A pragmatic randomised controlled trial of preferred intensity exercise in depressed adult women in the United Kingdom: secondary analysis of individual variability of depression

Background This study is a secondary analysis of the trial by Callaghan et al. (2011), which reported higher antidepressant effects for preferred intensity (n = 19) vs. prescribed intensity (n = 19) exercise of three sessions/week over four weeks in depressed women. In particular, the present study sought to examine whether greater clinically significant individual change/recovery was observed in the preferred compared to the prescribed exercise group. Methods The reliable change index and the Ccutoff score criteria described by Jacobson and Truax (1991) were employed to determine clinical significance. These criteria examined if individual change in depression scores from pre- to post-intervention in the preferred intensity group were statistically significant beyond the standard error of difference derived from the active comparator prescribed group, and subsequently within a normal population range. Patients fulfilling the first or both criteria were classified as improved or recovered, respectively. Results Post-intervention depression scores of six patients in the preferred intensity exercise group (32%) demonstrated statistically reliable improvement (p  0.05), although eight of them showed a non-significant improvement in post-intervention depression scores and three could not technically show an improvement in depression due to floor effects (baseline depression within normal range). Conclusions Preferred intensity exercise of three sessions/week over four weeks led almost a third of the patients to record scores consistent with recovery from depression. Health professionals may consider that short-term preferred intensity exercise provides clinically significant antidepressant effects comparing favourably to exercise on prescription

Myocardial biomechanics and the consequent differentially expressed genes of the left atrial ligation chick embryonic model of hypoplastic left heart syndrome

Left atrial ligation (LAL) of the chick embryonic heart is a model of the hypoplastic left heart syndrome (HLHS) where a purely mechanical intervention without genetic or pharmacological manipulation is employed to initiate cardiac malformation. It is thus a key model for understanding the biomechanical origins of HLHS. However, its myocardial mechanics and subsequent gene expressions are not well-understood. We performed finite element (FE) modeling and single-cell RNA sequencing to address this. 4D high-frequency ultrasound imaging of chick embryonic hearts at HH25 (ED 4.5) were obtained for both LAL and control. Motion tracking was performed to quantify strains. Image-based FE modeling was conducted, using the direction of the smallest strain eigenvector as the orientations of contractions, the Guccione active tension model and a Fung-type transversely isotropic passive stiffness model that was determined via micro-pipette aspiration. Single-cell RNA sequencing of left ventricle (LV) heart tissues was performed for normal and LAL embryos at HH30 (ED 6.5) and differentially expressed genes (DEG) were identified.After LAL, LV thickness increased by 33%, strains in the myofiber direction increased by 42%, while stresses in the myofiber direction decreased by 50%. These were likely related to the reduction in ventricular preload and underloading of the LV due to LAL. RNA-seq data revealed potentially related DEG in myocytes, including mechano-sensing genes (Cadherins, NOTCH1, etc.), myosin contractility genes (MLCK, MLCP, etc.), calcium signaling genes (PI3K, PMCA, etc.), and genes related to fibrosis and fibroelastosis (TGF-β, BMP, etc.). We elucidated the changes to the myocardial biomechanics brought by LAL and the corresponding changes to myocyte gene expressions. These data may be useful in identifying the mechanobiological pathways of HLHS

Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation

Naturally occurring regulatory T (Treg) cells, which specifically express the transcription factor forkhead box P3 (Foxp3), are engaged in the maintenance of immunological self-tolerance and homeostasis. By transcriptional start site cluster analysis, we assessed here how genome-wide patterns of DNA methylation or Foxp3 binding sites were associated with Treg-specific gene expression. We found that Treg-specific DNA hypomethylated regions were closely associated with Treg up-regulated transcriptional start site clusters, whereas Foxp3 binding regions had no significant correlation with either up- or down-regulated clusters in nonactivated Treg cells. However, in activated Treg cells, Foxp3 binding regions showed a strong correlation with down-regulated clusters. In accordance with these findings, the above two features of activation-dependent gene regulation in Treg cells tend to occur at different locations in the genome. The results collectively indicate that Treg-specific DNA hypomethylation is instrumental in gene up-regulation in steady state Treg cells, whereas Foxp3 down-regulates the expression of its target genes in activated Treg cells. Thus, the two events seem to play distinct but complementary roles in Treg-specific gene expression

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome.

X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X's gene content, gene expression, and evolution

Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach

Many standard statistical techniques are effective on data that are normally distributed with constant variance. Microarray data typically violate these assumptions since they come from non-Gaussian distributions with a non-trivial mean–variance relationship. Several methods have been proposed that transform microarray data to stabilize variance and draw its distribution towards the Gaussian. Some methods, such as log or generalized log, rely on an underlying model for the data. Others, such as the spread-versus-level plot, do not. We propose an alternative data-driven multiscale approach, called the Data-Driven Haar–Fisz for microarrays (DDHFm) with replicates. DDHFm has the advantage of being ‘distribution-free’ in the sense that no parametric model for the underlying microarray data is required to be specified or estimated; hence, DDHFm can be applied very generally, not just to microarray data