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  • 1
    Online Resource
    Online Resource
    Vocal learning–associated convergent evolution in mammalian proteins and regulatory elements
    American Association for the Advancement of Science (AAAS) ; 2024
    In:  Science Vol. 383, No. 6690 ( 2024-03-29)
    Details
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 383, No. 6690 ( 2024-03-29)
    Abstract: Vocal production learning (“vocal learning”), or the ability to modify vocalizations according to the social environment, forms the basis of human speech production. Among the Boreoeutherian mammals, this trait has evolved independently in four different lineages: humans, bats, cetaceans, and pinnipeds. In vertebrates, the evolution of vocal learning behavior has been associated with the evolution of brain anatomical features, including cortical long-range projection neurons (e.g., songbirds and humans). Moreover, neural circuits for the production of learned vocalization display convergent evolution in patterns of gene expression. RATIONALE Despite evidence for the convergent evolution of vocal learning at the behavioral, anatomical, and gene expression levels in vertebrates, the genetic underpinnings of vocal learning and human speech in mammals are poorly understood. New machine learning approaches and the newly sequenced mammalian genomes of the Zoonomia Consortium provide the foundation to rigorously study this question. The repeated evolution of vocal learning across mammals allows us to determine which parts of the genome are significantly associated with the behavior. RESULTS First, we studied convergent evolution in protein-coding regions using the RERconverge and HyPhy methods to find 200 significantly associated genes. The genes that tend to be under higher constraint in vocal learning mammals are enriched for genes involved in human autism. However, the vast majority of genes are driven by signals from only one or two clades of vocal learning mammals, suggesting that a large component of the genetic basis for the trait may lie instead in the convergent evolution of regulatory elements. To explore that hypothesis, we performed an anatomical and functional characterization of the Egyptian fruit bat motor cortex. We identified a subregion of the motor cortex that is implicated in vocal production and directly projects to the motoneurons controlling the bat’s larynx. This allowed us to profile candidate regulatory elements active in this vocalization-associated subregion of the motor cortex by measuring open chromatin. These open chromatin regions and 222 mammalian genomes of the Zoonomia Consortium served as input to the Tissue-Aware Conservation Inference Toolkit (TACIT) machine learning approach, which was applied to find 50 candidate regulatory elements whose predicted motor cortex open chromatin measurements across mammals are highly correlated with the presence of vocal learning behavior. Many of these open chromatin regions were near genes associated with autism, and they tended to overlap with open chromatin specific to the long-range projection neurons that have been implicated in the evolution of vocal learning. CONCLUSION Although it is impossible to know which parts of the genome evolved for human speech production, we are able to use the repeated evolution of a component of that behavior, vocal learning, to find significantly associated genes and noncoding regions. Our results demonstrate that the presence of vocal learning behavior in a given clade leads to weak selective pressure across a broad range of genes and stronger selective pressure across a smaller number of motor cortex noncoding regions. These genes and noncoding regions show an association with autism, which suggests that there are shared regulatory networks for vocal and social behavior that tend to adapt in similar ways when a lineage evolves vocal learning behavior. More broadly, our results suggest that the evolutionary history of selective pressures across a location in the genome can provide insight into how that region might influence human behavior. Finding vocal learning–associated regions of the mammalian genome. We compared the evolution of vocal learning behavior to the evolution of coding and noncoding elements of the genome, leveraging anatomical, electrophysiological, and epigenomic experiments in the Egyptian fruit bat orofacial motor cortex (ofM1). We show convergent evidence of the importance of long-range projection neurons and autism-associated gene networks.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    URL: Article
    DOI: 10.1126/science.abn3263
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
    detail.hit.zdb_id: 128410-1
    detail.hit.zdb_id: 2066996-3
    SSG: 11
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  • 2
    Online Resource
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    Evolutionary constraint and innovation across hundreds of placental mammals
    American Association for the Advancement of Science (AAAS) ; 2023
    In:  Science Vol. 380, No. 6643 ( 2023-04-28)
    Details
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 380, No. 6643 ( 2023-04-28)
    Abstract: A major challenge in genomics is discerning which bases among billions alter organismal phenotypes and affect health and disease risk. Evidence of past selective pressure on a base, whether highly conserved or fast evolving, is a marker of functional importance. Bases that are unchanged in all mammals may shape phenotypes that are essential for organismal health. Bases that are evolving quickly in some species, or changed only in species that share an adaptive trait, may shape phenotypes that support survival in specific niches. Identifying bases associated with exceptional capacity for cellular recovery, such as in species that hibernate, could inform therapeutic discovery. RATIONALE The power and resolution of evolutionary analyses scale with the number and diversity of species compared. By analyzing genomes for hundreds of placental mammals, we can detect which individual bases in the genome are exceptionally conserved (constrained) and likely to be functionally important in both coding and noncoding regions. By including species that represent all orders of placental mammals and aligning genomes using a method that does not require designating humans as the reference species, we explore unusual traits in other species. RESULTS Zoonomia’s mammalian comparative genomics resources are the most comprehensive and statistically well-powered produced to date, with a protein-coding alignment of 427 mammals and a whole-genome alignment of 240 placental mammals representing all orders. We estimate that at least 10.7% of the human genome is evolutionarily conserved relative to neutrally evolving repeats and identify about 101 million significantly constrained single bases (false discovery rate 〈 0.05). We cataloged 4552 ultraconserved elements at least 20 bases long that are identical in more than 98% of the 240 placental mammals. Many constrained bases have no known function, illustrating the potential for discovery using evolutionary measures. Eighty percent are outside protein-coding exons, and half have no functional annotations in the Encyclopedia of DNA Elements (ENCODE) resource. Constrained bases tend to vary less within human populations, which is consistent with purifying selection. Species threatened with extinction have few substitutions at constrained sites, possibly because severely deleterious alleles have been purged from their small populations. By pairing Zoonomia’s genomic resources with phenotype annotations, we find genomic elements associated with phenotypes that differ between species, including olfaction, hibernation, brain size, and vocal learning. We associate genomic traits, such as the number of olfactory receptor genes, with physical phenotypes, such as the number of olfactory turbinals. By comparing hibernators and nonhibernators, we implicate genes involved in mitochondrial disorders, protection against heat stress, and longevity in this physiologically intriguing phenotype. Using a machine learning–based approach that predicts tissue-specific cis - regulatory activity in hundreds of species using data from just a few, we associate changes in noncoding sequence with traits for which humans are exceptional: brain size and vocal learning. CONCLUSION Large-scale comparative genomics opens new opportunities to explore how genomes evolved as mammals adapted to a wide range of ecological niches and to discover what is shared across species and what is distinctively human. High-quality data for consistently defined phenotypes are necessary to realize this potential. Through partnerships with researchers in other fields, comparative genomics can address questions in human health and basic biology while guiding efforts to protect the biodiversity that is essential to these discoveries. Comparing genomes from 240 species to explore the evolution of placental mammals. Our new phylogeny (black lines) has alternating gray and white shading, which distinguishes mammalian orders (labeled around the perimeter). Rings around the phylogeny annotate species phenotypes. Seven species with diverse traits are illustrated, with black lines marking their branch in the phylogeny. Sequence conservation across species is described at the top left. IMAGE CREDIT: K. MORRILL
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    URL: Article
    DOI: 10.1126/science.abn3943
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
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  • 3
    Online Resource
    Online Resource
    Relating enhancer genetic variation across mammals to complex phenotypes using machine learning
    American Association for the Advancement of Science (AAAS) ; 2023
    In:  Science Vol. 380, No. 6643 ( 2023-04-28)
    Details
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 380, No. 6643 ( 2023-04-28)
    Abstract: Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of 〉 400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    URL: Article
    DOI: 10.1126/science.abm7993
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
    detail.hit.zdb_id: 128410-1
    detail.hit.zdb_id: 2066996-3
    SSG: 11
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  • 4
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    Assessment of white matter hyperintensity severity using multimodal magnetic resonance imaging
    Oxford University Press (OUP) ; 2023
    In:  Brain Communications Vol. 5, No. 6 ( 2023-11-01)
    Details
    In: Brain Communications, Oxford University Press (OUP), Vol. 5, No. 6 ( 2023-11-01)
    Abstract: White matter hyperintensities are radiological abnormalities reflecting cerebrovascular dysfunction detectable using MRI. White matter hyperintensities are often present in individuals at the later stages of the lifespan and in prodromal stages in the Alzheimer’s disease spectrum. Tissue alterations underlying white matter hyperintensities may include demyelination, inflammation and oedema, but these are highly variable by neuroanatomical location and between individuals. There is a crucial need to characterize these white matter hyperintensity tissue alterations in vivo to improve prognosis and, potentially, treatment outcomes. How different MRI measure(s) of tissue microstructure capture clinically-relevant white matter hyperintensity tissue damage is currently unknown. Here, we compared six MRI signal measures sampled within white matter hyperintensities and their associations with multiple clinically-relevant outcomes, consisting of global and cortical brain morphometry, cognitive function, diagnostic and demographic differences and cardiovascular risk factors. We used cross-sectional data from 118 participants: healthy controls (n = 30), individuals at high risk for Alzheimer’s disease due to familial history (n = 47), mild cognitive impairment (n = 32) and clinical Alzheimer’s disease dementia (n = 9). We sampled the median signal within white matter hyperintensities on weighted MRI images [T1-weighted (T1w), T2-weighted (T2w), T1w/T2w ratio, fluid-attenuated inversion recovery (FLAIR)] as well as the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. qT2* and fluid-attenuated inversion recovery signals within white matter hyperintensities displayed different age- and disease-related trends compared to normal-appearing white matter signals, suggesting sensitivity to white matter hyperintensity-specific tissue deterioration. Further, white matter hyperintensity qT2*, particularly in periventricular and occipital white matter regions, was consistently associated with all types of clinically-relevant outcomes in both univariate and multivariate analyses and across two parcellation schemes. qT1 and fluid-attenuated inversion recovery measures showed consistent clinical relationships in multivariate but not univariate analyses, while T1w, T2w and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to white matter hyperintensities. Our results suggest that combining volumetric and signal measures of white matter hyperintensity should be considered to fully characterize the severity of white matter hyperintensities in vivo. These findings may have implications in determining the reversibility of white matter hyperintensities and the potential efficacy of cardio- and cerebrovascular treatments.
    Type of Medium: Online Resource
    ISSN: 2632-1297
    URL: Article
    DOI: 10.1093/braincomms/fcad279
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
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  • 5
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    Drosophila Muller F Elements Maintain a Distinct Set of Genomic Properties Over 40 Million Years of Evolution
    Oxford University Press (OUP) ; 2015
    In:  G3 Genes|Genomes|Genetics Vol. 5, No. 5 ( 2015-05-01), p. 719-740
    Details
    In: G3 Genes|Genomes|Genetics, Oxford University Press (OUP), Vol. 5, No. 5 ( 2015-05-01), p. 719-740
    Abstract: The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25–50%) than euchromatic reference regions (3–11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11–27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4–3.6 vs. 8.4–8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu.
    Type of Medium: Online Resource
    ISSN: 2160-1836
    URL: Article
    DOI: 10.1534/g3.114.015966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
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  • 6
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    Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2016 to 2019
    Scientific Societies ; 2020
    In:  Plant Health Progress Vol. 21, No. 4 ( 2020-01-01), p. 238-247
    Details
    In: Plant Health Progress, Scientific Societies, Vol. 21, No. 4 ( 2020-01-01), p. 238-247
    Abstract: Annual reductions in corn (Zea mays L.) yield caused by diseases were estimated by university Extension-affiliated plant pathologists in 26 corn-producing states in the United States and in Ontario, Canada, from 2016 through 2019. Estimated loss from each disease varied greatly by state or province and year. Gray leaf spot (caused by Cercospora zeae-maydis Tehon & E.Y. Daniels) caused the greatest estimated yield loss in parts of the northern United States and Ontario in all years except 2019, and Fusarium stalk rot (caused by Fusarium spp.) also greatly reduced yield. Tar spot (caused by Phyllachora maydis Maubl.), a relatively new disease in the United States, was estimated to cause substantial yield loss in 2018 and 2019 in several northern states. Gray leaf spot and southern rust (caused by Puccinia polysora Underw.) caused the most estimated yield losses in the southern United States. Unfavorable wet and delayed harvest conditions in 2018 resulted in an estimated 2.5 billion bushels (63.5 million metric tons) of grain contaminated with mycotoxins. The estimated mean economic loss due to reduced yield caused by corn diseases in the United States and Ontario from 2016 to 2019 was US$55.90 per acre (US$138.13 per hectare). Results from this survey provide scientists, corn breeders, government agencies, and educators with data to help inform and prioritize research, policy, and educational efforts in corn pathology and disease management.
    Type of Medium: Online Resource
    ISSN: 1535-1025
    URL: Article
    DOI: 10.1094/PHP-05-20-0038-RS
    Language: English
    Publisher: Scientific Societies
    Publication Date: 2020
    detail.hit.zdb_id: 2115009-6
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  • 7
    Online Resource
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    Leveraging base-pair mammalian constraint to understand genetic variation and human disease
    American Association for the Advancement of Science (AAAS) ; 2023
    In:  Science Vol. 380, No. 6643 ( 2023-04-28)
    Details
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 380, No. 6643 ( 2023-04-28)
    Abstract: Thousands of genetic variants have been associated with human diseases and traits through genome-wide association studies (GWASs). Translating these discoveries into improved therapeutics requires discerning which variants among hundreds of candidates are causally related to disease risk. To date, only a handful of causal variants have been confirmed. Here, we leverage 100 million years of mammalian evolution to address this major challenge. RATIONALE We compared genomes from hundreds of mammals and identified bases with unusually few variants (evolutionarily constrained). Constraint is a measure of functional importance that is agnostic to cell type or developmental stage. It can be applied to investigate any heritable disease or trait and is complementary to resources using cell type– and time point–specific functional assays like Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTEx). RESULTS Using constraint calculated across placental mammals, 3.3% of bases in the human genome are significantly constrained, including 57.6% of coding bases. Most constrained bases (80.7%) are noncoding. Common variants (allele frequency ≥ 5%) and low-frequency variants (0.5% ≤ allele frequency 〈 5%) are depleted for constrained bases (1.85 versus 3.26% expected by chance, P 〈 2.2 × 10 −308 ). Pathogenic ClinVar variants are more constrained than benign variants ( P 〈 2.2 × 10 −16 ). The most constrained common variants are more enriched for disease single-nucleotide polymorphism (SNP)–heritability in 63 independent GWASs. The enrichment of SNP-heritability in constrained regions is greater (7.8-fold) than previously reported in mammals and is even higher in primates (11.1-fold). It exceeds the enrichment of SNP-heritability in nonsynonymous coding variants (7.2-fold) and fine-mapped expression quantitative trait loci (eQTL)–SNPs (4.8-fold). The enrichment peaks near constrained bases, with a log-linear decrease of SNP-heritability enrichment as a function of the distance to a constrained base. Zoonomia constraint scores improve functionally informed fine-mapping. Variants at sites constrained in mammals and primates have greater posterior inclusion probabilities and higher per-SNP contributions. In addition, using both constraint and functional annotations improves polygenic risk score accuracy across a range of traits. Finally, incorporating constraint information into the analysis of noncoding somatic variants in medulloblastomas identifies new candidate driver genes. CONCLUSION Genome-wide measures of evolutionary constraint can help discern which variants are functionally important. This information may accelerate the translation of genomic discoveries into the biological, clinical, and therapeutic knowledge that is required to understand and treat human disease. Using evolutionary constraint in genomic studies of human diseases. ( A ) Constraint was calculated across 240 mammal species, including 43 primates (teal line). ( B ) Pathogenic ClinVar variants ( N = 73,885) are more constrained across mammals than benign variants ( N = 231,642; P 〈 2.2 × 10 −16 ). ( C ) More-constrained bases are more enriched for trait-associated variants (63 GWASs). ( D ) Enrichment of heritability is higher in constrained regions than in functional annotations (left), even in a joint model with 106 annotations (right). ( E ) Fine-mapping (PolyFun) using a model that includes constraint scores identifies an experimentally validated association at rs1421085. Error bars represent 95% confidence intervals. BMI, body mass index; LF, low frequency; PIP, posterior inclusion probability.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    URL: Article
    DOI: 10.1126/science.abn2937
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
    detail.hit.zdb_id: 128410-1
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    SSG: 11
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  • 8
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    Insulin-like growth factor binding protein-2 in at-risk adults and autopsy-confirmed Alzheimer brains
    Oxford University Press (OUP) ; 2024
    In:  Brain Vol. 147, No. 5 ( 2024-05-03), p. 1680-1695
    Details
    In: Brain, Oxford University Press (OUP), Vol. 147, No. 5 ( 2024-05-03), p. 1680-1695
    Abstract: Insulin, insulin-like growth factors (IGF) and their receptors are highly expressed in the adult hippocampus. Thus, disturbances in the insulin-IGF signalling pathway may account for the selective vulnerability of the hippocampus to nascent Alzheimer's disease (AD) pathology. In the present study, we examined the predominant IGF-binding protein in the CSF, IGFBP2. CSF was collected from 109 asymptomatic members of the parental history-positive PREVENT-AD cohort. CSF levels of IGFBP2, core AD and synaptic biomarkers were measured using proximity extension assay, ELISA and mass spectrometry. Cortical amyloid-beta (Aβ) and tau deposition were examined using 18F-NAV4694 and flortaucipir. Cognitive assessments were performed during up to 8 years of follow-up, using the Repeatable Battery for the Assessment of Neuropsychological Status. T1-weighted structural MRI scans were acquired, and neuroimaging analyses were performed on pre-specified temporal and parietal brain regions. Next, in an independent cohort, we allocated 241 dementia-free ADNI-1 participants into four stages of AD progression based on the biomarkers CSF Aβ42 and total-tau (t-tau). In this analysis, differences in CSF and plasma IGFBP2 levels were examined across the pathological stages. Finally, IGFBP2 mRNA and protein levels were examined in the frontal cortex of 55 autopsy-confirmed AD and 31 control brains from the Quebec Founder Population (QFP) cohort, a unique population isolated from Eastern Canada. CSF IGFBP2 progressively increased over 5 years in asymptomatic PREVENT-AD participants. Baseline CSF IGFBP2 was positively correlated with CSF AD biomarkers and synaptic biomarkers, and negatively correlated with longitudinal changes in delayed memory (P = 0.024) and visuospatial abilities (P = 0.019). CSF IGFBP2 was negatively correlated at a trend-level with entorhinal cortex volume (P = 0.082) and cortical thickness in the piriform (P = 0.039), inferior temporal (P = 0.008), middle temporal (P = 0.014) and precuneus (P = 0.033) regions. In ADNI-1, CSF (P = 0.009) and plasma (P = 0.001) IGFBP2 were significantly elevated in Stage 2 [CSF Aβ(+)/t-tau(+)]. In survival analyses in ADNI-1, elevated plasma IGFBP2 was associated with a greater rate of AD conversion (hazard ratio = 1.62, P = 0.021). In the QFP cohort, IGFBP2 mRNA was reduced (P = 0.049); however, IGFBP2 protein levels did not differ in the frontal cortex of autopsy-confirmed AD brains (P = 0.462). Nascent AD pathology may induce an upregulation in IGFBP2 in asymptomatic individuals. CSF and plasma IGFBP2 may be valuable markers for identifying CSF Aβ(+)/t-tau(+) individuals and those with a greater risk of AD conversion.
    Type of Medium: Online Resource
    ISSN: 0006-8950 , 1460-2156
    URL: Article
    DOI: 10.1093/brain/awad398
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
    detail.hit.zdb_id: 1474117-9
    detail.hit.zdb_id: 80072-7
    SSG: 12
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  • 9
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    Expression quantitative trait locus fine mapping of the 17q12–21 asthma locus in African American children: a genetic association and gene expression study
    Elsevier BV ; 2020
    In:  The Lancet Respiratory Medicine Vol. 8, No. 5 ( 2020-05), p. 482-492
    Details
    In: The Lancet Respiratory Medicine, Elsevier BV, Vol. 8, No. 5 ( 2020-05), p. 482-492
    Type of Medium: Online Resource
    ISSN: 2213-2600
    URL: Article
    DOI: 10.1016/S2213-2600(20)30011-4
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2686754-0
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  • 10
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    Operation and performance of the ATLAS semiconductor tracker in LHC Run 2
    IOP Publishing ; 2022
    In:  Journal of Instrumentation Vol. 17, No. 01 ( 2022-01-01), p. P01013-
    Details
    In: Journal of Instrumentation, IOP Publishing, Vol. 17, No. 01 ( 2022-01-01), p. P01013-
    Abstract: The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules. During Run 2 (2015–2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb -1 to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector. Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2. It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%. Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules.
    Type of Medium: Online Resource
    ISSN: 1748-0221
    URL: Article
    DOI: 10.1088/1748-0221/17/01/P01013
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2235672-1
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