TBX3 over-expression causes mammary gland hyperplasia and increases mammary stem-like cells in an inducible transgenic mouse model

<p>Abstract</p> <p>Background</p> <p>The T-box transcription factor TBX3 is necessary for early embryonic development and for the normal development of the mammary gland. Homozygous mutations, in mice, are embryonic lethal while heterozygous mutations result in perturbed mammary gland development. In humans, mutations that result in the haploinsufficiency of TBX3 causes Ulnar Mammary Syndrome (UMS) characterized by mammary gland hypoplasia as well as other congenital defects. In addition to its role in mammary gland development, various studies have also supported a role for Tbx3 in breast cancer development. TBX3 is over-expressed in various breast cancer cell lines as well as cancer tissue and has been found to contribute to breast cancer cell migration. Previous studies have suggested that TBX3 contributes to cancer development by its ability to bypass senescence by repressing the expression of p14^ARF-tumor suppressor. Although many studies have shown that a dysregulation of TBX3 expression may contribute to cancer progression, no direct evidence shows TBX3 causes breast cancer.</p> <p>Results</p> <p>In this study, we created doxycycline inducible double transgenic mice (MMTV-rtTA;tet-myc-TBX3-IRES-Luciferase) to test whether TBX3 over-expression can induce tumor formation within the mammary gland. Although over-expression of TBX3, alone, did not induce tumor formation it did promote accelerated mammary gland development by increasing mammary epithelial cell proliferation. We also show that TBX3 directly binds to and represses <it>NFκBIB</it>, an inhibitor of the NF-κB pathway known to play a role in regulating cell proliferation. Lastly, we also show that the over-expression of TBX3 is associated with an increase in mammary stem-like cells.</p> <p>Conclusions</p> <p>Overall, our data suggests that over-expression of TBX3 may contribute to breast cancer development by promoting accelerated mammary gland development through the inhibition of the NF-κB pathway and stimulation of both mammary epithelial cell and stem-like cell proliferation.</p

Brain information processing capacity modeling

Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that-for a given cognitive task and subject-higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity-as estimated from fMRI data-predicted task and age-related differences in reaction times, speaking to the model's predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making

miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase

Abstract Background miRBase is the primary repository for published miRNA sequence and annotation data, and serves as the “go-to” place for miRNA research. However, the definition and annotation of miRNAs have been changed significantly across different versions of miRBase. The changes cause inconsistency in miRNA related data between different databases and articles published at different times. Several tools have been developed for different purposes of querying and converting the information of miRNAs between different miRBase versions, but none of them individually can provide the comprehensive information about miRNAs in miRBase and users will need to use a number of different tools in their analyses. Results We introduce miRBaseConverter, an R package integrating the latest miRBase version 22 available in Bioconductor to provide a suite of functions for converting and retrieving miRNA name (ID), accession, sequence, species, version and family information in different versions of miRBase. The package is implemented in R and available under the GPL-2 license from the Bioconductor website ( http://bioconductor.org/packages/miRBaseConverter/ ). A Shiny-based GUI suitable for non-R users is also available as a standalone application from the package and also as a web application at http://nugget.unisa.edu.au:3838/miRBaseConverter . miRBaseConverter has a built-in database for querying miRNA information in all species and for both pre-mature and mature miRNAs defined by miRBase. In addition, it is the first tool for batch querying the miRNA family information. The package aims to provide a comprehensive and easy-to-use tool for miRNA research community where researchers often utilize published miRNA data from different sources. Conclusions The Bioconductor package miRBaseConverter and the Shiny-based web application are presented to provide a suite of functions for converting and retrieving miRNA name, accession, sequence, species, version and family information in different versions of miRBase. The package will serve a wide range of applications in miRNA research and could provide a full view of the miRNAs of interest.https://deepblue.lib.umich.edu/bitstream/2027.42/146768/1/12859_2018_Article_2531.pd

Emotional Cues during Simultaneous Face and Voice Processing: Electrophysiological Insights

Both facial expression and tone of voice represent key signals of emotional communication but their brain processing correlates remain unclear. Accordingly, we constructed a novel implicit emotion recognition task consisting of simultaneously presented human faces and voices with neutral, happy, and angry valence, within the context of recognizing monkey faces and voices task. To investigate the temporal unfolding of the processing of affective information from human face-voice pairings, we recorded event-related potentials (ERPs) to these audiovisual test stimuli in 18 normal healthy subjects; N100, P200, N250, P300 components were observed at electrodes in the frontal-central region, while P100, N170, P270 were observed at electrodes in the parietal-occipital region. Results indicated a significant audiovisual stimulus effect on the amplitudes and latencies of components in frontal-central (P200, P300, and N250) but not the parietal occipital region (P100, N170 and P270). Specifically, P200 and P300 amplitudes were more positive for emotional relative to neutral audiovisual stimuli, irrespective of valence, whereas N250 amplitude was more negative for neutral relative to emotional stimuli. No differentiation was observed between angry and happy conditions. The results suggest that the general effect of emotion on audiovisual processing can emerge as early as 200 msec (P200 peak latency) post stimulus onset, in spite of implicit affective processing task demands, and that such effect is mainly distributed in the frontal-central region

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A novel single-cell based method for breast cancer prognosis

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Xiaomei Li, Lin Liu, Gregory J. Goodall, Andreas Schreiber, Taosheng Xu, Jiuyong Li, Thuc D. L