Robust dynamic balance of AP-1 transcription factors in a neuronal gene regulatory network

Glucosyl hesperidin exhibits more potent anxiolytic activity than hesperidin accompanied by the attenuation of noradrenaline induction in a zebrafish model

Anxiety is a symptom of various mental disorders, including depression. Severe anxiety can significantly affect the quality of life. Hesperidin (Hes), a flavonoid found in the peel of citrus fruits, reportedly has various functional properties, one of which is its ability to relieve acute and chronic stress. However, Hes is insoluble in water, resulting in a low absorption rate in the body and low bioavailability. Glucosyl hesperidin (GHes) is produced by adding one glucose molecule to hesperidin. Its water solubility is significantly higher than that of Hes, which is expected to improve its absorption into the body and enhance its effects. However, its efficacy in alleviating anxiety has not yet been investigated. Therefore, in this study, the anxiolytic effects of GHes were examined in a zebrafish model of anxiety. Long-term administration of diets supplemented with GHes did not cause any toxicity in the zebrafish. In the novel tank test, zebrafish in the control condition exhibited an anxious behavior called freezing, which was significantly suppressed in GHes-fed zebrafish. In the black-white preference test, which also induces visual stress, GHes-fed zebrafish showed significantly increased swimming time in the white side area. Furthermore, in tactile (low water-level stress) and olfactory-mediated stress (alarm substance administration test) tests, GHes suppressed anxious behavior, and these effects were stronger than those of Hes. Increased noradrenaline levels in the brain generally cause freezing; however, in zebrafish treated with GHes, the amount of noradrenaline after stress was lower than that in the control group. Activation of c-fos/ERK/Th, which is upstream of the noradrenaline synthesis pathway, was also suppressed, while activation of the CREB/BDNF system, which is vital for neuroprotective effects, was significantly increased. These results indicate that GHes has a more potent anxiolytic effect than Hes in vivo, which may have potential applications in drug discovery and functional food development.

Model-based virtual patient analysis of human liver regeneration predicts critical perioperative factors controlling the dynamic mode of response to resection

BACKGROUND

Liver has the unique ability to regenerate following injury, with a wide range of variability of the regenerative response across individuals. Existing computational models of the liver regeneration are largely tuned based on rodent data and hence it is not clear how well these models capture the dynamics of human liver regeneration. Recent availability of human liver volumetry time series data has enabled new opportunities to tune the computational models for human-relevant time scales, and to predict factors that can significantly alter the dynamics of liver regeneration following a resection.

METHODS

We utilized a mathematical model that integrates signaling mechanisms and cellular functional state transitions. We tuned the model parameters to match the time scale of human liver regeneration using an elastic net based regularization approach for identifying optimal parameter values. We initially examined the effect of each parameter individually on the response mode (normal, suppressed, failure) and extent of recovery to identify critical parameters. We employed phase plane analysis to compute the threshold of resection. We mapped the distribution of the response modes and threshold of resection in a virtual patient cohort generated in silico via simultaneous variations in two most critical parameters.

RESULTS

Analysis of the responses to resection with individual parameter variations showed that the response mode and extent of recovery following resection were most sensitive to variations in two perioperative factors, metabolic load and cell death post partial hepatectomy. Phase plane analysis identified two steady states corresponding to recovery and failure, with a threshold of resection separating the two basins of attraction. The size of the basin of attraction for the recovery mode varied as a function of metabolic load and cell death sensitivity, leading to a change in the multiplicity of the system in response to changes in these two parameters.

CONCLUSIONS

Our results suggest that the response mode and threshold of failure are critically dependent on the metabolic load and cell death sensitivity parameters that are likely to be patient-specific. Interventions that modulate these critical perioperative factors may be helpful to drive the liver regenerative response process towards a complete recovery mode.

MiRNA-target interactions in osteogenic signaling pathways involving zinc via the metal regulatory element

Adequate zinc nutriture is necessary for normal bone growth and development, though the precise mechanisms for zinc-mediated bone growth remain poorly defined. A key transcription factor activated by zinc is metal response element-binding transcription factor 1 (MTF-1), which binds to the metal regulatory element (MRE). We hypothesize that MREs will be found upstream of miRNA genes as well as miRNA target genes in the following bone growth and development signaling pathways: TGF-β, MAPK, and Wnt. A Bioconductor-based workflow in R was designed to identify interactions between MREs, miRNAs, and target genes. MRE sequences were found upstream from 64 mature miRNAs that interact with 213 genes which have MRE sequences in their own promoter regions. MAPK1 exhibited the most miRNA-target interactions (MTIs) in the TGF-β and MAPK signaling pathways; CCND2 exhibited the most interactions in the Wnt signaling pathway. Hsa-miR-124-3p exhibited the most MTIs in the TGF-β and MAPK signaling pathways; hsa-miR-20b-5p exhibited the most MTIs in the Wnt signaling pathway. MYC and hsa-miR-34a-5p were shared between all three signaling pathways, also forming an MTI unit. JUN exhibited the most protein-protein interactions, followed by MAPK8. These in silico data support the hypothesis that intracellular zinc status plays a role in osteogenesis through the transcriptional regulation of miRNA genes via the zinc/MTF-1/MRE complex.

Modeling the Dynamics of Human Liver Failure Post Liver Resection

Liver resection is an important clinical intervention to treat liver disease. Following liver resection, patients exhibit a wide range of outcomes including normal recovery, suppressed recovery, or liver failure, depending on the regenerative capacity of the remnant liver. The objective of this work is to study the distinct patient outcomes post hepatectomy and determine the processes that are accountable for liver failure. Our model based approach shows that cell death is one of the important processes but not the sole controlling process responsible for liver failure. Additionally, our simulations showed wide variation in the timescale of liver failure that is consistent with the clinically observed timescales of post hepatectomy liver failure scenarios. Liver failure can take place either instantaneously or after a certain delay. We analyzed a virtual patient cohort and concluded that remnant liver fraction is a key regulator of the timescale of liver failure, with higher remnant liver fraction leading to longer time delay prior to failure. Our results suggest that, for a given remnant liver fraction, modulating a combination of cell death controlling parameters and metabolic load may help shift the clinical outcome away from post hepatectomy liver failure towards normal recovery.

Modeling cytokine regulatory network dynamics driving neuroinflammation in central nervous system disorders

A central goal of pharmacological efforts to treat central nervous system (CNS) diseases is to develop systemic therapeutics that can restore CNS homeostasis. Achieving this goal requires a fundamental understanding of CNS function within the organismal context so as to leverage the mechanistic insights on the molecular basis of cellular and tissue functions towards novel drug target identification. The immune system constitutes a key link between the periphery and CNS, and many neurological disorders and neurodegenerative diseases are characterized by immune dysfunction. We review the salient opportunities for applying computational models to CNS disease research, and summarize relevant approaches from studies of immune function and neuroinflammation. While the accurate prediction of disease-related phenomena is often considered the central goal of modeling studies, we highlight the utility of computational modeling applications beyond making predictions, particularly for drawing counterintuitive insights from model-based analysis of multi-parametric and time series data sets.

Computational modeling of cytokine signaling in microglia

Neuroinflammation due to glial activation has been linked to many CNS diseases. We developed a computational model of a microglial cytokine interaction network to study the regulatory mechanisms of microglia-mediated neuroinflammation. We established a literature-based cytokine network, including TNFα, TGFβ, and IL-10, and fitted a mathematical model to published data from LPS-treated microglia. The addition of a previously unreported TGFβ autoregulation loop to our model was required to account for experimental data. Global sensitivity analysis revealed that TGFβ- and IL-10-mediated inhibition of TNFα was critical for regulating network behavior. We assessed the sensitivity of the LPS-induced TNFα response profile to the initial TGFβ and IL-10 levels. The analysis showed two relatively shifted TNFα response profiles within separate domains of initial condition space. Further analysis revealed that TNFα exhibited adaptation to sustained LPS stimulation. We simulated the effects of functionally inhibiting TGFβ and IL-10 on TNFα adaptation. Our analysis showed that TGFβ and IL-10 knockouts (TGFβ KO and IL-10 KO) exert divergent effects on adaptation. TFGβ KO attenuated TNFα adaptation whereas IL-10 KO enhanced TNFα adaptation. We experimentally tested the hypothesis that IL-10 KO enhances TNFα adaptation in murine macrophages and found supporting evidence. These opposing effects could be explained by differential kinetics of negative feedback. Inhibition of IL-10 reduced early negative feedback that results in enhanced TNFα-mediated TGFβ expression. We propose that differential kinetics in parallel negative feedback loops constitute a novel mechanism underlying the complex and non-intuitive pro- versus anti-inflammatory effects of individual cytokine perturbations.

Intracellular Information Processing through Encoding and Decoding of Dynamic Signaling Features

Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses.

Single cell studies have shown that differential patterns in the dynamics of signaling proteins, transcription factor activity, gene expression, etc. produce distinct downstream outcomes. The opposite also holds true where particular cellular outcomes have been found to be associated with the dynamical pattern of one or more signaling molecules. Signaling pathways, therefore, serve as signal processing units to inform specific downstream regulation. However, the functional capabilities of the dynamic aspects of signaling are not well understood. To address this issue, we developed a new approach that evaluates information processing between dynamic features in signaling patterns and transcriptional regulatory activity. Our work demonstrates that the information transfer occur through decoding of temporal history of signals rather than only through instantaneous correlations. Moreover, our results identify regulatory network motifs as the critical components in the information processing and filtering of variability in signaling dynamics to produce distinct patterns of downstream transcriptional responses. Our methodology can be broadly applied to single cell scale data on experimentally accessible downstream measures to infer dynamic aspects of upstream signaling.

Multiscale model of dynamic neuromodulation integrating neuropeptide-induced signaling pathway activity with membrane electrophysiology

We developed a multiscale model to bridge neuropeptide receptor-activated signaling pathway activity with membrane electrophysiology. Typically, the neuromodulation of biochemical signaling and biophysics have been investigated separately in modeling studies. We studied the effects of Angiotensin II (AngII) on neuronal excitability changes mediated by signaling dynamics and downstream phosphorylation of ion channels. Experiments have shown that AngII binding to the AngII receptor type-1 elicits baseline-dependent regulation of cytosolic Ca(2+) signaling. Our model simulations revealed a baseline Ca(2+)-dependent response to AngII receptor type-1 activation by AngII. Consistent with experimental observations, AngII evoked a rise in Ca(2+) when starting at a low baseline Ca(2+) level, and a decrease in Ca(2+) when starting at a higher baseline. Our analysis predicted that the kinetics of Ca(2+) transport into the endoplasmic reticulum play a critical role in shaping the Ca(2+) response. The Ca(2+) baseline also influenced the AngII-induced excitability changes such that lower Ca(2+) levels were associated with a larger firing rate increase. We examined the relative contributions of signaling kinases protein kinase C and Ca(2+)/Calmodulin-dependent protein kinase II to AngII-mediated excitability changes by simulating activity blockade individually and in combination. We found that protein kinase C selectively controlled firing rate adaptation whereas Ca(2+)/Calmodulin-dependent protein kinase II induced a delayed effect on the firing rate increase. We tested whether signaling kinetics were necessary for the dynamic effects of AngII on excitability by simulating three scenarios of AngII-mediated KDR channel phosphorylation: (1), an increased steady state; (2), a step-change increase; and (3), dynamic modulation. Our results revealed that the kinetics emerging from neuromodulatory activation of the signaling network were required to account for the dynamical changes in excitability. In summary, our integrated multiscale model provides, to our knowledge, a new approach for quantitative investigation of neuromodulatory effects on signaling and electrophysiology.

MicroRNA network changes in the brain stem underlie the development of hypertension

Hypertension is a major chronic disease whose molecular mechanisms remain poorly understood. We compared neuroanatomical patterns of microRNAs in the brain stem of the spontaneous hypertensive rat (SHR) to the Wistar Kyoto rat (WKY, control). We quantified 419 well-annotated microRNAs in the nucleus of the solitary tract (NTS) and rostral ventrolateral medulla (RVLM), from SHR and WKY rats, during three main stages of hypertension development. Changes in microRNA expression were stage- and region-dependent, with a majority of SHR vs. WKY differential expression occurring at the hypertension onset stage in NTS versus at the prehypertension stage in RVLM. Our analysis identified 24 microRNAs showing time-dependent differential expression in SHR compared with WKY in at least one brain region. We predicted potential gene regulatory targets corresponding to catecholaminergic processes, neuroinflammation, and neuromodulation using the miRWALK and RNA22 databases, and we tested those bioinformatics predictions using high-throughput quantitative PCR to evaluate correlations of differential expression between the microRNAs and their predicted gene targets. We found a novel regulatory network motif consisting of microRNAs likely downregulating a negative regulator of prohypertensive processes such as angiotensin II signaling and leukotriene-based inflammation. Our results provide new evidence on the dynamics of microRNA expression in the development of hypertension and predictions of microRNA-mediated regulatory networks playing a region-dependent role in potentially altering brain-stem cardiovascular control circuit function leading to the development of hypertension.

Genome-wide alteration of 5-hydroxymethylcytosine in a mouse model of fragile X-associated tremor/ataxia syndrome

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a late-onset neurodegenerative disorder in which patients carry premutation alleles of 55-200 CGG repeats in the FMR1 gene. To date, whether alterations in epigenetic regulation modulate FXTAS has gone unexplored. 5-Hydroxymethylcytosine (5hmC) converted from 5-methylcytosine (5mC) by the ten-eleven translocation (TET) family of proteins has been found recently to play key roles in neuronal functions. Here, we undertook genome-wide profiling of cerebellar 5hmC in a FXTAS mouse model (rCGG mice) and found that rCGG mice at 16 weeks showed overall reduced 5hmC levels genome-wide compared with age-matched wild-type littermates. However, we also observed gain-of-5hmC regions in repetitive elements, as well as in cerebellum-specific enhancers, but not in general enhancers. Genomic annotation and motif prediction of wild-type- and rCGG-specific differential 5-hydroxymethylated regions (DhMRs) revealed their high correlation with genes and transcription factors that are important in neuronal developmental and functional pathways. DhMR-associated genes partially overlapped with genes that were differentially associated with ribosomes in CGG mice identified by bacTRAP ribosomal profiling. Taken together, our data strongly indicate a functional role for 5hmC-mediated epigenetic modulation in the etiology of FXTAS, possibly through the regulation of transcription.

Analysis and design of a genetic circuit for dynamic metabolic engineering

Recent advances in synthetic biology have equipped us with new tools for bioprocess optimization at the genetic level. Previously, we have presented an integrated in silico design for the dynamic control of gene expression based on a density-sensing unit and a genetic toggle switch. In the present paper, analysis of a serine-producing Escherichia coli mutant shows that an instantaneous ON-OFF switch leads to a maximum theoretical productivity improvement of 29.6% compared to the mutant. To further the design, global sensitivity analysis is applied here to a mathematical model of serine production in E. coli coupled with a genetic circuit. The model of the quorum sensing and the toggle switch involves 13 parameters of which 3 are identified as having a significant effect on serine concentration. Simulations conducted in this reduced parameter space further identified the optimal ranges for these 3 key parameters to achieve productivity values close to the maximum theoretical values. This analysis can now be used to guide the experimental implementation of a dynamic metabolic engineering strategy and reduce the time required to design the genetic circuit components.

Adaptive transcriptional dynamics of A2 neurons and central cardiovascular control pathways

In order to provide an example of useful interaction between systems biology and computational neuroscience traditions, here we aim to identify the molecular response process through which elevated blood pressure induces a temporal sequence of gene expression changes. This initial response may then continually evolve as an adaptive response, altering central blood pressure set-point control. Our approach involves using a set of 96 quantitative PCR gene assays, which represent the molecular process associated with neuronal responses to angiotensin II type 1 receptor (AT1R) activation. We use this set as a probe to search for the AT1R signalling-triggered expression programme in individual neurons and groups of neurons involved in homeostatic regulation of cardiorespiratory function. Specifically, we focus on tissue samples from the nucleus tractus solitarii, as well as groups of A2 neurons and individual A2 cells within the nucleus tractus solitarii, and in the ventrolateral medulla and central amygdala. We assay these neural samples at rest and in response to elevated blood pressure. Analysis of the resulting high-dimensional data set reveals a remarkable complexity and heterogeneity of the samples and of their response to changes in blood pressure. These results demonstrate differential expression programmes for each anatomically distinct neuronal group and neuronal type. Single-cell expression analysis shows that A2 cells also are variable, and that the subset that responds to blood pressure with elevated Fos expression differs from other A2 cells. We present models of gene regulatory networks and of signalling cascades related to AT1R and broadly discuss the opportunities for valuable interactions between systems biology and computational neuroscience.