High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

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.

Interferon and B-cell Signatures Inform Precision Medicine in Lupus Nephritis

Introduction

Current therapeutic management of lupus nephritis (LN) fails to induce long-term remission in over 50% of patients, highlighting the urgent need for additional options.

Methods

We analyzed differentially expressed genes (DEGs) in peripheral blood from patients with active LN (n = 41) and active nonrenal lupus (n = 62) versus healthy controls (HCs) (n = 497) from the European PRECISESADS project (NTC02890121), and dysregulated gene modules in a discovery (n = 26) and a replication (n = 15) set of active LN cases.

Results

Replicated gene modules qualified for correlation analyses with serologic markers, and regulatory network and druggability analysis. Unsupervised coexpression network analysis revealed 20 dysregulated gene modules and stratified the active LN population into 3 distinct subgroups. These subgroups were characterized by low, intermediate, and high interferon (IFN) signatures, with differential dysregulation of the "B cell" and "plasma cells/Ig" modules. Drugs annotated to the IFN network included CC-motif chemokine receptor 1 (CCR1) inhibitors, programmed death-ligand 1 (PD-L1) inhibitors, and irinotecan; whereas the anti-CD38 daratumumab and proteasome inhibitor bortezomib showed potential for counteracting the "plasma cells/Ig" signature. In silico analysis demonstrated the low-IFN subgroup to benefit from calcineurin inhibition and the intermediate-IFN subgroup from B-cell targeted therapies. High-IFN patients exhibited greater anticipated response to anifrolumab whereas daratumumab appeared beneficial to the intermediate-IFN and high-IFN subgroups.

Conclusion

IFN upregulation and B and plasma cell gene dysregulation patterns revealed 3 subgroups of LN, which may not necessarily represent distinct disease phenotypes but rather phases of the inflammatory processes during a renal flare, providing a conceptual framework for precision medicine in LN.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods

Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.

Results

Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.

Discussion

The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

BACKGROUND

Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.

METHODS

By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.

RESULTS

A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.

CONCLUSIONS

The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

Short- and long-term effects of radiation exposure at low dose and low dose rate in normal human VH10 fibroblasts

Introduction

Experimental studies complement epidemiological data on the biological effects of low doses and dose rates of ionizing radiation and help in determining the dose and dose rate effectiveness factor.

Methods

Human VH10 skin fibroblasts exposed to 25, 50, and 100 mGy of 137Cs gamma radiation at 1.6, 8, 12 mGy/h, and at a high dose rate of 23.4 Gy/h, were analyzed for radiation-induced short- and long-term effects. Two sample cohorts, i.e., discovery (n = 30) and validation (n = 12), were subjected to RNA sequencing. The pool of the results from those six experiments with shared conditions (1.6 mGy/h; 24 h), together with an earlier time point (0 h), constituted a third cohort (n = 12).

Results

The 100 mGy-exposed cells at all abovementioned dose rates, harvested at 0/24 h and 21 days after exposure, showed no strong gene expression changes. DMXL2, involved in the regulation of the NOTCH signaling pathway, presented a consistent upregulation among both the discovery and validation cohorts, and was validated by qPCR. Gene set enrichment analysis revealed that the NOTCH pathway was upregulated in the pooled cohort (p = 0.76, normalized enrichment score (NES) = 0.86). Apart from upregulated apical junction and downregulated DNA repair, few pathways were consistently changed across exposed cohorts. Concurringly, cell viability assays, performed 1, 3, and 6 days post irradiation, and colony forming assay, seeded just after exposure, did not reveal any statistically significant early effects on cell growth or survival patterns. Tendencies of increased viability (day 6) and reduced colony size (day 21) were observed at 12 mGy/h and 23.4 Gy/min. Furthermore, no long-term changes were observed in cell growth curves generated up to 70 days after exposure.

Discussion

In conclusion, low doses of gamma radiation given at low dose rates had no strong cytotoxic effects on radioresistant VH10 cells.

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.

SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types

Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.

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.

An SPM-Enriched Marine Oil Supplement Shifted Microglia Polarization toward M2, Ameliorating Retinal Degeneration in rd10 Mice

Retinitis pigmentosa (RP) is the most common inherited retinal dystrophy causing progressive vision loss. It is accompanied by chronic and sustained inflammation, including M1 microglia activation. This study evaluated the effect of an essential fatty acid (EFA) supplement containing specialized pro-resolving mediators (SPMs), on retinal degeneration and microglia activation in rd10 mice, a model of RP, as well as on LPS-stimulated BV2 cells. The EFA supplement was orally administered to mice from postnatal day (P)9 to P18. At P18, the electrical activity of the retina was examined by electroretinography (ERG) and innate behavior in response to light were measured. Retinal degeneration was studied via histology including the TUNEL assay and microglia immunolabeling. Microglia polarization (M1/M2) was assessed by flow cytometry, qPCR, ELISA and histology. Redox status was analyzed by measuring antioxidant enzymes and markers of oxidative damage. Interestingly, the EFA supplement ameliorated retinal dysfunction and degeneration by improving ERG recording and sensitivity to light, and reducing photoreceptor cell loss. The EFA supplement reduced inflammation and microglia activation attenuating M1 markers as well as inducing a shift to the M2 phenotype in rd10 mouse retinas and LPS-stimulated BV2 cells. It also reduced oxidative stress markers of lipid peroxidation and carbonylation. These findings could open up new therapeutic opportunities based on resolving inflammation with oral supplementation with SPMs such as the EFA supplement.

miR-222 exerts negative regulation on insulin signaling pathway in 3T3-L1 adipocytes

Increased miR-222 levels are associated with metabolic syndrome, insulin resistance, and diabetes. Moreover, rats fed an obesogenic diet during lactation have higher miR-222 content in breast milk and the offspring display greater body fat mass and impaired insulin sensitivity in adulthood. In order to investigate the molecular mechanisms involved and to dissect the specific effects of miR-222 on adipocytes, transfection with a mimic or an inhibitor of miR-222 has been conducted on 3T3-L1 preadipocytes. 3T3-L1 cells were transfected with either a mimic or an inhibitor of miR-222 and collected after 2 days (preadipocytes) or 8 days (mature adipocytes) for transcriptomic analysis. Results showed a relevant impact on pathways associated with insulin signaling, lipid metabolism and adipogenesis. Outcomes in key genes and proteins were further analyzed with quantitative reverse transcription polymerase chain reaction and Western Blotting, respectively, which displayed a general inhibition in important effectors of the identified routes under miR-222 mimic treatment in preadipocytes. Although to a lesser extent, this overall signature was maintained in differentiated adipocytes. Altogether, miR-222 exerts a direct effect in metabolic pathways of 3T3-L1 adipocytes that are relevant to adipocyte function, limiting adipogenesis and insulin signaling pathways, offering a mechanistic explanation for its reported association with metabolic diseases.

Subpathway Analysis of Transcriptome Profiles Reveals New Molecular Mechanisms of Acquired Chemotherapy Resistance in Breast Cancer

Chemoresistance has been a major challenge in the treatment of patients with breast cancer. The diverse omics platforms and small sample sizes reported in the current studies of chemoresistance in breast cancer limit the consensus regarding the underlying molecular mechanisms of chemoresistance and the applicability of these study findings. Therefore, we built two transcriptome datasets for patients with chemotherapy-resistant breast cancers—one comprising paired transcriptome samples from 40 patients before and after chemotherapy and the second including unpaired samples from 690 patients before and 45 patients after chemotherapy. Subsequent conventional pathway analysis and new subpathway analysis using these cohorts uncovered 56 overlapping upregulated genes (false discovery rate [FDR], 0.018) and 36 downregulated genes (FDR, 0.016). Pathway analysis revealed the activation of several pathways in the chemotherapy-resistant tumors, including those of drug metabolism, MAPK, ErbB, calcium, cGMP-PKG, sphingolipid, and PI3K-Akt, as well as those activated by Cushing’s syndrome, human papillomavirus (HPV) infection, and proteoglycans in cancers, and subpathway analysis identified the activation of several more, including fluid shear stress, Wnt, FoxO, ECM-receptor interaction, RAS signaling, Rap1, mTOR focal adhesion, and cellular senescence (FDR < 0.20). Among these pathways, those associated with Cushing’s syndrome, HPV infection, proteoglycans in cancer, fluid shear stress, and focal adhesion have not yet been reported in breast cancer chemoresistance. Pathway and subpathway analysis of a subset of triple-negative breast cancers from the two cohorts revealed activation of the identical chemoresistance pathways.

Endoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modulation of Cell–Matrix Interactions

Endoglin (ENG) is a mesenchymal stem cell (MSC) marker typically expressed by active endothelium. This transmembrane glycoprotein is shed by matrix metalloproteinase 14 (MMP14). Our previous work demonstrated potent preclinical activity of first-in-class anti-ENG antibody-drug conjugates as a nascent strategy to eradicate Ewing sarcoma (ES), a devastating rare bone/soft tissue cancer with a putative MSC origin. We also defined a correlation between ENG and MMP14 expression in ES. Herein, we show that ENG expression is significantly associated with a dismal prognosis in a large cohort of ES patients. Moreover, both ENG/MMP14 are frequently expressed in primary ES tumors and metastasis. To deepen in their functional relevance in ES, we conducted transcriptomic and proteomic profiling of in vitro ES models that unveiled a key role of ENG and MMP14 in cell mechano-transduction. Migration and adhesion assays confirmed that loss of ENG disrupts actin filament assembly and filopodia formation, with a concomitant effect on cell spreading. Furthermore, we observed that ENG regulates cell–matrix interaction through activation of focal adhesion signaling and protein kinase C expression. In turn, loss of MMP14 contributed to a more adhesive phenotype of ES cells by modulating the transcriptional extracellular matrix dynamics. Overall, these results suggest that ENG and MMP14 exert a significant role in mediating correct spreading machinery of ES cells, impacting the aggressiveness of the disease.

NPC transplantation rescues sci-driven cAMP/EPAC2 alterations, leading to neuroprotection and microglial modulation

Neural progenitor cell (NPC) transplantation represents a promising treatment strategy for spinal cord injury (SCI); however, the underlying therapeutic mechanisms remain incompletely understood. We demonstrate that severe spinal contusion in adult rats causes transcriptional dysregulation, which persists from early subacute to chronic stages of SCI and affects nearly 20,000 genes in total tissue extracts. Functional analysis of this dysregulated transcriptome reveals the significant downregulation of cAMP signalling components immediately after SCI, involving genes such as EPAC2 (exchange protein directly activated by cAMP), PKA, BDNF, and CAMKK2. The ectopic transplantation of spinal cord-derived NPCs at acute or subacute stages of SCI induces a significant transcriptional impact in spinal tissue, as evidenced by the normalized expression of a large proportion of SCI-affected genes. The transcriptional modulation pattern driven by NPC transplantation includes the rescued expression of cAMP signalling genes, including EPAC2. We also explore how the sustained in vivo inhibition of EPAC2 downstream signalling via the intrathecal administration of ESI-05 for 1 week impacts therapeutic mechanisms involved in the NPC-mediated treatment of SCI. NPC transplantation in SCI rats in the presence and absence of ESI-05 administration prompts increased rostral cAMP levels; however, NPC and ESI-05 treated animals exhibit a significant reduction in EPAC2 mRNA levels compared to animals receiving only NPCs treatment. Compared with transplanted animals, NPCs + ESI-05 treatment increases the scar area (as shown by GFAP staining), polarizes microglia into an inflammatory phenotype, and increases the magnitude of the gap between NeuN + cells across the lesion. Overall, our results indicate that the NPC-associated therapeutic mechanisms in the context of SCI involve the cAMP pathway, which reduces inflammation and provides a more neuropermissive environment through an EPAC2-dependent mechanism.

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.

Mutational Characterization of Cutaneous Melanoma Supports Divergent Pathways Model for Melanoma Development

According to the divergent pathway model, cutaneous melanoma comprises a nevogenic group with a propensity to melanocyte proliferation and another one associated with cumulative solar damage (CSD). While characterized clinically and epidemiologically, the differences in the molecular profiles between the groups have remained primarily uninvestigated. This study has used a custom gene panel and bioinformatics tools to investigate the potential molecular differences in a thoroughly characterized cohort of 119 melanoma patients belonging to nevogenic and CSD groups. We found that the nevogenic melanomas had a restricted set of mutations, with the prominently mutated gene being BRAF. The CSD melanomas, in contrast, showed mutations in a diverse group of genes that included NF1, ROS1, GNA11, and RAC1. We thus provide evidence that nevogenic and CSD melanomas constitute different biological entities and highlight the need to explore new targeted therapies.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Lower miR-26a levels in breastmilk affect gene expression in adipose tissue of offspring

Breastmilk miRNAs may act as epigenetic regulators of metabolism and energy homeostasis in offspring. Here, we aimed to investigate the regulatory effects of miR-26a on adipose tissue development. First, the 3T3-L1 cell model was used to identify putative target genes for miR-26a. Then, target genes were analysed in adipose tissue of offspring from dams that supplied lower levels of breastmilk miR-26a to determine whether miR-26a milk concentration might have a long-lasting impact on adipose tissue in the progeny. In the in vitro model, both over- and under-expression of miR-26a were induced by transfecting into 3T3-L1 with miR-26a mimic and inhibitor. Array analysis was performed after induction of miR-26a to ascertain the impact on mRNA target genes and influence of differentiation status. Focusing on genes related to adipose tissue development, transfection with miR-26a mimic reduced the expression of Pten, Hmga1, Stk11, Rb1, and Adam17 in both pre- and mature adipocytes. Data mostly confirmed the results found in the animal model. After weaning, descendants of cafeteria-fed dams breastfed with lower levels of miR-26a displayed greater expression of Hmag1, Rb1, and Adam17 in retroperitoneal white adipose tissue in comparison with controls. Hence, alterations in the amount of miR-26a supplied through milk during lactation is able to alter the expression of target genes in the descendants and may affect adipose tissue development. Thus, milk miR-26a may act as an epigenetic regulator influencing early metabolic program in the progeny, which emerges as a relevant component of an optimal milk composition for correct development.

Human Embryos, Induced Pluripotent Stem Cells, and Organoids: Models to Assess the Effects of Environmental Plastic Pollution

For a long time, animal models were used to mimic human biology and diseases. However, animal models are not an ideal solution due to numerous interspecies differences between humans and animals. New technologies, such as human-induced pluripotent stem cells and three-dimensional (3D) cultures such as organoids, represent promising solutions for replacing, refining, and reducing animal models. The capacity of organoids to differentiate, self-organize, and form specific, complex, biologically suitable structures makes them excellent in vitro models of development and disease pathogenesis, as well as drug-screening platforms. Despite significant potential health advantages, further studies and considerable nuances are necessary before their clinical use. This article summarizes the definition of embryoids, gastruloids, and organoids and clarifies their appliance as models for early development, diseases, environmental pollution, drug screening, and bioinformatics.

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.

A comprehensive database for integrated analysis of omics data in autoimmune diseases

BACKGROUND

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.

RESULTS

Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

CONCLUSIONS

This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

Common pathways and functional profiles reveal underlying patterns in Breast, Kidney and Lung cancers

Background

Cancer is a major health problem which presents a high heterogeneity. In this work we explore omics data from Breast, Kidney and Lung cancers at different levels as signalling pathways, functions and miRNAs, as part of the CAMDA 2019 Hi-Res Cancer Data Integration Challenge. Our goal is to find common functional patterns which give rise to the generic microenvironment in these cancers and contribute to a better understanding of cancer pathogenesis and a possible clinical translation down further studies.

Results

After a tumor versus normal tissue comparison of the signaling pathways and cell functions, we found 828 subpathways, 912 Gene Ontology terms and 91 Uniprot keywords commonly significant to the three studied tumors. Such features interestingly show the power to classify tumor samples into subgroups with different survival times, and predict tumor state and tissue of origin through machine learning techniques. We also found cancer-specific alternative activation subpathways, such as the ones activating STAT5A in ErbB signaling pathway. miRNAs evaluation show the role of miRNAs, such as mir-184 and mir-206, as regulators of many cancer pathways and their value in prognoses.

Conclusions

The study of the common functional and pathway activities of different cancers is an interesting approach to understand molecular mechanisms of the tumoral process regardless of their tissue of origin. The existence of platforms as the CAMDA challenges provide the opportunity to share knowledge and improve future scientific research and clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13062-021-00293-8.

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

Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios.

Hepatic steatosis and steatohepatitis: a functional meta-analysis of sex-based differences in transcriptomic studies

Background

Previous studies have described sex-based differences in the epidemiological and clinical patterns of non-alcoholic fatty liver disease (NAFLD); however, we understand relatively little regarding the underlying molecular mechanisms. Herein, we present the first systematic review and meta-analysis of NAFLD transcriptomic studies to identify sex-based differences in the molecular mechanisms involved during the steatosis (NAFL) and steatohepatitis (NASH) stages of the disease.

Methods

Transcriptomic studies in the Gene Expression Omnibus database were systematically reviewed following the PRISMA statement guidelines. For each study, NAFL and NASH in premenopausal women and men were compared using a dual strategy: gene-set analysis and pathway activity analysis. Finally, the functional results of all studies were integrated into a meta-analysis.

Results

We reviewed a total of 114 abstracts and analyzed seven studies that included 323 eligible patients. The meta-analyses identified significantly altered molecular mechanisms between premenopausal women and men, including the overrepresentation of genes associated with DNA regulation, vinculin binding, interleukin-2 responses, negative regulation of neuronal death, and the transport of ions and cations in premenopausal women. In men, we discovered the overrepresentation of genes associated with the negative regulation of interleukin-6 and the establishment of planar polarity involved in neural tube closure.

Conclusions

Our meta-analysis of transcriptomic data provides a powerful approach to identify sex-based differences in NAFLD. We detected differences in relevant biological functions and molecular terms between premenopausal women and men. Differences in immune responsiveness between men and premenopausal women with NAFLD suggest that women possess a more immune tolerant milieu, while men display an impaired liver regenerative response.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13293-021-00368-1.

A Deep Look into the Program of Rapid Tumor Growth of Hepatocellular Carcinoma

BACKGROUND AND AIMS

Great efforts have been made towards increasing our understanding of the pathogenesis involved in hepatocellular carcinoma (HCC), but the rapid growth inherent to such tumor development remains to be explored.

METHODS

We identified distinct gene coexpression modes upon liver tumor growth using weighted gene coexpression network analysis. Modeling of tumor growth as signaling activity was employed to understand the main cascades responsible for the growth. Hub genes in the modules were determined, examined in vitro, and further assembled into the growth signature.

RESULTS

We revealed modules related to the different growth states in HCC, especially the fastest growth module, which is preserved among different HCC cohorts. Moreover, signaling flux in the cell cycle pathway was found to act as a driving force for rapid growth. Twenty hub genes in the module were identified and assembled into the growth signature, and two genes (NCAPH, and RAD54L) were tested for their growth potential in vitro. Genetic alteration of the growth signature affected the global gene expression. The activity of the signature was associated with tumor metabolism and immunity in HCC. Finally, the prognosis effect of the growth signature was reproduced in nine cancers.

CONCLUSIONS

These results collectively demonstrate the molecule organization of rapid tumor growth in HCC, which is a highly synergistic process, with implications for the future management of patients.

A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

The level of oncogenic Ras determines the malignant transformation of Lkb1 mutant tissue in vivo

The genetic and metabolic heterogeneity of RAS-driven cancers has confounded therapeutic strategies in the clinic. To address this, rapid and genetically tractable animal models are needed that recapitulate the heterogeneity of RAS-driven cancers in vivo. Here, we generate a Drosophila melanogaster model of Ras/Lkb1 mutant carcinoma. We show that low-level expression of oncogenic Ras (RasLow) promotes the survival of Lkb1 mutant tissue, but results in autonomous cell cycle arrest and non-autonomous overgrowth of wild-type tissue. In contrast, high-level expression of oncogenic Ras (RasHigh) transforms Lkb1 mutant tissue resulting in lethal malignant tumors. Using simultaneous multiview light-sheet microcopy, we have characterized invasion phenotypes of Ras/Lkb1 tumors in living larvae. Our molecular analysis reveals sustained activation of the AMPK pathway in malignant Ras/Lkb1 tumors, and demonstrate the genetic and pharmacologic dependence of these tumors on CaMK-activated Ampk. We further show that LKB1 mutant human lung adenocarcinoma patients with high levels of oncogenic KRAS exhibit worse overall survival and increased AMPK activation. Our results suggest that high levels of oncogenic KRAS is a driving event in the malignant transformation of LKB1 mutant tissue, and uncovers a vulnerability that may be used to target this aggressive genetic subset of RAS-driven tumors.

Mechanistic modeling of the SARS-CoV-2 disease map

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.

Human pluripotent stem cells - Unique tools to decipher the effects of environmental and intracellular plastic pollution on human health

Increase in plastic pollution causes irreparable harm to the environment lasting for decades. While current data of plastic pollution include marine and terrestrial ecology, the impacts of degraded or intentionally produced microscopic-sized plastics on human health remain unknown. Here, we are proposing the usage of pluripotent stem cells, modern transcriptomics, and bioinformatics as a unique scientific tool to define the link between environmental and intracellular pollution, its outcome on early human development and origin of diseases. This commentary is an urgent appeal to the scientific and policy communities to invest more time and resources to establish reliable standards and methods to define and address the consequences of plastic pollution on human health.

Transcriptomic Analysis of a Diabetic Skin-Humanized Mouse Model Dissects Molecular Pathways Underlying the Delayed Wound Healing Response

Defective healing leading to cutaneous ulcer formation is one of the most feared complications of diabetes due to its consequences on patients' quality of life and on the healthcare system. A more in-depth analysis of the underlying molecular pathophysiology is required to develop effective healing-promoting therapies for those patients. Major architectural and functional differences with human epidermis limit extrapolation of results coming from rodents and other small mammal-healing models. Therefore, the search for reliable humanized models has become mandatory. Previously, we developed a diabetes-induced delayed humanized wound healing model that faithfully recapitulated the major histological features of such skin repair-deficient condition. Herein, we present the results of a transcriptomic and functional enrichment analysis followed by a mechanistic analysis performed in such humanized wound healing model. The deregulation of genes implicated in functions such as angiogenesis, apoptosis, and inflammatory signaling processes were evidenced, confirming published data in diabetic patients that in fact might also underlie some of the histological features previously reported in the delayed skin-humanized healing model. Altogether, these molecular findings support the utility of such preclinical model as a valuable tool to gain insight into the molecular basis of the delayed diabetic healing with potential impact in the translational medicine field.

Why do pathway methods work better than they should?

Pathway analysis methods are frequently applied to cancer gene expression data to identify dysregulated pathways. These methods often infer pathway activity based on the expression of genes belonging to a given pathway, even though the proteins ultimately determine the activity of a given pathway. Furthermore, the association between gene expression levels and protein activities is not well-characterized. Here, we posit that pathway-based methods are effective not because of the correlation between expression and activity of members of a given pathway, but because pathway gene sets overlap with the genes regulated by transcription factors (TFs). Thus, pathway-based methods do not inform about the activity of the pathway of interest but rather reflect changes in TF activities.

A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway

Simple Summary

Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine.

Abstract

A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.

Platform to study intracellular polystyrene nanoplastic pollution and clinical outcomes

Increased pollution by plastics has become a serious global environmental problem, but the concerns for human health have been raised after reported presence of microplastics (MPs) and nanoplastics (NPs) in food and beverages. Unfortunately, few studies have investigate the potentially harmful effects of MPs/NPs on early human development and human health. Therefore, we used a new platform to study possible effects of polystyrene NPs (PSNPs) on the transcription profile of preimplantation human embryos and human induced pluripotent stem cells (hiPSCs). Two pluripotency genes, LEFTY1 and LEFTY2, which encode secreted ligands of the transforming growth factor-beta, were downregulated, while CA4 and OCLM, which are related to eye development, were upregulated in both samples. The gene set enrichment analysis showed that the development of atrioventricular heart valves and the dysfunction of cellular components, including extracellular matrix, were significantly affected after exposure of hiPSCs to PSNPs. Finally, using the HiPathia method, which uncovers disease mechanisms and predicts clinical outcomes, we determined the APOC3 circuit, which is responsible for increased risk for ischemic cardiovascular disease. These results clearly demonstrate that better understanding of NPs bioactivities and its implications for human health is of extreme importance. Thus, the presented platform opens further aspects to study interactions between different environmental and intracellular pollutions with the aim to decipher the mechanism and origin of human diseases.

Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments

Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.

Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape

Single-cell RNA sequencing is revealing an unexpectedly large degree of heterogeneity in gene expression levels across cell populations. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell fate decisions to the collective behavior of the tissues these cells are part of. Here, we use mechanistic modeling of signaling circuits, which reveals a complex functional landscape at single-cell level. Different clusters of neoplastic glioblastoma cells have been defined according to their differences in signaling circuit activity profiles triggering specific cancer hallmarks, which suggest different functional strategies with distinct degrees of aggressiveness. Moreover, mechanistic modeling of effects of targeted drug inhibitions at single-cell level revealed, how in some cells, the substitution of VEGFA, the target of bevacizumab, by other expressed proteins, like PDGFD, KITLG and FGF2, keeps the VEGF pathway active, insensitive to the VEGFA inhibition by the drug. Here, we describe for the first time mechanisms that individual cells use to avoid the effect of a targeted therapy, providing an explanation for the innate resistance to the treatment displayed by some cells. Our results suggest that mechanistic modeling could become an important asset for the definition of personalized therapeutic interventions.

Using mechanistic models for the clinical interpretation of complex genomic variation

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.

A comparison of mechanistic signaling pathway activity analysis methods

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Fibroblast activation and abnormal extracellular matrix remodelling as common hallmarks in three cancer-prone genodermatoses

BACKGROUND

Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three cancer-prone genodermatoses whose causal genetic mutations cannot fully explain, on their own, the array of associated phenotypic manifestations. Recent evidence highlights the role of the stromal microenvironment in the pathology of these disorders.

OBJECTIVES

To investigate, by means of comparative gene expression analysis, the role played by dermal fibroblasts in the pathogenesis of RDEB, KS and XPC.

METHODS

We conducted RNA-Seq analysis, which included a thorough examination of the differentially expressed genes, a functional enrichment analysis and a description of affected signalling circuits. Transcriptomic data were validated at the protein level in cell cultures, serum samples and skin biopsies.

RESULTS

Interdisease comparisons against control fibroblasts revealed a unifying signature of 186 differentially expressed genes and four signalling pathways in the three genodermatoses. Remarkably, some of the uncovered expression changes suggest a synthetic fibroblast phenotype characterized by the aberrant expression of extracellular matrix (ECM) proteins. Western blot and immunofluorescence in situ analyses validated the RNA-Seq data. In addition, enzyme-linked immunosorbent assay revealed increased circulating levels of periostin in patients with RDEB.

CONCLUSIONS

Our results suggest that the different causal genetic defects converge into common changes in gene expression, possibly due to injury-sensitive events. These, in turn, trigger a cascade of reactions involving abnormal ECM deposition and underexpression of antioxidant enzymes. The elucidated expression signature provides new potential biomarkers and common therapeutic targets in RDEB, XPC and KS. What's already known about this topic? Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three genodermatoses with high predisposition to cancer development. Although their causal genetic mutations mainly affect epithelia, the dermal microenvironment likely contributes to the physiopathology of these disorders. What does this study add? We disclose a large overlapping transcription profile between XPC, KS and RDEB fibroblasts that points towards an activated phenotype with high matrix-synthetic capacity. This common signature seems to be independent of the primary causal deficiency, but reflects an underlying derangement of the extracellular matrix via transforming growth factor-β signalling activation and oxidative state imbalance. What is the translational message? This study broadens the current knowledge about the pathology of these diseases and highlights new targets and biomarkers for effective therapeutic intervention. It is suggested that high levels of circulating periostin could represent a potential biomarker in RDEB.

Accelerated phosphatidylcholine turnover in macrophages promotes adipose tissue inflammation in obesity

White adipose tissue (WAT) inflammation contributes to the development of insulin resistance in obesity. While the role of adipose tissue macrophage (ATM) pro-inflammatory signalling in the development of insulin resistance has been established, it is less clear how WAT inflammation is initiated. Here, we show that ATMs isolated from obese mice and humans exhibit markers of increased rate of de novo phosphatidylcholine (PC) biosynthesis. Macrophage-specific knockout of phosphocholine cytidylyltransferase A (CCTα), the rate-limiting enzyme of de novo PC biosynthesis pathway, alleviated obesity-induced WAT inflammation and insulin resistance. Mechanistically, CCTα-deficient macrophages showed reduced ER stress and inflammation in response to palmitate. Surprisingly, this was not due to lower exogenous palmitate incorporation into cellular PCs. Instead, CCTα-null macrophages had lower membrane PC turnover, leading to elevated membrane polyunsaturated fatty acid levels that negated the pro-inflammatory effects of palmitate. Our results reveal a causal link between obesity-associated increase in de novo PC synthesis, accelerated PC turnover and pro-inflammatory activation of ATMs.

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

BACKGROUND

In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.

RESULTS

The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.

CONCLUSIONS

The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients

Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be incorporated in the logical model by modifying the activity of the node, the initial conditions or the state transition rates. The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time. We observe that two highly relevant cancer phenotypes derived from personalized models, Proliferation and Apoptosis, are biologically consistent prognostic factors: patients with both high proliferation and low apoptosis have the worst survival rate, and conversely. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments by physicians.

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

BACKGROUND

Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

RESULTS

Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

CONCLUSION

We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

REVIEWERS

This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.

Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape

Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies.Significance: Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. Cancer Res; 78(21); 6059-72. ©2018 AACR.

The effects of death and post-mortem cold ischemia on human tissue transcriptomes

Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.

RNA levels in post-mortem tissue can differ greatly from those before death. Studying the effect of post-mortem interval on the transcriptome in 36 human tissues, Ferreira et al. find that the response to death is largely tissue-specific and develop a model to predict time since death based on RNA data.

sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses

MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.