Roadmap and standard operating procedures for biobanking and discovery of neurochemical markers in ALS

Despite major advances in deciphering the neuropathological hallmarks of amyotrophic lateral sclerosis (ALS), validated neurochemical biomarkers for monitoring disease activity, earlier diagnosis, defining prognosis and unlocking key pathophysiological pathways are lacking. Although several candidate biomarkers exist, translation into clinical application is hindered by small sample numbers, especially longitudinal, for independent verification. This review considers the potential routes to the discovery of neurochemical markers in ALS, and provides a consensus statement on standard operating procedures that will facilitate multicenter collaboration, validation and ultimately clinical translation

The use of omic technologies applied to traditional Chinese medicine research

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Multiomics tools for improved atherosclerotic cardiovascular disease management

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care

Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows

The transition from late pregnancy to early lactation is a critical period in a dairy cow's life due to the rapidly increasing drain of nutrients from the maternal organism towards the foetus and into colostrum and milk. In order to cope with the challenges of parturition and lactation, comprehensive adaptive reactions comprising the endocrine and the immune system need to be accomplished. There is high variation in this coping ability and both metabolic and infectious diseases, summarized as \ue2\u80\u9cproduction diseases\ue2\u80\u9c, such as hypocalcaemia (milk fever), fatty liver syndrome, laminitis and ketosis, may occur and impact welfare, productive lifespan and economic outcomes. Proteomics and metabolomics have emerged as valuable techniques to characterize proteins and metabolite assets from tissue and biological fluids, such as milk, blood and urine. In this review we provide an overview on metabolic status and physiological changes during the transition period and the related production diseases in dairy cows, and summarize the state of art on proteomics and metabolomics of biological fluids and tissues involved in metabolic stress during the peripartum period. We also provide a current and prospective view of the application of the recent achievements generated by omics for biomarker discovery and their potential in diagnosis. Biological significance: For high-yielding dairy cows there are several \ue2\u80\u9coccupational diseases\ue2\u80\u9c that occur mainly during the metabolic challenges related to the transition from pregnancy to lactation. Such diseases and their sequelae form a major concern for dairy production, and often lead to early culling of animals. Beside the economical perspective, metabolic stress may severely influence animal welfare. There is a multitude of studies about the metabolic backgrounds of such so called production diseases like ketosis, fatty liver, or hypocalcaemia, although the investigations aiming to assess the complexity of the pathophysiological reactions are largely focused on gene expression, i.e. transcriptomics. For extending the knowledge towards the proteome and the metabolome, the respective technologies are of increasing importance and can provide an overall view of how dairy cows react to metabolic stress, which is needed for an in-depth understanding of the molecular mechanisms of the related diseases. We herein review the current findings from studies applying proteomics and metabolomics to transition-related diseases, including fatty liver, ketosis, endometritis, hypocalcaemia and laminitis. For each disease, a brief overview of the up to date knowledge about its pathogenesis is provided, followed by an insight into the most recent achievements on the proteome and metabolome of tissues and biological fluids, such as blood serum and urine, highlighting potential biomarkers. We believe that this review would help readers to be become more familiar with the recent progresses of molecular background of transition-related diseases thus encouraging research in this field

Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets

Metabolomics of COPD Pulmonary Rehabilitation Outcomes via Exhaled Breath Condensate

: Chronic obstructive pulmonary disease (COPD) is characterized by different phenotypes and clinical presentations. Therefore, a single strategy of pulmonary rehabilitation (PR) does not always yield the expected clinical outcomes as some individuals respond excellently, others discreetly, or do not respond at all. Fifty consecutive COPD patients were enrolled. Of them, 35 starting a 5-week PR program were sampled at admission (T0), after 2 (T2W) and 5 (T5W) weeks, while 15 controls not yet on PR were tested at T0 and T5W. Nuclear magnetic resonance (NMR) profiling of exhaled breath condensate (EBC) and multivariate statistical analysis were applied to investigate the relationship between biomarkers and clinical parameters. The model including the three classes correctly located T2W between T0 and T5W, but 38.71% of samples partially overlapped with T0 and 32.26% with T5W, suggesting that for some patients PR is already beneficial at T2W (32.26% overlapping with T5W), while for others (38.71% overlapping with T0) more time is required. Rehabilitated patients presented several altered biomarkers. In particular, methanol from T0 to T5W decreased in parallel with dyspnea and fatigue, while the walk distance increased. Methanol could be ascribed to lung inflammation. We demonstrated that the metabolic COPD phenotype clearly evolves during PR, with a strict relationship between clinical and molecular parameters. Methanol, correlating with clinical parameters, represents a useful biomarker for monitoring personalized outcomes and establishing more targeted protocols

Innovative analytical methods for the study of low and high weight molecules involved in diseases.

Personalized or precision medicine is an emerging approach to the treatment and prevention of disease, which takes into account the individual variability of genes, environment and lifestyles of each person. High Resolution Mass Spectrometry (HRMS) coupled with Ultra High Performance Liquid Chromatography (UHPLC) plays a fundamental role in characterizing and quantifying proteins and metabolites involved in the disease. The proteomic and metabolomic approaches allow the global identification, not guided by a priori hypothesis, of thousands of proteins and hundreds of metabolites present in a biological fluid and the evaluation of how these can vary in the presence of a specific disease compared to a non-pathological condition, allowing to characterize and discriminate between different study groups. In the first part PhD project has been studied the proteomic profile of autoinflammatory diseases to understand the molecular mechanisms underlying genetic diseases. Here, we analyzed the proteomic signature of unstimulated and stimulated monocytes of patients with FMF, TRAPS and MKD, describing the dysregulated intracellular pathways associated with each condition for the identification of possible biomarkers and possible novel therapeutic targets. The objective of the second part of the project is to build, optimize and share spectral MSMS with Accurate Mass Retention Time (AMRT) database libraries, using different metabolic standards (MSMLS, IROA Technologies), involved in key biological processes, to develop robust identification and quantitative methods because the compound identification process being often challenging and requiring a high degree of confidence