Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury

The identification and prediction of atrial fibrillation in coronary artery disease patients: a multicentre retrospective study based on Bayesian network

BACKGROUND

Atrial fibrillation (AF) coexisting with coronary artery disease (CAD) remains a prevailing issue that often results in poor short- and long-term patient outcomes. Screening has been proposed as a method to increase AF detection rates and reduce the incidence of poor prognosis through early intervention. Nevertheless, due to the cost implications and uncertainty over the benefits of a systematic screening programme, the International Task Force currently recommends against screening. This study is to employ Bayesian networks (BN) for assessing the pre-test probability (PTP) of AF in patients with CAD.

METHODS

A total of 12,552 patients with CAD were divided into the CAD patients with AF group (CHD-AF group) and the CAD patients without AF group (non-AF group). Univariate analysis and LASSO regression method were used to screen for potential risk factors. The maximum-minimum climb (MMHC) algorithm was used to construct the directed acyclic graph (DAG) of BN. Predictive power was tested using internal validation, external validation and 10-fold internal cross-validation. Finally, the generated BN model was compared with four machine learning algorithms.

RESULTS

Fourteen indicators were included in the BN, including age, gender, systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), serum uric acid (UA), gamma-glutamyltransferase (GGT), direct bilirubin (DBIL), lipoproteins [LP(a)], NYHA cardiac function grading, diabetes mellitus and hypertension, palpitation, dyspnoea and the left atrial diameter. The BN model performs well on both the test set (AUC = 0.90) and internal 10-fold cross-validation (AUC = 0.89 ± 0.01).

CONCLUSION

The prediction model of AF with CAD constructed based on BN has high prediction performance and may provide a new tool for large-scale AF screening.

Inhibitor of DNA Binding Protein 3 (ID3) and Nuclear Respiratory Factor 1 (NRF1) Mediated Transcriptional Gene Signatures are Associated with the Severity of Cerebral Amyloid Angiopathy

Cerebral amyloid angiopathy (CAA) is a degenerative vasculopathy. We have previously shown that transcription regulating proteins- inhibitor of DNA binding protein 3 (ID3) and the nuclear respiratory factor 1 (NRF1) contribute to vascular dysregulation. In this study, we have identified sex specific ID3 and NRF1-mediated gene networks in CAA patients diagnosed with Alzheimer's Disease (AD). High expression of ID3 mRNA coupled with low NRF1 mRNA levels was observed in the temporal cortex of men and women CAA patients. Low NRF1 mRNA expression in the temporal cortex was found in men with severe CAA. High ID3 expression was found in women with the genetic risk factor APOE4. Low NRF1 expression was also associated with APOE4 in women with CAA. Genome wide transcriptional activity of both ID3 and NRF1 paralleled their mRNA expression levels. Sex specific differences in transcriptional gene signatures of both ID3 and NRF1 were observed. These findings were further corroborated by Bayesian machine learning and the GeNIe simulation models. Dynamic machine learning using a Monte Carlo Markov Chain (MCMC) gene ordering approach revealed that ID3 was associated with disease severity in women. NRF1 was associated with CAA and severity of this disease in men. These findings suggest that aberrant ID3 and NRF1 activity presumably plays a major role in the pathogenesis and severity of CAA. Further analyses of ID3- and NRF1-regulated molecular drivers of CAA may provide new targets for personalized medicine and/or prevention strategies against CAA.

Network analysis to evaluate complexities in relationships among fermentation variables measured within continuous culture experiments

The objective of this study was to leverage a frequentist (ELN) and Bayesian learning (BLN) network analyses to summarize quantitative associations among variables measured in 4 previously published dual-flow continuous culture fermentation experiments. Experiments were originally designed to evaluate effects of nitrate, defaunation, yeast, and/or physiological shifts associated with pH or solids passage rates on rumen conditions. Measurements from these experiments that were used as nodes within the networks included concentrations of individual volatile fatty acids, mM and nitrate, NO3-,%; outflows of non-ammonia nitrogen (NAN, g/d), bacterial N (BN, g/d), residual N (RN, g/d), and ammonia N (NH3-N, mg/dL); degradability of neutral detergent fiber (NDFd, %) and degradability of organic matter (OMd, %); dry matter intake (DMI, kg/d); urea in buffer (%); fluid passage rate (FF, L/d); total protozoa count (PZ, cells/mL); and methane production (CH4, mmol/d). A frequentist network (ELN) derived using a graphical LASSO (least absolute shrinkage and selection operator) technique with tuning parameters selected by Extended Bayesian Information Criteria (EBIC) and a BLN were constructed from these data. The illustrated associations in the ELN were unidirectional yet assisted in identifying prominent relationships within the rumen that were largely consistent with current understanding of fermentation mechanisms. Another advantage of the ELN approach was that it focused on understanding the role of individual nodes within the network. Such understanding may be critical in exploring candidates for biomarkers, indicator variables, model targets, or other measurement-focused explorations. As an example, acetate was highly central in the network suggesting it may be a strong candidate as a rumen biomarker. Alternatively, the major advantage of the BLN was its unique ability to imply causal directionality in relationships. Because the BLN identified directional, cascading relationships, this analytics approach was uniquely suited to exploring the edges within the network as a strategy to direct future work researching mechanisms of fermentation. For example, in the BLN acetate responded to treatment conditions such as the source of N used and the quantity of substrate provided, while acetate drove changes in the protozoal populations, non-NH3-N and residual N flows. In conclusion, the analyses exhibit complementary strengths in supporting inference on the connectedness and directionality of quantitative associations among fermentation variables that may be useful in driving future studies.

A novel swine model of the acute respiratory distress syndrome using clinically relevant injury exposures

To date, existing animal models of the acute respiratory distress syndrome (ARDS) have failed to translate preclinical discoveries into effective pharmacotherapy or diagnostic biomarkers. To address this translational gap, we developed a high-fidelity swine model of ARDS utilizing clinically relevant lung injury exposures. Fourteen male swine were anesthetized, mechanically ventilated, and surgically instrumented for hemodynamic monitoring, blood, and tissue sampling. Animals were allocated to one of three groups: (1) Indirect lung injury only: animals were inoculated by direct injection of Escherichia coli into the kidney parenchyma, provoking systemic inflammation and distributive shock physiology; (2) Direct lung injury only: animals received volutrauma, hyperoxia, and bronchoscope-delivered gastric particles; (3) Combined indirect and direct lung injury: animals were administered both above-described indirect and direct lung injury exposures. Animals were monitored for up to 12 h, with serial collection of physiologic data, blood samples, and radiographic imaging. Lung tissue was acquired postmortem for pathological examination. In contrast to indirect lung injury only and direct lung injury only groups, animals in the combined indirect and direct lung injury group exhibited all of the physiological, radiographic, and histopathologic hallmarks of human ARDS: impaired gas exchange (mean PaO₂ /FiO₂ ratio 124.8 ± 63.8), diffuse bilateral opacities on chest radiographs, and extensive pathologic evidence of diffuse alveolar damage. Our novel porcine model of ARDS, built on clinically relevant lung injury exposures, faithfully recapitulates the physiologic, radiographic, and histopathologic features of human ARDS and fills a crucial gap in the translational study of human lung injury.

Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer

Using a machine learning method, this study aimed to identify unique causal networks of genes associated with bone, brain, and lung metastasis of breast cancer. Bayesian network analysis identified differentially expressed genes in primary breast cancer tissues, in bone, brain, and lung breast cancer metastatic tissues, and the clinicopathological features of patients obtained from the Gene Expression Omnibus microarray datasets. We evaluated the causal Bayesian networks of breast metastasis to distant sites (bone, brain, or lung) by (i) measuring how well the structures of each specific type of breast cancer metastasis fit the data, (ii) comparing the structures with known experimental evidence, and (iii) reporting predictive capabilities of the structures. We report for the first time that the molecular gene signatures are specific to the different types of breast cancer metastasis. Several genes, including CHPF, ARC, ANGPTL4, NR2E1, SH2D1A, CTSW, POLR2J4, SPTLC1, ILK, ALDH3B1, PDE6A, SCTR, ADM, HEY1, KCNF1, and UVRAG, were found to be predictors of the risk for site-specific metastasis of breast cancer. Expression of POLR2JA, SPTLC1, ILK, ALDH3B1, and the estrogen receptor was significantly associated with breast cancer bone metastasis. Expression of PDE6A and NR2E1 was causally linked to breast cancer brain metastasis. Expression of HEY1, KCNF1, UVRAG, and the estrogen and progesterone receptors was strongly associated with breast cancer lung metastasis. The causal Bayesian network structures of these genes identify potential interactions among the genes in distant metastases of breast cancer, including to the bone, brain, and lung, and may serve as target candidates for treatment of breast cancer metastasis.

Flufenamic acid alleviates sepsis-induced lung injury by up-regulating CBR1

Investigate the effect of flufenamic acid (FFA) on lung injury of sepsis rats. Rat sepsis model was established using cecal ligation and puncture (CLP). The pathomorphology of lung tissue was detected by Hematoxylin-eosin (H&E) staining. The expression levels of tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), and high mobility group box-1 (HMGB-1) in serum and TNF-α, IL-6, malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase (SOD) in lung tissues. The viability of RLE-6TN cells was detected by CCK-8 assay. The expression of carbonyl reductase 1 (CBR1) in RLE-6TN cells was analyzed by Western blot analysis and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. The inflammatory response was obviously enhanced in CLP-constructed sepsis rats and alleviated by FFA treatment. Sepsis induced the increase of W/D ratio, promoted the levels of TNF-α, IL-6, HMGBR1, and MDA and inhibited the levels of SOD and GSH. FFA could effectively alleviate the sepsis-induced lung injury. The viability of RLE-6TN cells induced by LPS was improved with the treatment of FFA. CBR1 expression in LPS-induced RLE-6TN cells was decreased and FFA could up-regulate the CBR1 expression. In addition, LPS-induced lung injury promoted the inflammatory response in lung tissues, increased the W/D ratio and levels of TNF-α, IL-6, HMGBR1, and MDA while inhibited the levels of SOD and GSH. FFA could effectively improve the LPS-induced lung injury while the effect of FFA on LPS-induced lung injury was alleviated by CBR1 interference. FFA may alleviate sepsis-induced lung injury by up-regulating CBR1.

RNA-Seq Bayesian Network Exploration of Immune System in Bovine

Background:

The stress is one of main factors effects on production system. Several factors (both genetic and environmental elements) regulate immune response to stress.

Objectives:

In order to determine the major immune system regulatory genes underlying stress responses, a learning Bayesian network approach for those regulatory genes was applied to RNA-Seq data from a bovine leukocyte model system.

Material and Methods:

The transcriptome dataset GSE37447 was used from GEO and a Bayesian network on differentially expressed genes was learned to investigate the gene regulatory network.

Results:

Applying the method produced a strongly interconnected network with four genes (TERF2IP, PDCD10, DDX10 and CENPE) acting as nodes, suggesting these genes may be important in the transcriptome regulation program of stress response. Of these genes TERF2IP has been shown previously to regulate gene expression, act as a regulator of the nuclear factor-kappa B (NF-κB) signalling, and to activate expression of NF-κB target genes; PDCD10 encodes a conserved protein associated with cell apoptosis; DDX10 encodes a DEAD box protein and is believed to be associated with cellular growth and division; and CENPE involves unstable spindle microtubule capture at kinetochores. Together these genes are involved in DNA damage of apoptosis, RNA splicing, DNA repairing, and regulating cell division in the bovine genome. The topology of the learned Bayesian gene network indicated that the genes had a minimal interrelationship with each other. This type of structure, using the publically available computational tool, was also observed on human orthologous genes of the differentially expressed genes.

Conclusions:

Overall, the results might be used in transcriptomic-assisted selection and design of new drug targets to treat stress-related problems in bovines.

Clinical decision support system to assess the risk of sepsis using Tree Augmented Bayesian networks and electronic medical record data

Early and accurate diagnoses of sepsis enable practitioners to take timely preventive actions. The existing diagnostic criteria suffer from deficiencies, such as triggering false alarms or leaving conditions undiagnosed. This study aims to develop a clinical decision support system to predict the risk of sepsis using tree augmented naive Bayesian network by identifying the optimal set of biomarkers. The key feature of our approach is that we captured the dynamics among biomarkers. With an area under receiver operating characteristic of 0.84, the proposed model outperformed the competing diagnostic criteria (systemic inflammatory response syndrome = 0.59, quick sepsis-related organ failure assessment = 0.65, modified early warning system = 0.75, sepsis-related organ failure assessment = 0.80). The richness of our proposed model is measured not only by achieving high accuracy, but also by utilizing fewer biomarkers. We also propose a left-center-right imputation method suitable for electronic medical record data. This method uses the individual patient’s visit, instead of aggregated (mean or median) value, to impute the missing data.

Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks

Background

The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC.

Methods

We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model.

Results

We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC.

Conclusions

The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.