Systems biology: current status and challenges

Camellia Japonica Radix modulates gut microbiota and 9(S)-HpODE-mediated ferroptosis to alleviate oxidative stress against MASLD

BACKGROUND

Camellia japonica radix (CJR), derived from the root of Camellia japonica L., has the potential to function as an herbal tea substitute for the prevention and intervention of metabolic dysfunction-associated steatotic liver disease (MASLD). It can provide systemic therapeutic benefits, boast a favorable safety profile, facilitate convenient consumption, and support long-term applicability. Despite its potential, research on CJR remains limited.

PURPOSE

The aim of this study aims is to elucidate the therapeutic mechanisms of CJR in MASLD, thereby providing evidence to support its clinical application.

METHODS

The therapeutic effects of CJR were evaluated using a water-supplementation model in MASLD mice. Integrated microbiome, transcriptome, proteome, and metabolome analyses were employed to comprehensively explore the mechanisms involved. A drug-target pull-down assay was performed to identify specific protein targets of small molecule metabolites in vitro. Fecal microbiota transplantation in antibiotic-treated ABX mice was conducted to confirm the critical role of gut microbiota and its metabolites. Furthermore, customized medicated feed supplemented with linoleic acid was used to explore the intervention effect of its metabolite, 9(S)-HpODE, as well as to evaluate its dietary intervention potential.

RESULTS

This present study explicitly elucidates the efficacy of CJR extract in alleviating hepatic inflammation and steatosis in a MASLD model mice, with its pharmacological mechanism associated with gut microbiota, linoleic acid metabolism, and GPX4-mediated ferroptosis. Notably, 9(S)-HpODE was discovered to be a key metabolite of linoleic acid, which could target both KEAP1 and SLC7A11, bidirectionally regulating GPX4-mediated ferroptosis, while acting as a signaling molecule at low doses to induce redox adaptation via oxidative preconditioning, thus ameliorating oxidative stress in MASLD.

CONCLUSION

Our findings indicate that both CJR and linoleic acid exhibit significant potential as dietary interventions for the management of MASLD, offering promising avenues for future research and clinical application.

Omics Approaches in Understanding Insecticide Resistance in Mosquito Vectors

In recent years, the emergence of insecticide resistance has been a major challenge to global public health. Understanding the molecular mechanisms of this phenomenon in mosquito vectors is paramount for the formulation of effective vector control strategies. This review explores the current knowledge of insecticide resistance mechanisms through omics approaches. Genomic, transcriptomic, proteomic, and metabolomics approaches have proven crucial to understand these resilient vectors. Genomic studies have identified multiple genes associated with insecticide resistance, while transcriptomics has revealed dynamic gene expression patterns in response to insecticide exposure and other environmental stimuli. Proteomics and metabolomics offer insights into protein expression and metabolic pathways involved in detoxification and resistance. Integrating omics data holds immense potential to expand our knowledge on the molecular basis of insecticide resistance in mosquitoes via information obtained from different omics platforms to understand regulatory mechanisms and differential expression of genes and protein, and to identify the transcription factors and novel molecules involved in the detoxification of insecticides. Eventually, these data will help construct predictive models, identify novel strategies, and develop targeted interventions to control vector-borne diseases.

Dynamics of spindle assembly and position checkpoints: Integrating molecular mechanisms with computational models

Mitotic checkpoints orchestrate cell division through intricate molecular networks that ensure genomic stability. While experimental research has uncovered key aspects of checkpoint function, the complexity of protein interactions and spatial dynamics necessitates computational modeling for a deeper, system-level understanding. This review explores mathematical frameworks-from ordinary differential equations to stochastic simulations, which reveal checkpoint dynamics across multiple scales, encompassing models ranging from simple protein interactions to whole-system simulations with thousands of parameters. These approaches have elucidated fundamental properties, including bistable switches driving spindle assembly checkpoint (SAC) activation, spatial organization principles underlying spindle position checkpoint (SPOC) signaling, and critical system-level features ensuring checkpoint robustness. This study evaluates diverse modeling approaches, from rule-based models to chemical organization theory, highlighting their successful application in predicting protein localization patterns and checkpoint response dynamics validated through live-cell imaging. Contemporary challenges persist in integrating spatial and temporal scales, refining parameter estimation, and enhancing spatial modeling fidelity. However, recent advances in single-molecule imaging, data-driven algorithms, and machine learning techniques, particularly deep learning for parameter optimization, present transformative opportunities for improving model accuracy and predictive power. By bridging molecular mechanisms with system-level behaviors through validated computational frameworks, this review offers a comprehensive perspective on the mathematical modeling of cell cycle control, with practical implications for cancer research and therapeutic development.

Insight into the complexity of male infertility: a multi-omics review

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

Towards Precision Sports Nutrition for Endurance Athletes: A Scoping Review of Application of Omics and Wearables Technologies

BACKGROUND

Endurance athletes require tailored nutrition strategies to optimize performance, recovery, and training adaptations. While traditional sports nutrition guidelines provide a foundational framework, individual variability in metabolic responses underscores the need for precision nutrition, informed by genetic, biological, and environmental factors. This scoping review evaluates the application of systems biology-driven sports nutrition for endurance athletes, focusing on 'omics' and wearable technologies.

METHODS

A scoping review of the literature was conducted in PubMed, Scopus, and Web of Science in accordance with the PRISMA-ScR checklist. Research questions, search strategies, and eligibility criteria were guided by the Population-Concept-Context framework with the following inclusion criteria: original research in English, involving endurance athletes, systems biology approaches, and nutritional interventions or continuous glucose monitoring (CGM).

RESULTS

Fifty-two studies were included, with distance runners as the most studied cohort. Eleven studies used metagenomics, eleven CGM, ten nutrigenetics, ten metabolomics, seven multi-omics, one proteomics, one epigenomics, and one lipidomics. Over half (n = 31; 60%) were randomized controlled trials (RCTs) with generally high methodological quality.

CONCLUSIONS

Most studies were proof-of-concept investigations aimed at assessing biomarkers; however, the evidence linking these biomarkers to performance, recovery, and long-term health outcomes in endurance athletes remains insufficient. Future research should focus on well-powered replicated crossover RCTs, multivariate N-of-1 clinical trials, 360-degree systems-wide approaches, and the validation of genetic impacts on nutritional interventions to refine dietary guidelines.

The identification of novel stroke-related sphingolipid biomarkers using UPLC-MS/MS

BACKGROUND

Stroke is a prominent contributor to global mortality and morbidity, thus necessitating the establishment of dependable diagnostic indicators. The objective of this study was to ascertain metabolites linked to sphingolipid metabolism and assess their viability as diagnostic markers for stroke.

METHODS

Two cohorts, consisting of 56 S patients and 56 healthy volunteers, were incorporated into this investigation. Metabolite data was obtained through the utilization of Ultra Performance Liquid Chromatography and Tandem Mass Spectrometry (UPLC-MS/MS). The mass spectrometry data underwent targeted analysis and quantitative evaluation utilizing the multiple reaction monitoring mode of triple quadrupole mass spectrometry. Various data analysis techniques, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM), logistic regression, and Receiver Operating Characteristic (ROC) curves were employed.

RESULTS

A comprehensive analysis detected a total of 129 metabolites related to sphingolipid metabolism, encompassing ceramides, 1-phosphoceramides, phytoceramides, glycosphingolipids, sphingomyelins, and sphingomyelins. The implementation of OPLS-DA analysis revealed significant disparities between individuals with stroke and controls, as it successfully identified 31 metabolites that exhibited significant differential expression between the two groups. Furthermore, functional enrichment analysis indicated the participation of these metabolites in diverse biological processes. Six metabolic markers, namely CerP(d18:1/20:3), CerP(d18:1/18:1), CerP(d18:1/18:0), CerP(d18:1/16:0), SM(d18:1/26:1), and Cer(d18:0/20:0), were successfully validated as potential diagnostic markers for stroke. The utilization of ROC analysis further confirmed their diagnostic potential, while a logistic regression model incorporating these markers demonstrated robust efficacy in distinguishing stroke patients from healthy controls.

CONCLUSION

these identified metabolic markers exhibit clinical significance and hold promise as valuable tools for the diagnosis of stroke.

Network pharmacology and metabolomics elucidate the underlying mechanisms of Venenum Bufonis in the treatment of colorectal cancer

ETHNOPHARMACOLOGICAL RELEVANCE

The present study aims to evaluate the efficacy of Venenum Bufonis (VBF), a traditional Chinese medicine derived from the dried secretions of the Chinese toad, in treating colorectal cancer (CRC). The comprehensive roles of VBF in CRC through systems biology and metabolomics approaches have been rarely investigated.

AIMS OF THE STUDY

The study sought to uncover the potential underlying mechanisms of VBF's anti-cancer effects by investigating the impact of VBF on cellular metabolic balance.

MATERIALS AND METHODS

An integrative approach combining biological network analysis, molecular docking and multi-dose metabolomics was used to predict the effects and mechanisms of VBF in CRC treatment. The prediction was verified by cell viability assay, EdU assay and flow cytometry.

RESULTS

The results of the study indicate that VBF presents anti-CRC effects and impacts cellular metabolic balance through its impact on cell cycle-regulating proteins, such as MTOR, CDK1, and TOP2A. The results of the multi-dose metabolomics analysis suggest a dose-dependent reduction of metabolites related to DNA synthesis after VBF treatment, while the EdU and flow cytometry results indicate that VBF inhibits cell proliferation and arrests the cell cycle at the S and G2/M phases.

CONCLUSIONS

These findings suggest that VBF disrupts purine and pyrimidine pathways in CRC cancer cells, leading to cell cycle arrest. This proposed workflow integrating molecular docking, multi-dose metabolomics, and biological validation, which contented EdU assay, cell cycle assay, provides a valuable framework for future similar studies.

Characterization of the enzyme kinetics of EMP and HMP pathway in Corynebacterium glutamicum: reference for modeling metabolic networks

The model of intracellular metabolic network based on enzyme kinetics parameters plays an important role in understanding the intracellular metabolic process of Corynebacterium glutamicum, and constructing such a model requires a large number of enzymological parameters. In this work, the genes encoding the relevant enzymes of the EMP and HMP metabolic pathways from Corynebacterium glutamicum ATCC 13032 were cloned, and engineered strains for protein expression with E.coli BL21 and P.pastoris X33 as hosts were constructed. The twelve enzymes (GLK, GPI, TPI, GAPDH, PGK, PMGA, ENO, ZWF, RPI, RPE, TKT, and TAL) were successfully expressed and purified by Ni2+ chelate affinity chromatography in their active forms. In addition, the kinetic parameters (V max, K m, and K cat) of these enzymes were measured and calculated at the same pH and temperature. The kinetic parameters of enzymes associated with EMP and the HMP pathway were determined systematically and completely for the first time in C.glutamicum. These kinetic parameters enable the prediction of key enzymes and rate-limiting steps within the metabolic pathway, and support the construction of a metabolic network model for important metabolic pathways in C.glutamicum. Such analyses and models aid in understanding the metabolic behavior of the organism and can guide the efficient production of high-value chemicals using C.glutamicum as a host.

Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis

Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

Progress in kidney transplantation: The role for systems immunology

The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.

Analysis of the Potential Relationship between Aging and Pulmonary Fibrosis Based on Transcriptome

Idiopathic pulmonary fibrosis (IPF) is an age-related interstitial lung disease with a high incidence in the elderly. Although many reports have shown that senescence can initiate pulmonary fibrosis, the relationship between aging and pulmonary fibrosis has not been explained systematically. In our study, young and old rats were intratracheally instilled with bleomycin (1 mg/kg), and the basic pathological indexes were determined using a commercial kit, hematoxylin, and eosin (H&E) and Masson's Trichrome staining, immunohistochemistry, immunohistofluorescence, and q-PCR. Then, the lung tissues of rats were sequenced by next-generation sequencing for transcriptome analysis. Bioinformatics was performed to analyze the possible differences in the mechanism of pulmonary fibrosis between aged and young rats. Finally, the related cytokines were determined by q-PCR and ELISA. The results indicate that pulmonary fibrosis in old rats is more serious than that in young rats under the same conditions. Additionally, transcriptomic and bioinformatics analysis with experimental validation indicate that the differences in pulmonary fibrosis between old and young rats are mainly related to the differential expression of cytokines, extracellular matrix (ECM), and other important signaling pathways. In conclusion, aging mainly affects pulmonary fibrosis through the ECM-receptor interaction, immune response, and chemokines.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

Weighted gene co-expression network analysis identifies dysregulated B-cell receptor signaling pathway and novel genes in pulmonary arterial hypertension

Background

Pulmonary arterial hypertension (PAH) is a devastating cardio-pulmonary vascular disease in which chronic elevated pulmonary arterial pressure and pulmonary vascular remodeling lead to right ventricular failure and premature death. However, the exact molecular mechanism causing PAH remains unclear.

Methods

RNA sequencing was used to analyze the transcriptional profiling of controls and rats treated with monocrotaline (MCT) for 1, 2, 3, and 4 weeks. Weighted gene co-expression network analysis (WGCNA) was employed to identify the key modules associated with the severity of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential biological processes and pathways of key modules. Real-time PCR and western blot analysis were used to validate the gene expression. The hub genes were validated by an independent dataset obtained from the Gene Expression Omnibus database.

Results

A total of 26 gene modules were identified by WGCNA. Of these modules, two modules showed the highest correlation with the severity of PAH and were recognized as the key modules. GO analysis of key modules showed the dysregulated inflammation and immunity, particularly B-cell-mediated humoral immunity in MCT-induced PAH. KEGG pathway analysis showed the significant enrichment of the B-cell receptor signaling pathway in the key modules. Pathview analysis revealed the dysregulation of the B-cell receptor signaling pathway in detail. Moreover, a series of humoral immune response-associated genes, such as BTK, BAFFR, and TNFSF4, were found to be differentially expressed in PAH. Additionally, five genes, including BANK1, FOXF1, TLE1, CLEC4A1, and CLEC4A3, were identified and validated as the hub genes.

Conclusion

This study identified the dysregulated B-cell receptor signaling pathway, as well as novel genes associated with humoral immune response in MCT-induced PAH, thereby providing a novel insight into the molecular mechanisms underlying inflammation and immunity and therapeutic targets for PAH.

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel therapeutic compounds. As a result, some of these biomolecules are in their testing phase. During these efforts, the whole-genome sequencing of SARS-CoV-2 has given the direction to explore the omics systems and approaches to overcome this unprecedented health challenge globally. Genome, proteome, and metagenome sequence analyses have helped identify virus nature, thereby assisting in understanding the molecular mechanism, structural understanding, and disease propagation. The multi-omics approaches offer various tools and strategies for identifying potential therapeutic biomolecules for COVID-19 and exploring the plants producing biomolecules that can be used as biopharmaceutical products. This review explores the available multi-omics approaches and their scope to investigate the therapeutic promises of plant-based biomolecules in treating SARS-CoV-2 infection.

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel therapeutic compounds. As a result, some of these biomolecules are in their testing phase. During these efforts, the whole-genome sequencing of SARS-CoV-2 has given the direction to explore the omics systems and approaches to overcome this unprecedented health challenge globally. Genome, proteome, and metagenome sequence analyses have helped identify virus nature, thereby assisting in understanding the molecular mechanism, structural understanding, and disease propagation. The multi-omics approaches offer various tools and strategies for identifying potential therapeutic biomolecules for COVID-19 and exploring the plants producing biomolecules that can be used as biopharmaceutical products. This review explores the available multi-omics approaches and their scope to investigate the therapeutic promises of plant-based biomolecules in treating SARS-CoV-2 infection.