Co-regulation Patterns Research Articles

Biclustering Analysis of Co-regulation Patterns in Nuclear-Encoded Mitochondrial Genes and Metabolic Pathways.

Transcription of a large set of nuclear-encoded genes underlies biogenesis of mitochondria, regulated by a complex network of transcription factors and co-regulators. A remarkable heterogeneity can be detected in the expression of these genes in different cell types and tissues, and the recent availability of large gene expression compendiums allows the quantification of specific mitochondrial biogenesis patterns. We have developed a method to effectively perform this task. Massively correlated biclustering (MCbiclust) is a novel bioinformatics method that has been successfully applied to identify co-regulation patterns in large genesets, underlying essential cellular functions and determining cell types. The method has been recently evaluated and made available as a package in Bioconductor for R. One of the potential applications of the method is to compare expression of nuclear-encoded mitochondrial genes or larger sets of metabolism-related genes between different cell types or cellular metabolic states. Here we describe the essential steps to use MCbiclust as a tool to investigate co-regulation of mitochondrial genes and metabolic pathways.

The Association between Symptom Accommodation and Emotional Coregulation in Couples with Binge Eating Disorder.

Intense negative emotions and maladaptive behavioral strategies to reduce emotional distress occur not only in patients with various forms of psychopathology but also in their committed partners. One common strategy to reduce distress is for partners to accommodate to the symptoms of the disorder, which reduces distress short term but maintains symptoms long term. Accommodation is believed to be motivated by the partner reacting behaviorally to the patient's emotions, but the emotions of the partner in this context have yet to be examined. This pilot study examined how partner accommodation related to specific patterns of emotional coregulation between patients with binge eating disorder (BED) and their partners, before and after a couple-based intervention for BED. Vocally encoded emotional arousal was measured during couples' (n=11) conversations about BED. As predicted, partners' emotional reactivity to patients' emotional arousal was associated with high accommodation before treatment. Thus, partners may use accommodation as a strategy to reduce both the patients' and their own distress. After treatment, partners' arousal was no longer associated with the patients' emotional arousal; instead, partners showed greater emotional stability over time, specifically when accommodation was low. Additionally, patients were less emotionally aroused after treatment. Therefore, treatment may have decreased overall emotionality of patients and altered the association between accommodation and partners' emotional reactivity. If replicated, this understanding of the emotional context associated with accommodation in BED can inform couple-based treatment by targeting specific emotional precipitants of behaviors that maintain symptoms.

Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses.

The immune response to pathogens varies substantially among people. While both genetic and non-genetic factors contribute to inter-person variation, their relative contributions and potential predictive power have remained largely unknown. By systematically correlating host factors in 534 healthy volunteers, including baseline immunological parameters and molecular profiles (genome, metabolome and gut microbiome), with cytokine-production capacity after stimulation with 20 pathogens, we identified distinct patterns of co-regulation. Among the 91 different cytokine–stimulus pairs, 11 categories of host factors together explained up to 67% of inter-individual variation in cytokine production induced by stimulation. A computational model based on genetic data predicted the genetic component of stimulus-induced cytokine-production (correlation 0.28-0.89), while non-genetic factors influenced cytokine production as well.

Systemic bioinformatics analysis of skeletal muscle gene expression profiles of sepsis.

Sepsis is a type of systemic inflammatory response syndrome with high morbidity and mortality. Skeletal muscle dysfunction is one of the major complications of sepsis that may also influence the outcome of sepsis. The aim of the present study was to explore and identify potential mechanisms and therapeutic targets of sepsis. Systemic bioinformatics analysis of skeletal muscle gene expression profiles from the Gene Expression Omnibus was performed. Differentially expressed genes (DEGs) in samples from patients with sepsis and control samples were screened out using the limma package. Differential co-expression and coregulation (DCE and DCR, respectively) analysis was performed based on the Differential Co-expression Analysis package to identify differences in gene co-expression and coregulation patterns between the control and sepsis groups. Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways of DEGs were identified using the Database for Annotation, Visualization and Integrated Discovery, and inflammatory, cancer and skeletal muscle development-associated biological processes and pathways were identified. DCE and DCR analysis revealed several potential therapeutic targets for sepsis, including genes and transcription factors. The results of the present study may provide a basis for the development of novel therapeutic targets and treatment methods for sepsis.

IntLIM: integration using linear models of metabolomics and gene expression data

BackgroundIntegration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites.ResultsThe proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally.ConclusionsIntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub (https://github.com/mathelab/IntLIM) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

Mother-Child Coregulation of Parasympathetic Processes Differs by Child Maltreatment Severity and Subtype.

Parasympathetic processes appear to underlie maladaptive parent-child interactions in maltreating families, but it is unknown whether parent-child coregulation of respiratory sinus arrhythmia (RSA) differs by child maltreatment severity and subtype. RSA coregulation in maltreating and nonmaltreating mother-child dyads ( N = 146; age 3-5 years) during two dyadic tasks was analyzed using dynamic time series modeling. Nonmaltreating dyads showed positive RSA concordance but maltreating dyads (when examined as one group) did not. However, when examined separately by subtype, physically abusive dyads showed positive concordance and neglectful dyads no concordance, in dyadic RSA. Patterns were further modified by maltreatment severity, which predicted discordant RSA (one partner's RSA predicting decreases in the other's) in both groups. Specifically, higher physical abuse severity predicted lower resting child RSA, declining mother RSA over time, and mother RSA predicting declines in child RSA over time, suggesting a mother-driven dyadic stress response. Higher neglect severity predicted increasing child RSA over time and child RSA predicting declines in mother RSA over time, suggesting a child-driven maternal stress response. These findings show there are distinct patterns of RSA coregulation in nonmaltreating, physically abusive, and neglectful mother-child dyads, which may inform etiology and intervention with respect to stress regulation in maltreating families.

The effects of maternal respiratory sinus arrhythmia and behavioral engagement on mother‐child physiological coregulation

Parent-child coregulation is thought to be an important precursor for children's developing self-regulation, but we know little about how individual parent factors shape parent-child physiological coregulation. We examined whether maternal baseline respiratory sinus arrhythmia (RSA), teaching, and disengagement were associated with stronger or weaker coregulation of RSA between mothers and their 3-year-old children (N = 47), modeled across 18 min of observed dyadic interaction using multilevel coupled autoregressive models. Whereas greater maternal teaching was associated with stronger coregulation in mother and child RSA over time, maternal disengagement was related to weaker coregulation, specifically more divergent parent and child RSA at higher levels of maternal disengagement. Coregulation of mother-child RSA was also weaker when mothers' baseline RSA was higher. Findings contribute to the emerging knowledge base on real-time patterns of parent-child physiological coregulation in early childhood and suggest that mothers' physiology and behavioral engagement with the child play an important role in mother-child physiological coregulation patterns.

Coregulation and the Quality of the Relationship in Full‐Term and Very Low‐Birthweight Preterm Infant‐Mother Dyads During Face‐To‐Face Interactions

Interactions with parents build the foundation for infants' social–emotional development. This study investigated coregulation of the interaction and quality of relationship between mothers and their 6‐month‐old full‐term (n = 43) and very low‐birthweight/preterm (VLBW/preterm; n = 44; ages corrected for prematurity) infants. The objectives were to examine (1) how coregulation changed following a perturbed interaction, (2) how coregulation differed between full‐term and VLBW/preterm infant–mother dyads, and (3) the association between coregulation and relationship quality. Coregulation was coded using the Revised Relational Coding System (Fogel et al., 2003). Quality of the relationship was measured using the Emotional Availability scales (Biringen et al., 2014; Carter et al., 1998). Dyads participated in the Still‐Face (SF) procedure (Tronick et al., 1978) consisting of two natural and one SF period where mothers assumed a neutral expression, refraining from interacting with their infants. Following the SF period, dyads engaged in more symmetrical and more disruptive patterns of coregulation. While full‐term dyads engaged in more sequential‐symmetrical, VLBW/preterm dyads engaged in more resonant‐symmetrical coregulation. These results suggest that VLBW/preterm dyads may show more emotional reactivity in their interactions than full‐term dyads; however, in both groups infant responsiveness and parenting stress influenced the types of coregulation exhibited.

Disorder-specific patterns of emotion coregulation in couples: Comparing obsessive compulsive disorder and anorexia nervosa.

Impaired emotion regulation and maladaptive strategies to manage distress are central to psychopathology, including obsessive-compulsive disorder (OCD) and anorexia nervosa (AN). Emotion regulation can be fostered or thwarted by romantic partners, and the tendency to rely on interpersonally oriented emotion regulation may vary by disorder. This study examined coregulation as a form of interpersonal emotion regulation in OCD and AN. We hypothesized that OCD is associated with exaggerated and AN with diminished coregulation, and that OCD patients have greater overall levels of emotional arousal than AN patients. Greater symptom severity was expected to exacerbate these opposing tendencies. Vocally encoded emotional arousal was measured during couple conversations in 34 AN patients, 18 OCD patients, and their partners. Two indicators of coregulation (covariation and coupling) were analyzed using cross-lagged actor-partner interdependence and coupled linear oscillator models. As hypothesized, OCD was associated with greater overall emotional arousal than AN. Symptom severity was not associated with emotional arousal or coregulation. Covariation differed in the opposite direction of the hypothesis; there was no difference in coupling. AN patients exhibited consistent coregulation, indicating high reactivity to partners' emotional arousal which may contribute to interpersonal avoidance. OCD couples showed limited predictability of patients' arousal over time, while partners were affected by the patients' emotional arousal; thus, symptom accommodation may in part be partners' attempts at managing their own distress along with the patients'. A better understanding of interpersonal emotion regulation in OCD and AN can inform treatment by targeting interaction patterns that may maintain symptoms. (PsycINFO Database Record

Large-Scale trans-eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation

Efforts to decipher the causal relationships between differences in gene regulation and corresponding differences in phenotype have been stymied by several basic technical challenges. Although detecting local, cis-eQTLs is now routine, trans-eQTLs, which are distant from the genes of origin, are far more difficult to find because millions of SNPs must currently be compared to thousands of transcripts. Here, we demonstrate an alternative approach: we looked for SNPs associated with the expression of many genes simultaneously and found that hundreds of trans-eQTLs each affect hundreds of transcripts in lymphoblastoid cell lines across three African populations. These trans-eQTLs target the same genes across the three populations and show the same direction of effect. We discovered that target transcripts of a high-confidence set of trans-eQTLs encode proteins that interact more frequently than expected by chance, are bound bythe same transcription factors, and are enriched for pathway annotations indicative of roles in basic cell homeostasis. We thus demonstrate that our approach can uncover trans-acting transcriptional control circuits that affect co-regulated groups of genes: a key to understanding how cellular pathways and processes are orchestrated.

Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach

Microarray analysis based on gene coexpression is widely used to investigate the coregulation pattern of a group (or cluster) of genes in a specific phenotype condition. Recent approaches go one step beyond and look for differential coexpression pattern, wherein there exists a significant difference in coexpression pattern between two phenotype conditions. These changes of coexpression patterns generally arise due to significant change in regulatory mechanism across different conditions governed by natural progression of diseases. Here we develop a novel multiobjective framework DiffCoMO, to identify differentially coexpressed modules that capture altered coexpression in gene modules across different stages of HIV-1 progression. The objectives are built to emphasize the distance between coexpression pattern of two phenotype stages. The proposed method is assessed by comparing with some state-of-the-art techniques. We show that DiffCoMO outperforms the state-of-the-art for detecting differential coexpressed modules. Moreover, we have compared the performance of all the methods using simulated data. The biological significance of the discovered modules is also investigated using GO and pathway enrichment analysis. Additionally, miRNA enrichment analysis is carried out to identify TF to miRNA and miRNA to TF connections. The gene modules discovered by DiffCoMO manifest regulation by miRNA-28, miRNA-29 and miRNA-125 families.

Emotion Coregulation in Mother-Child Dyads: A Dynamic Systems Analysis of Children with and without Autism Spectrum Disorder.

Few studies have investigated patterns of emotion coregulation in families of children with Autism Spectrum Disorder (ASD) or contrasted the ways in which their emotion coregulation patterns differ from families of typically developing (TD) children. To address this gap, we used a dynamic systems approach to compare flexible structure and emotional content of coregulation between mothers and children (3-7years) with ASD (n=47) and TD children (n=26). Mother-child play interactions in the home were videotaped and emotion-engagement states were coded in micro-level 5-s intervals based on behavioral and affective expressions. Analyses indicated that mother-child dyads in the ASD group spent more time than dyads in the TD group in mismatched emotion-engagement states (e.g., child negative/mother positive), and children with ASD spent more time than TD children engaged exclusively with objects. Mother-child dyads in the TD group stayed longer in mutual positive engagement states. Compared to dyads in the TD group, mother-child dyads in the ASD group exhibited greater flexibility (i.e., a wider range of emotional-engagement states, more frequent changes in states, and less time in each state). These findings suggest that mothers and their children with ASD do not sustain dyadic positive engagement patterns in a low-stress environment. Findings confirmed the preference of children with ASD for objects over social partners, even when they are at home with their mothers, and elucidated a challenging mother-child interactional style. Results have implications for mother-child interventions aimed at regulating negative emotional states and sustaining positive ones in families raising children with ASD.

Disorder-specific patterns of emotion coregulation in couples: Comparing obsessive compulsive disorder and anorexia nervosa

Disorder-specific patterns of emotion coregulation in couples: Comparing obsessive compulsive disorder and anorexia nervosa

Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer.

Analysis of gene co-expression networks is a powerful "data-driven" tool, invaluable for understanding cancer biology and mechanisms of tumor development. Yet, despite of completion of thousands of studies on cancer gene expression, there were few attempts to normalize and integrate co-expression data from scattered sources in a concise "meta-analysis" framework. Here we describe an integrated approach to cancer expression meta-analysis, which combines generation of "data-driven" co-expression networks with detailed statistical detection of promoter sequence motifs within the co-expression clusters. First, we applied Weighted Gene Co-Expression Network Analysis (WGCNA) workflow and Pearson's correlation to generate a comprehensive set of over 3000 co-expression clusters in 82 normalized microarray datasets from nine cancers of different origin. Next, we designed a genome-wide statistical approach to the detection of specific DNA sequence motifs based on similarities between the promoters of similarly expressed genes. The approach, realized as cisExpress software module, was specifically designed for analysis of very large data sets such as those generated by publicly accessible whole genome and transcriptome projects. cisExpress uses a task farming algorithm to exploit all available computational cores within a shared memory node.We discovered that although co-expression modules are populated with different sets of genes, they share distinct stable patterns of co-regulation based on promoter sequence analysis. The number of motifs per co-expression cluster varies widely in accordance with cancer tissue of origin, with the largest number in colon (68 motifs) and the lowest in ovary (18 motifs). The top scored motifs are typically shared between several tissues; they define sets of target genes responsible for certain functionality of cancerogenesis. Both the co-expression modules and a database of precalculated motifs are publically available and accessible for further studies.

Discovering Perturbation of Modular Structure in HIV Progression by Integrating Multiple Data Sources Through Non-Negative Matrix Factorization.

Detecting perturbation in modular structure during HIV-1 disease progression is an important step to understand stage specific infection pattern of HIV-1 virus in human cell. In this article, we proposed a novel methodology on integration of multiple biological information to identify such disruption in human gene module during different stages of HIV-1 infection. We integrate three different biological information: gene expression information, protein-protein interaction information, and gene ontology information in single gene meta-module, through non negative matrix factorization (NMF). As the identified meta-modules inherit those information so, detecting perturbation of these, reflects the changes in expression pattern, in PPI structure and in functional similarity of genes during the infection progression. To integrate modules of different data sources into strong meta-modules, NMF based clustering is utilized here. Perturbation in meta-modular structure is identified by investigating the topological and intramodular properties and putting rank to those meta-modules using a rank aggregation algorithm. We have also analyzed the preservation structure of significant GO terms in which the human proteins of the meta-modules participate. Moreover, we have performed an analysis to show the change of coregulation pattern of identified transcription factors (TFs) over the HIV progression stages.

Perspectives on Technological Enhancements of Co-Thermoregulation in Research and Therapy

It is by now well known that one of the key predictors for a healthier life is high quality relationships. But why is this so? It has been suggested that co-regulation - regulating each other’s physiological and emotional states - is one crucial mediating mechanism. Indeed, couples that co-regulate have healthier and happier relationships. Improving co-regulation has been central to classical therapies like Emotionally Focused Therapy or Cognitive Behavioral Therapy. The authors argue that novel technological interventions can help improve these therapies. The authors detail mechanisms surrounding processes they deem central and suggest that co-thermoregulation, helping to regulate each other’s bodily temperature, is central to more general co-regulation patterns. They outline how novel technological advancements make it possible to improve co-thermoregulation, and how this can aid complex social emotion regulation. The authors further review the steps needed to understand and apply co-thermoregulation in a methodologically sound fashion, after which they indicate how therapeutic interventions may be crafted using these devices. Accordingly, they warn against too hastily applying these theoretical perspectives, but that digitally mediated social thermoregulation is an important way forward in relationship therapy.

Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes

Combining path consistency (PC) algorithms with conditional mutual information (CMI) are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference), to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.

Relationships of Leaf Net Photosynthesis, Stomatal Conductance, and Mesophyll Conductance to Primary Metabolism: A Multispecies Meta-Analysis Approach.

Plant metabolism drives plant development and plant-environment responses, and data readouts from this cellular level could provide insights in the underlying molecular processes. Existing studies have already related key in vivo leaf gas-exchange parameters with structural traits and nutrient components across multiple species. However, insights in the relationships of leaf gas-exchange with leaf primary metabolism are still limited. We investigated these relationships through a multispecies meta-analysis approach based on data sets from 17 published studies describing net photosynthesis (A) and stomatal (gs) and mesophyll (gm) conductances, alongside the 53 data profiles from primary metabolism of 14 species grown in different experiments. Modeling results highlighted the conserved patterns between the different species. Consideration of species-specific effects increased the explanatory power of the models for some metabolites, including Glc-6-P, Fru-6-P, malate, fumarate, Xyl, and ribose. Significant relationships of A with sugars and phosphorylated intermediates were observed. While gs was related to sugars, organic acids, myo-inositol, and shikimate, gm showed a more complex pattern in comparison to the two other traits. Some metabolites, such as malate and Man, appeared in the models for both conductances, suggesting a metabolic coregulation between gs and gm The resulting statistical models provide the first hints for coregulation patterns involving primary metabolism plus leaf water and carbon balances that are conserved across plant species, as well as species-specific trends that can be used to determine new biotechnological targets for crop improvement.

Phenotypic variability in human skin mast cells.

Mast cells (MCs) are unique constituents of the human body. While inter-individual differences may influence the ways by which MCs operate in their skin habitat, they have not been surveyed in a comprehensive manner so far. We therefore set out to quantify skin MC variability in a large cohort of subjects. Pathophysiologically relevant key features were quantified and correlated: transcripts of c-kit, FcεRIα, FcεRIβ, FcεRIγ, histidine decarboxylase, tryptase, and chymase; surface expression of c-Kit, FcεRIα; activity of tryptase, and chymase; histamine content and release triggered by FcεRI and Ca(2+) ionophore. While there was substantial variability among subjects, it strongly depended on the feature under study (coefficient of variation 33-386%). Surface expression of FcεRI was positively associated with FcεRIα mRNA content, histamine content with HDC mRNA, and chymase activity with chymase mRNA. Also, MC signature genes were co-regulated in distinct patterns. Intriguingly, histamine levels were positively linked to tryptase and chymase activity, whereas tryptase and chymase activity appeared to be uncorrelated. FcεRI triggered histamine release was highly variable and was unrelated to FcεRI expression but unexpectedly tightly correlated with histamine release elicited by Ca(2+) ionophore. This most comprehensive and systematic work of its kind provides not only detailed insights into inter-individual variability in MCs, but also uncovers unexpected patterns of co-regulation among signature attributes of the lineage. Differences in MCs among humans may well underlie clinical responses in settings of allergic reactions and complex skin disorders alike.

Exploring the behavioral patterns of Co-regulation in mobile computer-supported collaborative learning

This study examined the behavioral patterns of co-regulation in a mobile computer-supported collaborative learning context. Participants in this study included 101 undergraduate students majoring in law or Chinese language and literature. Content analysis and lag sequential analysis were conducted to analyze the behavioral patterns of co-regulation for four weeks. The results indicated that the main co-regulation behaviors included establishing goals, making plans, enacting strategies, monitoring and controlling, reflecting and evaluating, and adapting metacognition. The behavioral sequences from week 1 to week 4 demonstrated different characteristics. In addition, the high-achievement groups and low-achievement groups presented distinct differences in behavioral sequences. The implications for CSCL and limitations are also discussed.