Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data

RSEA: A Web Server for Pathway Enrichment Analysis of Metabolic Reaction Sets

Changes in biological pathways provide essential clues about metabolism. Genome-scale metabolic models (GEM) are network-based templates that computationally describe all stoichiometric associations and gene-protein reaction (GPR) relations found in an organism for all its metabolic genes and metabolites. Using reaction stoichiometry as input, GEMs mathematically simulate metabolic reaction fluxes occurring in an organism and predict changes in the metabolic system under the relevant condition. Multiple tools and approaches in the literature can capture fluxes sensitive to a given condition by using GEMs. However, functional enrichment analysis of these reaction lists in a systems biology perspective is not straightforward. Here, we introduce RSEA to annotate given reaction sets to significantly related metabolic pathways: Reaction Set Enrichment Analysis web server tool. RSEA converts given reaction list derived from GEMs into proper reaction identifiers and statistically analyze its enrichment in metabolic pathways. RSEA is designed to provide researchers with a practical and user-friendly platform to explore and interpret sets of reactions in biological pathways and freely available online (https://rseatool.com/).

drexml: A command line tool and Python package for drug repurposing

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Single cell RNA sequencing of human FAPs reveals different functional stages in Duchenne muscular dystrophy

Background: Duchenne muscular dystrophy is a genetic disease produced by mutations in the dystrophin gene characterized by early onset muscle weakness leading to severe and irreversible disability. Muscle degeneration involves a complex interplay between multiple cell lineages spatially located within areas of damage, termed the degenerative niche, including inflammatory cells, satellite cells (SCs) and fibro-adipogenic precursor cells (FAPs). FAPs are mesenchymal stem cell which have a pivotal role in muscle homeostasis as they can either promote muscle regeneration or contribute to muscle degeneration by expanding fibrotic and fatty tissue. Although it has been described that FAPs could have a different behavior in DMD patients than in healthy controls, the molecular pathways regulating their function as well as their gene expression profile are unknown. Methods: We used single-cell RNA sequencing (scRNAseq) with 10X Genomics and Illumina technology to elucidate the differences in the transcriptional profile of isolated FAPs from healthy and DMD patients. Results: Gene signatures in FAPs from both groups revealed transcriptional differences. Seurat analysis categorized cell clusters as proliferative FAPs, regulatory FAPs, inflammatory FAPs, and myofibroblasts. Differentially expressed genes (DEGs) between healthy and DMD FAPs included upregulated genes CHI3L1, EFEMP1, MFAP5, and TGFBR2 in DMD. Functional analysis highlighted distinctions in system development, wound healing, and cytoskeletal organization in control FAPs, while extracellular organization, degradation, and collagen degradation were upregulated in DMD FAPs. Validation of DEGs in additional samples (n = 9) using qPCR reinforced the specific impact of pathological settings on FAP heterogeneity, reflecting their distinct contribution to fibro or fatty degeneration in vivo. Conclusion: Using the single-cell RNA seq from human samples provide new opportunities to study cellular coordination to further understand the regulation of muscle homeostasis and degeneration that occurs in muscular dystrophies.

Rosemary (Rosmarinus officinalis L.) hydrosol based on serotonergic synapse for insomnia

ETHNOPHARMACOLOGICAL RELEVANCE

Rosemary (Rosmarinus officinalis L.) has been widely used as a traditional remedy for insomnia, depression and anxiety in China and Western countries. Modern pharmacological studies have shown that rosemary has important applications in neurological disorders. However, the mechanism of action of rosemary hydrosol in the treatment of insomnia is not known.

AIMS OF THE STUDY

Insomnia is closely linked to anxiety and depression, and its pathogenesis is related to biology, psychology, and sociology. Rosemary is a natural plant that has been used to treat insomnia and depression and has good biological activity, but its material basis and mechanism for the treatment of insomnia are not clear. Here, we report on the role of aqueous extracts of rosemary in the treatment of insomnia.

MATERIALS AND METHODS

The study was based on network pharmacology, using a combination of RNA-sequencing, "quantity-effect" weighting coefficients, and pharmacodynamic experiments. DL-4-chlorophenylalanine (PCPA) was intraperitoneally injected into SD rats to replicate the insomnia model with a blank, model, diazepam, and rosemary hydrosol low-, medium-, and high-dose groups were set up for the experiment. The key pathways in the treatment of insomnia with rosemary hydrosol were analyzed by molecular docking, open field assay, ELISA, western-Blot, Rt-PCR, and immunohistochemical assay.

RESULTS

Rosemary hydrosol was analyzed by GC-MS to identify 19 components. 1579 differential genes were obtained by RNA-Seq analysis, 533 targets for rosemary hydrosol and 2705 targets for insomnia, and 29 key targets were obtained by intersection. The KEGG results were ranked by "quantity-effect" weighting coefficients, resulting in serotonergic synapse was the key pathway for the treatment of insomnia with rosemary hydrosol. Molecular docking results showed that 1,7,7-trimethylbicyclo[2.2.1] heptan-2-one, 3-methyl-4-isopropylphenol, caryophyllene, and citronellol of rosemary hydrosol acted synergistically to achieve a therapeutic effect on insomnia. Caryophyllene acts on the HTR1A target by upregulating 5-HT1AR, leading to increased 5-HT release, and upregulation of ADCY5, cAMP, PKA and GABAA at serotonergic synapses; citronellol upregulated ADCY5 and 1,7,7-trimethylbicyclo[2.2.1] heptan-2-one, and 3-methyl-4-isopropylphenol up-regulated GABAA to improve insomnia symptoms. In open-field experiments, ELISA kits (5-HT, GABA, and DA), Western-blotting, Rt-PCR and immunohistochemical assay experiments, insomnia rats in the low-, medium- and high-dose groups of rosemary hydrosol showed different degrees of improvement compared with the model group.

CONCLUSIONS

It was shown that rosemary hydrosol may exert its therapeutic effects on insomnia through serotonergic synapses by combining RNA-Seq, "quantity-effect" weighting coefficients network pharmacology and pharmacodynamic experiments. We have provided a preliminary theoretical study for the development of rosemary hydrosol additive into a beverage for the treatment of insomnia, but it needs to be studied in depth. This study was conducted in rats and the results have limitations and may not apply to humans.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

Functional Profiling of Soft Tissue Sarcoma Using Mechanistic Models

Soft tissue sarcoma is an umbrella term for a group of rare cancers that are difficult to treat. In addition to surgery, neoadjuvant chemotherapy has shown the potential to downstage tumors and prevent micrometastases. However, finding effective therapeutic targets remains a research challenge. Here, a previously developed computational approach called mechanistic models of signaling pathways has been employed to unravel the impact of observed changes at the gene expression level on the ultimate functional behavior of cells. In the context of such a mechanistic model, RNA-Seq counts sourced from the Recount3 resource, from The Cancer Genome Atlas (TCGA) Sarcoma project, and non-diseased sarcomagenic tissues from the Genotype-Tissue Expression (GTEx) project were utilized to investigate signal transduction activity through signaling pathways. This approach provides a precise view of the relationship between sarcoma patient survival and the signaling landscape in tumors and their environment. Despite the distinct regulatory alterations observed in each sarcoma subtype, this study identified 13 signaling circuits, or elementary sub-pathways triggering specific cell functions, present across all subtypes, belonging to eight signaling pathways, which served as predictors for patient survival. Additionally, nine signaling circuits from five signaling pathways that highlighted the modifications tumor samples underwent in comparison to normal tissues were found. These results describe the protective role of the immune system, suggesting an anti-tumorigenic effect in the tumor microenvironment, in the process of tumor cell detachment and migration, or the dysregulation of ion homeostasis. Also, the analysis of signaling circuit intermediary proteins suggests multiple strategies for therapy.

Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer

The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.

Polystyrene nanoplastics affect transcriptomic and epigenomic signatures of human fibroblasts and derived induced pluripotent stem cells: Implications for human health

Plastic pollution is increasing at an alarming rate yet the impact of this pollution on human health is poorly understood. Because human induced pluripotent stem cells (hiPSC) are frequently derived from dermal fibroblasts, these cells offer a powerful platform for the identification of molecular biomarkers of environmental pollution in human cells. Here, we describe a novel proof-of-concept for deriving hiPSC from human dermal fibroblasts deliberately exposed to polystyrene (PS) nanoplastic particles; unexposed hiPSC served as controls. In parallel, unexposed hiPSC were exposed to low and high concentrations of PS nanoparticles. Transcriptomic and epigenomic signatures of all fibroblasts and hiPSCs were defined using RNA-seq and whole genome methyl-seq, respectively. Both PS-treated fibroblasts and derived hiPSC showed alterations in expression of ESRRB and HNF1A genes and circuits involved in the pluripotency of stem cells, as well as in pathways involved in cancer, inflammatory disorders, gluconeogenesis, carbohydrate metabolism, innate immunity, and dopaminergic synapse. Similarly, the expression levels of identified key transcriptional and DNA methylation changes (DNMT3A, ESSRB, FAM133CP, HNF1A, SEPTIN7P8, and TTC34) were significantly affected in both PS-exposed fibroblasts and hiPSC. This study illustrates the power of human cellular models of environmental pollution to narrow down and prioritize the list of candidate molecular biomarkers of environmental pollution. This knowledge will facilitate the deciphering of the origins of environmental diseases.

Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks

Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single-cell proteomics or large-scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers.

Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling

Recent studies have demonstrated a relevant role of the host genetics in the coronavirus disease 2019 (COVID-19) prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum of genetic variability remains yet unexplored with respect to possible interactions with COVID-19. Here, a mathematical mechanistic model of the COVID-19 molecular disease mechanism has been used to detect potential interactions between rare disease genes and the COVID-19 infection process and downstream consequences. Out of the 2518 disease genes analyzed, causative of 3854 rare diseases, a total of 254 genes have a direct effect on the COVID-19 molecular disease mechanism and 207 have an indirect effect revealed by a significant strong correlation. This remarkable potential of interaction occurs for >300 rare diseases. Mechanistic modeling of COVID-19 disease map has allowed a holistic systematic analysis of the potential interactions between the loss of function in known rare disease genes and the pathological consequences of COVID-19 infection. The results identify links between disease genes and COVID-19 hallmarks and demonstrate the usefulness of the proposed approach for future preventive measures in some rare diseases.