Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

A multi-omics approach reveals differences in toxicity and mechanisms in rice (Oryza sativa L.) exposed to anatase or rutile TiO2 nanoparticles

Titanium dioxide nanoparticles (TiO2 NPs) have been widely used in agriculture, which increased the risk to soil-plant systems. Studies have demonstrated that TiO2 NPs can induce phytotoxicity. However, the toxicity mechanisms, particularly under the stress of TiO2 NPs with different crystalline forms, remain inadequately reported. In this study, we combined transcriptomics and metabolomics to analyze the toxicity mechanisms in rice (Oryza sativa L.) under the stress of anatase (AT) or rutile (RT) TiO2 NPs (50 mg/kg, 40 days). The length (decreased by 1.1-fold, p = 0.021) and malondialdehyde concentration (decreased by 1.4-fold, p = 0.0027) of rice shoots was significantly reduced after AT exposure, while no significant changes were observed following RT exposure. Antioxidant enzyme activities were significantly altered both in the AT and RT groups, indicating TiO2 NPs induced rice oxidative damage (with changes of 1.1 to 1.4-fold, p < 0.05). Additionally, compared to the control, AT exposure altered 3247 differentially expressed genes (DEGs) and 56 significantly differentially metabolites in rice (collectively involved in pyrimidine metabolism, TCA cycle, fatty acid metabolism, and amino acid metabolism). After RT exposure, 2814 DEGs and 55 significantly differentially metabolites were identified, which were collectively involved in fatty acid metabolism and amino acid metabolism. Our results indicated that AT exposure led to more pronounced changes in biological responses related to oxidative stress and had more negative effects on rice growth compared to RT exposure. These findings provide new insights into the phytotoxic mechanisms of TiO2 NPs with different crystalline forms. Based on the observed adverse effects, the study emphasizes that any form of TiO2 NPs should be used with caution in rice ecosystems. This study is the first to demonstrate that AT is more toxic than RT in paddy ecosystems, providing crucial insights into the differential impacts and toxic mechanisms of TiO2 NPs with different crystalline forms. These findings suggest prioritizing the use of RT when TiO2 NPs are necessary in agricultural development to minimize toxicity risks.

Role of Nanofertilization in Plant Nutrition under Abiotic Stress Conditions

Plants require nutrients for growth, which they obtain from the soil via the root system. Fertilizers offer the essential nutrients (nitrogen, phosphorus, and potassium, as well as critical secondary elements) required by plants. Soil productivity falls with each crop until nutrients are provided. A wide range of so-called fertilizer products, such as organic fertilizers, argon mineral fertilizers, and mineral fertilizers, can assist farmers in adjusting fertilization methods based on the environment and agricultural conditions (inhibitors, restricted materials, growth mediums, plant bio-stimulants, etc.). Agricultural land is reduced by erosion, pollution, careless irrigation, and fertilization. On the other hand, more agricultural production is needed to meet the demands of expanding industries and the nutritional needs of a growing population. Nano fertilizers have recently started to be manufactured to obtain the highest yield and its quality per unit area. Previous researchers found that nano fertilizers could improve plant nutrient uptake efficiency, lower soil toxicity, mitigate the potential negative effects of excessive chemical fertilizer use, and reduce the frequency of fertilization. To maximize crop yields and optimize nutrient use while reducing the overuse of chemical fertilizers, nano fertilizersNFs are crucial in agriculture. The key component of these fertilizers is that they contain one or more macro- and micronutrients that can be applied regularly in minute doses while not damaging the environment. However, they have a minimal effect on plant growth and agricultural yields when employed in high numbers, like synthetic fertilizers. This article explains the features, relevance and classification of nano-fertilizers, their use in plant development, their advantages and disadvantages, and the results achieved in this field.

Explore the mechanism of yishenjiangya formula in the treatment of senile hypertension based on multi-omics technology

ETHNOPHARMACOLOGICAL RELEVANCE

The Yishenjiangya formula (YSJ) is a traditional Chinese medicine (TCM) primarily composed of qi-tonifying components. This classic formula is commonly utilized to treat kidney qi deficiency in elderly patients with hypertension. According to TCM, maintaining a balance between qi and blood is crucial for stable blood pressure. Kidney qi deficiency can disrupt this balance, altering fluid shear force and, ultimately, leading to hypertension, particularly in elderly populations. Despite YSJ's efficacy in treating hypertension, its specific anti-hypertensive mechanisms remain unclear.

AIM OF THE STUDY

YSJ is commonly prescribed for elderly patients with hypertension. Earlier metabolomics studies demonstrated that YSJ exerts antihypertensive effects by influencing four key pathways: linoleic acid metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and steroid hormone biosynthesis. This study aims to combine metabolomic and proteomic analyses to thoroughly understand the molecular biological mechanisms responsible for YSJ's anti-hypertensive properties.

METHODS

Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) metabolomics, combined with Label-Free Quantitation (LFQ) proteomics, was employed to analyze serum samples from elderly individuals with and without hypertension pre- and post-YSJ intervention. Serum levels of candidate proteins were assessed using enzyme-linked immunosorbent assay, and receiver operating characteristic curves were used to evaluate the diagnostic performance of the target proteins.

RESULTS

Eight differentially expressed metabolites and three differentially expressed proteins were identified as potential therapeutic targets of YSJ. These substances are primarily involved in unsaturated fatty acid metabolism, fluid shear stress and atherosclerosis pathway, primary bile acid biosynthesis, proline metabolism, apoptosis, and endoplasmic reticulum stress. YSJ exerts its therapeutic effects on hypertension in the elderly by modulating these pathways.

CONCLUSIONS

YSJ effectively treats senile hypertension. By analyzing the correlation between therapeutic targets and pathways, YSJ's anti-hypertensive effect was achieved by inhibiting lipid peroxidation and matrix degeneration. Combining metabolomics and proteomics provides an effective method for uncovering YSJ's anti-hypertensive mechanisms.

Exploratory Metabolomics and Lipidomics Profiling Contributes to Understanding How Curcumin Improves Quality of Goat Semen Stored at 16 °C in Tropical Areas

Reactive oxygen species (ROS) exert a vital role in sperm quality during semen preservation, where excessive ROS leads to oxidative damage and undermines sperm integrity. Curcumin, a botanical extract, is capable of neutralizing ROS and enhancing the activity of antioxidant enzymes. This study was aimed at evaluating the effects of curcumin on sperm viability, acrosome integrity, and antioxidant levels, as well as metabolomic and lipidomic profiles. The results demonstrated that curcumin at 25 µmol/L significantly enhanced sperm motility, plasma membrane, and acrosome integrity, elevated the levels of antioxidant enzymes (T-AOC, CAT, SOD), and decreased ROS production (p < 0.05). Metabolomic analysis identified 93 distinct metabolites that showed significant differences between the control and curcumin-treated groups. KEGG pathways emphasized the participation of these metabolites in key metabolic processes such as the citric acid cycle, cholesterol metabolism, and fatty acid metabolism. Curcumin treatment brought about notable variations in lipid profiles, including increased levels of phosphatidylcholine, acylcarnitine, and triglyceride over the storage time, suggesting enhanced lipid anabolic activity. Overall, the supplementation of curcumin at 25 µmol/L effectively mitigates oxidative stress and prolongs the viability of semen storage at 16 °C by modulating specific metabolic and lipid profiles.

Multilayer biological networks to upscale marine research to global change-smart management and sustainable resource use

Human activities are having a massive negative impact on biodiversity and ecological processes worldwide. The rate and magnitude of ecological transformations induced by climate change, habitat destruction, overexploitation and pollution are now so substantial that a sixth mass extinction event is currently underway. The biodiversity crisis of the Anthropocene urges scientists to put forward a transformative vision to promote the conservation of biodiversity, and thus indirectly the preservation of ecosystem functions. Here, we identify pressing issues in global change biology research and propose an integrative framework based on multilayer biological networks as a tool to support conservation actions and marine risk assessments in multi-stressor scenarios. Multilayer networks can integrate different levels of environmental and biotic complexity, enabling us to combine information on molecular, physiological and behaviour responses, species interactions and biotic communities. The ultimate aim of this framework is to link human-induced environmental changes to species physiology, fitness, biogeography and ecosystem impacts across vast seascapes and time frames, to help guide solutions to address biodiversity loss and ecological tipping points. Further, we also define our current ability to adopt a widespread use of multilayer networks within ecology, evolution and conservation by providing examples of case-studies. We also assess which approaches are ready to be transferred and which ones require further development before use. We conclude that multilayer biological networks will be crucial to inform (using reliable multi-levels integrative indicators) stakeholders and support their decision-making concerning the sustainable use of resources and marine conservation.

Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles

Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Multi-Omics Exploration of the Mechanism of Curcumol to Reduce Invasion and Metastasis of Nasopharyngeal Carcinoma by Inhibiting NCL/EBNA1-Mediated UBE2C Upregulation

Nasopharyngeal carcinoma (NPC) is closely linked to Epstein-Barr virus (EBV) infection. Curcumae Rhizoma, a traditional Chinese herb, has shown antitumor effects, primarily through its component curcumol (Cur), which has been shown to reduce NPC cell invasion and migration by targeting nucleolin (NCL) and Epstein-Barr Virus Nuclear Antigen 1 (EBNA1). We constructed an EBV-positive NPC cell model using C666-1 cells and performed transcriptomics studies after treatment with curcumol, which revealed a significant enrichment of ubiquitin-mediated proteolysis, the PI3K-AKT and mTOR signaling pathways, cell cycle and apoptosis involved in tumor invasion and migration. To investigate the importance of NCL and EBNA1 in curcumol-resistant EBV-positive NPC, we performed a multi-omics study using short hairpin NCL (shNCL) and shEBNA1 EBV-positive NPC cells, and the proteomics results showed enrichment in complement and coagulation cascades and ubiquitin-mediated proteolysis signaling pathways. Here, we focused on ubiquitin-conjugating enzyme E2C (UBE2C), which plays an important role in the ubiquitin-mediated proteolysis signaling pathway. In addition, metabolomics revealed that UBE2C is highly associated with 4-Aminobutanoic acid (GABA). In vitro studies further validated the function of the key targets, suggesting that UBE2C plays an important role in NCL and EBNA1-mediated curcumol resistance to nasopharyngeal carcinoma invasion and metastasis.

Potential application of artificial intelligence in cancer therapy

PURPOSE OF REVIEW

This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence.

RECENT FINDINGS

Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs.

SUMMARY

Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.

Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

Genomic Insights into Idiopathic Granulomatous Mastitis through Whole-Exome Sequencing: A Case Report of Eight Patients

Idiopathic granulomatous mastitis (IGM) is a rare condition characterised by chronic inflammation and granuloma formation in the breast. The aetiology of IGM is unclear. By focusing on the protein-coding regions of the genome, where most disease-related mutations often occur, whole-exome sequencing (WES) is a powerful approach for investigating rare and complex conditions, like IGM. We report WES results on paired blood and tissue samples from eight IGM patients. Samples were processed using standard genomic protocols. Somatic variants were called with two analytical pipelines: nf-core/sarek with Strelka2 and GATK4 with Mutect2. Our WES study of eight patients did not find evidence supporting a clear genetic component. The discrepancies between variant calling algorithms, along with the considerable genetic heterogeneity observed amongst the eight IGM cases, indicate that common genetic drivers are not readily identifiable. With only three genes, CHIT1, CEP170, and CTR9, recurrently altering in multiple cases, the genetic basis of IGM remains uncertain. The absence of validation for somatic variants by Sanger sequencing raises further questions about the role of genetic mutations in the disease. Other potential contributors to the disease should be explored.

Planetary health: A new approach to healing the Anthropocene

The Anthropocene is a proposed geological epoch reflecting the large-scale impact of human activity on the Earth's natural systems. This era is also characterized by other significant threats to ecologic well-being that are less evident in the sedimentary records. Extensive environmental changes with industrialization and urbanization have also contributed to declining biodiversity and microbial dysbiosis in essential ecosystems-the original and foundational lifeforms that continue to sustain virtually all ecosystems today, including our own. These changes, along with numerous other social and ecologic disruptions at all scales are implicated in the rising rates of physical and mental ill-health, particularly the immune dysregulation and noncommunicable diseases that characterize the Anthropocene. This narrative review considers how urgent structural changes in how we live are essential to the future of human health and the flourishing of all life on Earth. It explores planetary health as a solutions-oriented, transdisciplinary field and social movement aimed at addressing these interconnected global challenges through integrated ecologic approaches. Planetary health considers not only the vital biophysical "planetary boundaries" required to support human flourishing, but also the upstream social, political, and economic ecosystems that support (or undermine) well-being at all scales. The value systems and the worldviews that have contributed to our global challenges are a central consideration in the planetary health agenda- emphasizing the imperative to address structural inequalities, injustices, and the social, emotional, and spiritual dimensions of unrealized human potential. Promoting these inner assets is essential to human flourishing and fostering the cultural capacities necessary to ensure sustainable planetary health.

Unlocking novel therapeutic avenues in glioblastoma: Harnessing 4-amino cyanine and miRNA synergy for next-gen treatment convergence

Glioblastoma (GBM) poses a formidable challenge in oncology due to its aggressive nature and dismal prognosis, with average survival rates around 15 months despite conventional treatments. This review proposes a novel therapeutic strategy for GBM by integrating microRNA (miRNA) therapy with 4-amino cyanine molecules possessing near-infrared (NIR) properties. miRNA holds promise in regulating gene expression, particularly in GBM, making it an attractive therapeutic target. 4-amino cyanine molecules, especially those with NIR properties, have shown efficacy in targeted tumor cell degradation. The combined approach addresses gene expression regulation and precise tumor cell degradation, offering a breakthrough in GBM treatment. Additionally, the review explores noncoding RNAs classification and characteristics, highlighting their role in GBM pathogenesis. Advanced technologies such as antisense oligonucleotides (ASOs), locked nucleic acids (LNAs), and peptide nucleic acids (PNAs) show potential in targeting noncoding RNAs therapeutically, paving the way for precision medicine in GBM. This synergistic combination presents an innovative approach with the potential to advance cancer therapy in the challenging landscape of GBM.

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

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

Advances in Parkinson's disease research - A computational network pharmacological approach

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.

Integrated Bioinformatics-Based Identification and Validation of Neuroinflammation-Related Hub Genes in Primary Open-Angle Glaucoma

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene-miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation-related genes were found to overlap between the differentially expressed genes and neuroinflammation-related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene-miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Transcriptomics and epigenetic data integration learning module on Google Cloud

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

HIGHLIGHTS

Unraveling the noncoding RNA landscape in glioblastoma: from pathogenesis to precision therapeutics

Glioblastoma (GBM) is an aggressive type IV brain tumor that originates from astrocytes and has a poor prognosis. Despite intensive research, survival rates have not significantly improved. Noncoding RNAs (ncRNAs) are emerging as critical regulators of carcinogenesis, progression, and increased treatment resistance in GBM cells. They influence angiogenesis, migration, epithelial-to-mesenchymal transition, and invasion in GBM cells. ncRNAs, such as long ncRNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are commonly dysregulated in GBM. miRNAs, such as miR-21, miR-133a, and miR-27a-3p, are oncogenes that increase cell proliferation, metastasis, and migration by targeting TGFBR1 and BTG2. In contrast, lncRNAs, such as HOXD-AS2 and LINC00511, are oncogenes that increase the migration, invasion, and proliferation of cells. CircRNAs, such as circ0001730, circENTPD7, and circFOXO3, are oncogenes responsible for cell growth, angiogenesis, and viability. Developing novel therapeutic strategies targeting ncRNAs, cell migration, and angiogenesis is a promising approach for GBM. By targeting these dysregulated ncRNAs, we can potentially restore a healthy balance in gene expression and influence disease progression. ncRNAs abound within GBM, demonstrating significant roles in governing the growth and behavior of these tumors. They may also be useful as biomarkers or targets for therapy. The use of morpholino oligonucleotides (MOs) suppressing the oncogene expression of HOTAIR, BCYRN1, and cyrano, antisense oligonucleotides (ASOs) suppressing the expression of ncRNAs such as MALAT1 and miR-10b, locked nucleic acids (LNAs) suppressing miR-21, and peptide nucleic acids (PNAs) suppressing the expression of miR-155 inhibited the PI3K pathway, tumor growth, angiogenesis, proliferation, migration, and invasion. Targeting oncogenic ncRNAs with RNA-interfering strategies such as MOs, ASOs, LNAs, CRISPR-Cas9 gene editing, and PNA approaches may represent a promising therapeutic strategy for GBM. This review emphasizes the critical role of ncRNAs in GBM pathogenesis, as well as the potential for new therapeutic strategies targeting these pathways to improve the prognosis and quality of life for GBM patients.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Multi-omics reveals the role of MCM2 and hnRNP K phosphorylation in mouse renal aging through genomic instability

The process of aging is characterized by structural degeneration and functional decline, as well as diminished adaptability and resistance. The aging kidney exhibits a variety of structural and functional impairments. In aging mice, thinning and graying of fur were observed, along with a significant increase in kidney indices compared to young mice. Biochemical indicators revealed elevated levels of creatinine, urea nitrogen and serum uric acid, suggesting impaired kidney function. Histological analysis unveiled glomerular enlargement and sclerosis, severe hyaline degeneration, capillary occlusion, lymphocyte infiltration, tubular and glomerular fibrosis, and increased collagen deposition. Observations under electron microscopy showed thickened basement membranes, altered foot processes, and increased mesangium and mesangial matrix. Molecular marker analysis indicated upregulation of aging-related β-galactosidase, p16-INK4A, and the DNA damage marker γH2AX in the kidneys of aged mice. In metabolomics, a total of 62 significantly different metabolites were identified, and 10 pathways were enriched. We propose that citrulline, dopamine, and indoxyl sulfate have the potential to serve as markers of kidney damage related to aging in the future. Phosphoproteomics analysis identified 6656 phosphosites across 1555 proteins, annotated to 62 pathways, and indicated increased phosphorylation at the Ser27 site of Minichromosome maintenance complex component 2 (Mcm2) and decreased at the Ser284 site of heterogeneous nuclear ribonucleoprotein K (hnRNP K), with these modifications being confirmed by western blotting. The phosphorylation changes in these molecules may contribute to aging by affecting genome stability. Eleven common pathways were detected in both omics, including arginine biosynthesis, purine metabolism and biosynthesis of unsaturated fatty acids, etc., which are closely associated with aging and renal insufficiency.

Facing the challenges to shorten the diagnostic odyssey: first Whole Genome Sequencing experience of a Colombian cohort with suspected rare diseases

Exome and genome sequencing (ES/GS) are routinely used for the diagnosis of genetic diseases in developed countries. However, their implementation is limited in countries from Latin America. We aimed to describe the results of GS in patients with suspected rare genetic diseases in Colombia. We studied 501 patients from 22 healthcare sites from January to December 2022. GS was performed in the index cases using dried blood spots on filtercards. Ancestry analysis was performed under iAdmix. Multiomic testing was performed when needed (biomarker, enzymatic activity, RNA-seq). All tests were performed at an accredited genetic laboratory. Ethnicity prediction data confirmed that 401 patients (80%) were mainly of Amerindian origin. A genetic diagnosis was established for 142 patients with a 28.3% diagnostic yield. The highest diagnostic yield was achieved for pathologies with a metabolic component and syndromic disorders (p < 0.001). Young children had a median of 1 year of diagnostic odyssey, while the median time for adults was significantly longer (15 years). Patients with genetic syndromes have spent more than 75% of their life without a diagnosis, while for patients with neurologic and neuromuscular diseases, the time of the diagnostic odyssey tended to decrease with age. Previous testing, specifically karyotyping or chromosomal microarray were significantly associated with a longer time to reach a definitive diagnosis (p < 0.01). Furthermore, one out of five patients that had an ES before could be diagnosed by GS. The Colombian genome project is the first Latin American study reporting the experience of systematic use of diagnostic GS in rare diseases.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Comparative Analysis of mRNA, microRNA of Transcriptome, and Proteomics on CIK Cells Responses to GCRV and Aeromonas hydrophila

Grass Carp Reovirus (GCRV) and Aeromonas hydrophila (Ah) are the causative agents of haemorrhagic disease in grass carp. This study aimed to investigate the molecular mechanisms and immune responses at the miRNA, mRNA, and protein levels in grass carp kidney cells (CIK) infected by Grass Carp Reovirus (GCRV, NV) and Aeromonas hydrophilus (Bacteria, NB) to gain insight into their pathogenesis. Within 48 h of infection with Grass Carp Reovirus (GCRV), 99 differentially expressed microRNA (DEMs), 2132 differentially expressed genes (DEGs), and 627 differentially expressed proteins (DEPs) were identified by sequencing; a total of 92 DEMs, 3162 DEGs, and 712 DEPs were identified within 48 h of infection with Aeromonas hydrophila. It is worth noting that most of the DEGs in the NV group were primarily involved in cellular processes, while most of the DEGs in the NB group were associated with metabolic pathways based on KEGG enrichment analysis. This study revealed that the mechanism of a grass carp haemorrhage caused by GCRV infection differs from that caused by the Aeromonas hydrophila infection. An important miRNA-mRNA-protein regulatory network was established based on comprehensive transcriptome and proteome analysis. Furthermore, 14 DEGs and 6 DEMs were randomly selected for the verification of RNA/small RNA-seq data by RT-qPCR. Our study not only contributes to the understanding of the pathogenesis of grass carp CIK cells infected with GCRV and Aeromonas hydrophila, but also serves as a significant reference value for other aquatic animal haemorrhagic diseases.

Comprehensive toxicological, metabolomic, and transcriptomic analysis of the biodegradation and adaptation mechanism by Achromobacter xylosoxidans SL-6 to diuron

Biodegradation was considered a promising and environmentally friendly method for treating environmental pollution caused by diuron. However, the mechanisms of biodegradation of diuron required further research. In this study, the degradation process of diuron by Achromobacter xylosoxidans SL-6 was systematically investigated. The results suggested that the antioxidant system of strain SL-6 was activated by adding diuron, thereby alleviating their oxidative stress response. In addition, degradation product analysis showed that diuron in strain SL-6 was mainly degraded by urea bridge cleavage, dehalogenation, deamination, and ring opening, and finally cis, cis-muconic acid was generated. The combined analysis of metabolomics and transcriptomics revealed the biodegradation and adaptation mechanism of strain SL-6 to diuron. Metabolomics analysis showed that after the strain SL-6 was exposed to diuron, metabolic pathways such as tricarboxylic acid cycle (cis, cis-muconic acid), glutathione metabolism (oxidized glutathione), and urea cycle (arginine) were reprogrammed in the cells. Furthermore, diuron could induce the production of membrane transport proteins in strain SL-6 cells and overexpress antioxidant enzyme genes, finally ultimately promoting the up-regulation of genes encoding amide hydrolases and dioxygenases, which was revealed by transcriptomics studies. This work enriched the biodegradation mechanism of phenylurea herbicides and provided guidance for the removal of diuron residues in the environment and promoting agriculture sustainable development.

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.

METHOD

Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis.

RESULTS

Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others.

CONCLUSIONS

This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

Carpal tunnel syndrome

Carpal tunnel syndrome (CTS) is the most common nerve entrapment disorder worldwide. The epidemiology and risk factors, including family burden, for developing CTS are multi-factorial. Despite much research, its intricate pathophysiological mechanism(s) are not fully understood. An underlying subclinical neuropathy may indicate an increased susceptibility to developing CTS. Although surgery is often performed for CTS, clear international guidelines to indicate when to perform non-surgical or surgical treatment, based on stage and severity of CTS, remain to be elucidated. Neurophysiological examination, using electrophysiology or ultrasonography, performed in certain circumstances, should correlate with the history and findings in clinical examination of the person with CTS. History and clinical examination are particularly relevant globally owing to lack of other equipment. Various instruments are used to assess CTS and treatment outcomes as well as the effect of the disorder on quality of life. The surgical treatment options of CTS - open or endoscopic - offer an effective solution to mitigate functional impairments and pain. However, there are risks of post-operative persistent or recurrent symptoms, requiring meticulous diagnostic re-evaluation before any additional surgery. Health-care professionals should have increased awareness about CTS and all its implications. Future considerations of CTS include use of linked national registries to understand risk factors, explore possible screening methods, and evaluate diagnosis and treatment with a broader perspective beyond surgery, including psychological well-being.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Inhibition of UHRF1 Improves Motor Function in Mice with Spinal Cord Injury

Spinal-cord injury (SCI) is a severe condition that can lead to limb paralysis and motor dysfunction, and its pathogenesis is not fully understood. The objective of this study was to characterize the differential gene expression and molecular mechanisms in the spinal cord of mice three days after spinal cord injury. By analyzing RNA sequencing data, we identified differentially expressed genes and discovered that the immune system and various metabolic processes play crucial roles in SCI. Additionally, we identified UHRF1 as a key gene that plays a significant role in SCI and found that SCI can be improved by suppressing UHRF1. These findings provide important insights into the molecular mechanisms of SCI and identify potential therapeutic targets that could greatly contribute to the development of new treatment strategies for SCI.

Rummagene: massive mining of gene sets from supporting materials of biomedical research publications

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

Yak genome database: a multi-omics analysis platform

BACKGROUND

The yak (Bos grunniens) is a large ruminant species that lives in high-altitude regions and exhibits excellent adaptation to the plateau environments. To further understand the genetic characteristics and adaptive mechanisms of yak, we have developed a multi-omics database of yak including genome, transcriptome, proteome, and DNA methylation data.

DESCRIPTION

The Yak Genome Database ( http://yakgenomics.com/ ) integrates the research results of genome, transcriptome, proteome, and DNA methylation, and provides an integrated platform for researchers to share and exchange omics data. The database contains 26,518 genes, 62 transcriptomes, 144,309 proteome spectra, and 22,478 methylation sites of yak. The genome module provides access to yak genome sequences, gene annotations and variant information. The transcriptome module offers transcriptome data from various tissues of yak and cattle strains at different developmental stages. The proteome module presents protein profiles from diverse yak organs. Additionally, the DNA methylation module shows the DNA methylation information at each base of the whole genome. Functions of data downloading and browsing, functional gene exploration, and experimental practice were available for the database.

CONCLUSION

This comprehensive database provides a valuable resource for further investigations on development, molecular mechanisms underlying high-altitude adaptation, and molecular breeding of yak.

Key Proteomics Tools for Fundamental and Applied Microalgal Research

Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

Comparative transcriptomic analysis of circulating endothelial cells in sickle cell stroke

Ischemic stroke (IS) is one of the most impairing complications of sickle cell anemia (SCA), responsible for 20% of mortality in patients. Rheological alterations, adhesive properties of sickle reticulocytes, leukocyte adhesion, inflammation and endothelial dysfunction are related to the vasculopathy observed prior to ischemic events. The role of the vascular endothelium in this complex cascade of mechanisms is emphasized, as well as in the process of ischemia-induced repair and neovascularization. The aim of the present study was to perform a comparative transcriptomic analysis of endothelial colony-forming cells (ECFCs) from SCA patients with and without IS. Next, to gain further insights of the biological relevance of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction network (PPI) construction and in silico prediction of regulatory factors were performed. Among the 2469 DEGs, genes related to cell proliferation (AKT1, E2F1, CDCA5, EGFL7), migration (AKT1, HRAS), angiogenesis (AKT1, EGFL7) and defense response pathways (HRAS, IRF3, TGFB1), important endothelial cell molecular mechanisms in post ischemia repair were identified. Despite the severity of IS in SCA, widely accepted molecular targets are still lacking, especially related to stroke outcome. The comparative analysis of the gene expression profile of ECFCs from IS patients versus controls seems to indicate that there is a persistent angiogenic process even after a long time this complication has occurred. Thus, this is an original study which may lead to new insights into the molecular basis of SCA stroke and contribute to a better understanding of the role of endothelial cells in stroke recovery.

Components study on gastroprotective effect and holistic mechanism of the herbal pair Alpinia officinarum - Cyperus rotundus based on spectrum-effect relationship and integrated transcriptome and metabolome analyses

ETHNOPHARMACOLOGICAL RELEVANCE

The herbal pair Alpinia officinarum-Cyperus rotundus (HPAC) has an extended history of use in the treatment of gastric ulcers, and its curative effect is definite.

AIM OF THE STUDY

To explore the material basis and holistic mechanism of HPAC on ethanol-induced gastric ulcers.

MATERIALS AND METHODS

Three chemometrics, GRA, OPLS, and BCA, were used to construct the spectrum-effect relationship between the HPLC fingerprints of HPAC extracts and the bioactivity indices (cell viability; the levels of TNF-α, IL-6, COX-2, and PGE2; and wound healing rate) against GES-1 cell damage to screen the bioactive ingredients. The bioactive components were isolated and validated in vitro. Simultaneously, the effects of HPAC with concentrated bioactive ingredients was tested on ethanol-induced gastric ulcers in vivo, and the mechanism was investigated using transcriptomics and metabolomics. The mechanism was further validated by Western blotting. Finally, the contents of the main components of HPAC were determined before and after compatibility.

RESULTS

Twelve bioactive components were screened, and the structures of nine compounds were confirmed. An in vitro verification test showed that DPHA and galangin could protect GES-1 cells from injury, and that their content increased after compatibility. The CH2Cl2 fraction of HPAC (HP-CH2Cl2) can protect mice from ethanol-induced gastric mucosal injury by reducing hemorrhage and decreasing inflammatory cell infiltration. Western blot analysis indicated that this fraction may up-regulate TRPV1 protein and down-regulate PI3K and AKT proteins.

CONCLUSIONS

DPHA and galangin may be the bioactive components against ethanol-induced GES-1 cell injury. HP-CH2Cl2 may exert gastroprotective effects by regulating PI3K, AKT and TRPV1 proteins.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

Pangenomics: A new era in the field of neurodegenerative diseases

A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.

Trends in the application of "omics" to Alzheimer's disease: a bibliometric and visualized study

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease with an insidious onset. The widespread application of omics techniques in AD has attracted considerable attention. We aimed to make a comprehensive analysis of published omics articles on AD in order to determine the research profile and application trends of omics techniques in AD.

METHODS

This study utilizes bibliometric and visual methods including a map collaboration map, co-citations, and keywords to identify knowledge structures, hot topics, and research trends based on 6,828 publications from the Web of Science Core Collection (WoSCC) database.

RESULTS

The results of this study showed that 5654 institutions from 91 countries published articles in this field. The USA, China, and the UK played a leading role in publishing numerous articles in relevant journals as well as prolific institutions and authors, respectively. This paper collects a large number of literatures on the application of AD omics technology from the WoSCC database and found the omics technology applied to AD is mainly based on genomics technology. The application of transcriptomics technology has shown an increasing trend in recent years, and the application of multi-omics technology will be the general trend in the future.

CONCLUSION

The development status, frontier hotspots, and general trends of omics application technologies are reviewed. This article will provide intelligence support to researchers and institutions in the field of Alzheimer's omics research and applications from a practical perspective.

Omics-based investigation of pathological liver injury induced by Echinococcus multilocularis infection in mice

BACKGROUND

Alveolar echinococcosis (AE) can cause severe liver injury and be fatal if left untreated. Currently, there are no effective therapeutic options for AE-induced liver injury. Therefore, by exploring the changes of gene proteins in mice with damaged liver, we attempted to identify the key molecules of liver damage, and provide data that will enable the development of drugs targeting hepatic AE.

METHODS

BALB/c mice were inoculated with protoscoleces via the hepatic portal vein. Three months later, B-ultrasound examination and Hematoxylin-eosin (H&E) staining were used to confirm liver damage in mice. RNA sequencing and Liquid chromatography-mass spectrometry (LC-MS) were used to screen differentially expressed molecules associated with liver damage through bioinformatics, and Quantitative Real-Time PCR (qRT-PCR) was used to verify their expression.

RESULTS

B-ultrasound examination showed liver lesions in the infected group, and H&E staining showed liver inflammation, fibrosis and liver necrosis. RNA sequencing and LC-MS results showed changes in the levels of more than 1000 genes and proteins, with upregulation of immune and inflammation pathways. By contrast, the downregulated genes and proteins were mostly involved in various metabolic reactions. Correlation analysis was conducted between the transcriptome data and proteome data. The results revealed 240 differentially expressed genes, of which 192 were upregulated, and 48 were downregulated. Many of these genes were involved in metabolic reactions, such as Catalase (Cat), fatty acid synthase (Fasn), and IL-16 genes, which may have relevance to liver injury. The results of qRT-PCR were consistent with those of bioinformatics analysis.

CONCLUSIONS

The mechanisms of liver injury in mice infected with Echinococcus multilocularis are complex, involving abnormal metabolism, oxidative stress, inflammatory response, and many other factors. This study provides the data for preliminary exploration for the development of targeted therapies against AE.

Combined transcriptomics and proteomics studies on the effect of electrical stimulation on spinal cord injury in rats

Electrical stimulation (ES) of the spinal cord is a promising therapy for functional rehabilitation after spinal cord injury (SCI). However, the specific mechanism of action is poorly understood. We designed and applied an implanted ES device in the SCI area in rats and determined the effect of ES on the treatment of motor dysfunction after SCI using behavioral scores. Additionally, we examined the molecular characteristics of the samples using proteomic and transcriptomic sequencing. The differential molecules between groups were identified using statistical analyses. Molecular, network, and pathway-based analyses were used to identify group-specific biological features. ES (0.5 mA, 0.1 ms, 50 Hz) had a positive effect on motor dysfunction and neuronal regeneration in rats after SCI. Six samples (three independent replicates in each group) were used for transcriptome sequencing; we obtained 1026 differential genes, comprising 274 upregulated genes and 752 downregulated genes. A total of 10 samples were obtained: four samples in the ES group and six samples in the SCI group; for the proteome sequencing, 48 differential proteins were identified, including 45 up-regulated and three down-regulated proteins. Combined transcriptomic and proteomic studies have shown that the main enrichment pathway is the hedgehog signaling pathway. Western blot results showed that the expression levels of Sonic hedgehog (SHH) (P < 0.001), Smoothened (SMO) (P = 0.0338), and GLI-1 (P < 0.01) proteins in the ES treatment group were significantly higher than those in the SCI group. The immunofluorescence results showed significantly increased expression of SHH (P = 0.0181), SMO (P = 0.021), and GLI-1 (P = 0.0126) in the ES group compared with that in the SCI group. In conclusion, ES after SCI had a positive effect on motor dysfunction and anti-inflammatory effects in rats. Moreover, transcriptomic and proteomic sequencing also provided unique perspectives on the complex relationships between ES on SCI, where the SHH signaling pathway plays a critical role. Our study provides a significant theoretical foundation for the clinical implementation of ES therapy in patients with SCI.

Integrated Transcriptomics-Proteomics Analysis Identifies Molecular Phenotypic Alterations Associated with Colorectal Cancer

Understanding the pathogenesis and finding diagnostic markers for colorectal cancer (CRC) are the key to its diagnosis and treatment. Integrated transcriptomics and proteomics analysis can be used to characterize alterations of molecular phenotypes and reveal the hidden pathogenesis of CRC. This study employed a novel strategy integrating transcriptomics and proteomics to identify pathological molecular pathways and diagnostic biomarkers of CRC. First, differentially expressed proteins and coexpressed genes generated from weighted gene coexpression network analysis (WGCNA) were intersected to obtain key genes of the CRC phenotype. In total, 63 key genes were identified, and pathway enrichment analysis showed that the process of coagulation and peptidase regulator activity could both play important roles in the development of CRC. Second, protein-protein interaction analysis was then conducted on these key genes to find the central genes involved in the metabolic pathways underpinning CRC. Finally, Itih3 and Lrg1 were further screened out as diagnostic biomarkers of CRC by applying statistical analysis on central genes combining transcriptomics and proteomics data. The deep involvement of central genes in tumorigenesis demonstrates the accuracy and reliability of this novel transcriptomics-proteomics integration strategy in biomarker discovery. The identified candidate biomarkers and enriched metabolic pathways provide insights for CRC diagnosis and treatment.

Implementation of Exome Sequencing to Identify Rare Genetic Diseases

Modern high-throughput genomic testing using next-generation sequencing (NGS) has led to a significant increase in the successful diagnosis of rare genetic disorders. Recent advances in NGS tools and techniques have led to accurate and timely diagnosis of a large proportion of genetic diseases by finding sequence variations in clinical samples. One of the NGS techniques, exome sequencing (ES), is considered as a powerful and easily approachable method for genetic disorders in terms of rapid and cost-effective diagnostic yields. In this chapter, we describe an overview of whole exome sequencing (ES) in the context of experimental and analytical methodologies. Approaches to ES include sequencing capture technique, quality control processes at various stages of sequencing analysis, exome data filtering strategy that incorporates both primary and secondary filtering, and prioritization of candidate variants in diagnosing genetic diseases.

Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches

The incorporation of AI models into bioinformatics has brought about a revolutionary era in the analysis and interpretation of biological data. This mini-review offers a succinct overview of the indispensable role AI plays in the convergence of computational techniques and biological research. The search strategy followed PRISMA guidelines, encompassing databases such as PubMed, Embase, and Google Scholar to include studies published between 2018 and 2024, utilizing specific keywords. We explored the diverse applications of AI methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), across various domains of bioinformatics. These domains encompass genome sequencing, protein structure prediction, drug discovery, systems biology, personalized medicine, imaging, signal processing, and text mining. AI algorithms have exhibited remarkable efficacy in tackling intricate biological challenges, spanning from genome sequencing to protein structure prediction, and from drug discovery to personalized medicine. In conclusion, this study scrutinizes the evolving landscape of AI-driven tools and algorithms, emphasizing their pivotal role in expediting research, facilitating data interpretation, and catalyzing innovations in biomedical sciences.

Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles

Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.