Systems Biology Research Articles

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Single-cell transcriptional landscape of long non-coding RNAs orchestrating mouse heart development

Long non-coding RNAs (lncRNAs) comprise the most representative transcriptional units of the mammalian genome. They are associated with organ development linked with the emergence of cardiovascular diseases. We used bioinformatic approaches, machine learning algorithms, systems biology analyses, and statistical techniques to define co-expression modules linked to heart development and cardiovascular diseases. We also uncovered differentially expressed transcripts in subpopulations of cardiomyocytes. Finally, from this work, we were able to identify eight cardiac cell-types; several new coding, lncRNA, and pcRNA markers; two cardiomyocyte subpopulations at four different time points (ventricle E9.5, left ventricle E11.5, right ventricle E14.5 and left atrium P0) that harbored co-expressed gene modules enriched in mitochondrial, heart development and cardiovascular diseases. Our results evidence the role of particular lncRNAs in heart development and highlight the usage of co-expression modular approaches in the cell-type functional definition.

Cellular metabolomics: From sample preparation to high-throughput data analysis

Cellular metabolomics, which involves targeted and untargeted analyses of metabolites in in vitro systems, plays a pivotal role in system biology. It offers insights into cell phenotypes and provides a means to monitor cellular activities. The power of cellular metabolomics stems from its abilities to quantify and identify metabolites in a cellular system and to distill the acquired information with various data analysis tools. In comparison to in vivo studies that utilize sera, tissues, and body fluids, cellular metabolomics is not only more cost-effective and controllable but also highly informative about biological functions. This review delves into the intricacies of sample preparation workflows, data acquisition platforms, and data mining and analysis techniques for cellular metabolomic studies, with 127 references cited. We explore the merits and limitations of metabolomics strategies, i.e., targeted and untargeted, aiming to provide readers with a clear understanding of the step-by-step procedures and what can be achieved when executing a cellular metabolomics experiment.

Evaluating the causal relationship between human blood metabolites and gastroesophageal reflux disease.

Gastroesophageal reflux disease (GERD) affects approximately 13% of the global population. However, the pathogenesis of GERD has not been fully elucidated. The development of metabolomics as a branch of systems biology in recent years has opened up new avenues for the investigation of disease processes. As a powerful statistical tool, Mendelian randomization (MR) is widely used to explore the causal relationship between exposure and outcome. To analyze of the relationship between 486 blood metabolites and GERD. Two-sample MR analysis was used to assess the causal relationship between blood metabolites and GERD. A genome-wide association study (GWAS) of 486 metabolites was the exposure, and two different GWAS datasets of GERD were used as endpoints for the base analysis and replication and meta-analysis. Bonferroni correction is used to determine causal correlation features (P < 1.03 × 10-4). The results were subjected to sensitivity analysis to assess heterogeneity and pleiotropy. Using the MR Steiger filtration method to detect whether there is a reverse causal relationship between metabolites and GERD. In addition, metabolic pathway analysis was conducted using the online database based MetaboAnalyst 5.0 software. In MR analysis, four blood metabolites are negatively correlated with GERD: Levulinate (4-oxovalerate), stearate (18:0), adrenate (22:4n6) and p-acetamidophenylglucuronide. However, we also found a positive correlation between four blood metabolites and GERD: Kynurenine, 1-linoleoylglycerophosphoethanolamine, butyrylcarnitine and guanosine. And bonferroni correction showed that butyrylcarnitine (odd ratio 1.10, 95% confidence interval: 1.05-1.16, P = 7.71 × 10-5) was the most reliable causal metabolite. In addition, one significant pathways, the "glycerophospholipid metabolism" pathway, can be involved in the pathogenesis of GERD. Our study found through the integration of genomics and metabolomics that butyrylcarnitine may be a potential biomarker for GERD, which will help further elucidate the pathogenesis of GERD and better guide its treatment. At the same time, this also contributes to early screening and prevention of GERD. However, the results of this study require further confirmation from both basic and clinical real-world studies.

Discovering common pathogenetic processes between COVID-19 and tuberculosis by bioinformatics and system biology approach.

The coronavirus disease 2019 (COVID-19) pandemic, stemming from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has persistently threatened the global health system. Meanwhile, tuberculosis (TB) caused by Mycobacterium tuberculosis (M. tuberculosis) still continues to be endemic in various regions of the world. There is a certain degree of similarity between the clinical features of COVID-19 and TB, but the underlying common pathogenetic processes between COVID-19 and TB are not well understood. To elucidate the common pathogenetic processes between COVID-19 and TB, we implemented bioinformatics and systematic research to obtain shared pathways and molecular biomarkers. Here, the RNA-seq datasets (GSE196822 and GSE126614) are used to extract shared differentially expressed genes (DEGs) of COVID-19 and TB. The common DEGs were used to identify common pathways, hub genes, transcriptional regulatory networks, and potential drugs. A total of 96 common DEGs were selected for subsequent analyses. Functional enrichment analyses showed that viral genome replication and immune-related pathways collectively contributed to the development and progression of TB and COVID-19. Based on the protein-protein interaction (PPI) network analysis, we identified 10 hub genes, including IFI44L, ISG15, MX1, IFI44, OASL, RSAD2, GBP1, OAS1, IFI6, and HERC5. Subsequently, the transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory network identified 61 TFs and 29 miRNAs. Notably, we identified 10 potential drugs to treat TB and COVID-19, namely suloctidil, prenylamine, acetohexamide, terfenadine, prochlorperazine, 3'-azido-3'-deoxythymidine, chlorophyllin, etoposide, clioquinol, and propofol. This research provides novel strategies and valuable references for the treatment of tuberculosis and COVID-19.

Department of Pharmacology of the Military Medical Academy in the 21st century: New achievements based on historical traditions (2000–2023)

In the early 2000s at the Department of Pharmacology, active research was launched into the mechanisms of hypoxia, creation of antihypoxic drugs, study of means and technologies for increasing performance, and drugs with immunomodulatory and reparative effects. The mechanisms of hypoxia and life support in extreme conditions have been a traditional area of scientific research of the department. Starting back in the 1960s under the leadership of V. M. Vinogradov, this direction gained development and scientific recognition by the end of the 1990s. During this period, new antihypoxants of direct and indirect actions were created and thoroughly studied, namely, gutimin, bemithyl, amtizol, trimetazidine, and others. Nearly all employees of the department and numerous applicants for academic degrees from among both academic and nonacademic employees participated in the research. The use of approaches and concepts of molecular physiology and pharmacology achieved very interesting and promising results for implementation. An unconditional success in scientific research in this area has been the elucidation of the molecular mechanisms of action of aminothiol-type antihypoxants, which consist in optimizing energy production and energy consumption in cells and the ability of compounds to reduce membrane damage caused by lipid peroxidation and clearly activate antioxidant systems. The second direction was the study of the molecular mechanisms of sustainable adaptation to various factors, which was started by Prof. A. V. Smirnov. Thus, long-term adaptation patterns were established in the body’s adaptation to high-altitude hypoxia, overheating, and heavy repeated loads. The contribution of individual organs to the development of these adaptive phenomena was quantitatively determined. In the experiment, effective schemes for emergency adaptation were developed using “pulse modes” of training influences against the use of pharmacological adaptogens. A significantly more pronounced adaptogenic activity has been established for drugs such as thiobenzimidazole (bemythyl) and trecrezan in comparison with known adaptogens, such as dibazole and eleutherococcus. Based on experimental data, similar schemes for pharmacologically enhanced and accelerated emergency adaptation have been developed and tested on healthy volunteers, particularly to sharp changes in climatic conditions (overheating, hypothermia, and hypoxia) and metabolic cocktails based on the antihypoxant bemityl (B-100, B-200, and B-300). These schemes and pharmacological interventions make it possible, in just a few days, to make the body noticeably adapt to the action of certain climatic factors. The third direction was the creation and study of new pharmacological agents for the correction of the effects of hypoxia, toxic pulmonary edema, and liver and immune system damage based on condensed indole systems and their product transformations. Consequently, indole derivatives (approximately 60 compounds have been synthesized and studied) are low toxic and have antihypoxic, antiedema, hepatoprotective, actoprotective and antiviral activities.

Windows Scanning Multiomics: Integrated Metabolomics and Proteomics.

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.

PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing

BackgroundThe standardization of biological data using unique identifiers is vital for seamless data integration, comprehensive interpretation, and reproducibility of research findings, contributing to advancements in bioinformatics and systems biology. Despite being widely accepted as a universal identifier, scientific names for biological species have inherent limitations, including lack of stability, uniqueness, and convertibility, hindering their effective use as identifiers in databases, particularly in natural product (NP) occurrence databases, posing a substantial obstacle to utilizing this valuable data for large-scale research applications.ResultTo address these challenges and facilitate high-throughput analysis of biological data involving scientific names, we developed PhyloSophos, a Python package that considers the properties of scientific names and taxonomic systems to accurately map name inputs to entries within a chosen reference database. We illustrate the importance of assessing multiple taxonomic databases and considering taxonomic syntax-based pre-processing using NP occurrence databases as an example, with the ultimate goal of integrating heterogeneous information into a single, unified dataset.ConclusionsWe anticipate PhyloSophos to significantly aid in the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, enabling full-scale bioinformatics analysis using these valuable data resources.

Appropriate patient population for future visual system axon regeneration therapies.

A number of blinding diseases caused by damage to the optic nerve result in progressive vision loss or loss of visual acuity. Secondary glaucoma results from traumatic injuries, pseudoexfoliation or pigmentary dispersion syndrome. Progressive peripheral vision loss is common to all secondary glaucoma irrespective of the initial event. Axon regeneration is a potential therapeutic avenue to restore lost vision in these patients. In contrast to the usual approach of having the worst possible patient population for initial therapies, axon regeneration may require consideration of appropriate patient population even for initial treatment trials. The current state of axon regeneration therapies, their potential future and suitable patient population when ready is discussed in this perspective. The selection of patients are important for adoption of axon regeneration specifically in the areas of central nervous system regenerative medicine. This article is categorized under: Neurological Diseases > Molecular and Cellular Physiology Neurological Diseases > Biomedical Engineering Metabolic Diseases > Molecular and Cellular Physiology.

Saccharomyces cerevisiae biofactory to produce naringenin using a systems biology approach and a bicistronic vector expression strategy in flavonoid production.

Flavonoids are a group of compounds generally produced by plants with proven biological activity, which have recently beeen recommended for the treatment and prevention of diseases and ailments with diverse causes. In this study, naringenin was produced in adequate amounts in yeast after in silico design. The four genes of the involved enzymes from several organisms (bacteria and plants) were multi-expressed in two vectors carrying each two genes linked by a short viral peptide sequence. The batch kinetic behavior of the product, substrate, and biomass was described at lab scale. The engineered strain might be used in a more affordable and viable bioprocess for industrial naringenin procurement.

Taming the SARS-CoV-2-mediated proinflammatory response with BromAc®.

In the present study, the impact of BromAc®, a specific combination of bromelain and acetylcysteine, on the SARS-CoV-2-specific inflammatory response was evaluated. An in vitro stimulation system was standardized using blood samples from 9 healthy donors, luminex assays and flow cytometry were performed. BromAc® demonstrated robust anti-inflammatory activity in human peripheral blood cells upon SARS-CoV-2 viral stimuli, reducing the cytokine storm, composed of chemokines, growth factors, and proinflammatory and regulatory cytokines produced after short-term in vitro culture with the inactivated virus (iSARS-CoV-2). A combined reduction in vascular endothelial growth factor (VEGF) induced by SARS-CoV-2, in addition to steady-state levels of platelet recruitment-associated growth factor-PDGFbb, was observed, indicating that BromAc® may be important to reduce thromboembolism in COVID-19. The immunophenotypic analysis of the impact of BromAc® on leukocytes upon viral stimuli showed that BromAc® was able to downmodulate the populations of CD16+ neutrophils and CD14+ monocytes observed after stimulation with iSARS-CoV-2. Conversely, BromAc® treatment increased steady-state HLA-DR expression in CD14+ monocytes and preserved this activation marker in this subset upon iSARS-CoV-2 stimuli, indicating improved monocyte activation upon BromAc® treatment. Additionally, BromAc® downmodulated the iSARS-CoV-2-induced production of TNF-a by the CD19+ B-cells. System biology approaches, utilizing comprehensive correlation matrices and networks, showed distinct patterns of connectivity in groups treated with BromAc®, suggesting loss of connections promoted by the compound and by iSARS-CoV-2 stimuli. Negative correlations amongst proinflammatory axis and other soluble and cellular factors were observed in the iSARS-CoV-2 group treated with BromAc® as compared to the untreated group, demonstrating that BromAc® disengages proinflammatory responses and their interactions with other soluble factors and the axis orchestrated by SARS-CoV-2. These results give new insights into the mechanisms for the robust anti-inflammatory effect of BromAc® in the steady state and SARS-CoV-2-specific immune leukocyte responses, indicating its potential as a therapeutic strategy for COVID-19.

Retracted: A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy.

[This retracts the article DOI: 10.1155/2022/5337380.].

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet’s ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.

Methylation analysis by targeted bisulfite sequencing in large for gestational age (LGA) newborns: the LARGAN cohort

BackgroundIn 1990, David Barker proposed that prenatal nutrition is directly linked to adult cardiovascular disease. Since then, the relationship between adult cardiovascular risk, metabolic syndrome and birth weight has been widely documented. Here, we used the TruSeq Methyl Capture EPIC platform to compare the methylation patterns in cord blood from large for gestational age (LGA) vs adequate for gestational age (AGA) newborns from the LARGAN cohort.ResultsWe found 1672 differentially methylated CpGs (DMCs) with a nominal p < 0.05 and 48 differentially methylated regions (DMRs) with a corrected p < 0.05 between the LGA and AGA groups. A systems biology approach identified several biological processes significantly enriched with genes in association with DMCs with FDR < 0.05, including regulation of transcription, regulation of epinephrine secretion, norepinephrine biosynthesis, receptor transactivation, forebrain regionalization and several terms related to kidney and cardiovascular development. Gene ontology analysis of the genes in association with the 48 DMRs identified several significantly enriched biological processes related to kidney development, including mesonephric duct development and nephron tubule development. Furthermore, our dataset identified several DNA methylation markers enriched in gene networks involved in biological pathways and rare diseases of the cardiovascular system, kidneys, and metabolism.ConclusionsOur study identified several DMCs/DMRs in association with fetal overgrowth. The use of cord blood as a material for the identification of DNA methylation biomarkers gives us the possibility to perform follow-up studies on the same patients as they grow. These studies will not only help us understand how the methylome responds to continuum postnatal growth but also link early alterations of the DNA methylome with later clinical markers of growth and metabolic fitness.

Oncolytic Viruses in the Era of Omics, Computational Technologies, and Modeling: Thesis, Antithesis, and Synthesis.

Oncolytic viruses (OVs) are the frontier therapy for refractory cancers, especially in integration with immunomodulation strategies. In cancer immunovirotherapy, the many available "omics" and systems biology technologies generate at a fast pace a challenging huge amount of data, where apparently clashing information mirrors the complexity of individual clinical situations and OV used. In this review, we present and discuss how currently big data analysis, on one hand and, on the other, simulation, modeling, and computational technologies, provide invaluable support to interpret and integrate "omic" information and drive novel synthetic biology and personalized OV engineering approaches for effective immunovirotherapy. Altogether, these tools, possibly aided in the future by artificial intelligence as well, will allow for the blending of the information into OV recombinants able to achieve tumor clearance in a patient-tailored way. Various endeavors to the envisioned "synthesis" of turning OVs into personalized theranostic agents are presented.

Evaluation of genes and molecular pathways involved in the oxidative stress in preeclampsia: A systems biology approach

BackgroundOxidative stress is one of the main factors in the pathogenesis of preeclampsia (PE). Many genes and molecular pathways play a role in its occurrence, however, so far no study has been conducted with a bioinformatics approach to identify genes. Therefore, we investigated this issue in this study. Material and methodsIn this study, microarray data (GSE30186 dataset) sourced from the Gene Expression Omnibus (GEO) database were analyzed. This dataset consists of 12 placental tissue samples; six was obtained from normal individuals and six from patients diagnosed with preeclampsia. Differential gene analysis was conducted with the LIMMA package in R software using the following criteria 1: absolute log fold change >1, 2: adjusted P-value <0.05. Oxidative stress-related genes were sourced from Genecard, and the overlap with DEGs was extracted via a Venn diagram. Gene ontology and KEGG pathway analysis were performed using Enrichr software. A protein-protein interaction (PPI) network was designed using the STRING database and visualized with Cytoscape. Hub genes were identified using various methods. Associations between hub genes, transcription factors, and microRNAs were assessed using the miRTarBase and TRRUST databases. Hub gene validation was conducted on the GSE75010 dataset, which comprises 80 preeclampsia and 77 control placenta samples. Differential expression analysis was performed using a two-sample t-test (P < 0.05); boxplots were also generated for visualization. ResultAnalysis of the GSE30186 dataset identified 717 DEGs in PE compared to normal samples with 448 upregulated and 269 downregulated DEGs. To investigate oxidative stress in PE, a list of 391 genes related to oxidative stress was initially collected from Genecard. Subsequently, an intersection was identified between these oxidative stress-related genes and DEGs, resulting in the identification of 26 shared genes between the DEGs and the oxidative stress-related genes. PPI network and hub gene analyses identified 8 hub genes, including STAT1, SERPINE1, CXCL8, and FN1. According to the results, hsa-miR-146a-5p has the most interactions with hub genes among miRNAs, and RELA has the most interactions among TFs. Finally, validation using the GSE75010 dataset confirmed significant differential expression of SERPINE1, CXCL8, and FN1 in PE; it suggested cited genes as hub genes, while STAT1 did not exhibit significant differences. ConclusionIdentifying pathways related to the expression of hub genes can be useful in designing appropriate treatment strategies. On the other hand, examining the pathways activated by hub genes can be effective in preventing ROS production.

System biology mediated assessment of molecular mechanism for sinapic acid against breast cancer: via network pharmacology and molecular dynamic simulation

Sinapic acid is a hydroxycinnamic acid widespread in the plant kingdom, known to be a potent anti-oxidant used for the treatment of cancer, infections, oxidative stress, and inflammation. However, the mode of action for its chemotherapeutic properties has yet not been unleashed. Hence, we aimed to identify potential targets to propose a possible molecular mechanism for sinapic acid against breast cancer. We utilized multiple system biology tools and databases like DisGeNET, DIGEP-Pred, Cytoscape, STRING, AutoDock 4.2, AutoDock vina, Schrodinger, and gromacs to predict a probable molecular mechanism for sinapic acid against breast cancer. Targets for the disease breast cancer, were identified via DisGeNET database which were further matched with proteins predicted to be modulated by sinapic acid. In addition, KEGG pathway analysis was used to identify pathways; a protein-pathway network was constructed via Cytoscape. Molecular docking was performed using three different algorithms followed by molecular dynamic simulations and MMPBSA analysis. Moreover, cluster analysis and gene ontology (GO) analysis were performed. A total of 6776 targets were identified for breast cancer; 95.38% of genes predicted to be modulated by sinapic acid were common with genes of breast cancer. The ‘Pathways in cancer’ was predicted to be modulated by most umber of proteins. Further, PRKCA, CASP8, and CTNNB1 were predicted to be the top 3 hub genes. In addition, molecular docking studies revealed CYP3A4, CYP1A1, and SIRT1 to be the lead proteins identified from AutoDock 4.2, AutoDock Vina, and Schrodinger suite Glide respectively. Molecular dynamic simulation and MMPBSA were performed for the complex of sinapic acid with above mentioned proteins which revealed a stable complex throughout simulation. The predictions revealed that the mechanism of sinapic acid in breast cancer may be due to regulation of multiple proteins like CTNNB1, PRKCA, CASP8, SIRT1, and cytochrome enzymes (CYP1A1 & CYP3A4); the majorly regulated pathway was predicted to be ‘Pathways in cancer’. This indicates the rationale for sinapic acid to be used in the treatment of breast cancer. However, these are predictions and need to be validated and looked upon in-depth to confirm the exact mechanism of sinapic acid in the treatment of breast cancer; this is future scope as well as a drawback of the current study.

Therapeutic effect of microRNA-21 on differentially expressed hub genes in gastric cancer based on systems biology

Gastric cancer (GC) is a leading cause of mortality for many people. Cancer’s initiating factors are poorly understood. miR-21 has a crucial function in several malignancies, particularly GC. Furthermore, it has been shown that miR-21 is critical for the emergence and advancement of GC. This work intends to identify new genes which expression is associated with the activity of mir-21 in GC and to investigate the effect of downregulation of mir-21 on these genes and gastric tumorigenesis. We utilized the gene expression profiles of GCs from an Array database (GSE13911) from the Gene Expression Omnibus (GEO) dataset to find differentially expressed genes (DEGs) between control and gastric cancer groups. Using weighted gene correlation network analysis (WGCNA) in R, the Gene co-expression network was reconstructed. The microRNA–mRNA network was then reconstructed using the miRWalk database, and by investigating the microRNA–mRNA network, the genes that have an association with mir-21 were found. To implement the functional investigation, MKN and AGS cell lines were transfected with anti-miR-21 next. Subsequently, MTT proliferation was utilized to assess the cell's vitality. qRT-PCR was then used to evaluate the anticipated levels of gene expression in both GC cell lines. This study discovered and predicted CCL28, NR3C2, and SNYPO2 as the targets of miR-21 (GC), which are downregulated through gastric tumorigenesis, showing great potential as therapeutic and diagnostic targets. The suppression of miR-21 in gastric GC cells led to the inhibition of cell proliferation and decreased expression of CCL28, NR3C2, and SNYPO2 genes. This study established that miR-21, via downregulating these genes, contributes significantly to the development of GC. In addition, systems biology techniques identified CCL28, NR3C2, and SNYPO2 genes as possible GC surveillance and therapy components.

Generalized Michaelis-Menten rate law with time-varying molecular concentrations.

The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

A Network-gene-mutation-based model for large network dynamic application reliability evaluation

Network evolution model (NEM) is a framework proposed recently for dynamic application reliability evaluation which integrates various factors that affect application failure. The existing component-based NEM relies on an exhaustive enumeration of all potential states of network components, which can lead to a computational bottleneck, especially when dealing with large networks. Therefore, to tackle this challenge, this paper presents a Network-gene-mutation-based NEM. The model introduces novel concepts of Network-cells and Network-genes inspired by the gene-cell-function model framework used in system biology to address complex disease problems. Network-cells are constructed according to the statistical classification of components. Network-genes are constructed by the average failure rate of components within a Network-cell. Dynamic network states are simulated by the Network-gene-mutation function of Network-cells instead of the failure and repair function of network components. Validation of our proposed model is performed through the comparison with benchmark models. The results demonstrate the validity of our proposed model in calculating dynamic application reliability. Besides, the comparison results with the component-based NEM also suggest that our proposed model reduces the time complexity of the algorithm from square order [Formula: see text] to constant order [Formula: see text] and improves the simulation efficiency by roughly 104. Applied to networks of varying scales, our proposed model proves to be an efficient method for calculating dynamic application reliability in large networks. In summary, this paper offers a promising solution to address the computational bottlenecks encountered in the component-based NEM when evaluating the reliability of dynamic applications.