Biology subjects

Lee, N.

Publications and source records attributed to Lee, N..

5 recordsLinked to original sources

bioRxiv2018

The jellyfish genome sheds light on the early evolution of active predation

BackgroundUnique among cnidarians, jellyfish have remarkable morphological and biochemical innovations that allow them to actively hunt in the water column. One of the first animals to become free-swimming, jellyfish employ pulsed jet propulsion and venomous tentacles to capture prey.\n\nResultsTo understand these key innovations, we sequenced the genome of the giant Nomuras jellyfish (Nemopilema nomurai), the transcriptomes of its bell and tentacles, and transcriptomes across tissues and developmental stages of the Sanderia malayensis jellyfish. Analyses of Nemopilema and other cnidarian genomes revealed adaptations associated with swimming, marked by codon bias in muscle contraction and expansion of neurotransmitter genes, along with expanded Myosin type II family and venom domains; possibly contributing to jellyfish mobility and active predation. We also identified gene family expansions of Wnt and posterior Hox genes, and discovered the important role of retinoic acid signaling in this ancient lineage of metazoans, which together may be related to the unique jellyfish body plan (medusa formation).\n\nConclusionsTaken together, the jellyfish genome and transcriptomes genetically confirm their unique morphological and physiological traits that have combined to make these animals one of the worlds earliest and most successful multi-cellular predators.

genomics↗

bioRxiv2018

Palbociclib and fulvestrant act in synergy to modulate central carbon metabolism in breast cancer cells

Palbociclib, is a selective inhibitor of cyclin-dependent kinases 4 and 6 and used as a first-line treatment for patients with estrogen receptor positive breast cancer. It has been shown that patients have improved progression-free survival when treated in combination with fulvestrant, an estrogen receptor antagonist. However, the mechanisms for this survival advantage are not known. We sought to analyze metabolic and transcriptomic changes in MCF-7 adenocarcinoma breast cancer cells following single and combined treatments to determine if selective metabolic pathways are targeted during combination therapy. Our results showed that individually, the drugs caused metabolic disruption to the same metabolic pathways, however fulvestrant additionally attenuated the pentose phosphate pathway and the production of important coenzymes. A comprehensive effect was observed when the drugs were applied together, confirming the combinatory therapy's synergism in the cell model. This study highlights the power of merging high-dimensional datasets to unravel mechanisms involved in cancer metabolism and therapy.\n\nHighlights{circ} First study employing multi-omics to investigate combined therapy on breast cancer cells\n{circ}Fulvestrant attenuates the pentose phosphate pathway and coenzyme production\n{circ}Synergism of palbociclib and fulvestrant was confirmed in vitro\n{circ}Altered key pathways have been identified\n\n\neTOC BlurbJohnson et al. applied an innovative multi-omics approach to decipher metabolic pathways affected by single versus combination dosing of palbociclib and fulvestrant in estrogen receptor positive breast cancer. Key metabolites and genes were correlated within metabolic pathways and shown to be involved in the drugs' synergism.

biochemistry↗

bioRxiv2018

Spatiotemporally controlled Myosin relocalization and internal pressure cause biased cortical extension to generate sibling cell size asymmetry

Metazoan cells can generate unequal sized sibling cells during cell division. This form of asymmetric cell division depends on spindle geometry and Myosin distribution but the underlying mechanics are unclear. Here, we use atomic force microscopy and live cell imaging to elucidate the biophysical forces involved in the establishment of physical asymmetry in Drosophila neural stem cells. We show that the force driving initial apical membrane expansion is provided by hydrostatic pressure, peaking shortly after anaphase onset, and enabled by a relieve of actomyosin contractile tension on the apical cell cortex. The subsequent increase in contractile forces at the cleavage furrow, combined with the relocalization of basally located Myosin results in basal membrane extension and sustained apical expansion. We propose that spatiotemporally controlled actomyosin contractile tension and hydrostatic pressure enables stereotypic biased membrane expansion to generate sibling cell size asymmetry.

cell biology↗

bioRxiv2018

Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.\n\nAuthor SummaryFood security is a growing global concern. Farmers, plant breeders, and geneticists are hastening to address the challenges presented to agriculture by climate change, dwindling arable land, and population growth. Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency. However, the terabytes of image data require new computational methods to extract useful information. Machine learning algorithms are effective in recognizing select parts of images, but they require high quality data curated by people to train them, a process that can be laborious and costly. We examined how well crowdsourcing works in providing training data for plant phenomics, specifically, segmenting a corn tassel - the male flower of the corn plant - from the often-cluttered images of a cornfield. We provided images to students, and to Amazon MTurkers, the latter being an on-demand workforce brokered by Amazon.com and paid on a task-by-task basis. We report on best practices in crowdsourcing image labeling for phenomics, and compare the different groups on measures such as fatigue and accuracy over time. We find that crowdsourcing is a good way of generating quality labeled data, rivaling that of experts.

plant biology↗

bioRxiv2016

Mindboggling morphometry of human brains

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains every conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, and more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggles algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.\n\nAuthor SummaryBrains vary in many ways, including their shape. Analysing differences in shape between brains or changes in brain shape over time has been used to characterize morphology of diseased brains, but these analyses conventionally rely on simple volumetric shape measures. We believe that access to a greater variety of shape measures could provide greater sensitivity and specificity to morphological disturbances, and could aid in diagnosis, tracking, and prediction of the progression of mental health disorders. Mindboggle is open source software that provides neuroscientists (and indeed, anyone interested in computing shapes) tools for computing a variety of shape measures, including area, volume, thickness, curvature, geodesic depth, travel depth, Laplace-Beltrami spectra, and Zernike moments. In addition to algorithmic contributions, we conducted evaluations and applied Mindboggle to conduct the most detailed shape analysis of human brains.

neuroscience↗