Browsing by Subject "Medical genetics"

Now showing 1 - 10 of 13
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    Craniofacial variation and growth in the Prader-Labhart-Willi syndrome
    (Wiley, 1987-12) Meaney, F. John; Butler, Merlin G.; Medical and Molecular Genetics, School of Medicine
    A study of anthropometric variation and craniofacial growth in individuals with the Prader-Labhart-Willi syndrome (PLWS) illustrates the utility of anthropometry in clinical evaluation and research. Anthropometric measurements, including head length and breadth, minimum frontal diameter, and head circumference, were obtained on 38 PLWS individuals (21 with chromosome 15 deletions) with an age range from 2 weeks to 39 years. No anthropometric differences were found between the two chromosome subgroups. A relative deceleration in the growth of certain craniofacial dimensions (head circumference and length) is suggested by the negative correlations between age and Z-scores for the measurements. Raw values for minimum frontal diameter and head breadth were near or below the 5th percentile curve, while almost all values for head length and circumference fell within normal limits. The data support suggestions that dolichocephaly be considered an early diagnostic feature of PLWS. Furthermore, the status of narrow bifrontal diameter as a major feature of PLWS is confirmed.
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    Erk1 and Erk2 in hematopoiesis, mast cell function, and the management of Nf1-associated leukemia and tumors
    (2012-03) Staser, Karl W.; Clapp, D. Wade; Yang, Feng-Chun; Goebl, Mark, 1958-; Harrington, Maureen A.
    Neurofibromatosis type 1 is a genetic disease that results from either heritable or spontaneous autosomal dominant mutations in the NF1 gene, which encodes a protein serving, at least in part, to accelerate the intrinsic hydrolysis of active Ras-GTP to inactive Ras-GDP. A second-hit NF1 mutation precedes predominant NF1 neoplasms, including juvenile myelomoncytic leukemia (JMML) and plexiform neurofibroma formation, potentially fatal conditions with no medical therapy. While NF1 loss of heterozygosity (LOH) in myeloid progenitor cells sufficiently engenders leukemogenesis, plexiform neurofibroma formation depends on LOH in Schwann cells and Nf1 heterozygosity in the hematopoietic system. Specifically, recruited Nf1+/- mast cells accelerate tumorigenesis through secreted cytokines and growth factors. Nf1+/- mast cells depend upon deregulated signaling in c-kit pathways, a receptor system conserved in hematopoietic stem cells (HSCs). Accordingly, Nf1-/- myeloid progenitor cells, which can induce a JMML-like disease in mice, also demonstrate deregulated c-kit receptor signaling. C-kit-activated Nf1+/- mast cells and Nf1-/- myeloid progenitors both show increased latency and potency of active Erk1 and Erk2, the principal cytosolic-to-nuclear effectors of canonical Ras-Raf-Mek signaling. Thus, Erk represents a potential regulator of leukemogenesis and tumor-associated inflammation. However, single and combined Erk1 and Erk2 roles in HSC function, myelopoiesis, and mature mast cell physiology remain unknown, and recent hematopoietic studies relying on chemical Mek-Erk inhibitors have produced conflicting results. Here, we show that hematopoietic stability, myelopoiesis, and mast cell generation require Erk1 or Erk2, but individual isoforms are largely dispensable. Principally, Erk-disrupted hematopoietic stem cells incorporate BrdU but are incapable of dividing, a novel and cell type-specific Erk function. Similarly, mast cell proliferation requires Erk but cytokine production proceeds through other pathways, elucidating molecule-specific functions within the c-kit cascade. Based on these findings, we have reduced tumor mast cell infiltration by treating genetically-engineered tumor model mice with PD0325901, a preclinical Mek-Erk inhibitor. Moreover, we have devised a quadruple transgenic HSC transplantation model to examine dual Erk disruption in the context of Nf1 nullizygosity, testing whether diseased hematopoiesis requires Erk. These insights illuminate cell-specific Erk functions in normal and Nf1-deficient hematopoiesis, informing the feasibility of targeting Mek-Erk in NF1-associated disease.
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    Genetic linkage studies in Huntington's disease
    (1978) Pericak-Vance, Margaret Ann
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    Luminescence-Based MicroRNA Detection Methods
    (2012-08-27) Cissell, Kyle A.; Deo, Sapna K.; Long, Eric C. (Eric Charles); Simpson, Garth; Mao, Chengde
    MicroRNAs (miRNA) are short, 18-24 nucleotide long noncoding RNAs. These small RNAs, which are initially transcribed in the nucleus, are transported into the cell cytoplasm where they regulate protein translation either through direct cleavage of mRNA, or indirect inhibition through binding to mRNA and disrupting the protein translation machinery. Recently, miRNAs have gained much attention due to their implication in numerous diseases and cancers. It has been found that heightened or lowered levels of miRNA in diseased cells vs. healthy cells are linked to disease progression. It is therefore immensely important to be able to detect these small molecules. Current detection methods of Northern blotting, microarrays, and qRT-PCR suffer from drawbacks including low sensitivity, a lack of simplicity, being semi-quantitative in nature, time-consuming, and requiring expensive instruments. This work aims to develop novel miRNA technologies which will address these above problems. Bioluminescent labels are promising alternatives to current methods of miRNA detection. Bioluminescent labels are relatively small, similar in size to fluorescent proteins, and they emit very intense signals upon binding to their substrate. Bioluminescent labels are advantageous to fluorescent labels in that they do not require an external excitation source, rather, the excitation energy is supplied through a biochemical reaction. Therefore, background signal due to excitation is eliminated. They also have the advantage of being produced in large amounts through bacterial expression. Four miRNA detection methods are presented which utilize luminescence-based methods. Three employ Renilla luciferase, a bioluminescent protein, and one is based on fluorescence. The presented methods are capable of detecting miRNA from the picomole (nanomolar) level down to the femtomole (picomolar) level. These methods are rapid, sensitive, simple, and quantitative, can be employed in complex matrices, and do not require expensive instruments. All methods are hybridization-based and do not require amplification steps.
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    Mining brain imaging and genetics data via structured sparse learning
    (2015-04-29) Yan, Jingwen; Wu, Huanmei; Shen, Li; Fang, Shiaofen; Liu, Xiaowen
    Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of brain functions, usually preceded by memory impairments. It has been widely affecting aging Americans over 65 old and listed as 6th leading cause of death. More importantly, unlike other diseases, loss of brain function in AD progression usually leads to the significant decline in self-care abilities. And this will undoubtedly exert a lot of pressure on family members, friends, communities and the whole society due to the time-consuming daily care and high health care expenditures. In the past decade, while deaths attributed to the number one cause, heart disease, has decreased 16 percent, deaths attributed to AD has increased 68 percent. And all of these situations will continue to deteriorate as the population ages during the next several decades. To prevent such health care crisis, substantial efforts have been made to help cure, slow or stop the progression of the disease. The massive data generated through these efforts, like multimodal neuroimaging scans as well as next generation sequences, provides unprecedented opportunities for researchers to look into the deep side of the disease, with more confidence and precision. While plenty of efforts have been made to pull in those existing machine learning and statistical models, the correlated structure and high dimensionality of imaging and genetics data are generally ignored or avoided through targeted analysis. Therefore their performances on imaging genetics study are quite limited and still have plenty to be improved. The primary contribution of this work lies in the development of novel prior knowledge-guided regression and association models, and their applications in various neurobiological problems, such as identification of cognitive performance related imaging biomarkers and imaging genetics associations. In summary, this work has achieved the following research goals: (1) Explore the multimodal imaging biomarkers toward various cognitive functions using group-guided learning algorithms, (2) Development and application of novel network structure guided sparse regression model, (3) Development and application of novel network structure guided sparse multivariate association model, and (4) Promotion of the computation efficiency through parallelization strategies.