Integrated transcriptomic landscape of the effect of anti-steatotic treatments in high-fat diet mouse models of non-alcoholic fatty liver disease.

High-fat diet (HFD) mouse models are widely used in research to develop medications to treat non-alcoholic fatty liver disease (NAFLD), as they mimic the steatosis, inflammation, and hepatic fibrosis typically found in this complex human disease. The aims of this study were to identify a complete transcriptomic signature of these mouse models and to characterize the transcriptional impact exerted by different experimental anti-steatotic treatments. For this reason, we conducted a systematic review and meta-analysis of liver transcriptomic studies performed in HFD-fed C57BL/6J mice, comparing them with control mice and HFD-fed mice receiving potential anti-steatotic treatments. Analyzing 21 studies broaching 24 different treatments, we obtained a robust HFD transcriptomic signature that included 2,670 differentially expressed genes and 2,567 modified gene ontology biological processes. Treated HFD mice generally showed a reversion of this HFD signature, although the extent varied depending on the treatment. The biological processes most frequently reversed were those related to lipid metabolism, response to stress, and immune system, whereas processes related to nitrogen compound metabolism were generally not reversed. When comparing this HFD signature with a signature of human NAFLD progression, we identified 62 genes that were common to both; 10 belonged to the group that were reversed by treatments. Altered expression of most of these 10 genes was confirmed in vitro in hepatocytes and hepatic stellate cells exposed to a lipotoxic or a profibrogenic stimulus, respectively. In conclusion, this study provides a vast amount of information about transcriptomic changes induced during the progression and regression of NAFLD and identifies some relevant targets. Our results may help in the assessment of treatment efficacy, the discovery of unmet therapeutic targets, and the search for novel biomarkers. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The role of microRNAs in understanding sex-based differences in Alzheimer's disease.

BACKGROUND: The incidence of Alzheimer's disease (AD)-the most frequent cause of dementia-is expected to increase as life expectancies rise across the globe. While sex-based differences in AD have previously been described, there remain uncertainties regarding any association between sex and disease-associated molecular mechanisms. Studying sex-specific expression profiles of regulatory factors such as microRNAs (miRNAs) could contribute to more accurate disease diagnosis and treatment. METHODS: A systematic review identified six studies of microRNA expression in AD patients that incorporated information regarding the biological sex of samples in the Gene Expression Omnibus repository. A differential microRNA expression analysis was performed, considering disease status and patient sex. Subsequently, results were integrated within a meta-analysis methodology, with a functional enrichment of meta-analysis results establishing an association between altered miRNA expression and relevant Gene Ontology terms. RESULTS: Meta-analyses of miRNA expression profiles in blood samples revealed the alteration of sixteen miRNAs in female and 22 miRNAs in male AD patients. We discovered nine miRNAs commonly overexpressed in both sexes, suggesting a shared miRNA dysregulation profile. Functional enrichment results based on miRNA profiles revealed sex-based differences in biological processes; most affected processes related to ubiquitination, regulation of different kinase activities, and apoptotic processes in males, but RNA splicing and translation in females. Meta-analyses of miRNA expression profiles in brain samples revealed the alteration of six miRNAs in female and four miRNAs in male AD patients. We observed a single underexpressed miRNA in female and male AD patients (hsa-miR-767-5p); however, the functional enrichment analysis for brain samples did not reveal any specifically affected biological process. CONCLUSIONS: Sex-specific meta-analyses supported the detection of differentially expressed miRNAs in female and male AD patients, highlighting the relevance of sex-based information in biomedical data. Further studies on miRNA regulation in AD patients should meet the criteria for comparability and standardization of information.

Alzheimer's disease (AD)­a neurodegenerative disease mainly affecting older patients­is characterized by cognitive deterioration, memory loss, and progressive incapacitation in daily activities. While AD affects almost twice as many females as males, and cognitive deterioration and brain atrophy develop more rapidly in females, the biological causes of these differences remain poorly understood. MicroRNAs (miRNAs) regulate gene expression and impact a wide variety of biological processes; therefore, studying the differential expression of miRNAs in female and male AD patients could contribute to a better understanding of the disease. We reviewed studies of miRNA expression in female and male AD patients and integrated results using a meta-analysis methodology and then identified those genes regulated by the altered miRNAs to establish an association with biological processes. We found 16 (females) and 22 (males) miRNAs altered in the blood of AD patients. Functional enrichment revealed sex-based differences in the affected altered biological processes­protein modification and degradation and cell death in male AD patients and RNA processing in female AD patients. A similar analysis in the brains of AD patients revealed six (females) and four (males) miRNAs with altered expression; however, our analysis failed to highlight any specifically altered biological processes. Overall, we highlight the sex-based differential expression of miRNAs (and biological processes affected) in the blood and brain of AD patients.

A Comprehensive Transcriptional Signature in Pancreatic Ductal Adenocarcinoma Reveals New Insights into the Immune and Desmoplastic Microenvironments.

Pancreatic ductal adenocarcinoma (PDAC) prognoses and treatment responses remain devastatingly poor due partly to the highly heterogeneous, aggressive, and immunosuppressive nature of this tumor type. The intricate relationship between the stroma, inflammation, and immunity remains vaguely understood in the PDAC microenvironment. Here, we performed a meta-analysis of stroma-, and immune-related gene expression in the PDAC microenvironment to improve disease prognosis and therapeutic development. We selected 21 PDAC studies from the Gene Expression Omnibus and ArrayExpress databases, including 922 samples (320 controls and 602 cases). Differential gene enrichment analysis identified 1153 significant dysregulated genes in PDAC patients that contribute to a desmoplastic stroma and an immunosuppressive environment (the hallmarks of PDAC tumors). The results highlighted two gene signatures related to the immune and stromal environments that cluster PDAC patients into high- and low-risk groups, impacting patients' stratification and therapeutic decision making. Moreover, HCP5, SLFN13, IRF9, IFIT2, and IFI35 immune genes are related to the prognosis of PDAC patients for the first time.

A deep transcriptome meta-analysis reveals sex differences in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS), a chronic auto-immune, inflammatory, and degenerative disease of the central nervous system, affects both males and females; however, females suffer from a higher risk of developing MS (2-3:1 ratio relative to males). The precise sex-based factors influencing risk of MS are currently unknown. Here, we explore the role of sex in MS to identify molecular mechanisms underlying observed MS sex differences that may guide novel therapeutic approaches tailored for males or females. METHODS: We performed a rigorous and systematic review of genome-wide transcriptome studies of MS that included patient sex data in the Gene Expression Omnibus and ArrayExpress databases following PRISMA statement guidelines. For each selected study, we analyzed differential gene expression to explore the impact of the disease in females (IDF), in males (IDM) and our main goal: the sex differential impact of the disease (SDID). Then, for each scenario (IDF, IDM and SDID) we performed 2 meta-analyses in the main tissues involved in the disease (brain and blood). Finally, we performed a gene set analysis in brain tissue, in which a higher number of genes were dysregulated, to characterize sex differences in biological pathways. RESULTS: After screening 122 publications, the systematic review provided a selection of 9 studies (5 in blood and 4 in brain tissue) with a total of 474 samples (189 females with MS and 109 control females; 82 males with MS and 94 control males). Blood and brain tissue meta-analyses identified, respectively, 1 (KIR2DL3) and 13 (ARL17B, CECR7, CEP78, IFFO2, LOC401127, NUDT18, RNF10, SLC17A5, STMP1, TRAF3IP2-AS1, UBXN2B, ZNF117, ZNF488) MS-associated genes that differed between males and females (SDID comparison). Functional analyses in the brain revealed different altered immune patterns in females and males (IDF and IDM comparisons). The pro-inflammatory environment and innate immune responses related to myeloid lineage appear to be more affected in females, while adaptive responses associated with the lymphocyte lineage in males. Additionally, females with MS displayed alterations in mitochondrial respiratory chain complexes, purine, and glutamate metabolism, while MS males displayed alterations in stress response to metal ion, amine, and amino acid transport. CONCLUSION: We found transcriptomic and functional differences between MS males and MS females (especially in the immune system), which may support the development of new sex-based research of this disease. Our study highlights the importance of understanding the role of biological sex in MS to guide a more personalized medicine.

Deciphering the sex bias in housekeeping gene expression in adipose tissue: a comprehensive meta-analysis of transcriptomic studies.

BACKGROUND: As the housekeeping genes (HKG) generally involved in maintaining essential cell functions are typically assumed to exhibit constant expression levels across cell types, they are commonly employed as internal controls in gene expression studies. Nevertheless, HKG may vary gene expression profile according to different variables introducing systematic errors into experimental results. Sex bias can indeed affect expression display, however, up to date, sex has not been typically considered as a biological variable. METHODS: In this study, we evaluate the expression profiles of six classical housekeeping genes (four metabolic: GAPDH, HPRT, PPIA, and UBC, and two ribosomal: 18S and RPL19) to determine expression stability in adipose tissues (AT) of Homo sapiens and Mus musculus and check sex bias and their overall suitability as internal controls. We also assess the expression stability of all genes included in distinct whole-transcriptome microarrays available from the Gene Expression Omnibus database to identify sex-unbiased housekeeping genes (suHKG) suitable for use as internal controls. We perform a novel computational strategy based on meta-analysis techniques to identify any sexual dimorphisms in mRNA expression stability in AT and to properly validate potential candidates. RESULTS: Just above half of the considered studies informed properly about the sex of the human samples, however, not enough female mouse samples were found to be included in this analysis. We found differences in the HKG expression stability in humans between female and male samples, with females presenting greater instability. We propose a suHKG signature including experimentally validated classical HKG like PPIA and RPL19 and novel potential markers for human AT and discarding others like the extensively used 18S gene due to a sex-based variability display in adipose tissue. Orthologs have also been assayed and proposed for mouse WAT suHKG signature. All results generated during this study are readily available by accessing an open web resource ( https://bioinfo.cipf.es/metafun-HKG ) for consultation and reuse in further studies. CONCLUSIONS: This sex-based research proves that certain classical housekeeping genes fail to function adequately as controls when analyzing human adipose tissue considering sex as a variable. We confirm RPL19 and PPIA suitability as sex-unbiased human and mouse housekeeping genes derived from sex-specific expression profiles, and propose new ones such as RPS8 and UBB.

Housekeeping genes (HKG) are involved in the maintenance of essential cellular functions. They usually present constant expression levels and are relevant because of their usefulness as internal controls in gene expression studies. However, HKG can vary the gene expression profile depending on different variables such as sex, introducing errors in the experimental results. In this study, we have performed an exhaustive systematic review and applied a massive analysis of expression data to check which HKG presents this bias and which do not. The results confirm that certain classical HKG do not perform adequately as controls when analyzing human adipose tissue considering sex as a variable. We further confirm the suitability of RPL19 and PPIA as human and mouse HKG without sex bias derived from sex-specific expression profiles, and propose new ones such as RPS8 and UBB. These results will be of great use in upcoming studies where expression data need to be normalized without the inclusion of sex bias.

Unveiling sex-based differences in Parkinson's disease: a comprehensive meta-analysis of transcriptomic studies.

BACKGROUND: In recent decades, increasing longevity (among other factors) has fostered a rise in Parkinson's disease incidence. Although not exhaustively studied in this devastating disease, the impact of sex represents a critical variable in Parkinson's disease as epidemiological and clinical features differ between males and females. METHODS: To study sex bias in Parkinson's disease, we conducted a systematic review to select sex-labeled transcriptomic data from three relevant brain tissues: the frontal cortex, the striatum, and the substantia nigra. We performed differential expression analysis on each study chosen. Then we summarized the individual differential expression results with three tissue-specific meta-analyses and a global all-tissues meta-analysis. Finally, results from the meta-analysis were functionally characterized using different functional profiling approaches. RESULTS: The tissue-specific meta-analyses linked Parkinson's disease to the enhanced expression of MED31 in the female frontal cortex and the dysregulation of 237 genes in the substantia nigra. The global meta-analysis detected 15 genes with sex-differential patterns in Parkinson's disease, which participate in mitochondrial function, oxidative stress, neuronal degeneration, and cell death. Furthermore, functional analyses identified pathways, protein-protein interaction networks, and transcription factors that differed by sex. While male patients exhibited changes in oxidative stress based on metal ions, inflammation, and angiogenesis, female patients exhibited dysfunctions in mitochondrial and lysosomal activity, antigen processing and presentation functions, and glutamic and purine metabolism. All results generated during this study are readily available by accessing an open web resource ( http://bioinfo.cipf.es/metafun-pd/ ) for consultation and reuse in further studies. CONCLUSIONS: Our in silico approach has highlighted sex-based differential mechanisms in typical Parkinson Disease hallmarks (inflammation, mitochondrial dysfunction, and oxidative stress). Additionally, we have identified specific genes and transcription factors for male and female Parkinson Disease patients that represent potential candidates as biomarkers to diagnosis.

Minimal hepatic encephalopathy is associated to alterations in eye movements.

Minimal hepatic encephalopathy (MHE) is diagnosed using PHES battery, but other tests are more sensitive, and a simple tool for early MHE detection is required. Assessment of saccadic eye movements is useful for early detection of cognitive alterations in different pathologies. We characterized the alterations in saccadic eye movements in MHE patients, its relationship with cognitive alterations and its utility for MHE diagnosis. One-hundred and eighteen cirrhotic patients (86 without and 32 with MHE) and 35 controls performed PHES and Stroop test and an eye movements test battery by OSCANN system: visual saccades, antisaccades, memory-guided saccades, fixation test and smooth pursuit. We analyzed 177 parameters of eye movements, assessed their diagnostic capacity for MHE, and correlated with cognitive alterations. MHE patients showed alterations in 56 of the 177 variables of eye movements compared to NMHE patients. MHE patients showed longer latencies and worse performance in most eye movements tests, which correlated with mental processing speed and attention impairments. The best correlations found were for antisaccades and memory-guided saccades, and some parameters in these tests could be useful for discriminating MHE and NMHE patients. Eye movements analysis could be a new, rapid, reliable, objective, and reproducible tool for early diagnose MHE.

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.

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.

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.

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.

Functional Signatures in Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis of Sex-Based Differences in Transcriptomic Studies.

While studies have established the existence of differences in the epidemiological and clinical patterns of lung adenocarcinoma between male and female patients, we know relatively little regarding the molecular mechanisms underlying such sex-based differences. In this study, we explore said differences through a meta-analysis of transcriptomic data. We performed a meta-analysis of the functional profiling of nine public datasets that included 1366 samples from Gene Expression Omnibus and The Cancer Genome Atlas databases. Meta-analysis results from data merged, normalized, and corrected for batch effect show an enrichment for Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to the immune response, nucleic acid metabolism, and purinergic signaling. We discovered the overrepresentation of terms associated with the immune response, particularly with the acute inflammatory response, and purinergic signaling in female lung adenocarcinoma patients, which could influence reported clinical differences. Further evaluations of the identified differential biological processes and pathways could lead to the discovery of new biomarkers and therapeutic targets. Our findings also emphasize the relevance of sex-specific analyses in biomedicine, which represents a crucial aspect influencing biological variability in disease.

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.

Unveiling Sex-Based Differences in the Effects of Alcohol Abuse: A Comprehensive Functional Meta-Analysis of Transcriptomic Studies.

The abuse of alcohol, one of the most popular psychoactive substances, can cause several pathological and psychological consequences, including alcohol use disorder (AUD). An impaired ability to stop or control alcohol intake despite adverse health or social consequences characterize AUD. While AUDs predominantly occur in men, growing evidence suggests the existence of distinct cognitive and biological consequences of alcohol dependence in women. The molecular and physiological mechanisms participating in these differential effects remain unknown. Transcriptomic technology permits the detection of the biological mechanisms responsible for such sex-based differences, which supports the subsequent development of novel personalized therapeutics to treat AUD. We conducted a systematic review and meta-analysis of transcriptomics studies regarding alcohol dependence in humans with representation from both sexes. For each study, we processed and analyzed transcriptomic data to obtain a functional profile of pathways and biological functions and then integrated the resulting data by meta-analysis to characterize any sex-based transcriptomic differences associated with AUD. Global results of the transcriptomic analysis revealed the association of decreased tissue regeneration, embryo malformations, altered intracellular transport, and increased rate of RNA and protein replacement with female AUD patients. Meanwhile, our analysis indicated that increased inflammatory response and blood pressure and a reduction in DNA repair capabilities are associated with male AUD patients. In summary, our functional meta-analysis of transcriptomic studies provides evidence for differential biological mechanisms of AUD patients of differing sex.

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.

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.

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.

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.