Unraveling the mechanistic complexity of Alzheimer's disease through systems biology

Rosemarinic Acid-Induced Destabilization of Aβ Peptides: Insights from Molecular Dynamics Simulations

Alzheimer's disease (AD) is a neurodegenerative disorder marked by the progressive accumulation of amyloid-β (Aβ) plaques and tau protein tangles in the brain. These pathological aggregates interfere with neuronal function, leading to the disruption of cognitive processes, particularly memory. The deposition of Aβ forms senile plaques, while tau protein, in its hyperphosphorylated state, forms neurofibrillary tangles, both of which contribute to the underlying neurodegeneration observed in AD. Rosmarinic acid (RosA), a natural compound found in plants such as Rosmarinus officinalis, is known for its antioxidant, anti-inflammatory, and antimicrobial properties. Due to its ability to cross the blood-brain barrier, RosA holds promise as a nutritional supplement that may support brain health. In this study, molecular dynamics (MD) simulations were used to investigate the impact of RosA on the structural stability of Aβ peptides. The results indicated that the addition of RosA increased the instability of Aβ, as evidenced by an increase in the Root Mean Square Deviation (RMSD), a decrease in the Radius of Gyration (Rg), and an expansion of the Solvent Accessible Surface Area (SASA). This destabilization is primarily attributed to the disruption of native hydrogen bonds and hydrophobic interactions in the presence of two RosA molecules. The free energy landscape (FEL) analysis and MM-PBSA (Poisson-Boltzmann Surface Area Mechanics) results further support the notion that RosA can effectively bind to the hydrophobic pocket of the protein, highlighting its potential as a nutritional component that may contribute to maintaining brain health and function.

The need for systems thinking to advance Alzheimer's disease research

Despite extensive research efforts to mechanistically understand late-onset Alzheimer's disease (LOAD) and other complex mental health disorders, curative treatments remain elusive. We emphasize the multiscale multicausality inherent to LOAD, highlighting the interplay between interconnected pathophysiological processes and risk factors. Systems thinking methods, such as causal loop diagrams and systems dynamic models, offer powerful means to capture and study this complexity. Recent studies developed and validated a causal loop diagram and system dynamics model using multiple longitudinal data sets, enabling the simulation of personalized interventions on various modifiable risk factors in LOAD. The results indicate that targeting factors like sleep disturbance and depressive symptoms could be promising and yield synergistic benefits. Furthermore, personalized interventions showed significant potential, with top-ranked intervention strategies differing significantly across individuals. We argue that systems thinking approaches can open new prospects for multifactorial precision medicine. In future research, systems thinking may also guide structured, model-driven data collection on the multiple interactions in LOAD's complex multicausality, facilitating theory development and possibly resulting in effective prevention and treatment options.

A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis

Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.

Simulating the multicausality of Alzheimer's disease with system dynamics

INTRODUCTION

In Alzheimer's disease (AD), cognitive decline is driven by various interlinking causal factors. Systems thinking could help elucidate this multicausality and identify opportune intervention targets.

METHODS

We developed a system dynamics model (SDM) of sporadic AD with 33 factors and 148 causal links calibrated with empirical data from two studies. We tested the SDM's validity by ranking intervention outcomes on 15 modifiable risk factors to two sets of 44 and 9 validation statements based on meta-analyses of observational data and randomized controlled trials, respectively.

RESULTS

The SDM answered 77% and 78% of the validation statements correctly. Sleep quality and depressive symptoms yielded the largest effects on cognitive decline with which they were connected through strong reinforcing feedback loops, including via phosphorylated tau burden.

DISCUSSION

SDMs can be constructed and validated to simulate interventions and gain insight into the relative contribution of mechanistic pathways.

Potential bioactive compounds and mechanisms of Fibraurea recisa Pierre for the treatment of Alzheimer's disease analyzed by network pharmacology and molecular docking prediction

Introduction

Heat-clearing and detoxifying Chinese medicines have been documented to have anti-Alzheimer's disease (AD) activities according to the accumulated clinical experience and pharmacological research results in recent decades. In this study, Fibraurea recisa Pierre (FRP), the classic type of Heat-clearing and detoxifying Chinese medicine, was selected as the object of research.

Methods

12 components with anti-AD activities were identified in FRP by a variety of methods, including silica gel column chromatography, multiple databases, and literature searches. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Consequently, it was found that these 12 compounds could act on 235 anti-AD targets, of which AKT and other targets were the core targets. Meanwhile, among these 235 targets, 71 targets were identified to be significantly correlated with the pathology of amyloid beta (Aβ) and Tau.

Results and discussion

In view of the analysis results of the network of active ingredients and targets, it was observed that palmatine, berberine, and other alkaloids in FRP were the key active ingredients for the treatment of AD. Further, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were the most significant signaling pathways for FRP to play an anti-AD role. Findings in our study suggest that multiple primary active ingredients in FRP can play a multitarget anti-AD effect by regulating key physiological processes such as neurotransmitter transmission and anti-inflammation. Besides, key ingredients such as palmatine and berberine in FRP are expected to be excellent leading compounds of multitarget anti-AD drugs.

Identification of Multi-Target Anti-AD Chemical Constituents From Traditional Chinese Medicine Formulae by Integrating Virtual Screening and In Vitro Validation

Alzheimer's disease (AD) is a neurodegenerative disease that seriously threatens the health of the elderly. At present, no drugs have been proven to cure or delay the progression of the disease. Due to the multifactorial aetiology of this disease, the multi-target-directed ligand (MTDL) approach provides an innovative and promising idea in search for new drugs against AD. In order to find potential multi-target anti-AD drugs from traditional Chinese medicine (TCM) formulae, a compound database derived from anti-AD Chinese herbal formulae was constructed and predicted by the anti-AD multi-target drug prediction platform established in our laboratory. By analyzing the results of virtual screening, 226 chemical constituents with 3 or more potential AD-related targets were collected, from which 16 compounds that were predicted to combat AD through various mechanisms were chosen for biological validation. Several cell models were established to validate the anti-AD effects of these compounds, including KCl, Aβ, okadaic acid (OA), SNP and H₂O₂ induced SH-SY5Y cell model and LPS induced BV2 microglia model. The experimental results showed that 12 compounds including Nonivamide, Bavachromene and 3,4-Dimethoxycinnamic acid could protect model cells from AD-related damages and showed potential anti-AD activity. Furthermore, the potential targets of Nonivamide were investigated by molecular docking study and analysis with CDOCKER revealed the possible binding mode of Nonivamide with its predicted targets. In summary, 12 potential multi-target anti-AD compounds have been found from anti-AD TCM formulae by comprehensive application of computational prediction, molecular docking method and biological validation, which laid a theoretical and experimental foundation for in-depth study, also providing important information and new research ideas for the discovery of anti-AD compounds from traditional Chinese medicine.

Untargeted metabolomics reveal dysregulations in sugar, methionine, and tyrosine pathways in the prodromal state of AD

INTRODUCTION

Altered metabolism may occur years before clinical manifestations of Alzheimer's disease (AD). We used untargeted metabolomics on the cerebrospinal fluid of patients with mild cognitive impairment (MCI) to uncover metabolomic derangements.

METHODS

CSF from 92 normal controls and 93 MCI underwent untargeted metabolomics using high-resolution mass spectrometry with liquid chromatography. Partial least squares discriminant analysis was used followed by metabolite annotation and pathway enrichment analysis (PES). Significant features were correlated with disease phenotypes.

RESULTS

We identified 294 features differentially expressed between the two groups and 94 were annotated. PES showed that sugar regulation (N-glycan, P = .0007; sialic acid, P = .0014; aminosugars, P = .0042; galactose, P = .0054), methionine regulation (P = .0081), and tyrosine metabolism (P = .019) pathways were differentially activated and significant features within these pathways correlated with multiple disease phenotypes.

CONCLUSION

There is a metabolic signature characterized by impairments in sugars, methionine, and tyrosine regulation in MCI. Targeting these pathways may offer new therapeutic approaches to AD.

Mapping the multicausality of Alzheimer's disease through group model building

Alzheimer’s disease (AD) is a complex, multicausal disorder involving several spatiotemporal scales and scientific domains. While many studies focus on specific parts of this system, the complexity of AD is rarely studied as a whole. In this work, we apply systems thinking to map out known causal mechanisms and risk factors ranging from intracellular to psychosocial scales in sporadic AD. We report on the first systemic causal loop diagram (CLD) for AD, which is the result of an interdisciplinary group model building (GMB) process. The GMB was based on the input of experts from multiple domains and all proposed mechanisms were supported by scientific literature. The CLD elucidates interaction and feedback mechanisms that contribute to cognitive decline from midlife onward as described by the experts. As an immediate outcome, we observed several non-trivial reinforcing feedback loops involving factors at multiple spatial scales, which are rarely considered within the same theoretical framework. We also observed high centrality for modifiable risk factors such as social relationships and physical activity, which suggests they may be promising leverage points for interventions. This illustrates how a CLD from an interdisciplinary GMB process may lead to novel insights into complex disorders. Furthermore, the CLD is the first step in the development of a computational model for simulating the effects of risk factors on AD.

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

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

Novel Perspective on Alzheimer's Disease Treatment: Rosmarinic Acid Molecular Interplay with Copper(II) and Amyloid β

Alzheimer's disease is a severe disorder that affects millions of people worldwide. It is a very debilitating disease with no cure at the moment. The necessity of finding an effective treatment is very demanding, and the entire scientific community is putting in a lot of effort to address this issue. The major hallmark of Alzheimer's disease is the presence of toxic aggregated species in the brain, impaired metal homeostasis, and high levels of oxidative stress. Rosmarinic acid is a well-known potent antioxidant molecule, the efficacy of which has been proved both in vitro and in vivo. In this study, we investigated the possible role played by rosmarinic acid as a mediator of the copper(II)-induced neurotoxicity. Several spectroscopic techniques and biological assays were applied to characterize the metal complexes and to evaluate the cytotoxicity and the mutagenicity of rosmarinic acid and its Cu(II) complex. Our data indicate that rosmarinic acid is able to interfere with the interaction between amyloid β and Cu(II) by forming an original ternary association.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer's disease

Sex differences in Alzheimer’s disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer’s and followed up to 10 years in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aβ42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having “high” or “low” likelihood for MCI due to Alzheimer’s. Data were analyzed using mixed-effects models incorporating all follow-ups. The worsening from baseline in Alzheimer’s Disease Assessment Scale-Cognitive score (mean, SD) (9 ± 12) in subjects with high likelihood of MCI due to Alzheimer’s was markedly greater than that in subjects with low likelihood (1 ± 6, p < 0.0001). Among MCI due to AD subjects, the mean worsening in cognitive score was significantly greater in females (11.58 ± 14) than in males (6.87 ± 11, p = 0.006). Our findings highlight the need to further investigate these findings in other populations and develop sex specific timelines for Alzheimer’s disease progression.

Staging Alzheimer's Disease Risk by Sequencing Brain Function and Structure, Cerebrospinal Fluid, and Cognition Biomarkers

This study aims to develop a composite biomarker that can accurately measure the sequential biological stages of Alzheimer’s disease (AD) on an individual level. We selected 144 subjects from the Alzheimer’s Disease Neuroimaging Initiative 2 datasets. Ten biomarkers, from brain function and structure, cerebrospinal fluid, and cognitive performance, were integrated using the event-based probabilistic model to estimate their optimal temporal sequence (S^optimal). We identified the numerical order of the S^optimal as the characterizing Alzheimer’s disease risk events (CARE) index to measure disease stage. The results show that, in the S^optimal, hippocampal and posterior cingulate cortex network biomarkers occur first, followed by aberrant cerebrospinal fluid amyloid-β and p-tau levels, then cognitive deficit, and finally regional gray matter loss and fusiform network abnormality. The CARE index significantly correlates with disease severity and exhibits high reliability. Our findings demonstrate that use of the CARE index would advance AD stage measurement across the whole AD continuum and facilitate personalized treatment of AD.

Towards precision medicine in Alzheimer's disease: deciphering genetic data to establish informative biomarkers

INTRODUCTION

Developing biomarker tools for identification of individuals at high-risk for late-onset Alzheimer's disease (LOAD) is important for prognosis and early treatment. This review focuses on genetic factors and their potential role for precision medicine in LOAD.

AREAS COVERED

APOEe4 is the strongest genetic risk factor for non-Mendelian LOAD, and the APOE-linkage disequilibrium (LD) region has produced the most significant association signal in multi-center genome-wide-association-studies (GWAS). Consideration of extended haplotypes in the APOE-LD region and specifically, non-coding variants in putative enhancer elements, such as the TOMM40-polyT, in-addition to the coding variants that comprise the APOE-genotypes, may be useful for predicting subjects at high-risk of developing LOAD and estimating age-of-onset of early disease-stage symptoms. A genetic-biomarker based on APOE-TOMM40-polyT haplotypes, and age is currently applied in a clinical trial for prevention/delay of LOAD onset. Additionally, we discuss LOAD-GWAS discoveries and the development of new genetic risk scores based on LOAD-GWAS findings other than the APOE-LD region.

EXPERT COMMENTARY

Deciphering the precise causal genetic-variants within LOAD-GWAS regions will advance the development of genetic-biomarkers to complement and refine the APOE-LD region based prediction model. Collectively, the genetic-biomarkers will be translational for early diagnosis and enrichment of clinical trials with subjects at high-risk.

PRECISION MEDICINE - The Golden Gate for Detection, Treatment and Prevention of Alzheimer's Disease

During this decade, breakthrough conceptual shifts have commenced to emerge in the field of Alzheimer's disease (AD) recognizing risk factors and the non-linear dynamic continuum of complex pathophysiologies amongst a wide dimensional spectrum of multi-factorial brain proteinopathies/neurodegenerative diseases. As is the case in most fields of medicine, substantial advancements in detecting, treating and preventing AD will likely evolve from the generation and implementation of a systematic precision medicine strategy. This approach will likely be based on the success found from more advanced research fields, such as oncology. Precision medicine will require integration and transfertilization across fragmented specialities of medicine and direct reintegration of Neuroscience, Neurology and Psychiatry into a continuum of medical sciences away from the silo approach. Precision medicine is biomarker-guided medicine on systems-levels that takes into account methodological advancements and discoveries of the comprehensive pathophysiological profiles of complex multi-factorial neurodegenerative diseases, such as late-onset sporadic AD. This will allow identifying and characterizing the disease processes at the asymptomatic preclinical stage, where pathophysiological and topographical abnormalities precede overt clinical symptoms by many years to decades. In this respect, the uncharted territory of the AD preclinical stage has become a major research challenge as the field postulates that early biomarker guided customized interventions may offer the best chance of therapeutic success. Clarification and practical operationalization is needed for comprehensive dissection and classification of interacting and converging disease mechanisms, description of genomic and epigenetic drivers, natural history trajectories through space and time, surrogate biomarkers and indicators of risk and progression, as well as considerations about the regulatory, ethical, political and societal consequences of early detection at asymptomatic stages. In this scenario, the integrated roles of genome sequencing, investigations of comprehensive fluid-based biomarkers and multimodal neuroimaging will be of key importance for the identification of distinct molecular mechanisms and signaling pathways in subsets of asymptomatic people at greatest risk for progression to clinical milestones due to those specific pathways. The precision medicine strategy facilitates a paradigm shift in Neuroscience and AD research and development away from the classical "one-size-fits-all" approach in drug discovery towards biomarker guided "molecularly" tailored therapy for truly effective treatment and prevention options. After the long and winding decade of failed therapy trials progress towards the holistic systems-based strategy of precision medicine may finally turn into the new age of scientific and medical success curbing the global AD epidemic.

Presenilin 1 mutations influence processing and trafficking of the ApoE receptor apoER2

Presenilin (PS)-1 is an intramembrane protease serving as the catalytic component of γ-secretase. Mutations in the PS1 gene are the most common cause of familial Alzheimer's disease (FAD). The low-density lipoprotein (LDL)-receptor family member apoER2 is a γ-secretase substrate that has been associated with AD in several ways, including acting as a receptor for apolipoprotein E (ApoE). ApoER2 is processed by γ-secretase into a C-terminal fragment (γ-CTF) that appears to regulate gene expression. FAD PS1 mutations were tested for effects on apoER2. PS1 mutation R278I showed impaired γ-secretase activity for apoER2 in the basal state or after exposure to Reelin. PS1 M146V mutation permitted accumulation of apoER2 CTFs after Reelin treatment, whereas no difference was seen between wild-type (WT) and M146V in the basal state. PS1 L282V mutation, combined with the γ-secretase inhibitor N-(N-[3,5-Difluorophenacetyl]-L-alanyl)-S-phenylglycine t-butyl ester, greatly reduced the cell-surface levels of apoER2 without affecting total apoER2 levels, suggesting a defect in receptor trafficking. These findings indicate that impaired processing or localization of apoER2 may contribute to the pathogenic effects of FAD mutations in PS1.