Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the “guilt-by-association” principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.     

TCF4 and HuR mediated-METTL14 suppresses dissemination of colorectal cancer via N6-methyladenosine-dependent silencing of ARRDC4

Metastasis remains the major obstacle to improved survival for colorectal cancer (CRC) patients. Dysregulation of N6-methyladenosine (m6A) is causally associated with the development of metastasis through poorly understood mechanisms. Here, we report that METTL14, a key component of m6A methylation, is functionally related to the inhibition of ARRDC4/ZEB1 signaling and to the consequent suppression of CRC metastasis. We unveil METTL14-mediated m6A modification profile and identify ARRDC4 as a direct downstream target of METTL14. Knockdown of METTL14 significantly enhanced ARRDC4 mRNA stability relying on the “reader” protein YHTDF2 dependent manner. Moreover, we demonstrate that TCF4 can induce METTL14 protein expression, and HuR suppress METTL14 expression by directly binding to its promoter. Clinically, our results show that decreased METTL14 is correlated with poor prognosis and acts as an independent predictor of CRC survival. Collectively, our findings propose that METTL14 functions as a metastasis suppressor, and define a novel signaling axis of TCF4/HuR-METTL14-YHTDF2-ARRDC4-ZEB1 in CRC, which might be potential therapeutic targets for CRC.Subject terms: Cancer prevention, Post-translational modifications     

Anomalies in Network Bridges Involved in Bile Acid Metabolism Predict Outcomes of Colorectal Cancer Patients

Biomarkers prognostic for colorectal cancer (CRC) would be highly desirable in clinical practice. Proteins that regulate bile acid (BA) homeostasis, by linking metabolic sensors and metabolic enzymes, also called bridge proteins, may be reliable prognostic biomarkers for CRC. Based on a devised metric, “bridgeness,” we identified bridge proteins involved in the regulation of BA homeostasis and identified their prognostic potentials. The expression patterns of these bridge proteins could distinguish between normal and diseased tissues, suggesting that these proteins are associated with CRC pathogenesis. Using a supervised classification system, we found that these bridge proteins were reproducibly prognostic, with high prognostic ability compared to other known markers.     

Construction of PARPi Resistance-related Competing Endogenous RNA Network

Objective: Ovarian cancer is a kind of common gynecological malignancy in women. PARP inhibitors (PARPi) have been approved for ovarian cancer treatment. However, the primary and acquired resistance have limited the application of PARPi. The mechanisms remain to be elucidated.Methods: In this study, we characterized the expression profiles of mRNA and nonconding RNAs (ncRNAs) and constructed the regulatory networks based on RNA sequencing in PARPi Olaparib-induced ovarian cancer cells.Results: We found that the functions of the differentially expressed genes were enriched in “PI3K/AKT signaling pathway,” “MAPK signaling pathway” and “metabolic process”. The functions of DELs (cis) were enriched in “Human papillomavirus infection”“tight junction” “MAPK signaling pathway”. As the central regulator of ceRNAs, the differentially expressed miRNAs were enriched in “Human papillomavirus infection” “MAPK signaling pathway” “Ras signaling pathway”. According to the degree of interaction, we identified 3 lncRNAs, 2 circRNAs, 7 miRNAs, and 12 mRNA as the key regulatory ceRNA axis, in which miR-320b was the important mediator.Conclusion: Here, we revealed the key regulatory lncRNA (circRNA)-miRNA-mRNA axis and their involved pathways in the PARPi resistant ovarian cancer cells. These findings provide new insights into exploring the ceRNA regulatory networks and developing new targets for PARPi resistance.     

Network Signatures of Survival in Glioblastoma Multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.     

Age,but Not Experience,Affects Courtship Gene Expression in Male Drosophila melanogaster

Mutation screens in model organisms have helped identify the foundation of many fundamental organismal phenotypes. An emerging question in evolutionary and behavioral biology is the extent to which these “developmental” genes contribute to the subtle individual variation that characterizes natural populations. A related question is whether individual differences arise from static differences in gene expression that arose during previous life stages, or whether they are due to dynamic regulation of expression during the life stage under investigation. Here, we address these questions using genes that have been discovered to control the development of normal courtship behavior in male Drosophila melanogaster. We examined whether these genes have static or dynamic expression in the heads of adult male flies of different ages and with different levels of social experience. We found that 16 genes of the 25 genes examined were statically expressed, and 9 genes were dynamically expressed with changes related to adult age. No genes exhibited rapid dynamic expression changes due to social experience or age*experience interaction. We therefore conclude that a majority of fly “courtship” genes are statically expressed, while a minority are regulated in adults with respect to age, but not with respect to relevant social experience. These results are consistent with those from a recent microarray analysis that found none of the canonical courtship genes changed expression in male flies after brief exposure to females.     

Static and Dynamic Factors Limit Chromosomal Replication Complexity in Escherichia coli,Avoiding Dangers of Runaway Overreplication

We define chromosomal replication complexity (CRC) as the ratio of the copy number of the most replicated regions to that of unreplicated regions on the same chromosome. Although a typical CRC of eukaryotic or bacterial chromosomes is 2, rapidly growing Escherichia coli cells induce an extra round of replication in their chromosomes (CRC = 4). There are also E. coli mutants with stable CRC∼6. We have investigated the limits and consequences of elevated CRC in E. coli and found three limits: the “natural” CRC limit of ∼8 (cells divide more slowly); the “functional” CRC limit of ∼22 (cells divide extremely slowly); and the “tolerance” CRC limit of ∼64 (cells stop dividing). While the natural limit is likely maintained by the eclipse system spacing replication initiations, the functional limit might reflect the capacity of the chromosome segregation system, rather than dedicated mechanisms, and the tolerance limit may result from titration of limiting replication factors. Whereas recombinational repair is beneficial for cells at the natural and functional CRC limits, we show that it becomes detrimental at the tolerance CRC limit, suggesting recombinational misrepair during the runaway overreplication and giving a rationale for avoidance of the latter.     

Network Topology Reveals Key Cardiovascular Disease Genes

The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and “driver genes.” We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies “key” genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.     

The Association between the Use of Proton Pump Inhibitors and the Risk of Hypomagnesemia: A Systematic Review and Meta-Analysis

Background

Although many case reports have described patients with proton pump inhibitor (PPI)-induced hypomagnesemia, the impact of PPI use on hypomagnesemia has not been fully clarified through comparative studies. We aimed to evaluate the association between the use of PPI and the risk of developing hypomagnesemia by conducting a systematic review with meta-analysis.

Methods

We conducted a systematic search of MEDLINE, EMBASE, and the Cochrane Library using the primary keywords “proton pump,” “dexlansoprazole,” “esomeprazole,” “ilaprazole,” “lansoprazole,” “omeprazole,” “pantoprazole,” “rabeprazole,” “hypomagnesemia,” “hypomagnesaemia,” and “magnesium.” Studies were included if they evaluated the association between PPI use and hypomagnesemia and reported relative risks or odds ratios or provided data for their estimation. Pooled odds ratios with 95% confidence intervals were calculated using the random effects model. Statistical heterogeneity was assessed with Cochran’s Q test and I ² statistics.

Results

Nine studies including 115,455 patients were analyzed. The median Newcastle-Ottawa quality score for the included studies was seven (range, 6–9). Among patients taking PPIs, the median proportion of patients with hypomagnesemia was 27.1% (range, 11.3–55.2%) across all included studies. Among patients not taking PPIs, the median proportion of patients with hypomagnesemia was 18.4% (range, 4.3–52.7%). On meta-analysis, pooled odds ratio for PPI use was found to be 1.775 (95% confidence interval 1.077–2.924). Significant heterogeneity was identified using Cochran’s Q test (df = 7, P<0.001, I ² = 98.0%).

Conclusions

PPI use may increase the risk of hypomagnesemia. However, significant heterogeneity among the included studies prevented us from reaching a definitive conclusion.     

The roles of phospholipase C activation and alternative ADAR1 and ADAR2 pre-mRNA splicing in modulating serotonin 2C-receptor editing in vivo

The serotonin 2C receptor (5-HT2CR), a Gq-protein-coupled neurotransmitter receptor, exists in multiple isoforms that result from RNA editing of five exonic adenosines that are converted to inosines. In the adult brain, editing of 5-HT2C pre-mRNA exhibits remarkable plasticity in response to environmental and neurochemical stimuli. Here, we investigated two potential mechanisms underlying these plastic changes in adult 5-HT2CR editing phenotypes in vivo: activation of phospholipase C (PLC) and alternative splicing of pre-mRNA encoding the editing enzymes ADAR1 and ADAR2. Studies on two inbred strains of mice (C57Bl/6 and Balb/c) revealed that sustained stimulation of PLC—a downstream effector of activated Gαq protein—increased editing of forebrain neocortical 5-HT2C pre-mRNA at two sites known to be targeted by ADAR2. Moreover, changes in relative expression of the alternatively spliced “a” and “b” mRNA isoforms of ADAR1 and ADAR2 also correlate with changes in 5-HT2CR editing. The site-specific changes in 5-HT2CR editing detected in mice with different “a” over “b” ADAR mRNA isoform ratios only partially overlap with those evoked by sustained PLC activation and are best explained by the increased editing efficiency of ADAR1. Thus, activation of PLC and alternative splicing of ADAR pre-mRNA have both overlapping and specific roles in modulating 5-HT2CR editing phenotypes.     

Metastatic Tissue Proteomic Profiling Predicts 5-Year Outcomes in Patients with Colorectal Liver Metastases

Colorectal cancer (CRC) is one of the most common cancers in the developed countries, and nearly 70% of patients with CRC develop colorectal liver metastases (CRLMs). During the last decades, several scores have been proposed to predict recurrence after CRLM resection. However, these risk scoring systems do not accurately reflect the prognosis of these patients. Therefore, this investigation was designed to identify a proteomic profile in human hepatic tumor samples to classify patients with CRLM as “mild” or “severe” based on the 5-year survival. The study was performed on 85 CRLM tumor samples. Firstly, to evaluate any distinct tumor proteomic signatures between mild and severe CRLM patients, a training group of 57 CRLM tumor samples was characterized by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, and a classification and regression tree (CART) analysis was subsequently performed. Finally, 28 CRLM tumor samples were used to confirm and validate the results obtained. Based on all the protein peaks detected in the training group, the CART analysis was generated, and four peaks were considered to be the most relevant to construct a diagnostic algorithm. Indeed, the multivariate model yielded a sensitivity of 85.7% and a specificity of 86.1%, respectively. In addition, the receiver operating characteristic (ROC) curve showed an excellent diagnostic accuracy to discriminate mild from severe CRLM patients (area under the ROC: 0.903). Finally, the validation process yielded a sensitivity and specificity of 68.8% and 83.3%, respectively. We identified a proteomic profile potentially useful to determine the prognosis of CRLM patients based on the 5-year survival.     

Gene Expression Profiling as a Tool for Positional Cloning of Genes-Shortcut or the Longest Way Round

The identification of quantitative trait loci, QTL, in arthritis animal models is a straight forward process. However, to identify the underlying genes is a great challenge. One strategy frequently used, is to combine QTL analysis with genomic/proteomic screens. This has resulted in a number of publications where carefully performed genomic analyses present likely candidate genes for their respective QTL´s. However, seldom the findings are reconnected to the QTL controlled phenotypes. In this review, we use our own data as an illustrative example that “very likely candidate genes” identified by genomic/proteomics is not necessarily the same as true QTL underlying genes.     

Enhancements to the Rosetta Energy Function Enable Improved Identification of Small Molecules that Inhibit Protein-Protein Interactions

Protein-protein interactions are among today’s most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more “traditional” drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of “decoys.” Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new “Talaris” update and the “pwSHO” solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta’s ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta’s ability to identify small-molecule inhibitors of protein-protein interactions.     

Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data

Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The “common input” problem presents a major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons versus correlations induced by common input from unobserved neurons. Available techniques allow us to simultaneously record, with sufficient temporal resolution, only a small fraction of the network. Consequently, naive connectivity estimators that neglect these common input effects are highly biased. This work proposes a “shotgun” experimental design, in which we observe multiple sub-networks briefly, in a serial manner. Thus, while the full network cannot be observed simultaneously at any given time, we may be able to observe much larger subsets of the network over the course of the entire experiment, thus ameliorating the common input problem. Using a generalized linear model for a spiking recurrent neural network, we develop a scalable approximate expected loglikelihood-based Bayesian method to perform network inference given this type of data, in which only a small fraction of the network is observed in each time bin. We demonstrate in simulation that the shotgun experimental design can eliminate the biases induced by common input effects. Networks with thousands of neurons, in which only a small fraction of the neurons is observed in each time bin, can be quickly and accurately estimated, achieving orders of magnitude speed up over previous approaches.