The Emerging Role of Proteomics in Precision Medicine: Applications in Neurodegenerative Diseases and Neurotrauma

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies

Disruptions of brain metabolism are considered integral to the pathogenesis of dementia, but thus far little is known of how dementia with Lewy bodies (DLB) impacts the brain metabolome. DLB is less well known than other neurodegenerative diseases such as Alzheimer's and Parkinson's disease which is perhaps why it is under-investigated. This exploratory study aimed to address current knowledge gaps in DLB research and search for potentially targetable biochemical pathways for therapeutics. It also aimed to better understand metabolic similarities and differences with other dementias. Combined metabolomic analyses of ¹H NMR and tandem mass spectrometry of neocortical post-mortem brain tissue (Brodmann region 7) from autopsy confirmed cases of DLB (n = 15) were compared with age/gender-matched, non-cognitively impaired healthy controls (n = 30). Following correction for multiple comparisons, only 2 metabolites from a total of 219 measured compounds significantly differed. Putrescine was suppressed (55.4%) in DLB and O-phosphocholine was elevated (52.5%). We identified a panel of 5 metabolites (PC aa C38:4, O-Phosphocholine, putrescine, 4-Aminobutyrate, and SM C16:0) capable of accurately discriminating between DLB and control subjects. Deep Learning (DL) provided the best predictive model following 10-fold cross validation (AUROC (95% CI) = 0.80 (0.60-1.0)) with sensitivity and specificity equal to 0.92 and 0.88, respectively. Altered brain levels of putrescine and O-phosphocholine indicate that the Kennedy pathway and polyamine metabolism are perturbed in DLB. These are accompanied by a consistent underlying trend of lipid dysregulation. As yet it is unclear whether these are a cause or consequence of DLB onset.

Protein Expression Analysis of an In Vitro Murine Model of Prostate Cancer Progression: Towards Identification of High-Potential Therapeutic Targets

Background: Prostate cancer (PC) is the most frequently diagnosed cancer among men worldwide. The poor prognosis of PC is largely due to late diagnosis of the disease when it has progressed to advanced stages marked by androgen-independence. We interrogated proteomic signatures that embody the transition of PC from an androgen-dependent (AD) to an androgen-independent (AI) state. Methods: We have previously established AD and AI murine PC cell lines, PLum-AD and PLum-AI, respectively, which recapitulate primary and progressive PC at phenotypic and subcellular levels. We statistically surveyed global protein expression profiles in these cell lines. Differential profiles were functionally interrogated by pathways and protein–protein interaction network analyses. Results: Protein expression pattern analysis revealed a total of 683 proteins, among which 99 were significantly differentially altered in PLum-AI cells as compared to PLum-AD cells (45 increased and 54 decreased). Principal component analysis (PCA) revealed that the two different cell lines clearly separated apart, indicating a significant proteome expression difference between them. Four of the proteins (vimentin, catalase, EpCAM, and caspase 3) that were differentially expressed in PLum-AI cells compared to PLum-AD cells were subjected to biochemical validation by Western blotting. Biological process gene ontology (GO) analysis of the differentially expressed proteins demonstrated enrichment of biological functions and pathways in PLum-AI cells that are central to PI3 kinase and androgen receptor pathways. Besides, other relevant biological processes that are enriched in PLum-AI cells included cell adhesion and cell migration processes, cell and DNA damage, apoptosis, and cell cycle regulation. Conclusions: Our protein expression analysis of a murine in vitro model of PC progression identified differential protein spots that denote this progression and that comprise high-potential targets for early treatment of PC with a personalized patient-specific approach. Efforts are underway to functionally assess the potential roles of these proteins as therapeutic targets for PC progression.

Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease

Life expectancy has gradually grown over the last century. This has deeply affected healthcare costs, since the growth of an aging population is correlated to the increasing burden of chronic diseases. This represents the interesting challenge of how to manage patients with chronic diseases in order to improve health care budgets. Effective primary prevention could represent a promising route. To this end, precision, together with personalized medicine, are useful instruments in order to investigate pathological processes before the appearance of clinical symptoms and to guide physicians to choose a targeted therapy to manage the patient. Cardiovascular and neurodegenerative diseases represent suitable models for taking full advantage of precision medicine technologies applied to all stages of disease development. The availability of high technology incorporating artificial intelligence and advancement progress made in the field of biomedical research have been substantial to understand how genes, epigenetic modifications, aging, nutrition, drugs, microbiome and other environmental factors can impact health and chronic disorders. The aim of the present review is to address how precision and personalized medicine can bring greater clarity to the clinical and biological complexity of these types of disorders associated with high mortality, involving tremendous health care costs, by describing in detail the methods that can be applied. This might offer precious tools for preventive strategies and possible clues on the evolution of the disease and could help in predicting morbidity, mortality and detecting chronic disease indicators much earlier in the disease course. This, of course, will have a major effect on both improving the quality of care and quality of life of the patients and reducing time efforts and healthcare costs.

Modeling Parkinson's Disease Using Induced Pluripotent Stem Cells

Parkinson's disease (PD) is the second most common neurodegenerative disease. The molecular mechanisms of PD at the cellular level involve oxidative stress, mitochondrial dysfunction, autophagy, axonal transport, and neuroinflammation. Induced pluripotent stem cells (iPSCs) with patient-specific genetic background are capable of directed differentiation into dopaminergic neurons. Cell models based on iPSCs are powerful tools for studying the molecular mechanisms of PD. The iPSCs used for PD studies were mainly from patients carrying mutations in synuclein alpha (SNCA), leucine-rich repeat kinase 2 (LRRK2), PTEN-induced putative kinase 1 (PINK1), parkin RBR E3 ubiquitin protein ligase (PARK2), cytoplasmic protein sorting 35 (VPS35), and variants in glucosidase beta acid (GBA). In this review, we summarized the advances in molecular mechanisms of Parkinson's disease using iPSC models.

Proteomic Analysis of Baboon Cerebral Artery Reveals Potential Pathways of Damage by Prenatal Alcohol Exposure

Alcohol is one of the most widely misused substances in the world. Alcohol consumption by pregnant women often results in an array of fetal developmental abnormalities, but the damage to the fetus by alcohol remains poorly understood. The limited knowledge regarding the molecular targets of alcohol in the developing fetus constitutes one of the major obstacles in developing effective pharmacological interventions that could prevent fetal damage after alcohol consumption by pregnant women. The fetal cerebral artery is emerging as an important mediator of fetal cerebral damage by maternal alcohol drinking. In the present work, we conduct proteomics analysis of cerebral (basilar) artery lysates of near-term fetal baboons to search for protein targets of fetal alcohol exposure. Our study demonstrates that 3 episodes of binge alcohol exposure during the second trimester-equivalent of human pregnancy are sufficient to render profound changes in fetal cerebral artery proteome. These changes persisted, as they were detected in near-term fetuses. In particular, the relative abundance of 238 proteins differed significantly between control and alcohol-exposed fetuses. Enrichment analysis pointed at the group of metabolic activity proteins as a major class targeted by alcohol. Western blotting confirmed upregulation of the aldehyde dehydrogenase 6 family member A1 (ALDH6A1) in cerebral artery lysates from alcohol-exposed fetuses. This upregulation translated to greater ALDH activity of cerebral artery lysate of near-term fetuses following prenatal alcohol exposure when compared with controls.

Towards precision medicine: the role and potential of protein and peptide microarrays

Techniques to comprehensively analyze protein signatures are pivotal to unravel disease mechanisms, develop novel biomarkers and targeted therapies. In this frame, protein and peptide microarrays can play a major role in fuelling precision medicine.