Construction Of Gene Networks Research Articles

A Blood-Based Immune Gene Signature with Prognostic Significance in Localized Prostate Cancer.

Prostate cancer (PCa) is one of the most common male cancers worldwide and one of the deadliest if unsuccessfully treated. Τhe need for reliable, easily accessible immune-related molecular biomarkers that could be combined with clinically defined criteria, including PSA and Gleason score, to accurately predict PCa patients' clinical outcomes is emerging. Herein, we describe for the first time a blood-identified immune-related gene signature comprising eight upregulated multi-functional genes associated with poor prognosis. Next-generation sequencing (NGS) analysis of PCa patients' peripheral blood samples revealed a more than three-fold upregulation of each of the eight genes as compared to samples originating from healthy donors. The construction of gene and protein interaction networks revealed different extents of the functional implications of these genes in the regulation of cell proliferation and immune responses. Analysis of the available data from The Cancer Genome Atlas (TCGA) regarding gene expression and survival of prostate adenocarcinoma (PRAD) and pan-cancer (PANCAN) patients revealed that intra-tumoral upregulation of this eight-gene signature (8-GS) was associated with poor 5-year progression-free intervals in PCa patients, even in those with high Gleason scores, and also with an unfavorable prognosis for cancer patients irrespective of the cancer type and even in the early stages. These observations suggest that further investigation of the 8-GS prospectively in randomized clinical trials, in which clinical benefit in terms of evaluating time to disease progression can be assessed, is warranted.

Altered Expression of CYSLTR1 is Associated With Adverse Clinical Outcome in Triple Negative Breast Tumors: An In Silico Approach.

Triple negative breast cancer (TNBC) has high relapse rates due to dysregulated inflammatory signaling pathways and significant changes in the tumor microenvironment, probably influencing the failure of several therapies. The Cysteinyl Leukotriene Receptor 1 (CYSLTR1), a leukotriene modulator of inflammation, has been shown to play an important role in cancer pathogenesis and survival but few studies have been reported on its role in breast cancer. The present work was conducted using publicly available platforms that have omics data to assess the clinical potential of CYSLTR1 expression and its prognostic validation in large cohorts of samples from breast cancer patients. Web platforms containing clinical information, RNA-seq and protein data were selected to perform in silico analyses of the potential marker CYLSTR1. Added together, the platforms included modules for correlation, expression, prognosis, drug interactions, and construction of gene networks. Kaplan-Meier curves revealed that reduced levels of CYSLTR1 corresponded to an unfavorable outcome for overall survival (p<0.005) as well as relapse-free survival (p<0.001) in the basal subtype. Additionally, CYSLTR1 was downregulated in breast tumor samples compared to adjacent healthy tissue (p<0.01) and the basal subtype exhibited the lowest expression of CYSLTR1 relative to the other subtypes (p<0.0001). Furthermore, gene networking analysis showed strong associations of CYSLTR1 with two protein-coding genes (P2RY10 and XCR1) when tested on a TNBC dataset. Our data highlighted the relevance of CYSLTR1 since it may play an important role in TNBC therapy. However, further in vitro and in vivo studies should be directed towards validating our findings in an effort to improve our understanding of TNBC pathology.

Network analysis of hepatocellular carcinoma liquid biopsies augmented by single-cell sequencing data.

Liquid biopsy, the analysis of body fluids, represents a promising approach for disease diagnosis and prognosis with minimal intervention. Sequencing cell-free RNA derived from liquid biopsies has been very promising for the diagnosis of several diseases. Cancer research, in particular, has emerged as a prominent candidate since early diagnosis has been shown to be a critical determinant of disease prognosis. Although high-throughput analysis of liquid biopsies has uncovered many differentially expressed genes in the context of cancer, the functional connection between these genes is not investigated in depth. An important approach to remedy this issue is the construction of gene networks which describes the correlation patterns between different genes, thereby allowing to infer their functional organization. In this study, we aimed at characterizing extracellular transcriptome gene networks of hepatocellular carcinoma patients compared to healthy controls. Our analysis revealed a number of genes previously associated with hepatocellular carcinoma and uncovered their association network in the blood. Our study thus demonstrates the feasibility of performing gene co-expression network analysis from cell-free RNA data and its utility in studying hepatocellular carcinoma. Furthermore, we augmented cell-free RNA network analysis with single-cell RNA sequencing data which enables the contextualization of the identified network modules with cell-type specific transcriptomes from the liver.

Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.

Independent Component Analysis is a matrix factorization method for data dimension reduction. ICA has been widely applied for the analysis of transcriptomic data for blind separation of biological, environmental, and technical factors affecting gene expression. The study aimed to analyze the publicly available esophageal cancer data using the ICA for identification and comprehensive analysis of reproducible signaling pathways and molecular signatures involved in this cancer type. In this study, four independent esophageal cancer transcriptomic datasets from GEO databases were used. A bioinformatics tool « BiODICA—Independent Component Analysis of Big Omics Data» was applied to compute independent components (ICs). Gene Set Enrichment Analysis (GSEA) and ToppGene uncovered the most significantly enriched pathways. Construction and visualization of gene networks and graphs were performed using the Cytoscape, and HPRD database. The correlation graph between decompositions into 30 ICs was built with absolute correlation values exceeding 0.3. Clusters of components—pseudocliques were observed in the structure of the correlation graph. The top 1,000 most contributing genes of each ICs in the pseudocliques were mapped to the PPI network to construct associated signaling pathways. Some cliques were composed of densely interconnected nodes and included components common to most cancer types (such as cell cycle and extracellular matrix signals), while others were specific to EC. The results of this investigation may reveal potential biomarkers of esophageal carcinogenesis, functional subsystems dysregulated in the tumor cells, and be helpful in predicting the early development of a tumor.

Conserved and species-specific chromatin remodeling and regulatory dynamics during mouse and chicken limb bud development

Chromatin remodeling and genomic alterations impact spatio-temporal regulation of gene expression, which is central to embryonic development. The analysis of mouse and chicken limb development provides important insights into the morphoregulatory mechanisms, however little is known about the regulatory differences underlying their morphological divergence. Here, we identify the underlying shared and species-specific epigenomic and genomic variations. In mouse forelimb buds, we observe striking synchrony between the temporal dynamics of chromatin accessibility and gene expression, while their divergence in chicken wing buds uncovers species-specific regulatory heterochrony. In silico mapping of transcription factor binding sites and computational footprinting establishes the developmental time-restricted transcription factor-DNA interactions. Finally, the construction of target gene networks for HAND2 and GLI3 transcriptional regulators reveals both conserved and species-specific interactions. Our analysis reveals the impact of genome evolution on the regulatory interactions orchestrating vertebrate limb bud morphogenesis and provides a molecular framework for comparative Evo-Devo studies.

Network pharmacology-based analysis for unraveling potential cancer-related molecular targets of Egyptian propolis phytoconstituents accompanied with molecular docking and in vitro studies.

Cancer is one of the predominant causes of death worldwide. The new trend nowadays is to exploit natural products with the hope of developing new anticancer agents with fewer side effects. Propolis is one of these natural products which showed effectiveness in cancer treatment. The aim of this study is to understand the multi-level mechanism of action of propolis constituents in cancer treatment using an integrated approach of network pharmacology-based analysis, molecular docking and in vitro cytotoxicity testing. An inhouse database of chemical constituents from Egyptian propolis was compiled and assessed for its ADME properties using the QikProp module in the Schrodinger software. STITCH, UniProt, STRING, KEGG and DAVID databases were used for construction of constituent-target gene, gene-pathway, and constituent-target gene-pathway networks with the aid of Cytoscape 3.8.2. The network pharmacology-based analysis showed that the hit propolis constituents related to cancer targets were genistein, luteolin, benzoic acid, quercetin and vanillic acid, whereas the main cancer-associated targets were CYP1A1, CYP19A1, ESR1, NOS3, CASP3 and AKT1. Twenty-four cancer-related pathways were recognized where the most enriched ones were pathways in cancer and estrogen signaling pathway. The most enriched biological processes involved in the mechanism of action of propolis constituents in cancer treatment were negative regulation of the apoptotic process and the metabolic process and negative regulation of cellular glucuronidation. Molecular docking analysis of the top hit compounds against the most enriched target proteins in the constructed networks was carried out using the Maestro interface of the Schrodinger software. Among hit compounds, quercetin and genistein exhibited the most stabilized interaction. Finally, confirmation of the potential anticancer activity of propolis was assured by in vitro cytotoxicity testing of propolis extract on human prostate cancer (DU-145), breast adenocarcinoma (MCF-7) and colorectal adenocarcinoma (Caco-2) cell lines. This study presents deeper insights about propolis molecular mechanisms of action in cancer for the first time using an integrated approach of network pharmacology, molecular docking and in vitro testing.

Limit cycles in models of circular gene networks regulated by negative feedback loops

BackgroundThe regulatory feedback loops that present in structural and functional organization of molecular-genetic systems and the phenomenon of the regulatory signal delay, a time period between the moment of signal reception and its implementation, provide natural conditions for complicated dynamic regimes in these systems. The delay phenomenon at the intracellular level is a consequence of the matrix principle of data transmission, implemented through the rather complex processes of transcription and translation.However, the rules of the influence of system structure on system dynamics are not clearly understood. Knowledge of these rules is particularly important for construction of synthetic gene networks with predetermined properties.ResultsWe study dynamical properties of models of simplest circular gene networks regulated by negative feedback mechanisms. We have shown existence and stability of oscillating trajectories (cycles) in these models. Two algorithms of construction and localization of these cycles have been proposed. For one of these models, we have solved an inverse problem of parameters identification.ConclusionsThe modeling results demonstrate that non-stationary dynamics in the models of circular gene networks with negative feedback loops is achieved by a high degree of non-linearity of the mechanism of the autorepressor influence on its own expression, by the presence of regulatory signal delay, the value of which must exceed a certain critical value, and transcription/translation should be initiated from a sufficiently strong promoter/Shine-Dalgarno site. We believe that the identified patterns are key elements of the oscillating construction design.

Characterization of a five-microRNA signature as a prognostic biomarker for esophageal squamous cell carcinoma

This study aims to identify a miRNAs signature for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients. MiRNA expression profiles and corresponding clinical information of 119 ESCC patients were obtained from NCBI GEO and used as the training set. Differentially expressed miRNAs (DEmiRNAs) were screened between early-stage and late-stage samples. Cox regression analysis, recursive feature elimination (RFE)-support vector machine (SVM) algorithm, and LASSO Cox regression model were used to identify prognostic miRNAs and consequently build a prognostic scoring model. Moreover, promising target genes of these prognostic miRNAs were predicted followed by construction of miRNA-target gene networks. Functional relevance of predicted target genes of these prognostic miRNAs in ESCC was analyzed by performing function enrichment analyses. There were 46 DEmiRNAs between early-stage and late-stage samples in the training set. A risk score model based on five miRNAs was built. The five-miRNA risk score could classify the training set into a high-risk group and a low-risk group with significantly different OS time. Risk stratification ability of the five-miRNA risk score was successfully validated on an independent set from the Cancer Genome Atlas (TCGA). Various biological processes and pathways were identified to be related to these miRNAs, such as Wnt signaling pathway, inflammatory mediator regulation of TRP channels pathway, and estrogen signaling pathway. The present study suggests a pathological stage-related five-miRNA signature that may have clinical implications in predicting prognosis of ESCC patients.

Gene Modules Co-regulated with Biosynthetic Gene Clusters for Allelopathy between Rice and Barnyardgrass.

Allelopathy is a central process in crop–weed interactions and is mediated by the release of allelochemicals that result in adverse growth effects on one or the other plant in the interaction. The genomic mechanism for the biosynthesis of many critical allelochemicals is unknown but may involve the clustering of non-homologous biosynthetic genes involved in their formation and regulatory gene modules involved in controlling the coordinated expression within these gene clusters. In this study, we used the transcriptomes from mono- or co-cultured rice and barnyardgrass to investigate the nature of the gene clusters and their regulatory gene modules involved in the allelopathic interactions of these two plants. In addition to the already known biosynthetic gene clusters in barnyardgrass we identified three potential new clusters including one for quercetin biosynthesis and potentially involved in allelopathic interaction with rice. Based on the construction of gene networks, we identified one gene regulatory module containing hub transcription factors, significantly positively co-regulated with both the momilactone A and phytocassane clusters in rice. In barnyardgrass, gene modules and hub genes co-expressed with the gene clusters responsible for 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) biosynthesis were also identified. In addition, we found three genes in barnyardgrass encoding indole-3-glycerolphosphate synthase that regulate the expression of the DIMBOA cluster. Our findings offer new insights into the regulatory mechanisms of biosynthetic gene clusters involved in allelopathic interactions between rice and barnyardgrass, and have potential implications in controlling weeds for crop protection.

Prioritization of potato genes involved in the formation of agronomically valuable traits using the SOLANUM TUBEROSUM knowledge base

The development of highly efficient technologies in genomics, transcriptomics, proteomics and metabolomics, as well as new technologies in agriculture has led to an “information explosion” in plant biology and crop production, including potato production. Only a small part of the information reaches formalized databases (for example, Uniprot, NCBI Gene, BioGRID, IntAct, etc.). One of the main sources of reliable biological data is the scientific literature. The well-known PubMed database contains more than 18 thousand abstracts of articles on potato. The effective use of knowledge presented in such a number of non-formalized documents in natural language requires the use of modern intellectual methods of analysis. However, in the literature, there is no evidence of a widespread use of intelligent methods for automatically extracting knowledge from scientific publications on cultures such as potatoes. Earlier we developed the SOLANUM TUBEROSUM knowledge base (http://www-bionet.sysbio.cytogen. ru/and/plant/). Integrated into the knowledge base information about the molecular genetic mechanisms underlying the selection of significant traits helps to accelerate the identification of candidate genes for the breeding characteristics of potatoes and the development of diagnostic markers for breeding. The article searches for new potential participants of the molecular genetic mechanisms of resistance to adverse factors in plants. Prioritizing candidate genes has shown that the PHYA, GF14, CNIH1, RCI1A, ABI5, CPK1, RGS1, NHL3, GRF8, and CYP21-4 genes are the most promising for further testing of their relationships with resistance to adverse factors. As a result of the analysis, it was shown that the molecular genetic relationships responsible for the formation of significant agricultural traits are complex and include many direct and indirect interactions. The construction of associative gene networks and their analysis using the SOLANUM TUBEROSUM knowledge base is the basis for searching for target genes for targeted mutagenesis and marker-oriented selection of potato varieties with valuable agricultural characteristics.

Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Here we find, using a fast-acting, optogenetic transcription factor in Saccharomyces cerevisiae, that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or the construction of synthetic gene networks.

Fluorescent Guide RNAs Facilitate Development of Layered Pol II-Driven CRISPR Circuits.

Efficient clustered regularly interspaced short palindromic repeat (CRISPR) guide RNA (gRNA) expression from RNA Polymerase II (Pol II) promoters will aid in construction of complex CRISPR-based synthetic gene networks. Yet, we require tools to properly visualize gRNA directly to quantitatively study the corresponding network behavior. To address this need, we employed a fluorescent gRNA (fgRNA) to visualize synthetic CRISPR network dynamics without affecting gRNA functionality. We show that studying gRNA dynamics directly enables circuit modification and improvement of network function in Pol II-driven CRISPR circuits. This approach generates information necessary for optimizing the overall function of these networks and provides insight into the hurdles remaining in Pol II-regulated gRNA expression.

Synthetic Genomics: From DNA Synthesis to Genome Design.

Rapid technological advances enabling the construction of designer gene networks, biosynthetic pathways, and even entire genomes are moving the fields of genetics and genomics from descriptive to synthetic applications. Following the synthesis of small viral genomes, advances in DNA assembly and rewriting have enabled the hierarchical synthesis of bacterial genomes, such as Mycoplasma genitalium, as well as recoding of the Escherichia coli genome by reducing the number of codons from 64 to 57. The field has advanced to the point of synthesizing an entire eukaryotic genome. The Synthetic Yeast Genome Project (Sc2.0) is underway and aims to rewrite all 16 Saccharomyces cerevisiae chromosomes by 2018; to date, 6.5 chromosomes have been designed and synthesized. Using bottom-up assembly and applying genome-wide alterations will improve our understanding of genome structure and function. This approach will not only provide a platform for systematic studies of eukaryotic chromosomes but will also generate diverse "streamlined" strains that are potentially suitable for medical and industrial applications. Herein, we review the current state of synthetic genome research and discuss potential applications of this emerging technology.

Decoupling Resource-Coupled Gene Expression in Living Cells.

Synthetic biology aspires to develop frameworks that enable the construction of complex and reliable gene networks with predictable functionalities. A key limitation is that increasing network complexity increases the demand for cellular resources, potentially causing resource-associated interference among noninteracting circuits. Although recent studies have shown the effects of resource competition on circuit behaviors, mechanisms that decouple such interference remain unclear. Here, we constructed three systems in Escherichia coli, each consisting of two independent circuit modules where the complexity of one module (Circuit 2) was systematically increased while the other (Circuit 1) remained identical. By varying the expression level of Circuit 1 and measuring its effect on the expression level of Circuit 2, we demonstrated computationally and experimentally that indirect coupling between these seemingly unconnected genetic circuits can occur in three different regulatory topologies. More importantly, we experimentally verified the computational prediction that negative feedback can significantly reduce resource-coupled interference in regulatory circuits. Our results reveal a design principle that enables cells to reliably multitask while tightly controlling cellular resources.

Bernstein approximations in glasso‐based estimation of biological networks

AbstractThe Gaussian graphical model (GGM) is one of the common dynamic modelling approaches in the construction of gene networks. In inference of this modelling the interaction between genes can be detected mainly via graphical lasso (glasso) or coordinate descent‐based approaches. Although these methods are successful in moderate networks, their performances in accuracy decrease when the system becomes sparser. We here implement a particular type of polynomial transformations, called the Bernstein polynomials, of the network data in advance of their inference to raise the accuracy. From comparative Monte Carlo studies and real data analyses we show that these polynomials are successful in terms of the precision, specificity and F‐measure when the scale‐free networks are modelled via GGM and estimated by glasso, and accordingly they can be used as a preprocessing step in inference of these networks. The Canadian Journal of Statistics 45: 62–76; 2017 © 2017 Statistical Society of Canada

A computational method for the investigation of multistable systems and its application to genetic switches.

BackgroundGenetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology.ResultsHere we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour.ConclusionsOur results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0375-z) contains supplementary material, which is available to authorized users.

Learning from Co-expression Networks: Possibilities and Challenges.

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

A gene network engineering platform for lactic acid bacteria.

Recent developments in synthetic biology have positioned lactic acid bacteria (LAB) as a major class of cellular chassis for applications. To achieve the full potential of LAB, one fundamental prerequisite is the capacity for rapid engineering of complex gene networks, such as natural biosynthetic pathways and multicomponent synthetic circuits, into which cellular functions are encoded. Here, we present a synthetic biology platform for rapid construction and optimization of large-scale gene networks in LAB. The platform involves a copy-controlled shuttle for hosting target networks and two associated strategies that enable efficient genetic editing and phenotypic validation. By using a nisin biosynthesis pathway and its variants as examples, we demonstrated multiplex, continuous editing of small DNA parts, such as ribosome-binding sites, as well as efficient manipulation of large building blocks such as genes and operons. To showcase the platform, we applied it to expand the phenotypic diversity of the nisin pathway by quickly generating a library of 63 pathway variants. We further demonstrated its utility by altering the regulatory topology of the nisin pathway for constitutive bacteriocin biosynthesis. This work demonstrates the feasibility of rapid and advanced engineering of gene networks in LAB, fostering their applications in biomedicine and other areas.

Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for BMD Variation

Integration of multiple profiling data and construction of functional gene networks may provide additional insights into the molecular mechanisms of complex diseases. Osteoporosis is a worldwide public health problem, but the complex gene-gene interactions, post-transcriptional modifications and regulation of functional networks are still unclear. To gain a comprehensive understanding of osteoporosis etiology, transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing were performed simultaneously in 5 high hip BMD (Bone Mineral Density) subjects and 5 low hip BMD subjects. SPIA (Signaling Pathway Impact Analysis) and PCST (Prize Collecting Steiner Tree) algorithm were used to perform pathway-enrichment analysis and construct the interaction networks. Through integrating the transcriptomic and epigenomic data, firstly we identified 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) which showed the consistent association evidence from both gene expression and methylation data; secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status. In conclusion, the integration of multiple layers of omics can yield in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology.

A platform for rapid prototyping of synthetic gene networks in mammalian cells.

Mammalian synthetic biology may provide novel therapeutic strategies, help decipher new paths for drug discovery and facilitate synthesis of valuable molecules. Yet, our capacity to genetically program cells is currently hampered by the lack of efficient approaches to streamline the design, construction and screening of synthetic gene networks. To address this problem, here we present a framework for modular and combinatorial assembly of functional (multi)gene expression vectors and their efficient and specific targeted integration into a well-defined chromosomal context in mammalian cells. We demonstrate the potential of this framework by assembling and integrating different functional mammalian regulatory networks including the largest gene circuit built and chromosomally integrated to date (6 transcription units, 27kb) encoding an inducible memory device. Using a library of 18 different circuits as a proof of concept, we also demonstrate that our method enables one-pot/single-flask chromosomal integration and screening of circuit libraries. This rapid and powerful prototyping platform is well suited for comparative studies of genetic regulatory elements, genes and multi-gene circuits as well as facile development of libraries of isogenic engineered cell lines.