A data integration methodology for systems biology: experimental verification

An innovative MGM-BPNN-ARIMA model for China's energy consumption structure forecasting from the perspective of compositional data

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023-2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China.

Neural network based integration of assays to assess pathogenic potential

Limited data significantly hinders our capability of biothreat assessment of novel bacterial strains. Integration of data from additional sources that can provide context about the strain can address this challenge. Datasets from different sources, however, are generated with a specific objective and which makes integration challenging. Here, we developed a deep learning-based approach called the neural network embedding model (NNEM) that integrates data from conventional assays designed to classify species with new assays that interrogate hallmarks of pathogenicity for biothreat assessment. We used a dataset of metabolic characteristics from a de-identified set of known bacterial strains that the Special Bacteriology Reference Laboratory (SBRL) of the Centers for Disease Control and Prevention (CDC) has curated for use in species identification. The NNEM transformed results from SBRL assays into vectors to supplement unrelated pathogenicity assays from de-identified microbes. The enrichment resulted in a significant improvement in accuracy of 9% for biothreat. Importantly, the dataset used in our analysis is large, but noisy. Therefore, the performance of our system is expected to improve as additional types of pathogenicity assays are developed and deployed. The proposed NNEM strategy thus provides a generalizable framework for enrichment of datasets with previously collected assays indicative of species.

Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes

We report a proteogenomic analysis of pancreatic ductal adenocarcinoma (PDAC). Mutation-phosphorylation correlations identified signaling pathways associated with somatic mutations in significantly mutated genes. Messenger RNA-protein abundance correlations revealed potential prognostic biomarkers correlated with patient survival. Integrated clustering of mRNA, protein and phosphorylation data identified six PDAC subtypes. Cellular pathways represented by mRNA and protein signatures, defining the subtypes and compositions of cell types in the subtypes, characterized them as classical progenitor (TS1), squamous (TS2-4), immunogenic progenitor (IS1) and exocrine-like (IS2) subtypes. Compared with the mRNA data, protein and phosphorylation data further classified the squamous subtypes into activated stroma-enriched (TS2), invasive (TS3) and invasive-proliferative (TS4) squamous subtypes. Orthotopic mouse PDAC models revealed a higher number of pro-tumorigenic immune cells in TS4, inhibiting T cell proliferation. Our proteogenomic analysis provides significantly mutated genes/biomarkers, cellular pathways and cell types as potential therapeutic targets to improve stratification of patients with PDAC.

Cntnap2-dependent molecular networks in autism spectrum disorder revealed through an integrative multi-omics analysis

Autism spectrum disorder (ASD) is a major neurodevelopmental disorder in which patients present with core symptoms of social communication impairment, restricted interest, and repetitive behaviors. Although various studies have been performed to identify ASD-related mechanisms, ASD pathology is still poorly understood. CNTNAP2 genetic variants have been found that represent ASD genetic risk factors, and disruption of Cntnap2 expression has been associated with ASD phenotypes in mice. In this study, we performed an integrative multi-omics analysis by combining quantitative proteometabolomic data obtained with Cntnap2 knockout (KO) mice with multi-omics data obtained from ASD patients and forebrain organoids to elucidate Cntnap2-dependent molecular networks in ASD. To this end, a mass spectrometry-based proteometabolomic analysis of the medial prefrontal cortex in Cntnap2 KO mice led to the identification of Cntnap2-associated molecular features, and these features were assessed in combination with multi-omics data obtained on the prefrontal cortex in ASD patients to identify bona fide ASD cellular processes. Furthermore, a reanalysis of single-cell RNA sequencing data obtained from forebrain organoids derived from patients with CNTNAP2-associated ASD revealed that the aforementioned identified ASD processes were mainly linked to excitatory neurons. On the basis of these data, we constructed Cntnap2-associated ASD network models showing mitochondrial dysfunction, axonal impairment, and synaptic activity. Our results may shed light on the Cntnap2-dependent molecular networks in ASD.

Ethylene responsive factor34 mediates stress-induced leaf senescence by regulating salt stress-responsive genes

Leaf senescence proceeds with age but is modulated by various environmental stresses and hormones. Salt stress is one of the most well-known environmental stresses that accelerate leaf senescence. However, the molecular mechanisms that integrate salt stress signalling with leaf senescence programmes remain elusive. In this study, we characterised the role of ETHYLENE RESPONSIVE FACTOR34 (ERF34), an Arabidopsis APETALA2 (AP2)/ERF family transcription factor, in leaf senescence. ERF34 was differentially expressed under various leaf senescence-inducing conditions, and negatively regulated leaf senescence induced by age, dark, and salt stress. ERF34 also promoted salt stress tolerance at different stages of the plant life cycle such as seed germination and vegetative growth. Transcriptome analysis revealed that the overexpression of ERF34 increased the transcript levels of salt stress-responsive genes including COLD-REGULATED15A (COR15A), EARLY RESPONSIVE TO DEHYDRATION10 (ERD10), and RESPONSIVE TO DESICCATION29A (RD29A). Moreover, ERF34 directly bound to ERD10 and RD29A promoters and activated their expression. Our findings indicate that ERF34 plays a key role in the convergence of the salt stress response with the leaf senescence programmes, and is a potential candidate for crop improvement, particularly by enhancing salt stress tolerance.

Nogo-A regulates myogenesis via interacting with Filamin-C

Among the three isoforms encoded by Rtn4, Nogo-A has been intensely investigated as a central nervous system inhibitor. Although Nogo-A expression is increased in muscles of patients with amyotrophic lateral sclerosis, its role in muscle homeostasis and regeneration is not well elucidated. In this study, we discovered a significant increase in Nogo-A expression in various muscle-related pathological conditions. Nogo^−/− mice displayed dystrophic muscle structure, dysregulated muscle regeneration following injury, and altered gene expression involving lipid storage and muscle cell differentiation. We hypothesized that increased Nogo-A levels might regulate muscle regeneration. Differentiating myoblasts exhibited Nogo-A upregulation and silencing Nogo-A abrogated myoblast differentiation. Nogo-A interacted with filamin-C, suggesting a role for Nogo-A in cytoskeletal arrangement during myogenesis. In conclusion, Nogo-A maintains muscle homeostasis and integrity, and pathologically altered Nogo-A expression mediates muscle regeneration, suggesting Nogo-A as a novel target for the treatment of myopathies in clinical settings.

Natural allelic variation of GVS1 confers diversity in the regulation of leaf senescence in Arabidopsis

Leaf senescence affects plant fitness. Plants that evolve in different environments are expected to acquire distinct regulations of leaf senescence. However, the adaptive and evolutionary roles of leaf senescence are largely unknown. We investigated leaf senescence in 259 natural accessions of Arabidopsis by quantitatively assaying dark-induced senescence responses using a high-throughput chlorophyll fluorescence imaging system. A meta-analysis of our data with phenotypic and climatic information demonstrated biological and environmental links with leaf senescence. We further performed genome-wide association mapping to identify the genetic loci underlying the diversity of leaf senescence responses. We uncovered a new locus, Genetic Variants in leaf Senescence (GVS1), with high similarity to reductase, where a single nonsynonymous nucleotide substitution at GVS1 mediates the diversity of the senescence trait. Loss-of-function mutations of GVS1 in Columbia-0 delayed leaf senescence and increased sensitivity to oxidative stress, suggesting that this GVS1 variant promotes optimal responses to developmental and environmental signals. Intriguingly, gvs1 loss-of-function mutants display allele- and accession-dependent phenotypes, revealing the functional diversity of GVS1 alleles not only in leaf senescence, but also oxidative stress. Our discovery of GVS1 as the genetic basis of natural variation in senescence programs reinforces its adaptive potential in modulating life histories across diverse environments.

Proteogenomic Characterization of Human Early-Onset Gastric Cancer

We report proteogenomic analysis of diffuse gastric cancers (GCs) in young populations. Phosphoproteome data elucidated signaling pathways associated with somatic mutations based on mutation-phosphorylation correlations. Moreover, correlations between mRNA and protein abundances provided potential oncogenes and tumor suppressors associated with patient survival. Furthermore, integrated clustering of mRNA, protein, phosphorylation, and N-glycosylation data identified four subtypes of diffuse GCs. Distinguishing these subtypes was possible by proteomic data. Four subtypes were associated with proliferation, immune response, metabolism, and invasion, respectively; and associations of the subtypes with immune- and invasion-related pathways were identified mainly by phosphorylation and N-glycosylation data. Therefore, our proteogenomic analysis provides additional information beyond genomic analyses, which can improve understanding of cancer biology and patient stratification in diffuse GCs.

Rho-kinase/AMPK axis regulates hepatic lipogenesis during overnutrition

Obesity is a major risk factor for developing nonalcoholic fatty liver disease (NAFLD). NAFLD is the most common form of chronic liver disease and is closely associated with insulin resistance, ultimately leading to cirrhosis and hepatocellular carcinoma. However, knowledge of the intracellular regulators of obesity-linked fatty liver disease remains incomplete. Here we showed that hepatic Rho-kinase 1 (ROCK1) drives obesity-induced steatosis in mice through stimulation of de novo lipogenesis. Mice lacking ROCK1 in the liver were resistant to diet-induced obesity owing to increased energy expenditure and thermogenic gene expression. Constitutive expression of hepatic ROCK1 was sufficient to promote adiposity, insulin resistance, and hepatic lipid accumulation in mice fed a high-fat diet. Correspondingly, liver-specific ROCK1 deletion prevented the development of severe hepatic steatosis and reduced hyperglycemia in obese diabetic (ob/ob) mice. Of pathophysiological significance, hepatic ROCK1 was markedly upregulated in humans with fatty liver disease and correlated with risk factors clustering around NAFLD and insulin resistance. Mechanistically, we found that hepatic ROCK1 suppresses AMPK activity and a ROCK1/AMPK pathway is necessary to mediate cannabinoid-induced lipogenesis in the liver. Furthermore, treatment with metformin, the most widely used antidiabetes drug, reduced hepatic lipid accumulation by inactivating ROCK1, resulting in activation of AMPK downstream signaling. Taken together, our findings establish a ROCK1/AMPK signaling axis that regulates de novo lipogenesis, providing a unique target for treating obesity-related metabolic disorders such as NAFLD.

Rewiring of cisplatin-resistant bladder cancer cells through epigenetic regulation of genes involved in amino acid metabolism

Alterations in DNA methylation are important epigenetic markers in bladder cancer (BC). These epigenome modifications may drive the mechanisms of aggressive chemo-resistant BC. Clinicopathological biomarkers that indicate chemotherapeutic resistance are critical for better assessing treatment strategies for individual patients. Thus, in this study, we aimed to determine whether DNA methylation of certain metabolic enzymes is significantly altered in cisplatin-resistant BC cells.

Methods: To characterize CpG methylation and nucleosome accessibility in cisplatin-resistant BC cells, the Illumina Infinium HM450 DNA methylation assay was performed. Perturbed gene expression was found to be associated with cisplatin resistance, and the biological roles of spermidine/spermine N¹-acetyltransferase (SAT1) and argininosuccinate synthase 1 (ASS1) were further studied using qRT-PCR analysis and various cell biology assays, including western blot.

Results:ASS1 and SAT1, genes for amino acid and polyamine metabolism catalysts, respectively, were found to be vastly hypermethylated, resulting in greatly downregulated expression. ASS1 expression is of particular interest because prior studies have demonstrated its potential association with BC stage and recurrence. In regard to chemoresistance, we found that aberrant expression or induced stimulation of SAT1 restored cisplatin sensitivity in the cell culture system. We also found that the addition of exogenous arginine deiminase through administration of ADI-PEG 20 (pegylated arginine deiminase) increased ASS1 expression and enhanced cisplatin's apoptotic effects.

Conclusions: Our study demonstrates a novel mechanistic link between the epigenetic perturbation of SAT1 and ASS1 and cancer metabolism in cisplatin-resistant bladder cancer cells. These findings suggest potential utility of SAT1 and ASS1 as predictive biomarkers in re-sensitizing bladder cancer to chemotherapy and personalizing therapy.

BDNF Val66Met polymorphism, life stress and depression: A meta-analysis of gene-environment interaction

BACKGROUND

Depression is thought to be multifactorial in etiology, including genetic and environmental components. While a number of gene-environment interaction studies have been carried out, meta-analyses are scarce. The present meta-analysis aimed to quantify evidence on the interaction between brain-derived neurotrophic factor (BDNF) Val66Met polymorphism and stress in depression.

METHODS

Included were 31 peer-reviewed with a pooled total of 21060 participants published before October 2016 and literature searches were conducted using PubMed, Wolters Kluwer, Web of Science, EBSCO, Elsevier Science Direct and Baidu Scholar databases.

RESULTS

The results indicated that the Met allele of BDNF Val66Met polymorphism significantly moderated the relationship between stress and depression (Z=2.666, p = 0.003). The results of subgroup analysis concluded that stressful life events and childhood adversity separately interacted with the Met allele of BDNF Val66Met polymorphism in depression (Z = 2.552, p = 0.005; Z = 1.775, p = 0.03).

LIMITATIONS

The results could be affected by errors or bias in primary studies which had small sample sizes with relatively lower statistic power. We could not estimate how strong the interaction effect between gene and environment was.

CONCLUSIONS

We found evidence that supported the hypothesis that BDNF Val66Met polymorphism moderated the relationship between stress and depression, despite the fact that many included individual studies did not show this effect.

Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response

Mathematical models of cancer stem cells are useful in translational cancer research for facilitating the understanding of tumor growth dynamics and for predicting treatment response and resistance to combined targeted therapies. In this chapter, we describe appealing aspects of different methods used in mathematical oncology and discuss compelling questions in oncology that can be addressed with these modeling techniques. We describe a simplified version of a model of the breast cancer stem cell niche, illustrate the visualization of the model, and apply stochastic simulation to generate full distributions and average trajectories of cell type populations over time. We further discuss the advent of single-cell data in studying cancer stem cell heterogeneity and how these data can be integrated with modeling to advance understanding of the dynamics of invasive and proliferative populations during cancer progression and response to therapy.

The Promise of Systems Biology Approaches for Revealing Host Pathogen Interactions in Malaria

Despite global eradication efforts over the past century, malaria remains a devastating public health burden, causing almost half a million deaths annually (WHO, 2016). A detailed understanding of the mechanisms that control malaria infection has been hindered by technical challenges of studying a complex parasite life cycle in multiple hosts. While many interventions targeting the parasite have been implemented, the complex biology of Plasmodium poses a major challenge, and must be addressed to enable eradication. New approaches for elucidating key host-parasite interactions, and predicting how the parasite will respond in a variety of biological settings, could dramatically enhance the efficacy and longevity of intervention strategies. The field of systems biology has developed methodologies and principles that are well poised to meet these challenges. In this review, we focus our attention on the Liver Stage of the Plasmodium lifecycle and issue a “call to arms” for using systems biology approaches to forge a new era in malaria research. These approaches will reveal insights into the complex interplay between host and pathogen, and could ultimately lead to novel intervention strategies that contribute to malaria eradication.

Requirement of zinc transporter ZIP10 for epidermal development: Implication of the ZIP10-p63 axis in epithelial homeostasis

Skin tissues, in particular the epidermis, are severely affected by zinc deficiency. However, the zinc-mediated mechanisms that maintain the cells that form the epidermis have not been established. Here, we report that the zinc transporter ZIP10 is highly expressed in the outer root sheath of hair follicles and plays critical roles in epidermal development. We found that ZIP10 marked epidermal progenitor cell subsets and that ablating Zip10 caused significant epidermal hypoplasia accompanied by down-regulation of the transactivation of p63, a master regulator of epidermal progenitor cell proliferation and differentiation. Both ZIP10 and p63 are significantly increased during epidermal development, in which ZIP10-mediated zinc influx promotes p63 transactivation. Collectively, these results indicate that ZIP10 plays important roles in epidermal development via, at least in part, the ZIP10-zinc-p63 signaling axis, thereby highlighting the physiological significance of zinc regulation in the maintenance of skin epidermis.

Requirement of Zinc Transporter SLC39A7/ZIP7 for Dermal Development to Fine-Tune Endoplasmic Reticulum Function by Regulating Protein Disulfide Isomerase

Skin is the first area that manifests zinc deficiency. However, the molecular mechanisms by which zinc homeostasis affects skin development remain largely unknown. Here, we show that zinc-regulation transporter-/iron-regulation transporter-like protein 7 (ZIP7) localized to the endoplasmic reticulum plays critical roles in connective tissue development. Mice lacking the Slc39a7/Zip7 gene in collagen 1-expressing tissue exhibited dermal dysplasia. Ablation of ZIP7 in mesenchymal stem cells inhibited cell proliferation thereby preventing proper dermis formation, indicating that ZIP7 is required for dermal development. We also found that mesenchymal stem cells lacking ZIP7 accumulated zinc in the endoplasmic reticulum, which triggered zinc-dependent aggregation and inhibition of protein disulfide isomerase, leading to endoplasmic reticulum dysfunction. These results suggest that ZIP7 is necessary for endoplasmic reticulum function in mesenchymal stem cells and, as such, is essential for dermal development.

Transcriptional regulatory networks underlying the reprogramming of spermatogonial stem cells to multipotent stem cells

Spermatogonial stem cells (SSCs) are germline stem cells located along the basement membrane of seminiferous tubules in testes. Recently, SSCs were shown to be reprogrammed into multipotent SSCs (mSSCs). However, both the key factors and biological networks underlying this reprogramming remain elusive. Here, we present transcriptional regulatory networks (TRNs) that control cellular processes related to the SSC-to-mSSC reprogramming. Previously, we established intermediate SSCs (iSSCs) undergoing the transition to mSSCs and generated gene expression profiles of SSCs, iSSCs and mSSCs. By comparing these profiles, we identified 2643 genes that were up-regulated during the reprogramming process and 15 key transcription factors (TFs) that regulate these genes. Using the TF-target relationships, we developed TRNs describing how these TFs regulate three pluripotency-related processes (cell proliferation, stem cell maintenance and epigenetic regulation) during the reprogramming. The TRNs showed that 4 of the 15 TFs (Oct4/Pou5f1, Cux1, Zfp143 and E2f4) regulated cell proliferation during the early stages of reprogramming, whereas 11 TFs (Oct4/Pou5f1, Foxm1, Cux1, Zfp143, Trp53, E2f4, Esrrb, Nfyb, Nanog, Sox2 and Klf4) regulated the three pluripotency-related processes during the late stages of reprogramming. Our TRNs provide a model for the temporally coordinated transcriptional regulation of pluripotency-related processes during the SSC-to-mSSC reprogramming, which can be further tested in detailed functional studies.

NORE1/SAUL1 integrates temperature-dependent defense programs involving SGT1b and PAD4 pathways and leaf senescence in Arabidopsis

Leaf senescence is not only primarily governed by developmental age but also influenced by various internal and external factors. Although some genes that control leaf senescence have been identified, the detailed regulatory mechanisms underlying integration of diverse senescence-associated signals into the senescence programs remain to be elucidated. To dissect the regulatory pathways involved in leaf senescence, we isolated the not oresara1-1 (nore1-1) mutant showing accelerated leaf senescence phenotypes from an EMS-mutagenized Arabidopsis thaliana population. We found that altered transcriptional programs in defense response-related processes were associated with the accelerated leaf senescence phenotypes observed in nore1-1 through microarray analysis. The nore1-1 mutation activated defense program, leading to enhanced disease resistance. Intriguingly, high ambient temperature effectively suppresses the early senescence and death phenotypes of nore1-1. The gene responsible for the phenotypes of nore1-1 contains a missense mutation in SENESCENCE-ASSOCIATED E3 UBIQUITIN LIGASE 1 (SAUL1), which was reported as a negative regulator of premature senescence in the light intensity- and PHYTOALEXIN DEFICIENT 4 (PAD4)-dependent manner. Through extensive double mutant analyses, we recently identified suppressor of the G2 Allele of SKP1b (SGT1b), one of the positive regulators for disease resistance conferred by many resistance (R) proteins, as a downstream signaling component in NORE1-mediated senescence and cell death pathways. In conclusion, NORE1/SAUL1 is a key factor integrating signals from temperature-dependent defense programs and leaf senescence in Arabidopsis. These findings provide a new insight that plants might utilize defense response program in regulating leaf senescence process, possibly through recruiting the related genes during the evolution of the leaf senescence program.

Network-Based Protein Biomarker Discovery Platforms

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.

RNA helicase HEL-1 promotes longevity by specifically activating DAF-16/FOXO transcription factor signaling in Caenorhabditis elegans

The homeostatic maintenance of the genomic DNA is crucial for regulating aging processes. However, the role of RNA homeostasis in aging processes remains unknown. RNA helicases are a large family of enzymes that regulate the biogenesis and homeostasis of RNA. However, the functional significance of RNA helicases in aging has not been explored. Here, we report that a large fraction of RNA helicases regulate the lifespan of Caenorhabditis elegans. In particular, we show that a DEAD-box RNA helicase, helicase 1 (HEL-1), promotes longevity by specifically activating the DAF-16/forkhead box O (FOXO) transcription factor signaling pathway. We find that HEL-1 is required for the longevity conferred by reduced insulin/insulin-like growth factor 1 (IGF-1) signaling (IIS) and is sufficient for extending lifespan. We further show that the expression of HEL-1 in the intestine and neurons contributes to longevity. HEL-1 enhances the induction of a large fraction of DAF-16 target genes. Thus, the RNA helicase HEL-1 appears to promote longevity in response to decreased IIS as a transcription coregulator of DAF-16. Because HEL-1 and IIS are evolutionarily well conserved, a similar mechanism for longevity regulation via an RNA helicase-dependent regulation of FOXO signaling may operate in mammals, including humans.

IMMUNOLOGY. An interactive reference framework for modeling a dynamic immune system

Immune cells function in an interacting hierarchy that coordinates the activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology.

From big data analysis to personalized medicine for all: challenges and opportunities

Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

The Yeast Nucleosome Atlas (YNA) database: an integrative gene mining platform for studying chromatin structure and its regulation in yeast

Background

Histone modification and remodeling play crucial roles in regulating gene transcription. These post-translational modifications of histones function in a combinatorial fashion and can be recognized by specific histone-binding proteins, thus regulating gene transcription. Therefore, understanding the combinatorial patterns of the histone code is vital to understanding the associated biological processes. However, most of the datasets regarding histone modification and chromatin regulation are scattered across various studies, and no comprehensive search and query tool has yet been made available to retrieve genes bearing specific histone modification patterns and regulatory proteins.

Description

For this reason, we developed the Yeast Nucleosome Atlas database, or the YNA database, which integrates the available experimental data on nucleosome occupancy, histone modifications, the binding occupancy of regulatory proteins, and gene expression data, and provides the genome-wide gene miner to retrieve genes with a specific combination of these chromatin-related datasets. Moreover, the biological significance analyzer, which analyzes the enrichments of histone modifications, binding occupancy, transcription rate, and functionality of the retrieved genes, was constructed to help researchers to gain insight into the correlation among chromatin regulation and transcription.

Conclusions

Compared to previously established genome browsing databases, YNA provides a powerful gene mining and retrieval interface, and is an investigation tool that can assist users to generate testable hypotheses for studying chromatin regulation during transcription. YNA is available online at http://cosbi3.ee.ncku.edu.tw/yna/.

cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila

BACKGROUND

Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs.

DESCRIPTION

It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality.

CONCLUSIONS

cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cisMEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes.

Hyperosmotic stress reduces melanin production by altering melanosome formation

Many tissues of the human body encounter hyperosmotic stress. The effect of extracellular osmotic changes on melanin production has not yet been elucidated. In this study, we determined that hyperosmotic stress induced by organic osmolytes results in reduced melanin production in human melanoma MNT-1 cells. Under hyperosmotic stress, few pigmented mature melanosomes were detected, but there was an increase in swollen vacuoles. These vacuoles were stained with an anti-M6PR antibody that recognizes late endosomal components and with anti-TA99 and anti-HMB45 antibodies, implying that melanosome formation was affected by hyperosmotic stress. Electron microscopic analysis revealed that the M6PR-positive swollen vacuoles were multi-layered and contained melanized granules, and they produced melanin when L-DOPA was applied, indicating that these vacuoles were still capable of producing melanin, but the inner conditions were not compatible with melanin production. The vacuolation phenomenon induced by hyperosmotic conditions disappeared with treatment with the PI3K activator 740 Y-P, indicating that the PI3K pathway is affected by hyperosmotic conditions and is responsible for the proper formation and maturation of melanosomes. The microarray analysis showed alterations of the vesicle organization and transport under hyperosmotic stress. Our findings suggest that melanogenesis could be regulated by physiological conditions, such as osmotic pressure.

iNID: an analytical framework for identifying network models for interplays among developmental signaling in Arabidopsis

Integration of internal and external cues into developmental programs is indispensable for growth and development of plants, which involve complex interplays among signaling pathways activated by the internal and external factors (IEFs). However, decoding these complex interplays is still challenging. Here, we present a web-based platform that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID) in Arabidopsis. iNID provides a comprehensive resource of (1) transcriptomes previously collected under the conditions treated with a broad spectrum of IEFs and (2) protein and genetic interactome data in Arabidopsis. In addition, iNID provides an array of tools for identifying key regulators and network models related to interplays among IEFs using transcriptome and interactome data. To demonstrate the utility of iNID, we investigated the interplays of (1) phytohormones and light and (2) phytohormones and biotic stresses. The results revealed 34 potential regulators of the interplays, some of which have not been reported in association with the interplays, and also network models that delineate the involvement of the 34 regulators in the interplays, providing novel insights into the interplays collectively defined by phytohormones, light, and biotic stresses. We then experimentally verified that BME3 and TEM1, among the selected regulators, are involved in the auxin-brassinosteroid (BR)-blue light interplay. Therefore, iNID serves as a useful tool to provide a basis for understanding interplays among IEFs.

Decreased diversity of nasal microbiota and their secreted extracellular vesicles in patients with chronic rhinosinusitis based on a metagenomic analysis

BACKGROUND

Chronic rhinosinusitis (CRS) is an inflammatory process in the nasal cavity and paranasal sinuses, and bacteria have been considered to be a cause. Indeed, recent evidence indicates that bacteria-derived extracellular vesicles (EV) appear to be an important causative agent of inflammatory diseases. Here, we aimed to evaluate the diversity of nasal microbiota and their secreted EV in patients with CRS.

METHODS

Nasal lavage (NAL) fluid samples were obtained from five patients with CRS with polyposis, three patients with CRS without polyposis, and three non-CRS controls. After preparation of bacteria and EV from samples using differential centrifugation, genomic DNA was extracted and 16S-rDNA amplicons were subjected to high-throughput pyrosequencing on a Roche 454 GS-FLX platform.

RESULTS

Metagenomics showed that bacteria composition was positively correlated with EV composition. Samples from patients with CRS had greater bacterial abundance and lower diversity, both from bacteria and the EV portion of samples, compared with non-CRS samples. At each phylogenetic level, Bacteroidetes decreased while Proteobacteria increased in the CRS group at the phylum level. At the genus level, Prevotella spp. decreased in the CRS group, while Staphylococcus spp. increased from both bacteria and EV. Moreover, Staphylococcus aureus and its secreting EV compositions were higher in samples from CRS with polyps compared with CRS without polyps.

CONCLUSIONS

These results suggest that patients with CRS have altered nasal microbiota and decreased diversity in bacterial compositions as well as increased S. aureus abundance in those patients with polyps.

Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data

Background

Technological improvements have shifted the focus from data generation to data analysis. The availability of large amounts of data from transcriptomics, protemics and metabolomics experiments raise new questions concerning suitable integrative analysis methods. We compare three integrative analysis techniques (co-inertia analysis, generalized singular value decomposition and integrative biclustering) by applying them to gene and protein abundance data from the six life cycle stages of Plasmodium falciparum. Co-inertia analysis is an analysis method used to visualize and explore gene and protein data. The generalized singular value decomposition has shown its potential in the analysis of two transcriptome data sets. Integrative Biclustering applies biclustering to gene and protein data.

Results

Using CIA, we visualize the six life cycle stages of Plasmodium falciparum, as well as GO terms in a 2D plane and interpret the spatial configuration. With GSVD, we decompose the transcriptomic and proteomic data sets into matrices with biologically meaningful interpretations and explore the processes captured by the data sets. IBC identifies groups of genes, proteins, GO Terms and life cycle stages of Plasmodium falciparum. We show method-specific results as well as a network view of the life cycle stages based on the results common to all three methods. Additionally, by combining the results of the three methods, we create a three-fold validated network of life cycle stage specific GO terms: Sporozoites are associated with transcription and transport; merozoites with entry into host cell as well as biosynthetic and metabolic processes; rings with oxidation-reduction processes; trophozoites with glycolysis and energy production; schizonts with antigenic variation and immune response; gametocyctes with DNA packaging and mitochondrial transport. Furthermore, the network connectivity underlines the separation of the intraerythrocytic cycle from the gametocyte and sporozoite stages.

Conclusion

Using integrative analysis techniques, we can integrate knowledge from different levels and obtain a wider view of the system under study. The overlap between method-specific and common results is considerable, even if the basic mathematical assumptions are very different. The three-fold validated network of life cycle stage characteristics of Plasmodium falciparum could identify a large amount of the known associations from literature in only one study.

YTRP: a repository for yeast transcriptional regulatory pathways

Regulatory targets of transcription factors (TFs) can be identified by the TF perturbation experiments, which reveal the expression changes owing to the perturbation (deletion or overexpression) of TFs. But the identified targets of a given TF consist of both direct and indirect regulatory targets. It has been shown that most of the TFPE-identified regulatory targets are indirect, indicating that TF-gene regulation is mainly through transcriptional regulatory pathways (TRPs) consisting of intermediate TFs. Without identification of these TRPs, it is not easy to understand how a TF regulates its indirect targets. Because there is no such database depositing the potential TRPs for Saccharomyces cerevisiae now, this motivates us to construct the YTRP (Yeast Transcriptional Regulatory Pathway) database. For each TF-gene regulatory pair under different experimental conditions, all possible TRPs in two underlying networks (constructed using experimentally verified TF-gene binding pairs and TF-gene regulatory pairs from the literature) for the specified experimental conditions were automatically enumerated by TRP mining procedures developed from the graph theory. The enumerated TRPs of a TF-gene regulatory pair provide experimentally testable hypotheses for the molecular mechanisms behind a TF and its regulatory target. YTRP is available online at http://cosbi3.ee.ncku.edu.tw/YTRP/. We believe that the TRPs deposited in this database will greatly improve the usefulness of TFPE data for yeast biologists to study the regulatory mechanisms between a TF and its knocked-out targets.

Database URL: http://cosbi3.ee.ncku.edu.tw/YTRP/

Endobiogeny: a global approach to systems biology (part 1 of 2)

Endobiogeny is a global systems approach to human biology that may offer an advancement in clinical medicine based in scientific principles of rigor and experimentation and the humanistic principles of individualization of care and alleviation of suffering with minimization of harm. Endobiogeny is neither a movement away from modern science nor an uncritical embracing of pre-rational methods of inquiry but a synthesis of quantitative and qualitative relationships reflected in a systems-approach to life and based on new mathematical paradigms of pattern recognition.

DualChip®microarray as a new tool in cancer research

Over the last 5 years, the emergence of gene expression profiling using high-density DNA microarrays led to a better understanding of tumor development and identified new prognostic markers. However, high-density microarrays failed to leap from the researcher's bench to the clinical practice due to their cost, data management and lack of standardization. DualChip low-density DNA microarrays were developed as a new flexible tool that is able to reliably quantify the expression of a limited number of genes of clinical relevance. This review will illustrate how DualChip technology can be applied to tumor diagnosis and tumor-acquired drug resistance.

A network perspective on unraveling the role of TRP channels in biology and disease

Transient receptor potential (TRP) channels are a large family of non-selective cation channels that mediate numerous physiological and pathophysiological processes; however, still largely unknown are the underlying molecular mechanisms. With data generated on an unprecedented scale, network-based approaches have been revolutionizing the way in which we understand biology and disease, discover disease genes, and develop therapeutic strategies. These circumstances have created opportunities to encounter TRP channel research to data-intensive science. In this review, we provide an introduction of network-based approaches in biomedical science, describe the current state of TRP channel network biology, and discuss the future direction of TRP channel research. Network perspective will facilitate the discovery of latent roles and underlying mechanisms of TRP channels in biology and disease.

Effects of matrine on JAK-STAT signaling transduction pathways in bleomycin-induced pulmonary fibrosis

The current study aims to investigate the effects of matrine on the JAK-STAT signaling transduction pathways in bleomycin (BLM)-induced pulmonary fibrosis (PF) and to explore its action mechanism. A total of 72 male C57BL/6 mice were randomized into the control, model, and treatment groups. PF models were established by instilling BLM intratracheally. The treatment group was given daily matrine through gastric lavage. Six mice were sacrificed in each group at 3, 7, 14, and 28 days. The lung tissues were observed using hematoxylin-eosin staining. The expression of JAK, STAT1, and STAT3 was observed using immunohistochemistry and then determined using real-time polymerase chain reaction. Alveolitis and PF significantly improved in the treatment group compared with the model group (P < 0.05). The expression of JAK, STAT1, and STAT3 in the model group increased at day 7, peaked at day 14 and then decreased, but the expression was still higher than that in the control group at day 28 (P < 0.05). The three indices in the treatment group were significantly lower than those in the model group at any detection time point (P < 0.05). PF causes high expression of JAK, STAT1, and STAT3. Matrine exerts an anti-PF effect by inhibiting the JAK-STAT signaling transduction pathways.

Pig immune response to general stimulus and to porcine reproductive and respiratory syndrome virus infection: a meta-analysis approach

BACKGROUND

The availability of gene expression data that corresponds to pig immune response challenges provides compelling material for the understanding of the host immune system. Meta-analysis offers the opportunity to confirm and expand our knowledge by combining and studying at one time a vast set of independent studies creating large datasets with increased statistical power. In this study, we performed two meta-analyses of porcine transcriptomic data: i) scrutinized the global immune response to different challenges, and ii) determined the specific response to Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) infection. To gain an in-depth knowledge of the pig response to PRRSV infection, we used an original approach comparing and eliminating the common genes from both meta-analyses in order to identify genes and pathways specifically involved in the PRRSV immune response. The software Pointillist was used to cope with the highly disparate data, circumventing the biases generated by the specific responses linked to single studies. Next, we used the Ingenuity Pathways Analysis (IPA) software to survey the canonical pathways, biological functions and transcription factors found to be significantly involved in the pig immune response. We used 779 chips corresponding to 29 datasets for the pig global immune response and 279 chips obtained from 6 datasets for the pig response to PRRSV infection, respectively.

RESULTS

The pig global immune response analysis showed interconnected canonical pathways involved in the regulation of translation and mitochondrial energy metabolism. Biological functions revealed in this meta-analysis were centred around translation regulation, which included protein synthesis, RNA-post transcriptional gene expression and cellular growth and proliferation. Furthermore, the oxidative phosphorylation and mitochondria dysfunctions, associated with stress signalling, were highly regulated. Transcription factors such as MYCN, MYC and NFE2L2 were found in this analysis to be potentially involved in the regulation of the immune response. The host specific response to PRRSV infection engendered the activation of well-defined canonical pathways in response to pathogen challenge such as TREM1, toll-like receptor and hyper-cytokinemia/ hyper-chemokinemia signalling. Furthermore, this analysis brought forth the central role of the crosstalk between innate and adaptive immune response and the regulation of anti-inflammatory response. The most significant transcription factor potentially involved in this analysis was HMGB1, which is required for the innate recognition of viral nucleic acids. Other transcription factors like interferon regulatory factors IRF1, IRF3, IRF5 and IRF8 were also involved in the pig specific response to PRRSV infection.

CONCLUSIONS

This work reveals key genes, canonical pathways and biological functions involved in the pig global immune response to diverse challenges, including PRRSV infection. The powerful statistical approach led us to consolidate previous findings as well as to gain new insights into the pig immune response either to common stimuli or specifically to PRRSV infection.

Using Systems Biology Approaches to Predict New Players in the Innate Immune System

Toll-like receptors (TLRs) are critical players in the innate immune response to pathogens. However, transcriptional regulatory mechanisms in the TLR activation pathways are still relatively poorly characterized. To address this question, the author of this chapter applied a systematic approach to predict transcription factors that temporally regulate differentially expressed genes under diverse TLR stimuli. Time-course microarray data were selected from mouse bone marrow-derived macrophages stimulated by six TLR agonists. Differentially regulated genes were clustered on the basis of their dynamic behavior. The author then developed a computational method to identify positional overlapping transcription factor (TF) binding sites in each cluster, so as to predict possible TFs that may regulate these genes. A second microarray dataset, on wild-type, Myd88-/- and Trif-/- macrophages stimulated by lipopolysaccharide (LPS), was used to provide supporting evidence on this combined approach. Overall, the author was able to identify known TLR TFs, as well as to predict new TFs that may be involved in TLR signaling.

Disruption of sorting nexin 5 causes respiratory failure associated with undifferentiated alveolar epithelial type I cells in mice

Sorting nexin 5 (Snx5) has been posited to regulate the degradation of epidermal growth factor receptor and the retrograde trafficking of cation-independent mannose 6-phosphate receptor/insulin-like growth factor II receptor. Snx5 has also been suggested to interact with Mind bomb-1, an E3 ubiquitin ligase that regulates the activation of Notch signaling. However, the in vivo functions of Snx5 are largely unknown. Here, we report that disruption of the Snx5 gene in mice (Snx5^-/- mice) resulted in partial perinatal lethality; 40% of Snx5^-/- mice died shortly after birth due to cyanosis, reduced air space in the lungs, and respiratory failure. Histological analysis revealed that Snx5^-/- mice exhibited thickened alveolar walls associated with undifferentiated alveolar epithelial type I cells. In contrast, alveolar epithelial type II cells were intact, exhibiting normal surfactant synthesis and secretion. Although the expression levels of surfactant proteins and saturated phosphatidylcholine in the lungs of Snx5^-/- mice were comparable to those of Snx5^+/+ mice, the expression levels of T1α, Aqp5, and Rage, markers for distal alveolar epithelial type I cells, were significantly decreased in Snx5^-/- mice. These results demonstrate that Snx5 is necessary for the differentiation of alveolar epithelial type I cells, which may underlie the adaptation to air breathing at birth.

Molecular signatures from omics data: from chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.

Patterns of gene and metabolite define the effects of extracellular osmolality on kidney collecting duct

To investigate the effects of changes in extracellular osmolality on the function of kidney collecting duct cells, particularly on water and sodium reabsorption in the conditions of diuresis and antidiuresis, we generated transcriptome and metabolome profiles of primary cultured inner medullary collecting duct (IMCD) cells. They were grown in hyperosmolar culture medium (640 mOsm) for 4 days and then exposed to either reduced (300 mOsm) or same osmolality for 1 or 2 days more. Integrated analysis of the transcriptome and metabolome revealed that decreased extracellular osmolality was associated with decreased levels of organic osmolytes, glucose, intermediates of citric acid cycle, and branched-chain amino acids (BCAA) in IMCD cells, along with significantly decreased gene expression and protein abundance of P-type transporters (ATP1B1), ABC transporters (ABCC5 and ABCG1), and insulin signaling pathways (IRS2). Quantitative real-time RT-PCR and semiquantitative immunoblotting confirmed the changes of transcript levels of differentially expressed genes and protein levels. Taken together, integrated analysis of omics data demonstrated that water and sodium reabsorption could be reduced by decreased extracellular osmolality per se, through decreased levels of ABC transporters and IRS2, which play a potential role in the transport of organic osmolytes, BCAA, glucose, and trafficking of epithelial sodium channel.

Computational modeling of mitochondrial energy transduction

Mitochondria are the power plant of the heart, burning fat and sugars to supply the muscle with the adenosine triphosphate (ATP) free energy that drives contraction and relaxation during each heart beat. This function was first captured in a mathematical model in 1967. Today, interest in such a model has been rekindled by ongoing in silico integrative physiology efforts such as the Cardiac Physiome project. Here, the status of the field of computational modeling of mitochondrial ATP synthetic function is reviewed.

Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets

Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, "-omics" methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments "constraints" using previously reported protein-protein and protein-DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions "optimization". A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.

CD133+ liver cancer stem cells modulate radioresistance in human hepatocellular carcinoma

CD133 is a cancer stem-cell (CSC) marker associated with radioresistance and chemoresistance in various cancers. In the present study, CD133-expressing liver cancer cells following radiation exposure showed higher activation of MAPK/PI3K signaling pathway and reduction in reactive oxygen species levels compared to CD133- cells. The in vivo study with a xenograft model showed increased tumor formation in irradiated CD133+ cell-injected nude mice compared to the CD133- group, suggesting that CD133 contributes to radioresistance in HCC. Therefore, CD133-expressing liver cancer cells have anti-apoptotic and radioresistance properties that may be useful to improve anti-cancer treatments, including chemotherapy/radiotherapy of HCC.

Understanding the dynamic behavior of genetic regulatory networks by functional decomposition

A number of mechanistic and predictive genetic regulatory networks (GRNs) comprising dozens of genes have already been characterized at the level of cis-regulatory interactions. Reconstructions of networks of 100's to 1000's of genes and their interactions are currently underway. Understanding the organizational and functional principles underlying these networks is probably the single greatest challenge facing genomics today. We review the current approaches to deciphering large-scale GRNs and discuss some of their limitations. We then propose a bottom-up approach in which large-scale GRNs are first organized in terms of functionally distinct GRN building blocks of one or a few genes. Biological processes may then be viewed as the outcome of functional interactions among these simple, well-characterized functional building blocks. We describe several putative GRN functional building blocks and show that they can be located within GRNs on the basis of their interaction topology and additional, simple and experimentally testable constraints.

Fusion methodologies for biomedical data

Data fusion methods are powerful tools for integrating the different views of an organism provided by various types of experimental data. We describe various methodologies for integrating and drawing inferences from a collection of biomedical data, primarily focusing on protein and gene expression data. Computational experiments performed using biomedical data, including known protein-protein interactions, hydropathy profiles, gene expression data and amino acid sequences, demonstrate the utility of this approach. Overall, studies agree in that methodologies using carefully selected data of various types to predict particular classes, groups and interactions, perform better than when applied to a single type of data.

Trade-off between responsiveness and noise suppression in biomolecular system responses to environmental cues

When living systems detect changes in their external environment their response must be measured to balance the need to react appropriately with the need to remain stable, ignoring insignificant signals. Because this is a fundamental challenge of all biological systems that execute programs in response to stimuli, we developed a generalized time-frequency analysis (TFA) framework to systematically explore the dynamical properties of biomolecular networks. Using TFA, we focused on two well-characterized yeast gene regulatory networks responsive to carbon-source shifts and a mammalian innate immune regulatory network responsive to lipopolysaccharides (LPS). The networks are comprised of two different basic architectures. Dual positive and negative feedback loops make up the yeast galactose network; whereas overlapping positive and negative feed-forward loops are common to the yeast fatty-acid response network and the LPS-induced network of macrophages. TFA revealed remarkably distinct network behaviors in terms of trade-offs in responsiveness and noise suppression that are appropriately tuned to each biological response. The wild type galactose network was found to be highly responsive while the oleate network has greater noise suppression ability. The LPS network appeared more balanced, exhibiting less bias toward noise suppression or responsiveness. Exploration of the network parameter space exposed dramatic differences in system behaviors for each network. These studies highlight fundamental structural and dynamical principles that underlie each network, reveal constrained parameters of positive and negative feedback and feed-forward strengths that tune the networks appropriately for their respective biological roles, and demonstrate the general utility of the TFA approach for systems and synthetic biology.

Biological systems constantly balance noise suppression with responsiveness. In a fluctuating environment, some changes are insignificant to living cells while others represent cues to which they must respond. These stimuli are interpreted by molecular circuits that enable the cell to strike an appropriate balance between responsiveness and noise suppression. This trade-off is governed by the structure and kinetic parameters of molecular networks, which have been tuned by evolutionary selection for different stimuli and responses. We consider three regulatory circuits (two from yeast and one from mammalian cells), which respond to different environments and involve very different physiological processes. To investigate the responses to a time varying signal, we developed a generalized time-frequency analysis framework for studying such trade-offs using mathematical models of regulatory circuits and explore how the structure and parameters of the circuit affect the trade-offs between noise suppression and responsiveness. The generalized TFA approach represents an effective tool for exploring and analyzing different systems-level dynamical properties. Making use of such properties can facilitate prediction and network control for systems- and synthetic biology applications.

Systematic computational prediction of protein interaction networks

Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use.

Strengthening insights into host responses to mastitis infection in ruminants by combining heterogeneous microarray data sources

Background

Gene expression profiling studies of mastitis in ruminants have provided key but fragmented knowledge for the understanding of the disease. A systematic combination of different expression profiling studies via meta-analysis techniques has the potential to test the extensibility of conclusions based on single studies. Using the program Pointillist, we performed meta-analysis of transcription-profiling data from six independent studies of infections with mammary gland pathogens, including samples from cattle challenged in vivo with S. aureus, E. coli, and S. uberis, samples from goats challenged in vivo with S. aureus, as well as cattle macrophages and ovine dendritic cells infected in vitro with S. aureus. We combined different time points from those studies, testing different responses to mastitis infection: overall (common signature), early stage, late stage, and cattle-specific.

Results

Ingenuity Pathway Analysis of affected genes showed that the four meta-analysis combinations share biological functions and pathways (e.g. protein ubiquitination and polyamine regulation) which are intrinsic to the general disease response. In the overall response, pathways related to immune response and inflammation, as well as biological functions related to lipid metabolism were altered. This latter observation is consistent with the milk fat content depression commonly observed during mastitis infection. Complementarities between early and late stage responses were found, with a prominence of metabolic and stress signals in the early stage and of the immune response related to the lipid metabolism in the late stage; both mechanisms apparently modulated by few genes, including XBP1 and SREBF1.

The cattle-specific response was characterized by alteration of the immune response and by modification of lipid metabolism. Comparison of E. coli and S. aureus infections in cattle in vivo revealed that affected genes showing opposite regulation had the same altered biological functions and provided evidence that E. coli caused a stronger host response.

Conclusions

This meta-analysis approach reinforces previous findings but also reveals several novel themes, including the involvement of genes, biological functions, and pathways that were not identified in individual studies. As such, it provides an interesting proof of principle for future studies combining information from diverse heterogeneous sources.

Using Systems Biology Approaches to Predict New Players in the Innate Immune System

Toll-like receptors (TLRs) are critical players in the innate immune response to pathogens. However, transcriptional regulatory mechanisms in the TLR activation pathways are still relatively poorly characterized. To address this question, the author of this chapter applied a systematic approach to predict transcription factors that temporally regulate differentially expressed genes under diverse TLR stimuli. Time-course microarray data were selected from mouse bone marrow-derived macrophages stimulated by six TLR agonists. Differentially regulated genes were clustered on the basis of their dynamic behavior. The author then developed a computational method to identify positional overlapping transcription factor (TF) binding sites in each cluster, so as to predict possible TFs that may regulate these genes. A second microarray dataset, on wild-type, Myd88-/- and Trif-/- macrophages stimulated by lipopolysaccharide (LPS), was used to provide supporting evidence on this combined approach. Overall, the author was able to identify known TLR TFs, as well as to predict new TFs that may be involved in TLR signaling.

Data integration for dynamic and sustainable systems biology resources: challenges and lessons learned

Systems-biology and infectious-disease (host-pathogen-environment) research and development is becoming increasingly dependent on integrating data from diverse and dynamic sources. Maintaining integrated resources over long periods of time presents distinct challenges. This review describes experiences and lessons learned from integrating data in two five-year projects focused on pathosystems biology: the Pathosystems Resource Integration Center (PATRIC, http://patric.vbi.vt.edu/), with a goal of developing bioinformatics resources for the research and countermeasures-development communities based on genomics data, and the Resource Center for Biodefense Proteomics Research (RCBPR, http://www.proteomicsresource.org/), with a goal of developing resources based on the experiment data such as microarray and proteomics data from diverse sources and technologies. Some challenges include integrating genomic sequence and experiment data, data synchronization, data quality control, and usability engineering. We present examples of a variety of data-integration problems drawn from our experiences with PATRIC and RBPRC, as well as open research questions related to long-term sustainability, and describe the next steps to meeting these challenges. Novel contributions of this work include 1) an approach for addressing discrepancies between experiment results and interpreted results, and 2) expanding the range of data-integration techniques to include usability engineering at the presentation level.

Advantages and limitations of current network inference methods

Network inference, which is the reconstruction of biological networks from high-throughput data, can provide valuable information about the regulation of gene expression in cells. However, it is an underdetermined problem, as the number of interactions that can be inferred exceeds the number of independent measurements. Different state-of-the-art tools for network inference use specific assumptions and simplifications to deal with underdetermination, and these influence the inferences. The outcome of network inference therefore varies between tools and can be highly complementary. Here we categorize the available tools according to the strategies that they use to deal with the problem of underdetermination. Such categorization allows an insight into why a certain tool is more appropriate for the specific research question or data set at hand.