X2K/ analysis tool to find the essential transcription factors and kinases engaged in or linked with differentially expressed genes in GEO datasets. Transcription factors and kinases with a hypergeometric p-value ≤ 0.05 are considered significant. Transcription factor analysis of upregulated and downregulated genes was performed separately and provided in Supplementary 2. The network analyst tool networkanalyst.ca/ was used to identify the potential pathways that these transcription factors and kinases are engaged in, as well as the target genes of these transcription factors and kinases.
Proteomics data analysis
Proteomics analysis was performed for three tissues blood serum, brain cortex, and cerebrospinal fluid using Enrichr maayanlab.cloud/Enrichr/. The datasets were obtained from existing published literature. To understand the dynamics at the protein level in different tissues (1) Brain Cortex60: 58 differentially expressed significant proteins were obtained from a published dataset containing 100 Alzheimer’s samples to find the deregulated pathways. (2) Blood Serum61: 22 differentially expressed significant proteins with p-value < 0.05 and fold change(upregulated—1.5 and downregulated—0.67) were obtained from a published dataset with 20 Alzheimer’s patient samples and 20 control samples to find the deregulated pathways. (3). Cerebrospinal Fluid62: 65 differentially expressed significant proteins (63upregulated and 2 downregulated) with p-value < 0.0001 were obtained from a published dataset with 20 Alzheimer’s patient samples and 20 cognitively normal individuals samples to find the deregulated pathways. A detailed information of number of human subjects, gender and age is provided in Supplementary file 12.
The mice data selected had only proteomics and metabolomics. We also find that some of the data sets had very small number of metabolites which did not provide any significant pathways or overlap completely with pathways obtained from the present data set used in the study. The mice model is used to compare with human AD data sets to demonstrate that irrespective of different study settings, taxa and techniques used there is a considerable overlap of deregulated pathways among them. In addition, mice model is well appreciated and the overlapping pathways could be considered the core deregulated pathways that might be involved in disease progression. Mice proteomics analysis was performed for brain and blood tissues, datasets were obtained from the literature. The details of mice used for blood proteomics is triple transgenic mice 3xTg-AD mice expressing the human mutations APPSwe, PS1 M146V, and Tau P301L and strain B6129SF2/J. Details of mice used for brain proteomics is 3xTg AD mice and control strain used C57/BL6. Differentially expressed mice proteins are converted into human orthologs using DIOPT flyrnai.org/diopt and used for pathways analysis. Overall 148 significant differential proteins from the cortex and 93 hippocampal proteins are used for pathway analysis63. 17 significantly deregulated proteins from blood serum are used for pathway analysis64. Detailed information of number of mice, gender, age and strain of all mice datasets are provided in Supplementary file 12.
Metabolomic data analysis
AD metabolomics studies give us insight into the pathophysiology of disease as the metabolites are collective termini of transcriptomics and proteomic profiles. We analysed three published datasets of AD patients obtained from the literature. Metaboanalyst 5.0 metaboanalyst.ca/ a web-based tool was used to analyse the pathways associated with the metabolites. Pathways with FDR (false discovery rate) ≤ 0.25 are considered significant and used for further analysis.
Metabolomic analysis was performed on three tissues. The datasets were obtained from published literature. (1). Brain Cortex66: significant metabolites with p-value ≤ 0.05 were obtained from a dataset containing 21 AD patients and 19 control samples (2). Blood Serum67: 16 decreased and 7 increased differential metabolite s with adj. p-value ≤ 0.05 were obtained from a published dataset with 23 AD and 21 control samples (3). Cerebrospinal fluid68: 54 significant differential metabolites were obtained from a published dataset with 10 control samples and 10 AD patient samples. A detailed information of number of human subjects, gender and age is provided in Supplementary file 12.
Mice metabolomics analysis was performed for the brain, blood and cerebrospinal fluid. The datasets were obtained from literature. Mice strain used for studies was C57BL/6J with APP/PS1 coexpressing the Swedish mutation(K595N/M596L) and the detaE9 with PS1 exon deletion in blood and brain and K670N, M671L mutations in CSF mice not expressing transgene was used as wildtype controls in blood, brain and CSF. A total of 73 significantly deregulated metabolites from brain are used for pathway analysis and69. 15 significantly deregulated metabolites from blood plasma of APP/PS1 are used for pathway analysis112. 20 significantly deregulated metabolites from cerebrospinal fluid are used for pathway analysis71. Detailed information of number of mice, gender, age and strain of all mice datasets are provided in Supplementary file 12.
Commonality study
Common significant pathways, cell types, transcription factors and kinases from 5 datasets are identified using multiple list comparator tool molbiotools.com/listcompare.php. Pathways and cell types with p-value ≤ 0.05 are selected and further top 20 pathways and cell types with the highest combined score are selected for commonality study. Commonality study and venn diagram generation for proteomics and metabolomics datasets were carried out by venny online tool bioinfogp.cnb.csic.es/tools/venny/.
Cofactor-protein interacting network and its overlapping genes with enriched pathways from transcriptomics datasets
A cofactor-protein interaction network for vitamin B2 (riboflavin), B5 (pantothenate), and B6 (pyridoxine) and its interacting proteins were generated using pre-published data in cytoscape 3.8.2113,114. A network was generated for each transcriptomic dataset. A comparative analysis was carried out for common genes between the cofactor-protein network and those involved in the enriched pathways from transcriptomic dataset analysis. Further, Cytoscape 3.8.2 was used to generate the integrated Transcriptomic-Cofactor-protein interaction network using the network merge tool for visualization. The generated network highlights key overlapping genes that are represented in both the cofactor-protein network and enriched pathway network from transcriptomics as shown.
Data availability
All the transcriptomic datasets analysed in this study are taken from NCBI GEO (Gene Expression Omnibus) database which are already publicly available. Accession numbers for GEO datasets used in the study are GSE5281- (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), GSE36980-(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), GSE44770 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), GSE48350 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), GSE140829 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi). All the proteomic and metabolomic datasets analysed during this study are taken from literature (published article and its supplementary files). The detailed information of all the datasets used in this study are given in Supplementary file 13 and appropriately referenced. No animals and humans are directly involved in the study.
References
Murphy, M. P. & Levine, H. Alzheimer’s disease and the amyloid-β peptide. J Alzheimer’s Dis. https://doi.org/10.3233/JAD-2010-1221 (2010).
McCurdy, S. R. et al. The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to Aβ plaques. Physiol Behav. 176(3), 139–148. https://doi.org/10.1016/j.celrep.2019.03.099 (2017).
Cruchaga, C. et al. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer’s disease families. PLoS ONE https://doi.org/10.1371/journal.pone.0031039 (2012).
Vélez, J. I. et al. Familial Alzheimer’s disease and recessive modifiers. Mol Neurobiol. https://doi.org/10.1007/s12035-019-01798-0 (2020).
Tanzi, R. E. The genetics of Alzheimer disease. Cold Spring Harb Perspect Med. 2(10), 55. https://doi.org/10.1101/cshperspect.a006296 (2012).
Liu, C. C., Kanekiyo, T., Xu, H. & Bu, G. Apolipoprotein e and Alzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol. https://doi.org/10.1038/nrneurol.2012.263 (2013).
Bu, G. Apolipoprotein e and its receptors in Alzheimer’s disease: pathways, pathogenesis and therapy. Nat Rev Neurosci. https://doi.org/10.1038/nrn2620 (2009).
Blanchard, J. W. et al. APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes. Nature 611(7937), 769–779. https://doi.org/10.1038/s41586-022-05439-w (2022).
Chen, Y. et al. Interplay between microglia and Alzheimer’s disease—focus on the most relevant risks: APOE genotype, sex and age. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2021.631827 (2021).
Huang, Y. et al. Microglia use TAM receptors to detect and engulf amyloid β plaques. Nat Immunol. https://doi.org/10.1038/s41590-021-00913-5 (2021).
Rossor, M. N., Scahill, R. I., Fox, N. C., Schott, J. M. & Stevens, J. M. Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. Proc Natl Acad Sci USA 99(7), 4703–4707 (2002).
Tampi, R. R., Forester, B. P. & Agronin, M. Aducanumab: evidence from clinical trial data and controversies. Drugs Context https://doi.org/10.7573/dic.2021-7-3 (2021).
Swanson, C. J. et al. A randomized, double-blind, phase 2b proof-of-concept clinical trial in early Alzheimer’s disease with lecanemab, an anti-Aβ protofibril antibody. Alzheimer’s Res Ther. https://doi.org/10.1186/s13195-021-00813-8 (2021).
Li, Q. S. & De Muynck, L. Differentially expressed genes in Alzheimer’s disease highlighting the roles of microglia genes including OLR1 and astrocyte gene CDK2AP1. Brain Behav Immun - Heal. 13(February), 100227. https://doi.org/10.1016/j.bbih.2021.100227 (2021).
Wang, W., Zhao, F., Ma, X., Perry, G. & Zhu, X. Mitochondria dysfunction in the pathogenesis of Alzheimer’s disease: recent advances. Mol Neurodegener. https://doi.org/10.1186/s13024-020-00376-6 (2020).
Thota, S. M. et al. Multimodal imaging and visual evoked potentials reveal key structural and functional features that distinguish symptomatic from Presymptomatic Huntington’s disease brain, Neurol India https://doi.org/10.4103/0028-3886.329528 (2021).
Johnson, K. A., Fox, N. C., Sperling, R. A. & Klunk, W. E. Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med. https://doi.org/10.1101/cshperspect.a006213 (2012).
Mani, S. et al. Mitochondrial defects: an emerging theranostic avenue towards Alzheimer’s associated dysregulations. Life Sci. 285(June), 119985. https://doi.org/10.1016/j.lfs.2021.119985 (2021).
Song, T. et al. Mitochondrial dysfunction, oxidative stress, neuroinflammation, and metabolic alterations in the progression of Alzheimer’s disease: a meta-analysis of in vivo magnetic resonance spectroscopy studies. Ageing Res Rev. https://doi.org/10.1016/j.arr.2021.101503 (2021).
Bhatia, V. & Sharma, S. Role of mitochondrial dysfunction, oxidative stress and autophagy in progression of Alzheimer’s disease. J Neurol Sci. Published online https://doi.org/10.1016/j.jns.2020.117253 (2021).
Yao, J. et al. Mitochondrial bioenergetic deficit precedes Alzheimer’s pathology in female mouse model of Alzheimer’s disease. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.0903563106 (2009).
Maurer, I., Zierz, S. & Möller, H. J. A selective defect of cytochrome c oxidase is present in brain of Alzheimer disease patients. Neurobiol Aging. 21(3), 455–462. https://doi.org/10.1016/S0197-4580(00)00112-3 (2000).
Demarest, T. G. et al. Biological sex and DNA repair deficiency drive Alzheimer’s disease via systemic metabolic remodeling and brain mitochondrial dysfunction. Acta Neuropathol. https://doi.org/10.1007/s00401-020-02152-8 (2020).
Zhang, X., Alshakhshir, N. & Zhao, L. Glycolytic metabolism, brain resilience, and Alzheimer’s disease. Front Neurosci. https://doi.org/10.3389/fnins.2021.662242 (2021).
Hascup, E. R., Sime, L. N., Peck, M. R. & Hascup, K. N. Amyloid-β42 stimulated hippocampal lactate release is coupled to glutamate uptake. Sci Rep. https://doi.org/10.1038/s41598-022-06637-2 (2022).
Mosconi, L. Glucose metabolism in normal aging and Alzheimer’s disease: Methodological and physiological considerations for PET studies. Clin Transl Imaging. https://doi.org/10.1007/s40336-013-0026-y (2013).
Mosconi L, Pupi A, De Leon MJ. Brain glucose hypometabolism and oxidative stress in preclinical Alzheimer’s disease. In: Annals of the New York Academy of Sciences. 2008. doi:https://doi.org/10.1196/annals.1427.007
González-Reyes, R. E., Nava-Mesa, M. O., Vargas-Sánchez, K., Ariza-Salamanca, D. & Mora-Muñoz, L. Involvement of astrocytes in Alzheimer’s disease from a neuroinflammatory and oxidative stress perspective. Front Mol Neurosci. https://doi.org/10.3389/fnmol.2017.00427 (2017).
Murray, M. E. et al. Early Alzheimer’s disease neuropathology detected by proton MR spectroscopy. J Neurosci. Published online https://doi.org/10.1523/JNEUROSCI.2027-14.2014 (2014).
Lauer, A. A. et al. Mechanistic link between vitamin B12 and Alzheimer’s disease. Biomolecules https://doi.org/10.3390/biom12010129 (2022).
Calderón-Ospina, C. A. & Nava-Mesa, M. O. B Vitamins in the nervous system: current knowledge of the biochemical modes of action and synergies of thiamine, pyridoxine, and cobalamin. CNS Neurosci Ther. 26(1), 5–13. https://doi.org/10.1111/cns.13207 (2020).
Paul, B. D. Neuroprotective roles of the reverse transsulfuration pathway in Alzheimer’s disease. Front Aging Neurosci. 13(March), 1–9. https://doi.org/10.3389/fnagi.2021.659402 (2021).
Mielech, A., Puścion-Jakubik, A., Markiewicz-żukowska, R. & Socha, K. Vitamins in alzheimer’s disease: review of the latest reports. Nutrients 12(11), 1–15. https://doi.org/10.3390/nu12113458 (2020).
Rai, S. N. et al. The role of vitamins in neurodegenerative disease: an update. Biomedicines. https://doi.org/10.3390/biomedicines9101284 (2021).
Ames, B. N., Elson-Schwab, I. & Silver, E. A. High-dose vitamin therapy stimulates variant enzymes with decreased coenzyme binding affinity (increased Km): relevance to genetic disease and polymorphisms. Am J Clin Nutr. https://doi.org/10.1093/ajcn/75.4.616 (2002).
Sang, C. et al. Coenzyme A-dependent tricarboxylic acid cycle enzymes are decreased in Alzheimer’s disease consistent with cerebral pantothenate deficiency. Front Aging Neurosci. 14(June), 1–13. https://doi.org/10.3389/fnagi.2022.893159 (2022).
Zhao, R., Wang, H., Qiao, C. & Zhao, K. Vitamin B2 blocks development of Alzheimer’s disease in APP/PS1 transgenic mice via anti-oxidative mechanism. Trop J Pharm Res. 17(6), 1049–1054. https://doi.org/10.4314/tjpr.v17i6.10 (2018).
Douaud, G. et al. Preventing Alzheimer’s disease-related gray matter atrophy by B-vitamin treatment. Proc Natl Acad Sci USA. 110(23), 9523–9528. https://doi.org/10.1073/pnas.1301816110 (2013).
Kennedy, D. O. B vitamins and the brain: mechanisms, dose and efficacy: a review. Nutrients https://doi.org/10.3390/nu8020068 (2016).
Yan, X., Hu, Y., Wang, B., Wang, S. & Zhang, X. Metabolic dysregulation contributes to the progression of Alzheimer’s disease. Front Neurosci. https://doi.org/10.3389/fnins.2020.530219 (2020).
Peña-Bautista, C., Baquero, M., Vento, M. & Cháfer-Pericás, C. Omics-based biomarkers for the early Alzheimer disease diagnosis and reliable therapeutic targets development. Curr Neuropharmacol. https://doi.org/10.2174/1570159x16666180926123722 (2018).
Magistri, M., Velmeshev, D., Makhmutova, M. & Faghihi, M. A. Transcriptomics profiling of Alzheimer’s disease reveal neurovascular defects, altered amyloid-β homeostasis, and deregulated expression of long noncoding RNAs. J Alzheimer’s Dis. https://doi.org/10.3233/JAD-150398 (2015).
Santiago, J. A., Bottero, V. & Potashkin, J. A. Transcriptomic and network analysis highlight the association of diabetes at different stages of Alzheimer’s disease. Front Neurosci. https://doi.org/10.3389/fnins.2019.01273 (2019).
Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature https://doi.org/10.1038/s41586-019-1195-2 (2019).
Srivastava, S. Emerging insights into the metabolic alterations in aging using metabolomics. Metabolites https://doi.org/10.3390/metabo9120301 (2019).
Ogawa, M., Fukuyama, H., Ouchi, Y., Yamauchi, H. & Kimura, J. Altered energy metabolism in Alzheimer’s disease. J Neurol Sci. https://doi.org/10.1016/0022-510X(96)00033-0 (1996).
Griffin, J. W. D. & Bradshaw, P. C. Amino acid catabolism in Alzheimer’s disease brain: friend or foe?. Oxid Med Cell Longev. 2017, 5. https://doi.org/10.1155/2017/5472792 (2017).
Yin, F. Lipid metabolism and Alzheimer’s disease: clinical evidence, mechanistic link and therapeutic promise. FEBS J. https://doi.org/10.1111/febs.16344 (2022).
Pulukool, S. K. et al. Elevated ATP, cytokines and potential microglial inflammation distinguish exfoliation glaucoma from exfoliation syndrome. Cytokine https://doi.org/10.1016/j.cyto.2022.155807 (2022).
Swaroop, R. S., Pradhan, S. S., Darshan, V. M. D., Phalguna, K. S. & Sivaramakrishnan, V. Integrated network pharmacology approach shows a potential role of Ginseng catechins and ginsenosides in modulating protein aggregation in Amyotrophic Lateral Sclerosis. 3 Biotech. 12(12), 1–19. https://doi.org/10.1007/s13205-022-03401-1 (2022).
Bhagavatham, S. K. S. et al. Adenosine deaminase modulates metabolic remodeling and orchestrates joint destruction in rheumatoid arthritis. Sci Rep. https://doi.org/10.1038/s41598-021-94607-5 (2021).
Liang, W. S. et al. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol Genomics. 28(3), 311–322. https://doi.org/10.1152/physiolgenomics.00208.2006 (2007).
Hokama, M. et al. Altered expression of diabetes-related genes in Alzheimer’s disease brains: the Hisayama study. Cereb Cortex. 24(9), 2476–2488. https://doi.org/10.1093/cercor/bht101 (2014).
Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153(3), 19–22. https://doi.org/10.1016/j.cell.2013.03.030 (2013).
Berchtold, N. C. et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc Natl Acad Sci USA https://doi.org/10.1073/pnas.0806883105 (2008).
Nachun D, Ramos EM, Karydas A, et al. Systems-level analysis of peripheral blood gene expression in dementia patients reveals an innate immune response shared across multiple disorders. bioRxiv. 2019. doi:https://doi.org/10.1101/2019.12.13.875112
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44(1), W90–W97. https://doi.org/10.1093/nar/gkw377 (2016).
Chen EY, Tan CM, Kou Y, et al. Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 2013;14. doi:https://doi.org/10.1186/1471-2105-14-128
Clarke, D. J. B. et al. EXpression2Kinases (X2K) Web: Linking expression signatures to upstream cell signaling networks. Nucleic Acids Res. 46(W1), W171–W179. https://doi.org/10.1093/nar/gky458 (2018).
Bai, B. et al. Deep multilayer brain proteomics identifies molecular networks in Alzheimer’s disease progression. Neuron 105(6), 975–991. https://doi.org/10.1016/j.neuron.2019.12.015 (2020).
Shen, L. et al. Proteomics analysis of blood serums from Alzheimer’s disease patients using iTRAQ labeling technology. J Alzheimer’s Dis. 56(1), 361–378. https://doi.org/10.3233/JAD-160913 (2017).
Higginbotham, L. et al. Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease. Sci Adv. https://doi.org/10.1126/sciadv.aaz9360 (2020).
Martin, B. et al. iTRAQ analysis of complex proteome alterations in 3xTgAD Alzheimer’s mice: Understanding the interface between physiology and disease. PLoS ONE https://doi.org/10.1371/journal.pone.0002750 (2008).
Sui, X. et al. Proteomic analysis of serum proteins in triple transgenic Alzheimer’s disease mice: Implications for identifying biomarkers for use to screen potential candidate therapeutic drugs for early alzheimer’s disease. J Alzheimer’s Dis. https://doi.org/10.3233/JAD-131823 (2014).
Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49(W1), W388–W396. https://doi.org/10.1093/nar/gkab382 (2021).
Giuseppe Paglia, Matteo Stocchero and GA. Unbiased metabolomic investigation of Alzheimer’s disease brain points to dysregulation of mitochondrial aspartate metabolism.
González-Domínguez, R., García-Barrera, T. & Gómez-Ariza, J. L. Metabolite profiling for the identification of altered metabolic pathways in Alzheimer’s disease. J Pharm Biomed Anal. 107, 75–81. https://doi.org/10.1016/j.jpba.2014.10.010 (2015).
Nagata, Y. et al. Comparative analysis of cerebrospinal fluid metabolites in Alzheimer’s disease and idiopathic normal pressure hydrocephalus in a Japanese cohort. Biomark Res. 6(1), 1–11. https://doi.org/10.1186/s40364-018-0119-x (2018).
González-Domínguez, R., García-Barrera, T., Vitorica, J. & Gómez-Ariza, J. L. Metabolomic screening of regional brain alterations in the APP/PS1 transgenic model of Alzheimer’s disease by direct infusion mass spectrometry. J Pharm Biomed Anal. 102, 425–435. https://doi.org/10.1016/j.jpba.2014.10.009 (2015).
Bhatti AB, Usman M, Ali F, Satti SA. Vitamin supplementation as an adjuvant treatment for Alzheimer’s disease. J Clin Diagnostic Res. 2016;10(8):OE07–OE11. doi:https://doi.org/10.7860/JCDR/2016/20273.8261
Trushina E, Nemutlu E, Zhang S, et al. Defects in mitochondrial dynamics and metabolomic signatures of evolving energetic stress in mouse models of familial Alzheimer’s disease. PLoS One. 2012;7(2). doi:https://doi.org/10.1371/journal.pone.0032737
Fagan AM, Head D, Shah AR, et al. Decreased CSF Aβ42 correlates with brain atrophy in cognitively normal elderly. Ann Neurol. Published online 2009.
Zhuo, J. M. & Praticò, D. Acceleration of brain amyloidosis in an Alzheimer’s disease mouse model by a folate, vitamin B6 and B12-deficient diet. Exp Gerontol. https://doi.org/10.1016/j.exger.2009.12.005 (2010).
Bayraktar, A. et al. Revealing the molecular mechanisms of alzheimer’s disease based on network analysis. Int J Mol Sci. https://doi.org/10.3390/ijms222111556 (2021).
Mufson, E. J., Counts, S. E., Perez, S. E. & Ginsberg, S. D. Cholinergic system during the progression of Alzheimer’s disease: therapeutic implications. Expert Rev Neurother. 8(11), 1703–1718. https://doi.org/10.1586/14737175.8.11.1703 (2008).
Xu, J. et al. Cerebral deficiency of vitamin B5 (D-pantothenic acid; pantothenate) as a potentially-reversible cause of neurodegeneration and dementia in sporadic Alzheimer’s disease. Biochem Biophys Res Commun. 527(3), 676–681. https://doi.org/10.1016/j.bbrc.2020.05.015 (2020).
Paul, B. D., Sbodio, J. I. & Snyder, S. H. Cysteine metabolism in neuronal redox homeostasis. Trends Pharmacol Sci. https://doi.org/10.1016/j.tips.2018.02.007 (2018).
Torres, L. L. et al. Peripheral oxidative stress biomarkers in mild cognitive impairment and alzheimer’s disease. J Alzheimer’s Dis. https://doi.org/10.3233/JAD-2011-110284 (2011).
De Toda, I. M., Vida, C., Miguel, L. S. S. & De La Fuente, M. Function, oxidative, and inflammatory stress parameters in immune cells as predictive markers of lifespan throughout aging. Oxid Med Cell Longev. https://doi.org/10.1155/2019/4574276 (2019).
Arslan, J., Jamshed, H. & Qureshi, H. Early detection and prevention of Alzheimer’s disease: role of oxidative markers and natural antioxidants. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2020.00231 (2020).
Chen, X. et al. FMN reduces Amyloid-β toxicity in yeast by regulating redox status and cellular metabolism. Nat Commun. Published online https://doi.org/10.1038/s41467-020-14525-4 (2020).
Malouf R, Grimley-Evans J. Vitamin B6 for cognition. Cochrane Database Syst Rev. 2003;(4). doi:https://doi.org/10.1002/14651858.cd004393
Martínez-Reyes, I. & Chandel, N. S. Mitochondrial TCA cycle metabolites control physiology and disease. Nat Commun. https://doi.org/10.1038/s41467-019-13668-3 (2020).
Fleszar, M. G. et al. Targeted metabolomic analysis of nitric oxide/L-arginine pathway metabolites in dementia: association with pathology, severity, and structural brain changes. Sci Rep. https://doi.org/10.1038/s41598-019-50205-0 (2019).
Wu, G. & Morris, S. M. Arginine metabolism: nitric oxide and beyond. Biochem J. https://doi.org/10.1042/bj3360001 (1998).
Shen, K. Z., Cox, B. A. & Johnson, S. W. L-arginine potentiates GABA-mediated synaptic transmission by a nitric oxide-independent mechanism in rat dopamine neurons. Neuroscience https://doi.org/10.1016/S0306-4522(97)00024-9 (1997).
Sai Swaroop, R. et al. Integrated multi-omic data analysis and validation with yeast model show oxidative phosphorylation modulates protein aggregation in amyotrophic lateral sclerosis. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2022.2090441 (2022).
Pinto, M. et al. Adult-onset deficiency of mitochondrial complex III in a mouse model of Alzheimer’s disease decreases amyloid beta plaque formation. Mol Neurobiol. 59(10), 6552–6566. https://doi.org/10.1007/s12035-022-02992-3 (2022).
Pradhan, S. S. et al. Integrated multi-omics analysis of Huntington disease identifies pathways that modulate protein aggregation. Dis Model Mech. https://doi.org/10.1242/dmm.049492 (2022).
Pearce, E. L. & Pearce, E. J. Metabolic pathways in immune cell activation and quiescence. Immunity https://doi.org/10.1016/j.immuni.2013.04.005 (2013).
Sun, L., Yang, X., Yuan, Z. & Wang, H. Metabolic reprogramming in immune response and tissue inflammation. Arterioscler Thromb Vasc Biol. https://doi.org/10.1161/ATVBAHA.120.314037 (2020).
Ganeshan, K. & Chawla, A. Metabolic regulation of immune responses. Annu Rev Immunol. https://doi.org/10.1146/annurev-immunol-032713-120236 (2014).
Zorov, D. B., Juhaszova, M. & Sollott, S. J. Mitochondrial reactive oxygen species (ROS) and ROS-induced ROS release. Physiol Rev. https://doi.org/10.1152/physrev.00026.2013 (2014).
Forrester, S. J., Kikuchi, D. S., Hernandes, M. S., Xu, Q. & Griendling, K. K. Reactive oxygen species in metabolic and inflammatory signaling. Circ Res. https://doi.org/10.1161/CIRCRESAHA.117.311401 (2018).
Kowalczyk, P. et al. Mitochondrial oxidative stress: a causative factor and therapeutic target in many diseases. Int J Mol Sci. https://doi.org/10.3390/ijms222413384 (2021).
Gebre, A. K., Altaye, B. M., Atey, T. M., Tuem, K. B. & Berhe, D. F. Targeting Renin–Angiotensin system against Alzheimer’s disease. Front Pharmacol. https://doi.org/10.3389/fphar.2018.00440 (2018).
Loera-Valencia, R., Eroli, F., Garcia-Ptacek, S. & Maioli, S. Brain renin–angiotensin system as novel and potential therapeutic target for Alzheimer’s disease. Int J Mol Sci. https://doi.org/10.3390/ijms221810139 (2021).
Cai, X. et al. Imaging the effect of the circadian light–dark cycle on the glymphatic system in awake rats. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.1914017117 (2020).
Mortensen, K. N. et al. Impaired glymphatic transport in spontaneously hypertensive rats. J Neurosci. https://doi.org/10.1523/JNEUROSCI.1974-18.2019 (2019).
Fairley, L. H., Wong, J. H. & Barron, A. M. Mitochondrial regulation of microglial immunometabolism in Alzheimer’s disease. Front Immunol. https://doi.org/10.3389/fimmu.2021.624538 (2021).
Conway, M. E. Alzheimer’s disease: targeting the glutamatergic system. Biogerontology https://doi.org/10.1007/s10522-020-09860-4 (2020).
Bukke, V. N. et al. The dual role of glutamatergic neurotransmission in Alzheimer’s disease: from pathophysiology to pharmacotherapy. Int J Mol Sci. https://doi.org/10.3390/ijms21207452 (2020).
Jacob, C. P. et al. Alterations in expression of glutamatergic transporters and receptors in sporadic Alzheimer’s disease. J Alzheimer’s Dis. https://doi.org/10.3233/JAD-2007-11113 (2007).
Xu, Y., Zhao, M., Han, Y. & Zhang, H. GABAergic inhibitory interneuron deficits in Alzheimer’s disease: implications for treatment. Front Neurosci. https://doi.org/10.3389/fnins.2020.00660 (2020).
Druart, M. & Le Magueresse, C. Emerging roles of complement in psychiatric disorders. Front Psychiatry. https://doi.org/10.3389/fpsyt.2019.00573 (2019).
Lim, T. K. Y. & Ruthazer, E. S. Microglial trogocytosis and the complement system regulate axonal pruning in vivo. Elife https://doi.org/10.7554/eLife.62167 (2021).
Shah, A., Kishore, U. & Shastri, A. Complement system in Alzheimer’s disease. Int J Mol Sci. 22(24), 5. https://doi.org/10.3390/ijms222413647 (2021).
Uttara, B., Singh, A., Zamboni, P. & Mahajan, R. Oxidative stress and neurodegenerative diseases: a review of upstream and downstream antioxidant therapeutic options. Curr Neuropharmacol. https://doi.org/10.2174/157015909787602823 (2009).
Hunt, G. P. et al. GEOexplorer: a webserver for gene expression analysis and visualisation. Nucleic Acids Res. 50(W1), W367–W374. https://doi.org/10.1093/nar/gkac364 (2022).
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw1092 (2017).
Pilarczyk, M. et al. Connecting omics signatures and revealing biological mechanisms with iLINCS. Nat Commun. 13(1), 1–13. https://doi.org/10.1038/s41467-022-32205-3 (2022).
Pan X, Nasaruddin M Bin, Elliott CT, et al. Alzheimer’s disease-like pathology has transient effects on the brain and blood metabolome. Neurobiol Aging. 2016;38:151–163. doi:https://doi.org/10.1016/j.neurobiolaging.2015.11.014
Naik, A. A. et al. Systems analysis of avascular necrosis of femoral head using integrative data analysis and literature mining delineates pathways associated with disease. Sci Rep. 10(1), 1–20. https://doi.org/10.1038/s41598-020-75197-0 (2020).
Scott-Boyer, M. P. et al. A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases. Sci Rep. https://doi.org/10.1038/srep19633 (2016).
Acknowledgements
We thank SSSIHL, CRIF and BITS computational infrastructure facility. We dedicate this work to late Dr. Ramakrishna Vadrevu. P.K is grateful to University Grants Commission (UGC), India for financial support in the form of a fellowship. We acknowledge the grant support from the Department of Science and Technology-The Science and Engineering Research Board–Extra Mural Research DST-SERB-EMR: (EMR/2017/005381), Department of Biotechnology—Bioinformatics facility (DBT-BIF): (BT/BI/25/063/2012), Department of Science and Technology—(DST-FIST): (SR/FST/LSI 616/2014), University Grants Commission-Special Assistance Program (UGC-SAP III: F.3-19 /2018/DRS-III(SAP- II)) for infrastructure funding.
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P.K.: Carried out transcriptomic, proteomic and metabolomic pathway annotation analysis, cofactor network and integrated network analysis, interpreted results and contributed towards manuscript preparation. S.S.R and S.S.P : contributed towards standardising the analysis, compiling literature and helped in the generation of figures. R.V and V.S.: conceptualized the work, guided in data analysis and interpretation, and contributed towards manuscript preparation.
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Kodam, P., Sai Swaroop, R., Pradhan, S.S. et al. Integrated multi-omics analysis of Alzheimer’s disease shows molecular signatures associated with disease progression and potential therapeutic targets. Sci Rep 13, 3695 (2023). https://doi.org/10.1038/s41598-023-30892-6
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DOI: https://doi.org/10.1038/s41598-023-30892-6