Multi-omics approaches to disease

Artificial intelligence and the impact of multiomics on the reporting of case reports

The integration of artificial intelligence (AI) and multiomics has transformed clinical and life sciences, enabling precision medicine and redefining disease understanding. Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022, with AI research tripling during this period. Multiomics fields, including genomics and proteomics, also advanced, exemplified by the Human Proteome Project achieving a 90% complete blueprint by 2021. This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting. A review of studies and case reports was conducted to evaluate AI and multiomics integration. Key areas analyzed included diagnostic accuracy, predictive modeling, and personalized treatment approaches driven by AI tools. Case examples were studied to assess impacts on clinical decision-making. AI and multiomics enhanced data integration, predictive insights, and treatment personalization. Fields like radiomics, genomics, and proteomics improved diagnostics and guided therapy. For instance, the “AI radiomics, genomics, oncopathomics, and surgomics project” combined radiomics and genomics for surgical decision-making, enabling preoperative, intraoperative, and postoperative interventions. AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data. AI and multiomics enable standardized data analysis, dynamic updates, and predictive modeling in case reports. Traditional reports often lack objectivity, but AI enhances reproducibility and decision-making by processing large datasets. Challenges include data standardization, biases, and ethical concerns. Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine. AI and multiomics integration is revolutionizing clinical research and practice. Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential. Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.

Advancing fish disease research through CRISPR-Cas genome editing: Recent developments and future perspectives

CRISPR-Cas genome editing technology has transformed genetic research, by enabling unprecedented precision in modifying DNA sequences across various organisms, including fish. This review explores the significant advancements and potential uses of CRISPR-Cas technology in the study and management of fish diseases, which pose serious challenges to aquaculture and wild fish populations. Fish diseases cause significant economic losses and environmental impacts, therefore effective disease control a top priority. The review highlights the pivotal role of CRISPR-Cas in identifying disease-associated genes, which is critical to comprehending the genetic causes of disease susceptibility and resistance. Some studies have reported key genetic factors that influence disease outcomes, using targeted gene knockouts and modifications to pave the way for the development of disease-resistant fish strains. The creation of such genetically engineered fish holds great promise for enhancing aquaculture sustainability by reducing the reliance on antibiotics and other conventional disease control measures. In addition, CRISPR-Cas has facilitated in-depth studies of pathogen-host interactions, offering new insights into the mechanisms by which pathogens infect and proliferate within their hosts. By manipulating both host and pathogen genes, this technology provides a powerful tool for uncovering the molecular underpinnings of these interactions, leading to the development of more effective treatment strategies. While CRISPR-Cas has shown great promise in fish research, its application remains limited to a few species, primarily model organisms and some freshwater fish. In addition, challenges such as off-target effects, ecological risks, and ethical concerns regarding the release of genetically modified organisms into the environment must be carefully addressed. This review also discusses these challenges and emphasizes the need for robust regulatory frameworks and ongoing research to mitigate risks. Looking forward, the integration of CRISPR-Cas with other emerging technologies, such as multi-omics approaches, promises to further advance our understanding and management of fish diseases. This review concludes by envisioning the future directions of CRISPR-Cas applications in fish health, underscoring its potential to its growing in the field.

A novel tool for multi-omics network integration and visualization: A study of glioma heterogeneity

Gliomas are highly heterogeneous tumors with generally poor prognoses. Leveraging multi-omics data and network analysis holds great promise in uncovering crucial signatures and molecular relationships that elucidate glioma heterogeneity. However, the complexity of the problem and the high dimensionality of the data increase the challenges of integrating information across various biological levels. This study develops a comprehensive framework aimed at identifying potential glioma-type-specific biomarkers through innovative variable selection and integrated network visualization. We designed a two-step framework for variable selection using sparse network estimation across various omics datasets. This framework incorporates MINGLE (Multi-omics Integrated Network for GraphicaL Exploration), a novel methodology designed to merge distinct multi-omics information into a single network, enabling the identification of underlying relations through an innovative integrated visualization. The analysis was conducted using glioma omics datasets, with patients grouped based on the latest glioma classification guidelines. Our investigation of the glioma data led to the identification of variables potentially serving as glioma-type-specific biomarkers. The integration of multi-omics data into a single network through MINGLE facilitated the discovery of molecular relationships that reflect glioma heterogeneity, supporting the biological interpretation. Scripts and files for reproducing the analysis or adapting it to other applications, are available in R software.

An Attention-Aware Multi-Task Learning Framework Identifies Candidate Targets for Drug Repurposing in Sarcopenia

BACKGROUND

Sarcopenia presents a pressing public health concern due to its association with age-related muscle mass decline, strength loss and reduced physical performance, particularly in the growing older population. Given the absence of approved pharmacological therapies for sarcopenia, the need to discover effective pharmacological interventions has become critical.

METHODS

To address this challenge and discover new therapies, we developed a novel Multi-Task Attention-aware method for Multi-Omics data (MTA-MO) to extract complex biological insights from various biomedical data sources, including transcriptome, methylome and genome data to identify drug targets and discover new therapies. Additionally, MTA-MO integrates human protein-protein interaction (PPI) networks and drug-target networks to improve target identification. The novel method is applied to a multi-omics dataset that included 1055 participants aged 20-50 (mean (± SD) age 36.88 (± 8.64)), comprising 37.82% African-American and 62.18% Caucasian/White individuals. Physical activity levels were self-reported and categorized into three groups: ≥ 3 times/week, < 3 times/week and no regular exercise. Mean (± SD) measures for grip strength, appendicular lean mass (ALM), exercise frequency and smoking status (no/yes, n (%)) were 38.72 (± 8.93) kg, 28.65 (± 4.63) kg, 4.31 (± 1.79) and 30.81%/69.19%, respectively. Significant differences (p < 0.05) were found between groups in age, ALM, smoking, and consumption of milk, alcohol, beer and wine.

RESULTS

Using the MTA-MO method, we identified 639 gene targets, and by analysing PPIs and querying public databases, we narrowed this list down to seven potential hub genes associated with sarcopenia (ESR1, ATM, CDC42, EP300, PIK3CA, EGF and PTK2B). These findings were further validated through diverse levels of pathobiological evidence associated with sarcopenia. Gene Ontology and KEGG pathways analysis highlighted five key functions and signalling pathways relevant to skeletal muscle. The interaction network analysis identified three transcriptional factors (GATA2, JUN and FOXC1) as the key transcriptional regulators of the seven potential genes. In silico analysis of 1940 drug candidates identified canagliflozin as a promising candidate for repurposing in sarcopenia, demonstrating the strongest binding affinity to the PTK2B protein (inhibition constant 6.97 μM). This binding is stabilized by hydrophobic bonds, Van der Waals forces, pi-alkyl interactions and pi-anion interactions around PTK2B's active residues, suggesting its potential as a therapeutic option.

CONCLUSIONS

Our novel approach effectively integrates multi-omics data to identify potential treatments for sarcopenia. The findings suggest that canagliflozin could be a promising therapeutic candidate for sarcopenia.

Paeoniflorin inhibits pyruvate dehydrogenase kinase 3 and promotes BDNF activity by modulating neuronal activity and TNF-α

Metabolic dysregulation causes diseases like diabetes and cancer, making PDKs attractive targets. However, a thorough investigation into the unique roles played by the different members of the PDK family, especially PDK3, about memory loss and related diseases like Alzheimer's disease (AD) is still lacking. The current study investigates PF's potential to reduce PDK3-associated toxicity in neurodegenerative illnesses, including AD. The association between PF and PDK3 presents a significant opportunity for medication development and AD therapy approaches. PF efficiently suppresses PDK3 activity, as demonstrated by molecular docking and biophysical characterization, providing an in-depth understanding of their molecular interactions. PF significantly inhibited PDK3 in a concentration-dependent manner with an IC50 value of 4.88 µM. Considering this, the current investigation also explores the biological component of PF, which exhibits potential in treating AD and is primarily associated with neuroprotection. In the present study, a 3-hour pre-treatment of PF was administered at varying concentrations (4, 6, and 8 µM) in response to the 24-hour SCP (2 mM)-mediated toxicity. Based on the results of in silico and biophysical characterization, it is concluded that PF inhibits the PDK3 activity. Additionally, it can enhance cell viability, suppress ROS expression, impede apoptosis, and downregulate TNF-α expression. When combined, these actions help to prevent neuronal death in an in vitro model of SCP. PF strengthens the memory marker, which is confirmed through BDNF expression. This study found that all results were more effective at lower and moderate doses of PF. Our research indicates that PF boosts memory, decelerates the progression of oxidative stress, and could potentially serve as a dose-dependent treatment for AD.

Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases

Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.

Metabolic and proteomic profiles provide insights on mechanism of late onset Pompe disease

Late onset Pompe disease (LOPD) is caused by a deficiency of the enzyme acid α-glucosidase, resulting in glycogen accumulation in lysosomes. The mechanism of LOPD has been less explored. In this study, we used an integrative analysis of the proteomics and metabolomics of LOPD muscle samples to reveal the potential mechanisms. Proteomic analysis identified 635 upregulated proteins and 89 downregulated proteins in the LOPD group. Similarly, metabolomic analysis revealed 15 upregulated and 143 downregulated metabolites; notably, L-arginine levels were significantly decreased in the LOPD group. Lysosome-related GO terms were significantly upregulated, while GO terms related to neurofilament, cytoskeleton, axon ensheathment, and myelin sheath were significantly downregulated. KEGG pathway analysis demonstrated that the lysosome, autophagy, and mTOR pathways were distinctly upregulated. Correlation analysis indicated that CALML3 showed a potential correlation with LOPD severity. Our study highlighted the potential crosstalk among these LOPD-related pathways. Supplementation with L-arginine could represent a promising therapeutic approach for LOPD, and CALML3 could serve as a potential biomarker for LOPD severity. These findings provide valuable insights into the pathogenesis of LOPD and suggest avenues for future therapeutic development.

Lipidomics-driven drug discovery and delivery strategies in glioblastoma

With few viable treatment options, glioblastoma (GBM) is still one of the most aggressive and deadly types of brain cancer. Recent developments in lipidomics have demonstrated the potential of lipid metabolism as a therapeutic target in GBM. The thorough examination of lipids in biological systems, or lipidomics, is essential to comprehending the changed lipid profiles found in GBM, which are linked to the tumor's ability to grow, survive, and resist treatment. The use of lipidomics in drug delivery and discovery is examined in this study, focusing on how it may be used to find new biomarkers, create multi-target directed ligands, and improve drug delivery systems. We also cover the use of FDA-approved medications, clinical trials that use lipid-targeted medicines, and the integration of lipidomics with other omics technologies. This study emphasizes lipidomics as a possible tool in developing more effective treatment methods for GBM by exploring various lipid-centric techniques.

Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction

Computational systems biology employs computational algorithms and integrates diverse data sources, such as gene expression profiles, molecular interactions, and network modeling, to identify promising drug candidates through repurposing existing compounds in response to urgent healthcare needs. This study tackles the urgent need for rapid therapeutic development against emerging infectious diseases. We introduce a novel analytic expression for sensitivity analysis based on the Kronecker product and enhance model prediction performance using Graph Convolutional Networks (GCNs) with sensitivity-based graph reduction. Our algorithm refines prediction performance by leveraging sensitivity-based graph reduction. By integrating RNA-seq data, molecular interactions, and GCNs, we identify disease-related genes and pathways, construct heterogeneous graph models, and predict potential drugs. This approach involves novel analytical expressions that assess sensitivity to model loss, employing the Kronecker product approach. Subgraph analysis identifies nodes for removal, leading to a refined graph used for model retraining. This cost-effective pipeline focuses on computational methods for drug repurposing, targeting infectious diseases such as Zika virus and COVID-19 infection. Applied to these infections, our methodology integrates 659 proteins and 703 drugs for Zika virus, and 495 proteins and 468 drugs for COVID-19, along with their interactions derived from gene expression profiles. Top candidate drugs, such as Betamethasone phosphate and Bizelesin for Zika virus, and Chloroquine, Heparin Disaccharide, and Resveratrol for COVID-19, were validated through literature review or docking analysis. This scalable approach demonstrates promise in repurposing drugs for urgent healthcare challenges.

Systematic construction of composite radiation therapy dataset using automated data pipeline for prognosis prediction

BACKGROUND

Existing research on medical data has primarily focused on single time-points or single-modality data. This study aims to collect all data generated during radiotherapy comprehensively to improve the treatment and prognosis of patients with malignant tumors.

METHODS

The data collected from each medical institution were transmitted to the lead organization, where they underwent a file integrity check and were processed using a data pipeline. The key metadata of the collected data were compiled into a database, which were examined by data analysts to identify outliers based on theoretical and institution-specific characteristics. Appropriate filters were applied and the filtered data were subsequently reviewed by artificial intelligence (AI)-based models and researchers for radiotherapy organ slides. Finally, they were annotated by specialists.

RESULTS

The final dataset included 30,136 three-dimensional cone-beam computed tomography scans and 5,019 tabular data entries collected from 5,019 patients. It comprised 2,043,162 Digital Imaging and Communications in Medicine-format files with a total file size of 832 GB. Quality verification of the data using AI models revealed high classification performance for most organs, with relatively poor performance for the rectum. Overall, the macro AUROC value was 0.947.

CONCLUSIONS

This study implemented an automated data pipeline and AI-based verification to enhance the quality of collected radiotherapy data. The constructed dataset can be utilized for various types of future research and is expected to contribute to the improvement of radiotherapy efficiency.

Exploring oncology treatment strategies with tyrosine kinase inhibitors through advanced 3D models (Review)

The limitations of two-dimensional (2D) models in cancer research have hindered progress in fully understanding the complexities of drug resistance and therapeutic failures. However, three-dimensional (3D) models provide a more accurate representation of in vivo environments, capturing critical cellular interactions and dynamics that are essential in evaluating the efficacy and toxicity of tyrosine kinase inhibitors (TKIs). These advanced models enable researchers to explore drug resistance mechanisms with greater precision, optimizing treatment strategies and improving the predictive accuracy of clinical outcomes. By leveraging 3D models, it will be possible to deepen the current understanding of TKIs and drive forward innovations in cancer treatment. The present review discusses the limitations of 2D models and the transformative impact of 3D models on oncology research, highlighting their roles in addressing the challenges of 2D systems and advancing TKI studies.

Fast and efficient method for parallel construction of targeted exome and methylome single-stranded DNA sequencing libraries

Based on single-stranded DNA library method, we established an efficient workflow to parallelly construct targeted genomic and epigenomic sequencing libraries from a small amount of DNA. We applied the protocol to nine pediatric brain cancer DNA samples containing various extents of damage from formalin fixation and/or DNA oxidation. Compared to our previous study, the new exome protocol showed superior uniformity of coverage. Many artifactual mutation calls introduced by DNA damages were eliminated by bioinformatics filtering tools. After filtration, 89.4-97.0% of somatic single nucleotide variant (SNV) calls generated by double-stranded DNA library were reproduced in formalin-fixed paraffin-embedded (FFPE) samples, which was achieved with substantially reduced DNA input amounts (26.7-50ng). In methylome analysis, we obtained methylation calls for 78-92% of target CpGs with at least 10x coverage when using 100ng of FFPE DNA, which is comparable to those obtained from fresh frozen samples. We also obtained SNV calls from methylome data, recovering 39-76% of filtered SNVs from exome data in nine brain cancer samples. In conclusion, we present a simple protocol for parallel construction of targeted exome and methylome sequencing libraries, which was successfully applied to damaged brain cancer DNA samples from FFPE tissues stored for prolonged periods.

TDMFS: Tucker decomposition multimodal fusion model for pan-cancer survival prediction

Integrated analysis of multimodal data offers a more comprehensive view for cancer survival prediction, yet it faces challenges like computational intensity, overfitting, and challenges in achieving a unified representation due to data heterogeneity. To address the above issues, the first Tucker decomposition multimodal fusion model was hereby proposed for pan-cancer survival prediction (TDMFS). The model employed Tucker decomposition to limit complex tensor parameters during fusion, achieving deep modality integration with reduced computational cost and lower overfitting risk. The individual modality-specific representations were then fully exploited by signal modulation mechanisms in a bilinear pooling decomposition to serve as complementary information for the deep fusion representation. Furthermore, the performance of TDMFS was evaluated using a 5-fold cross-validation method with two modal data, gene expression (GeneExpr), and copy number variation (CNV), for 33 cancers from The Cancer Genome Atlas (TCGA) database. The experiments demonstrated that the proposed TDMFS model achieved an average C-index of 0.757 across 33 cancer datasets, with a C-index exceeding 0.80 on 10 of these datasets. Survival curves for both high and low risk patients plotted on 27 cancer datasets were statistically significant. The TDMFS model demonstrated superior performance in survival prediction, outperforming models like LinearSum and Multimodal Factorisation Higher Order Pooling, making it a valuable asset for advancing clinical cancer research.

Multimodal insights into adult neurogenesis: An integrative review of multi-omics approaches

Adult neural stem cells divide to produce neurons that migrate to preexisting neuronal circuits in a process named adult neurogenesis. Adult neurogenesis is one of the most exciting areas of current neuroscience, and it may be involved in a range of brain functions, including cognition, learning, memory, and social and behavior changes. While there is a growing number of multi-omics studies on adult neurogenesis, generalized analyses from a multi-omics perspective are lacking. In this review, we summarize studies related to genomics, metabolomics, proteomics, epigenomics, transcriptomics, and microbiomics of adult neurogenesis, and then discuss their future research priorities and potential neighborhoods. This will provide theoretical guidance and new directions for future research on adult neurogenesis.

Applications of Single-Cell Omics Technologies for Induced Pluripotent Stem Cell-Based Cardiovascular Research

Single-cell omics technologies have transformed our investigation of genomic, transcriptomic, and proteomic landscapes at the individual cell level. In particular, the application of single-cell RNA sequencing has unveiled the complex transcriptional variations inherent in cardiac cells, offering valuable perspectives into their dynamics. This review focuses on the integration of single-cell omics with induced pluripotent stem cells (iPSCs) in the context of cardiovascular research, offering a unique avenue to deepen our understanding of cardiac biology. By synthesizing insights from various single-cell technologies, we aim to elucidate the molecular intricacies of heart health and diseases. Beyond current methodologies, we explore the potential of emerging paradigms such as single-cell/spatial omics, delving into their capacity to reveal the spatial organization of cellular components within cardiac tissues. Furthermore, we anticipate their transformative role in shaping the future of cardiovascular research. This review aims to contribute to the advancement of knowledge in the field, offering a comprehensive perspective on the synergistic potential of transcriptomic analyses, iPSC applications, and the evolving frontier of spatial omics.

An integrated approach to understanding the effects of Exposome on Neuroplasticity

Anthropogenic factors are those that occur due to human activities. The exposome is proposed to complement the genome, wherein an individual's exposure begins before birth. The range of exposures includes physical, chemical, dietary, lifestyle, biological, and occupational sources. Exposome has a positive or negative influence on neuroplasticity during different stages of life. A comprehensive study of the exposome is thus necessary to incorporate these factors and their influence on the individual, community, and the population as a whole. Exposomic research and global health present significant opportunities for interdisciplinary research. This review gives an overview of the exposome and its influence on neuroplasticity. It proposes methods to study the exposome on neuroplasticity across the lifespan of the individual. This is possible with the use of self-reported data, large-scale cohort formation, physiological sensors, neuroimaging, omics, molecular biology, and systems approaches. These approaches aim to provide a holistic understanding of an individual's neurological well-being and its implications for the population at large. This will also enable the designing of novel preventive and treatment strategies for managing neurological disorders.

Harnessing ferroptosis for precision oncology: challenges and prospects

The discovery of diverse molecular mechanisms of regulated cell death has opened new avenues for cancer therapy. Ferroptosis, a unique form of cell death driven by iron-catalyzed peroxidation of membrane phospholipids, holds particular promise for targeting resistant cancer types. This review critically examines current literature on ferroptosis, focusing on its defining features and therapeutic potential. We discuss how molecular profiling of tumors and liquid biopsies can generate extensive multi-omics datasets, which can be leveraged through machine learning-based analytical approaches for patient stratification. Addressing these challenges is essential for advancing the clinical integration of ferroptosis-driven treatments in cancer care.

Retinal Biomarkers in Diabetic Retinopathy: From Early Detection to Personalized Treatment

Diabetic retinopathy (DR) is a leading cause of vision loss globally, with early detection and intervention critical to preventing severe outcomes. This narrative review examines the role of retinal biomarkers-molecular and imaging-in improving early diagnosis, tracking disease progression, and advancing personalized treatment for DR. Key biomarkers, such as inflammatory and metabolic markers, imaging findings from optical coherence tomography and fluorescence angiography and genetic markers, provide insights into disease mechanisms, help predict progression, and monitor responses to treatments, like anti-VEGF and corticosteroids. While challenges in standardization and clinical integration remain, these biomarkers hold promise for a precision medicine approach that could transform DR management through early, individualized care.

Exploring Molecular Pathways in Refractive Errors Associated with Inherited Retinal Dystrophies

The term inherited retinal dystrophies (IRDs) refers to a diverse range of conditions characterized by retinal dysfunction, and mostly deterioration, leading to a gradual decay of the visual function and eventually to total vision loss. IRDs have a global impact on about 1 in every 3000 to 4000 individuals. However, the prevalence statistics might differ significantly depending on the exact type of dystrophy and the demographic being examined. The cellular pathophysiology and genetic foundation of IRDs have been extensively studied, however, knowledge regarding associated refractive errors remain limited. This review aims to clarify the cellular and molecular processes that underlie refractive errors in IRDs. We did a thorough search of the current literature (Pubmed, accession Feb 2024), selecting works describing phenotypic differences among genes-related to IRDs, particularly in relation to refractive errors. First, we summarize the wide range of IRDs and their genetic causes, describing the genes and biological pathways connected to the etiology of the disease. We then explore the complex relationship between refractive errors and retinal dysfunction, including how the impairment of the vision-related mechanisms in the retina can affect ocular biometry and optical characteristics. New data about the involvement of aberrant signaling pathways, photoreceptor degeneration, and dysfunctional retinal pigment epithelium (RPE) in the development of refractive errors in IRDs have been examined. We also discuss the therapeutic implications of refractive defects in individuals with IRD, including possible approaches to treating visual impairments. In addition, we address the value of using cutting-edge imaging methods and animal models to examine refractive errors linked to IRDs and suggest future lines of inquiry for identifying new targets for treatment. In summary, this study presents an integrated understanding of the cellular and molecular mechanisms underlying refractive errors in IRDs. It illuminates the intricacies of ocular phenotypes in these conditions and offers a tool for understanding mechanisms underlying isolated refractive errors, besides the IRD-related forms.

iSODA: A Comprehensive Tool for Integrative Omics Data Analysis in Single- and Multi-Omics Experiments

Thanks to the plummeting costs of continuously evolving omics analytical platforms, research centers collect multiomics data more routinely. They are, however, confronted with the lack of a versatile software solution to harmoniously analyze single-omics and interpret multiomics data. We have developed iSODA, a web-based application for the analysis of single- and multiomics data. The tool emphasizes intuitive interactive visualizations designed for user-driven data exploration. Researchers can access a variety of functions ranging from simple visualization like volcano plots and PCA to advanced functional analyses like enrichment analysis and lipid saturation analysis. For integrated multiomics, iSODA incorporates multi-omics factor analysis and similarity network fusion. The ability to adapt the data on-the-fly allows for tasks, such as the removal of outlier samples or failed features, imputation, or normalization. All results are presented through interactive plots, the modular design supports extensions, and tooltips and tutorials provide comprehensive guidance for users. iSODA is accessible under http://isoda.online/.

Researcher views on returning results from multi-omics data to research participants: insights from The Molecular Transducers of Physical Activity Consortium (MoTrPAC) Study

BACKGROUND

There is growing consensus in favor of returning individual specific research results that are clinically actionable, valid, and reliable. However, deciding what and how research results should be returned remains a challenge. Researchers are key stakeholders in return of results decision-making and implementation. Multi-omics data contains medically relevant findings that could be considered for return. We sought to understand researchers' views regarding the potential for return of results for multi-omics data from a large, national consortium generating multi-omics data.

METHODS

Researchers from the Molecular Transducers of Physical Activity Consortium (MoTrPAC) were recruited for in-depth semi-structured interviews. To assess understanding of potential clinical utility for types of data collected and attitudes towards return of results in multi-omic clinical studies, we devised an interview guide focusing on types of results generated in the study for hypothetical return based on review of the literature and professional expertise of team members. The semi-structured interviews were recorded, transcribed verbatim and co-coded. Thematic trends were identified for reporting.

RESULTS

We interviewed a total of 16 individuals representative of 11 sites and 6 research roles across MoTrPAC. Many respondents expressed positive attitudes regarding hypothetical multi-omics results return, citing participant rights to their data and perception of minimal harm. Ethical and logistical concerns around the return of multi-omics results were raised, and they often mirrored those in the published literature for genomic return of results including: uncertain clinical validity, a lack of expertise to communicate results, and an unclear obligation regarding whether to return multi-omics results. With the exception of privacy concerns, respondents were able to give examples within multi-omics of how each point was relevant. Further, researchers called for more guidance from funding agencies and increased researcher education regarding return of results.

CONCLUSION

Overall, researchers expressed positive attitudes toward multi-omic return of results in principle, particularly if medically actionable. However, competing ethical considerations, logistical constraints, and need for more external guidance were raised as key implementation concerns. Future studies should consider views and experiences of other relevant stakeholders, specifically clinical genomics professionals and study participants, regarding the clinical utility of multi-omics information and multi-omics results return.

Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine

Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI's potential in advancing the field of ophthalmology and improving patient care.

Investigating novel pharmacological strategies for treatment-resistant depression: focus on new mechanisms and approaches

INTRODUCTION

A substantial number of patients exhibit treatment-resistant depression (TRD), posing significant challenges to clinicians. The discovery of novel molecules or mechanisms that may underlie TRD pathogenesis and antidepressant actions is highly needed.

AREAS COVERED

Using the PubMed database, the authors searched for emerging evidence of novel approaches for TRD based on experimental and human studies. Herein, the authors discuss the mechanisms underlying glutamatergic antagonists, modulators of the opioid system, and tryptamine-derivate psychedelics as well as the emerging platforms to investigate novel pharmacological targets for TRD. A search for clinical trials investigating novel agents and interventions for TRD was also conducted.

EXPERT OPINION

The understanding of the multiple pathophysiological mechanisms involved in TRD may add further value to the effective treatment, contributing to a more personalized approach. Esketamine was approved for the treatment of TRD and novel drugs with rapid antidepressant actions such as psilocybin and buprenorphine have also been investigated as potential therapeutic strategies. Over the past decades, technological advances such as omics approaches have broadened our knowledge regarding molecular and genetic underpinnings of complex conditions like TRD. Omics approaches could open new avenues for investigating glial-mediated mechanisms, including their crosstalk with neurons, as therapeutic targets in TRD.

Intrinsic-dimension analysis for guiding dimensionality reduction and data fusion in multi-omics data processing

Multi-omics data have revolutionized biomedical research by providing a comprehensive understanding of biological systems and the molecular mechanisms of disease development. However, analyzing multi-omics data is challenging due to high dimensionality and limited sample sizes, necessitating proper data-reduction pipelines to ensure reliable analyses. Additionally, its multimodal nature requires effective data-integration pipelines. While several dimensionality reduction and data fusion algorithms have been proposed, crucial aspects are often overlooked. Specifically, the choice of projection space dimension is typically heuristic and uniformly applied across all omics, neglecting the unique high dimension small sample size challenges faced by individual omics. This paper introduces a novel multi-modal dimensionality reduction pipeline tailored to individual views. By leveraging intrinsic dimensionality estimators, we assess the curse-of-dimensionality impact on each view and propose a two-step reduction strategy for significantly affected views, combining feature selection with feature extraction. Compared to traditional uniform reduction pipelines in a crucial and supervised multi-omics analysis setting, our approach shows significant improvement. Additionally, we explore three effective unsupervised multi-omics data fusion methods rooted in the main data fusion strategies to gain insights into their performance under crucial, yet overlooked, settings.

Nanopore approaches for single-molecule temporal omics: promises and challenges

The great molecular heterogeneity within single cells demands omics analysis from a single-molecule perspective. Moreover, considering the perpetual metabolism and communication within cells, it is essential to determine the time-series changes of the molecular library, rather than obtaining data at only one time point. Thus, there is an urgent need to develop a single-molecule strategy for this omics analysis to elucidate the biosystem heterogeneity and temporal dynamics. In this Perspective, we explore the potential application of nanopores for single-molecule temporal omics to characterize individual molecules beyond mass, in both a single-molecule and high-throughput manner. Accordingly, recent advances in nanopores available for single-molecule temporal omics are reviewed from the view of single-molecule mass identification, revealing single-molecule heterogeneity and illustrating temporal evolution. Furthermore, we discuss the primary challenges associated with using nanopores for single-molecule temporal omics in complex biological samples, and present the potential strategies and notes to respond to these challenges.

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

Multimodal data integration in early-stage breast cancer

The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors. The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers. This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.

Integral-Omics: Serial Extraction and Profiling of Metabolome, Lipidome, Genome, Transcriptome, Whole Proteome and Phosphoproteome Using Biopsy Tissue

The integrative multiomics characterization of minute amounts of clinical tissue specimens has become increasingly important. Here, we present an approach called Integral-Omics, which enables sequential extraction of metabolites, lipids, genomic DNA, total RNA, proteins, and phosphopeptides from a single biopsy-level tissue specimen. We benchmarked this method with various samples, applied the workflow to perform multiomics profiling of tissues from six patients with colorectal cancer, and found that tumor tissues exhibited suppressed ferroptosis pathways at multiomics levels. Together, this study presents a methodology that enables sequential extraction and profiling of metabolomics, lipidomics, genomics, transcriptomics, proteomics, and phosphoproteomics using biopsy tissue specimens.

Understanding Genetic Screening: Harnessing Health Information to Prevent Disease Risks

Genetic screening analyzes an individual's genetic information to assess disease risk and provide personalized health recommendations. This article introduces the public to genetic screening, explaining its definition, principles, history, and common types, including prenatal, newborn, adult disease risk, cancer, and pharmacogenetic screening. It elaborates on the benefits of genetic screening, such as early risk detection, personalized prevention, family risk assessment, and reproductive decision-making. The article also notes limitations, including result interpretation uncertainty, psychological and ethical issues, and privacy and discrimination risks. It provides advice on selecting suitable screening, consulting professionals, choosing reliable institutions, and understanding screening purposes and limitations. Finally, it discusses applying screening results through lifestyle adjustments, regular check-ups, and preventive treatments. By comprehensively introducing genetic screening, the article aims to raise public awareness and encourage utilizing this technology to prevent disease and maintain health.

From Genes to Clinical Practice: Exploring the Genomic Underpinnings of Endometrial Cancer

Endometrial cancer (EC), a prevalent gynecological malignancy, presents significant challenges due to its genetic complexity and heterogeneity. The genomic landscape of EC is underpinned by genetic alterations, such as mutations in PTEN, PIK3CA, and ARID1A, and chromosomal abnormalities. The identification of molecular subtypes-POLE ultramutated, microsatellite instability (MSI), copy number low, and copy number high-illustrates the diverse genetic profiles within EC and underscores the need for subtype-specific therapeutic strategies. The integration of multi-omics technologies such as single-cell genomics and spatial transcriptomics has revolutionized our understanding and approach to studying EC and offers a holistic perspective that enhances the ability to identify novel biomarkers and therapeutic targets. The translation of these multi-omics findings into personalized medicine and precision oncology is increasingly feasible in clinical practice. Targeted therapies such as PI3K/AKT/mTOR inhibitors have demonstrated the potential for improved treatment efficacy tailored to specific genetic alterations. Despite these advancements, challenges persist in terms of variability in patient responses, the integration of genomic data into clinical workflows, and ethical considerations. This review explores the genomic underpinnings of EC, from genes to clinical practice. It highlights the ongoing need for multidisciplinary research and collaboration to address the complexities of EC and improve diagnosis, treatment, and patient outcomes.

Update on Blastocystis: highlights from the Fourth International Blastocystis Conference

While the stramenopile Blastocystis, first discovered in 1911, is considered the most prevalent enteric protist in humans, its biology remains largely unexplored. Clinical studies have only recently begun investigating the role of Blastocystis in the gut and its relationship with the gut microbiome, and whether it plays a pathogenic role in human and animal health. Aiming to gather leading researchers in the field to encourage and stimulate cross-disciplinary dialogue while fostering long-term international collaborations, the Fourth International Blastocystis Conference was hosted from the 17 th to the 19 th of September 2024 in Heraklion (Crete, Greece). The event was mainly supported by the COST Action CA21105, " Blastocystis under One Health", and the Microbiology Society. The multi- and interdisciplinary conference programme covered all aspects related to Blastocystis evolutionary biology and advances in omics, intestinal ecology (gut microbiome), clinical significance and association with disease, diagnosis and molecular characterisation, as well as epidemiology and One Health. The high-quality presentations discussed at the conference provided researchers with a synthesis of recent advancements, while key research questions, knowledge gaps, and future steps in Blastocystis research were identified. Herein, we aim to provide a thorough overview of the presentations at the congress. The COST Action CA21105, 'Blastocystis under One Health,' will build on the insights and collaborations fostered during the conference, promoting integrative research approaches, advancing our understanding of Blastocystis, and driving future efforts to translate these findings into improved public health strategies.

The Era of Preemptive Medicine: Developing Medical Digital Twins through Omics, IoT, and AI Integration

Preemptive medicine represents a paradigm shift from reactive treatment to proactive disease prevention. The integration of omics technologies, the Internet of Things (IoT), and artificial intelligence (AI) has facilitated the development of personalized, predictive, and preemptive healthcare strategies. Omic technologies, such as genomics, proteomics, and metabolomics, provide comprehensive insights into molecular profile of an individual, revealing potential disease predispositions and health trajectories. IoT devices, such as wearables and smartphones, enable continuous and periodic monitoring of physiological parameters, thus providing a dynamic view of an individual's health status. AI algorithms analyze comprehensive and complex data from omics and IoT technologies to identify patterns and correlations that inform predictive models of disease risk, progression, and response to interventions. Medical digital twins, or virtual replicas of an individual's biological processes, have emerged as the cornerstone of preemptive medicine. The integration of omics, IoT, and AI enables the development of medical digital twins, which in turn allows for precise simulation of human physiological profiles, prediction of future health outcomes, and virtual individual clinical trials, facilitating personalized proactive interventions and preemptive disease control. This review demonstrates the convergence of omics, IoT, and AI in preemptive medicine, highlighting their potential to revolutionize healthcare by enabling early disease detection, personalized treatment strategies, and chronic disease prevention. We show how AI leverages omics and IoT in preemptive medicine through several case studies while also discussing the necessary data for developing medical digital twins and addressing ethical and social aspects that warrant consideration. Medical digital twins signify a fundamental transformation in health management, shifting from treating diseases after their occurrence to controlling them before their occurrence. This approach enhances the effectiveness of medical interventions and improves overall health outcomes, preparing for a healthier future.

Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression

BACKGROUND

Inclusion Body Myositis is an acquired muscle disease. Its pathogenesis is unclear due to the co-existence of inflammation, muscle degeneration and mitochondrial dysfunction. We aimed to provide a more advanced understanding of the disease by combining multi-omics analysis with prior knowledge. We applied molecular subnetwork identification to find highly interconnected subnetworks with a high degree of change in Inclusion Body Myositis. These could be used as hypotheses for potential pathomechanisms and biomarkers that are implicated in this disease.

RESULTS

Our multi-omics analysis resulted in five subnetworks that exhibit changes in multiple omics layers. These subnetworks are related to antigen processing and presentation, chemokine-mediated signaling, immune response-signal transduction, rRNA processing, and mRNA splicing. An interesting finding is that the antigen processing and presentation subnetwork links the underexpressed miR-16-5p to overexpressed HLA genes by negative expression correlation. In addition, the rRNA processing subnetwork contains the RPS18 gene, which is not differentially expressed, but has significant variant association. The RPS18 gene could potentially play a role in the underexpression of the genes involved in 18 S ribosomal RNA processing, which it is highly connected to.

CONCLUSIONS

Our analysis highlights the importance of interrogating multiple omics to enhance knowledge discovery in rare diseases. We report five subnetworks that can provide additional insights into the molecular pathogenesis of Inclusion Body Myositis. Our analytical workflow can be reused as a method to study disease mechanisms involved in other diseases when multiple omics datasets are available.

COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer

Single-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting the intricate relationships between biological features-both within and across different data views-remains a bottleneck. To address these challenges, we introduce COSIME (Cooperative Multi-view Integration and Scalable Interpretable Model Explainer). COSIME uses backpropagation of Learnable Optimal Transport (LOT) to deep neural networks, enabling the learning of latent features from multiple views to predict disease phenotypes. In addition, COSIME incorporates Monte Carlo sampling to efficiently estimate Shapley values and Shapley-Taylor indices, enabling the assessment of both feature importance and their pairwise interactions-synergistically or antagonistically-in predicting disease phenotypes. We applied COSIME to both simulated data and real-world datasets, including single-cell transcriptomics, single-cell spatial transcriptomics, epigenomics, and metabolomics, specifically for Alzheimer's disease-related phenotypes. Our results demonstrate that COSIME significantly improves prediction performance while offering enhanced interpretability of feature relationships. For example, we identified that synergistic interactions between microglia and astrocyte genes associated with AD are more likely to be active at the edges of the middle temporal gyrus as indicated by spatial locations. Finally, COSIME is open-source and available for general use.

Bioinert UHPLC system improves sensitivity and peak shapes for ionic metabolites

The analysis of ionic compounds by liquid chromatography is challenging due to the interaction of analytes with the metal surface of the instrument and the column, leading to poor peak shape and decreased sensitivity. The use of bioinert materials in the chromatographic system minimizes these unrequired interactions. In this work, the ultrahigh-performance liquid chromatography (UHPLC) with bioinert components was connected to a high-resolution mass spectrometer to develop a method for untargeted metabolomic analysis. 81 standards of metabolites were used for the development and optimization of the method. In comparison to the conventional chromatographic system, the application of bioinert technology resulted in significantly improved peak shapes and increased sensitivity, especially for metabolites containing phosphate groups. The calibration curves were constructed for the evaluation of the method performance, showing a wide dynamic range, low limit of detection, and linear regression coefficients higher than 0.99 for all standards. The optimized method was applied to the analysis of NIST SRM 1950 human plasma, which allowed the detection of 156 metabolites and polar lipids based on the combination of mass accuracy in the full-scan mass spectra in both polarity modes, characteristic fragment ions in MS/MS, and logical chromatographic behavior leading to the high confidence level of annotation/identification. We have demonstrated an improvement in the peak shapes and sensitivity of ionic metabolites using bioinert technology, which indicates the potential for the analysis of other ionic compounds, e.g., molecules containing phosphate groups.

Integrative bioinformatics approach identifies novel drug targets for hyperaldosteronism, with a focus on SHMT1 as a promising therapeutic candidate

Primary aldosteronism (PA), characterized by autonomous aldosterone overproduction, is a major cause of secondary hypertension with significant cardiovascular complications. Current treatments mainly focus on symptom management rather than addressing underlying mechanisms. This study aims to discover novel therapeutic targets for PA using integrated bioinformatics and experimental validation approaches. We employed a systematic approach combining: gene identification through transcriptome-wide association studies (TWAS); causal inference using summary data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (MR) analyses; additional analyses included phenome-wide association analysis, enrichment analysis, protein-protein interaction (PPI) networks, drug repurposing, molecular docking and clinical validation through aldosterone-producing adenomas (APAs) tissue. Through systematic screening and prioritization, we identified 163 PA-associated genes, of which seven emerged as potential drug targets: CEP104, HIP1, TONSL, ZNF100, SHMT1, and two long non-coding RNAs (AC006369.2 and MRPL23-AS1). SHMT1 was identified as the most promising target, showing significantly elevated expression in APAs compared to adjacent non-tumorous tissues. Drug repurposing analysis identified four potential SHMT1-targeting compounds (Mimosine, Pemetrexed, Leucovorin, and Irinotecan), supported by molecular docking studies. The integration of multiple bioinformatics methods and experimental validation successfully identified novel drug targets for hyperaldosteronism. SHMT1, in particular, represents a promising candidate for future therapeutic development. These findings provide new opportunities for developing causative treatments for PA, though further clinical validation is warranted.

Exploring the molecular mechanisms of tirzepatide in alleviating metabolic dysfunction-associated fatty liver in mice through integration of metabolomics, lipidomics, and proteomics

Clinical studies have suggested that tirzepatide may also possess hepatoprotective effects; however, the molecular mechanisms underlying this association remain unclear. In our study, we performed biochemical analyses of serum and histopathological examinations of liver tissue in mice. To preliminarily explore the molecular mechanisms of tirzepatide on metabolic dysfunction-associated fatty liver disease (MAFLD), liquid chromatography-mass spectrometry (LC-MS) was employed for comprehensive metabolomic, lipidomic, and proteomic analyses in MAFLD mice fed a high-fat diet (HFD). The results demonstrated that tirzepatide significantly reduced serum levels of alanine transaminase (ALT) and aspartate transaminase (AST), as well as hepatic triglycerides (TG) and total cholesterol (TC), indicating its efficacy in treating MAFLD. Further findings revealed that tirzepatide reduced fatty acid uptake by downregulating Cd36 and Fabp2/4, as well as enhance the mitochondrial-lysosomal function by upregulating Lamp1/2. In addition, tirzepatide promoted cholesterol efflux and reduced cholesterol reabsorption by upregulating the expression of Hnf4a, Abcg5, and Abcg8. These results suggest that tirzepatide exerts its therapeutic effects on MAFLD by reducing fatty acid uptake, promoting cholesterol excretion, and enhancing mitochondrial-lysosomal function, providing a theoretical basis for a comprehensive understanding of tirzepatide.

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery for all organisms using multi-omics data

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/ and can be utilized for all organisms.

Multi-omics approaches to deciphering complex pathological mechanisms of migraine: a systematic review

Background

Migraine represents a chronic neurological disorder characterized by high prevalence, substantial disability rates, and significant economic burden. Its pathogenesis is complex, and there is currently no cure. The rapid progress in multi-omics technologies has provided new tools to uncover the intricate pathological mechanisms underlying migraine. This systematic review aims to synthesize the findings of multi-omics studies on migraine to further elucidate the complex mechanisms of disease onset, thereby laying a scientific foundation for identifying new therapeutic targets.

Methods

We conducted a comprehensive systematic review, specifically focusing on clinical observational studies that investigate various aspects of migraine through the integration of genomics, transcriptomics, proteomics, and metabolomics. Our search encompassed multiple databases including PubMed, EMBASE, the Web of Science Core Collection, the Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, the Wanfang database, and the China Biology Medicine Database to cover studies from database inception until 20 March 2024., The scope of our review included various aspects of migraine such as ictal and interictal phases; episodic or chronic migraine; menstrual-related migraine; and migraine with or without aura (PROSPERO registration number: CRD42024470268).

Results

A total of 38 studies were ultimately included, highlighting a range of genetic variations, transcriptional abnormalities, protein function alterations, and disruptions in metabolic pathways associated with migraine.These multi-omics findings underscore the pivotal roles played by mitochondrial dysfunction, inflammatory responses, and oxidative stress in the pathophysiology of migraine.

Conclusion

Multi-omics approaches provide novel perspectives and tools for comprehending the intricate pathophysiology of migraine, facilitating the identification of potential biomarkers and therapeutic targets.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=470268, identifier CRD42024470268.

Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWASs) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWASs) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWASs) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWASs and TWASs can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p values across all the genes, which suggests high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Proteomics: An In-Depth Review on Recent Technical Advances and Their Applications in Biomedicine

Proteins hold pivotal importance since many diseases manifest changes in protein activity. Proteomics techniques provide a comprehensive exploration of protein structure, abundance, and function in biological samples, enabling the holistic characterization of overall changes in organisms. Nowadays, the breadth of emerging methodologies in proteomics is unprecedentedly vast, with constant optimization of technologies in sample processing, data collection, data analysis, and its scope of application is steadily transitioning from the bench to the clinic. Here, we offer an insightful review of the technical developments in proteomics and its applications in biomedicine over the past 5 years. We focus on its profound contributions in profiling disease spectra, discovering new biomarkers, identifying promising drug targets, deciphering alterations in protein conformation, and unearthing protein-protein interactions. Moreover, we summarize the cutting-edge technologies and potential breakthroughs in the proteomics pipeline and provide the principal challenges in proteomics. Based on these, we aspire to broaden the applicability of proteomics and inspire researchers to enhance our understanding of complex biological systems by utilizing such techniques.

Advancing environmental epidemiologic methods to confront the cancer burden

Even though many environmental carcinogens have been identified, studying their effects on specific cancers has been challenging in nonoccupational settings, where exposures may be chronic but at lower levels. Although exposure measurement methods have improved considerably, along with key opportunities to integrate multi-omic platforms, there remain challenges that need to be considered, particularly around the design of studies. Cancer studies typically exclude individuals with prior cancers and start recruitment in midlife. This translates into a failure to capture individuals who may have been most susceptible because of both germline susceptibility and higher early-life exposures that lead to premature mortality from cancer and/or other environmentally caused diseases like lung diseases. Using the example of breast cancer, we demonstrate how integration of susceptibility, both for cancer risk and for exposure windows, may provide a more complete picture regarding the harm of many different environmental exposures. Choice of study design is critical to examining the effects of environmental exposures, and it will not be enough to just rely on the availability of existing cohorts and samples within these cohorts. In contrast, new, diverse, early-onset case-control studies may provide many benefits to understanding the impact of environmental exposures on cancer risk and mortality. This article is part of a Special Collection on Environmental Epidemiology.

Eleven Grand Challenges for Inflammatory Bowel Disease Genetics and Genomics

The past 2 decades have witnessed extraordinary advances in our understanding of the genetic factors influencing inflammatory bowel disease (IBD), providing a foundation for the approaching era of genomic medicine. On behalf of the NIDDK IBD Genetics Consortium, we herein survey 11 grand challenges for the field as it embarks on the next 2 decades of research utilizing integrative genomic and systems biology approaches. These involve elucidation of the genetic architecture of IBD (how it compares across populations, the role of rare variants, and prospects of polygenic risk scores), in-depth cellular and molecular characterization (fine-mapping causal variants, cellular contributions to pathology, molecular pathways, interactions with environmental exposures, and advanced organoid models), and applications in personalized medicine (unmet medical needs, working toward molecular nosology, and precision therapeutics). We review recent advances in each of the 11 areas and pose challenges for the genetics and genomics communities of IBD researchers.

Diagnostic Application of Bronchoalveolar Lavage Fluid Analysis in Cases of Idiopathic Pulmonary Fibrosis in which Diagnosis Cannot Be Confirmed by High-Resolution Computed Tomography

PURPOSE

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disorder characterized by dry cough, fatigue, and exacerbated dyspnea. The prognosis of IPF is notably unfavorable, becoming extremely poor when the disease advances acutely. Effective therapeutic intervention is essential to mitigate disease progression; hence, early diagnosis and treatment are paramount. When high-resolution computed tomography (HRCT) reveals usual interstitial pneumonia (UIP), a diagnosis of IPF can be established. However, when HRCT fails to conclusively confirm IPF, the diagnostic pathway becomes intricate and necessitates a multidisciplinary approach involving clinicians, radiologists, and pathologists. Consequently, the objective of this study was to investigate new diagnostic approaches through bronchoalveolar lavage (BAL) analysis.

METHODS

BAL is a commonly utilized diagnostic tool for interstitial lung diseases. We review the application of bronchoalveolar lavage (BALF) in idiopathic pulmonary fibrotic disease, emphasizing that the cellular and solute composition of the lower respiratory tract offers valuable insights.

RESULTS

This review delineates the advancements in diagnosing IPF cases that remain indeterminate via HRCT, leveraging BALF analysis. In contrast to surgical lung biopsy, BAL is minimally invasive and offers potential diagnostic utility through the identification of specific BALF biomarkers.

CONCLUSION

Augment the clinical diagnostic armamentarium for IPF, particularly in scenarios where HRCT findings are inconclusive.

AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power. Key challenges in predictive modeling include scarcity of labeled data, generalization across different domains, and disentangling causation from correlation. In light of recent advances in multi-omics data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale modeling framework to tackle these issues. This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict genotype-environment-phenotype relationships under various conditions. AI models inspired by biology may identify novel molecular targets, biomarkers, pharmaceutical agents, and personalized medicines for presently unmet medical needs.

Novel Computational and Artificial Intelligence Models in Cancer Research

The ICIBM 2023 marked the 11th annual conference of its kind, with the ICIBM recently becoming the official conference of the International Association for Intelligent Biology and Medicine (IAIBM), showcasing cutting-edge advancements at the intersection of computation and biomedical research [...].

Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies

Advances in high-throughput omics technologies have enabled system-wide characterization of biological samples across multiple molecular levels, such as the genome, transcriptome, and proteome. However, as sample sizes rapidly increase in large-scale multi-omics studies, sample mix-ups have become a prevalent issue, compromising data integrity and leading to erroneous conclusions. The interconnected nature of multi-omics data presents an opportunity to identify and correct these errors. This review examines the potential sources of sample mix-ups and evaluates the methodologies and tools developed for detecting and correcting these errors, with an emphasis on approaches applicable to proteomics data. We categorize existing tools into three main groups: expression/protein quantitative trait loci-based, genotype concordance-based, and gene/protein expression correlation-based approaches. Notably, only a handful of tools currently utilize the proteogenomics approach for correcting sample mix-ups at the proteomics level. Integrating the strengths of current tools across diverse data types could enable the development of more versatile and comprehensive solutions. In conclusion, verifying sample identity is a critical first step to reduce bias and increase precision in subsequent analyses for large-scale multi-omics studies. By leveraging these tools for identifying and correcting sample mix-ups, researchers can significantly improve the reliability and reproducibility of biomedical research.

Omics based technology application in poultry meat research

Omics techniques, including genomics, transcriptomics, proteomics, metabolomics, and lipidomics, analyze entire sets of biological molecules to seek comprehensive knowledge on a particular phenotype. These approaches have been extensively utilized to identify both biomarkers and biological mechanisms for various physiological conditions in livestock and poultry. The purpose of this symposium was not only to focus on how recent omics technologies can be used to gather, integrate, and interpret data produced by various methodologies in poultry research, but also to highlight how omics and bioinformatics have increased our understanding of poultry meat quality problems and other complex traits. This Poultry Science Association symposium paper includes 5 sections that cover: 1) functional annotation of cis-regulatory elements in the genome informs genetic control of complex traits in poultry, 2) mass spectrometry for proteomics, metabolomics, and lipidomics, 3) proteomic approaches to investigate meat quality, 4) spatial transcriptomics and metabolomics studies of wooden breast disease, and 5) multiomics analyses on chicken meat quality and spaghetti meat. These topics provide insights into the molecular components that contribute to the structure, function, and dynamics of the underlying mechanisms influencing meat quality traits, including chicken breast myopathies. This information will ultimately contribute to improving the quality and composition of poultry products.

Rap1 and mTOR signaling pathways drive opposing immunotoxic effects of structurally similar aryl-OPFRs, TPHP and TOCP

Aryl organophosphorus flame retardants (aryl-OPFRs), commonly used product additives with close ties to daily life, have been regrettably characterized by multiple well-defined toxicity risks. Triphenyl phosphate (TPHP) and tri-o-cresyl phosphate (TOCP), two structurally similar aryl-OPFRs, were observed in our previous study to exhibit contrasting immunotoxic effects on THP-1 macrophages, yet the underlying mechanisms remain unclear. This study sought to address the knowledge gap by integrating transcriptomic and metabolomic analyses to elucidate the intricate mechanisms. During individual omics analyses, we unfortunately only obtained highly similar results for both TPHP and TOCP, failing to identify the key reasons for their differences. These results revealed comparable disturbances induced by both compounds, including disruptions in nucleic acid synthesis and energy metabolism, blocking ADP to ATP conversion by reducing TCA cycle intermediates, consequently leading to ATP depletion. However, through integrative analysis, specific pathways affected by each compound were successfully identified, shedding light on their unique effects. TPHP reduced GTP levels necessary for Rap1 activation, thereby inhibiting phagocytosis and adhesion of THP-1 macrophages. Conversely, TOCP stimulated the mTOR signaling pathway, enhancing phosphorylation of downstream proteins S6K, RHOA, and PKC, consequently promoting immune responses. This study not only clarified the distinct immunotoxic mechanisms of TPHP and TOCP but also provided critical insights into how structural variations in aryl-OPFRs can lead to markedly different immune responses, thereby informing future risk assessments and regulatory strategies for these compounds.