Short-Term, Combined Fasting and Exercise Improves Body Composition in Healthy Males

Fasting enhances the beneficial metabolic outcomes of exercise; however, it is unknown whether body composition is favorably modified on the short term. A baseline-follow-up study was carried out to assess the effect of an established protocol involving short-term combined exercise with fasting on body composition. One hundred seven recreationally exercising males underwent a 10-day intervention across 15 fitness centers in the Netherlands involving a 3-day gradual decrease of food intake, a 3-day period with extremely low caloric intake, and a gradual 4-day increase to initial caloric intake, with daily 30-min submaximal cycling. Using dual-energy X-ray absorptiometry analysis, all subjects substantially lost total body mass (-3.9 ± 1.9 kg; p < .001) and fat mass (-3.3 ± 1.3 kg; p < .001). Average lean mass was lost (-0.6 ± 1.5 kg; p < .001), but lean mass as a percentage of total body mass was not reduced. The authors observed a loss of -3.9 ± 1.9% android fat over total fat mass (p < .001), a loss of -2.2 ± 1.9% gynoid over total fat mass (p < .001), and reduced android/gynoid ratios (-0.05 ± 0.1; p < .001). Analyzing 15 preselected single-nucleotide polymorphisms in 13 metabolism-related genes revealed trending associations for thyroid state-related single-nucleotide polymorphisms rs225014 (deiodinase 2) and rs35767 (insulin-like growth factor1), and rs1053049 (PPARD). In conclusion, a short period of combined fasting and exercise leads to a substantial loss of body and fat mass without a loss of lean mass as a percentage of total mass.

A map of tumor-host interactions in glioma at single-cell resolution

BACKGROUND

Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets.

RESULTS

We present a novel method, single-cell Tumor-Host Interaction tool (scTHI), to identify significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand-receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma.

CONCLUSIONS

Our results provide a complete map of the active tumor-host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

The genomic landscape of schwannoma

Schwannomas are common peripheral nerve sheath tumors that can cause debilitating morbidities. We performed an integrative analysis to determine genomic aberrations common to sporadic schwannomas. Exome sequence analysis with validation by targeted DNA sequencing of 125 samples uncovered, in addition to expected NF2 disruption, recurrent mutations in ARID1A, ARID1B and DDR1. RNA sequencing identified a recurrent in-frame SH3PXD2A-HTRA1 fusion in 12/125 (10%) cases, and genomic analysis demonstrated the mechanism as resulting from a balanced 19-Mb chromosomal inversion on chromosome 10q. The fusion was associated with male gender predominance, occurring in one out of every six men with schwannoma. Methylation profiling identified distinct molecular subgroups of schwannomas that were associated with anatomical location. Expression of the SH3PXD2A-HTRA1 fusion resulted in elevated phosphorylated ERK, increased proliferation, increased invasion and in vivo tumorigenesis. Targeting of the MEK-ERK pathway was effective in fusion-positive Schwann cells, suggesting a possible therapeutic approach for this subset of tumors.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.