Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities

A four-lncRNA risk signature for prognostic prediction of osteosarcoma

Aim: Osteosarcoma is the most common primary malignant tumor of bone. However, our understanding of the prognostic indicators and the genetic mechanisms of the disease progression are still incomplete. The aim of this study was to identify a long noncoding RNA (lncRNA) risk signature for osteosarcoma survival prediction. Methods: RNA sequencing data and relevant clinical information of osteosarcoma patients were downloaded from the database of Therapeutically Applicable Research to Generate Effective Treatments (TARGET). We analyzed the differentially expressed lncRNAs between deceased and living patients by univariate and multivariate Cox regression analysis to identify a risk signature. We calculated a prognostic risk score for each sample according to this prognosis signature, and divided patients into high-risk and low-risk groups according to the median value of the risk score (0.975). Kaplan-Meier analysis and receiver operating characteristic (ROC) curve statistics were used to evaluate the performance of the signature. Next, we analyzed the signature's potential function through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene-set enrichment analysis (GSEA). Lastly, qRT-PCR was used to validate the expression levels of the four lncRNAs in clinical samples. Results: Twenty-six differentially expressed lncRNAs were identified between deceased and living patients. Four of these lncRNAs (CTB-4E7.1, RP11-553A10.1, RP11-24N18.1, and PVRL3-AS1) were identified as independent prognostic factors, and a risk signature of these four lncRNAs for osteosarcoma survival prediction was constructed. Kaplan-Meier analysis showed that the five-year survival time in high-risk and low-risk groups was 33.1% and 82.5%, and the area under the curve (AUC) of the ROC was 0.784, which demonstrated that the prognostic signature was reliable and had the potential to predict the survival of patients with osteosarcoma. The expression level of the four lncRNAs in osteosarcoma tissues and cells was determined by qRT-PCR. Functional enrichment analysis suggested that the signature might be related to osteosarcoma through regulation of the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the extracellular matrix and also provided new insights into the study of osteosarcoma, including the role of papillomavirus infection, olfactory receptor activity, and olfactory transduction in osteosarcoma. Conclusion: We constructed a novel lncRNA risk signature that served as an independent biomarker for predicting the prognosis of osteosarcoma patients.

Genetic association of ANRIL with susceptibility to Ischemic stroke: A comprehensive meta-analysis

Background

Ischemic stroke (IS) is a complex polygenic disease with a strong genetic background. The relationship between the ANRIL (antisense non-coding RNA in the INK4 locus) in chromosome 9p21 region and IS has been reported across populations worldwide; however, these studies have yielded inconsistent results. The aim of this study is to clarify the types of single-nucleotide polymorphisms on the ANRIL locus associated with susceptibility to IS using meta-analysis and comprehensively assess the strength of the association.

Methods

Relevant studies were identified by comprehensive and systematic literature searches. The quality of each study was assessed using the Newcastle-Ottawa Scale. Allele and genotype frequencies were extracted from each of the included studies. Odds ratios with corresponding 95% confidence intervals of combined analyses were calculated under three genetic models (allele frequency comparison, dominant model, and recessive model) using a random-effects or fixed-effects model. Heterogeneity was tested using the chi-square test based on the Cochran Q statistic and I2 metric, and subgroup analyses and a meta-regression model were used to explore sources of heterogeneity. The correction for multiple testing used the false discovery rate method proposed by Benjamini and Hochberg. The assessment of publication bias employed funnel plots and Egger’s test.

Results

We identified 25 studies (15 SNPs, involving a total of 11,527 cases and 12,216 controls maximum) and performed a meta-analysis. Eight SNPs (rs10757274, rs10757278, rs2383206, rs1333040, rs1333049, rs1537378, rs4977574, and rs1004638) in ANRIL were significantly associated with IS risk. Six of these SNPs (rs10757274, rs10757278, rs2383206, rs1333040, rs1537378, and rs4977574) had a significant relationship to the large artery atherosclerosis subtype of IS. Two SNPs (rs2383206 and rs4977574) were associated with IS mainly in Asians, and three SNPs (rs10757274, rs1333040, and rs1333049) were associated with susceptibility to IS mainly in Caucasians. Sensitivity analyses confirmed the reliability of the original results. Ethnicity and individual studies may be the main sources of heterogeneity in ANRIL.

Conclusions

Our results suggest that some single-nucleotide polymorphisms on the ANRIL locus may be associated with IS risk. Future studies with larger sample numbers are necessary to confirm this result. Additional functional analyses of causal effects of these polymorphisms on IS subtypes are also essential.

Factors associated with health intentions and behaviour among health checkup participants in Japan

Health intentions and behaviours are essential for improving the health of individuals and society. This study used cross-sectional data from 20,155 health checkup participants in the Yamagata study to identify factors associated with health intentions and behaviours. Information regarding the current level of health intentions and behaviours was collected using a baseline survey questionnaire. Participants were categorised into three groups: having no intention (no intention), having intentions to improve but not acting on them (intention), and already active (action). The associations between background factors and the presence/absence of health intentions and behaviours were assessed using logistic regression analysis. Of the participants, 35.4%, 37.7%, and 26.9% belonged to the no intention, intention, and action groups, respectively. Multivariate analysis revealed that the factors associated with health intentions were being young, being female, longer duration of education, higher body mass index and abdominal circumference, diabetes, and dyslipidaemia. The factors associated with health behaviours were being older and male, not consuming alcohol, not smoking, performing daily exercise, and having diabetes. These results indicate that health guidance considering background factors, including age, gender, education, and comorbidities, may be useful for effectively promoting health intentions and health behaviours in the Japanese population.

Challenges of the heterogeneous nutrition response: interpreting the group mean

Extensive research demonstrates unequivocally that nutrition plays a fundamental role in maintaining health and preventing disease. In parallel nutrition research provides evidence that the risks and benefits of diet and lifestyle choices do not affect people equally, as people are inherently variable in their responses to nutrition and associated interventions to maintain health and prevent disease. To simplify the inherent complexity of human subjects and their nutrition, with the aim of managing expectations for dietary guidance required to ensure healthy populations and individuals, nutrition researchers often seek to group individuals based on commonly used criteria. This strategy relies on demonstrating meaningful conclusions based on comparison of group mean responses of assigned groups. Such studies are often confounded by the heterogeneous nutrition response. Commonly used criteria applied in grouping study populations and individuals to identify mechanisms and determinants of responses to nutrition often contribute to the problem of interpreting the results of group comparisons. Challenges of interpreting the group mean using diverse populations will be discussed with respect to studies in human subjects, in vivo and in vitro model systems. Future advances in nutrition research to tackle inter-individual variation require a coordinated approach from funders, learned societies, nutrition scientists, publishers and reviewers of the scientific literature. This will be essential to develop and implement improved study design, data recording, analysis and reporting to facilitate more insightful interpretation of the group mean with respect to population diversity and the heterogeneous nutrition response.

Agent-based re-engineering of ErbB signaling: a modeling pipeline for integrative systems biology

Motivation

Experiments in systems biology are generally supported by a computational model which quantitatively estimates the parameters of the system by finding the best fit to the experiment. Mathematical models have proved to be successful in reverse engineering the system. The data generated is interpreted to understand the dynamics of the underlying phenomena. The question we have sought to answer is that - is it possible to use an agent-based approach to re-engineer a biological process, making use of the available knowledge from experimental and modelling efforts? Can the bottom-up approach benefit from the top-down exercise so as to create an integrated modelling formalism for systems biology? We propose a modelling pipeline that learns from the data given by reverse engineering, and uses it for re-engineering the system, to carry out in-silico experiments.

Results

A mathematical model that quantitatively predicts co-expression of EGFR-HER2 receptors in activation and trafficking has been taken for this study. The pipeline architecture takes cues from the population model that gives the rates of biochemical reactions, to formulate knowledge-based rules for the particle model. Agent-based simulations using these rules, support the existing facts on EGFR-HER2 dynamics. We conclude that, re-engineering models, built using the results of reverse engineering, opens up the possibility of harnessing the power pack of data which now lies scattered in literature. Virtual experiments could then become more realistic when empowered with the findings of empirical cell biology and modelling studies.

Availability and Implementation

Implemented on the Agent Modelling Framework developed in-house. C ++ code templates available in Supplementary material .

Contact

liz.csir@gmail.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

Essential O2-responsive genes of Pseudomonas aeruginosa and their network revealed by integrating dynamic data from inverted conditions

Identification of the gene network through which Pseudomonas aeruginosa PAO1 (PA) adapts to altered oxygen-availability environments is essential for a better understanding of stress responses and pathogenicity of PA. We performed high-time-resolution (HTR) transcriptome analyses of PA in a continuous cultivation system during the transition from high oxygen tension to low oxygen tension (HLOT) and the reversed transition from low to high oxygen tension (LHOT). From those genes responsive to both transient conditions, we identified 85 essential oxygen-availability responsive genes (EORGs), including the expected ones (arcDABC) encoding enzymes for arginine fermentation. We then constructed the regulatory network for the EORGs of PA by integrating information from binding motif searching, literature and HTR data. Notably, our results show that only the sub-networks controlled by the well-known oxygen-responsive transcription factors show a very high consistency between the inferred network and literature knowledge, e.g. 87.5% and 83.3% of the obtained sub-network controlled by the anaerobic regulator (ANR) and a quorum sensing regulator RhIR, respectively. These results not only reveal stringent EORGs of PA and their transcription regulatory network, but also highlight that achieving a high accuracy of the inferred regulatory network might be feasible only for the apparently affected regulators under the given conditions but not for all the expressed regulators on a genome scale.

Data Integration Protocol In Ten-steps (DIPIT): a new standard for medical researchers

INTRODUCTION

The exponential increase in data, computing power and the availability of readily accessible analytical software has allowed organisations around the world to leverage the benefits of integrating multiple heterogeneous data files for enterprise-level planning and decision making. Benefits from effective data integration to the health and medical research community include more trustworthy research, higher service quality, improved personnel efficiency, reduction of redundant tasks, facilitation of auditing and more timely, relevant and specific information. The costs of poor quality processes elevate the risk of erroneous outcomes, an erosion of confidence in the data and the organisations using these data. To date there are no documented set of standards for best practice integration of heterogeneous data files for research purposes. Therefore, the aim of this paper is to describe a set of clear protocol for data file integration (Data Integration Protocol In Ten-steps; DIPIT) translational to any field of research.

METHODS AND RESULTS

The DIPIT approach consists of a set of 10 systematic methodological steps to ensure the final data are appropriate for the analysis to meet the research objectives, legal and ethical requirements are met, and that data definitions are clear, concise, and comprehensive. This protocol is neither file specific nor software dependent, but aims to be transportable to any data-merging situation to minimise redundancy and error and translational to any field of research. DIPIT aims to generate a master data file that is of the optimal integrity to serve as the basis for research analysis.

CONCLUSION

With linking of heterogeneous data files becoming increasingly common across all fields of medicine, DIPIT provides a systematic approach to a potentially complex task of integrating a large number of files and variables. The DIPIT protocol will ensure the final integrated data is consistent and of high integrity for the research requirements, useful for practical application across all fields of medical research.

Genome sequencing of high-penicillin producing industrial strain of Penicillium chrysogenum

BACKGROUND

Due to the importance of Penicillium chrysogenum holding in medicine, the genome of low-penicillin producing laboratorial strain Wisconsin54-1255 had been sequenced and fully annotated. Through classical mutagenesis of Wisconsin54-1255, product titers and productivities of penicillin have dramatically increased, but what underlying genome structural variations is still little known. Therefore, genome sequencing of a high-penicillin producing industrial strain is very meaningful.

RESULTS

To reveal more insights into the genome structural variations of high-penicillin producing strain, we sequenced an industrial strain P. chrysogenum NCPC10086. By whole genome comparative analysis, we observed a large number of mutations, insertions and deletions, and structural variations. There are 69 new genes that not exist in the genome sequence of Wisconsin54-1255 and some of them are involved in energy metabolism, nitrogen metabolism and glutathione metabolism. Most importantly, we discovered a 53.7 Kb "new shift fragment" in a seven copies of determinative penicillin biosynthesis cluster in NCPC10086 and the arrangement type of amplified region is unique. Moreover, we presented two large-scale translocations in NCPC10086, containing genes involved energy, nitrogen metabolism and peroxysome pathway. At last, we found some non-synonymous mutations in the genes participating in homogentisate pathway or working as regulators of penicillin biosynthesis.

CONCLUSIONS

We provided the first high-quality genome sequence of industrial high-penicillin strain of P. chrysogenum and carried out a comparative genome analysis with a low-producing experimental strain. The genomic variations we discovered are related with energy metabolism, nitrogen metabolism and so on. These findings demonstrate the potential information for insights into the high-penicillin yielding mechanism and metabolic engineering in the future.

Lignin plays a negative role in the biochemical process for producing lignocellulosic biofuels

A biochemical platform holds the most promising route toward lignocellulosic biofuels, in which polysaccharides are hydrolyzed by cellulase enzymes into simple sugars and fermented to ethanol by microbes. However, these polysaccharides are cross-linked in the plant cell walls with the hydrophobic network of lignin that physically impedes enzymatic deconstruction. A thermochemical pretreatment process is often required to remove or delocalize lignin, which may also generate inhibitors that hamper enzymatic hydrolysis and fermentation. Here we review recent advances in understanding lignin structure in the plant cell walls and the negative roles of lignin in the processes of converting biomass to biofuels. Perspectives and future directions to improve the biomass conversion process are also discussed.

PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells

Human FOXP3(+)CD25(+)CD4(+) regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and underlying molecular networks controlling the suppressor function still largely remain unclear. Here, we describe a strategy to identify the key genes directly from an undirected correlation network which we reconstruct from a very high time-resolution (HTR) transcriptome during the activation of human Tregs/CD4(+) T-effector cells. We show that a predicted top-ranked new key gene PLAU (the plasminogen activator urokinase) is important for the suppressor function of both human and murine Tregs. Further analysis unveils that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways. Our study demonstrates the potential for identifying novel key genes for complex dynamic biological processes using a network strategy based on HTR data, and reveals a critical role for PLAU in Treg suppressor function.

Multiscale models of breast cancer progression

Breast cancer initiation, invasion and metastasis span multiple length and time scales. Molecular events at short length scales lead to an initial tumorigenic population, which left unchecked by immune action, acts at increasingly longer length scales until eventually the cancer cells escape from the primary tumor site. This series of events is highly complex, involving multiple cell types interacting with (and shaping) the microenvironment. Multiscale mathematical models have emerged as a powerful tool to quantitatively integrate the convective-diffusion-reaction processes occurring on the systemic scale, with the molecular signaling processes occurring on the cellular and subcellular scales. In this study, we reviewed the current state of the art in cancer modeling across multiple length scales, with an emphasis on the integration of intracellular signal transduction models with pro-tumorigenic chemical and mechanical microenvironmental cues. First, we reviewed the underlying biomolecular origin of breast cancer, with a special emphasis on angiogenesis. Then, we summarized the development of tissue engineering platforms which could provide high-fidelity ex vivo experimental models to identify and validate multiscale simulations. Lastly, we reviewed top-down and bottom-up multiscale strategies that integrate subcellular networks with the microenvironment. We present models of a variety of cancers, in addition to breast cancer specific models. Taken together, we expect as the sophistication of the simulations increase, that multiscale modeling and bottom-up agent-based models in particular will become an increasingly important platform technology for basic scientific discovery, as well as the identification and validation of potentially novel therapeutic targets.