cRegulome: an R package for accessing microRNA and transcription factor-gene expression correlations in cancer

Background Transcription factors and microRNAs play a critical role in regulating the gene expression in normal physiology and pathological conditions. Many bioinformatics tools were built to predict and identify transcription factor and microRNA targets and their role in the development of diseases including cancers. The availability of public access high-throughput data allows researchers to make data-driven predictions. Implementation Here, we developed an R package called cRegulome to access, manage and visualize data from open source databases. The package provides a programmatic access to the regulome (transcription factor and microRNA) expression correlations with target genes of different cancer types. It obtains a local instance of Cistrome Cancer and miRCancerdb databases and provides classes and methods to query, interact with and visualize the correlation data. Availability cRegulome is available on the comprehensive R archive network (CRAN) and the source code is hosted on GitHub as part of the ROpenSci on-boarding collection, https://github.com/ropensci/cRegulome

Maximizing the utility of public data

The human genome project galvanized the scientific community around an ambitious goal. Upon completion, the project delivered several discoveries, and a new era of research commenced. More importantly, novel technologies and analysis methods materialized during the project period. The cost reduction allowed many more labs to generate high-throughput datasets. The project also served as a model for other extensive collaborations that generated large datasets. These datasets were made public and continue to accumulate in repositories. As a result, the scientific community should consider how these data can be utilized effectively for the purposes of research and the public good. A dataset can be re-analyzed, curated, or integrated with other forms of data to enhance its utility. We highlight three important areas to achieve this goal in this brief perspective. We also emphasize the critical requirements for these strategies to be successful. We draw on our own experience and others in using publicly available datasets to support, develop, and extend our research interest. Finally, we underline the beneficiaries and discuss some risks involved in data reuse

Disease-specific Proteins from Rheumatoid Arthritis Patients

Rheumatoid arthritis (RA) is a chronic inflammatiory disease that mainly destroys cartilages or bones at the joints. This inflammatory disorder is initiated by self-attack using own immune system, but the detail of pathological mechanism is unclear. Features of autoantigens leading to autoimmune disease are also under veil although several candidates including type II collagen have been suggested to play a role in pathogenesis. In this report, we tried to identify proteins responding to antibodies purified from RA patients and screen proteins up-regulated or down-regulated in RA using proteomic approach. Fibronectin, semaphorin 7A precursor, growth factor binding protein 7 (GRB7), and immunoglobulin µ chain were specifically associated with antibodies isolated from RA synovial fluids. In addition, some metabolic proteins such as adipocyte fatty acid binding protein, galectin-1 and apolipoprotein A1 precursor were overexpressed in RA synovium. Also, expression of peroxiredoxin 2 was up-regulated in RA. On the contrary, expression of vimentin was severely suppressed in RA synoviocytes. Such findings might give some insights into understanding of pathological mechanism in RA

A Case of Hypothyroidism and Type 2 Diabetes Associated with Type V Hyperlipoproteinemia and Eruptive Xanthomas

Primary hypothyroidism and type 2 diabetes are both typically associated with the increased level of triglycerides. To date, there have been only a few case reports of type 2 diabetes patients with both type V hyperlipoproteinemia and eruptive xanthomas, but there have been no reports of hypothyroidism patients associated with eruptive xanthomas. We report here on a case of a 48-yr old female patient who was diagnosed with type 2 diabetes and primary hypothyroidism associated with both type V hyperlipoproteinemia and eruptive xanthomas. We found rouleaux formation of RBCs in peripheral blood smear, elevated TSH, and low free T4 level, and dyslipidemia (total cholesterol 18.1 mM/L, triglyceride 61.64 mM/L, HDL 3.0 mM/L, and LDL 2.54 mM/L). She has taken fenofibrate, levothyroxine, and oral hypoglycemic agent for 4 months. After treatment, both TSH level and lipid concentration returned to normal range, and her yellowish skin nodules have also disappeared

A Case of Hyperglycemic Hyperosmolar State Associated with Graves' Hyperthyroidism: A Case Report

Hyperglycemic hyperosmolar state (HHS) is an acute complication mostly occurring in elderly type 2 diabetes mellitus (DM). Thyrotoxicosis causes dramatic increase of glycogen degradation and/or gluconeogenesis and enhances breakdown of triglycerides. Thus, in general, it augments glucose intolerance in diabetic patients. A 23-yr-old female patient with Graves' disease and type 2 DM, complying with methimazole and insulin injection, had symptoms of nausea, polyuria and generalized weakness. Her serum glucose and osmolarity were 32.7 mM/L, and 321 mosm/kg, respectively. Thyroid function tests revealed that she had more aggravated hyperthyroid status; 0.01 mU/L TSH and 2.78 pM/L free T3 (reference range, 0.17-4.05, 0.31-0.62, respectively) than when she was discharged two weeks before (0.12 mU/L TSH and 1.41 pM/L free T3). Being diagnosed as HHS and refractory Graves' hyperthyroidism, she was treated successfully with intravenous fluids, insulin and high doses of methimazole (90 mg daily). Here, we described the case of a woman with Graves' disease and type 2 DM developing to HHS

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

pcr: an R package for quality assessment, analysis and testing of qPCR data

Background Real-time quantitative PCR (qPCR) is a broadly used technique in the biomedical research. Currently, few different analysis models are used to determine the quality of data and to quantify the mRNA level across the experimental conditions. Methods We developed an R package to implement methods for quality assessment, analysis and testing qPCR data for statistical significance. Double Delta CT and standard curve models were implemented to quantify the relative expression of target genes from CT in standard qPCR control-group experiments. In addition, calculation of amplification efficiency and curves from serial dilution qPCR experiments are used to assess the quality of the data. Finally, two-group testing and linear models were used to test for significance of the difference in expression control groups and conditions of interest. Results Using two datasets from qPCR experiments, we applied different quality assessment, analysis and statistical testing in the pcr package and compared the results to the original published articles. The final relative expression values from the different models, as well as the intermediary outputs, were checked against the expected results in the original papers and were found to be accurate and reliable. Conclusion The pcr package provides an intuitive and unified interface for its main functions to allow biologist to perform all necessary steps of qPCR analysis and produce graphs in a uniform way

Validating a re-implementation of an algorithm to integrate transcriptome and ChIP-seq data

Transcription factor binding to a gene regulatory region induces or represses its expression. Binding and expression target analysis (BETA) integrates the binding and gene expression data to predict this function. First, the regulatory potential of the factor is modeled based on the distance of its binding sites from the transcription start sites in a decay function. Then the differential expression statistics from an experiment where this factor was perturbed represent the binding effect. The rank product of the two values is employed to order in importance. This algorithm was originally implemented in Python. We reimplemented the algorithm in R to take advantage of existing data structures and other tools for downstream analyses. Here, we attempted to replicate the findings in the original BETA paper. We applied the new implementation to the same datasets using default and varying inputs and cutoffs. We successfully replicated the original results. Moreover, we showed that the method was appropriately influenced by varying the input and was robust to choices of cutoffs in statistical testing