Systems Biology Perspective Research Articles

Detecting Common Pathways and Key Molecules of Neurodegenerative Diseases from the Topology of Molecular Networks.

MotivationNeurodegenerative diseases (NDs), including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, occur as a result of neurodegenerative processes. Thus, it has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels. However, traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Discovering this overlap offers hope for therapeutic advances that could ameliorate many ND simultaneously. In parallel, in the last decade, systems biology approaches have become a reliable choice in complex disease analysis for gaining more delicate biological insights and have enabled the comprehension of the higher order functions of the biological systems.ResultsToward this orientation, we developed a systems biology approach for the identification of common links and pathways of ND, based on well-established and novel topological and functional measures. For this purpose, a molecular pathway network was constructed, using molecular interactions and relations of four main neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease). Our analysis captured the overlapped subregions forming molecular subpathways fully enriched in these four NDs. Also, it exported molecules that act as bridges, hubs, and key players for neurodegeneration concerning either their topology or their functional role.ConclusionUnderstanding these common links and central topologies under the perspective of systems biology and network theory and greater insights are provided to uncover the complex neurodegeneration processes.

Targeting abnormal metabolism in Alzheimer's disease: The Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study.

Drug discovery for disease‐modifying therapies for Alzheimer's disease and related dementias (ADRD) based on the traditional paradigm of experimental animal models has been disappointing. We describe the rationale and design of the Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study, an innovative multidisciplinary alternative to traditional drug discovery. First, we use a systems biology perspective in the "hypothesis generation" phase to identify metabolic abnormalities that may either precede or interact with the accumulation of ADRD neuropathology, accelerating the expression of clinical symptoms of the disease. Second, in the "hypothesis refinement" phase we propose use of large patient cohorts to test whether drugs approved for other indications that also target metabolic drivers of ADRD pathogenesis might alter the trajectory of the disease. We emphasize key challenges in population‐based pharmacoepidemiologic studies aimed at quantifying the association between medication use and ADRD onset and outline robust causal inference principles to safeguard against common pitfalls. Candidate ADRD treatments emerging from this approach will hold promise as plausible disease‐modifying therapies for evaluation in randomized controlled trials.

Omics Studies and Systems Biology Perspective towards Abiotic Stress Response in Plants

Plant abiotic stress responses are vital yield-restricting aspect in agriculture. Recent technology in plant biology allows research of such stress responses at a molecular scale in plants. Network analysis provides in-depth knowledge regarding omics information visualisation as it reduces the intrinsic intricacy of such data. The use of integrated functional genomics helps to understand the relationship between the genomic profile and the phenotypic profile in different environmental conditions of an organism. Plants’ responses to abiotic stress are often considered as a complex process. Systems biology approaches allow visualising and understanding how plant life works to overcome abiotic stress. The combination of integrated functional genomics along with bioinformatics will put a hand in additional in-depth research knowledge on stress tolerance to plants by exploiting available genetic information and continuously improving techniques and strategies. Most of the omics technologies are high throughput with very rapid data generation rates and humongous outputs. These technologies have made noticeable contributions to the modern-day improvements in our knowledge of plant biology. So, in this review, omics studies and the system biology approach towards abiotic stress tolerance in plants are highlighted.

Toward an Integrated View of Operational Tolerance in Human Renal Transplantation: A Systems Biology Perspective.

Operational tolerance (OT) is the phenomenon occurring in human renal and liver transplantation in which the body does not reject the organ after discontinuing immunosuppression for at least a year. We revisited the data generated by The Brazilian Multicenter Study on Operational Tolerance involving different conceptual fields - antigen-specific cytokine response, immune cell numbers and repertoire, signaling pathways, and epigenetics. We integrated our data to pave the way to systems biology thinking and harness debate on potential mechanisms in OT. We present original data on systems biology in OT, connecting potential mechanistic players. Using bioinformatics, we identified three dominant features that discriminate OT from its opposing clinical outcome, chronic rejection (CR). The OT-CR discriminative molecules were FOXP3, GATA3 and STAT6, each corresponding to a differential profile: (1) In FOXP3, OT presents preserved regulatory T cell (Treg) numbers but decreased numbers in CR; (2) in GATA3, increased expression is seen in OT; and (3) in STAT6, decreased monocyte activation is seen in OT. With these variables, we built molecular networks to identify interactions related to OT versus CR. Our first systems biology endeavor gave rise to novel potentially relevant interconnected players in OT mechanisms: FOXP3 connecting to interleukin-9 (IL-9) and IL-35 signaling, suggesting their immunoregulatory involvement in OT. Likewise, GATA3/FOXP3 interactions incrementing/stabilizing FOXP3 transcription suggest participation in keeping healthy FOXP3+ Tregs in OT. We envision that systems biology thinking will greatly contribute to advancing knowledge in human transplantation tolerance in an interactive perspective.

Complement activation by drug carriers and particulate pharmaceuticals: Principles, challenges and opportunities

Considering the multifaceted protective and homeostatic roles of the complement system, many consequences arise when drug carriers, and particulate pharmaceutical formulations clash with complement proteins, and trigger complement cascade. Complement activation may induce formulation destabilization, promote opsonization, and affect biological and therapeutic performance of pharmaceutical nano- and micro-particles. In some cases, complement activation is beneficial, where complement may play a role in prophylactic protection, whereas uncontrolled complement activation is deleterious, and contributes to disease progression. Accordingly, design initiatives with particulate medicines should consider complement activation properties of the end formulation within the context of administration route, dosing, systems biology, and therapeutic perspective. Here we examine current progress in mechanistic processes underlying complement activation by pre-clinical and clinical particles, identify opportunities and challenges ahead, and suggest future directions in nanomedicine-complement interface research.

Multiple system-level feedback loops control life-and-death decisions in endoplasmic reticulum stress.

Scientific results have revealed that autophagy is able to promote cell survival in response to endoplasmic reticulum (ER) stress, while drastic events result in apoptotic cell death. Here, we analyse the important crosstalk of life-and-death decisions from a systems biological perspective by studying the regulatory modules of the unfolded protein response (UPR). While a double-negative loop between autophagy and apoptosis inducers is crucial for the switch-like characteristic of the stress response mechanism, a positive feedback loop between ER stress sensors is also essential. Corresponding to experimental data, here, we show the dynamical significance of Gadd34-CHOP connections inside the PERK branch of the UPR. The multiple system-level feedback loops seem to be crucial for managing a robust life-and-death decision depending on the level and durability of cellular stress.

Machine Learning Unveils Proteotypic Mimicry in Genetically Defined SCN Variants

Background Novel computational algorithms for multi-omics analysis bear great potential to highlight pathomechanisms of monogenic diseases. We recently defined the in-depth proteome of primary human neutrophil granulocytes (PMID 30630937). Here, we ask the question whether proteotypic patterns differ between defined genetic subtypes associated with severe congenital neutropenia (SCN). We focus on two novel genetic variants in constituents of the signal recognition particle (SRPRA and SRP19) and previously reported SCN genotypes SRP54, HAX1, and ELANE. Methods We analyzed proteomes of highly purified neutrophil granulocytes from a total of 26 SCN patients, including 5 with homozygous splice site mutations in SRP19, one patient with a de-novo heterozygous missense mutation in SRPRA (using 5 biological replicates collected months apart) as well as 6 patients with SRP54, 8 with HAX1 and 6 with ELANE mutations. Samples of 70 healthy donors (HD) served as controls. Whole cell proteome analysis was based on data-independent acquisition using a Thermo Fisher QExactive HF mass spectrometer. Data analysis was performed in R and Cytoscape, machine learning approaches included lasso regression and random forest. Results Differential expression analysis in comparison to HD showed in all genotypes overexpression of ribosomes, the translational apparatus, mitochondria, cell-substrate junctions and response to unfolded proteins. Underexpressed proteins showed genotype specific enrichment for granule subsets. Whereas ELANE showed deficiency of primary and secretory granules, HAX1 showed deficiency of specific, tertiary and secretory granules. All SRP genotypes showed markedly reduced abundance of proteins in all granule subsets. Principal component analysis showed clear separation of healthy and diseased proteotypes on the first component, whereas the separation of patient genotypes became clear only using five dimensions. We derived genotype specific proteome signatures by lasso regression, consisting of 26 (minimal specific set) to 128 (comprehensive signature) proteins, and a signature of 48 proteins when joining the SRP genotypes as one group. This signatures allow for perfect separation of the genotypes, demonstrating a clear genotype specific effect on protein abundance levels. We asked the question if the genotypes SRP19 and SRPRA show more similar proteomic profile to SRP54 than the other genotypes (ELANE, HAX1) by training a random forest model on proteome data from SRP54, HAX1, ELANE and HD and subsequently testing if the other SRP samples get classified as SRP54. We observe only few misclassifications using either all proteins (7/10) or using the lasso derived genotype defining proteins (8/10). This strongly supports our hypothesis that mutations in different subunits of the same complex lead to similar proteotype changes, a phenomenon we propose to call "proteotypic mimicry". For a systems biology perspective we selected proteins that were exclusively regulated in the SRP genotypes and restricted a network of interactions to these proteins together with their direct interactors (based on APID level 1). The resulting network contained 464 proteins and 3587 interactions. MCODE analysis identified 16 clusters that were consequently annotated using BINGO enrichment analysis. The SRP specific network shows features of the translational apparatus, the proteasome, the septin complex, splicing and cell-metabolic processes. Further studies to dissect specific pathomechanisms are under way. Conclusion Here we provide for the first time evidence for the correlation between SCN causing genotypes and their corresponding neutrophil proteotypes. In particular, we demonstrate significant overlap of all SRP related proteotypes, indicating a phenomenon we propose to be called "proteotypic mimicry". Studies on similarities and disparities of neutrophil proteotypes will help to raise new hypothesis on distinct cellular dysfunction in defined genetic defects of neutrophil granulocytes. Disclosures No relevant conflicts of interest to declare.

A Double Negative Feedback Loop between mTORC1 and AMPK Kinases Guarantees Precise Autophagy Induction upon Cellular Stress.

Cellular homeostasis is controlled by an evolutionary conserved cellular digestive process called autophagy. This mechanism is tightly regulated by the two sensor elements called mTORC1 and AMPK. mTORC1 is one of the master regulators of proteostasis, while AMPK maintains cellular energy homeostasis. AMPK is able to promote autophagy by phosphorylating ULK1, the key inducer of autophagosome formation, while mTORC1 downregulates the self-eating process via ULK1 under nutrient rich conditions. We claim that the feedback loops of the AMPK–mTORC1–ULK1 regulatory triangle guarantee the appropriate response mechanism when nutrient and/or energy supply changes. In our opinion, there is an essential double negative feedback loop between mTORC1 and AMPK. Namely, not only does AMPK downregulate mTORC1, but mTORC1 also inhibits AMPK and this inhibition is required to keep AMPK inactive at physiological conditions. The aim of the present study was to explore the dynamical characteristic of AMPK regulation upon various cellular stress events. We approached our scientific analysis from a systems biology perspective by incorporating both theoretical and molecular biological techniques. In this study, we confirmed that AMPK is essential to promote autophagy, but is not sufficient to maintain it. AMPK activation is followed by ULK1 induction, where protein has a key role in keeping autophagy active. ULK1-controlled autophagy is always preceded by AMPK activation. With both ULK1 depletion and mTORC1 hyper-activation (i.e., TSC1/2 downregulation), we demonstrate that a double negative feedback loop between AMPK and mTORC1 is crucial for the proper dynamic features of the control network. Our computer simulations have further proved the dynamical characteristic of AMPK–mTORC1–ULK1 controlled cellular nutrient sensing.

Protein interactome network analysis predicts novel breast cancer candidate genes with molecular signatures

Abstract Background Advancement in molecular biology research has allowed the measurement of multiomics data points from a single tumor biopsy sample in a reasonable time frame for making significant clinical decisions. There have been tremendous advances from the bioinformatics and systems biology perspective to analyse integrated multi‐scale analysis of the data will prove invaluable for a combined systems‐level understanding of the important biological network processes contributing to the initiation, progression and management of cancer. Cancer networks have greater challenge due to the aberrant functions of hundreds of genes that are translated to altered protein function within each cancer cell, to understand this complexity. Network-based methods have been used to analyse the complexity molecular interactions in the cell and can contribute to prediction of candidate genes responsible for cancer. Methods HI-II- 14 is the largest experimentally determined binary protein – protein interaction map is used for the study. These interactions were mapped to NCBI Entrez gene ID and excluded self-loop and redundant interactions. The known human breast cancer genes were obtained from the Sanger Cancer Gene Census which is a comprehensive catalogue of genes implicated in breast cancer and from Uniprot database. In total, we obtained 913 genes from both for analyses. The genes degree of connections and the centrality in the protein -protein networks were considered. The genome-wide mutation datasets of breast cancer were downloaded from TCGA as maf files. For each gene, we obtained the mutation frequency for each gene is calculated which is defined as the number of mutated samples divided by the total number of samples. The frequency of the top 100 ranked genes and the bottom 100 ranked genes were compared by Wilcox rank sum test. Results In this study, HI-II-14 human protein-protein interactome network is applied to prediction of novel breast cancer genes. Conclusions Our study suggests integrating interactome networks with multiomics datasets could provide novel insights into breast cancer-associated genes and underlying molecular mechanisms. Legal entity responsible for the study M. Peer Mohammed. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.

Systems biology perspective for studying the gut microbiota in human physiology and liver diseases.

The advancement in high-throughput sequencing technologies and systems biology approaches have revolutionized our understanding of biological systems and opened a new path to investigate unacknowledged biological phenomena. In parallel, the field of human microbiome research has greatly evolved and the relative contribution of the gut microbiome to health and disease have been systematically explored. This review provides an overview of the network-based and translational systems biology-based studies focusing on the function and composition of gut microbiota. We also discussed the association between the gut microbiome and the overall human physiology, as well as hepatic diseases and other metabolic disorders.

Estrogen receptors promote NSCLC progression by modulating the membrane receptor signaling network: a systems biology perspective

BackgroundEstrogen receptors (ERs) are thought to play an important role in non-small cell lung cancer (NSCLC). However, the effect of ERs in NSCLC is still controversial and needs further investigation. A new consideration is that ERs may affect NSCLC progression through complicated molecular signaling networks rather than individual targets. Therefore, this study aims to explore the effect of ERs in NSCLC from the perspective of cancer systems biology.MethodsThe gene expression profile of NSCLC samples in TCGA dataset was analyzed by bioinformatics method. Variations of cell behaviors and protein expression were detected in vitro. The kinetic process of molecular signaling network was illustrated by a systemic computational model. At last, immunohistochemical (IHC) and survival analysis was applied to evaluate the clinical relevance and prognostic effect of key receptors in NSCLC.ResultsBioinformatics analysis revealed that ERs might affect many cancer-related molecular events and pathways in NSCLC, particularly membrane receptor activation and signal transduction, which might ultimately lead to changes in cell behaviors. Experimental results confirmed that ERs could regulate cell behaviors including cell proliferation, apoptosis, invasion and migration; ERs also regulated the expression or activation of key members in membrane receptor signaling pathways such as epidermal growth factor receptor (EGFR), Notch1 and Glycogen synthase kinase-3β/β-Catenin (GSK3β/β-Catenin) pathways. Modeling results illustrated that the promotive effect of ERs in NSCLC was implemented by modulating the signaling network composed of EGFR, Notch1 and GSK3β/β-Catenin pathways; ERs maintained and enhanced the output of oncogenic signals by adding redundant and positive-feedback paths into the network. IHC results echoed that high expression of ERs, EGFR and Notch1 had a synergistic effect on poor prognosis of advanced NSCLC.ConclusionsThis study indicated that ERs were likely to promote NSCLC progression by modulating the integrated membrane receptor signaling network composed of EGFR, Notch1 and GSK3β/β-Catenin pathways and then affecting tumor cell behaviors. It also complemented the molecular mechanisms underlying the progression of NSCLC and provided new opportunities for optimizing therapeutic scheme of NSCLC.

A systems biology approach for studying Wolbachia metabolism reveals points of interaction with its host in the context of arboviral infection.

Wolbachia are alpha-proteobacteria known to infect arthropods, which are of interest for disease control since they have been associated with improved resistance to viral infection. Although several genomes for different strains have been sequenced, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their dependency for survival. Motivated by the potential applications on disease control, we developed genome-scale models of four Wolbachia strains known to infect arthropods: wAlbB (Aedes albopictus), wVitA (Nasonia vitripennis), wMel and wMelPop (Drosophila melanogaster). The obtained metabolic reconstructions exhibit a metabolism relying mainly on amino acids for energy production and biomass synthesis. A gap analysis was performed to detect metabolic candidates which could explain the endosymbiotic nature of this bacterium, finding that amino acids, requirements for ubiquinone precursors and provisioning of metabolites such as riboflavin could play a crucial role in this relationship. This work provides a systems biology perspective for studying the relationship of Wolbachia with its host and the development of new approaches for control of the spread of arboviral diseases. This approach, where metabolic gaps are key objects of study instead of just additions to complete a model, could be applied to other endosymbiotic bacteria of interest.

Rethinking the pragmatic systems biology and systems-theoretical biology divide: Toward a complexity-inspired epistemology of systems biomedicine

This paper examines some methodological and epistemological issues underlying the ongoing “artificial” divide between pragmatic-systems biology and systems-theoretical biology. The pragmatic systems view of biology has encountered problems and constraints on its explanatory power because pragmatic systems biologists still tend to view systems as mere collections of parts, not as “emergent realities” produced by adaptive interactions between the constituting components. As such, they are incapable of characterizing the higher-level biological phenomena adequately. The attempts of systems-theoretical biologists to explain these “emergent realities” using mathematics also fail to produce satisfactory results.Given the increasing strategic importance of systems biology, both from theoretical and research perspectives, we suggest that additional epistemological and methodological insights into the possibility of further integration between traditional experimental studies and complex modeling are required. This integration will help to improve the currently underdeveloped pragmatic-systems biology and system-theoretical biology.The “epistemology of complexity,” I contend, acts as a glue that connects and integrates different and sometimes opposing viewpoints, perspectives, streams, and practices, thus maintaining intellectual and research coherence of systems research of life. It allows scientists to shift the focus from traditional experimental research to integrated, modeling-based holistic practices capable of providing a comprehensive knowledge of organizing principles of living systems. It also opens the possibility of the development of new practical and theoretical foundations of systems biology to build a better understanding of complex organismic functions.

Traditional Chinese Medicine-Based Subtyping of Early-Stage Type 2 Diabetes Using Plasma Metabolomics Combined with Ultra-Weak Photon Emission

The prevalence of type 2 diabetes mellitus (T2DM) is increasing rapidly worldwide. Because of the limited success of generic interventions, the focus of the disease study has shifted toward personalized strategies, particularly in the early stages of the disease. Traditional Chinese medicine (TCM) is based on a systems view combined with personalized strategies and has improved our knowledge of personalized diagnostics. From a systems biology perspective, the understanding of personalized diagnostics can be improved to yield a biochemical basis for such strategies; for example, metabolomics can be used in combination with other system-based diagnostic methods such as ultra-weak photon emission (UPE). In this study, we investigated the feasibility of using plasma metabolomics obtained from 44 pre-T2DM subjects to stratify the following TCM-based subtypes: Qi-Yin deficiency, Qi-Yin deficiency with dampness, and Qi-Yin deficiency with stagnation. We studied the relationship between plasma metabolomics and UPE with respect to TCM-based subtyping in order to obtain biochemical information for further interpreting disease subtypes. Principal component analysis of plasma metabolites revealed differences among the TCM-based pre-T2DM subtypes. Relatively high levels of lipids (e.g., cholesterol esters and triglycerides) were important discriminators of two of the three subtypes and may be associated with a higher risk of cardiovascular disease. Plasma metabolomics data indicate that the lipid profile is an essential component captured by UPE with respect to stratifying subtypes of T2DM. The results suggest that metabolic differences exist among different TCM-based subtypes of pre-T2DM, and profiling plasma metabolites can be used to discriminate among these subtypes. Plasma metabolomics thus provides biochemical insights into system-based UPE measurements. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.

Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective.

There is an inherent need to identify differentially expressed genes (DEGs), characterize these genes and provide functional enrichment analysis to the publicly available lung cancer datasets, primarily coming from next-generation sequencing data or microarray gene expression studies. The risk of lung cancer in patients with smokers is manifold, and with chronic obstructive pulmonary disease (COPD) it is 2- to 5-fold greater, compared with smokers without COPD. In the present study, differential expression analysis and gene functional enrichment analysis of lung cancer gene expression datasets obtained from NCBI-GEO were performed. The result identifies a significant number of DEGs which have at least a 2-fold change in their expression. Among them, six genes were found to have a 4-fold change in the expression level, and 47 genes exhibited a 3-fold change in the expression. It was also observed that most of the genes were upregulated and few genes were downregulated.

Transcriptomic and Phytochemical Analyses Reveal Root-Mediated Resource-Based Defense Response to Leaf Herbivory by Ectropis oblique in Tea Plant ( Camellia sinensis).

Leaf herbivory on tea plants ( Camellia sinensis) by tea geometrids ( Ectropis oblique) severely threaten the yield and quality of tea. In previous work, we found that local defense response was induced in damaged leaves by geometrids at the transcriptome level. Here, we investigated the systemic response triggered in undamaged roots and the potential role of roots in response to leaf herbivory. Comparative transcriptome analysis and carbohydrate dynamics indicated that leaf herbivory activated systemic carbon reallocation to enhance resource investment for local secondary metabolism. The crucial role of jasmonic acid and the involvement of other potential hormone signals for local and systemic signaling networks were supported by phytohormone quantification and dynamic expression analysis of phytohormone-related genes. This work represents a deep understanding of the interaction of tea plants and geometrids from the perspective of systems biology and reveals that tea plants have evolved an intricate root-mediated resource-based resistance strategy to cope with geometrid attack.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Decoding competing endogenous RNA networks for cancer biomarker discovery.

Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis; thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. pseudogenes, competing mRNAs, long non-coding RNAs, and circular RNAs, the perturbation of crucial interactions in ceRNA networks may contribute to carcinogenesis by affecting the balance of cellular regulatory system. Therefore, discovering biomarkers that indicate cancer initiation, development, and/or therapeutic responses via reconstructing and analyzing ceRNA networks is of clinical significance. In this review, the regulatory function of ceRNAs in cancer and crucial determinants of ceRNA crosstalk are firstly discussed to gain a global understanding of ceRNA-mediated carcinogenesis. Then, computational and experimental approaches for ceRNA network reconstruction and ceRNA validation, respectively, are described from a systems biology perspective. We focus on strategies for biomarker identification based on analyzing ceRNA networks and highlight the translational applications of ceRNA biomarkers for cancer management. This article will shed light on the significance of miRNA-mediated ceRNA interactions and provide important clues for discovering ceRNA network-based biomarker in cancer biology, thereby accelerating the pace of precision medicine and healthcare for cancer patients.

Predictable gene expression related to behavioral variation in parenting

AbstractDifferential gene expression has been associated with transitions between behavioral states for a wide variety of organisms and behaviors. Heterochrony, genetic toolkits, and predictable pathways underlying behavioral transitions have been hypothesized to explain the relationship between transcription and behavioral changes. Less studied is how variation in transcription is related to variation within a behavior, and if the genes that are associated with this variation are predictable. Here, we adopt an evolutionary systems biology perspective to address 2 hypotheses relating differential expression to changes within and between behavior. We predicted fewer genes will be associated with variation within a behavior than with transitions between states, and the genes underlying variation within a behavior will represent a narrower set of biological functions. We tested for associations with parenting variation within a state with a set of genes known a priori to be differentially expressed (DE) between parenting states in the burying beetle Nicrophorus vespilloides. As predicted, we found that far fewer genes are DE related to variation within parenting. Moreover, these were not randomly distributed among categories or pathways in the gene set we tested and primarily involved genes associated with neurotransmission. We suggest that this means candidate genes will be easier to identify for associations within a behavior, as descriptions of behavioral state may include more than a single phenotype.

Advances in Analyzing Virus-Induced Alterations of Host Cell Splicing.

Alteration of host cell splicing is a common feature of many viral infections which is underappreciated because of the complexity and technical difficulty of studying alternative splicing (AS) regulation. Recent advances in RNA sequencing technologies revealed that up to several hundreds of host genes can show altered mRNA splicing upon viral infection. The observed changes in AS events can be either a direct consequence of viral manipulation of the host splicing machinery or result indirectly from the virus-induced innate immune response or cellular damage. Analysis at a higher resolution with single-cell RNAseq, and at a higher scale with the integration of multiple omics data sets in a systems biology perspective, will be needed to further comprehend this complex facet of virus-host interactions.