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

Genetic insights into the role of mitochondria-related genes in mental disorders: An integrative multi-omics analysis

BACKGROUND

Mitochondrial dysfunction has been implicated in the development of mental disorders, yet the underlying mechanisms remain unclear. In this study, we employed summary-data-based Mendelian randomization (SMR) analysis to explore the associations between mitochondrial-related genes and seven common mental disorders across gene expression, DNA methylation, and protein levels.

METHOD

Summary statistics from genome-wide association studies were used for seven mental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, anxiety, bipolar disorder, major depressive disorder, post-traumatic stress disorder, and schizophrenia (SCZ). Instrumental variables associated with 1136 mitochondria-related genes were derived from summary statistics for DNA methylation, gene expression, and protein quantitative trait loci. SMR analyses and colocalization analyses were then conducted across these three biological levels to explore the associations with each of the seven mental disorders.

RESULTS

We identified mitochondria-related genes associated with mental disorders with multi-omics evidence: RMDN1 for ADHD, and ACADVL, ETFA, MMAB, and PPA2 for SCZ. Specifically, an increase of one standard deviation in the level of RMDN1 was linked to a 12 % decrease in the risk of developing ADHD (OR = 0.88, 95 % CI: 0.83-0.94). Increased levels of ETFA (OR = 1.79, 95 % CI: 1.24-2.60) and MMAB (OR = 1.10, 95 % CI: 1.05-1.16) were significantly associated with increased risk of SCZ. Conversely, high levels of ACADVL (OR = 0.50, 95 % CI: 0.33-0.77) and PPA2 (OR = 0.68, 95 % CI: 0.55-0.85) were associated with a reduced risk of SCZ.

CONCLUSIONS

These findings suggested that dysfunction in mitochondria-related genes may underlie the molecular mechanisms of ADHD and SCZ, providing novel biomarkers for diagnosis and therapeutic interventions.

From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcare

BACKGROUND

The integration of data from diverse sources is not only crucial for addressing data scarcity in health informatics but also enables the use of complementary information from multiple datasets. However, the isolated nature of data collected from disparate sources (referred to as 'Silos') presents significant challenges in multi-source data integration due to inherent heterogeneity and differences in data structures, formats, and standards. Domain adaptation emerges as a key framework to transition from 'Silos' to 'Synthesis' by measuring and mitigating such discrepancies, enabling uniform representation and harmonization of multi-source data.

METHODS

This study explores different approaches to healthcare data integration, highlighting the challenges associated with each type and discussing both general-purpose and healthcare-specific adaptation methods. We examine key research challenges and evaluate leading domain adaptation approaches, demonstrating their effectiveness and limitations in advancing healthcare data integration.

RESULTS

The findings highlight the potential of domain adaptation methods to significantly improve healthcare data integration while laying a foundation for future research.

CONCLUSION

Current research often lacks a comprehensive analysis of how domain adaptation can effectively address the challenges associated with integrating multi-source and multi-modal healthcare datasets. This study serves as a valuable resource for healthcare professionals and researchers, providing guidance on leveraging domain adaptation techniques to mitigate domain discrepancies in healthcare data integration.

Role of Maternal Obesity in Offspring Cardiovascular Development and Congenital Heart Defects

BACKGROUND

Congenital heart disease is a leading cause of death in newborns, yet many of its molecular mechanisms remain unknown. Both maternal obesity and diabetes increase the risk of congenital heart disease in offspring, with recent studies suggesting these conditions may have distinct teratogenic mechanisms. The global prevalence of obesity is rising, and while maternal obesity is a known risk factor for fetal congenital heart disease, the specific mechanisms are largely unexplored.

METHODS AND RESULTS

We used a murine model of diet-induced maternal obesity, without diabetes, to produce dams that were overweight but had normal blood glucose levels. Embryos were generated and their developing hearts analyzed. Transcriptome analysis was performed using single-nucleus and bulk RNA sequencing. Global and phospho-enriched proteome analysis was performed using tandem mass tag-mass spectroscopy. Immunobloting and histologic evaluation were also performed. Analysis revealed disrupted oxidative phosphorylation and reactive oxygen species formation, with reduced antioxidant capacity, evidenced by downregulation of genes Sod1 and Gp4x, and disrupted Hif1a signaling. Evidence of oxidative stress, cell death signaling, and alteration in Rho GTPase and actin cytoskeleton signaling was also observed. Genes involved in cardiac morphogenesis, including Hand2, were downregulated, and fewer mature cardiomyocytes were present. Histologic analysis confirmed increased cardiac defects in embryos exposed to maternal obesity.

CONCLUSIONS

These findings demonstrate that maternal obesity alone can result in cardiac defects through mechanisms similar to those associated with maternal hyperglycemia. This study provides valuable insight into the role of maternal obesity, a growing and modifiable risk factor, in the development of the most common birth defect, congenital heart disease.

Research Progress of Genomics Applications in Secondary Metabolites of Medicinal Plants: A Case Study in Safflower

Medicinal plants, recognized as significant natural resources, have gained prominence in response to the increasing global demand for herbal medicines, necessitating the large-scale production of these plants and their derivatives. Medicinal plants are exposed to a variety of internal and external factors that interact to influence the biosynthesis and accumulation of secondary metabolites. With the rapid development of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics, multi-omics technologies have become important tools for revealing the complexity and functionality of organisms. They are conducive to further uncovering the biological activities of secondary metabolites in medicinal plants and clarifying the molecular mechanisms underlying the production of secondary metabolites. Also, artificial intelligence (AI) technology accelerates the comprehensive utilization of high-dimensional datasets and offers transformative potential for multi-omics analysis. However, there is currently no systematic review summarizing the genomic mechanisms of secondary metabolite biosynthesis in medicinal plants. Safflower (Carthamus tinctorius L.) has rich and diverse bioactive flavonoids, among of which Hydroxysafflor yellow A (HSYA) is specific to safflower and emerging as a potential medication for treating a wide range of diseases. Hence, significant progress has been made in the study of safflower as an excellent example for the regulation of secondary metabolites in medicinal plants in recent years. Here, we review the progress on the understanding of the regulation of main secondary metabolites at the multi-omics level, and summarize the influence of various factors on their types and contents, with a particular focus on safflower flavonoids. This review aims to provide a comprehensive insight into the regulatory mechanisms of secondary metabolite biosynthesis from the perspective of genomics.

Systems biology of dry eye: Unraveling molecular mechanisms through multi-omics integration

Dry eye disease (DED) is a multifactorial condition with complex and incompletely understood molecular mechanisms. Advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and microbiomics, have provided new insights into the pathophysiology of DED. Genomic analyses have identified key genetic variants linked to immune regulation and lacrimal gland function. Transcriptomic studies reveal upregulated inflammatory pathways in ocular surface tissues, implicating these as core drivers of chronic inflammation. Proteomic research highlights significant alterations in tear protein composition, especially proteins involved in inflammation and tissue repair. Metabolomics studies focus on disrupted lipid metabolism and oxidative stress, which are crucial in maintaining tear film stability. Furthermore, microbiome research has demonstrated reduced microbial diversity and increased pathogenic bacteria, exacerbating inflammatory responses. The integration of multi-omics data allows for the identification of novel biomarkers and therapeutic targets, enabling precision diagnostics and personalized treatments. Therefore, this review highlights the critical importance of multi-omics approaches in deepening our understanding of DED's complex molecular mechanisms and their potential to transform clinical management and therapeutic innovations in this challenging field.

Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study

INTRODUCTION

Influenza viruses cause seasonal epidemics almost every year, and it is difficult to diagnose quickly and accurately. Machine learning and peripheral blood protein omics have brought new ideas to the research of clinical markers.

OBJECTIVES

Prediction of key molecular marker of influenza virus infection by the established machine learning model and peripheral blood protein omics.

METHODS

This study used the testing data of 850 patients (including influenza, COVID-19 and mixed infections) and 265 healthy individuals, to establish and validate a diagnostic prediction model for influenza infection and verified the potential value of this model in the differential diagnosis of influenza, COVID-19 and healthy people.

RESULTS

The overall analysis showed that there were significant differences in 9 clinical features in the influenza group. Principal component analysis can effectively group samples based on these clinical features. Based on the random forest model and LASSO regression model found that the selected features are clinical indicators that can accurately distinguish influenza patients. We performed proteome sequencing combined with machine learning and found a total of 26 DEPs. Through PPI and WGCNA analysis, we identified several genes related to the proportion of monocytes. We then analyzed the correlation of these factors with immune cell proportions and found that SAA1 and SAA2 were highly correlated with various vital immunocyte. ROC curve analysis shows that SERPINA3 can distinguish influenza, COVID-19, mixed infection and healthy people; SAA1 can distinguish COVID-19, mixed infection and healthy people; SAA2 can distinguish influenza and healthy people. In influenza, high expression of SERPINA3, SAA1, and SAA2 is associated with higher risk. Finally, we used the ELISA method to confirm that SAA2 protein can be used as an auxiliary diagnostic indicator for influenza infection.

CONCLUSIONS

Preliminary results showed that SAA2 is an important molecular marker specific to influenza infection.

Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group A Streptococcus

Group A Streptococcus (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these findings into predictive models remains challenging due to data complexity. The current study aimed to integrate deep learning models with genome-wide association study-derived genetic variants to predict pathogenic phenotypes in GAS. We evaluated the performance of several deep neural network models, including CNN, ResNet18, LSTM, and their ensemble approach in predicting GAS phenotypes. It was found that the ensemble model consistently achieved the highest prediction accuracy across phenotypes. Models trained on the full 4722-genotype set outperformed those trained on a reduced 175-genotype set, underscoring the importance of comprehensive variant data in capturing complex genotype-phenotype interactions. Performance changes in the reduced 175-genotype set compared to the full-set genotype scenarios revealed the impact of data dimensionality on model effectiveness, with CNN remaining robust, while ResNet18 and LSTM underperformed. Our findings emphasized the potential of deep learning in phenotype prediction and the critical role of data-model compatibility.

CytoPheno: Automated descriptive cell type naming in flow and mass cytometry

Advances in cytometry have led to increases in the number of cellular markers that are routinely measured. The resulting complexity of the data has prompted a shift from manual to automated analysis methods. Currently, numerous unsupervised methods are available to cluster cells based on marker expression values. However, phenotyping the resulting clusters is typically not part of the automated process. Manually identifying both marker definitions (e.g. CD4+, CCR7+, CD45RA+, CD19-) and descriptive cell type names (e.g. naïve CD4+ T cells) based on marker expression values can be time-consuming, subjective, and error-prone. In this work we propose an algorithm that addresses these problems through the creation of an automated tool, CytoPheno, that assigns marker definitions and cell type names to unidentified clusters. First, post-clustered expression data undergoes per-marker calculations to assign markers as positive or negative. Next, marker names undergo a standardization process to match to Protein Ontology identifier terms. Finally, marker descriptions are matched to cell type names within the Cell Ontology. Each part of the tool was tested with benchmark data to demonstrate performance. Additionally, the tool is encompassed in a graphical user interface (R Shiny) to increase user accessibility and interpretability. Overall, CytoPheno can aid researchers in timely and unbiased phenotyping of post-clustered cytometry data.

A review of the carcinogenic potential of human papillomavirus (HPV) in urological cancers

Direct skin-to-skin contact during intimate sexual contact with a human papillomavirus (HPV)-positive individual is often the cause of HPV infection. In addition, many studies have been written up to date that look at the role of HPV in the growth of other types of tumors. Not all urological cancers are associated with HPV. However, penile cancer (PC) is often caused by HPV, especially high-risk types. HPV-16 has been the most frequent (68.3%), followed by HPV-6 (8.1%) and HPV-18 (6.9%). An increased risk of getting certain types of urinary cancers like prostate, bladder, testicular, and kidney has also been linked to these infections. Additionally, HPV may play a part in continuous inflammation and cancer progression in different organs and tissues. So, making HPV vaccine programs available to more people of the male sex around the world could significantly lower the number of urinary cancers caused by HPV. The critical effects of HPV on different types of urologic cancers (UCs), such as testicular, prostate, penile, and kidney cancer, and the importance of HPV vaccination have been seen in this study.

Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation-maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene analysis and pathway enrichment for BRCA patients. The analysis compared 302 tumor samples to 113 normal samples across 60,660 genes. The highly upregulated gene COL10A1, promoting breast cancer progression and poor prognosis, and the consistently downregulated gene CDG300LG, linked to brain metastatic cancer, were identified. Pathway enrichment analysis revealed similarities in cellular matrix organization pathways across subtypes, with notable differences in functions like cell proliferation regulation and endocytosis by host cells. GO Semantic Similarity analysis quantified gene relationships in each subtype, identifying potential biomarkers like MATN2, similar to COL10A1. These insights suggest deeper relationships within clusters and highlight personalized treatment potential based on subtypes.

Harnessing multi-omics approaches to elucidate the role of Chinese herbal compounds in chemotherapy-induced gastrointestinal damage

In this editorial, we discuss the findings reported by Wang et al in the latest issue of the World Journal of Gastrointestinal Oncology. Various research methodologies, including microbiome analysis, assert that the Tzu-Chi Cancer-Antagonizing and Life-Protecting II Decoction of Chinese herbal compounds mitigates inflammatory responses by inhibiting the NF-κB signaling pathway. This action helps maintain the dynamic equilibrium of the intestinal microecology and lessens chemotherapy-induced gastrointestinal damage. The efficacy of these compounds is intimately linked to the composition of intestinal microbes. These compounds regulate intestinal microecology by virtue of their specific compatibility and effectiveness, thereby enhancing the overall therapeutic outcomes of cancer chemotherapy. Nonetheless, the exact mechanisms underlying these effects warrant further investigation. Multi-omics technologies offer a systematic approach to elucidate the mechanisms and effectiveness of Chinese herbal compounds in vivo. This manuscript reviews the application of multi-omics technologies to Chinese herbal compounds and explores their potential role in modulating the gastrointestinal microenvironment following cancer chemotherapy, thus providing a theoretical foundation for their continued use in adjunct cancer treatment.

Exploring the Effects of Metformin on the Body via the Urine Proteome

Metformin is the first-line medication for treating type 2 diabetes mellitus, with more than 200 million patients taking it daily. Its effects are extensive and play a positive role in multiple areas. Can its effects and potential mechanisms be explored through the urine proteome? In this study, 166 differential proteins were identified following the administration of 150 mg/(kg·d) of metformin to rats for five consecutive days. These included complement component C6, pyruvate kinase, coagulation factor X, growth differentiation factor 15, carboxypeptidase A4, chymotrypsin-like elastase family member 1, and L-lactate dehydrogenase C chain. Several of these proteins have been reported to be directly affected by metformin or associated with its effects. Multiple biological pathways enriched by these differential proteins, or proteins containing differentially modified peptides, have been reported to be associated with metformin, such as the glutathione metabolic process, negative regulation of gluconeogenesis, and the renin-angiotensin system. Additionally, some significantly changed proteins and enriched biological pathways, not yet reported to be associated with metformin's effects, may provide clues for exploring its potential mechanisms. In conclusion, the application of the urine proteome offers a comprehensive and systematic approach to exploring the effects of drugs, providing a new perspective on the study of metformin's mechanisms.

Development and Validation of Targeted Metabolomics Methods Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for the Quantification of 235 Plasma Metabolites

Plasma contains metabolites with diverse physicochemical properties, ranging from highly polar to highly apolar, and concentrations spanning at least nine orders of magnitude. Plasma metabolome analysis is valuable for monitoring health and evaluating medical interventions but is challenging due to the metabolome's diversity and complexity. This study aims to develop and validate targeted LC-MS/MS methods for quantifying 235 mammalian metabolites from 17 compound classes in porcine plasma without prior derivatization. Utilizing reversed-phase and hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry, each analyte is identified and quantified using two selected reaction monitoring (SRM) transitions. Fast polarity switching and scheduled SRM enhance the metabolome coverage and throughput, enabling the analysis of one sample in about 40 min. A simple "dilute and shoot" sample preparation protocol was employed, with samples injected at two dilution levels to align metabolite concentrations within calibration curve ranges. Validation in porcine plasma included assessments of carryover, linearity, detection and quantification limits, repeatability and recovery. The method was further applied to plasma samples from various animal species, demonstrating its applicability to human and animal studies. This study establishes two robust LC-MS/MS methods for comprehensive porcine plasma metabolome quantification, advancing large-scale targeted metabolomics in biomedical research.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

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.

Fibrinogen Alpha Chain as a Potential Serum Biomarker for Predicting Response to Cisplatin and Gemcitabine Doublet Chemotherapy in Lung Adenocarcinoma: Integrative Transcriptome and Proteome Analyses

Cisplatin combined with gemcitabine, a doublet regimen, is the first-line treatment for patients with advanced lung adenocarcinoma (ADC); however, the treatment response remains poor. This study aimed to identify potential biomarkers for predicting response to cisplatin and gemcitabine. Tissue transcriptome and blood proteome analyses were conducted on 27 patients with lung ADC. Blood-derived proteins that reflected tissue-specific biomarkers were obtained using Venn diagrams. The candidate proteins were validated by Western blotting. Lentivirus-mediated short hairpin RNA interference was used to verify the functional roles of the candidate proteins in human A549 cells. We identified 417 differentially expressed genes, including 52 upregulated and 365 downregulated genes, and 31 differentially expressed proteins, including 26 upregulated and 5 downregulated proteins. Integrative analysis revealed the presence of alpha-1-acid glycoprotein 1 (A1AG1) and fibrinogen alpha chain (FGA or FIBA) in both the tissue and serum. FGA levels were elevated in responders compared to non-responders, and reduced serum FGA levels were correlated with resistance to this regimen. Moreover, FGA knockdown in A549 cells resulted in resistance to the doublet regimen. Our findings indicate that FGA is a tissue-specific serum protein that may function as a blood-based biomarker to predict the response of patients with lung ADC to cisplatin plus gemcitabine chemotherapy.

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

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

Novel Protective Role for Gut Microbiota-derived Metabolite PAGln in Doxorubicin-induced Cardiotoxicity

PURPOSE

Doxorubicin (Dox) is a classic anthracycline chemotherapy drug with cause cumulative and dose-dependent cardiotoxicity. This study aimed to investigate the potential role and molecular mechanism of phenylacetylglutamine (PAGln), a novel gut microbiota metabolite, in Dox-induced cardiotoxicity (DIC).

METHODS

DIC models were established in vivo and in vitro, and a series of experiments were performed to verify the cardioprotective effect of PAGln. RNA sequencing (RNA-seq) was employed to explore the mechanism of PAGln in DIC. Subsequently, the differentially expressed genes (DEGs) were subjected to comprehensive analysis using diverse public databases, and RT-PCR was used to confirm the expression levels of the candidate genes. Finally, molecular docking techniques were used for validation.

RESULTS

PAGln effectively prevented both in vivo and in vitro Dox-induced myocardial injury and cell apoptosis. RNA-seq results showed that 40 genes were up-regulated and 54 down-regulated in the Dox group compared to the Con group, displaying opposite changes in the Dox + PAGln group. Enrichment analysis highlighted several mechanisms by which PAGln alleviated Dox-induced cardiotoxicity, including the lipid metabolic process, calcium-mediated signaling, positive regulation of store-operated calcium channel activity, and hypertrophic cardiomyopathy. In vitro and in vivo experiments confirmed that PAGln treatment could reverse the changes in the expression levels of Klb, Ece2, Nmnat2, Casq1, Pak1, and Apob in Dox. Molecular docking results showed that these genes had good binding activity with PAGln.

CONCLUSIONS

PAGln shows potential in alleviating Dox-induced cardiotoxicity, with Ece2 identified as key regulatory molecules related to endothelial dysfunction.

Metabolomics insights into doxorubicin and 5-fluorouracil combination therapy in triple-negative breast cancer: a xenograft mouse model study

Background

Breast cancer is one of the most prevalent malignancies and a leading cause of death among women worldwide. Among its subtypes, triple-negative breast cancer (TNBC) poses significant clinical challenges due to its aggressive behavior and limited treatment options. This study aimed to investigate the effects of doxorubicin (DOX) and 5-fluorouracil (5-FU) as monotherapies and in combination using an established MDA-MB-231 xenograft model in female BALB/C nude mice employing advanced metabolomics analysis to identify molecular alterations induced by these treatments.

Methods

We conducted comprehensive plasma and tumor tissue sample profiling using ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS).

Results

Each treatment group exhibited unique metabolic profiles in plasma and tumor analysis. Univariate and enrichment analyses identified alterations in metabolic pathways. The combination treatment of DOX + 5-FU induced the most extensive metabolic alterations disrupting key pathways including purine, pyrimidine, beta-alanine, and sphingolipid metabolism. It significantly reduced critical metabolites such as guanine, xanthine, inosine, L-fucose, and sphinganine, demonstrating enhanced cytotoxic effects compared to individual treatments. The DOX treatment uniquely increased ornithine levels, while 5-FU altered sphingolipid metabolism, promoting apoptosis.

Significance

This in vivo study highlights TNBC's metabolic alterations to chemotherapeutics, identifying potential biomarkers like L-fucose and beta-alanine, and provides insights for improving treatment strategies.

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

Background

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

Methods

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

Results

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

Conclusion

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

Systematic Review Registration

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

Application of a high-throughput swarm-based deep neural network Algorithm reveals SPAG5 downregulation as a potential therapeutic target in adult AML

Gene‒gene interactions play pivotal roles in disease pathogenesis and are fundamental in the development of targeted therapeutics, particularly through the elucidation of oncogenic gene drivers in cancer. The systematic analysis of pathways and gene interactions is critical in the drug discovery process for various cancer subtypes. SPAG5, known for its role in spindle formation during cell division, has been identified as an oncogene in several cancers, although its specific impact on AML remains underexplored. This study leverages a high-throughput swarm-based deep neural network (SDNN) and transcriptomic data-an approach that enhances predictive accuracy and robustness through collective intelligence-to augment, model, and enhance the understanding of the TP53 pathway in AML cohorts. Our integrative systems biology approach identified SPAG5 as a uniquely downregulated driver in adult AML, underscoring its potential as a novel therapeutic target. The interaction of SPAG5 with key hub genes such as MDM2 and CDK1 not only reinforces its role in tumour suppression through negative regulation but also highlights its potential in moderating the phenotypic and genomic alterations associated with AML progression. This study of the role and interaction dynamics of SPAG5 sets the stage for future research aimed at developing targeted and personalized treatment approaches for AML, utilizing the capabilities of genetic interventions.

iTRAQ-based quantitative proteomic analysis of herbicide stress in Avena ludoviciana Durieu

Winter wild oat (Avena sterilis subsp. ludoviciana (Durieu) Gillet & Magne) has been considered the most common and troublesome weed in wheat fields of Iran. The widespread and continuous use of herbicides has led to the emergence and development of resistant biotypes in A. ludoviciana, making it one of the most important herbicide-resistant weeds within field crops. Considering the importance of understanding the mechanisms underlying resistance to herbicides and identifying key proteins involved in the response to Acetyl-coenzyme A carboxylase (ACCase) and Acetolactate synthase (ALS) inhibitor herbicides in A. ludoviciana. This study aimed to identify the proteins involved in herbicide resistance in A. ludoviciana using the Isobaric Tags for Relative and Absolute Quantification (iTRAQ) technique. In this study, a total of 18,313 peptides were identified with ≤ 0.01 FDR, which could be classified into 484 protein groups. Additionally, 138 differentially expressed proteins (DEPs) were identified in the resistant biotype (R), while 93 DEPs were identified in the susceptible biotype (S). Gene Ontology (GO) analysis revealed that these DEPs mainly consisted of proteins related to photosynthesis, respiration, amino acid synthesis and translation, secondary metabolite biosynthesis, defense proteins, and detoxification. Furthermore, enrichment pathway analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that the most important pathways included metabolic pathways, carbohydrate metabolism, secondary metabolites, amino acid synthesis, and photosynthesis. The function of DEPs indicated that some proteins, such as cytochrome P450, play a direct role in herbicide detoxification. Overall, the results of this study demonstrated the complex response of the resistant biotype to herbicides and its ability to increase antioxidant capacity through up-regulated detoxification proteins, particularly cytochrome P450 (Q6YSB4), and defense proteins, particularly superoxide dismutase (Q0DRV6) and polyamine oxidase (Q7XR46). In the resistant A. ludoviciana populations, in addition to the activation of enzymatic and non-enzymatic defense systems, other strategies such as reduced photosynthesis and respiration, increased transcription and translation activity, enhanced lipid metabolism, regulation of cellular processes and homeostasis, and up-regulation of proteins associated with signaling and ion channels play a role in resistance to herbicide. Overall these findings provide new insights into the role of different proteins in resistance to herbicides and contribute to a comprehensive understanding of herbicide resistance in A. ludoviciana.

Next-generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: A critical review

This critical review summarizes the current state of omics-based biomarkers in the alcohol research field. We first provide definitions and background information on alcohol and alcohol use disorder (AUD), biomarkers, and "omic" technologies. We next summarize using (1) genetic information as risk/prognostic biomarkers for the onset of alcohol-related problems and the progression from regular drinking to problematic drinking (including AUD), (2) epigenetic information as diagnostic biomarkers for AUD and risk biomarkers for alcohol consumption, (3) transcriptomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and (4) metabolomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and predictive biomarkers for response to acamprosate in subjects with AUD. In the final section, the clinical implications of the findings are discussed, and recommendations are made for future research.

Synthetic biology approaches to improve tolerance of inhibitors in lignocellulosic hydrolysates

Increasing attention is being focused on using lignocellulose for valuable products. Microbial decomposition can convert lignocellulose into renewable biofuels and other high-value bioproducts, contributing to sustainable development. However, the presence of inhibitors in lignocellulosic hydrolysates can negatively affect microorganisms during fermentation. Improving microbial tolerance to these hydrolysates is a major focus in metabolic engineering. Traditional detoxification methods increase costs, so there is a need for cheap and efficient cell-based detoxification strategies. Synthetic biology approaches offer several strategies for improving microbial tolerance, including redox balancing, membrane engineering, omics-guided technologies, expression of protectants and transcription factors, irrational engineering, cell flocculation, and other novel technologies. Advances in molecular biology, high-throughput sequencing, and artificial intelligence (AI) allow for precise strain modification and efficient industrial production. Developing AI-based computational models to guide synthetic biology efforts and creating large-scale heterologous libraries with automation and high-throughput technologies will be important for future research.

Mechanistic Modeling the Role of MicroRNAs and Transcription Factors in Disease Progression

In this chapter, we illustrate the utilization of network analysis and mechanistic modeling, two potent branches of systems biology, to simplify the representation of intricate biological processes such as cell signaling, gene regulation, and metabolic pathways. Specifically, we demonstrate the application of a well-established method to generate a microRNA-transcription factor-gene regulatory feed-forward loop network extracted from the GEO dataset GSE163877. Furthermore, we outline a method for constructing a deterministic model using the LSODA method based on the sub-network. This model furnishes insights into the roles of crucial differentially expressed microRNAs and transcription factors in gene expression associated with Alzheimer's disease progression. Our analysis of the model reveals elevated kinetics of synthesis for EGR1, miR-6891, miR-4786, and LTBP1. The model suggests the linear upregulation of miR-8080, miR-3921, HSPB6, and downregulation MX2 gene. The rest of the miRNA, TFs, and genes shows a momentary variation in expression and if the system is undisturbed, they attain equilibrium. Thus, we elucidate how mechanistic modeling, along with perturbation studies and network analysis of expression data, can yield diverse insights into the trajectory of disease progression.

Integration of metabolomics and other omics: from microbes to microbiome

Metabolomics is a cutting-edge omics technology that identifies metabolites in organisms and their environments and tracks their fluctuations. This field has been extensively utilized to elucidate previously unknown metabolic pathways and to identify the underlying causes of metabolic changes, given its direct association with phenotypic alterations. However, metabolomics inherently has limitations that can lead to false positives and false negatives. First, most metabolites function as intermediates in multiple biochemical reactions, making it challenging to pinpoint which specific reaction is responsible for the observed changes in metabolite levels. Consequently, metabolic processes that are anticipated to vary with metabolite concentrations may not exhibit significant changes, generating false positives. Second, the range of metabolites identified is contingent upon the analytical conditions employed. Until now, no analytical instrument or protocol has been developed that can capture all metabolites simultaneously. Therefore, some metabolites are changed but are not detected, generating false negatives. In this review, we offer a novel and systematic assessment of the limitations of omics technologies and propose-specific strategies to minimize false positives and false negatives through multi-omics approaches. Additionally, we provide examples of multi-omics applications in microbial metabolic engineering and host-microbiome interactions, helping other researchers gain a better understanding of these strategies. KEY POINTS: • Metabolomics identifies metabolic shifts but has inherent false positive/negatives. • Multi-omics approaches help overcome metabolomics' inherent limitations.

Spotting targets with 2D-DIGE proteomics

Two-dimensional difference gel electrophoresis (2D-DIGE) has been a staple of protein studies for almost three decades since first described in 1997. Although the advent of omic technologies has greatly expanded protein research and discovery, 2D-DIGE has consistently been the mainstay in biomedical applications. Differential protein expression is a hallmark of many disease states and identification of these biomarkers can improve diagnosis, prognosis and treatment. In this review, we examine the use of 2D-DIGE in exploring the cellular environment in physiologic and pathophysiologic states. We highlight this technology in protein identification and quantification, functional modification and biochemical pathways of interest. 2D-DIGE remains a useful tool due low cost and high resolving power for comparative and quantitative purposes in assessing disease states and facilitating identification of unique and novel biomarkers.

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.

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.

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.

Exploring molecular mechanisms of postoperative delirium through multi-omics strategies in plasma exosomes

Currently, the diagnosis of delirium is solely based on clinical observation, lacking objective diagnostic tools, and the regulatory networks and pathological mechanisms behind it are not yet fully understood. Exosomes have garnered considerable interest as potential biomarkers for a variety of illnesses. This research aimed to delineate both the proteomic and metabolomic landscapes inherent to exosomes, assessing their diagnostic utility in postoperative delirium (POD) and understanding the underlying pathophysiological frameworks. Integrated analyses of proteomics and metabolomics were conducted on exosomes derived from plasma of individuals from both the non-postoperative delirium (NPOD) control group and the POD group. Subsequently, the study utilized the Connectivity Map (CMap) methodology for the identification of promising small-molecule drugs and carried out molecular docking assessments to explore the binding affinities with the enzyme MMP9 of these identified molecules. We identified significant differences in exosomal metabolites and proteins between the POD and control groups, highlighting pathways related to neuroinflammation and blood-brain barrier (BBB) integrity. Our CMap analysis identified potential small-molecule therapeutics, and molecular docking studies revealed two compounds with high affinity to MMP9, suggesting a new therapeutic avenue for POD. This study highlights MMP9, TLR2, ICAM1, S100B, and glutamate as key biomarkers in the pathophysiology of POD, emphasizing the roles of neuroinflammation and BBB integrity. Notably, molecular docking suggests mirin and orantinib as potential inhibitors targeting MMP9, providing new therapeutic avenues. The findings broaden our understanding of POD mechanisms and suggest targeted strategies for its management, reinforcing the importance of multidimensional biomarker analysis and molecular targeting in POD intervention.

Circulating biomarkers in pulmonary arterial hypertension: State-of-the-art review and future directions

Pulmonary arterial hypertension is a complex and heterogeneous condition, associated with a considerable diagnostic delay, diminished exercise capacity, and poor outcomes. In pulmonary arterial hypertension, biomarker research has become a subject of intense inquiry, and novel circulating biomarkers acknowledged in a multitude of mechanistic pathways are emerging. Beyond the widely used natriuretic peptides, novel biomarkers may provide deeper pathophysiological understanding, support clinical decision-making, and prompt the incorporation of precision medicine by enabling a more precise individual phenotyping. In this state-of-the-art review, the recent advances in circulating biomarkers in pulmonary arterial hypertension from a clinical perspective are discussed, with particular emphasis on the current state of knowledge, gaps in evidence, and future perspectives.

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.

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.

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.

Leveraging the Genetics of Psychiatric Disorders to Prioritize Potential Drug Targets and Compounds

Background

Genetics has the potential to inform biologically relevant drug treatment and repurposing which may ultimately improve patient care. In this study, we combine methods which leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Methods

We used the largest available genome-wide association studies, in European ancestry, of four psychiatric disorders [i.e., attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] along with genes encoding drug targets. With this data, we conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We then conducted a series of molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder.

Findings

We reveal candidate drug targets shared across multiple disorders as well as disorder-specific targets. Drug prioritization indicated genetic support for several currently used psychotropic medications including the antipsychotic paliperidone as the top ranked drug for schizophrenia. We also observed genetic support for other commonly used psychotropics (e.g., clozapine, risperidone, duloxetine, lithium, and valproic acid). Opportunities for drug repurposing were revealed such as cholinergic drugs for ADHD, estrogens for depression, and gabapentin enacarbil for schizophrenia. Our findings also indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission.

Interpretation

Here we present a series of complimentary and comprehensive analyses that highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders. Our findings present novel opportunities for refining psychiatric treatment.

Decoding the Transcriptomics of Oil Palm Seed Germination

Seed dormancy and germination are critical factors affecting oil palm production efficiency. The typical dormancy-breaking process involves dry heat treatment (38-40 °C for 40-60 days) followed by germination at 30-32 °C. To understand the molecular mechanisms behind this process and improve germination rates and speed, we conducted transcriptome analysis at three stages: pre-incubation, 45-day incubation at 40 °C, and 14-day germination at 32 °C. Our findings, supported by qRT-PCR and DEGs analysis, identified four key stages: ABA degradation, energy mobilization, starch mobilization, and cell elongation and division. ABA pathway genes (SnRK2, PYR/PYL) were active during dormancy release, while GAE and GPI were upregulated after heat treatment, indicating increased energy metabolism and structural changes. During germination, genes involved in starch/sucrose metabolism (SPS, TPP, SS, MGAM) and cell wall biosynthesis (GAUT1, PE, GAE) supported embryo expansion, with BAM, PGM, GlgB fueling early growth. Auxin (TIR1, AUX/IAA, ARF), brassinosteroid (BRI1, BSK, BIN2, CYCD3), ethylene (ETR, CTR1), and jasmonic acid (JAR1, COI1) pathway genes regulated cell growth and stress response, promoting seedling development. Though gibberellins were not crucial for this oil palm variety, gene expression varied between varieties. This study provides information on oil palm seed germination that could be applied to other oil palm species, particularly in terms of incubation times and chemical treatments.

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.

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.