Investigating the mechanisms underlying resistance to chemoterapy and to CRISPR-Cas9 in cancer cell lines.

Cancer is one of the major causes of death worldwide and the development of multidrug resistance (MDR) in cancer cells is the principal cause of chemotherapy failure. To gain insights into the specific mechanisms of MDR in cancer cell lines, we developed a novel method for the combined analysis of recently published datasets on drug sensitivity and CRISPR loss-of-function screens for the same set of cancer cell lines. For our analysis, we first selected cell lines that consistently exhibit drug resistance across several classes of compounds. We then identified putative resistance genes for each class of compound and used inferred gene regulatory networks (GRNs) to study possible mechanisms underlying the development of MDR in the identified cancer cell lines. We show that the same method of analysis can also be used to identify cell lines that consistently exhibit resistance to the gene knockout effect of the CRISPR-Cas9 technique and to study the possible underlying mechanisms. In the GRN associated to the drug resistant cell lines, we identify genes previously associated with resistance (UHMK1, RALYL, MGST3, USP9X, and ESRG), genes for which an indirect association can be identified (SPINK13, LINC00664, MRPL38, and EMILIN3), and genes that are found to be overexpressed in non-resistant cancer cell lines (MRPL38, EMILIN3 and RALYL). In the GRNs associated to the CRISPR-Cas9 resistance mechanism, none of the identified genes has been previously reported in the admittedly sparse literature on the subject. However, some of these genes have a common role: APBB2, RUNX1T1, ZBTB7C, and ISX regulate transcription, while APBB2, BTG3, ZBTB7C, SZRD1 and LEF1 have a function in regulating proliferation, suggesting a role for these two pathways. While our results are specific for the lung cancer cell lines we selected for this work, our method of analysis can be applied to cell lines from other tissues and for which the required data is available.

Strengths and limitations of non-disclosive data analysis: a comparison of breast cancer survival classifiers using VisualSHIELD.

Preserving data privacy is an important concern in the research use of patient data. The DataSHIELD suite enables privacy-aware advanced statistical analysis in a federated setting. Despite its many applications, it has a few open practical issues: the complexity of hosting a federated infrastructure, the performance penalty imposed by the privacy-preserving constraints, and the ease of use by non-technical users. In this work, we describe a case study in which we review different breast cancer classifiers and report our findings about the limits and advantages of such non-disclosive suite of tools in a realistic setting. Five independent gene expression datasets of breast cancer survival were downloaded from Gene Expression Omnibus (GEO) and pooled together through the federated infrastructure. Three previously published and two newly proposed 5-year cancer-free survival risk score classifiers were trained in a federated environment, and an additional reference classifier was trained with unconstrained data access. The performance of these six classifiers was systematically evaluated, and the results show that i) the published classifiers do not generalize well when applied to patient cohorts that differ from those used to develop them; ii) among the methods we tried, the classification using logistic regression worked better on average, closely followed by random forest; iii) the unconstrained version of the logistic regression classifier outperformed the federated version by 4% on average. Reproducibility of our experiments is ensured through the use of VisualSHIELD, an open-source tool that augments DataSHIELD with new functions, a standardized deployment procedure, and a simple graphical user interface.

iColon, a patient-focused mobile application for perioperative care in colorectal surgery: Results from 444 patients.

AIM: The aim of this study is to assess if a patient-focused mobile application can increase compliance with active Enhanced Recovery After Surgery (ERAS) items and thereby improve surgery-related outcomes and patient satisfaction. METHOD: This is a prospective observational study of patients admitted for elective colorectal surgery, under the ERAS protocol, and having access to the mobile application iColon during all perioperative phases. RESULTS: The 444 participants were included in the study. The overall adherence to the use of iColon was 62.4%. The overall adherence to active ERAS items was 74.1%. Adherence to the use of iColon significantly impacted adherence to active ERAS items. The use of the application was negatively related with factors such as age, type of disease, and postoperative complications. In the postdischarge phase, low adherence to active ERAS items typically indicates an increased likelihood of readmission; however, the use of iColon correlated significantly with a reduction in the 30-day readmission rate. A survey regarding patient satisfaction and confidence in using iColon resulted in positive feedback in more than 94% of cases, while 92.7% reported better quality of care. CONCLUSION: Our findings suggest that digital health tools are beneficial and effective in the follow up of patients after early discharge. Our mobile application, iColon, represents user-friendly technology that is well-accepted. It has real-world implications in increasing adherence to active ERAS items, which results in an improvement in perceived quality of care by its users.

MOUSSE: Multi-Omics Using Subject-Specific SignaturEs.

High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.

Depression-Associated Gene Negr1-Fgfr2 Pathway Is Altered by Antidepressant Treatment.

The Negr1 gene has been significantly associated with major depression in genetic studies. Negr1 encodes for a cell adhesion molecule cleaved by the protease Adam10, thus activating Fgfr2 and promoting neuronal spine plasticity. We investigated whether antidepressants modulate the expression of genes belonging to Negr1-Fgfr2 pathway in Flinders sensitive line (FSL) rats, in a corticosterone-treated mouse model of depression, and in mouse primary neurons. Negr1 and Adam10 were the genes mostly affected by antidepressant treatment, and in opposite directions. Negr1 was down-regulated by escitalopram in the hypothalamus of FSL rats, by fluoxetine in the hippocampal dentate gyrus of corticosterone-treated mice, and by nortriptyline in hippocampal primary neurons. Adam10 mRNA was increased by nortriptyline administration in the hypothalamus, by escitalopram in the hippocampus of FSL rats, and by fluoxetine in mouse dorsal dentate gyrus. Similarly, nortriptyline increased Adam10 expression in hippocampal cultures. Fgfr2 expression was increased by nortriptyline in the hypothalamus of FSL rats and in hippocampal neurons. Lsamp, another IgLON family protein, increased in mouse dentate gyrus after fluoxetine treatment. These findings suggest that Negr1-Fgfr2 pathway plays a role in the modulation of synaptic plasticity induced by antidepressant treatment to promote therapeutic efficacy by rearranging connectivity in corticolimbic circuits impaired in depression.

rScudo: an R package for classification of molecular profiles using rank-based signatures.

SUMMARY: The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods. AVAILABILITY AND IMPLEMENTATION: The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.

Gene expression signature of antidepressant treatment response/non-response in Flinders Sensitive Line rats subjected to maternal separation.

Neurobiological underpinnings of treatment-resistant depression, a debilitating condition associated with significant functional impairment, have not been elucidated. Consequently, the aim of this study was to use animal models of response and resistance to antidepressant treatment, in an attempt to identify differences in associated transcriptional responses. Flinders Sensitive Line rats were subjected to maternal separation (MS) and chronically treated with Escitalopram or Nortriptyline. Antidepressants reduced immobility time in the forced swim test in non-MS rats, while lack of antidepressant behavioural response was observed in MS animals. We developed a novel bioinformatic algorithm that enabled identification of transcriptional signatures in hippocampus and pre-frontal cortex that discriminate vehicle- and antidepressant-treated subjects in both MS and non-MS rats. Functional annotation analysis showed that in antidepressant-responder rats the most enriched pathways included IQGAPs activation, toll-like receptor trafficking, energy metabolism, and regulation of endopeptidase activity. The analysis of interacting proteins implicated synaptic vesicles and neurotransmitter release, ubiquitin regulation, cytoskeleton organisation and carbohydrate metabolism. In contrast, in treatment-resistant MS rats, main expression changes were revealed in ribosomal proteins, inflammatory responses, transcriptional/epigenetic regulation, and small GTPases. Susceptibility signature shared Rtn1, Zdhhc5, Igsf6, and Sim1 genes with the latest depression GWAS meta-analysis, while antidepressant resistance signature shared Ctnnd1, Rbms3, Atp1a3, and Pla2r1 genes. In conclusion, this study demonstrated that distinct transcriptional signatures are associated with behavioural response or non-response to antidepressant treatment. The identification of genes involved in antidepressant response will increase the comprehension of the neurobiological underpinnings of treatment-resistant depression, thus contributing to identification of novel therapeutic targets.

Cross-disease analysis of Alzheimer's disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid diseases.

Evidence is accumulating that the main chronic diseases of aging Alzheimer's disease (AD) and type-2 diabetes mellitus (T2DM) share common pathophysiological mechanisms. This study aimed at applying systems biology approaches to increase the knowledge of the shared molecular pathways underpinnings of AD and T2DM. We analysed transcriptomic data of post-mortem AD and T2DM human brains to obtain disease signatures of AD and T2DM and combined them with protein-protein interaction information to construct two disease-specific networks. The overlapping AD/T2DM network proteins were then used to extract the most representative Gene Ontology biological process terms. The expression of genes identified as relevant was studied in two AD models, 3xTg-AD and ApoE3/ApoE4 targeted replacement mice. The present transcriptomic data analysis revealed a principal role for autophagy in the molecular basis of both AD and T2DM. Our experimental validation in mouse AD models confirmed the role of autophagy-related genes. Among modulated genes, Cyclin-Dependent Kinase Inhibitor 1B, Autophagy Related 16-Like 2, and insulin were highlighted. In conclusion, the present investigation revealed autophagy as the central dys-regulated pathway in highly co-morbid diseases such as AD and T2DM allowing the identification of specific genes potentially involved in disease pathophysiology which could become novel targets for therapeutic intervention.

Cross-species evidence from human and rat brain transcriptome for growth factor signaling pathway dysregulation in major depression.

An enhanced understanding of the pathophysiology of depression would facilitate the discovery of new efficacious medications. To this end, we examined hippocampal transcriptional changes in rat models of disease and in humans to identify common disease signatures by using a new algorithm for signature-based clustering of expression profiles. The tool identified a transcriptomic signature comprising 70 probesets able to discriminate depression models from controls in both Flinders Sensitive Line and Learned Helplessness animals. To identify disease-relevant pathways, we constructed an expanded protein network based on signature gene products and performed functional annotation analysis. We applied the same workflow to transcriptomic profiles of depressed patients. Remarkably, a 171-probesets transcriptional signature which discriminated depressed from healthy subjects was identified. Rat and human signatures shared the SCARA5 gene, while the respective networks derived from protein-based significant interactions with signature genes contained 25 overlapping genes. The comparison between the most enriched pathways in the rat and human signature networks identified a highly significant overlap (p-value: 3.85 × 10-6) of 67 terms including ErbB, neurotrophin, FGF, IGF, and VEGF signaling, immune responses and insulin and leptin signaling. In conclusion, this study allowed the identification of a hippocampal transcriptional signature of resilient or susceptible responses in rat MDD models which overlapped with gene expression alterations observed in depressed patients. These findings are consistent with a loss of hippocampal neural plasticity mediated by altered levels of growth factors and increased inflammatory responses causing metabolic impairments as crucial factors in the pathophysiology of MDD.

Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy.

Although the genetic basis of Duchenne muscular dystrophy has been known for almost thirty years, the cellular and molecular mechanisms characterizing the disease are not completely understood and an efficacious treatment remains to be developed. In this study we analyzed proteomics data obtained with the SomaLogic technology from blood serum of a cohort of patients and matched healthy subjects. We developed a workflow based on biomarker identification and network-based pathway analysis that allowed us to describe different deregulated pathways. In addition to muscle-related functions, we identified other biological processes such as apoptosis, signaling in the immune system and neurotrophin signaling as significantly modulated in patients compared with controls. Moreover, our network-based analysis identified the involvement of FoxO transcription factors as putative regulators of different pathways. On the whole, this study provided a global view of the molecular processes involved in Duchenne muscular dystrophy that are decipherable from serum proteome.

Consensus Clustering of temporal profiles for the identification of metabolic markers of pre-diabetes in childhood (EarlyBird 73).

In longitudinal clinical studies, methodologies available for the analysis of multivariate data with multivariate methods are relatively limited. Here, we present Consensus Clustering (CClust) a new computational method based on clustering of time profiles and posterior identification of correlation between clusters and predictors. Subjects are first clustered in groups according to a response variable temporal profile, using a robust consensus-based strategy. To discover which of the remaining variables are associated with the resulting groups, a non-parametric hypothesis test is performed between groups at every time point, and then the results are aggregated according to the Fisher method. Our approach is tested through its application to the EarlyBird cohort database, which contains temporal variations of clinical, metabolic, and anthropometric profiles in a population of 150 children followed-up annually from age 5 to age 16. Our results show that our consensus-based method is able to overcome the problem of the approach-dependent results produced by current clustering algorithms, producing groups defined according to Insulin Resistance (IR) and biological age (Tanner Score). Moreover, it provides meaningful biological results confirmed by hypothesis testing with most of the main clinical variables. These results position CClust as a valid alternative for the analysis of multivariate longitudinal data.

Integration of transcriptomic and genomic data suggests candidate mechanisms for APOE4-mediated pathogenic action in Alzheimer's disease.

Among the genetic factors known to increase the risk of late onset Alzheimer's diseases (AD), the presence of the apolipoproteine e4 (APOE4) allele has been recognized as the one with the strongest effect. However, despite decades of research, the pathogenic role of APOE4 in Alzheimer's disease has not been clearly elucidated yet. In order to investigate the pathogenic action of APOE4, we applied a systems biology approach to the analysis of transcriptomic and genomic data of APOE44 vs. APOE33 allele carriers affected by Alzheimer's disease. Network analysis combined with a novel technique for biomarker computation allowed the identification of an alteration in aging-associated processes such as inflammation, oxidative stress and metabolic pathways, indicating that APOE4 possibly accelerates pathological processes physiologically induced by aging. Subsequent integration with genomic data indicates that the Notch pathway could be the nodal molecular mechanism altered in APOE44 allele carriers with Alzheimer's disease. Interestingly, PSEN1 and APP, genes whose mutation are known to be linked to early onset Alzheimer's disease, are closely linked to this pathway. In conclusion, APOE4 role on inflammation and oxidation through the Notch signaling pathway could be crucial in elucidating the risk factors of Alzheimer's disease.

Systems view of adipogenesis via novel omics-driven and tissue-specific activity scoring of network functional modules.

The investigation of the complex processes involved in cellular differentiation must be based on unbiased, high throughput data processing methods to identify relevant biological pathways. A number of bioinformatics tools are available that can generate lists of pathways ranked by statistical significance (i.e. by p-value), while ideally it would be desirable to functionally score the pathways relative to each other or to other interacting parts of the system or process. We describe a new computational method (Network Activity Score Finder - NASFinder) to identify tissue-specific, omics-determined sub-networks and the connections with their upstream regulator receptors to obtain a systems view of the differentiation of human adipocytes. Adipogenesis of human SBGS pre-adipocyte cells in vitro was monitored with a transcriptomic data set comprising six time points (0, 6, 48, 96, 192, 384 hours). To elucidate the mechanisms of adipogenesis, NASFinder was used to perform time-point analysis by comparing each time point against the control (0 h) and time-lapse analysis by comparing each time point with the previous one. NASFinder identified the coordinated activity of seemingly unrelated processes between each comparison, providing the first systems view of adipogenesis in culture. NASFinder has been implemented into a web-based, freely available resource associated with novel, easy to read visualization of omics data sets and network modules.

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes.

Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multi-omics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.

Diversity of key players in the microbial ecosystems of the human body.

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different.

SCUDO: a tool for signature-based clustering of expression profiles.

SCUDO (Signature-based ClUstering for DiagnOstic purposes) is an online tool for the analysis of gene expression profiles for diagnostic and classification purposes. The tool is based on a new method for the clustering of profiles based on a subject-specific, as opposed to disease-specific, signature. Our approach relies on construction of a reference map of transcriptional signatures, from both healthy and affected subjects, derived from their respective mRNA or miRNA profiles. A diagnosis for a new individual can then be performed by determining the position of the individual's transcriptional signature on the map. The diagnostic power of our method has been convincingly demonstrated in an open scientific competition (SBV Improver Diagnostic Signature Challenge), scoring second place overall and first place in one of the sub-challenges.

Rank-based miRNA signatures for blood-based diagnosis of tuberculosis.

We describe a new signature definition and analysis method to be used as biomarker for blood-based diagnosis of tuberculosis. Our new approach is based on the construction of a reference map of transcriptional signatures of both healthy and affected individuals using circulating miRNA from a large number of subjects. Once such a map is available, the diagnosis for a new patient can be performed by observing the relative position on the map of his/her transcriptional signature. To demonstrate its efficacy for this specific application we report the results of the application of our method to published data sets of circulating miRNA. Two crucial features make this method an ideal candidate for large scale applications such as a mass screening tool, or for point-of-care diagnostics. Specifically, our method is minimally invasive because it works well with profiles of circulating miRNA. More importantly, it is robust with respect to lab-to-lab protocol variability, measurement errors and batch effects because it requires that only the relative ranking of miRNA species in a profile be accurate, not their absolute values.

Systems biology meets -omic technologies: novel approaches to biomarker discovery and companion diagnostic development.

The next generation of biomarkers and companion diagnostics will require the development of technologies capable of conjugating the advances in high-throughput techniques in biology with computational methods. Systems biology is poised to contribute through an integrated view, capturing the complexity of the system, both in terms of a collection of interacting molecular components and also in terms of multiple intersecting views. Following this system-centered view, novel approaches have been developed for the identification of signatures of both disease processes and drug modes of action with the promising perspectives of better diagnosis of disease and of the discovery of more efficacious and safe drugs. The application of systems biology to the development of companion diagnostics is very recent and to date a few pioneering steps have been made in this direction. In this review, we describe the ongoing studies and the potential developments in this area of research.

Rank-based miRNA signatures for early cancer detection.

We describe a new signature definition and analysis method to be used as biomarker for early cancer detection. Our new approach is based on the construction of a reference map of transcriptional signatures of both healthy and cancer affected individuals using circulating miRNA from a large number of subjects. Once such a map is available, the diagnosis for a new patient can be performed by observing the relative position on the map of his/her transcriptional signature. To demonstrate its efficacy for this specific application we report the results of the application of our method to published datasets of circulating miRNA, and we quantify its performance compared to current state-of-the-art methods. A number of additional features make this method an ideal candidate for large-scale use, for example, as a mass screening tool for early cancer detection or for at-home diagnostics. Specifically, our method is minimally invasive (because it works well with circulating miRNA), it is robust with respect to lab-to-lab protocol variability and batch effects (it requires that only the relative ranking of expression value of miRNA in a profile be accurate not their absolute values), and it is scalable to a large number of subjects. Finally we discuss the need for HPC capability in a widespread application of our or similar methods.

The central role of AMP-kinase and energy homeostasis impairment in Alzheimer's disease: a multifactor network analysis.

Alzheimer's disease is the most common cause of dementia worldwide, affecting the elderly population. It is characterized by the hallmark pathology of amyloid-ß deposition, neurofibrillary tangle formation, and extensive neuronal degeneration in the brain. Wealth of data related to Alzheimer's disease has been generated to date, nevertheless, the molecular mechanism underlying the etiology and pathophysiology of the disease is still unknown. Here we described a method for the combined analysis of multiple types of genome-wide data aimed at revealing convergent evidence interest that would not be captured by a standard molecular approach. Lists of Alzheimer-related genes (seed genes) were obtained from different sets of data on gene expression, SNPs, and molecular targets of drugs. Network analysis was applied for identifying the regions of the human protein-protein interaction network showing a significant enrichment in seed genes, and ultimately, in genes associated to Alzheimer's disease, due to the cumulative effect of different combinations of the starting data sets. The functional properties of these enriched modules were characterized, effectively considering the role of both Alzheimer-related seed genes and genes that closely interact with them. This approach allowed us to present evidence in favor of one of the competing theories about AD underlying processes, specifically evidence supporting a predominant role of metabolism-associated biological process terms, including autophagy, insulin and fatty acid metabolic processes in Alzheimer, with a focus on AMP-activated protein kinase. This central regulator of cellular energy homeostasis regulates a series of brain functions altered in Alzheimer's disease and could link genetic perturbation with neuronal transmission and energy regulation, representing a potential candidate to be targeted by therapy.