Novel biomarkers of preterm brain injury from blood transcriptome in sheep model of intrauterine asphyxia.

BACKGROUND: Infants born preterm have a higher incidence of neurological deficits. A key step in finding effective treatments is to identify biomarkers that reliably predict outcome. METHODS: Following umbilical cord occlusion (UCO) in pregnant sheep, whole fetal blood RNA was sequenced pre- and post-UCO, brain injury outcome was determined by battery of neuropathology scoring and the transcriptome signature correlated to the degree of brain injury. Additionally, we developed a novel analytical procedure to deduce cell blood composition over time. RESULTS: Sixty-one genes were identified with significant altered expression after UCO. In pre-UCO blood, the level of three mRNAs (Trex2, Znf280b, novel miRNA) and in post-UCO, four mRNAs (Fam184a, Angptl2, novel lincRNA and an unknown protein-coding gene) were associated to brain injury (FDR < 0.01). Several of these mRNAs are related to inflammation and angiogenesis. Pathway analysis highlighted genes playing a role in perinatal death and growth failure. Results also indicate that several leukocyte populations undergo significant changes after UCO. CONCLUSION: We have used a whole transcriptomic approach to uncover novel biomarkers in fetal blood that correlate to neuropathology in the preterm sheep brain. The current data forms a basis for future studies to investigate mechanisms of these mRNAs in the injury progression. IMPACT: Trend analysis of genes following asphyxia reveal a group of genes associated with perinatal death and growth failure. Several pre-asphyxia transcripts were associated to brain injury severity suggesting genomic susceptibility to injury. Several post-asphyxia transcripts were correlated to brain injury severity, thus, serve as potential novel biomarkers of injury outcome. Successfully adaptation of cell profiling algorithms suggests significant changes in blood cell composition following asphyxia.

A statistical and biological response to an informatics appraisal of healthy aging gene signatures.

Jacob and Speed did not identify even a single example of a '150-gene-set' that was statistically significant at classifying Alzheimer's disease (AD) samples, or age in independent studies. We attempt to clarify the various misunderstandings, below.

Longevity-related molecular pathways are subject to midlife "switch" in humans.

Emerging evidence indicates that molecular aging may follow nonlinear or discontinuous trajectories. Whether this occurs in human neuromuscular tissue, particularly for the noncoding transcriptome, and independent of metabolic and aerobic capacities, is unknown. Applying our novel RNA method to quantify tissue coding and long noncoding RNA (lncRNA), we identified ~800 transcripts tracking with age up to ~60 years in human muscle and brain. In silico analysis demonstrated that this temporary linear "signature" was regulated by drugs, which reduce mortality or extend life span in model organisms, including 24 inhibitors of the IGF-1/PI3K/mTOR pathway that mimicked, and 5 activators that opposed, the signature. We profiled Rapamycin in nondividing primary human myotubes (n = 32 HTA 2.0 arrays) and determined the transcript signature for reactive oxygen species in neurons, confirming that our age signature was largely regulated in the "pro-longevity" direction. Quantitative network modeling demonstrated that age-regulated ncRNA equaled the contribution of protein-coding RNA within structures, but tended to have a lower heritability, implying lncRNA may better reflect environmental influences. Genes ECSIT, UNC13, and SKAP2 contributed to a network that did not respond to Rapamycin, and was associated with "neuron apoptotic processes" in protein-protein interaction analysis (FDR = 2.4%). ECSIT links inflammation with the continued age-related downwards trajectory of mitochondrial complex I gene expression (FDR < 0.01%), implying that sustained inhibition of ECSIT may be maladaptive. The present observations link, for the first time, model organism longevity programs with the endogenous but temporary genome-wide responses to aging in humans, revealing a pattern that may ultimately underpin personalized rates of health span.

A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease.

Genome-wide association studies (GWAS), relying on hundreds of thousands of individuals, have revealed >200 genomic loci linked to metabolic disease (MD). Loss of insulin sensitivity (IS) is a key component of MD and we hypothesized that discovery of a robust IS transcriptome would help reveal the underlying genomic structure of MD. Using 1,012 human skeletal muscle samples, detailed physiology and a tissue-optimized approach for the quantification of coding (>18,000) and non-coding (>15,000) RNA (ncRNA), we identified 332 fasting IS-related genes (CORE-IS). Over 200 had a proven role in the biochemistry of insulin and/or metabolism or were located at GWAS MD loci. Over 50% of the CORE-IS genes responded to clinical treatment; 16 quantitatively tracking changes in IS across four independent studies (P = 0.0000053: negatively: AGL, G0S2, KPNA2, PGM2, RND3 and TSPAN9 and positively: ALDH6A1, DHTKD1, ECHDC3, MCCC1, OARD1, PCYT2, PRRX1, SGCG, SLC43A1 and SMIM8). A network of ncRNA positively related to IS and interacted with RNA coding for viral response proteins (P < 1 × 10-48), while reduced amino acid catabolic gene expression occurred without a change in expression of oxidative-phosphorylation genes. We illustrate that combining in-depth physiological phenotyping with robust RNA profiling methods, identifies molecular networks which are highly consistent with the genetics and biochemistry of human metabolic disease.

Autoreactive T effector memory differentiation mirrors ß cell function in type 1 diabetes.

In type 1 diabetes, cytotoxic CD8+ T cells with specificity for ß cell autoantigens are found in the pancreatic islets, where they are implicated in the destruction of insulin-secreting ß cells. In contrast, the disease relevance of ß cell-reactive CD8+ T cells that are detectable in the circulation, and their relationship to ß cell function, are not known. Here, we tracked multiple, circulating ß cell-reactive CD8+ T cell subsets and measured ß cell function longitudinally for 2 years, starting immediately after diagnosis of type 1 diabetes. We found that change in ß cell-specific effector memory CD8+ T cells expressing CD57 was positively correlated with C-peptide change in subjects below 12 years of age. Autoreactive CD57+ effector memory CD8+ T cells bore the signature of enhanced effector function (higher expression of granzyme B, killer-specific protein of 37 kDa, and CD16, and reduced expression of CD28) compared with their CD57- counterparts, and network association modeling indicated that the dynamics of ß cell-reactive CD57+ effector memory CD8+ T cell subsets were strongly linked. Thus, coordinated changes in circulating ß cell-specific CD8+ T cells within the CD57+ effector memory subset calibrate to functional insulin reserve in type 1 diabetes, providing a tool for immune monitoring and a mechanism-based target for immunotherapy.

iGEMS: an integrated model for identification of alternative exon usage events.

DNA microarrays and RNAseq are complementary methods for studying RNA molecules. Current computational methods to determine alternative exon usage (AEU) using such data require impractical visual inspection and still yield high false-positive rates. Integrated Gene and Exon Model of Splicing (iGEMS) adapts a gene-level residuals model with a gene size adjusted false discovery rate and exon-level analysis to circumvent these limitations. iGEMS was applied to two new DNA microarray datasets, including the high coverage Human Transcriptome Arrays 2.0 and performance was validated using RT-qPCR. First, AEU was studied in adipocytes treated with (n = 9) or without (n = 8) the anti-diabetes drug, rosiglitazone. iGEMS identified 555 genes with AEU, and robust verification by RT-qPCR (∼90%). Second, in a three-way human tissue comparison (muscle, adipose and blood, n = 41) iGEMS identified 4421 genes with at least one AEU event, with excellent RT-qPCR verification (95%, n = 22). Importantly, iGEMS identified a variety of AEU events, including 3'UTR extension, as well as exon inclusion/exclusion impacting on protein kinase and extracellular matrix domains. In conclusion, iGEMS is a robust method for identification of AEU while the variety of exon usage between human tissues is 5-10 times more prevalent than reported by the Genotype-Tissue Expression consortium using RNA sequencing.

A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status.

BACKGROUND: Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health. RESULTS: One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD. CONCLUSIONS: We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.