The complexity of biological processes – including aging and age-related diseases – is driven by protein level variations. Proteoforms, a modest term to encapsulate the scalable, functional diversity, reflect gene sequence variations, splicing, and post-translational modifications resulting from integrative effects of genetic, epigenetic, and endogenous/exogenous environmental factors. Consequently, changes are induced also in other signaling molecules, beyond proteins, such as lipids, and metabolites. The combination of advances in next-generation sequencing and mass spectrometry have initiated a multi-OMICs revolution with the potential to probe the genome, transcriptome, proteome, epigenome, lipidome and metabolome. Integration of these approaches will likely advance our understanding of pathogenic mechanisms to subclassify patients and thereby pave the way forward from a “one-size-fits-all” to a personalized medicine model.
Personalized diagnostics and treatments would dramatically alter the ability to address unmet medical needs of neurodegenerative syndromes such as Parkinson’s disease, Alzheimer’s dementia, and amyotrophic lateral sclerosis for which few effective disease-modifying therapeutics exist. These diseases are primarily diagnosed with neuropsychological assessments, brain imaging for presence of pathology where available, and limited laboratory tests. Further complicating matters is the clinical and molecular overlap of neurodegenerative diseases, which obfuscates diagnosis (especially in the early stages) and the long prodromal, neurodegenerative period prior to clinical diagnosis.
This Research Topic is focused on how leveraging OMICs makes it is possible to advance therapeutic
development for neurodegenerative diseases. We welcome submissions covering the use and integration of OMICs approaches to:
• Inform molecular mechanisms underlying disease pathogenesis and pathology, including but not limited to PTMs and the enzymes responsible, protein:protein interactions, aggregation, degradation (lysosomal, proteasomal), and effects on cellular function.
• Discover and validate biomarkers of (a) disease-relevant pharmacodynamics, (b) efficacy prediction, and (c) patient stratification/enrichment (including composite biomarkers) and potential integration with digital technologies.
• Understanding the utility and limitations of biomarkers in light of translation failures/misalignments.
• Detect different “strains” or proteoforms of pathological proteins (Aβ, aSyn, tau, TDP43, etc.) in diseases with overlapping neuropathologies and clinical symptoms.
• Elucidate phenotypic subtypes with distinct molecular heterogeneity, possibly driven by different underlying biochemistries.
• Implement machine learning approaches to integrate multi-OMICS/multi-modal data.
• Identify similarities and differences between monogenic versus sporadic age-related disease.
• Inform novel therapeutic targets and associated target engagement/target modulation biomarkers.
• Elucidate mechanism(s) of action of drugs.
• Aid in development of imaging agent(s) to reflect brain pathology or drug effects.
Reviews, perspectives, and commentaries offering a critical assessment of the field, identifying knowledge gaps, and how to strategically move forward with the goal of enabling disease-modifying therapeutics and biomarkers are also welcome. We recognize the importance of Machine Learning and Artificial Intelligence (ML/AI) in increasing insights large multi-OMIC datasets. These rapidly evolving computational tools could help build a comprehensive set of well-characterized, patient-specific molecular signatures that could potentially guide therapeutic decisions and disentangle disruptions in complex molecular networks that originally led to the disease. For this reason, we also welcome submissions in this area including critical discussion of the limitations and gaps of ML/AI.
Topic Editor Norelle Christine Wildburger has previously been employed by Evotec SE. Topic Editor Hilal Lashuel is a founder and Chief Scientific Officer of ND BioSciences SA, and has received financial support from Merck Serono, UCB, Abbvie and Idorsia. Topic Editor Kalpana Merchant has received financial support from Caraway Therapeutics, Nitrome Biosciences, Nura Bio, Retromer Therapeutics, Sinopia Biosciences, Vanqua Bio, and private venture companies. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
The complexity of biological processes – including aging and age-related diseases – is driven by protein level variations. Proteoforms, a modest term to encapsulate the scalable, functional diversity, reflect gene sequence variations, splicing, and post-translational modifications resulting from integrative effects of genetic, epigenetic, and endogenous/exogenous environmental factors. Consequently, changes are induced also in other signaling molecules, beyond proteins, such as lipids, and metabolites. The combination of advances in next-generation sequencing and mass spectrometry have initiated a multi-OMICs revolution with the potential to probe the genome, transcriptome, proteome, epigenome, lipidome and metabolome. Integration of these approaches will likely advance our understanding of pathogenic mechanisms to subclassify patients and thereby pave the way forward from a “one-size-fits-all” to a personalized medicine model.
Personalized diagnostics and treatments would dramatically alter the ability to address unmet medical needs of neurodegenerative syndromes such as Parkinson’s disease, Alzheimer’s dementia, and amyotrophic lateral sclerosis for which few effective disease-modifying therapeutics exist. These diseases are primarily diagnosed with neuropsychological assessments, brain imaging for presence of pathology where available, and limited laboratory tests. Further complicating matters is the clinical and molecular overlap of neurodegenerative diseases, which obfuscates diagnosis (especially in the early stages) and the long prodromal, neurodegenerative period prior to clinical diagnosis.
This Research Topic is focused on how leveraging OMICs makes it is possible to advance therapeutic
development for neurodegenerative diseases. We welcome submissions covering the use and integration of OMICs approaches to:
• Inform molecular mechanisms underlying disease pathogenesis and pathology, including but not limited to PTMs and the enzymes responsible, protein:protein interactions, aggregation, degradation (lysosomal, proteasomal), and effects on cellular function.
• Discover and validate biomarkers of (a) disease-relevant pharmacodynamics, (b) efficacy prediction, and (c) patient stratification/enrichment (including composite biomarkers) and potential integration with digital technologies.
• Understanding the utility and limitations of biomarkers in light of translation failures/misalignments.
• Detect different “strains” or proteoforms of pathological proteins (Aβ, aSyn, tau, TDP43, etc.) in diseases with overlapping neuropathologies and clinical symptoms.
• Elucidate phenotypic subtypes with distinct molecular heterogeneity, possibly driven by different underlying biochemistries.
• Implement machine learning approaches to integrate multi-OMICS/multi-modal data.
• Identify similarities and differences between monogenic versus sporadic age-related disease.
• Inform novel therapeutic targets and associated target engagement/target modulation biomarkers.
• Elucidate mechanism(s) of action of drugs.
• Aid in development of imaging agent(s) to reflect brain pathology or drug effects.
Reviews, perspectives, and commentaries offering a critical assessment of the field, identifying knowledge gaps, and how to strategically move forward with the goal of enabling disease-modifying therapeutics and biomarkers are also welcome. We recognize the importance of Machine Learning and Artificial Intelligence (ML/AI) in increasing insights large multi-OMIC datasets. These rapidly evolving computational tools could help build a comprehensive set of well-characterized, patient-specific molecular signatures that could potentially guide therapeutic decisions and disentangle disruptions in complex molecular networks that originally led to the disease. For this reason, we also welcome submissions in this area including critical discussion of the limitations and gaps of ML/AI.
Topic Editor Norelle Christine Wildburger has previously been employed by Evotec SE. Topic Editor Hilal Lashuel is a founder and Chief Scientific Officer of ND BioSciences SA, and has received financial support from Merck Serono, UCB, Abbvie and Idorsia. Topic Editor Kalpana Merchant has received financial support from Caraway Therapeutics, Nitrome Biosciences, Nura Bio, Retromer Therapeutics, Sinopia Biosciences, Vanqua Bio, and private venture companies. The other Topic Editors declare no competing interests with regard to the Research Topic subject.