ERMer: a serverless platform for navigating, analyzing, and visualizing Escherichia coli regulatory landscape through graph database

An important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for instance, simple statistics such as the average number of links between two types of entities (relations) are easily computed using a query on a relational database and may already provide valuable information. However, for the exploration of graph data, relational databases may not be most practical and scalable. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, however, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. An important component of such databases is the language that is used to enable aggregating queries, such as shortest path queries.In this paper, we propose an extension to a previously proposed query language. This extension allows for querying and analyzing databases by using aggregates and ranking. A notable feature of our language is that it also supports probabilistic graph queries by conceiving of such queries as aggregating queries. We demonstrate its value on a simple data analysis task.

Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and they claim to achieve high prediction accuracy in their tests; we show that these approaches do not generalize, that is, they fail to predict interactions between unknown proteins and unknown small molecules. To address these shortcomings, we develop a new compound-protein interaction predictor, Yuel, which predicts compound-protein interactions with a higher generalizability than the existing methods. Upon comprehensive tests on various data sets, we find that out of all the deep-learning approaches surveyed, Yuel manifests the best ability to predict interactions between unknown compounds and unknown proteins.

In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.

A GAL4-based yeast three-hybrid system for the identification of small molecule–target protein interactions

Modeling data as a graph enables users to quickly analyze networked phenomenon, such as social network-based marketing data (e.g., linking entities in social media based on their friends and their "likes", their friend-of-a-friend's "likes", etc.) and scientific data, such as in biology to assess a gene's proximity in a graph to particular disease phenotype. Numerous purpose-built graph databases (DBs) called “NoSQL” or “Not Only SQL” DBs are emerging to support this type of graph analysis. These DBs differ from traditional relational DBs in that they are optimized to improve storage and query performance across large, complex graphs. This document reviews the state of the art in graph DBs and identifies potential DBs that show promise for dynamic entity-scoring algorithms. Dynamic entity scoring is a new area of research that allows analysis of data within graphs using undirected methods to weigh the edges of the graph based on the proximity or ambient influence of near-by nodes. The EntityScore (eScore) methodology developed at Lawrence Livermore National Laboratory (LLNL) is one application of these emerging graph-analysis algorithms to help analysts identify and prioritize entities, based on complex, analyst-defined weighting criteria. Our review examines a wide range of different, well-known graph DBs in use since at least 2010 and identifies unique advantages and disadvantages that may impact eScore-specific requirements. Specifically, we review data structures employed, query features employed, load times, query calculation times, memory usage and several other important attributes reported in the literature. We limited our considerations for eScore to Blueprints-compliant graph DBs (https://github.com/tinkerpop/blueprints/) because they provide a standard, java application programming interface (API) based on the property graph model.

The accumulation of unfolded proteins in the endoplasmic reticulum (ER) triggers a stress response program that protects cells early in the response and can lead to apoptosis during prolonged stress. The basic leucine zipper transcription factor, CCAAT/enhancer-binding protein beta (C/EBPbeta), is one of the genes with increased expression during ER stress. Translation of the C/EBPbeta mRNA from different initiation codons leads to the synthesis of two transcriptional activators (LAP-1 and -2) and a transcriptional repressor (LIP). The LIP/LAP ratio is a critical factor in C/EBPbeta-mediated gene transcription. It is shown here that the LIP/LAP ratio decreased by 5-fold during the early phase of ER stress and increased by 20-fold during the late phase, mostly because of changes in LIP levels. The early decrease in LIP required degradation via the proteasome pathway and phosphorylation of the translation initiation factor, eIF2alpha. The increased LIP levels during the late phase were due to increased synthesis and increased stability of the protein. It is proposed that regulation of synthesis and degradation rates during ER stress controls the LIP/LAP ratio. The importance of C/EBPbeta in the ER-stress response program was demonstrated using C/EBPbeta-deficient mouse embryonic fibroblasts. It is shown that C/EBPbeta attenuates expression of pro-survival ATF4 target genes in late ER stress and enhances expression of cell death-associated genes downstream of CHOP. The inhibitory effect of LIP on ATF4-induced transcription was demonstrated for the cat-1 amino acid transporter gene. We conclude that regulation of LIP/LAP ratios during ER stress is a novel mechanism for modulating the cellular stress response.

Molecular chaperones, inducible by heat shock and a variety of other stresses, have critical roles in protein homeostasis, balancing cell stress with adaptation, survival, and cell death mechanisms. In transformed cells and tumors, chaperones are frequently overexpressed, with constitutive activation of the heat shock transcription factor HSF1 implicated in tumor formation. Here, we describe the activity of triptolide, a diterpene triepoxide from the plant Triptergium wilfordii, as an inhibitor of the human heat shock response. Triptolide treatment of human tissue culture cells prevented the inducible expression of heat shock genes, shown by suppression of an HSP70 promoter-reporter construct and by suppression of endogenous HSP70 gene expression. Upon examining the steps in the HSF1 activation pathway, we found that triptolide abrogates the transactivation function of HSF1 without interfering in the early events of trimer formation, hyperphosphorylation, and DNA binding. The ability of triptolide to inhibit the heat shock response renders these cells sensitive to stress-induced cell death, which may be of great relevance to cancer treatments.

Modulators (Ms) are proteins that modify the activity of transcription factors (TFs) and influence expression of their target genes (TGs). To discover modulators of NF-κB/RelA, we first identified 365 NF-κB/RelA-binding proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used a probabilistic model to infer 8349 (M, NF-κB/RelA, TG) triplets and their modes of modulatory action from our combined LC-MS/MS and ChIP-Seq (ChIP followed by next generation sequencing) data, published RelA modulators and TGs, and a compendium of gene expression profiles. Hierarchical clustering of the derived modulatory network revealed functional subnetworks and suggested new pathways modulating RelA transcriptional activity. The modulators with the highest number of TGs and most non-random distribution of action modes (measured by Shannon entropy) are consistent with published reports. Our results provide a repertoire of testable hypotheses for experimental validation. One of the NF-κB/RelA modulators we identified is STAT1. The inferred (STAT1, NF-κB/RelA, TG) triplets were validated by LC-selected reaction monitoring-MS and the results of STAT1 deletion in human fibrosarcoma cells. Overall, we have identified 562 NF-κB/RelA modulators, which are potential drug targets, and clarified mechanisms of achieving NF-κB/RelA multiple functions through modulators. Our approach can be readily applied to other TFs.

2. INTRODUCTION Since the inception of online social networks, the amount of social data that is being published on a daily basis has been increasing at an unprecedented rate. Smart, GPS-enabled, always-connected personal devices have taken the data generation to a new level by making it tremendously easy to generate and share social content like check-in information, likes, microblogs (e.g., Twitter), multimedia data, and so on. There is an enormous value in reasoning about such streaming data and deriving meaningful insights from it in real-time. Examples of potential applications include advertising, sentiment analysis, detecting natural disasters, social recommendations, personalized trends, spam detection, to name a few. There is thus an increasing need to build scalable systems to support such applications. Complex nature of social networks and their rapid evolution, coupled with the huge volume of streaming social data and the need for real-time processing, raise many computational challenges that have not been addressed in prior work. Social network data comprises of two major components. First, there is a network (linkage) component that captures the underlying interconnection structure among the entities in the social network. Second, there is content data that is typically associated with the nodes and the edges in the social network. The social network data stream contains updates to both these components. The structure of the network may itself change rapidly in many cases, especially when things like webpages and user tags (e.g., Twitter hashtags) are treated as nodes of the network. However, most of the social network data stream consists of updates to the data associated with the nodes and the edges, e.g., status updates and other content uploaded by the users, communication among the users, and so on. There is interest in performing a wide variety of queries and analytics over such data streams in real time. The queries can range from simple publish-subscribe queries, where a user is interested in being notified when something happens in his friend circle, to complex anomaly detection queries, where the goal is to identify anomalous behavior as early as possible. In this paper, we present an introduction to this new research area of stream querying and reasoning over social data. This area combines aspects from several well-studied research areas, chief among them, social network analysis, graph databases, and data streams. We provide a formal definition of the problem, survey the related prior work, and discuss some of the key research challenges that need to be addressed (and some of the solutions that have been proposed). We note that we use the term stream reasoning in this paper to encompass a broad range of tasks including various types of analytics, probabilistic reasoning, statistical inference, and logical reasoning. We contrast our use of this term with the recent work by Valle et al. [104, 105] who define this term more specifically to refer to integration of logical reasoning systems with data streams in the context of the Semantic Web. Given the vast amount of work on this and related topics, it is not our intention to be comprehensive in this brief overview. Rather we aim to cover some of the key ideas and representative work.

A Conditional Zebrafish MITF Mutation Reveals MITF Levels Are Critical for Melanoma Promotion vs. Regression In Vivo

Studies of transcription regulation are often focused on binding of transcription factors (TFs) to a small number of promoters of interest. It is often assumed that TFs are in great excess to their binding sites (TFBSs) and competition for TFs between DNA sites is seldom considered. With increasing evidence that TFBSs are exceedingly abundant for many TFs and significant variations in TF and TFBS numbers occur during growth, the interplay between a TF and all TFBSs should not be ignored. Here, we use additional decoy DNA sites to quantitatively analyze how the relative abundance of a TF to its TFBSs impacts the steady-state level and onset time of gene expression for the auto-activated Escherichia coli PhoB response regulator. We show that increasing numbers of decoy sites progressively delayed transcription activation and lowered promoter activities. Perturbation of transcription regulation by additional TFBSs did not require extreme numbers of decoys, suggesting that PhoB is approximately at capacity for its DNA sites. Addition of decoys also converted a graded response to a bi-modal response. We developed a binding competition model that captures the major features of experimental observations, providing a quantitative framework to assess how variations in TFs and TFBSs influence transcriptional responses.

KiSS-1 has been shown to function as a tumor metastasis suppressor gene and reduce the number of metastases in different cancers. The expression of KiSS-1 or KiSS1, like other tumor suppressor, is commonly reduced or completely ablated in a variety of cancers via an unknown mechanism. Here we show that the loss of KiSS-1 expression in highly metastatic breast cancer cell lines correlates directly with the expression levels of two transcription factors, activator protein-2alpha (AP-2alpha) and specificity protein 1 (Sp1), which synergistically activate the transcriptional regulation of KiSS-1 in breast cancer cells. Although the KiSS-1 promoter contains multiple AP-2alpha binding elements, AP-2alpha-mediated regulation occurs indirectly through Sp1 sites, as determined by deletion and mutation analysis. Overexpression of AP-2alpha into highly metastatic breast cell lines did not alter KiSS-1 promoter-driven luciferase gene activity. However, co-transfection of AP-2alpha wild-type or the dominant negative form of AP-2 lacking its C-terminal DNA-binding domain, AP-2B, together with Sp1, increased KiSS-1 promoter activity dramatically, suggesting that AP-2alpha regulation of KiSS-1 transcription does not require direct binding to the KiSS-1 promoter. Furthermore, we demonstrated that AP-2alpha directly interacted with Sp1 to form transcription complexes at two tandem Sp1-binding sites of the promoter to activate KiSS-1 transcription. Together, our results indicate that AP-2alpha and Sp1 are strong transcriptional regulators of KiSS-1 and that loss or decreased expression of AP-2alpha in breast cancer may account for the loss of tumor metastasis suppressor KiSS-1 expression and thus increased cancer metastasis.

Introduction to a Virtual Special Issue on cell biology at the plant-microbe interface.

MnSOD, which is an important oxygen free radical scavenger in organisms, has an effect to resist oxidative stress and tumor. The expression and regulation of MnSOD gene is a complicated process, which includes many kinds of transcription factors, cell signal molecules and cell signal pathways. It refers to three aspects including transcription regulation, post-transcription regulation and translation regulation. Transcription regulation is the primary step for MnSOD gene expression and plays a key role during the expression of MnSOD gene. The activity of transcription factors, which controls MnSOD gene expression, such as SP-1, AP-2, AP-1, NF-kB and so on, can be changed in the course of transcription regulation. Drugs and metalions can also affect those transcription factors' activity. Furthermore some genes mutation and depletion also have an influence on the activity of those transcription factors. Post-transcription regulation is in a way of changing the stability of mRNA and its translation. Translation regulation is a process to regulate edition, modification, binding to metalion and site-specific of MnSOD polypeptide. Recently a kind of manganese trafficking factor for mitochondrial MnSOD called MTMl which is very important for activation of MnSOD has been discovered. Here, we review the advances in this field with an emphasis on transcription regulation and translation regulation of MnSOD gene. And at last, we discussed the prospect of MnSOD gene expression and regulation.

Auditory hair cells transduce sound to the brain, and in mammals, these cells reside together with supporting cells in the sensory epithelium of the cochlea, called the organ of Corti. To establish the organ's delicate function during development and differentiation, spatiotemporal gene expression is strictly controlled by chromatin accessibility and cell type–specific transcription factors, jointly representing the regulatory landscape. Bulk sequencing technology and cellular heterogeneity obscured investigations on the interplay between transcription factors and chromatin accessibility in inner ear development. To study the formation of the regulatory landscape in hair cells, we collected single-cell chromatin accessibility profiles accompanied by single-cell RNA data from genetically labeled murine hair cells and supporting cells after birth. Using an integrative approach, we predicted cell type–specific activating and repressing functions of developmental transcription factors. Furthermore, by integrating gene expression and chromatin accessibility data sets, we reconstructed gene regulatory networks. Then, using a comparative approach, 20 hair cell–specific activators and repressors, including putative downstream target genes, were identified. Clustering of target genes resolved groups of related transcription factors and was used to infer their developmental functions. Finally, the heterogeneity in the single-cell data allowed us to spatially reconstruct transcriptional as well as chromatin accessibility trajectories, indicating that gradual changes in the chromatin accessibility landscape are lagging behind the transcriptional identity of hair cells along the organ's longitudinal axis. Overall, this study provides a strategy to spatially reconstruct the formation of a lineage-specific regulatory landscape using a single-cell multi-omics approach.