Simulation and visualization of multiple KEGG pathways using BioNSi

NF-κB signaling is the major inflammatory pathway for inducing insulin resistance

Insulin resistance is major factor in the development of metabolic syndrome and type 2 diabetes (T2D). We extracted 430 genes from literature associated with both insulin resistance and inflammation. The highly significant pathways were Toll-like receptor signaling, PI3K-Akt signaling, cytokine-cytokine receptor interaction, pathways in cancer, TNF signaling, and NF-kappa B signaling. Among the 297 common genes in all datasets of various T2D patients' tissues including blood, muscle, liver, pancreas, and adipose tissues, 71% and 60% of these genes were differentially expressed in pancreas (GSE25724) and liver (GSE15653), respectively. A total of 169 genes contain highly conserved motifs for various transcription factors involved in immune response, thereby suggesting coordinated expression. Through co-expression analysis, we obtained three modules. The respective modules had 78, 158, and 55 genes, and TRAF2, HMGA1, and RGS5 as hub genes. Further, we used the BioNSi pathways simulation tool and identified the following five KEGG pathways perturbed in four or more tissues, namely Toll-like receptor signaling pathway, RIG-1-like receptor signaling pathway, pathways in cancer, NF-kappa B signaling pathway, and insulin resistance pathway. The genes NFKBIA and IKBKB are common to all these five pathways. In addition, using the NF-κB computational activation model, we identified that the reversal of NF-κB constitutive activation through overexpression of NFKB1 (P50 homodimer), PPARG, PIAS3 could reduce insulin resistance by almost half of its original value. To conclude, co-expression studies, gene expression network simulation, and NF-κB computational modeling substantiate the causal role of NF-κB pathway in insulin resistance. These results taken together with other published evidence suggests that the TNF-TRAF2-IKBKB-NF-κB axis could be explored as a potential target in combination with available metabolic targets in the management of insulin resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04202-4.

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

PHENSIM: Phenotype Simulator

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool’s applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach’s reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. In this context, ’in silico’ simulations can be extensively applied in massive scales, testing thousands of hypotheses under various conditions, which is usually experimentally infeasible. At present, many simulation models have become available. However, complex biological networks might pose challenges to their performance.

We propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. We implemented our tool as a freely accessible web application, hoping to allow ’in silico’ simulations to play a more central role in the modeling and understanding of biological phenomena.

Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with very few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which - by leveraging available transcriptomic and proteomic databases - allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both > 96%) the viral effects on cellular host-immune response, resulting in a specific cellular SARS-CoV-2 signature and ii) utilize this specific signature to narrow down promising repurposable therapeutic strategies. Powered by this tool, coupled with domain expertise, we have identified several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential new druggable targets in COVID-19 pathogenesis.

iTRAQ-based proteomics analysis on insomnia rats treated with Mongolian medical warm acupuncture

OBJECTIVE

To explore the proteomic changes in the hypothalamus of rats treated with Mongolian medical warm acupuncture for insomnia therapy based proteomics.

METHOD

We used an iTRAQ-based quantitative proteomic approach to identify proteins that potential molecular mechanisms involved in the treatment of insomnia by Mongolian medical warm acupuncture.

RESULT

In total, 7477 proteins were identified, of which 36 proteins showed increased levels and 45 proteins showed decreased levels in insomnia model group (M) compared with healthy control group (C), 72 proteins showed increased levels and 44 proteins showed decreased levels from the warm acupuncture treated insomnia group (W) compared with healthy controls (C), 28 proteins showed increased levels and 17 proteins showed decreased levels from the warm acupuncture-treated insomnia group (W) compared with insomnia model group (M). Compared with healthy control groups, warm acupuncture-treated insomnia group showed obvious recovered. Bioinformatics analysis indicated that up-regulation of neuroactive ligand-receptor interaction and oxytocin signaling was the most significantly elevated regulate process of Mongolian medical warm acupuncture treatment for insomnia. Proteins showed that increased/decreased expression in the warm acupuncture-treated insomnia group included Prolargin (PRELP), NMDA receptor synaptonuclear-signaling and neuronal migration factor (NSMF), Transmembrane protein 41B (TMEM41B) and Microtubule-associated protein 1B (MAP1B) to adjust insomnia.

CONCLUSION

A combination of findings in the present study suggest that warm acupuncture treatment is efficacious in improving sleep by regulating the protein expression process in an experimental rat model and may be of potential benefit in treating insomnia patients with the added advantage with no adverse effects.

Systems Analysis of Insulin and IGF1 Receptors Networks in Breast Cancer Cells Identifies Commonalities and Divergences in Expression Patterns

Insulin and insulin-like growth factor-1 (IGF1), acting respectively via the insulin (INSR) and IGF1 (IGF1R) receptors, play key developmental and metabolic roles throughout life. In addition, both signaling pathways fulfill important roles in cancer initiation and progression. The present study was aimed at identifying mechanistic differences between INSR and IGF1R using a recently developed bioinformatics tool, the Biological Network Simulator (BioNSi). This application allows to import and merge multiple pathways and interaction information from the KEGG database into a single network representation. The BioNsi network simulation tool allowed us to exploit the availability of gene expression data derived from breast cancer cell lines with specific disruptions of the INSR or IGF1R genes in order to investigate potential differences in protein expression that might be linked to biological attributes of the specific receptor networks. Modeling-generated information was corroborated by experimental and biological assays. BioNSi analyses revealed that the expression of 75 and 71 genes changed during simulation of IGF1R-KD and INSR-KD, compared to control cells, respectively. Out of 16 proteins that BioNSi analysis was based on, validated by Western blotting, nine were shown to be involved in DNA repair, eight in cell cycle checkpoints, six in proliferation, eight in apoptosis, seven in oxidative stress, six in cell migration, two in energy homeostasis, and three in senescence. Taken together, analyses identified a number of commonalities and, most importantly, dissimilarities between the IGF1R and INSR pathways that might help explain the basis for the biological differences between these networks.