Omics Research Research Articles

Improving Replication in Endometrial Omics: Understanding the Influence of the Menstrual Cycle.

The dynamic nature of human endometrial tissue presents unique challenges in analysis. Despite extensive research into endometrial disorders such as endometriosis and infertility, recent systematic reviews have highlighted concerning issues with the reproducibility of omics studies attempting to identify biomarkers. This review examines factors contributing to poor reproducibility in endometrial omics research. Hormonal fluctuations in the menstrual cycle lead to widespread molecular changes in the endometrium, most notably in gene expression profiles. In this review, we examine the variability in omics data due to the menstrual cycle and highlight the importance of accurate menstrual cycle dating for effective statistical modelling. The current standards of endometrial dating lack precision and we make the case for using molecular-based modelling methods to estimate menstrual cycle time for endometrium tissue samples. Additionally, we discuss statistical considerations such as confounding and interaction effects, as well as the importance of recording the detailed and accurate clinical information of patients. By addressing these methodological challenges, we aim to establish more robust and reproducible research practises, increasing the reliability of endometrial omics research and biomarker discovery.

Insights into the molecular underpinning of type 2 diabetes complications.

Type 2 diabetes (T2D) complications pose a significant global health challenge. Omics technologies have been employed to investigate these complications and identify the biological pathways involved. In this review, we focus on four major T2D complications: diabetic kidney disease, diabetic retinopathy, diabetic neuropathy, and cardiovascular complications. We discuss advancements in omics research, summarizing findings from genetic, epigenomic, transcriptomic, proteomic, and metabolomic studies across different ancestries and disease-relevant tissues. We stress the importance of integrating multi-omics techniques to elucidate the biological mechanisms underlying T2D complications and advocate for ancestrally diverse studies. Ultimately, these insights will improve risk prediction for T2D complications and inform translation strategies.

The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.

Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi-omics datasets, AI can provide real-time, patient-specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI-driven discoveries to ensure their efficacy, relevance, and cost-effectiveness in patient care.

Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models.

Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research. Therefore, this study aimed to analyze RNA-seq data by ML method to identify differentially expressed genes (DEGs) affecting IMF deposition in pigs. In this study, a total of 74 RNA-seq data from muscle tissue samples were used. A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. KEGG pathway enrichment analysis of the intersecting genes revealed that these genes were enriched in pathways associated with lipid deposition. These pathways include α-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism. Four key genes affecting intramuscular fat deposition, PLA2G6, MPV17, NUDT2, and ND4L, were identified based on significant pathways. The results of this study are important for the elucidation of the molecular regulatory mechanism of intramuscular fat deposition and the effective improvement of IMF content in pigs.

Omics approaches: Role in acute myeloid leukemia biomarker discovery and therapy.

Acute myeloid leukemia (AML) is the most common acute leukemia in adults and has the highest fatality rate. Patients aged 65 and above exhibit the poorest prognosis, with a mere 30 % survival rate within one year. One important issue in optimizing outcomes for AML patients is their limited ability to predict responses to specific therapies, response duration, and likelihood of relapse. Despite rigorous therapeutic interventions, a significant proportion of patients experience relapse. Consequently, there is a pressing need to introduce new targets for therapy. Sequencing and biotechnology have come a long way in the last ten years. This has made it easier for many omics technologies, like genomics, transcriptomics, proteomics, and metabolomics, to study molecular mechanisms of AML. An integrative approach is necessary to understand a complex biological process fully and offers an important opportunity to understand the information underlying diseases. In this review, we studied papers published between 2010 and 2024 employing omics approaches encompassing diagnosis, prognosis, and risk stratification of AML. Finally, we discuss prospects and challenges in applying -omics technologies to the discovery of novel biomarkers and therapy targets. Our review may be helpful for omics researchers who want to study AML from different molecular aspects.

Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method.

Imaging mass spectrometry (IMS) is a technique for simultaneously acquiring the expression and distribution of molecules on the surface of a sample, and it plays a crucial role in spatial omics research. In IMS, the time cost and instrument load required for large data sets must be considered, as IMS typically involves tens of thousands of pixels or more. In this study, we developed a high-resolution method for IMS data reconstruction using a window-based Adversarial Autoencoder (AAE) method. We acquired IMS data from partial cerebellum regions of mice with a pitch size of 75 μm and then down-sampled the data to a pitch size of 150 μm, selecting 22 m/z peak intensity values per pixel. We established an AAE model to generate three pixels from the surrounding nine pixels within a window to reconstruct the image data at a pitch size of 75 μm. Compared with two alternative interpolation methods, Bilinear and Bicubic interpolation, our window-based AAE model demonstrated superior performance on image evaluation metrics for the validation data sets. A similar model was constructed for larger mouse kidney tissues, where the AAE model achieved high image evaluation metrics. Our method is expected to be valuable for IMS measurements of large animal organs across extensive areas.

Thraustochytrids: Evolution, Ultrastructure, Biotechnology, and Modeling.

The thraustochytrids are a group of marine protists known for their significant ecological roles as decomposers and parasites as well as for their potential biotechnological applications, yet their evolutionary and structural diversity remains poorly understood. Our review critically examines the phylogeny of this taxa, utilizing available up-to-date knowledge and their taxonomic classifications. Additionally, advanced imaging techniques, including electron microscopy, are employed to explore the ultrastructural characteristics of these organisms, revealing key features that contribute to their adaptive capabilities in varying marine environments. The integration of this knowledge with available omics data highlights the huge biotechnological potential of thraustochytrids, particularly in producing ω-3 fatty acids and other bioactive compounds. Our review underscores the importance of a systems biology approach in understanding thraustochytrids biology and highlights the urgent need for novel, accurate omics research to unlock their full biotechnological potential. Overall, this review aims to foster a deeper appreciation of thraustochytrids by synthesizing information on their evolution, ultrastructure, and practical applications, thereby providing a foundation for future studies in microbiology and biotechnology.

AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability.

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, highlighting successful applications and case studies across diverse sectors. From predicting genotoxicity and mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer a comprehensive framework for understanding chemical exposures and mitigating associated health risks. Future perspectives for advancing chemical risk assessment and cancer prevention through data integration, advanced machine learning techniques, translational research, and policy implementation are discussed. By implementing the predictive capabilities of AI/ML and omics technologies, researchers and policymakers can enhance public health protection, inform regulatory decisions, and promote sustainable development for a healthier future.

Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.

Alzheimer's disease (AD) is a multidimensional, complex condition that affects individuals all over the world. Despite decades of experimental and clinical research that has revealed various processes, many concerns concerning the origin of Alzheimer's disease remain unresolved. Despite the notion that there isn't a complete set of jigsaw pieces, the growing number of public data-sharing initiatives that collect biological, clinical, and lifestyle data from those suffering from Alzheimer's disease has resulted in virtually endless volumes of knowledge about the disorder, far beyond what humans can comprehend. Furthermore, combining Big Data from multi- -omics research gives a chance to investigate the pathophysiological processes underlying the whole biological spectrum of Alzheimer's disease. To improve knowledge on the subject of Alzheimer's disease, Artificial Intelligence (AI) offers a wide variety of approaches for evaluating complex and significant data. The introduction of next-generation sequencing and microarray technologies has resulted in significant growth in genetic data research. When it comes to assessing such complex projects, AI technology beats conventional statistical techniques of data processing. This review focuses on current research and potential challenges for AI in Alzheimer's disease research. This article, in particular, examines how AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies. Ultimately, scientists want to develop individualized, efficient medicines.

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.

Artificial intelligence and omics in malignant gliomas.

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemotherapy and radiotherapy, the prognosis remains unfavorable. The heterogeneity of GBM and the presence of the blood-brain barrier further complicate the therapeutic process. It is crucial to adopt a multifaceted approach in GBM research to understand its biology and advance toward effective treatments. In particular, omics research, which primarily includes genomics, transcriptomics, proteomics, and epigenomics, helps us understand how GBM develops, finds biomarkers, and discovers new therapeutic targets. The availability of large-scale multiomics data requires the development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large omics databases. Although the application of AI tools in GBM-omics is currently in its early stages, a thorough exploration of AI utilization to uncover different aspects of GBM (subtype classification, prognosis, and survival) would have a significant impact on both researchers and clinicians. Here, we aim to review and provide database resources of different AI-based techniques that have been used to study GBM pathogenesis using multiomics data over the past decade. We summarize different types of GBM-related omics resources that can be used to develop AI models. Furthermore, we explore various AI tools that have been developed using either individual or integrated multiomics data, highlighting their applications and limitations in the context of advancing GBM research and treatment.

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.