45 research outputs found
Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development
Advancing brain barriers RNA sequencing: guidelines from experimental design to publication
Background: RNA sequencing (RNA-Seq) in its varied forms has become an indispensable tool for analyzing differential gene expression and thus characterization of specific tissues. Aiming to understand the brain barriers genetic signature, RNA seq has also been introduced in brain barriers research. This has led to availability of both, bulk and single-cell RNA-Seq datasets over the last few years. If appropriately performed, the RNA-Seq studies provide powerful datasets that allow for significant deepening of knowledge on the molecular mechanisms that establish the brain barriers. However, RNA-Seq studies comprise complex workflows that require to consider many options and variables before, during and after the proper sequencing process.Main body: In the current manuscript, we build on the interdisciplinary experience of the European PhD Training Network BtRAIN (https://www.btrain-2020.eu/) where bioinformaticians and brain barriers researchers collaborated to analyze and establish RNA-Seq datasets on vertebrate brain barriers. The obstacles BtRAIN has identified in this process have been integrated into the present manuscript. It provides guidelines along the entire workflow of brain barriers RNA-Seq studies starting from the overall experimental design to interpretation of results. Focusing on the vertebrate endothelial bloodâbrain barrier (BBB) and epithelial blood-cerebrospinal-fluid barrier (BCSFB) of the choroid plexus, we provide a step-by-step description of the workflow, highlighting the decisions to be made at each step of the workflow and explaining the strengths and weaknesses of individual choices made. Finally, we propose recommendations for accurate data interpretation and on the information to be included into a publication to ensure appropriate accessibility of the data and reproducibility of the observations by the scientific community.Conclusion: Next generation transcriptomic profiling of the brain barriers provides a novel resource for understanding the development, function and pathology of these barrier cells, which is essential for understanding CNS homeostasis and disease. Continuous advancement and sophistication of RNA-Seq will require interdisciplinary approaches between brain barrier researchers and bioinformaticians as successfully performed in BtRAIN. The present guidelines are built on the BtRAIN interdisciplinary experience and aim to facilitate collaboration of brain barriers researchers with bioinformaticians to advance RNA-Seq study design in the brain barriers community
Genetic and genomic analyses underpin the feasibility of concomitant genetic improvement of milk yield and mastitis resistance in dairy sheep
Milk yield is the most important dairy sheep trait and constitutes the key genetic improvement goal via selective breeding. Mastitis is one of the most prevalent diseases, significantly impacting on animal welfare, milk yield and quality, while incurring substantial costs. Our objectives were to determine the feasibility of a concomitant genetic improvement programme for enhanced milk production and resistance to mastitis. Individual records for milk yield, and four mastitis-related traits (milk somatic cell count, California Mastitis Test score, total viable bacterial count in milk and clinical mastitis presence) were collected monthly throughout lactation for 609 ewes of the Chios breed. All ewes were genotyped with a mastitis specific custom-made 960 single nucleotide polymorphism (SNP) array. We performed targeted genomic association studies, (co)variance component estimation and pathway enrichment analysis, and characterised gene expression levels and the extent of allelic expression imbalance. Presence of heritable variation for milk yield was confirmed. There was no significant genetic correlation between milk yield and mastitis traits. Environmental factors appeared to favour both milk production and udder health. There were no overlapping of SNPs associated with mastitis resistance and milk yield in Chios sheep. Furthermore, four distinct Quantitative Trait Loci (QTLs) affecting milk yield were detected on chromosomes 2, 12, 16 and 19, in locations other than those previously identified to affect mastitis resistance. Five genes (DNAJA1, GHR, LYPLA1, NUP35 and OXCT1) located within the QTL regions were highly expressed in both the mammary gland and milk transcriptome, suggesting involvement in milk synthesis and production. Furthermore, the expression of two of these genes (NUP35 and OXCT1) was enriched in immune tissues implying a potentially pleiotropic effect or likely role in milk production during udder infection, which needs to be further elucidated in future studies. In conclusion, the absence of genetic antagonism between milk yield and mastitis resistance suggests that simultaneous genetic improvement of both traits be achievable
Gene activity in primary T cells infected with HIV89.6: intron retention and induction of genomic repeats
Prescription des bloqueurs du SRAA avant et aprĂšs lâadmission chez les patients hospitalisĂ©s pour COVID-19, et association avec la mortalitĂ©. Implications pour lâinterprĂ©tation des Ă©tudes observationnelles sur le rĂŽle des bloqueurs du SRAA durant lâinfection Ă SARS-CoV-2
Dietary challenges differentially affect activity and sleep/wake behavior in <i>mus musculus</i>: Isolating independent associations with diet/energy balance and body weight
<div><p>Background and aims</p><p>Associated with numerous metabolic and behavioral abnormalities, obesity is classified by metrics reliant on body weight (such as body mass index). However, overnutrition is the common cause of obesity, and may independently contribute to these obesity-related abnormalities. Here, we use dietary challenges to parse apart the relative influence of diet and/or energy balance from body weight on various metabolic and behavioral outcomes.</p><p>Materials and methods</p><p>Seventy male mice (<i>mus musculus)</i> were subjected to the diet switch feeding paradigm, generating groups with various body weights and energetic imbalances. Spontaneous activity patterns, blood metabolite levels, and unbiased gene expression of the nutrient-sensing ventral hypothalamus (using RNA-sequencing) were measured, and these metrics were compared using standardized multivariate linear regression models.</p><p>Results</p><p>Spontaneous activity patterns were negatively related to body weight (p<0.0001) but not diet/energy balance (p = 0.63). Both body weight and diet/energy balance predicted circulating glucose and insulin levels, while body weight alone predicted plasma leptin levels. Regarding gene expression within the ventral hypothalamus, only two genes responded to diet/energy balance (<i>neuropeptide y</i> [<i>npy</i>] and <i>agouti-related peptide</i> [<i>agrp</i>]), while others were related only to body weight.</p><p>Conclusions</p><p>Collectively, these results demonstrate that individual components of obesityâspecifically obesogenic diets/energy imbalance and elevated body massâcan have independent effects on metabolic and behavioral outcomes. This work highlights the shortcomings of using body mass-based indices to assess metabolic health, and identifies novel associations between blood biomarkers, neural gene expression, and animal behavior following dietary challenges.</p></div
Glucose, insulin, and leptin levels from trunk blood plasma (Day 7 Post-DS).
<p>(A) Glucose, (B) insulin, and (C) leptin levels measured from blood plasma, which was collected immediately after sacrifice (ZT6-7). ****p<0.0001 comparing RC â HFD vs HFD â RC; a: p<0.05, aâ: p<0.01, aâ: p<0.001, aââ: p<0.0001 compared to RC NoDS; b: p<0.05, bâ: p<0.01, bâ: p<0.001, bââ: p<0.0001 compared to HFD NoDS. Sample sizes for glucose: [RC NoDS: n = 16; RC â HFD: n = 21; HFD â RC: n = 19; HFD NoDS: n = 14]. Sample sizes for insulin: [RC NoDS: n = 13; RC â HFD: n = 17; HFD â RC: n = 12; HFD NoDS: n = 13]. Sample sizes for leptin: [RC NoDS: n = 13; RC â HFD: n = 17; HFD â RC: n = 13; HFD NoDS: n = 13].</p
Linear modeling results comparing weight-matched DS conditions.
<p>Linear modeling results comparing weight-matched DS conditions.</p