Self-compassion and relationship status moderate the relationship between sexual minority status and body image concerns

A growing body of research suggests that sexual minority men (SMM) experience greater body image concerns including body shame, body surveillance, drive for muscularity and drive for thinness than heterosexual men. However, little is known regarding the potential factors that can buffer these relationships. The aim of the present study was to examine the role that both self-compassion and relationship status may play in decreasing the strength of the relationship between sexual minority status and body image concerns. A sample of n = 106 SMM and n = 145 heterosexual men completed an online survey assessing body image concerns, self-compassion, and relationship status. Findings revealed that SMM reported higher levels of body image concerns (on all measures, except drive for muscularity) as compared to heterosexual men. Self-compassion moderated the link between sexual orientation and drive for muscularity: in men with higher levels of self-compassion, sexual orientation was no longer associated with drive for muscularity. But, among men with less self-compassion, SMM reported higher drive for muscularity than heterosexual men. Moreover, relationship status moderated the relationship between sexual orientation and body shame and drive for thinness, such that, among SMM only, being in a relationship was associated with lower levels of these concerns.

Short tandem repeats are important contributors to silencer elements in T cells

The action of cis-regulatory elements with either activation or repression functions underpins the precise regulation of gene expression during normal development and cell differentiation. Gene activation by the combined activities of promoters and distal enhancers has been extensively studied in normal and pathological contexts. In sharp contrast, gene repression by cis-acting silencers, defined as genetic elements that negatively regulate gene transcription in a position-independent fashion, is less well understood. Here, we repurpose the STARR-seq approach as a novel high-throughput reporter strategy to quantitatively assess silencer activity in mammals. We assessed silencer activity from DNase hypersensitive I sites in a mouse T cell line. Identified silencers were associated with either repressive or active chromatin marks and enriched for binding motifs of known transcriptional repressors. CRISPR-mediated genomic deletions validated the repressive function of distinct silencers involved in the repression of non-T cell genes and genes regulated during T cell differentiation. Finally, we unravel an association of silencer activity with short tandem repeats, highlighting the role of repetitive elements in silencer activity. Our results provide a general strategy for genome-wide identification and characterization of silencer elements.

Epigenetic regulation of transcription factor binding motifs promotes Th1 response in Chagas disease cardiomyopathy

Chagas disease, caused by the protozoan Trypanosoma cruzi, is an endemic parasitic disease of Latin America, affecting 7 million people. Although most patients are asymptomatic, 30% develop complications, including the often-fatal Chronic Chagasic Cardiomyopathy (CCC). Although previous studies have demonstrated some genetic deregulations associated with CCCs, the causes of their deregulations remain poorly described. Based on bulk RNA-seq and whole genome DNA methylation data, we investigated the genetic and epigenetic deregulations present in the moderate and severe stages of CCC. Analysis of heart tissue gene expression profile allowed us to identify 1407 differentially expressed transcripts (DEGs) specific from CCC patients. A tissue DNA methylation analysis done on the same tissue has permitted the identification of 92 regulatory Differentially Methylated Regions (DMR) localized in the promoter of DEGs. An in-depth study of the transcription factors binding sites (TFBS) in the DMRs corroborated the importance of TFBS’s DNA methylation for gene expression in CCC myocardium. TBX21, RUNX3 and EBF1 are the transcription factors whose binding motif appears to be affected by DNA methylation in the largest number of genes. By combining both transcriptomic and methylomic analysis on heart tissue, and methylomic analysis on blood, 4 biological processes affected by severe CCC have been identified, including immune response, ion transport, cardiac muscle processes and nervous system. An additional study on blood methylation of moderate CCC samples put forward the importance of ion transport and nervous system in the development of the disease.

OLOGRAM-MODL: mining enriched n-wise combinations of genomic features with Monte Carlo and dictionary learning

Most epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual tree-based representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtftk

Anomaly detection in genomic catalogues using unsupervised multi-view autoencoders

Background

Accurate identification of Transcriptional Regulator binding locations is essential for analysis of genomic regions, including Cis Regulatory Elements. The customary NGS approaches, predominantly ChIP-Seq, can be obscured by data anomalies and biases which are difficult to detect without supervision.

Results

Here, we develop a method to leverage the usual combinations between many experimental series to mark such atypical peaks. We use deep learning to perform a lossy compression of the genomic regions’ representations with multiview convolutions. Using artificial data, we show that our method correctly identifies groups of correlating series and evaluates CRE according to group completeness. It is then applied to the ReMap database’s large volume of curated ChIP-seq data. We show that peaks lacking known biological correlators are singled out and less confirmed in real data. We propose normalization approaches useful in interpreting black-box models.

Conclusion

Our approach detects peaks that are less corroborated than average. It can be extended to other similar problems, and can be interpreted to identify correlation groups. It is implemented in an open-source tool called atyPeak.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04359-2.

Explore, edit and leverage genomic annotations using Python GTF toolkit

MOTIVATION

While Python has become very popular in bioinformatics, a limited number of libraries exist for fast manipulation of gene coordinates in Ensembl GTF format.

RESULTS

We have developed the GTF toolkit Python package (pygtftk), which aims at providing easy and powerful manipulation of gene coordinates in GTF format. For optimal performances, the core engine of pygtftk is a C dynamic library (libgtftk) while the Python API provides usability and readability for developing scripts. Based on this Python package, we have developed the gtftk command line interface that contains 57 sub-commands (v0.9.10) to ease handling of GTF files. These commands may be used to (i) perform basic tasks (e.g. selections, insertions, updates or deletions of features/keys), (ii) select genes/transcripts based on various criteria (e.g. size, exon number, transcription start site location, intron length, GO terms) or (iii) carry out more advanced operations such as coverage analyses of genomic features using bigWig files to create faceted read-coverage diagrams. In conclusion, the pygtftk package greatly simplifies the annotation of GTF files with external information while providing advance tools to perform gene analyses.

AVAILABILITY AND IMPLEMENTATION

pygtftk and gtftk have been tested on Linux and MacOSX and are available from https://github.com/dputhier/pygtftk under the MIT license. The libgtftk dynamic library written in C is available from https://github.com/dputhier/libgtftk.

OLOGRAM: Determining significance of total overlap length between genomic regions sets

MOTIVATION

Various bioinformatics analyses provide sets of genomic coordinates of interest. Whether two such sets possess a functional relation is a frequent question. This is often determined by interpreting the statistical significance of their overlaps. However, only few existing methods consider the lengths of the overlap, and they do not provide a resolutive p-value.

RESULTS

Here, we introduce OLOGRAM, which performs overlap statistics between sets of genomic regions described in BEDs or GTF. It uses Monte Carlo simulation, taking into account both the distributions of region and inter-region lengths, to fit a negative binomial model of the total overlap length. Exclusion of user-defined genomic areas during the shuffling is supported.

AVAILABILITY

This tool is available through the command line interface of the pygtftk toolkit. It has been tested on Linux and OSX and is available on Bioconda and from https://github.com/dputhier/pygtftk under the GNU GPL license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.