Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

Background

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually.

Results

We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends.

Conclusions

MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

Early hyperbaric oxygen therapy through regulating the HIF-1α signaling pathway attenuates Neuroinflammation and behavioral deficits in a mouse model of Sepsis-associated encephalopathy

BACKGROUND

Sepsis-associated encephalopathy (SAE) presents a significant clinical challenge, associated with increased mortality and healthcare expenses. Hyperbaric oxygen therapy (HBOT), involving inhaling pure or highly concentrated oxygen under pressures exceeding one atmosphere, has demonstrated neuroprotective effects in various conditions. However, the precise mechanisms underlying its protective actions against sepsis-associated brain injury remain unclear. This study aimed to determine whether HBOT protects against SAE and to elucidate the impact of the hypoxia-inducible factor-1α (HIF-1α) signaling pathway on SAE.

METHODS

The experiment consisted of two parts. In the first part, C57BL/6 J male mice were divided into five groups using a random number table method: control group, sham surgery group, sepsis group, HBOT + sepsis group, and HBOT + sham surgery group. In the subsequent part, C57BL/6 J male mice were divided into four groups: sepsis group, HBOT + sepsis group, HIF-1α + HBOT + sepsis group, and HIF-1α + sepsis group. Sepsis was induced via cecal ligation and puncture (CLP). Hyperbaric oxygen therapy was administered at 1 h and 4 h post-CLP. After 24 h, blood and hippocampal tissue were collected for cytokine measurements. HIF-1α, TNF-α, IL-1β, and IL-6 expression were assessed via ELISA and western blotting. Microglial expression was determined by immunofluorescence. Blood-brain barrier permeability was quantified using Evans Blue. Barnes maze and fear conditioning were conducted 14 days post-CLP to evaluate learning and memory.

RESULTS

Our findings reveal that CLP-induced hippocampus-dependent cognitive deficits coincided with elevated HIF-1α and increased TNF-α, IL-1β, and IL-6 levels in both blood and hippocampus. Observable activation of microglial cells in the hippocampus and increased blood-brain barrier (BBB) permeability were also evident. HBOT mitigated HIF-1α, TNF-α, IL-1β, and IL-6 levels, attenuated microglial activation in the hippocampus, and significantly improved learning and memory deficits in CLP-exposed mice. Additionally, these outcomes were corroborated by injecting a lentivirus that overexpressed HIF-1α into the hippocampal region of the mice.

CONCLUSION

HIF-1α escalation induced peripheral and central inflammatory factors, promoting microglial activation, BBB impairment, and cognitive dysfunction. However, HBOT ameliorated these effects by reducing HIF-1α levels in Sepsis-Associated Encephalopathy.

Predicting weighted unobserved nodes in a regulatory network using answer set programming

BACKGROUND

The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network. Previously proposed methods that study this problem fail to deal with a real-size regulatory network, computing predictions sensitive to perturbation and quantifying the predicted species behaviour more finely.

RESULTS

To address previously mentioned limitations, we develop a new method based on Answer Set Programming, MajS. It takes a regulatory network and a discrete partial set of observations as input. MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool.

CONCLUSIONS

MajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species. It provides fine-grained discrete predictions by outputting the weight of the predicted sign as a piece of additional information. MajS' output, thanks to its weight, could easily be integrated with metabolic network modelling.

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

HIF-1α/BNIP3L induced cognitive deficits in a mouse model of sepsis-associated encephalopathy

Objective

Sepsis Associated Encephalopathy (SAE) is a common complication in critically ill patients and perioperative period, but its pathogenesis is still unclear. This study aimed to explore the effect of the HIF-1α (hypoxia-inducible factor-1α)/BNIP3L (Bcl-2/adenovirus E1B 19-kDa interaction protein) signaling pathway on SAE.

Methods

C57BL/6J male mice were divided into four groups, using a random number table method: control group, sham group, sepsis group, sepsis+HIF-1α activity inhibitor (echinomycin) group. Sepsis was induced by cecal ligation and puncture (CLP). At 24 h after surgery, brain tissue was sampled. HE was staining to observe changes in the hippocampus structure. Fluoroscopy observes changes in mitochondrial structure. Western blot, QT-PCR, and immunofluorescence were used to assess the amount of expression of HIF-1α and BNIP3L in the hippocampus and mitochondrion of hippocampus neurons. Observation of neuronal apoptosis by TUNEL staining. Seven days after surgery, mice were tested in a Morris water maze test to assess cognitive function after CLP.

Results

Our results show that CLP-induced hippocampus-dependent cognitive deficits were accompanied with increased HIF 1a and decreased BNIP3L, increased protein levels of TNF-α, IL-6, and IL-β, and damage to mitochondrial structures and neuronal apoptosis in the hippocampus. In addition, administration of echinomycin rescues cognitive deficits, ameliorates HIF-1α and BNIP3L-mediated neuronal pyroptosis and damaged mitochondrial structures, and decreases the expression of TNF-α and IL-6 in the hippocampus.

Conclusions

HIF-1α and the BNIP3L promote mitochondrial damage, and neuronal apoptosis and the expression of inflammatory factors may be the mechanism of SAE in critically ill patients and perioperative period.

MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY

A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.

TRIMER: Transcription Regulation Integrated with Metabolic Regulation

There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER’s applicability to both simulated and experimental data and provide performance comparison with other existing approaches.

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TRIMER models transcription-regulated metabolism using Bayesian network modeling;

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TRIMER integrates prior knowledge (regulatory interaction) with data (expression);

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TRIMER enables metabolic behavior prediction for general knockout strategies;

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TRIMER includes a simulator as an evaluation platform for similar hybrid models;

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TRIMER reliably predicts metabolite yields for both simulated and experimental data.

Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine

The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Analysis of the potential and mechanism of Ginkgo biloba in the treatment of Alzheimer's disease based on network pharmacology

Objective

Study the principle and possible mechanism of Ginkgo biloba in the treatment of Alzheimer's disease (AD) which is based on network pharmacology.

Methods

The potential targets of active ingredients of Ginkgo biloba were collected by Traditional Chinese Medicine Integrated Database platform (TCMSP). TCMSP is a pharmacological system for drug discovery from Chinese herbal medicine. The disease targets of AD were searched and collected by the database of gene-disease associations (DisGeNET) and literature. The obtained targets were standardized by the UniProt database. STING network platform and Cytoscape were used to construct protein-protein interaction network (PPI) of the key targets. According to Materscape, we clarify the possible mechanism of action including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis.

Results

The compound-target network contains 27 active ingredients, 191 related targets, 18 key targets, including PLAU, HMOX1, TNF, INSR, MPO, MAOB, IGF2, IL1B, ESR1, BCL2, ACHE, BAX, GSK3B, PPARG, SLC2A4, NOS3, CASP3, VEGFA. GO enrichment analysis has got a total of 640 GO items, including 609 biological process (BP) items (95.1%), 16 molecular function (MF) items (2.5%) and 15 cellular component (CC) items (2.4%). After KEGG enrichment, 44 pathways were obtained.

Conclusion

Through the construction of "component-target-pathway", GO biological function and KEGG pathway enrichment analysis were performed on core targets, and the possibility of Ginkgo biloba for the treatment of AD was explored from multiple targets and pathways, which provided a new approach for multi-target treatment.

Dysregulation of metabolic flexibility: The impact of mTOR on autophagy in neurodegenerative disease

Non-communicable diseases (NCDs) that involve neurodegenerative disorders and metabolic disease impact over 400 million individuals globally. Interestingly, metabolic disorders, such as diabetes mellitus, are significant risk factors for the development of neurodegenerative diseases. Given that current therapies for these NCDs address symptomatic care, new avenues of discovery are required to offer treatments that affect disease progression. Innovative strategies that fill this void involve the mechanistic target of rapamycin (mTOR) and its associated pathways of mTOR complex 1 (mTORC1), mTOR complex 2 (mTORC2), AMP activated protein kinase (AMPK), trophic factors that include erythropoietin (EPO), and the programmed cell death pathways of autophagy and apoptosis. These pathways are intriguing in their potential to provide effective care for metabolic and neurodegenerative disorders. Yet, future work is necessary to fully comprehend the entire breadth of the mTOR pathways that can effectively and safely translate treatments to clinical medicine without the development of unexpected clinical disabilities.