Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells

"Oh, Dear We Are in Tribble": An Overview of the Oncogenic Functions of Tribbles 1

Pseudokinases are catalytically inactive proteins in the human genome that lack the ability to transfer phosphate from ATP to their substrates. The Tribbles family of pseudokinases contains three members: Tribbles 1, 2, and 3. Tribbles 1 has recently gained importance because of its involvement in various diseases, including cancer. It acts as a scaffolding protein that brings about the degradation of its substrate proteins, such as C/EBPα/β, MLXIPL, and RAR/RXRα, among others, via the ubiquitin proteasome system. It also serves as an adapter protein, which sequesters different protein molecules and activates their downstream signaling, leading to processes, such as cell survival, cell proliferation, and lipid metabolism. It has been implicated in cancers such as AML, prostate cancer, breast cancer, CRC, HCC, and glioma, where it activates oncogenic signaling pathways such as PI3K-AKT and MAPK and inhibits the anti-tumor function of p53. TRIB1 also causes treatment resistance in cancers such as NSCLC, breast cancer, glioma, and promyelocytic leukemia. All these effects make TRIB1 a potential drug target. However, the lack of a catalytic domain renders TRIB1 "undruggable", but knowledge about its structure, conformational changes during substrate binding, and substrate binding sites provides an opportunity to design small-molecule inhibitors against specific TRIB1 interactions.

TRIB1 confers therapeutic resistance in GBM cells by activating the ERK and Akt pathways

GBM (Glioblastoma) is the most lethal CNS (Central nervous system) tumor in adults, which inevitably develops resistance to standard treatments leading to recurrence and mortality. TRIB1 is a serine/threonine pseudokinase which functions as a scaffold platform that initiates degradation of its substrates like C/EBPα through the ubiquitin proteasome system and also activates MEK and Akt signaling. We found that increased TRIB1 gene expression associated with worse overall survival of GBM patients across multiple cohorts. Importantly, overexpression of TRIB1 decreased RT/TMZ (radiation therapy/temozolomide)-induced apoptosis in patient derived GBM cell lines in vitro. TRIB1 directly bound to MEK and Akt and increased ERK and Akt phosphorylation/activation. We also found that TRIB1 protein expression was maximal during G2/M transition of cell cycle in GBM cells. Furthermore, TRIB1 bound directly to HDAC1 and p53. Importantly, mice bearing TRIB1 overexpressing tumors had worse overall survival. Collectively, these data suggest that TRIB1 induces resistance of GBM cells to RT/TMZ treatments by activating the cell proliferation and survival pathways thus providing an opportunity for developing new targeted therapeutics.

Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network

Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021. We identified social responsibility, vaccine safety and anticipated regret as prime candidates for interventions and revealed a complex network of variables that mediate their influences. Social responsibility's causal effect greatly exceeded that of other variables. The BN revealed that the causal impact of political affiliations was weak compared with more direct causal factors. This approach provides clearer targets for intervention than regression, suggesting it can be an effective way to explore multiple causal pathways of complex behavioural problems to inform interventions.

Differentiation of imatinib -resistant chronic myeloid leukemia cells with BCR-ABL-T315I mutation induced by Jiyuan Oridonin A

Chronic myeloid leukemia (CML) results from BCR-ABL oncogene, which blocks CML cells differentiation and protects these cells from apoptosis. T315I mutated BCR-ABL is the main cause of the resistance mediated by imatinib and second generation BCR-ABL inhibitor. CML with the T315I mutation has been considered to have poor prognosis. Here, we determined the effect of Jiyuan oridonin A (JOA), an ent-kaurene diterpenoid compound, on the differentiation blockade in imatinib-sensitive, particularly, imatinib-resistant CML cells with BCR-ABL-T315I mutation by cell proliferation assay, apoptosis analysis, cell differentiation analysis, cell cycle analysis and colony formation assay. We also investigated the possible molecular mechanism by mRNA sequencing, qRT-PCR and Western blotting. We found that JOA at lower concentration significantly inhibited the proliferation of CML cells expressing mutant BCR-ABL (T315I mutation included) and wild-type BCR-ABL, which was due to that JOA induced the cell differentiation and the cell cycle arrest at G0/G1 phase. Interestingly, JOA possessed stronger anti-leukemia activity than its analogues such as OGP46 and Oridonin, which has been investigated extensively. Mechanistically, the cell differentiation mediated by JOA may be originated from the inhibition of BCR-ABL/c-MYC signaling in CML cells expressing wild-type BCR-ABL and BCR-ABL-T315I. JOA displayed the activity of inhibiting the BCR-ABL and promoted differentiation of not only imatinib -sensitive but also imatinib -resistant cells with BCR-ABL mutation, which could become a potent lead compound to overcome the imatinib -resistant induced by inhibitors of BCR-ABL tyrosine kinase in CML therapy.

Bayesian network analysis of panomic biological big data identifies the importance of triglyceride-rich LDL in atherosclerosis development

Introduction

We sought to explore biomarkers of coronary atherosclerosis in an unbiased fashion.

Methods

We analyzed 665 patients (mean ± SD age, 56 ± 11 years; 47% male) from the GLOBAL clinical study (NCT01738828). Cases were defined by the presence of any discernable atherosclerotic plaque based on comprehensive cardiac computed tomography (CT). De novo Bayesian networks built out of 37,000 molecular measurements and 99 conventional biomarkers per patient examined the potential causality of specific biomarkers.

Results

Most highly ranked biomarkers by gradient boosting were interleukin-6, symmetric dimethylarginine, LDL-triglycerides [LDL-TG], apolipoprotein B48, palmitoleic acid, small dense LDL, alkaline phosphatase, and asymmetric dimethylarginine. In Bayesian analysis, LDL-TG was directly linked to atherosclerosis in over 95% of the ensembles. Genetic variants in the genomic region encoding hepatic lipase (LIPC) were associated with LIPC gene expression, LDL-TG levels and with atherosclerosis.

Discussion

Triglyceride-rich LDL particles, which can now be routinely measured with a direct homogenous assay, may play an important role in atherosclerosis development.

Clinical trial registration

GLOBAL clinical study (Genetic Loci and the Burden of Atherosclerotic Lesions); [https://clinicaltrials.gov/ct2/show/NCT01738828?term=NCT01738828&rank=1], identifier [NCT01738828].

Single Cell Transcriptome Analysis of Peripheral Blood Mononuclear Cells in Freshly Isolated versus Stored Blood Samples

BACKGROUND

Peripheral blood mononuclear cells (PBMCs) are widely used as a model in the study of different human diseases. There is often a time delay from blood collection to PBMC isolation during the sampling process, which can result in an experimental bias, particularly when performing single cell RNA-seq (scRNAseq) studies.

METHODS

This study examined the impact of different time periods from blood draw to PBMC isolation on the subsequent transcriptome profiling of different cell types in PBMCs by scRNAseq using the 10X Chromium Single Cell Gene Expression assay.

RESULTS

Examining the five major cell types constituting the PBMC cell population, i.e., CD4+ T cells, CD8+ T cells, NK cells, monocytes, and B cells, both common changes and cell-type-specific changes were observed in the single cell transcriptome profiling over time. In particular, the upregulation of genes regulated by NF-kB in response to TNF was observed in all five cell types. Significant changes in key genes involved in AP-1 signaling were also observed. RBC contamination was a major issue in stored blood, whereas RBC adherence had no direct impact on the cell transcriptome.

CONCLUSIONS

Significant transcriptome changes were observed across different PBMC cell types as a factor of time from blood draw to PBMC isolation and as a consequence of blood storage. This should be kept in mind when interpreting experimental results.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Exosomal circRNA Scm-like with four malignant brain tumor domains 2 (circ-SFMBT2) enhances the docetaxel resistance of prostate cancer via the microRNA-136-5p/tribbles homolog 1 pathway

Exosomal circular RNA was found to mediate cancer chemoresistance. However, whether exosomal circRNA Scm-like with four malignant brain tumor domains 2 (circ-SFMBT2) was involved in the chemoresistance of prostate cancer (PCa) remains unclear. The docetaxel (DTX) resistance of PCa cells was analyzed by Cell Counting Kit 8 assay. Quantitative real-time PCR was used to measure circSFMBT2, microRNA (miR)-136-5p and tribbles homolog 1 (TRIB1) expression. Cell proliferation, apoptosis, migration and invasion were analyzed by 5-ethynyl-2'-deoxyuridine (EdU) assay, flow cytometry, wound-healing assay and transwell assay. RNA interaction was verified by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Protein expression was measured by western blot analysis. Exosomes-extracted from cells were identified by transmission electron microscope, nanoparticles tracking analysis and western blot. Xenograft mice models were constructed to analyze the effect of exosomal circSFMBT2 on the DTX sensitivity of PCa tumors in vivo. CircSFMBT2 was upregulated in DTX-resistant PCa cells, and its knockdown enhanced the DTX sensitivity of DTX-resistant PCa cells by suppressing cell proliferation, migration, invasion and enhancing apoptosis. CircSFMBT2 severed as miR-136-5p sponge to positively regulate TRIB1. The regulation of circSFMBT2 knockdown on the DTX sensitivity of DTX-resistant PCa cells could be reversed by miR-136-5p inhibitor or TRIB1 overexpression. Exosomal circSFMBT2 from DTX-resistant PCa could increase the DTX resistance of normal PCa cells. In addition, exosomal circSFMBT2 also enhanced the DTX resistance of PCa tumors in vivo, and it was highly expressed in the serum of DTX-resistance PCa patients. Exosomal circSFMBT2 enhanced the DTX resistance of PCa by miR-136-5p/TRIB1 axis, indicating that circSFMBT2 might be a potential target for the treatment of PCa chemoresistance.

Jiyuan oridonin A induces differentiation of acute myeloid leukemia cells including leukemic stem-like cells

Acute myeloid leukemia (AML) is an aggressive form of hematological neoplasia characterized by failure of myeloid differentiation. AML is a leading cause of death from leukemia. Cytarabine chemotherapy resistance is a major source of refractory/relapsed AML. A major obstacle to the successful treatment of AML results from residual disease maintained by leukemic stem cells (LSCs), which are mostly resistant to conventional chemotherapy. Here, we determined the effect of a natural compound, Jiyuan oridonin A (JOA), on the differentiation blockade in the M2 subtype [particularly t (8;21)] of AML cells, M3 subtype of AML cells (APL cells), and leukemic stem-like cells both in vitro and in vivo. We found that JOA induced cell differentiation and suppressed the colony formation capacity in various AML cell lines (Kasumi-1, KG-1, MUTZ-8, NB4, and HL-60) without eliciting apoptosis. The mechanism of JOA-induced cell differentiation depends on the specificity of cell type. JOA mediated the differentiation of Kasumi-1 cells by activating the hematopoietic cell lineage signaling pathway, while inhibition of c-MYC was involved in the JOA-induced differentiation of NB4 cells. Moreover, JOA was identified to target leukemic stem-like cells by induced cell differentiation in vivo. These findings demonstrated that JOA could inhibit the proliferation of M2 and M3 subtypes of AML cells and leukemic stem-like cells by overcoming the differentiation blockade, which may offer a novel therapeutic strategy for AML to overcome relapse and drug resistance in patients with AML. Our findings highlight the possibility of using compounds like JOA as a promising differentiation-induced agent for the treatment of AML.

Causal discoveries for high dimensional mixed data

Causal relationships are of crucial importance for biological and medical research. Algorithms have been proposed for causal structure learning with graphical visualizations. While much of the literature focuses on biological studies where data often follow the same distribution, for example, the normal distribution for all variables, challenges emerge from epidemiological and clinical studies where data are often mixed with continuous, binary, and ordinal variables. We propose to use a mixed latent Gaussian copula model to estimate the underlying correlation structure via the rank correlation for mixed data. This correlation structure is then incorporated into a popular causal discovery algorithm, the PC algorithm, to identify causal structures. The proposed algorithm, called the latent-PC algorithm, is able to discover the true causal structure consistently under mild conditions in high dimensional settings. From simulation studies, the latent-PC algorithm delivers a competitive performance in terms of a similar or higher true positive rate and a similar or lower false positive rate, compared with other variants of the PC algorithm. In the high dimensional settings where the number of variables is more than the number of observations, the causal graphs identified by the latent-PC algorithm are closer to the true causal structures, compared to other competing algorithms. Further, we demonstrate the utility of the latent-PC algorithm in a real dataset for hepatocellular carcinoma. Causal structures for patient survival are visualized and connected with clinical interpretations in the literature.

The Pseudokinase TRIB1 in Immune Cells and Associated Disorders

Simple Summary

TRIB1 is at the center of major cell signaling pathways. In this review, we describe its role in immune cells and highlight TRIB1 interacting partners which suggests cell-specific functions and that TRIB1 is involved in cellular homeostasis and also in different cancers and immune-related disorders.

Abstract

Research advances in Tribbles homolog (TRIB) genes have established the consensus that this protein family plays roles in diverse biological conditions and regulates intracellular signaling networks and several human diseases. In this review, we focus on one member of the family, TRIB1, and its role at the crossroads of immune signaling. TRIB1 directly interacts with transcription factors such as FOXP3 and C/EBPα, with several signaling molecules such as MEK1 and MALT1 and directly acts on key cell signaling pathways such as the MAPK and NF-κB pathways. Altogether, these interactions emphasize that TRIB1 is at the center of major cell signaling pathways while TRIB1 has cell-specific roles, potentially depending on the expressing cells and binding partners. In this review, we describe its roles in immune cells and highlight the interacting partners explaining these functions which suggests TRIB1 as a precise mediator of cellular homeostasis as well as in different cancers and immune-related disorders.

Comprehensive Profiling of Mammalian Tribbles Interactomes Implicates TRIB3 in Gene Repression

The three human Tribbles (TRIB) pseudokinases have been implicated in a plethora of signaling and metabolic processes linked to cancer initiation and progression and can potentially be used as biomarkers of disease and prognosis. While their modes of action reported so far center around protein-protein interactions, the comprehensive profiling of TRIB interactomes has not been reported yet. Here, we have developed a robust mass spectrometry (MS)-based proteomics approach to characterize Tribbles' interactomes and report a comprehensive assessment and comparison of the TRIB1, -2 and -3 interactomes, as well as domain-specific interactions for TRIB3. Interestingly, TRIB3, which is predominantly localized in the nucleus, interacts with multiple transcriptional regulators, including proteins involved in gene repression. Indeed, we found that TRIB3 repressed gene transcription when tethered to DNA in breast cancer cells. Taken together, our comprehensive proteomic assessment reveals previously unknown interacting partners and functions of Tribbles proteins that expand our understanding of this family of proteins. In addition, our findings show that MS-based proteomics provides a powerful tool to unravel novel pseudokinase biology.

Novel cancer subtyping method based on patient-specific gene regulatory network

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

RFWD2 Knockdown as a Blocker to Reverse the Oncogenic Role of TRIB2 in Lung Adenocarcinoma

RFWD2, an E3 ubiquitin ligase, is overexpressed in numerous human cancers, including leukemia, lung cancer, breast cancer, renal cell carcinoma, and colorectal cancer. The roles of RFWD2 in cancer are related to the targeting of its substrates for ubiquitination and degradation. This study aimed to investigate the role of TRIB2 in relation to the regulation of protein degradation through RFWD2. inBio Discover™ results demonstrated that TRIB2 can perform its functions by interacting with RFWD2 or other factors. TRIB2 can interact with and regulate RFWD2, which further attends the proteasome-mediated degradation of the RFWD2 substrate p-IκB-α. TRIB2 colocalizes with RFWD2-related IκB-α to form a ternary complex and further affects the IκB-α degradation by regulating its phosphorylation. Specific domain analysis showed that TRIB2 may bind to RFWD2 via its C-terminus, whereas it binds to IκB via its pseudokinase domain. TRIB2 acts as an oncogene and promotes cancer cell proliferation and migration, whereas RFWD2 knockdown reversed the role of TRIB2 in promoting cancer cell growth and colony formation in vitro and in vivo. In summary, this study reveals that TRIB2 promotes the progression of cancer by affecting the proteasome-mediated degradation of proteins through the interaction with RFWD2.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Current Progress in Delineating the Roles of Pseudokinase TRIB1 in Controlling Human Diseases

Tribbles homolog 1 (TRIB1) is a member of the tribbles family of pseudoprotein kinases and is widely expressed in numerous tissues, such as bone marrow, skeletal muscle, liver, heart, and adipose tissue. It is closely associated with acute myeloid leukemia, prostate cancer, and tumor drug resistance, and can interfere with the hematopoietic stem cell cycle, promote tumor cell proliferation, and inhibit apoptosis. Recent studies have shown that TRIB1 can regulate acute and chronic inflammation by affecting the secretion of inflammatory factors, which is closely related to the occurrence of hyperlipidemia and cardiovascular diseases. Given the important biological functions of TRIB1, the reviews published till now are not sufficiently comprehensive. Therefore, this paper reviews the progress in TRIB1 research aimed at exploring its roles in cancer, hyperlipidemia, and cardiovascular disease, and providing a theoretical basis for further studies on the biological roles of TRIB1.

The tumor suppressor miR-642a-5p targets Wilms Tumor 1 gene and cell-cycle progression in prostate cancer

RNA-based therapeutics are emerging as innovative options for cancer treatment, with microRNAs being attractive targets for therapy development. We previously implicated microRNA-642a-5p (miR-642a-5p) as a tumor suppressor in prostate cancer (PCa), and here we characterize its mode of action, using 22Rv1 PCa cells. In an in vivo xenograft tumor model, miR-642a-5p induced a significant decrease in tumor growth, compared to negative control. Using RNA-Sequencing, we identified gene targets of miR-642a-5p which were enriched for gene sets controlling cell cycle; downregulated genes included Wilms Tumor 1 gene (WT1), NUAK1, RASSF3 and SKP2; and upregulated genes included IGFBP3 and GPS2. Analysis of PCa patient datasets showed a higher expression of WT1, NUAK1, RASSF3 and SKP2; and a lower expression of GPS2 and IGFBP3 in PCa tissue compared to non-malignant prostate tissue. We confirmed the prostatic oncogene WT1, as a direct target of miR-642a-5p, and treatment of 22Rv1 and LNCaP PCa cells with WT1 siRNA or a small molecule inhibitor of WT1 reduced cell proliferation. Taken together, these data provide insight into the molecular mechanisms by which miR-642a-5p acts as a tumor suppressor in PCa, an effect partially mediated by regulating genes involved in cell cycle control; and restoration of miR-642-5p in PCa could represent a novel therapeutic approach.

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning

MOTIVATION

Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages.

RESULTS

In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems.

AVAILABILITY

http://sbbi-panda.unl.edu/pin/.

Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health

INTRODUCTION

Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India.

METHODS

Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups.

RESULTS

Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning.

CONCLUSION

These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.

Structure vs. Function of TRIB1-Myeloid Neoplasms and Beyond

The Tribbles family of proteins-comprising TRIB1, TRIB2, TRIB3 and more distantly related STK40-play important, but distinct, roles in differentiation, development and oncogenesis. Of the four Tribbles proteins, TRIB1 has been most well characterised structurally and plays roles in diverse cancer types. The most well-understood role of TRIB1 is in acute myeloid leukaemia, where it can regulate C/EBP transcription factors and kinase pathways. Structure-function studies have uncovered conformational switching of TRIB1 from an inactive to an active state when it binds to C/EBPα. This conformational switching is centred on the active site of TRIB1, which appears to be accessible to small-molecule inhibitors in spite of its inability to bind ATP. Beyond myeloid neoplasms, TRIB1 plays diverse roles in signalling pathways with well-established roles in tumour progression. Thus, TRIB1 can affect both development and chemoresistance in leukaemia; glioma; and breast, lung and prostate cancers. The pervasive roles of TRIB1 and other Tribbles proteins across breast, prostate, lung and other cancer types, combined with small-molecule susceptibility shown by mechanistic studies, suggests an exciting potential for Tribbles as direct targets of small molecules or biomarkers to predict treatment response.

From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network.

High-dimensional and heterogeneous healthcare data, such as electronic health records or epidemiological study data, contain much information on yet unknown risk factors that are associated with disease development. The identification of these risk factors may help to improve prevention, diagnosis, and therapy. Bayesian networks are powerful statistical models that can decipher these complex relationships. However, high dimensionality and heterogeneity of data, together with missing values and high feature correlation, make it difficult to automatically learn a good model from data. To facilitate the use of network models, we present a novel, fully automated workflow that combines network learning with hierarchical clustering. The algorithm reveals groups of strongly related features and models the interactions among those groups. It results in simpler network models that are easier to analyze. We introduce a method of adaptive refinement of such models to ensure that disease-relevant parts of the network are modeled in great detail. Our approach makes it easy to learn compact, accurate, and easily interpretable biomarker interaction networks. We test our method extensively on simulated data as well as data from the Study of Health in Pomerania (SHIP-Trend) by learning models of hypertension and non-alcoholic fatty liver disease.

Tribbles-1 Expression and Its Function to Control Inflammatory Cytokines, Including Interleukin-8 Levels are Regulated by miRNAs in Macrophages and Prostate Cancer Cells

The pseudokinase TRIB1 controls cell function in a range of contexts, by regulating MAP kinase activation and mediating protein degradation via the COP1 ubiquitin ligase. TRIB1 regulates polarization of macrophages and dysregulated Trib1 expression in murine models has been shown to alter atherosclerosis burden and adipose homeostasis. Recently, TRIB1 has also been implicated in the pathogenesis of prostate cancer, where it is often overexpressed, even in the absence of genetic amplification. Well described TRIB1 effectors include MAP kinases and C/EBP transcription factors, both in immune cells and in carcinogenesis. However, the mechanisms that regulate TRIB1 itself remain elusive. Here, we show that the long and conserved 3’untranslated region (3’UTR) of TRIB1 is targeted by miRNAs in macrophage and prostate cancer models. By using a systematic in silico analysis, we identified multiple “high confidence” miRNAs potentially binding to the 3’UTR of TRIB1 and report that miR-101-3p and miR-132-3p are direct regulators of TRIB1 expression and function. Binding of miR-101-3p and miR-132-3p to the 3’UTR of TRIB1 mRNA leads to an increased transcription and secretion of interleukin-8. Our data demonstrate that modulation of TRIB1 by miRNAs alters the inflammatory profile of both human macrophages and prostate cancer cells.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data

Background

Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner.

Results

This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations.

Conclusions

Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

Genomic and Functional Regulation of TRIB1 Contributes to Prostate Cancer Pathogenesis

Prostate cancer is the most frequent malignancy in European men and the second worldwide. One of the major oncogenic events in this disease includes amplification of the transcription factor cMYC. Amplification of this oncogene in chromosome 8q24 occurs concomitantly with the copy number increase in a subset of neighboring genes and regulatory elements, but their contribution to disease pathogenesis is poorly understood. Here we show that TRIB1 is among the most robustly upregulated coding genes within the 8q24 amplicon in prostate cancer. Moreover, we demonstrate that TRIB1 amplification and overexpression are frequent in this tumor type. Importantly, we find that, parallel to its amplification, TRIB1 transcription is controlled by cMYC. Mouse modeling and functional analysis revealed that aberrant TRIB1 expression is causal to prostate cancer pathogenesis. In sum, we provide unprecedented evidence for the regulation and function of TRIB1 in prostate cancer.

Clinical significance of CCNE2 protein and mRNA expression in thyroid cancer tissues

PURPOSE

Thyroid carcinoma (TC) is the most common endocrinal malignancy worldwide. Cyclin E2 (CCNE2), a member of the cyclin family, acts as a regulatory subunit of cyclin-dependent kinases (CDKs). It controls the transition of quiescent cells into the cell cycle, regulates the G1/S transition, promotes DNA replication, and activates CDK2. This study explored the role and potential molecular mechanisms of CCNE2 expression in TC tissues.

MATERIAL/METHODS

Immunohistochemistry was used to evaluate the CCNE2 protein expression levels in TC. High-throughput data on CCNE2 in TC were obtained from RNA sequencing (RNA-seq), microarray, and literature data. The CCNE2 expression levels in TC were comprehensively assessed through an integrated analysis. Analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPIs) data facilitated the investigation of the relative molecular mechanisms of CCNE2 in TC.

RESULTS

The immunohistochemical experiment showed a significant increase in the expression of CCNE2 in the TC tissues. For 505 TC and 59 non-cancerous samples from RNA-seq data, the area under the curve (AUC) was 0.8016 (95% confidence interval [CI] 0.742-0.8612; p<0.001). With another 14 microarrays, the pool standard mean difference [SMD] was 1.01 (95% CI [0.82-1.19]). The pooled SMD of CCNE2 was 1.12 (95% CI [0.60-1.64]), and the AUC was 0.87 (95% CI [0.84-0.90]) for 1157 TC samples and 366 non-cancerous thyroid samples from all possible sources. Nine hub genes were upregulated in TC.

CONCLUSIONS

A high expression of CCNE2 may lead to carcinogenesis and the development of TC.

An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

BACKGROUND

A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention.

METHODS

In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by cross-platform normalization (CPN). Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from KEGG-indexed SLE, TCR, and BCR signaling pathways; the values of the node (gene) and edge (intergenic relationships) parameters were estimated within each integrated datasets. Parameters with the FDR < 0.05 were considered significant. Finally, a mixture model was performed to decipher the signaling pathway alterations in the SLE patients compared to healthy controls.

RESULTS

In the SLE signaling pathway, we identified the dysregulation of several nodes involved in the (1) clearance mechanism (SSB, MACROH2A2, TRIM21, H2AX, and C1Q gene family), (2) autoantigen presentation by MHCII (HLA gene family, CD80, IL10, TNF, and CD86), and (3) end-organ damage (FCGR1A, ELANE, and FCGR2A). As a remarkable finding, we demonstrated significant perturbation in CD80 and CD86 to CD28, CD40LG to CD40, C1QA and C1R to C2, and C1S to C4A edges. Moreover, we not only replicated previous studies regarding alterations of subnetworks involved in TCR and BCR signaling pathways (PI3K/AKT, MAPK, VAV gene family, AP-1 transcription factor) but also distinguished several significant edges between genes (PPP3 to NFATC gene families). Our findings unprecedentedly showed that different parameter values assign to the same node based on the pathway topology (the PIK3CB parameter values were 1.7 in TCR vs - 0.5 in BCR signaling pathway).

CONCLUSIONS

Applying the BNrich as a hybridized network construction method, we highlight under-appreciated systemic alterations of SLE, TCR, and BCR signaling pathways in SLE. Consequently, having such a systems biology approach opens new insights into the context of multifactorial disorders.

Bayesian networks established functional differences between breast cancer subtypes

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.

A Small Molecule Inhibitor, OGP46, Is Effective against Imatinib-Resistant BCR-ABL Mutations via the BCR-ABL/JAK-STAT Pathway

Chronic myeloid leukemia (CML) is caused by the Philadelphia (Ph⁺) chromosome carrying the BCR-ABL oncogene, a constitutively active tyrosine kinase. The discovery of imatinib represents a major success story in the treatment against CML. However, mutations in the BCR-ABL kinase domain are a major cause of resistance to imatinib, demonstrating that BCR-ABL remains a critical drug target. Here, we investigate a novel small molecule inhibitor, OGP46, for its inhibitory activity against K562, a panel of murine BaF3 cell lines stably expressing either wild-type BCR-ABL or its mutant forms, including T315I. OGP46 exhibits potent activity against imatinib-resistant BCR-ABL mutations, including T315I. OGP46 induced cell differentiation accompanied by G0/G1 cell-cycle arrest and suppressed the colony formation capacity of cells. Treatment with OGP46 significantly decreased the mRNA and protein expression of BCR-ABL in K562 and BaF3-p210-T315I cells. Mechanistically, the anti-cancer activity of OGP46 induced by cell differentiation is likely through the BCR-ABL/JAK-STAT pathway in native BCR-ABL and mutant BCR-ABL, including T315I, of CML cells. Our findings highlight that OGP46 is active against not only native BCR-ABL but also 11 clinically relevant BCR-ABL mutations, including T315I mutation, which are resistant to imatinib. Thus, OGP46 may be a novel strategy for overcoming imatinib-resistance BCR-ABL mutations, including T315I.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Trib1 Contributes to Recovery From Ischemia/Reperfusion-Induced Acute Kidney Injury by Regulating the Polarization of Renal Macrophages

Increasing evidence suggests that macrophage polarization is involved in the recovery from ischemia-reperfusion (I/R)-induced acute kidney injury (AKI), implying that the regulation of macrophage polarization homeostasis might mediate AKI recovery. Trib1 is a key regulator of macrophage differentiation, but its role in AKI remains unclear. Here, we aimed to investigate the role of Trib1 and its link with the macrophage phenotype in the process of adaptive recovery from I/R-induced renal injury. Lentiviral vector-mediated RNA interference (RNAi) was used to knock down Trib1 expression in vitro and in vivo, and a mouse model of moderate AKI was established by the induction of I/R injury. Renal function measurements and inflammatory factors were determined by the corresponding kits. Histomorphology was assessed by hematoxylin-eosin, Masson and PAS staining. Western blot and flow cytometry were employed for the analysis of signal transduction, cell apoptosis and macrophage phenotypes. Trib1 knockdown inhibited cell viability of tubular epithelial cells (TECs) by inhibiting proliferation and enhancing apoptosis in vitro. I/R-induced AKI significantly impaired renal function in mice via increasing the levels of BUN, Scr, NGAL and renal tubular damage, leading to renal fibrosis from days 1 to 3. Through the adaptive self-repair mechanism, renal dysfunction recovered over time and returned to almost normal levels on day 28 after I/R intervention. However, Trib1 depletion worsened renal damage on day 3 and blunted the adaptive repair process of the renal tissue. Mechanistically, Trib1 inhibition suppressed renal tubular cell proliferation under adaptive self-repair conditions by affecting the expression of the proliferation-related proteins cyclin D1, cyclin B, p21, and p27, the apoptosis-related proteins Bcl-2 and Bax, and the fibrosis-related proteins collagen I and III. Furthermore, the M1/M2 macrophage ratio increased in the first 3 days and decreased from day 7 to day 28, consistent with changes in the expression of inflammatory factors, including TNFα, IL-6, IL-12, IL-10, and IL-13. Trib1 inhibition blocked macrophage polarization during adaptive recovery from I/R-induced moderate AKI. Our results show that Trib1 plays a role in kidney recovery and regeneration via the regulation of renal tubular cell proliferation by affecting macrophage polarization. Thus, Trib1 might be a viable therapeutic target to improve renal adaptive repair following I/R injury.

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.

Stem cell paracrine actions in tissue regeneration and potential therapeutic effect in human endometrium: a retrospective study

OBJECTIVE

Determining genetic and paracrine mechanisms behind endometrial regeneration in Asherman's syndrome and endometrial atrophy (AS/EA) patients after autologous CD133⁺ bone marrow-derived stem cell (CD133⁺ BMDSC) transplantation.

DESIGN

Retrospective study using human endometrial biopsies and mouse models.

SETTING

Fundación-IVI, IIS-La Fe, Valencia, Spain.

SAMPLES

Endometrial biopsies collected before and after CD133⁺ BMDSC therapy, from eight women with AS/EA (NCT02144987) from the uterus of five mice with only left horns receiving CD133⁺ BMDSC therapy.

METHODS

In human samples, haematoxylin and eosin (H&E) staining, RNA arrays, PCR validation, and neutrophil elastase (NE) immunohistochemistry (IHQ). In mouse samples, PCR validation and protein immunoarrays.

MAIN OUTCOME MEASURES

H&E microscopic evaluation, RNA expression levels, PCR, and growth/angiogenic factors quantification, NE IHQ signal.

RESULTS

Treatment improved endometrial morphology and thickness for all patients. In human samples, Jun, Serpine1, and Il4 were up-regulated whereas Ccnd1 and Cxcl8 were down-regulated after treatment. The significant decrease of NE signal corroborated Cxcl8 expression. Animal model analysis confirmed human results and revealed a higher expression of pro-angiogenic cytokines (IL18, HGF, MCP-1, MIP2) in treated uterine horns.

CONCLUSIONS

CD133⁺ BMDSC seems to activate several factors through a paracrine mechanism to help tissue regeneration, modifying endometrial behaviour through an immunomodulatory milieu that precedes proliferation and angiogenic processes. Insight into these processes could bring us one step closer to a non-invasive treatment for AS/EA patients.

TWEETABLE ABSTRACT

CD133⁺ BMDSC therapy regenerates endometrium, modifying the immunological milieu that precedes proliferation and angiogenesis.

Pseudokinases: a tribble-edged sword

Advances in the understanding of the Tribbles family of pseudokinases (TRIB1, TRIB2 and TRIB3) reveal these proteins as potentially valuable biomarkers of disease diagnosis, prognosis, prediction and clinical strategy. In their role as signalling mediators and scaffolding proteins, TRIBs lead to changes in protein stability and activity, which impact on diverse cellular processes such as proliferation, differentiation, cell cycle and cell death. We review the role of TRIB proteins as promising therapeutic targets, with an emphasis on their role in cancer, and as biomarkers, with potential application across diverse pathological processes.

The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro

Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data

Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.

Reactive Oxygen Species Are Involved in the Development of Gastric Cancer and Gastric Cancer-Related Depression through ABL1-Mediated Inflammation Signaling Pathway

Background

The mechanisms of crosstalk between depression and gastric cancer (GC) remain ill defined. Given that reactive oxygen species (ROS) is involved in the pathophysiology of both GC and depression, we try to explore the activities of ROS in the development of GC and GC-related depression.

Methods

110 patients with newly diagnosed GC were recruited in our study. The clinical characteristics of these patients were recorded. Inflammation and oxidative stress markers were detected by ELISA. The depression status of patients with GC was assessed during follow-up. The association between ROS, ABL1, and inflammation factors was evaluated in H₂O₂-treated GC cell lines and The Cancer Genome Atlas (TCGA) database. The effect of ABL1 on inflammation was detected with Imatinib/Nilotinib-treated GC cell lines. A chronic mild stress- (CMS-) induced patient-derived xenograft (PDX) mice model was established to assess the crosstalk between depression and GC.

Results

Depression was correlated with poor prognosis of patients with GC. GC patients with depression were under a high level of oxidative status as well as dysregulated inflammation. In the CMS-induced GC PDX mice model, CMS could facilitate the development of GC. Additionally, tumor bearing could induce depressive-like behaviors of mice. With the treatment of ROS, the activities of ABL1 and inflammatory signaling were enhanced both in vitro and in vivo, and blocking the activities of ABL1 inhibited inflammatory signaling.

Conclusions

ROS-activated ABL1 mediates inflammation through regulating NF-κB1 and STAT3, which subsequently leads to the development of GC and GC-related depression.

TRIB1 induces macrophages to M2 phenotype by inhibiting IKB-zeta in prostate cancer

Immunotherapy has made great breakthroughs in the field of cancer. However, the immunotherapeutic effect of prostate cancer is unsatisfactory. We found that the expression of TRIB1 was significantly correlated with the infiltration of CD163+ macrophages in prostate cancer. This study focused on the effects of TRIB1 on macrophage polarization in the immune microenvironment of prostate cancer. RNA sequencing analysis demonstrated that TRIB1 has significant effects on the regulation of the nuclear factor (NF)-κB signaling pathway and downstream cytokines. Flow cytometry and enzyme-linked immunosorbent assay were used to examine THP-1 cells cultured in conditioned medium from prostate cancer cells overexpressing TRIB1 and showed that overexpression of TRIB1 promoted the secretion of CXCL2 and interleukin (IL)8 by PC3 cells, which increased the secretion of IL12 by THP-1 cells as well as the expression of CD163 on THP-1 cells. IKB-zeta, regulated by TRIB1, was expressed in PC3 cells but was barely detectable in DU145 cells. The reductions in CXCL2 and IL8 by the inhibition of TRIB1 were rescued by the deletion of IKB-zeta. Here we showed that TRIB1 promoted the secretion of cytokines from prostate cancer cells and induced the differentiation of monocytes/macrophages into M2 macrophages.

Knockdown of TRIM27 expression suppresses the dysfunction of mesangial cells in lupus nephritis by FoxO1 pathway

TRIM27 (tripartite motif-containing 27) is a member of the TRIM (tripartite motif) protein family and participates in a variety of biological processes. Some research has reported that TRIM27 was highly expressed in certain kinds of carcinoma cells and tissues and played an important role in the proliferation of carcinoma cells. However, whether TRIM27 takes part in the progression of lupus nephritis (LN) especially in cells proliferation remains unclear. Our study revealed that the overexpression of TRIM27 was observed in the kidneys of patients with LN, lupus mice and mesangial cells exposed to LN plasma which correlated with the proliferation of mesangial cells and ECM (extracellular matrix) deposition. Downregulation of TRIM27 expression suppressed the proliferation of mesangial cells and ECM accumulation in MRL/lpr mice and cultured human mesangial cells (HMCs) by regulating the FoxO1 pathway. Furthermore, the overexpression of FoxO1 remarkably decreased HMCs proliferation level and ECM accumulation in LN plasma-treated HMCs. In addition, the protein kinase B (Akt) signal pathway inhibitor LY294002 significantly reduced the expression of TRIM27 and inhibited the dysfunction of mesangial cells. These above data suggested that TRIM27 mediated abnormal mesangial cell proliferation in kidney of lupus and might be the potential target for treating mesangial cell proliferation of lupus nephritis.

Sex-related DNA methylation differences in B cell chronic lymphocytic leukemia

BACKGROUND

Men are at higher risk of developing chronic lymphocytic leukemia (CLL) than women. DNA methylation has been shown to play important roles in a number of cancers. There are differences in the DNA methylation pattern between men and women. In this study, we investigated whether this contributes to the sex-related difference of B cell CLL risk.

METHODS

Using the HumanMethylation450 BeadChip, we profiled the genome-wide DNA methylation pattern of CD19⁺ B cells from 48 CLL patients (29 female patients and 19 male patients) and 28 healthy people (19 women and 9 men).

RESULTS

We identified 1043 sex-related differentially methylated positions (DMPs) related to CLL, 56 of which are located on autosomes and 987 on the X chromosome. Using published B cell RNA-sequencing data, we found 18 genes covered by the DMPs also have different expression levels in male and female CLL patients. Among them, TRIB1, an autosome gene, has been shown to promote tumor growth by suppressing apoptosis.

CONCLUSIONS

Our study represents the first epigenome-wide association study (EWAS) that investigates the sex-related differences in cancer, and indicated that DNA methylation differences might contribute to the sex-related difference in CLL risk.

IER family proteins are regulators of protein phosphatase PP2A and modulate the phosphorylation status of CDC25A

Proteins encoded by immediate-early response (IER) family genes, IER2, IER5, and IER5L, share homology at their N-terminal regions. IER5 binds to protein phosphatase 2A (PP2A) and enhances dephosphorylation of PP2A target proteins such as heat shock factor HSF1. Here, we show the expression of IER family genes and the target protein-specific function of IER proteins. The IER homology regions of IER2 and IER5L are required for the interaction with PP2A. Expression of IER2 and IER5L in cells leads to reduced phosphorylation of HSF1 and derepression of its transcriptional activity. Although IER5 and IER5L enhance dephosphorylation of ribosomal protein S6 kinase, IER2 fails to do so. IER2, IER5, and IER5L all bind to the cell cycle regulator CDC25A and convert it to the hypophosphorylated form, which causes dissociation from 14-3-3 regulatory protein. IER5 differentially regulates CDC25A levels in cells under normal and thermal stress conditions. These results suggest that IER proteins are target protein-specific regulators of PP2A activity and modulate cell proliferation through CDC25A activity.

Adaptively capturing the heterogeneity of expression for cancer biomarker identification

Background

Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.

Results

In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer.

Conclusions

Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use.

Software availability

The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2437-2) contains supplementary material, which is available to authorized users.

Association Analysis of a Microsatellite Repeat in the TRIB1 Gene With Prostate Cancer Risk, Aggressiveness and Survival

With an estimated 1.1 million men worldwide diagnosed with prostate cancer yearly, effective and more specific biomarkers for early diagnosis could lead to better patient outcome. As such, novel genetic markers are sought for this purpose. The tribbles homologue 1 gene (TRIB1) has recently shown to have a role in prostate tumorigenesis and data-mining of prostate cancer expression data confirmed clinical significance of TRIB1 in prostate cancer. For the first time, a polymorphic microsatellite in this gene was studied for its potential association with prostate cancer risk and aggressiveness. Genomic DNA was extracted from a cohort of 1,152 prostate cancer patients and 1,196 cancer-free controls and the TTTTG-TRIB1 microsatellite was genotyped. The socio-demographic and clinical characteristics were analyzed using the non-parametric t-test and two-way ANOVA. Association of the TTTTG-TRIB1 microsatellite and prostate cancer risk and aggressiveness were analyzed by binary logistic regression and confirmed by bootstrapping. Total and prostate cancer mortality was analyzed using the Kaplan Meier test. Genotype and allele correlation with TRIB1 mRNA levels was analyzed using the non-parametric Kolmogorov-Smirnov test. To predict the effect that the TTTTG-TRIB1 polymorphisms had on the mRNA structure, the in silico RNA folding predictor tool, mfold, was used. By analyzing the publicly available data, we confirmed a significant over-expression of TRIB1 in prostate cancer compared to other cancer types, and an over-expression in prostate cancerous tissue compared to adjacent benign. Three alleles (three-five repeats) were observed for TTTTG-TRIB1. The three-repeat allele was associated with prostate cancer risk at the allele (OR = 1.16; P = 0.044) and genotypic levels (OR = 1.70; P = 0.006) and this association was age-independent. The four-repeat allele was inversely associated with prosatet cancer risk (OR = 0.57; P < 0.0001). TRIB1 expression was upregulated in tumors when compared to adjacent cancer-free tissue but was not allele specific. In silico analysis suggested that the TTTTG-TRIB1 alleles may alter TRIB1 mRNA structure. In summary, the three-repeat allele was significantly associated with prostate cancer risk, suggesting a biomarker potential for this microsatellite to predict prostate cancer. Further studies are needed to elucidate the functional role of this microsatellite in regulating TRIB1 expression, perhaps by affecting the TRIB1 mRNA structure and stability.

Resveratrol inhibits monocrotaline-induced pulmonary arterial remodeling by suppression of SphK1-mediated NF-κB activation

AIMS

This study aims to explore the molecular mechanisms underlying sphingosine kinase 1 (SphK1) inducing pulmonary vascular remodeling and resveratrol suppressing pulmonary arterial hypertension (PAH).

MATERIAL AND METHODS

monocrotaline (MCT) was used to induce PAH in rats. The right ventricular systolic pressure (RVSP), right ventricle hypertrophy index (RVHI) and histological analyses including hematoxylin and eosin staining, the percentage of medial wall thickness (%MT), α-SMA staining and Ki67 staining were performed to evaluate the development of PAH. Protein levels of SphK1, nuclear factor-kappaB (NF-κB)-p65 and cyclin D1 were determined using immunoblotting. Sphingosine-1-phosphate (S1P) concentration was measured using enzyme-linked immunosorbent assay.

KEY FINDINGS

SphK1 protein level, S1P production, NF-κB activation and cyclin D1 expression were significantly increased in MCT-induced PAH rats. Inhibition of SphK1 by PF543 suppressed S1P synthesis and NF-κB activation and down-regulated cyclin D1 expression in PAH rats. Suppression of NF-κB by pyrrolidine dithiocarbamate (PDTC) also reduced cyclin D1 expression in PAH model. Treatment of PAH rats with either PF543 or PDTC dramatically decreased RVSP, RVHI and %MT and reduced pulmonary arterial smooth muscle cells proliferation and pulmonary vessel muscularization. In addition, resveratrol effectively inhibited the development of PAH by suppression of SphK1/S1P-mediated NF-κB activation and subsequent cyclin D1 expression.

SIGNIFICANCE

SphK1/S1P signaling induces the development of PAH by activation of NF-κB and subsequent up-regulation of cyclin D1 expression. Resveratrol inhibits the MCT-induced PAH by targeting on SphK1 and reverses the downstream changes of SphK1, indicating that resveratrol might be a therapeutic agent for the prevention of PAH.

Systems biology and in vitro validation identifies family with sequence similarity 129 member A (FAM129A) as an asthma steroid response modulator

BACKGROUND

Variation in response to the most commonly used class of asthma controller medication, inhaled corticosteroids, presents a serious challenge in asthma management, particularly for steroid-resistant patients with little or no response to treatment.

OBJECTIVE

We applied a systems biology approach to primary clinical and genomic data to identify and validate genes that modulate steroid response in asthmatic children.

METHODS

We selected 104 inhaled corticosteroid-treated asthmatic non-Hispanic white children and determined a steroid responsiveness endophenotype (SRE) using observations of 6 clinical measures over 4 years. We modeled each subject's cellular steroid response using data from a previously published study of immortalized lymphoblastoid cell lines under dexamethasone (DEX) and sham treatment. We integrated SRE with immortalized lymphoblastoid cell line DEX responses and genotypes to build a genome-scale network using the Reverse Engineering, Forward Simulation modeling framework, identifying 7 genes modulating SRE.

RESULTS

Three of these genes were functionally validated by using a stable nuclear factor κ-light-chain-enhancer of activated B cells luciferase reporter in A549 human lung epithelial cells, IL-1β cytokine stimulation, and DEX treatment. By using small interfering RNA transfection, knockdown of family with sequence similarity 129 member A (FAM129A) produced a reduction in steroid treatment response (P < .001).

CONCLUSION

With this systems-based approach, we have shown that FAM129A is associated with variation in clinical asthma steroid responsiveness and that FAM129A modulates steroid responsiveness in lung epithelial cells.

Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer

Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.

A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease

BACKGROUND

There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research.

OBJECTIVE

To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD.

METHODS

Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III.

RESULTS

The most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson's Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study.

CONCLUSIONS

Baseline function near the time of Parkinson's disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson's disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies.