From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis

Unveiling amyotrophic lateral sclerosis complexity: insights from proteomics, metabolomics and microbiomics

Amyotrophic lateral sclerosis is the most common motor neuron disease and manifests as a clinically and genetically heterogeneous neurodegenerative disorder mainly affecting the motor systems. To date, despite promising results and accumulating knowledge on the pathomechanisms of amyotrophic lateral sclerosis, a specific disease-modifying treatment is still not available. In vitro and in vivo disease models coupled with multiomics techniques have helped elucidate the pathomechanisms underlying this disease. In particular, omics approaches are powerful tools for identifying new potential disease biomarkers that may be particularly useful for diagnosis, prognosis and assessment of treatment response. In turn, these findings could support physicians in stratifying patients into clinically relevant subgroups for the identification of the best therapeutic targets. Here, we provide a comprehensive review of the most relevant literature highlighting the importance of proteomics approaches in determining the role of pathogenic misfolded/aggregated proteins and the molecular mechanisms involved in the pathogenesis and progression of amyotrophic lateral sclerosis. In addition, we explored new findings arising from metabolomic and lipidomic studies, which can aid to elucidate the intricate metabolic alterations underlying amyotrophic lateral sclerosis pathology. Moreover, we integrated these insights with microbiomics data, providing a thorough understanding of the interplay between metabolic dysregulation and microbial dynamics in disease progression. Indeed, a greater integration of these multiomics data could lead to a deeper understanding of disease mechanisms, supporting the development of specific therapies for amyotrophic lateral sclerosis.

Modeling ALS with Patient-Derived iPSCs: Recent Advances and Future Potentials

Amyotrophic lateral sclerosis (ALS) is a terminal complex neurodegenerative disease, with 10-15% of cases being familial and the majority being sporadic with no known cause. There are no animal models for the 85-90% of sporadic ALS cases. More creative, sophisticated models of ALS disease are required to unravel the mysteries of this complicated disease. While ALS patients urgently require new medications and treatments, suitable preclinical in vitro models for drug screening are lacking. Therefore, human-derived induced pluripotent stem cell (hiPSC) technology offers the opportunity to model diverse and unreachable cell types in a culture dish. In this review, we focus on recent hiPSC-derived ALS neuronal and non-neuronal models to examine the research progress of current ALS 2D monocultures, co-cultures, and more complex 3D-model organoids. Despite the challenges inherent to hiPSC-based models, their application to preclinical drug studies is enormous.

Exploiting the role of CSF NfL, CHIT1, and miR-181b as potential diagnostic and prognostic biomarkers for ALS

Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disorder characterized by relentless and progressive loss of motor neurons. A molecular diagnosis, supported by the identification of specific biomarkers, might promote the definition of multiple biological subtypes of ALS, improving patient stratification and providing prognostic information. Here, we investigated the levels of neurofilament light chain (NfL), chitotriosidase (CHIT1) and microRNA-181b (miR-181b) in the cerebrospinal fluid (CSF) of ALS subjects (N = 210) as well as neurologically healthy and neurological disease controls (N = 218, including N = 74 with other neurodegenerative diseases) from a large European multicentric cohort, evaluating their specific or combined utility as diagnostic and prognostic biomarkers. NfL, CHIT1 and miR-181b all showed significantly higher levels in ALS subjects compared to controls, with NfL showing the most effective diagnostic performance. Importantly, all three biomarkers were increased compared to neurodegenerative disease controls and, specifically, to patients with Alzheimer's disease (AD; N = 44), with NfL and CHIT1 being also higher in ALS than in alpha-synucleinopathies (N = 22). Notably, ALS patients displayed increased CHIT1 levels despite having, compared to controls, a higher prevalence of a polymorphism lowering CHIT1 expression. While no relationship was found between CSF miR-181b and clinical measures in ALS (disease duration, functional disability, and disease progression rate), CSF NfL was the best independent predictor of disease progression and survival. This study deepens our knowledge of ALS biomarkers, highlighting the relative specificity of CHIT1 for ALS among neurodegenerative diseases and appraising the potential diagnostic utility of CSF miR-181b.

A Quadruple Revolution: Deciphering Biological Complexity with Artificial Intelligence, Multiomics, Precision Medicine, and Planetary Health

A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability science" of precision/personalized medicine that aims to account for patient-to-patient and between-population differences in disease susceptibilities and responses to health interventions such as drugs, nutrition, vaccines, and radiation; and (4) planetary health scholarship that both scales up and integrates biological, clinical, and ecological contexts of health and disease. Against this overarching background, this article presents and highlights some of the salient challenges and prospects of multiomics research, emphasizing the attendant pivotal role of systems medicine and systems biology. In addition, we emphasize the rapidly growing importance of planetary health research for systems medicine, particularly amid climate emergency, ecological degradation, and loss of planetary biodiversity. Looking ahead, we anticipate that the integration and utilization of multiomics big data and artificial intelligence will drive further progress in systems medicine and systems biology, heralding a promising future for both human and planetary health.

From use of omics to systems biology: Identifying therapeutic targets for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a heterogeneous progressive neurodegenerative disorder with available treatments such as riluzole and edaravone extending survival by an average of 3-6 months. The lack of highly effective, widely available therapies reflects the complexity of ALS. Omics technologies, including genomics, transcriptomic and proteomics have contributed to the identification of biological pathways dysregulated and targeted by therapeutic strategies in preclinical and clinical trials. Integrating clinical, environmental and neuroimaging information with omics data and applying a systems biology approach can further improve our understanding of the disease with the potential to stratify patients and provide more personalised medicine. This chapter will review the omics technologies that contribute to a systems biology approach and how these components have assisted in identifying therapeutic targets. Current strategies, including the use of genetic screening and biosampling in clinical trials, as well as the future application of additional technological advances, will also be discussed.

Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords

Introduction

ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs).

Methods

This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified.

Results

The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037).

Conclusion

This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.

Integrating Transcriptomic and Structural Insights: Revealing Drug Repurposing Opportunities for Sporadic ALS

Amyotrophic lateral sclerosis (ALS) is a progressive and devastating neurodegenerative disorder characterized by the loss of upper and lower motor neurons, resulting in debilitating muscle weakness and atrophy. Currently, there are no effective treatments available for ALS, posing significant challenges in managing the disease that affects approximately two individuals per 100,000 people annually. To address the urgent need for effective ALS treatments, we conducted a drug repurposing study using a combination of bioinformatics tools and molecular docking techniques. We analyzed sporadic ALS-related genes from the GEO database and identified key signaling pathways involved in sporadic ALS pathogenesis through pathway analysis using DAVID. Subsequently, we utilized the Clue Connectivity Map to identify potential drug candidates and performed molecular docking using AutoDock Vina to evaluate the binding affinity of short-listed drugs to key sporadic ALS-related genes. Our study identified Cefaclor, Diphenidol, Flubendazole, Fluticasone, Lestaurtinib, Nadolol, Phenamil, Temozolomide, and Tolterodine as potential drug candidates for repurposing in sporadic ALS treatment. Notably, Lestaurtinib demonstrated high binding affinity toward multiple proteins, suggesting its potential as a broad-spectrum therapeutic agent for sporadic ALS. Additionally, docking analysis revealed NOS3 as the gene that interacts with all the short-listed drugs, suggesting its possible involvement in the mechanisms underlying the therapeutic potential of these drugs in sporadic ALS. Overall, our study provides a systematic framework for identifying potential drug candidates for sporadic ALS therapy and highlights the potential of drug repurposing as a promising strategy for discovering new therapies for neurodegenerative diseases.

A dataset profiling the multiomic landscape of the prefrontal cortex in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease, which still lacks effective disease-modifying therapies. Similar to other neurodegenerative disorders, such as Alzheimer and Parkinson disease, ALS pathology is presumed to propagate over time, originating from the motor cortex and spreading to other cortical regions. Exploring early disease stages is crucial to understand the causative molecular changes underlying the pathology. For this, we sampled human postmortem prefrontal cortex (PFC) tissue from Brodmann area 6, an area that exhibits only moderate pathology at the time of death, and performed a multiomic analysis of 51 patients with sporadic ALS and 50 control subjects. To compare sporadic disease to genetic ALS, we additionally analyzed PFC tissue from 4 transgenic ALS mouse models (C9orf72-, SOD1-, TDP-43-, and FUS-ALS) using the same methods. This multiomic data resource includes transcriptome, small RNAome, and proteome data from female and male samples, aimed at elucidating early and sex-specific ALS mechanisms, biomarkers, and drug targets.

Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics

Pharmaceutical design has made significant advancements in recent years, leading to the development of novel therapeutics with unprecedented efficacy and safety profiles. This review highlights the potential of these innovations to revolutionize healthcare and improve patient outcomes. The application of cutting-edge technologies like artificial intelligence, machine learning, and data mining in drug discovery and design has made it easier to find potential drug candidates. Combining big data and omics has led to the discovery of new therapeutic targets and personalized medicine strategies. Nanoparticles, liposomes, and microneedles are examples of advanced drug delivery systems that allow precise control over drug release, better bioavailability, and targeted delivery to specific tissues or cells. This improves the effectiveness of the treatment while reducing side effects. Stimuli-responsive materials and smart drug delivery systems enable drugs to be released on demand when specific internal or external signals are sent. Biologics and gene therapies are promising approaches in pharmaceutical design, offering high specificity and potency for treating various diseases like cancer, autoimmune disorders, and infectious diseases. Gene therapies hold tremendous potential for correcting genetic abnormalities, with recent breakthroughs demonstrating successful outcomes in inherited disorders and certain types of cancer. Advancements in nanotechnology and nanomedicine have paved the way for innovative diagnostic tools and therapeutics, such as nanoparticle-based imaging agents, targeted drug delivery systems, gene editing technologies, and regenerative medicine strategies. Finally, the review emphasizes the importance of regulatory considerations, ethical challenges, and future directions in pharmaceutical design. Regulatory agencies are adapting to the rapid advancements in the field, ensuring the safety and efficacy of novel therapeutics while fostering innovation. Ethical considerations regarding the use of emerging technologies, patient privacy, and access to advanced therapies also require careful attention.

Genomic and transcriptomic advances in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder and the most common motor neuron disease. ALS shows substantial clinical and molecular heterogeneity. In vitro and in vivo models coupled with multiomic techniques have provided important contributions to unraveling the pathomechanisms underlying ALS. To date, despite promising results and accumulating knowledge, an effective treatment is still lacking. Here, we provide an overview of the literature on the use of genomics, epigenomics, transcriptomics and microRNAs to deeply investigate the molecular mechanisms developing and sustaining ALS. We report the most relevant genes implicated in ALS pathogenesis, discussing the use of different high-throughput sequencing techniques and the role of epigenomic modifications. Furthermore, we present transcriptomic studies discussing the most recent advances, from microarrays to bulk and single-cell RNA sequencing. Finally, we discuss the use of microRNAs as potential biomarkers and promising tools for molecular intervention. The integration of data from multiple omic approaches may provide new insights into pathogenic pathways in ALS by shedding light on diagnostic and prognostic biomarkers, helping to stratify patients into clinically relevant subgroups, revealing novel therapeutic targets and supporting the development of new effective therapies.

Urinary biomarkers for amyotrophic lateral sclerosis: candidates, opportunities and considerations

Amyotrophic lateral sclerosis is a relentless neurodegenerative disease that is mostly fatal within 3-5 years and is diagnosed on evidence of progressive upper and lower motor neuron degeneration. Around 15% of those with amyotrophic lateral sclerosis also have frontotemporal degeneration, and gene mutations account for ∼10%. Amyotrophic lateral sclerosis is a variable heterogeneous disease, and it is becoming increasingly clear that numerous different disease processes culminate in the final degeneration of motor neurons. There is a profound need to clearly articulate and measure pathological process that occurs. Such information is needed to tailor treatments to individuals with amyotrophic lateral sclerosis according to an individual's pathological fingerprint. For new candidate therapies, there is also a need for methods to select patients according to expected treatment outcomes and measure the success, or not, of treatments. Biomarkers are essential tools to fulfil these needs, and urine is a rich source for candidate biofluid biomarkers. This review will describe promising candidate urinary biomarkers of amyotrophic lateral sclerosis and other possible urinary candidates in future areas of investigation as well as the limitations of urinary biomarkers.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

A Diagnostic Gene-Expression Signature in Fibroblasts of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease with limited treatment options. Diagnosis can be difficult due to the heterogeneity and non-specific nature of the initial symptoms, resulting in delays that compromise prompt access to effective therapeutic strategies. Transcriptome profiling of patient-derived peripheral cells represents a valuable benchmark in overcoming such challenges, providing the opportunity to identify molecular diagnostic signatures. In this study, we characterized transcriptome changes in skin fibroblasts of sporadic ALS patients (sALS) and controls and evaluated their utility as a molecular classifier for ALS diagnosis. Our analysis identified 277 differentially expressed transcripts predominantly involved in transcriptional regulation, synaptic transmission, and the inflammatory response. A support vector machine classifier based on this 277-gene signature was developed to discriminate patients with sALS from controls, showing significant predictive power in both the discovery dataset and in six independent publicly available gene expression datasets obtained from different sALS tissue/cell samples. Taken together, our findings support the utility of transcriptional signatures in peripheral cells as valuable biomarkers for the diagnosis of ALS.

CXCR2 Is Deregulated in ALS Spinal Cord and Its Activation Triggers Apoptosis in Motor Neuron-Like Cells Overexpressing hSOD1-G93A

Amyotrophic lateral sclerosis (ALS) is a multifactorial neurodegenerative disease characterized by progressive depletion of motor neurons (MNs). Recent evidence suggests a role in ALS pathology for the C-X-C motif chemokine receptor 2 (CXCR2), whose expression was found increased at both mRNA and protein level in cortical neurons of sporadic ALS patients. Previous findings also showed that the receptor inhibition is able to prevent iPSC-derived MNs degeneration in vitro and improve neuromuscular function in SOD1-G93A mice. Here, by performing transcriptional analysis and immunofluorescence studies, we detailed the increased expression and localization of CXCR2 and its main ligand CXCL8 in the human lumbar spinal cord of sporadic ALS patients. We further investigated the functional role of CXCR2/ligands axis in NSC-34 motor neuron-like cells expressing human wild-type (WT) or mutant (G93A) SOD1. A significant expression of CXCR2 was found in doxycycline-induced G93A-SOD1-expressing cells, but not in WT cells. In vitro assays showed CXCR2 activation by GROα and MIP2α, two murine endogenous ligands and functional homologs of CXCL8, reduces cellular viability and triggers apoptosis in a dose dependent manner, while treatment with reparixin, a non-competitive allosteric CXCR2 inhibitor, effectively counteracts GROα and MIP2α toxicity, significantly inhibiting the chemokine-induced cell death. Altogether, data further support a role of CXCR2 axis in ALS etiopathogenesis and confirm its pharmacological modulation as a candidate therapeutic strategy.

Functional genomics in stroke: current and future applications of iPSCs and gene editing to dissect the function of risk variants

Stroke is an important disease with unmet clinical need. To uncover novel paths for treatment, it is of critical importance to develop relevant laboratory models that may help to shed light on the pathophysiological mechanisms of stroke. Induced pluripotent stem cells (iPSCs) technology has enormous potential to advance our knowledge into stroke by creating novel human models for research and therapeutic testing. iPSCs models generated from patients with specific stroke types and specific genetic predisposition in combination with other state of art technologies including genome editing, multi-omics, 3D system, libraries screening, offer the opportunity to investigate disease-related pathways and identify potential novel therapeutic targets that can then be tested in these models. Thus, iPSCs offer an unprecedented opportunity to make rapid progress in the field of stroke and vascular dementia research leading to clinical translation. This review paper summarizes some of the key areas in which patient-derived iPSCs technology has been applied to disease modelling and discusses the ongoing challenges and the future directions for the application of this technology in the field of stroke research.

Integrative analysis of metabolomics and proteomics unravels purine metabolism disorder in the SOD1G93A mouse model of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with progressive paralysis of limbs and bulb in patients, the cause of which remains unclear. Accumulating studies suggest that motor neuron degeneration is associated with systemic metabolic impairment in ALS. However, the metabolic reprogramming and underlying mechanism in the longitudinal progression of the disease remain poorly understood. In this study, we aimed to investigate the molecular changes at both metabolic and proteomic levels during disease progression to identify the most critical metabolic pathways and underlying mechanisms involved in ALS pathophysiological changes. Utilizing liquid chromatography-mass spectrometry-based metabolomics, we analyzed the metabolites' levels of plasma, lumbar spinal cord, and motor cortex from SOD1^G93A mice and wildtype (WT) littermates at different stages. To elucidate the regulatory network underlying metabolic changes, we further analyzed the proteomics profile in the spinal cords of SOD1^G93A and WT mice. A group of metabolites implicated in purine metabolism, methionine cycle, and glycolysis were found differentially expressed in ALS mice, and abnormal expressions of enzymes involved in these metabolic pathways were also confirmed. Notably, we first demonstrated that dysregulation of purine metabolism might contribute to the pathogenesis and disease progression of ALS. Furthermore, we discovered that fatty acid metabolism, TCA cycle, arginine and proline metabolism, and folate-mediated one‑carbon metabolism were also significantly altered in this disease. The identified differential metabolites and proteins in our study could complement existing data on metabolic reprogramming in ALS, which might provide new insight into the pathological mechanisms and novel therapeutic targets of ALS.

Integrated network pharmacology approach shows a potential role of Ginseng catechins and ginsenosides in modulating protein aggregation in Amyotrophic Lateral Sclerosis

Amyotrophic lateral Sclerosis is an incurable, progressive neurodegenerative motor neuron disease. The disease is characterized by protein aggregates. The symptoms include weakness, denervation of muscles, atrophy and progressive paralysis of bulbar and respiratory muscles and dysphagia. Various secondary metabolites are evaluated for their ability to improve symptoms in ALS. Ginseng has been traditionally used for treating several neurodegenerative diseases. Several studies using model systems have shown a potential role of Ginseng catechins and Ginsenosides in clearing protein aggregation associated with ALS. We focus on Network pharmacology approach to understand the effect of Ginseng catechins or ginsenosides on protein aggregation associated with ALS. A catechin/ginsenoside-protein interaction network was generated and the pathways obtained were compared with those obtained from transcriptomic datasets of ALS from GEO database. Knock out of MAPK14, AKT and GSK from Catechin and BACE 1 from ginsenoside modulated pathways inhibited protein aggregation. Catechins and ginsenosides have potential as therapeutic agents in the management of ALS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03401-1.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

The revolution of pharmaco-omics: ready to open new avenues in materializing precision medicine?

Tweetable abstract

The pharmaco-omics revolution has started and, as a wild stream, sooner or later, will expand and dramatically improve drug discovery and individual response to pharmacotherapy. Hopefully, we will all be ready to follow the stream.

Clinical Proteomics for Meningioma: An Integrated Workflow for Quantitative Proteomics and Biomarker Validation in Formalin-Fixed Paraffin-Embedded Tissue Samples

Clinical proteomics is a rapidly emerging frontier in laboratory medicine. High-throughput proteomic investigations of biopsy tissues provide mechanistic insights into complex human diseases. For large-scale proteomics, formalin-fixed and paraffin-embedded (FFPE) tissue samples offer a viable alternative to fresh-frozen (FF) tissues that have restricted availability. In this context, meningioma is one of the most common primary brain tumors where innovation in diagnostics and therapeutic targets can benefit from clinical proteomics. We present here an integrated workflow for quantitative proteomics and biomarker validation of meningioma FFPE tissues. Applying label-free quantitative (LFQ) proteomics, we reproducibly (Pearson's correlation: 0.84-0.91) obtained an in-depth proteome coverage (nearly 4000 proteins per sample) from 120 min gradient of single unfractionated mass spectrometry run. Furthermore, building upon LFQ data and literature curated set of meningioma-associated proteins, we validated VIM, AHNAK, and CLU from FFPE tissues using selected reaction monitoring (SRM) assay and compared its performance with FF tissues. This study illustrates how knowledge from label-free proteomics can be integrated for selecting peptides for targeted validation and suggests that FFPE tissues are comparable to FF tissues for SRM assays. This quantitative clinical proteomics workflow is scalable for large-scale clinical diagnostics studies in the future, for example, utilizing the global repository of FFPE tissues in meningioma and possibly in other cancers.

Integrated multi-omic data analysis and validation with yeast model show oxidative phosphorylation modulates protein aggregation in amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis is a progressive, incurable amyloid aggregating neurodegenerative disease involving the motor neurons. Identifying potential biomarkers and therapeutic targets can assist in the better management of the disease. We used an integrative approach encompassing analysis of transcriptomic datasets of human and mice from the GEO database. Our analysis of ALS patient datasets showed deregulation in Non-alcoholic fatty acid liver disease and oxidative phosphorylation. Transgenic mice datasets of SOD1, FUS and TDP-43 showed deregulation in oxidative phosphorylation and ribosome-associated pathways. Commonality analysis between the human and mice datasets showed oxidative phosphorylation as a major deregulated pathway. Further, protein-protein and protein-drug interaction network analysis of mitochondrial electron transport chain showed enrichment of proteins and inhibitors of mitochondrial Complex III and IV. The results were further validated using the yeast model system. Inhibitor studies using metformin (Complex-I inhibitor) and malonate (Complex-II inhibitor) did not show any effect in mitigating the amyloids, while antimycin (Complex-III inhibitor) and azide (Complex-IV inhibitor) reduced amyloidogenesis. Knock-out of QCR8 (Complex-III) or COX8 (Complex-IV) cleared the amyloids. Taken together, our results show a critical role for mitochondrial oxidative phosphorylation in amyloidogenesis and as a potential therapeutic target in ALS.Communicated by Ramaswamy H. Sarma.

The Advent of Omics Sciences in Clinical Trials of Motor Neuron Diseases

The “omics revolution” has totally changed the scientific research approach and is contributing to the development of personalized therapies. In motor neuron diseases (MNDs), a set of complex, multifactorial, late-onset and chronic neurodegenerative diseases, the use of multi-omics approaches in clinical trials is providing new opportunities to stratify patients and develop target therapies. To show how omics science is gaining momentum in MNDs, in this work, we review the interventional clinical trials for MNDs based on the application of omics sciences. We analyze a total of 62 clinical trials listed in the ClinicalTrials database where different omics approaches have been applied in an initial phase, for diagnosis or patient selection, or in subsequent stages to cluster subjects, identify molecular signatures or evaluate drugs security or efficacy. The rise of omics sciences in clinical experimentation of MNDs is leading to an upheaval in their diagnosis and therapy that will require significant investments and means to ensure the correct and rapid evolution of personalized medicine.

A specific gene-splicing alteration in the SNRNP70 gene as a hallmark of an ALS subtype

Amyotrophic lateral sclerosis (ALS) has been considered as one of the progressive neurodegenerative diseases. Numerous genetic factors in divergent molecular pathways have been identified as causative factors of ALS. However, the underlying molecular mechanism that causes this disease remains undetermined; as a result, this has driven the search to find consensus disease-specific hallmarks. In this study, we focused on the alteration of the ratio of two specific gene-splicing events in the SNRNP70 gene from RNA-seq data derived from patients with ALS and control subjects. The splicing profile was significantly and specifically changed in one previously identified ALS subtype. Conversely, the gene expression profile of other ALS cases containing a splicing alteration in the SNRNP70 gene was similar to that of the subtype, whereas ALS cases without this change have exhibited less similarity. These results indicate that this splicing event in the SNRNP70 gene could represent a novel and broadly applicable molecular hallmark of a subtype of ALS.

Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.

Deciphering the Complex Molecular Pathogenesis of Myotonic Dystrophy Type 1 through Omics Studies

Omics studies are crucial to improve our understanding of myotonic dystrophy type 1 (DM1), the most common muscular dystrophy in adults. Employing tissue samples and cell lines derived from patients and animal models, omics approaches have revealed the myriad alterations in gene and microRNA expression, alternative splicing, 3′ polyadenylation, CpG methylation, and proteins levels, among others, that contribute to this complex multisystem disease. In addition, omics characterization of drug candidate treatment experiments provides crucial insight into the degree of therapeutic rescue and off-target effects that can be achieved. Finally, several innovative technologies such as single-cell sequencing and artificial intelligence will have a significant impact on future DM1 research.

A chemogenomic approach is required for effective treatment of amyotrophic lateral sclerosis

ALS is a fatal untreatable disease involving degeneration of motor neurons. Μultiple causative genes encoding proteins with versatile functions have been identified indicating that diverse biological pathways lead to ALS. Chemical entities still represent a promising choice to delay ALS progression, attenuate symptoms and/or increase life expectancy, but also gene-based and stem cell-based therapies are in the process of development, and some are tested in clinical trials. Various compounds proved effective in transgenic models overexpressing distinct ALS causative genes unfortunately though, they showed no efficacy in clinical trials. Notably, while animal models provide a uniform genetic background for preclinical testing, ALS patients are not stratified, and the distinct genetic forms of ALS are treated as one group, which could explain the observed discrepancies between treating genetically homogeneous mice and quite heterogeneous patient cohorts. We suggest that chemical entity-genotype correlation should be exploited to guide patient stratification for pharmacotherapy, that is administered drugs should be selected based on the ALS genetic background.

Simultaneous Isolation of High-Quality RNA and DNA From Postmortem Human Central Nervous System Tissues for Omics Studies

Multi-omics approaches are increasingly being adopted to understand the complex networks underlying disease. The coisolation of high-quality nucleotides from affected tissues is paramount for the parallel analysis of transcriptomic, genomic, and epigenomic data sets. Although nucleotides extracted from postmortem central nervous system (CNS) tissue are widely used in the study of neurodegenerative disease, assessment of methods for the simultaneous isolation of DNA and RNA is limited. Herein, we describe a strategy for the isolation of high-quality DNA and RNA from postmortem human tissue from 7 CNS regions. Motor cortex, frontal cortex, hippocampus, occipital cortex, anterior cingulate cortex, cerebellum, and spinal cord tissues were obtained from 22 individuals diagnosed with motor neuron disease (MND) and 13 neurologically normal controls (n = 245 tissues). We demonstrated that the Qiagen AllPrep DNA/RNA kit consistently isolated DNA and RNA of high yield and quality from all 6 brain regions. Importantly, phenol-chloroform-based extraction was required to isolate high-yield RNA from spinal cord. RNA sequencing using RNA extracted from 6 CNS regions (n = 60) generated high-quality transcriptomes. Hierarchical clustering of data from motor cortex, using an MND susceptibility gene panel and marker genes of disease-associated microglia, demonstrated that MND-specific gene expression signatures could be detected in the transcriptome data.

Advancing mechanistic understanding and biomarker development in amyotrophic lateral sclerosis

INTRODUCTION

Proteomic analysis has contributed significantly to the study of the neurodegenerative disease amyotrophic lateral sclerosis (ALS). It has helped to define the pathological change common to nearly all cases, namely intracellular aggregates of phosphorylated TDP-43, shifting the focus of pathogenesis in ALS toward RNA biology. Proteomics has also uniquely underpinned the delineation of disease mechanisms in model systems and has been central to recent advances in human ALS biomarker development.

AREAS COVERED

The contribution of proteomics to understanding the cellular pathological changes, disease mechanisms, and biomarker development in ALS are covered.

EXPERT OPINION

Proteomics has delivered unique insights into the pathogenesis of ALS and advanced the goal of objective measurements of disease activity to improve therapeutic trials. Further developments in sensitivity and quantification are expected, with application to the presymptomatic phase of human disease offering the hope of prevention strategies.

CXCR2 increases in ALS cortical neurons and its inhibition prevents motor neuron degeneration in vitro and improves neuromuscular function in SOD1G93A mice

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease characterized by depletion of motor neurons (MNs), for which effective medical treatments are still required. Previous transcriptomic analysis revealed the up-regulation of C-X-C motif chemokine receptor 2 (CXCR2)-mRNA in a subset of sporadic ALS patients and SOD1G93A mice. Here, we confirmed the increase of CXCR2 in human ALS cortex, and showed that CXCR2 is mainly localized in cell bodies and axons of cortical neurons. We also investigated the effects of reparixin, an allosteric inhibitor of CXCR2, in degenerating human iPSC-derived MNs and SOD1G93A mice. In vitro, reparixin rescued MNs from apoptotic cell death, preserving neuronal morphology, mitochondrial membrane potential and cytoplasmic membrane integrity, whereas in vivo it improved neuromuscular function of SOD1G93A mice. Altogether, these data suggest a role for CXCR2 in ALS pathology and support its pharmacological inhibition as a candidate therapeutic strategy against ALS at least in a specific subgroup of patients.

Taking Cellular Heterogeneity Into Consideration When Modeling Astrocyte Involvement in Amyotrophic Lateral Sclerosis Using Human Induced Pluripotent Stem Cells

Astrocytes are a large group of glial cells that perform a variety of physiological functions in the nervous system. They provide trophic, as well as structural, support to neuronal cells. Astrocytes are also involved in neuroinflammatory processes contributing to neuronal dysfunction and death. Growing evidence suggests important roles for astrocytes in non-cell autonomous mechanisms of motor neuron degeneration in amyotrophic lateral sclerosis (ALS). Understanding these mechanisms necessitates the combined use of animal and human cell-based experimental model systems, at least in part because human astrocytes display a number of unique features that cannot be recapitulated in animal models. Human induced pluripotent stem cell (hiPSC)-based approaches provide the opportunity to generate disease-relevant human astrocytes to investigate the roles of these cells in ALS. These approaches are facing the growing recognition that there are heterogenous populations of astrocytes in the nervous system which are not functionally equivalent. This review will discuss the importance of taking astrocyte heterogeneity into consideration when designing hiPSC-based strategies aimed at generating the most informative preparations to study the contribution of astrocytes to ALS pathophysiology.

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Novel Approaches to an Integrated Route for Trisomy 21 Evaluation

Trisomy 21 (T21) is one of the most commonly occurring genetic disorders, caused by the partial or complete triplication of chromosome 21. Despite the significant progress in the diagnostic tools applied for prenatal screening, commonly used methods are still imprecise and involve invasive diagnostic procedures that are related to a maternal risk of miscarriage. In this case, novel prenatal biomarkers are still being evaluated using highly specialized techniques, which could increase the diagnostic usefulness of biochemical prenatal screening for T21. From the other hand, the T21′s pathogenesis, caused by the improper division of genetic material, disrupting many metabolic pathways, could be further evaluated with the use of omics methods, which could result in bringing relevant insights for the evaluation of potential medical targets. Accordingly, a literature search was undertaken to collect novel information about prenatal screening for Down syndrome with the use of advanced technology, with a particular emphasis on the evaluation of novel screening biomarkers and the discovery of potential medical targets. These meta-analyses are focused on novel approaches designed with the use of omics techniques, representing the most rapidly developing and promising field in research today. Considering the limitations and progress of these methods, the use of omics techniques in evaluating T21 pathogenesis could bring beneficial results in prenatal screening, simultaneously uncovering novel potential medical targets.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

Artificial intelligence in early drug discovery enabling precision medicine

Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.

Mutant VAPB: Culprit or Innocent Bystander of Amyotrophic Lateral Sclerosis?

Nearly twenty years ago a mutation in the VAPB gene, resulting in a proline to serine substitution (p.P56S), was identified as the cause of a rare, slowly progressing, familial form of the motor neuron degenerative disease Amyotrophic Lateral Sclerosis (ALS). Since then, progress in unravelling the mechanistic basis of this mutation has proceeded in parallel with research on the VAP proteins and on their role in establishing membrane contact sites between the ER and other organelles. Analysis of the literature on cellular and animal models reviewed here supports the conclusion that P56S-VAPB, which is aggregation-prone, non-functional and unstable, is expressed at levels that are insufficient to support toxic gain-of-function or dominant negative effects within motor neurons. Instead, insufficient levels of the product of the single wild-type allele appear to be required for pathological effects, and may be the main driver of the disease. In light of the multiple interactions of the VAP proteins, we address the consequences of specific VAPB depletion and highlight various affected processes that could contribute to motor neuron degeneration. In the future, distinction of specific roles of each of the two VAP paralogues should help to further elucidate the basis of p.P56S familial ALS, as well as of other more common forms of the disease.

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.