Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer's and Parkinson's Diseases

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Retinoic acid in Parkinson's disease: Molecular insights, therapeutic advances, and future prospects

Parkinson's disease (PD) is a common and progressively worsening neurodegenerative disorder characterized by abnormal protein homeostasis and the degeneration of dopaminergic neurons, particularly in the substantia nigra pars compacta. The prevalence of PD has doubled in the past 25 years, now affecting over 8.5 million individuals worldwide, underscoring the need for effective management strategies. While current pharmacological therapies provide symptom relief, they face challenges in treating advanced PD stages. Recent research highlights the therapeutic benefits of retinoic acid (RA) in PD, demonstrating its potential to mitigate neuroinflammation and oxidative stress, regulate brain aging, promote neuronal plasticity, and influence circadian rhythm gene expression and retinoid X receptor heterodimerization. Additionally, RA helps maintain intestinal homeostasis and modulates the enteric nervous system, presenting significant therapeutic potential for managing PD. This review explores RA as a promising alternative to conventional therapies by summarizing the molecular mechanisms underlying its role in PD pathophysiology and presenting up-to-date insights into both preclinical and clinical studies of RA in PD treatment. It also delves into cutting-edge formulations incorporating RA, highlighting ongoing efforts to refine therapeutic strategies by integrating RA into novel treatments. This comprehensive overview aims to advance progress in the field, contribute to the development of effective, targeted treatments for PD, and enhance patient well-being. Further research is essential to fully explore RA's therapeutic potential and validate its efficacy in PD treatment.

Novel drug targets and molecular mechanisms for sarcopenia based on systems biology

Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing drugs.

Multilayer Analysis of RNA Sequencing Data in Alzheimer's Disease to Unravel Molecular Mysteries

Alzheimer's disease (AD) is a complex disease, and numerous cellular events may be involved in etiology. RNAseq-based transcriptome data hold multilayer information content, which could be crucial in unraveling molecular mysteries of AD. It enables quantification of gene expression levels, identification of genomic variants, and elucidation of splicing anomalies such as exon skipping and intron retention. Additional integration of this information into protein-protein interaction networks and genome-scale metabolic models from the literature has potential to decipher functional modules and affected mechanisms for complex scenarios such as AD. In this chapter, we review the application areas of the multilayer content of RNAseq and associated integrative approaches available, with a special focus on AD.

Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma

The discovery of potential disease-causing genes can aid medical progress. The post-genomic era has made this a more difficult task. Modern high-throughput methods have not solved the problem of identifying disease genes. Conventional methods cannot be used to investigate many rare or lethal diseases. Monitoring gene expression values in different samples using microarray technology is one of the best and most accurate ways to identify disease-causing genes. One of the most recent advances in experimental molecular biology is microarrays, which allow researchers to simultaneously monitor the expression levels of thousands of genes. Statistical analysis of microarray data might aid gene discovery by revealing pathways related to the target gene and facilitating identification of candidate genes. Systems biology, an interdisciplinary approach, has emerged as a crucial analytic tool with the potential to reveal previously unidentified causes and consequences of human illness. Genetic, environmental, immunological, or neurological factors have been implicated in the developing complex disorders like cancer. Because of this, it is important to approach the study of such disease from a novel perspective. The system biology approach allows us to rapidly identify disease-causing genes and assess their viability as therapeutic targets. This chapter demonstrates systems biology approaches to identify candidate genes using public database. Oral squamous cell carcinoma (OSCC) is used as a model disease to show how systems biology can be used successfully to identify and prioritize disease genes.

The role of SIRT2 inhibition on the aging process of brain in male rats

Background

Though the exact mechanisms regarding brain aging and its relation to neurodegenerative disorders are not precise, oxidative stress, the key regulators of apoptosis and autophagy, such as bcl-2 and beclin 1, seem to be the potential players in the aging of the cerebral cortex and hippocampus. As a type of nicotinamide adenine dinucleotide (NAD+)-dependent deacetylases, sirtuin 2 (SIRT2) has been associated to age-related diseases. However, the exact role of SIRT2 in brain aging is not well studied. The objective of the current study was to study the role of SIRT2 inhibition on brain aging through the neuroprotective mechanisms.

Methods

We tested the effects of AGK-2, a SIRT2 inhibitor, on oxidative stress parameters, apoptosis and autophagy regulators including bcl-2, bax, beclin1 in young and old rats. 24 Wistar albino rats (3 months-old and 22 months-old) were divided into four groups; Young-Control (4% DMSO+PBS), Young-AGK-2 (10 µM/bw, ip), Aged-Control, and Aged-AGK-2. Following the 30 days of drug administration period the rats were sacrificed and the cerebral cortex, hippocampus, and cerebellum were isolated. Total antioxidant status (TAS) and total oxidant status (TOS) were measured as oxidative stress parameters in all three brain regions. SIRT2, bcl-2, and bax protein expression levels were measured by western blot and gene expression level of beclin 1, Atg5, and SIRT2 by real-time PCR.

Results

The bcl-2, bcl-2/bax ratio, beclin 1, and TAS in the cerebral cortex of the aged group were significantly decreased; however, the TOS, oxidative stress index (OSI), and SIRT2 expression in the cerebral cortex and hippocampus increased. SIRT2 inhibition by AGK-2 reduced TOS and OSI levels in all brain regions and increased bcl-2, bcl-2/bax ratio. In aged animals, AGK-2 also increased the beclin 1 levels in the cortex and hippocampus.

Conclusion

Our results indicate that SIRT2 has an essential role in brain aging. The inhibition of SIRT2 by AGK-2 may increase cell survival and decrease aging related processes in the cerebral cortex and hippocampus via decreasing oxidative stress, and increasing bcl-2 and beclin 1 expression.

Non-motor manifestation of Parkinson's disease: a cross-sectional study in a teaching hospital in Jordan

Background

Parkinson's disease (PD) is the most common degenerative movement disorder. It is featured by motor manifestations and up till now the clinical diagnosis is based on them. Since the progress in the symptomatic treatment of PD and the longer survival of patients, non-motor manifestations (NMM) were more recognized and considered to be significant. The importance of NMM is that they reflect the more diffuse pathology of PD and may represent an opportunity of earlier diagnosis and treatment. Here in this cross-sectional study, we try to estimate the frequency of such manifestations in PD patients in the country. Using slightly modified PD non-motor (28 of 30 responses) questionnaire (NMS Quest), we studied the incidence of NMM in 100 PD patients attending one major teaching hospital and compared their occurrence in 130 age- and gender-matched non-PD controls.

Results

Out of 100 PD patients (40% females) mean age 67.4 ± 12 with disease duration of 7.3 ± 5.8, range < 1–33.2 years), and 130 control subjects (48.5% females), mean age 65.0 ± 7.0. PD patients had 8.6 ± 5.3 NMM while controls had 3.4 ± 3.3 NMM, respectively (p < 0.00001 t test). Constipation, urgency, insomnia, sad feeling, panic, light headedness and recent memory impairment were the most prevalent NMM in PD compared to controls, while nocturia, restless legs, encopresis and falling were not different in the two groups. The number of NMM ranged from 0 to 21 in PD patients with 50% having ≥ 8 manifestations. The number of NMM did not correlate with age, gender, or disease duration as defined by the classical motor symptoms. Frequency of 23 of these 28 manifestations differed significantly in PD patients compared to controls.

Conclusions

This study confirms that NMM in Jordanian PD patients are very common as reported in other populations. This signifies the universal prevalence of such NMM reflecting their important impact on their daily life and their relevant contribution to better understanding of this disease.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.

Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.

Neurogenic Inflammation: The Participant in Migraine and Recent Advancements in Translational Research

Migraine is a primary headache disorder characterized by a unilateral, throbbing, pulsing headache, which lasts for hours to days, and the pain can interfere with daily activities. It exhibits various symptoms, such as nausea, vomiting, sensitivity to light, sound, and odors, and physical activity consistently contributes to worsening pain. Despite the intensive research, little is still known about the pathomechanism of migraine. It is widely accepted that migraine involves activation and sensitization of the trigeminovascular system. It leads to the release of several pro-inflammatory neuropeptides and neurotransmitters and causes a cascade of inflammatory tissue responses, including vasodilation, plasma extravasation secondary to capillary leakage, edema, and mast cell degranulation. Convincing evidence obtained in rodent models suggests that neurogenic inflammation is assumed to contribute to the development of a migraine attack. Chemical stimulation of the dura mater triggers activation and sensitization of the trigeminal system and causes numerous molecular and behavioral changes; therefore, this is a relevant animal model of acute migraine. This narrative review discusses the emerging evidence supporting the involvement of neurogenic inflammation and neuropeptides in the pathophysiology of migraine, presenting the most recent advances in preclinical research and the novel therapeutic approaches to the disease.

Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis

The complex pathology of Alzheimer's disease (AD) emphasises the need for comprehensive modelling of the disease, which may lead to the development of efficient treatment strategies. To address this challenge, we analysed transcriptome data of post-mortem human brain samples of healthy elders and individuals with late-onset AD from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MayoRNAseq) studies in the AMP-AD consortium. In this context, we conducted several bioinformatics and systems medicine analyses including the construction of AD-specific co-expression networks and genome-scale metabolic modelling of the brain in AD patients to identify key genes, metabolites and pathways involved in the progression of AD. We identified AMIGO1 and GRPRASP2 as examples of commonly altered marker genes in AD patients. Moreover, we found alterations in energy metabolism, represented by reduced oxidative phosphorylation and ATPase activity, as well as the depletion of hexanoyl-CoA, pentanoyl-CoA, (2E)-hexenoyl-CoA and numerous other unsaturated fatty acids in the brain. We also observed that neuroprotective metabolites (e.g., vitamins, retinoids and unsaturated fatty acids) tend to be depleted in the AD brain, while neurotoxic metabolites (e.g., β-alanine, bilirubin) were more abundant. In summary, we systematically revealed the key genes and pathways related to the progression of AD, gained insight into the crucial mechanisms of AD and identified some possible targets that could be used in the treatment of AD.