Omics Research Research Articles

Why PNI scientists need to engage in exploratory hypothesis-generating biomarker studies

Multi-omics research is developing rapidly, offering extensive sample analysis options and advanced statistical solutions to identify and understand complex networks of biomarkers. This review encourages groups in the psychoneuroimmunology field with limited experience in omics research to embrace these advances. Cross-sectional studies can leverage existing sample collections to provide unique information that complements longitudinal studies, providing insights into which biological systems may warrant further investigation and building fundamental mechanistic knowledge of biological networks. The understanding of immune-brain interactions should inform ongoing developments in exploratory, hypothesis-generating research. Disregarding psychoneuroimmunological aspects may have led to challenges in some prior biomarker research. Moving forward, a more nuanced perspective on inflammation and psychological comorbidity is needed. The first steps in the conceptualization of an explorative cross-sectional omics study are discussed from a pragmatic perspective, highlighting who we choose to study and what we choose to measure.

AI-driven ethical issues in the development of personalised therapy for pediatric SIRS patients

Abstract Background Systemic Inflammatory Response Syndrome (SIRS) is a rare disease often presenting emergency. While the aetiology varies, current diagnostic criteria do not allow differentiation. Hopes are placed on OMICs research and development of Artificial intelligence (AI) algorithms for personalised disease management. Our report aims at identification and analysis of AI-driven ethical issues within the bigger ERA PerMed funded project of development of tailored immunotherapy for paediatric SIRS patients (TIPS) coordinated by Prof. Catharina Schütz from Technic University Dresden. Methods Systematic literature review followed by qualitative research methodology. Ten experts with different backgrounds were interviewed. Subsequent thematic analysis was performed by two independent researchers. Results Experts were on the opinion that AI can lead to a depart from individual approach in the medicine, as the physician may be induced to only act based on algorithms that are more or less representative (N = 2). Interviewees were aware that AI can lead to changes in the assessment of reality, as it is dependent on data quality (N = 4). AI-aided decision-making might be useful for special therapeutic situations, although it may not always capture the subtleties of individual situations (N = 2). Positive implications of AI were significantly more emphasised, though current frameworks were perceived as more limiting than enabling. Conclusions Personalised therapy is making its first steps while the ethical apprehensions has already been there with a strong emphasis on precautionary principle and resulting limitations on the application of AI-algorithms. The multifaceted nature of the AI-driven ethical issues in personalised medicine research requires an interdisciplinary discourse, strengthened ethics training of all professionals involved in the technology development and more active involvement of physicians in the shaping of AI. Key messages • AI algorithms promise therapeutic benefits, not achievable by other means, for specific categories of vulnerable pediatrics patients. • The multifaceted nature of the AI-driven ethical issues in personalised medicine research requires an interdisciplinary discourse and further ethics training of all stakeholders.

Omics in IgG4-related disease.

Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition - and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.

Omics research in atherosclerosis.

Atherosclerosis (AS) is a chronic inflammatory disease characterized by lipid deposition within the arterial intima, as well as fibrous tissue proliferation and calcification. AS has long been recognized as one of the primary pathological foundations of cardiovascular diseases in humans. Its pathogenesis is intricate and not yet fully elucidated. Studies have shown that AS is associated with oxidative stress, inflammatory response, lipid deposition, and changes in cell phenotype. Unfortunately, there is currently no effective prevention or targeted treatment for AS. The rapid advancement of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has opened up novel avenues to elucidate the fundamental pathophysiology and associated mechanisms of AS. Here, we review articles published over the past decade and focus on the current status, challenges, limitations, and prospects of omics in AS research and clinical practice. Emphasizing potential targets based on omics technologies will improve our understanding of this pathological condition and assist in the development of potential therapeutic approaches for AS-related diseases.

COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields.

The field of metabolomics, which is quintessential in today's omics research, involves the large-scale detection, identification, and quantification of small-molecule metabolites in a wide range of biological samples. Nuclear magnetic resonance spectroscopy (NMR) has emerged as a powerful tool for metabolomics due to its high resolution, reproducibility, and exceptional quantitative nature. One of the key bottlenecks of metabolomics studies, however, remains the accurate and automated analysis of the resulting NMR spectra with good accuracy and minimal human intervention. Here, we present the COLMAR1d platform, consisting of a public web server and an optimized database, for one-dimensional (1D) NMR-based metabolomics analysis to address these challenges. The COLMAR1d database comprises more than 480 metabolites from GISSMO enabling a database query of spectra measured at arbitrary magnetic field strengths, as is demonstrated for spectra acquired between 1H resonance frequencies of 80 MHz and 1.2 GHz of mouse serum, DMEM cell growth medium, and wine. COLMAR1d combines the GISSMO metabolomics database concept with the latest tools for automated processing, spectral deconvolution, database querying, and globally optimized mixture analysis for improved accuracy and efficiency. By leveraging advanced computational algorithms, COLMAR1d offers a user-friendly, automated platform for quantitative 1D NMR-based metabolomics analysis allowing a wide range of applications, including biomarker discovery, metabolic pathway elucidation, and integration with multiomics strategies.

SnPATHO-seq, a versatile FFPE single-nucleus RNA sequencing method to unlock pathology archives

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low RNA quality. In this study, leveraging a recent advance in single-cell genomic technology, we introduce snPATHO-seq, a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples. We benchmarked the performance of the snPATHO-seq workflow against existing 10x 3’ and Flex assays designed for frozen or fresh samples and highlighted the consistency in snRNA-seq data produced by all workflows. The snPATHO-seq workflow also demonstrated high robustness when tested across a wide range of healthy and diseased FFPE tissue samples. When combined with FFPE spatial transcriptomic technologies such as FFPE Visium, the snPATHO-seq provides a multi-modal sampling approach for FFPE samples, allowing more comprehensive transcriptomic characterization.

A review of genetic resources and trends of omics applications in donkey research: focus on China.

Omics methodologies, such as genomics, transcriptomics, proteomics, metabolomics, lipidomics and microbiomics, have revolutionized biological research by allowing comprehensive molecular analysis in livestock animals. However, despite being widely used in various animal species, research on donkeys has been notably scarce. China, renowned for its rich history in donkey husbandry, plays a pivotal role in their conservation and utilization. China boasts 24 distinct donkey breeds, necessitating conservation efforts, especially for smaller breeds facing extinction threats. So far, omics approaches have been employed in studies of donkey milk and meat, shedding light on their composition and quality. Similarly, omics methods have been utilized to explore the molecular basis associated with donkey growth, meat production, and quality traits. Omics analysis has also unraveled the critical role of donkey microbiota in health and nutrition, with gut microbiome studies revealing associations with factors such as pregnancy, age, transportation stress, and altitude. Furthermore, omics applications have addressed donkey health issues, including infectious diseases and reproductive problems. In addition, these applications have also provided insights into the improvement of donkey reproductive efficiency research. In conclusion, omics methodologies are essential for advancing knowledge about donkeys, their genetic diversity, and their applications across various domains. However, omics research in donkeys is still in its infancy, and there is a need for continued research to enhance donkey breeding, production, and welfare in China and beyond.

Potential of plasma biomarkers for heart failure prediction, management, and prognosis: A multiomics perspective.

Heart failure (HF) remains a major global health challenge, and more effective and comprehensive plasma biomarkers are needed to effectively treat HF patients. Multiomics studies have shown that DNA fragments, noncoding RNAs, proteins, and metabolites may be potential plasma biomarkers for HF. However, comprehensive reviews that focus on research on plasma biomarkers for HF from an omics perspective are lacking. This review summarizes the applications of various omics approaches in the exploration of biomarkers related to the risk assessment, diagnosis, subtype classification, medical management, and prognosis prediction of HF. Moreover, as heart transplantation and left ventricular assistant device (LVAD) implantation are terminal therapies for end-stage HF patients, this review also discusses the role of cell-free DNA as a biomarker for cardiac transplant rejection and omics studies of plasma biomarkers in patients who respond to LVAD therapy. Our findings suggest that future omics research on HF biomarkers should employ integrated multiomics methods and expand the sample size to increase the robustness of the results and that the identified biomarkers should be further validated in large cohorts.

Transcriptomic profiling of lung fibroblasts in silicosis: Regulatory roles of Nrf2 agonists in a mouse model

Silicosis is an occupational disease caused by long-term inhalation of free silica, resulting in a significant global health burden. Its pathogenesis remains unclear, and there is no effective treatment. Proliferative and activated myofibroblasts play a key role in the development of silicosis. Traditional studies have focused on fibroblast proliferation and collagen secretion, neglecting their functional heterogeneity. With the advancement of omics research, more pathogenic fibroblast subgroups and their functions have been identified. In this study, we applied transcriptomics to analyze gene changes in primary lung fibroblasts during silicosis development using a mouse model. Our results indicate that DEGs are enriched in collagen secretion, ECM synthesis, leukocyte migration, and chemotaxis functions. Altered core genes are associated with immune cell recruitment and cell migration. Nrf2 agonists, known for anti-inflammatory and antioxidant properties, have shown potential therapeutic effects in fibrotic diseases. However, their effects on fibroblasts in silicosis are not fully understood. We used four common Nrf2 agonists to study gene expression changes in lung fibroblasts at the transcriptome level, combined with histopathological and biochemical methods, to investigate their effects on silicosis in mice. Results show that Nrf2 agonists can exert anti-silicosis fibrosis functions by downregulating genes like Fos and Egr1, involved in cell differentiation, proliferation, and inflammation. In conclusion, this study suggests that inflammation-related co-functions of fibroblasts may be a potential mechanism in silicosis pathogenesis. Targeting Nrf2 may be a promising strategy to alleviate oxidative stress and inflammation in silicosis.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BackgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.ResultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism’s specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.ConclusionsWhile we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses.

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

The ROSMAP project: aging and neurodegenerative diseases through omic sciences.

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

Graph pooling in graph neural networks: methods and their applications in omics studies

Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications).

The Importance of Proteomics in Saliva and Tears as Potential Non-invasive Methods for Identifying Biomarkers in the Prognosis and Diagnosis of Multiple Sclerosis Patients

: Over the past two decades, the advancement of analytical examinations like proteomics for investigating neuronal-related biomarkers has emerged as one of the most important and effective tools for clinical evaluation and prognosis. However, there remains an unmet need in this area. With advances in quantifying methods and detection in omics research, including proteomics and metabolomics, the close association of the salivary glands and tears with the nervous system has become increasingly evident. As noninvasive specimens, saliva and tears serve as attractive substitutes for neuronal biomarkers, containing numerous proteins and metabolites that represent various ailments in neurodegenerative illnesses like multiple sclerosis (MS). These noninvasive biomarkers might potentially correlate with susceptibility, severity, and pathogenesis of neurological disorders and could be utilized in early diagnosis and prognosis. Therefore, tear and salivary proteomics present novel insights into understanding disease progression and offer personalized treatment options with greater sensitivity. This approach helps highlight the most relevant experimental outcomes related to tear and salivary biomarkers for multiple sclerosis.

Full-Length Transcriptome Construction and Systematic Characterization of Virulence Factor-Associated Isoforms in Vairimorpha (Nosema) Ceranae.

Vairimorpha (Nosema) ceranae is a single-cellular fungus that obligately infects the midgut epithelial cells of adult honeybees, causing bee microsporidiosis and jeopardizing bee health and production. This work aims to construct the full-length transcriptome of V. ceranae and conduct a relevant investigation using PacBio single-molecule real-time (SMRT) sequencing technology. Following PacBio SMRT sequencing, 41,950 circular consensus (CCS) were generated, and 25,068 full-length non-chimeric (FLNC) reads were then detected. After polishing, 4387 high-quality, full-length transcripts were gained. There are 778, 2083, 1202, 1559, 1457, 1232, 1702, and 3896 full-length transcripts that could be annotated to COG, GO, KEGG, KOG, Pfam, Swiss-Prot, eggNOG, and Nr databases, respectively. Additionally, 11 alternative splicing (AS) events occurred in 6 genes were identified, including 1 alternative 5' splice-site and 10 intron retention. The structures of 225 annotated genes in the V. ceranae reference genome were optimized, of which 29 genes were extended at both 5' UTR and 3' UTR, while 90 and 106 genes were, respectively, extended at the 5' UTR as well as 3' UTR. Furthermore, a total of 29 high-confidence lncRNAs were obtained, including 12 sense-lncRNAs, 10 lincRNAs, and 7 antisense-lncRNAs. Taken together, the high-quality, full-length transcriptome of V. ceranae was constructed and annotated, the structures of annotated genes in the V. ceranae reference genome were improved, and abundant new genes, transcripts, and lncRNAs were discovered. Findings from this current work offer a valuable resource and a crucial foundation for molecular and omics research on V. ceranae.

Bibliometric analysis and visualisation of research hotspots and frontiers on omics in osteosarcoma

Background/objectiveOmics technology has become a widely applied biological science that can be used to study the etiology, pathogenesis, and treatment of osteosarcoma(OS). Bibliometric analysis is still blank in this field.This study aimed to access the trends and hotspots of omics in OS research through the bibliometric analysis method.MethodsRelevant articles and reviews from 1999 to 2023 were retrieved from the Web of Science Core Collection. The data were processed with CiteSpace, and some graphs were generated with Graphpad, VOSviewer, Scimago Graphica, Bibliometrix and R Studio.ResultsA total of 1581 papers were included. China (569, 36.0%) and the United States (523, 33.1%) took the dominant position in the number of published papers, and the links between countries most frequently occurred between North America and East Asia, and between Australia and Europe. Top institutions with the highest number of publications were almost located in the United States, with The University of Texas MD Anderson Cancer Center contributing the most (44, 2.78%). Among the researchers in this field, Cleton-Jansen AM was the author with the highest number of articles in the field (20, 1.27%). According to the keyword cluster analysis, most studies focused on the “comparative genomic hybridization” before 2012. The latest surge words “tumor microenvironment” and “immune infiltration” in the keyword heatmap indicate future research directions.ConclusionOur study provided the current status of the omics research in OS on a global level and the hottest directions. The field of omics in OS was developing rapidly, and the main focuses of research were revealing the characteristics of tumor microenvironment of OS and how to activate the immune system to fight cancer cells. Research on the immune microenvironment and its relationship with genetic aberrations of OS will be a priority in the future.

Beyond a single factor: Cutting-Edge Approaches in Environmental Mixtures and Omics Research

Beyond a single factor: Cutting-Edge Approaches in Environmental Mixtures and Omics Research

Unveiling the Secrets of Oil Palm Genetics: A Look into Omics Research.

Oil palm is a versatile oil crop with numerous applications. Significant progress has been made in applying histological techniques in oil palm research in recent years. Whole genome sequencing of oil palm has been carried out to explain the function and structure of the order genome, facilitating the development of molecular markers and the construction of genetic maps, which are crucial for studying important traits and genetic resources in oil palm. Transcriptomics provides a powerful tool for studying various aspects of plant biology, including abiotic and biotic stresses, fatty acid composition and accumulation, and sexual reproduction, while proteomics and metabolomics provide opportunities to study lipid synthesis and stress responses, regulate fatty acid composition based on different gene and metabolite levels, elucidate the physiological mechanisms in response to abiotic stresses, and explain intriguing biological processes in oil palm. This paper summarizes the current status of oil palm research from a multi-omics perspective and hopes to provide a reference for further in-depth research on oil palm.

Understanding the Cryptosporidium species and their challenges to animal health and livestock species for informed development of new, specific treatment strategies

Cryptosporidium species are parasitic organisms of vertebrates with a worldwide distribution. They have an important impact globally upon human and animal health, and livestock productivity. The life cycle of these species is complex and difficult to disrupt to improve human health, animal health, food security and economic growth. This may contribute to the fact that no new treatment strategy has been widely accepted or applied in livestock for years. Here we consider the natural history of these parasites, their biochemistry and economic impact. Using recent developments in understanding these parasites we then consider viable and affordable approaches to enhancing control of their effects on livestock. These are based on advances in drug discovery, omics research and artificial intelligence applications to human and veterinary medicine that indicate putative new therapeutic approaches.

Validity and reproducibility of food group intakes in a self-administered food frequency questionnaire for genomic and omics research: the Tohoku Medical Megabank Project.

The Tohoku Medical Megabank Project (TMM) was established to realize personalized healthcare and medicine using genomic and omics data. This study evaluated the validity and reproducibility of food group intakes derived from a self-administered food frequency questionnaire (TMM-FFQ) that included the response option "constitutionally unable to eat/drink it" among community-dwelling Japanese adults. Participants comprised 89 men and 124 women aged ≥20 years from Miyagi Prefecture. Participants completed weighed food records (WFRs) for 3 consecutive days per season as reference intake and FFQs in 2019 (FFQ1) and 2021 (FFQ3). Spearman's rank correlation coefficients (CCs) were calculated for correlations between food group intakes estimated from the 12-day WFR and FFQ3 (validity), and for correlations between those estimated from the FFQ1 and FFQ3 (reproducibility). Cross-classification according to quintiles using FFQ and WFR data was also performed. The percentage of participants who chose the "constitutionally unable to eat/drink it" option was non-negligible for some food groups. In the validity analysis, CCs were >0.40 for many food groups; the median across 21 food groups was 0.49 in men and 0.45 in women. The median percentages of cross-classification into exact plus adjacent quintiles were 73.0% in men and 66.9% in women. In the reproducibility analysis, CCs were >0.50 for many food groups; the median across 21 food groups was 0.60 in men and 0.51 in women. The validity of the TMM-FFQ compared with 12-day WFR and the reproducibility of the TMM-FFQ were reasonable for food groups in the TMM cohort studies.