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• Flinders sensitive rats received maternal separation and antidepressant treatment. • Maternal separation hampered response of reduced immobility after antidepressants. • Transcriptional signatures discriminated controls from antidepressant treated rats. • Distinct brain transcriptional signatures are associated with treatment response. • Antidepressant response genes contribute to elucidate treatment-resistant depression. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein.Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team.The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams.Availability: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.Contact: gustavo@us.ibm.comSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Alzheimer’s disease is the most common cause of dementia worldwide, affecting the elderly population. It is characterized by the hallmark pathology of amyloid-β 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. [ABSTRACT FROM AUTHOR]

A novel algorithm for the identification of genetic networks from gene expression data is presented. Our approach is based on an ordinary differential equations (ODE) model of the network, and on an assumption of linearity around an equilibrium point of the cell machinery. Here we start by describing the application of NIR (Network Identification by multiple Regression) to a state-of-the-art in silico gene expression dataset provided by the Dialogue for Reverse Engineering Assessment and Methods 2 (DREAM2) reverse-engineering competition (challenge 4). Then we present NIRest (NIR with perturbation Estimate), a tool that builds upon the original NIR and extends its use to cases in which the generating perturbations are not known. The remarkable results obtained with in silico datasets support the validity of NIR and NIRest assumptions and the effectiveness of our approach. Comparison with other leading methods based on Bayesian networks and mutual information shows the performance advantage of NIRest, which at the same time provides directed and undirected versions of the inferred network and an estimate of the mode of action. [ABSTRACT FROM AUTHOR]

This paper focuses on the inverse problem of predicting inputs from measured outputs in the context of linear systems in steady-state. For system identification, we propose forward network identification regression (FNIR) and experimental planning involving simultaneously perturbing more than a single gene concentration using D-optimal designs. The proposed methods are compared with alternatives using simulation and data sets motivated by the SOS pathway for Escherichia coli bacteria. Findings include that the optimal experimental planning can likely improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks. Topics for further research are also suggested in this paper. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes methods for forward and inverse system modeling using Bayesian and least squares regression. These methods are based on both space-filling design criteria for multiple response problems and linear optimality criteria focusing on D-optimality. Modeling with and without the constant term is considered motivated by the case study application of genetic network modeling. We propose extended one-factor-at-a-time experimentation followed by augmentation of next stage design which offers biologists simplicity. Results are illustrated both numerical examples, a test problem from the literature, and a case study motivated by an real world biological research related to genetic network modeling. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Motivation: Sequence alignment is one of the most popular tools of modern biology. NCBI's PSI-BLAST utilizes iterative model building in order to better detect distant homologs with greater sensitivity than non-iterative BLAST. However, PSI-BLAST's performance is limited by the fact that it relies on deterministic alignments. Using a semi-probabilistic alignment scheme such as Hybrid alignment should allow for better informed model building and improved identification of homologous sequences, particularly remote homologs.Results: We have built a new version of the tool in which the Smith-Waterman alignment algorithm core is replaced by the hybrid alignment algorithm. The favorable statistical properties of the hybrid algorithm allow the introduction of position-specific gap penalties in Hybrid PSI-BLAST. This improves the position-specific modeling of protein families and results in an overall improvement of performance.Availability: Source code is freely available for download at http://bioserv.mps.ohio-state.edu/HybridPSI, implemented in C and supported on linux.Contact: bundschuh@mps.ohio-state.eduSupplementary information:Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Understanding the complexity of the cellular machinery represents a grand challenge in molecular biology. To contribute to the deconvolution of this complexity, a novel inference algorithm based on linear ordinary differential equations is proposed, based solely on high-throughput gene expression data. The algorithm can infer (i) gene–gene interactions from steady state expression profiles and (ii) mode-of-action of the components that can trigger changes in the system. Results demonstrate that the proposed algorithm can identify both information with high performances, thus overcoming the limitation of current algorithms that can infer reliably only one. [ABSTRACT FROM AUTHOR]

The article reports on the adaptation of the general framework to run two representative applications on our Organic Grid prototype: the basic local alignment search tool of National Center for Biotechnology Information in Bethesda, Maryland, and the Cannon's algorithm for matrix multiplication. The way computation is organized on the Organic Grid is determined completely on agent behavior. Computation and scheduling are decoupled from the underlying grid infrastructure through encapsulating application code and scheduling functionality into mobile agents.

Desktop grids have been used to perform some of the largest computations in the world and have the potential to grow by several more orders of magnitude. However, current approaches to utilizing desktop resources require either centralized servers or extensive knowledge of the underlying system, limiting their scalability. We propose a new design for desktop grids that relies on a self-organizing, fully decentralized approach to the organization of the computation. Our approach, called the organic grid, is a radical departure from current approaches and is modeled after the way complex biological systems organize themselves. Similar to current desktop grids, a large computational task is broken down into sufficiently small subtasks. Each subtask is encapsulated into a mobile agent, which is then released on the grid and discovers computational resources using autonomous behavior. In the process of "colonization" of available resources, the judicious design of the agent behavior produces the emergence of crucial properties of the computation that can be tailored to specific classes of applications. We demonstrate this concept with a reduced-scale proof-of-concept implementation that executes a data-intensive independent-task application on a set of heterogeneous, geographically distributed machines. We present a detailed exploration of the design space of our system and a performance evaluation of our implementation using metrics appropriate for assessing self-organizing desktop grids. [ABSTRACT FROM AUTHOR]

We discuss the efficient implementation of a collective operation called reduce-scatter, which is defined in the MPI standard. The reduce-scatter is equivalent to the combination of a reduction on vectors of length n with a scatter of the resulting n-vector to all processors.We describe the implementation issues and the performance characterization of two recently proposed algorithms for the reduce-scatter that have been proven to be highly efficient in theory under the assumption of fully connected parallel system.A performance comparison with existing mainstream implementations of the operation is presented which confirms the practical advantage of the new algorithms. Experiments show that the two algorithms have different characteristics which make them complementary in providing a performance gain over standard algorithms.Our study has been carried out on two different platforms: an SP2 and a Myrinet interconnected cluster of Pentium PRO. However, most of the results reported here are not specific for either MPI or the platforms used, and they hold in general for any message passing programming system. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Stem-cell functions require activation of stem-cell-intrinsic transcriptional programs and extracellular interaction with a niche microenvironment. How the transcriptional machinery controls residency of stem cells in the niche is unknown. Here we show that Id proteins coordinate stem-cell activities with anchorage of neural stem cells (NSCs) to the niche. Conditional inactivation of three Id genes in NSCs triggered detachment of embryonic and postnatal NSCs from the ventricular and vascular niche, respectively. The interrogation of the gene modules directly targeted by Id deletion in NSCs revealed that Id proteins repress bHLH-mediated activation of Rap1GAP, thus serving to maintain the GTPase activity of RAP1, a key mediator of cell adhesion. Preventing the elevation of the Rap1GAP level countered the consequences of Id loss on NSC-niche interaction and stem-cell identity. Thus, by preserving anchorage of NSCs to the extracellular environment, Id activity synchronizes NSC functions to residency in the specialized niche. [ABSTRACT FROM AUTHOR]

Simple Summary: Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]