Biology subjects

Liu, K. J.

Publications and source records attributed to Liu, K. J..

7 recordsLinked to original sources

bioRxiv2018

The osteogenic potential of the neural crest lineage may contribute to craniosynostosis

The craniofacial skeleton is formed from the neural crest and mesodermal lineages, both of which contribute mesenchymal precursors during formation of the skull bones. The large majority of cranial sutures also include a proportion of neural crest derived mesenchyme. While some studies have addressed the relative healing abilities of neural crest and mesodermal bone, relatively little attention has been paid to differences in intrinsic osteogenic potential. Here we use mouse models to compare neural crest osteoblasts (from frontal bones or dura mater) to mesodermal osteoblasts (from parietal bones). Using in vitro culture approaches we find that neural crest-derived osteoblasts readily generate bony nodules while mesodermal osteoblasts do so less efficiently. Furthermore, we find that co-culture of neural crest-derived osteoblasts with mesodermal osteoblasts is sufficient to nucleate ossification centres. All together, this suggests that the intrinsic osteogenic abilities of neural crest-derived mesenchyme may be a primary driver behind craniosynostosis.

developmental biology↗

bioRxiv2018

Non-parametric and semi-parametric support estimation using SEquential RESampling random walks on biomolecular sequences

Non-parametric and semi-parametric resampling procedures are widely used to perform support estimation in computational biology and bioinformatics. Among the most widely used methods in this class is the standard bootstrap method, which consists of random sampling with replacement. While not requiring assumptions about any particular parametric model for resampling purposes, the bootstrap and related techniques assume that sites are independent and identically distributed (i.i.d.). The i.i.d. assumption can be an over-simplification for many problems in computational biology and bioinformatics. In particular, sequential dependence within biomolecular sequences is often an essential biological feature due to biochemical function, evolutionary processes such as recombination, and other factors.\n\nTo relax the simplifying i.i.d. assumption, we propose a new non-parametric/semi-parametric sequential resampling technique that generalizes \"Heads-or-Tails\" mirrored inputs, a simple but clever technique due to Landan and Graur. The generalized procedure takes the form of random walks along either aligned or unaligned biomolecular sequences. We refer to our new method as the SERES (or \"SEquential RESampling\") method.\n\nTo demonstrate the flexibility of the new technique, we apply SERES to two different applications - one involving aligned inputs and the other involving unaligned inputs. Using simulated and empirical data, we show that SERES-based support estimation yields comparable or typically better performance compared to state-of-the-art methods for both applications.

bioinformatics↗

bioRxiv2018

GSK3 Controls Migration of the Neural Crest Lineage

Migration of the neural crest lineage is critical to its physiological function. Mechanisms controlling neural crest migration are comparatively unknown, due to difficulties accessing this cell population in vivo. Here, we uncover novel requirements of glycogen synthase kinase 3 (GSK3) in regulating the neural crest. We demonstrate that GSK3 is tyrosine phosphorylated (pY) in neural crest cells and that this activation depends on anaplastic lymphoma kinase (ALK), a protein associated with neuroblastoma. Consistent with this, neuroblastoma cells with pathologically increased ALK activity express high levels of pY-GSK3 and migration of these cells can be inhibited by GSK3 or ALK blockade. In normal neural crest cells, loss of GSK3 leads to increased pFAK and misregulation of Rac1 and lamellipodin, key regulators of cell migration. Genetic reduction of GSK-3 results in failure of migration. All together, this work identifies a role for GSK3 in cell migration during neural crest development and cancer.

developmental biology↗

bioRxiv2017

Sonic hedgehog is required for neural crest-dependent patterning of the intrinsic tongue musculature

The tongue is a highly specialized muscular organ important for breathing, speech, taste and swallowing. The secreted signaling molecule Sonic hedgehog (Shh) is expressed in dorsal tongue epithelium from the initial developmental stage. In this study, we utilized a series of genetic approaches to investigate the role of Shh during mouse tongue formation. Temporal-specific global deletion of Shh demonstrated a functional requirement for normal patterning of the intrinsic tongue muscles and establishment of the lingual tendon. These defects were reproduced in the mutant with a specific loss of signaling in oropharyngeal epithelium by a Shh cis-enhancer. In these mutants, Ptch1 was lost in the underlying cranial neural crest (CNC)-derived mesenchymal lineage. The importance of Shh was confirmed by generating tissue-specific deletions in the ciliopathy gene Ofd1, which transduces Shh signaling. These results revealed Shh roles in patterning of the mesodermal intrinsic tongue muscles through CNC-derived mesenchyme, including the lingual tendon.

developmental biology↗

bioRxiv2017

RAPGEF5 regulates nuclear transport of β-catenin

Canonical Wnt signaling coordinates many critical aspects of embryonic development, while dysregulated Wnt signaling contributes to common diseases, including congenital malformations and cancer. The nuclear localization of {beta}-catenin is the defining step in pathway activation. However, despite intensive investigation, the mechanisms regulating {beta}-catenin nuclear transport remain undefined. In a patient with congenital heart disease and heterotaxy, a disorder of left-right patterning, we previously identified the guanine nucleotide exchange factor, RAPGEF5. Here, we demonstrate that RAPGEF5 regulates left-right patterning via Wnt signaling. In particular, RAPGEF5, regulates the nuclear translocation of {beta}-catenin independently of both {beta}-catenin cytoplasmic stabilization and the importin {beta}1/Ran mediated transport system. We propose a model whereby RAPGEF5 activates the nuclear GTPases, Rap1/2, to facilitate the nuclear transport of {beta}-catenin, defining a parallel nuclear transport pathway to Ran. Our results suggest new targets for modulating Wnt signaling in disease states.

developmental biology↗

bioRxiv2017

Coal-Miner: A Coalescent-Based Method For GWA Studies Of Quantitative Traits With Complex Evolutionary Origins

Association mapping (AM) methods are used in genome-wide association (GWA) studies to test for statistically significant associations between genotypic and phenotypic data. The genotypic and phenotypic data share common evolutionary origins - namely, the evolutionary history of sampled organisms - introducing covariance which must be distinguished from the covariance due to biological function that is of primary interest in GWA studies. A variety of methods have been introduced to perform AM while accounting for sample relatedness. However, the state of the art predominantly utilizes the simplifying assumption that sample relatedness is effectively fixed across the genome. In contrast, population genetic theory and empirical studies have shown that sample relatedness can vary greatly across different loci within a genome; this phenomena - referred to as local genealogical variation - is commonly encountered in many genomic datasets. New AM methods are needed to better account for local variation in sample relatedness within genomes.\n\nWe address this gap by introducing Coal-Miner, a new statistical AM method. The Coal-Miner algorithm takes the form of a methodological pipeline. The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively causal markers. Subsequent stages of Coal-Miner perform test for association using a linear mixed model with multiple effects which account for sample relatedness locally within candidate loci and globally across the entire genome.\n\nUsing synthetic and empirical datasets, we compare the statistical power and type I error control of Coal-Miner against state-of-theart AM methods. The simulation conditions reflect a variety of genomic architectures for complex traits and incorporate a range of evolutionary scenarios, each with different evolutionary processes that can generate local genealogical variation. The empirical benchmarks include a large-scale dataset that appeared in a recent high-profile publication. Across the datasets in our study, we find that Coal-Miner consistently offers comparable or typically better statistical power and type I error control compared to the state-of-art methods.\n\nCCS CONCEPTSApplied computing [->] Computational genomics; Computational biology; Molecular sequence analysis; Molecular evolution; Computational genomics; Systems biology; Bioinformatics; Population genetics;\n\nACM Reference formatHussein A. Hejase, Natalie Vande Pol, Gregory M. Bonito, Patrick P. Edger, and Kevin J. Liu. 2017. Coal-Miner: a coalescent-based method for GWA studies of quantitative traits with complex evolutionary origins. In Proceedings of ACM BCB, Boston, MA, 2017 (BCB), 10 pages. DOI: 10.475/123 4

bioinformatics↗

bioRxiv2017

FastNet: Fast and accurate inference of phylogenetic networks using large-scale genomic sequence data

An emerging discovery in phylogenomics is that interspecific gene flow has played a major role in the evolution of many different organisms. To what extent is the Tree of Life not truly a tree reflecting strict \"vertical\" divergence, but rather a more general graph structure known as a phylogenetic network which also captures \"horizontal\"gene flow? The answer to this fundamental question not only depends upon densely sampled and divergent genomic sequence data, but also compu-tational methods which are capable of accurately and efficiently inferring phylogenetic networks from large-scale genomic sequence datasets. Re-cent methodological advances have attempted to address this gap. How-ever, in the 2016 performance study of Hejase and Liu, state-of-the-art methods fell well short of the scalability requirements of existing phy-logenomic studies.\n\nThe methodological gap remains: how can phylogenetic networks be ac-curately and efficiently inferred using genomic sequence data involving many dozens or hundreds of taxa? In this study, we address this gap by proposing a new phylogenetic divide-and-conquer method which we call FastNet. We conduct a performance study involving a range of evolu-tionary scenarios, and we demonstrate that FastNet outperforms state-of-the-art methods in terms of computational efficiency and topological accuracy.

bioinformatics↗