The experience of adversity in childhood can have life-long consequences on health outcomes. In search of mediators of this relationship, alterations of bio-behavioral and cellular regulatory systems came into focus, including those dealing with basic gene regulatory processes. System biology oriented approaches have been proposed to gain a more comprehensive understanding of the complex multiple interrelations between and within layers of analysis. Here, we used co-expression based, supervised and unsupervised single and multi-omics systems approaches to investigate the association between childhood adversity and gene expression, protein expression and DNA methylation in CD14+ monocytes in the context of psychosocial stress exposure, in a sample of healthy adults with (n = 29) or without (n = 27) a history of childhood adversity. Childhood adversity explained some variance at the single analyte level and within gene and protein co-expression structures. A single-omics, post-stress gene expression model differentiated best between participants with a history of childhood adversity and control participants in supervised analyses. In unsupervised analyses, a multi-omics based model showed best performance but separated participants based on sex only. Multi-omics analyses are a promising concept but might yield different results based on the specific approach taken and the omics-datasets supplied. We found that stress associated gene-expression pattern were most strongly associated with childhood adversity, and integrating multiple cellular layers did not results in better discriminatory performance in our rather small sample. The capacity and yield of different omics-profiling methods might currently limit the full potential of integrative approaches.

Sleep and circadian biology is fundamental to human health. Following the advancement in sleep medicine and availability of multi-omics technology, this review outlines the current knowledge regarding genetic basis and multi-omics research on circadian rhythm and the two most prevalent sleep disorders, obstructive sleep apnea (OSA) and insomnia.

Genome wide association analyses identified variants across circadian genes and genes pertinent to inflammation, obesity and neuronal function associated with OSA and insomnia. Multi-omics integration has led to novel breakthroughs in identifying systemic biomarkers and elucidating cascades, and causal associations underpinning these complex traits.

Multi-omics studies in sleep and circadian rhythm possess great potential in unveiling molecular mechanisms behind circadian rhythm and sleep, thereby advancing personalized medicine in the long term. Nevertheless, researchers should remain mindful of existing challenges in genetic and multi-omics sleep research, including data harmonization and existing racial and ethnic disparities in data collection and availability, limiting research generalizability.

Gastric cancer (GC), one of the most prevalent malignancies worldwide, is distinguished by extensive genetic and phenotypic heterogeneity, posing persistent challenges to conventional diagnostic and therapeutic strategies. The significant global burden of GC highlights an urgent need to unravel its complex underlying mechanisms, discover novel diagnostic and prognostic biomarkers, and develop more effective therapeutic interventions. In this context, this review comprehensively examines the transformative roles of cutting-edge technologies, including radiomics, pathomics, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, in advancing precision diagnosis and treatment for GC. Multiomics data analysis not only deepens our understanding of GC pathogenesis and molecular subtypes but also identifies promising biomarkers, facilitating the creation of tailored therapeutic approaches. Additionally, integrating multiomics approaches holds immense potential for elucidating drug resistance mechanisms, predicting patient outcomes, and uncovering novel therapeutic targets, thereby laying a robust foundation for precision medicine in the comprehensive management of GC.

Cryopreservation of boar semen is widely applied in the conservation of genetic resources and animal breeding to enhance the utilization efficiency of superior boars. However, accurately identifying individuals with good freezing tolerance in boar sperm remains challenging. In this study, based on the differences in sperm motility before and after cryopreservation from 328 boars, we selected six boars each from the Duroc, Landrace, and Large White breeds, and categorized them into poor freezability ejaculates (PFE) and good freezability ejaculates (GFE) groups for sperm metabolomic analysis. A total of 1288 metabolites were identified using both positive and negative ion modes. There were 148 differentially expressed metabolites between the GFE and PFE groups, which were enriched in pathways such as alanine, aspartate and glutamate metabolism; arginine biosynthesis; D-amino acid metabolism; histidine metabolism; beta-alanine metabolism; citrate cycle (TCA cycle); pantothenate and CoA biosynthesis; and pyruvate metabolism. Further analysis, including ROC curve evaluation, identified seven potential biomarkers for sperm cryopreservation. Argininosuccinic acid, asparagine, L-aspartate, fumarate, D-ornithine, DL-serine and histidine were tightly interconnected in a series of amino acids metabolism. In conclusion, our findings imply that differences in certain amino acid biosynthetic pathways contribute to the variations in freezing tolerance of boar sperm.

Laryngeal squamous cell carcinoma (LSCC), a complex cancer driven by genetic mutations, poses significant challenges for detection and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as a promising tool to uncover the cellular heterogeneity in cancer and identify novel therapeutic targets. In this study, we used scRNA-seq data (GSE252490) to explore molecular biomarkers for LSCC diagnosis and treatment. After processing and standardizing the data, we performed principal component analysis to identify highly variable genes. Cell clustering revealed 12 distinct clusters with unique molecular features. Differential gene expression analysis identified 6434 differentially expressed genes (DEGs), which were further analyzed using gene ontology enrichment to explore biological processes involved in LSCC progression. Protein-protein interaction (PPI) network analysis revealed 20 central genes associated with key cancer pathways. Pathway enrichment analysis through KEGG highlighted the involvement of these genes in various cancer-related pathways. Notably, genes such as CCL3, EPCAM, and IL8, with elevated expression, were linked to survival outcomes in LSCC. This comprehensive analysis provides valuable insights into the molecular landscape of LSCC, identifying potential biomarkers and therapeutic targets for improved diagnosis and treatment.

Since 2007, the South Yellow Sea region of China has endured the world's largest recurrent green tide disaster for 18 consecutive years, resulting in substantial economic losses and ecological risks. Ulva prolifera the dominant species in these green tides, demonstrates remarkable tolerance to both elevated and reduced temperatures, with its thermal adaptation mechanisms critically linked to seasonal outbreak dynamics. This study established three temperature regimes (5 °C, 20 °C, and 30 °C) based on recent extreme temperature records from the Yellow Sea region to systematically evaluate growth rates, photosynthetic performance, pigment profiles, antioxidant enzyme activities, and transcriptomic responses of U. prolifera. Results revealed optimal growth at 15-25 °C, with significant growth inhibition beyond this range. Elevated temperature (30 °C) induced modest increases in photosynthetic fluorescence parameters and pigment content, coupled with pronounced enhancement of peroxidase (POD) activity. Conversely, low-temperature exposure (5 °C) substantially suppressed both photosynthetic efficiency and pigment levels, while eliciting only marginal POD activation. Transcriptomic profiling demonstrated distinct survival strategies: Low temperatures triggered the activation of the protein synthesis pathway and basal metabolic maintenance to prolong viability, whereas high temperatures activated antioxidant defenses and metabolic reprogramming to sustain photosynthetic function and nutrient cycling. Machine learning analysis revealed that proteins related to protein modification and cell differentiation exhibited strong responses under temperature stress. These temperature-responsive regulatory networks underpin the seasonal proliferation patterns of U. prolifera green tides. The findings advance mechanistic understanding of its overwintering and summer endurance strategies, offering critical theoretical frameworks for ecological management and mitigation technologies.

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

To preserve functionality, bone is an active tissue that can constantly reconstruct itself through modeling and remodeling. It plays critical roles in the body, including maintaining mineral homeostasis, serving as the adult human body's core site of hematopoiesis, and supporting the structures of the body's soft tissues. It possesses the natural regeneration capacity, but large and complex lesions often require surgical intervention. Multiple omics integrate proteomics, metabolomics, genomics, and transcriptomics to provide a comprehensive understanding of biological processes like bone tissue injury and healing in bone tissue regeneration and engineering. Recently, bone tissue engineering and regenerative medicines have offered promising tools for bone regeneration using a multi-omics approach. Thus, this article will highlight the role of multiple omics in understanding bone tissue injury and healing. It will discuss the role of bone tissue engineering in developing bone substitutes that can replace translational medicine. Lastly, new developments in bone tissue engineering and regenerative medicine, along with multi-omics approaches, offer promising tools for bone regeneration.

Network representations of data are designed to encode relationships between concepts as sets of edges between nodes. Human biology is inherently complex and is represented by data that often exists in a hierarchical nature. One canonical example is the relationship that exists within and between various -omics datasets, including genomics, transcriptomics, and proteomics, among others. Encoding such data in a network-based or graph-based representation allows the explicit incorporation of such relationships into various biomedical big data tasks, including (but not limited to) disease subtyping, interaction prediction, biomarker identification, and patient classification. This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.

Current experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy.

This paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge.

Pi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.

Esophageal cancer ranks among the most lethal malignancies globally, with China accounting for more than half of worldwide esophageal squamous cell carcinoma (ESCC) cases. Late-stage diagnosis frequently precludes surgical intervention, contributing to poor outcomes. While precise clinical assessment is essential for treatment planning, therapeutic responses and prognosis exhibit substantial inter-patient heterogeneity, underscoring the urgent need for reliable biomarkers to enhance prognostic accuracy and guide personalized therapeutic strategies. Radiomics, an emerging field that extracts high-dimensional features from medical images, provides non-invasive approaches to improve diagnostic accuracy, predict survival, monitor adverse events, detect recurrence, and optimize treatment strategies. Radiomics has shown promising potential in the modern management of ESCC. Here, we review the critical contributions of radiomics to ESCC research and clinical practice, examining its workflow, applications, strengths, and limitations. Radiomics represents a compelling frontier with substantial potential to advance precision medicine for ESCC patients.

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

As personalized medicine becomes more prevalent, the objective measurement and visualization of an individual's health status are becoming increasingly crucial. However, as the dimensions of data collected from each individual increase, this task becomes more challenging. The Health Space (HS) model provides a statistical framework for visualizing an individual's health status on biologically meaningful axes. In our previous study, we developed HS models using statistical models such as logistic regression model (LRM) and the proportional odds model (POM). However, these statistical HS models are limited in their ability to accommodate complex non-linear biological relationships.

In order to model complex non-linear biological relationship, we developed deep learning HS models. Specifically, we formulated five distinct deep learning HS models: four standard binary deep neural networks (DNNs) for binary outcomes and one deep ordinal neural network (DONN) that accounts for the ordinality of the dependent variable. We trained these models using 32,140 samples from the Korea National Health and Nutrition Examination Survey (KNHANES) and validated them with data from the Ewha-Boramae cohort (862 samples) and the Korea Association Resource (KARE) project (3,199 samples).

The proposed deep learning HS models were compared with the existing statistical HS model based on the POM. Deep learning HS model using DONN demonstrated the best performance in discriminating health status in both the training and external datasets.

We developed deep learning HS models to capture complex non-linear biological relationships in HS and compared their performance with our previously best-performing statistical HS model. The deep learning HS models show promise as effective tools for objectively and meaningfully visualizing an individual's health status.

Not applicable.

The tumor microenvironment (TME) consists of various immune and non-immune cells, along with proteins from different origins, and plays a crucial role in tumor development, treatment response, and patient prognosis. Complement system is a key player in the TME. It is a proteolytic cascade that generates cleavage fragments capable to activate cells through specific receptors or deposit on cells and tissues. This review summarizes current data on the complement system as a potential biomarker in cancer. Transcriptomic analyses have classified tumors based on the impact of complement gene expression on prognosis. Immunostaining provides insights into the expression and deposition of complement proteins and fragments in tumors and TME cells. In body fluids such as blood, measuring complement activation fragments and detecting anti-complement autoantibodies have identified non-invasive biomarkers relevant to certain cancer types. With the rise of complement-targeting therapies and new tools for analyzing the complement system in tumors and body fluids, it is time to define its role in cancer management. This includes its potential for cancer detection, staging, and potentially for treatment monitoring.

Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artificial intelligence (AI). It is possible to identify disease-specific biomarkers using real-time and sensitive molecular interactions. These technologies are precious for genetic, epigenetic, and proteomic changes critical to disease progression and treatment response. AI improves multi-omics profiling by analyzing large, diverse data sets and common patterns traditional methods overlook. Machine learning tools Biomarkers Discovery is revolutionizing, drug resistance is being understood, and medicine is being personalized as the combination of AI and nanosensors has advanced the detection of DNA methylation and proteomic signatures and improved our understanding of cancer, cardiovascular disease and vascular disease. Despite these advances, challenges still exist. Difficulties in integrating data sets, retaining sensors, and building scalable computing tools are the biggest obstacles. It also examines various solutions with advanced AI algorithms and innovations, including fabrication in nanosensor design. Moreover, it highlights the potential of nanosensor-assisted, AI-driven multi-omics profiling to revolutionize disease diagnosis and treatment. As technology advances, these tools pave the way for faster diagnosis, more accurate treatment and improved patient outcomes, offering new hope for personalized medicine.

Cancers are complex diseases that have heterogeneous genetic drivers and varying clinical outcomes. A critical area of cancer research is organizing patient cohorts into subtypes and associating subtypes with clinical and biological outcomes for more effective prognosis and treatment. Large-scale studies have collected a plethora of omics data across multiple tumor types, providing an extensive dataset for stratifying patient cohorts. Network-based stratification (NBS) approaches have been presented to classify cancer tumors using somatic mutation data. A challenge in cancer stratification is integrating omics data to yield clinically meaningful subtypes. In this study, we investigate a novel approach to the NBS framework by integrating somatic mutation data with RNA sequencing data and investigating the effectiveness of integrated NBS on three cancers: ovarian, bladder, and uterine cancer.

We show that integrated NBS subtypes are more significantly associated with overall survival or histology. Specifically, we observe that integrated NBS subtypes for ovarian and bladder cancer were more significantly associated with patient survival than single-data type NBS subtypes, even when accounting for covariates. In addition, we show that integrated NBS subtypes for bladder and uterine are more significantly associated with tumor histology than single-data type NBS subtypes. Integrated NBS networks also reveal highly influential genes that span across multiple integrated NBS subtypes and subtype-specific genes. Pathway enrichment analysis of integrated NBS subtypes reveal overarching biological differences between subtypes. These genes and pathways are involved in a heterogeneous set of cell functions, including ubiquitin homeostasis, p53 regulation, cytokine and chemokine signaling, and cell proliferation, emphasizing the importance of identifying not only cancer-specific gene drivers but also subtype-specific tumor drivers.

Our study highlights the significance of integrating multi-omics data within the NBS framework to enhance cancer subtyping, specifically its utility in offering profound implications for personalized prognosis and treatment strategies. These insights contribute to the ongoing advancement of computational subtyping methods to uncover more targeted and effective therapeutic treatments while facilitating the discovery of cancer driver genes.

The development of high throughput technologies has resulted in the collection of large quantities of genomic and transcriptomic data. However, identifying disease-associated genes or networks from these data has remained an ongoing challenge. In recent years, graph neural networks (GNNs) have emerged as a promising analytical tool, but it is not well understood which characteristics of these models result in improved performance. We conducted a systematic search and review of publications that used GNNs to identify disease-associated biological interactions. Information was extracted about model characteristics and performance with the goal of examining the relationship between these factors and performance. Data leakage was found in 31% of these models. For node level tasks, univariate positive associations were identified between model accuracy and use of hyper parameter optimization, data leakage via hyperparameter optimization, test set size, and total dataset size. Among graph level tasks, an increase in AUC was identified in association with testing method and a decrease with optimization reporting. Data leakage may pose an issue for GNN-based approaches; the adoption of best practice guidelines and consistent reporting of model design would be beneficial for future studies.

A milestone in sickle cell disease (SCD) therapeutics was achieved in December 2023 with the FDA-approved gene therapy for patients aged 12 years and older. However, these therapies may only suit a fraction of patients because of cost or health risks. A better understanding of SCD outcome heterogeneity is needed to propose patient-specific pharmacological interventions. To achieve this, humanized and genetically diverse mouse models are essential for associating candidate genotypes with specific hematological traits, organ function, and disease resilience. Here, we discuss the challenges and opportunities in developing genetically diverse sickle cell mice (GDS mice). These models are expected to complement current approaches in SCD research and enhance our understanding of SCD heterogeneity and anemia.

Cervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus-based gene panel using multi-omics data that could effectively predict recurrence in early-stage cervical cancer patients. We utilized the "Multi-Omics Consensus Integration Analysis (MOVICS)" package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early-stage CC. We identified the "resting and naive" tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning-driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan-Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early-stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.

Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.

In recent years, interest in studying changes in cancer metabolites has resulted in significant advances in the metabolomics field. Glioblastoma remains the most aggressive and lethal brain malignancy, which presents with notable metabolic reprogramming.

We performed literature research from the PubMed database and considered research articles focused on the key metabolic pathways altered in glioblastoma (e.g., glycolysis, lipid metabolism, TCA cycle), the role of oncometabolites and metabolic plasticity, and the differential expression of metabolites in glioblastoma. Currently used metabolomics approaches can be either targeted, focusing on specific metabolites and pathways, or untargeted, which involves data-driven exploration of the metabolome and also results in the identification of new metabolites. Data processing and analysis is of great importance and can be improved with the integration of machine learning approaches for metabolite identification.

Changes in α/β-glucose, lactate, choline, and 2-hydroxyglutarate were detected in glioblastoma compared with non-tumor tissues. Different metabolites such as fumarate, tyrosine, and leucine, as well as citric acid, isocitric acid, shikimate, and GABA were detected in blood and CSF, respectively.

Although promising new technological and bioinformatic approaches help us understand glioblastoma better, challenges associated with biomarker availability, tumor heterogeneity, interpatient variability, standardization, and reproducibility still remain. Metabolomics research, either alone or combined with genomics or proteomics (i.e., multiomics) in glioblastoma, can lead to biomarker identification, tracking of metabolic therapy response, discovery of novel metabolites and pathways, and identification of potential therapeutic targets.

Gastric cancer (GC) remains a leading cause of cancer-related deaths and exhibits considerable heterogeneity among patients. Thus, accurate classifications are essential for predicting prognosis and developing personalized therapeutic strategies. To address this, we retrospectively analyzed multi-omics data from 359 GC samples, incorporating transcriptomic RNA (mRNA), DNA methylation, mutation data, and clinical parameters. Using ten clustering algorithms, we integrated these datasets to classify GC into molecular subtypes. The robustness of our clustering approach was externally validated using an independent cohort generated from different sequencing technologies, and we characterized the heterogeneity of each subtype. Our analysis identified three distinct molecular subtypes of GC, designated CS1, CS2, and CS3. These subtypes exhibited significant differences in survival outcomes, activation of cancer-related pathways, immune microenvironment composition, genomic alterations, and responses to immunotherapy and chemotherapy. Notably, Cathepsin V (CTSV) was significantly downregulated in the immunologically active and highly responsive CS3 subtype, while it was upregulated in the immunologically exhausted CS2 subtype. These findings suggest that CTSV could serve as both a prognostic marker and a molecular classifier. Furthermore, this study provides the first evidence of CTSV's high expression in GC and its potential role in tumor progression. The novel clustering approach, based on ten clustering algorithms and comprehensive analysis of multi-omics data in gastric cancer, can guide prognosis, characterize different clinical and biological features, and elucidate the tumor immune microenvironment, providing insights into the intratumor heterogeneity of GC and potential novel therapeutic strategies. Additionally, CTSV emerges as a prognostic marker linked to tumor immunity and disease progression, which lays the foundation for improved stratification strategies and the development of targeted therapeutic approaches in GC.

Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward.

The increasing feasibility of whole-genome sequencing has been highly anticipated, promising to transform our understanding of the biology of nonmodel species. Notably, dramatic cost reductions beginning around 2007 with the advent of high-throughput sequencing inspired publications heralding the 'genomics revolution', with predictions about its future impacts. Although such predictions served as useful guideposts, value is added when statements are evaluated with the benefit of hindsight. Here, we review 10 key predictions made early in the genomics revolution, highlighting those realised while identifying challenges limiting others. We focus on predictions concerning applied aspects of genomics and examples involving salmonid species which, due to their socioeconomic and ecological significance, have been frontrunners in applications of genomics in nonmodel species. Predicted outcomes included enhanced analytical power, deeper insights into the genetic basis of phenotype and fitness variation, disease management and breeding program advancements. Although many predictions have materialised, several expectations remain unmet due to technological, analytical and knowledge barriers. Additionally, largely unforeseen advancements, including the identification and management applicability of large-effect loci, close-kin mark-recapture, environmental DNA and gene editing have added under-anticipated value. Finally, emerging innovations in artificial intelligence and bioinformatics offer promising new directions. This retrospective evaluation of the impacts of the genomic revolution offers insights into the future of genomics for nonmodel species.

Translational research driven by large-scale biological testing requires significant volumes of blood for research testing. However, blood is also required for clinical management of research subjects, which must take priority. Paradoxically, much of the blood drawn for clinical management goes unused. Here, we present our approach for retrieving unused blood samples collected for clinical management and recycling them for research purposes.

Clinical Blood samples were collected for 60 days during a 61-day xenotransplantation experiment in a brain-dead decedent. Twice weekly, research staff went to the chemistry and hematology laboratories and collected stored blood, serum, and plasma samples that were >12 hours old. Sample collection and storage before retrieval was per standard clinical protocols. Samples were de-identified and relabeled and brought to a central biorepository for processing and storage. The quantity of plasma, serum, red blood cells (RBCs), and peripheral blood mononuclear cells (PBMCs) collected from clinical labs and bespoke research blood draws were compared.

Unused blood from clinical samples yielded a minimum of 6.0 ml per day of plasma, representing 62% of all plasma obtained. Serum was only recoverable on 13 days (22%), with a mean 2.3 ml collected on those days, representing 8% of all serum obtained. PBMCs were only recoverable on six days (10%).

Overdrawn clinical laboratory samples represent an untapped resource of blood samples for research and can help augment samples collected explicitly for research purposes. With careful planning, this represents an opportunity to minimize iatrogenic blood loss in clinical-translational research.

Colon cancer is a major cause of morbidity and mortality worldwide. Copper-induced cell death, known as cuproptosis, is a form of apoptosis that has been extensively studied in human diseases and is widely associated with tumor progression, prognosis, and immune response. However, the role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon cancer remains unclear.

This study aims to explore the role of cuproptosis-related long non-coding RNAs (lncRNAs) in predicting the prognosis of colon cancer and to establish a risk prediction model based on these lncRNAs to guide clinical decisions and improve patient outcomes.

A total of 19 cuproptosis-related genes were collected, and 1330 lncRNAs associated with cuproptosis were identified. Seven cuproptosis-related lncRNAs with prognostic value were selected from The Cancer Genome Atlas (TCGA) database. Using R software (version 4.1.0), the expression levels of the 19 genes were extracted, and the subjects were divided into high- and low-risk subgroups. A risk score model was developed based on cuproptosis-related genes and the seven co-expressed lncRNAs. The dataset was randomly split into a training set and a validation set. Analysis of clinicopathologic features, TME infiltration, and mutations was conducted, and nomogram predictions were validated using calibration plots to assess the predictive accuracy of the model.

The high-risk group had significantly shorter overall survival compared to the low-risk group (P<0.001), and the risk score was an independent prognostic factor (P<0.001). In the training set, the AUC values at 1, 3, and 5 years were 0.666, 0.621, and 0.669, respectively. Furthermore, low-risk patients had a higher survival rate. The genetic markers also correlated with tumor immune cell infiltration, clinical features, and prognosis.

This study established a novel method based on cuproptosis-related lncRNAs to predict the prognosis of colon cancer. The model has potential clinical applications in identifying patients sensitive to immunotherapy and antitumor treatments, thereby enhancing precision treatment strategies for colon cancer.

Spaghetti meat (SM), a recently emerging muscle myopathy in chicken breast meat, is characterized by a loss of muscle fiber integrity, resulting in a spaghetti-like appearance. Understanding the differences in proteins, metabolites, and lipids through a multiomics approach in SM can identify its quality traits and elucidate its exact causes. The purpose of this study was to investigate differentially abundant proteins, metabolites, and lipid molecules in SM compared to normal chicken breast meat (Control). The supernatant from sample homogenates was subjected to ultra-high performance liquid chromatography (UHPLC) analysis for multiomic profiling. A total of 16 chicken breast fillets (Pectoralis major) representing Control (n = 8) and SM (n = 8) groups were collected from a commercial slaughterhouse. A total of 2593 molecules were identified and composed of 1903 proteins, 506 lipids, 181 compounds and 3 electrolytes. There were 632 differential molecules composed of 503 proteins, 76 lipids, 50 metabolites, and 3 electrolytes. In comparing SM and Control, the protein, metabolite, and lipid molecules with the greatest fold change were calponin, decanoylcarnitine, and ceramide [N‑hydroxy-sphingosine] (Cer[NS]) d18:1_26:1, respectively. Plasmenylphosphatidylcholine (Plasmenyl-PC) and triglycerides (TG) were significantly decreased and increased, respectively, in SM compared to Control. Acylcarnitines (AC) were significantly decreased in SM compared to Control. Decanoylcarnitine, lauroylcarnitine, linoleyl-carnitine, oleoyl-carnitine, hexanoylcarnitine were downregulated in SM compared to Control, and adenosine 5'-diphosphoribose and nicotinamide adenine dinucleotide (NAD) were downregulated in SM. Carbon metabolism, glycolysis/glucogenesis, ribosome, biosynthesis of amino acids, and aminoacyl-tRNA biosynthesis were selected in the top 10 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, identified by using differential proteins. Hence, SM had different proteins, metabolites, and lipid molecules related to β-oxidation, carbon and energy metabolism, lipid formation, and protein and amino acid metabolism compared to Control. Results from this study showed physiological alterations found in SM myopathy. Therefore, to mitigate SM in broilers, interventions should: 1) increase NAD and carnitines, 2) reduce triglycerides, and 3) modulate β-oxidation and energy metabolism via nutritional, genetic, or systemic approaches.

Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approaches may facilitate more comprehensive examinations of the contributions of chemical exposures, sociodemographic factors, and maternal mental health to child neurodevelopment. A machine learning pipeline that utilized feature selection and ranking was applied to investigate which common prenatal chemical exposures and sociodemographic factors best predict neurodevelopmental outcomes in young children. Data from 406 maternal-child pairs enrolled in the APrON study were used. Maternal concentrations of 32 environmental chemical exposures (i.e., phthalates, bisphenols, per- and polyfluoroalkyl substances (PFAS), metals, trace elements) measured during pregnancy and 11 sociodemographic factors, as well as measures of maternal mental health and urinary creatinine were entered into the machine learning pipeline. The pipeline, which consisted of a RReliefF variable selection algorithm and support vector machine regression model, was used to identify and rank the best subset of variables predictive of cognitive, language, and motor development outcomes on the Bayley Scales of Infant Development-Third Edition (Bayley-III) at 2 years of age. Bayley-III cognitive scores were best predicted using 29 variables, resulting in a correlation coefficient of r = 0.27 (R2=0.07). For language outcomes, 45 variables led to the best result (r = 0.30; R2=0.09), whereas for motor outcomes 33 variables led to the best result (r = 0.28, R2=0.09). Environmental chemicals, sociodemographic factors, and maternal mental health were found to be highly ranked predictors of cognitive, language, and motor development in young children. Our findings demonstrate the potential of machine learning approaches to identify and determine the relative importance of different predictors of child neurodevelopmental outcomes. Future developmental neurotoxicology research should consider the prenatal chemical exposome as well as sample characteristics such as sociodemographic factors and maternal mental health as important predictors of child neurodevelopment.

Gold nanomaterials have garnered significant attention in biomedicine owing to their tunable size and morphology, facile surface modification capabilities, and distinctive optical properties. The surface functionalization of these nanoparticles can enhance their safety and efficacy in nanomedical applications. In this study, we examined the biological effects of gold nanoparticles (GNPs) with three distinct surface modifications (polyethylene glycol, chitosan, and polyethylenimine) in murine models, elucidating their mechanisms of action on hepatic tissue at both the transcriptomic and metabolomic levels. Our findings revealed that PEG-modified GNPs did not significantly alter any major metabolic pathway. In contrast, CS-GNPs markedly affected the metabolic pathways of retinol, arachidonic acid, linoleic acid, and glycerophospholipids (FDR < 0.05). Similarly, PEI-GNPs significantly influenced the metabolic pathways of retinol, arachidonic acid, linoleic acid, and sphingolipids (FDR < 0.05). Through a comprehensive analysis of the regulatory information within these pathways, we identified phosphatidylcholine compounds as potential biomarkers that may underlie the differential biological effects of the three functionalized GNPs. These findings provide valuable experimental data for evaluating the biological safety of functionalized GNPs.

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

A microbiome in the gut plays a significant role in cardiovascular health and disease. Dysbiosis is an imbalance in the gut microbiome, leading to multiple cardiovascular diseases (CVD) such as atherosclerosis, hypertension, and heart failure. Gut microbe-derived metabolites such as trimethylamine-N-oxide (TMAO) and short-chain fatty acids (SCFAs) are important mediators of the gut-heart axis. Evaluation of the relationship between the gut microbiome and host biomarkers with CVD requires the integration of metagenomics and metabolomics with meta-omics approaches. The literature review found that microbes and metabolic signatures are associated with the risk and progression of CVD. The development of precision therapeutic approaches for targeting gut microbiota includes preventing adverse microbial effects using probiotics, prebiotics, and the drug-as-bug approach to inhibit harmful metabolites of microbiomes, and fecal microbiota transplantation (FMT). However, the implication and practice of these findings in clinical settings face challenges due to the heterogeneity of study designs, difficulty in the determination of causality, and the impact of confounding factors such as diet, medication, and potential inter-individual gut microbiome variability. Future researchers are recommended to conduct longitudinal studies to further establish both gut microbiome associations with CVD and develop successful precision therapeutics approaches based on the microbiome for the treatment of CVD.

Cortex Dictamni (CD) is a traditional Chinese medicine that is commonly used to treat various skin diseases. Recently, clinical reports have highlighted its potential to induce severe hepatotoxicity. However, the underlying mechanisms of toxicity remain inadequately explored.

The aim of this study was to elucidate the intrinsic mechanisms of CD-induced hepatotoxicity.

Hepatotoxicity was assessed in SD rats, and human primary hepatocytes (HPHs) and differentiated HepaRG (dHepaRG) cells were used for in vitro testing.

The major components of CD were determined using ultra-performance liquid chromatography (UPLC). Rats were randomly divided into control, CD-high (CD-H), CD-middle (CD-M), CD-low (CD-L), and isoniazid (INH) groups and administered oral gavage for four weeks. Serum biochemical indices, histopathological changes, apoptotic markers, and liver function were evaluated to assess hepatotoxicity. A comprehensive analysis of rat liver samples was performed using transcriptomic, proteomic, and metabolomic approaches to identify key pathways involved in CD-induced hepatotoxicity. In vitro toxicity validation of CD was performed using HPHs and dHepaRG cells. The key pathway was validated in vivo and in vitro.

CD primarily contained obacunone, fraxinellone, and dictamine. Administration of CD-H (9 times the maximum daily clinical dose in adults) and CD-M (3 times the maximum daily clinical dose in adults) for 4 weeks induced varying degrees of hepatotoxicity in rats. The CD-H group presented increased absolute and relative liver weights, reduced alanine aminotransferase (ALT) and bile acid transporter levels, and increased albumin (ALB) and cytochrome P450 (CYP) 3A4 levels, indicating significant hepatotoxicity in rats. Integrated multiomics analysis revealed that NADH dehydrogenase (ubiquinone) Fe-S protein 2 (Ndufs2) is a critical regulator of CD-induced hepatotoxicity involving oxidative phosphorylation (OXPHOS). CD inhibited the viability of HPHs and dHepaRG cells, demonstrating its significant cytotoxicity. Mechanistic validation revealed that CD upregulated Ndufs2, reactive oxygen species (ROS) and mitochondrial respiratory chain complex (MRCC) I, leading to nuclear factor erythroid 2-related factor 2 (Nrf2) pathway activation, apoptosis, mitochondrial dysfunction, and hepatotoxicity.

In summary, our study presents a comprehensive picture of the toxicity of CD in terms of dose and sex and reveals, for the first time, the central role of Ndufs2-regulated OXPHOS in CD-induced hepatotoxicity.

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.

Neurodevelopmental disorders (NDDs) are a category of pervasive disorders of the developing nervous system with few or no recognized biomarkers. A significant portion of the risk for NDDs, including attention deficit hyperactivity disorder (ADHD), is contributed by the environment, and exposure to pyrethroid pesticides during pregnancy has been identified as a potential risk factor for NDD in the unborn child. We recently showed that low-dose developmental exposure to the pyrethroid pesticide deltamethrin in mice causes male-biased changes to ADHD- and NDD-relevant behaviors as well as the striatal dopamine system. Here, we used an integrated multiomics approach to determine the broadest possible set of biological changes in the mouse brain caused by developmental pyrethroid exposure (DPE). Using a litter-based, split-sample design, we exposed mouse dams during pregnancy and lactation to deltamethrin (3 mg/kg or vehicle every 3 days) at a concentration well below the EPA-determined benchmark dose used for regulatory guidance. We raised male offspring to adulthood, euthanized them, and pulverized and divided whole brain samples for split-sample transcriptomics, kinomics, and multiomics integration. Transcriptome analysis revealed alterations to multiple canonical clock genes, and kinome analysis revealed changes in the activity of multiple kinases involved in synaptic plasticity, including the mitogen-activated protein (MAP) kinase ERK. Multiomics integration revealed a dysregulated protein-protein interaction network containing primary clusters for MAP kinase cascades, regulation of apoptosis, and synaptic function. These results demonstrate that DPE causes a multimodal biophenotype in the brain relevant to ADHD and identifies new potential mechanisms of action.NEW & NOTEWORTHY Here, we provide the first evidence that low-dose developmental exposure to a pyrethroid pesticide, deltamethrin, results in molecular disruptions in the adult mouse brain in pathways regulating circadian rhythms and neuronal growth (MAP kinase). This same exposure causes a neurodevelopmental disorder (NDD)-relevant behavioral change in adult mice, making these findings relevant to the prevention of NDDs.

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

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.

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.

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.

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.

Insect populations are declining globally, with multiple potential drivers identified. However, experimental data are needed to understand their relative contributions. We highlight the sublethal effects of pesticides at field-relevant concentrations, often overlooked in standard environmental risk assessments (ERA), as significant contributors to these declines. Behavior, as an easily monitored high-level phenotype, reflects alterations at various phenotypic levels. We propose incorporating behavioral assays with AI-based analytical methods into ERA protocols to better assess the safety of molecules intended for large-scale field use. This approach aims to safeguard food supplies and protect vital ecosystems in the future.

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

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.

Rust fungi are highly destructive pathogens that pose a significant threat to crop production worldwide, especially cereals. Obligate biotrophy and, in many cases, complex life cycles make rust fungi particularly challenging to study. However, recent rapid advances in sequencing technologies and genomic analysis tools have revolutionised rust fungal research. It is anticipated that the increasing availability and ongoing substantial improvements in genome assemblies will propel the field of rust biology into the post-genomic era, instigating a cascade of research endeavours encompassing multi-omics and gene discoveries. This is especially the case for many cereal rust pathogens, for which continental-scale studies of virulence have been conducted over many years and historical collections of viable isolates have been sequenced and assembled. Genomic analysis plays a crucial role in uncovering the underlying causes of the high variability of virulence and the complexity of population dynamics in rust fungi. Here, we provide an overview of progress in rust genomics, discuss the strategies employed in genomic analysis, and elucidate the strides that will drive cereal rust biology into the post-genomic era.

The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate multi-omics data, systematic evaluation and comparison of these methods remain challenging. This review aims to analyze network-based approaches for multi-omics integration and evaluate their applications in drug discovery. We conducted a comprehensive review of literature (2015-2024) on network-based multi-omics integration methods in drug discovery, and categorized methods into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, and network inference models. We also discussed the applications of the methods in three scenario of drug discovery, including drug target identification, drug response prediction, and drug repurposing, and finally evaluated the performance of the methods by highlighting their advantages and limitations in specific applications. While network-based multi-omics integration has shown promise in drug discovery, challenges remain in computational scalability, data integration, and biological interpretation. Future developments should focus on incorporating temporal and spatial dynamics, improving model interpretability, and establishing standardized evaluation frameworks.

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Multi-omic data-driven studies are at the forefront of precision medicine by characterizing complex disease signaling systems across multiple views and levels. The integration and interpretation of multi-omic data are critical for identifying disease targets and deciphering disease signaling pathways. However, it remains an open problem due to the complex signaling interactions among many proteins. Herein, we propose a multi-scale multi-hop multi-omic network flow model, M3NetFlow, to facilitate both hypothesis-guided and generic multi-omic data analysis tasks. We evaluated M3NetFlow using two independent case studies: (1) uncovering mechanisms of synergy of drug combinations (hypothesis/anchor-target guided multi-omic analysis) and (2) identifying biomarkers of Alzheimer's disease (generic multi-omic analysis). The evaluation and comparison results showed that M3NetFlow achieved the best prediction accuracy and identified a set of drug combination synergy- and disease-associated targets. The model can be directly applied to other multi-omic data-driven studies.

The gut microbiome is involved in human health and disease, and its comprehensive understanding is necessary to exploit it as a diagnostic or therapeutic tool. Multi-omics approaches, including metagenomics, metatranscriptomics, metabolomics, and metaproteomics, enable depiction of the gut microbial ecosystem's complexity. However, these tools generate a large data stream in which integration is needed to produce clinically useful readouts, but, in turn, might be difficult to carry out with conventional statistical methods. Artificial intelligence and machine learning have been increasingly applied to multi-omics datasets in several conditions associated with microbiome disruption, from chronic disorders to cancer. Such tools have potential for clinical implementation, including discovery of microbial biomarkers for disease classification or prediction, prediction of response to specific treatments, and fine-tuning of microbiome-modulating therapies. The state of the art, potential, and limits, of artificial intelligence and machine learning in the multi-omics approach to gut microbiome are discussed.