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High-resolution temporal and regional mapping of MAPT expression and splicing in human brain development.

ABSTRACT: The microtubule associated protein tau plays a critical role in the pathogenesis of neurodegenerative disease. Recent studies suggest that tau also plays a role in disorders of neuronal connectivity, including epilepsy and post-traumatic stress disorder. Animal studies have shown that the MAPT gene, which codes for the tau protein, undergoes complex pre-mRNA alternative splicing to produce multiple isoforms during brain development. Human data, particularly on temporal and regional variation in tau splicing during development are however lacking. In this study, we present the first detailed examination of the temporal and regional sequence of MAPT alternative splicing in the developing human brain. We used a novel computational analysis of large transcriptomic datasets (total n = 502 patients), quantitative polymerase chain reaction (qPCR) and western blotting to examine tau expression and splicing in post-mortem human fetal, pediatric and adult brains. We found that MAPT exons 2 and 10 undergo abrupt shifts in expression during the perinatal period that are unique in the canonical human microtubule-associated protein family, while exon 3 showed small but significant temporal variation. Tau isoform expression may be a marker of neuronal maturation, temporally correlated with the onset of axonal growth. Immature brain regions such as the ganglionic eminence and rhombic lip had very low tau expression, but within more mature regions, there was little variation in tau expression or splicing. We thus demonstrate an abrupt, evolutionarily conserved shift in tau isoform expression during the human perinatal period that may be due to tau expression in maturing neurons. Alternative splicing of the MAPT pre-mRNA may play a vital role in normal brain development across multiple species and provides a basis for future investigations into the developmental and pathological functions of the tau protein.

SUBMITTER: Hefti MM

PROVIDER: S-EPMC5892924 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The MAPT gene, encoding the microtubule-associated protein tau on chromosome 17q21.31, is result of an inversion polymorphism, leading to two allelic variants (H1 and H2). Homozygosity for the more common haplotype H1 is associated with an increased risk for several tauopathies, but also for the synucleinopathy Parkinson's disease (PD). In the present study, we aimed to clarify whether the MAPT haplotype influences expression of MAPT and SNCA, encoding the protein α-synuclein (α-syn), on mRNA and protein levels in postmortem brains of PD patients and controls. We also investigated mRNA expression of several other MAPT haplotype-encoded genes. Postmortem tissues from cortex of fusiform gyrus (ctx-fg) and of the cerebellar hemisphere (ctx-cbl) of neuropathologically confirmed PD patients (n = 95) and age- and sex-matched controls (n = 81) were MAPT haplotype genotyped to identify cases homozygous for either H1 or H2. Relative expression of genes was quantified using real-time qPCR; soluble and insoluble protein levels of tau and α-syn were determined by Western blotting. Homozygosity for H1 versus H2 was associated with increased total MAPT mRNA expression in ctx-fg regardless of disease state. Inversely, H2 homozygosity was associated with markedly increased expression of the corresponding antisense MAPT-AS1 in ctx-cbl. PD patients had higher levels of insoluble 0N3R and 1N4R tau isoforms regardless of the MAPT genotype. The increased presence of insoluble α-syn in PD patients in ctx-fg validated the selected postmortem brain tissue. Our findings in this small, but well controlled cohort of PD and controls support a putative biological relevance of tau in PD. However, we did not identify any link between the disease-predisposing H1/H1 associated overexpression of MAPT with PD status. Further studies are required to gain a deeper understanding of the potential regulatory role of MAPT-AS1 and its association to the disease-protective H2/H2 condition in the context of PD.

Project description:Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Project description:Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74-75% for female literacy in Nigeria and Kenya, and in the 50-70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2-30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.

Project description:IntroductionMAPT encodes for tau, the predominant component of neurofibrillary tangles that are neuropathological hallmarks of Alzheimer's disease (AD). Genetic association of MAPT variants with late-onset AD (LOAD) risk has been inconsistent, although insufficient power and incomplete assessment of MAPT haplotypes may account for this.MethodsWe examined the association of MAPT haplotypes with LOAD risk in more than 20,000 subjects (n-cases = 9,814, n-controls = 11,550) from Mayo Clinic (n-cases = 2,052, n-controls = 3,406) and the Alzheimer's Disease Genetics Consortium (ADGC, n-cases = 7,762, n-controls = 8,144). We also assessed associations with brain MAPT gene expression levels measured in the cerebellum (n = 197) and temporal cortex (n = 202) of LOAD subjects. Six single nucleotide polymorphisms (SNPs) which tag MAPT haplotypes with frequencies greater than 1% were evaluated.ResultsH2-haplotype tagging rs8070723-G allele associated with reduced risk of LOAD (odds ratio, OR = 0.90, 95% confidence interval, CI = 0.85-0.95, p = 5.2E-05) with consistent results in the Mayo (OR = 0.81, p = 7.0E-04) and ADGC (OR = 0.89, p = 1.26E-04) cohorts. rs3785883-A allele was also nominally significantly associated with LOAD risk (OR = 1.06, 95% CI = 1.01-1.13, p = 0.034). Haplotype analysis revealed significant global association with LOAD risk in the combined cohort (p = 0.033), with significant association of the H2 haplotype with reduced risk of LOAD as expected (p = 1.53E-04) and suggestive association with additional haplotypes. MAPT SNPs and haplotypes also associated with brain MAPT levels in the cerebellum and temporal cortex of AD subjects with the strongest associations observed for the H2 haplotype and reduced brain MAPT levels (β = -0.16 to -0.20, p = 1.0E-03 to 3.0E-03).ConclusionsThese results confirm the previously reported MAPT H2 associations with LOAD risk in two large series, that this haplotype has the strongest effect on brain MAPT expression amongst those tested and identify additional haplotypes with suggestive associations, which require replication in independent series. These biologically congruent results provide compelling evidence to screen the MAPT region for regulatory variants which confer LOAD risk by influencing its brain gene expression.

Project description:OBJECTIVES:We studied in a clinical setting the age dependent T1 relaxation time as a marker of normal late brain maturation and compared it to conventional techniques, namely the apparent diffusion coefficient (ADC). MATERIALS AND METHODS:Forty-two healthy subjects ranging from ages 1 year to 20 years were included in our study. T1 brain maps in which the intensity of each pixel corresponded to T1 relaxation times were generated based on MR imaging data acquired using a MP2RAGE sequence. During the same session, diffusion tensor imaging data was collected. T1 relaxation times and ADC in white matter and grey matter were measured in seven clinically relevant regions of interest and were correlated to subjects' age. RESULTS:In the basal ganglia, there was a small, yet significant, decrease in T1 relaxation time (-0.45 ≤R≤-0.59, p<10-2) and ADC (-0.60≤R≤-0.65, p<10-4) as a function of age. In the frontal and parietal white matter, there was a significant decrease in T1 relaxation time (-0.62≤R≤-0.68, p<10-4) and ADC (-0.81≤R≤-0.85, p<10-4) as a function of age. T1 relaxation time changes in the corpus callosum and internal capsule were less relevant for this age range. There was no significant difference between the correlation of T1 relaxation time and ADC with respect to age (p-value = 0.39). The correlation between T1 relaxation and ADC is strong in the white matter but only moderate in basal ganglia over this age period. CONCLUSIONS:T1 relaxation time is a marker of brain maturation or myelination during late brain development. Between the age of 1 and 20 years, T1 relaxation time decreases as a function of age in the white matter and basal ganglia. The greatest changes occur in frontal and parietal white matter. These regions are known to mature in the final stage of development and are mainly composed of association circuits. Age-correlation is not significantly different between T1 relaxation time and ADC. Therefore, T1 relaxation time does not appear to be a superior marker of brain maturation than ADC but may be considered as complementary owing the intrinsic differences in bio-physical sensitivity. This work may serve as normative ranges in clinical imaging routines.

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High-resolution temporal and regional mapping of MAPT expression and splicing in human brain development.

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