Biology subjects

Lee, M.

Publications and source records attributed to Lee, M..

At least 235 records · Page 13Linked to original sources

bioRxiv2019

3D bioprinting of architected hydrogels from entangled microstrands

Hydrogels are an excellent biomimetic of the extracellular matrix and have found great use in tissue engineering. Nanoporous monolithic hydrogels have limited mass transport, restricting diffusion of key biomolecules. Structured microbead-hydrogels overcome some of these limitations, but suffer from lack of controlled anisotropy. Here we introduce a novel method for producing architected hydrogels based on entanglement of microstrands. The microstrands are mouldable and form a porous structure which is stable in water. Entangled microstrands are useable as bioinks for 3D bioprinting, where they align during the extrusion process. Cells co-printed with the microstrands show excellent viability and augmented matrix deposition resulting in a modulus increase from 2.7 kPa to 780.2 kPa after 6 weeks of culture. Entangled microstands are a new class of bioinks with unprecedented advantages in terms of scalability, material versatility, mass transport, showing foremost outstanding properties as a bioink for 3D printed tissue grafts.

bioengineering↗

bioRxiv2019

Measurements of 3-D morphology of individual red blood cells upon localized photothermal effects using gold nanorods

We investigated the in-situ photothermal response of human red blood cells (RBCs) by combining photothermal heat generation and 3D quantitative phase imaging techniques. Gold-nanorod-coated substrates were excited using near-infrared light to generate local heat to RBCs, and response was measured by imaging 3-D refractive index tomograms of cells under various near infrared (NIR) excitation conditions. On photothermal treatment, cell morphology changed from discoid to crescent shapes, cell volume and dry mass decreased, and hemoglobin concentration increased. We also investigated the irreversible deformation of RBCs when multiple intense excitation shocks are applied. These results provide a new understanding of thermodynamic aspects of cell biology and hematology. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biophysics↗

bioRxiv2019

Nanocomposite bioink exploits dynamic covalent bonds between nanoparticles and polysaccharides for precision bioprinting

The field of bioprinting has made significant recent progress towards engineering tissues with increasing complexity and functionality. It remains challenging, however, to develop bioinks with optimal biocompatibility and good printing fidelity. Here, we demonstrate enhanced printability of a polymer-based bioink based on dynamic covalent linkages between nanoparticles (NPs) and polymers, which retains good biocompatibility. Amine-presenting silica NPs (ca. 45 nm) were added to a polymeric ink containing oxidized alginate (OxA). The formation of reversible imine bonds between amines on the NPs and aldehydes of OxA lead to significantly improved rheological properties and high printing fidelity. In particular, the yield stress increased with increasing amounts of NPs (14.5 Pa without NPs, 79 Pa with 2 wt% NPs). In addition, the presence of dynamic covalent linkages in the gel provided improved mechanical stability over 7 days compared to ionically crosslinked gels. The nanocomposite ink retained high printability and mechanical strength, resulting in generation of centimetre-scale porous constructs and an ear structure with overhangs and high structural fidelity. Furthermore, the nanocomposite ink supported both in vitro and in vivo maturation of bioprinted gels containing chondrocytes. This approach based on simple oxidation can be applied to any polysaccharide, thus the widely applicability of the method is expected to advance the field towards the goal of precision bioprinting.

bioengineering↗

bioRxiv2019

BDKANN - Biological Domain Knowledge-based Artificial Neural Network for drug response prediction

MotivationOne of the main goals of precision oncology is to predict the response of a patient to a given cancer treatment based on their genomic profile. Although current models for drug response prediction are becoming more accurate, they are also black boxes and cannot explain their predictions, which is of particular importance in cancer treatment. Many models also do not leverage prior biological knowledge, such as the hierarchical information on how proteins form complexes and act together in pathways. ResultsIn this work, we use this prior biological knowledge to form the architecture of a deep neural network to predict cancer drug response from cell line gene expression data. We find that our approach not only has a low prediction error compared to baseline models but also allows meaningful interpretation of the network. These interpretations can both explain predictions made and discover novel connections in the biological knowledge that may lead to new hypotheses about mechanisms of drug action. AvailabilityCode at https://github.com/osnow/BDKANN Supplementary informationIncluded with submission

bioinformatics↗

bioRxiv2019

Regulation of islet function and gene expression by prolactin during pregnancy

Pancreatic islets adapt to insulin resistance of pregnancy by up regulating {beta}-cell proliferation and increase insulin secretion. Previously, we found that prolactin receptor (Prlr) signaling is important for this process, as heterozygous prolactin receptor-null (Prlr+/-) mice are glucose intolerant, had a lower number of {beta} cells and lower serum insulin levels than wild type mice during pregnancy. However, since Prlr expression is ubiquitous, to determine its {beta}-cell specific effects, we generated a transgenic mouse with a floxed Prlr allele under the control of an inducible promoter, allowing conditional deletion of Prlr from {beta} cells in adult mice. In this study, we found that {beta}-cell-specific Prlr reduction resulted in elevated blood glucose during pregnancy. Similar to our previous finding in mouse with global Prlr reduction, {beta}-cell-specific Prlr loss led to a lower {beta}-cell mass and a lower in vivo insulin level during pregnancy. However, these islets do not have an intrinsic insulin secretion defect when tested in vitro. Interestingly, when we compared the islet gene expression profile, using islets isolated from mice with global versus {beta}-cell-specific Prlr reduction, we found some important differences in genes that regulate apoptosis and insulin secretion. This suggests that Prlr has both cell-autonomous and non-cell-autonomous effect on {beta} cells, beyond its regulation of pro-proliferative genes.

physiology↗

bioRxiv2019

The genomic landscape of metastasis in treatment-naive breast cancer models

Metastasis remains the principle cause of mortality for breast cancer and presents a critical challenge because secondary lesions are often refractory to conventional treatments. While specific genetic alterations are tightly linked to primary tumour development and progression, the role of genetic alteration in the metastatic process is not well-understood. To investigate how somatic evolution might contribute to breast cancer metastasis, we performed exome, whole genome, and RNA sequencing of matched metastatic and primary tumours from pre-clinical mouse models of breast cancer. Here we show that in a treatment-naive setting, recurrent single nucleotide variants and copy number variation, but not gene fusion events, play key metastasis-driving roles in breast cancer. For instance, we identified recurrent mutations in Kras, a known driver of tumorigenesis that has not been previously implicated in breast cancer metastasis. The strategy presented here represents a novel framework to identify actionable metastasis-targeted therapies.

cancer biology↗

bioRxiv2019

Three-dimensional label-free observation of individual bacteria upon antibiotic treatment using optical diffraction tomography

Measuring alterations in bacteria upon antibiotic application is important for basic studies in microbiology, drug discovery, and clinical diagnosis, and disease treatment. However, imaging and 3D time-lapse response analysis of individual bacteria upon antibiotic application remain largely unexplored mainly due to limitations in imaging techniques. Here, we present a method to systematically investigate the alterations in individual bacteria in 3D and quantitatively analyze the effects of antibiotics. Using optical diffraction tomography, in-situ responses of Escherichia coli and Bacillus subtilis to various concentrations of ampicillin were investigated in a label-free and quantitative manner. The presented method reconstructs the dynamic changes in the 3D refractive-index distributions of living bacteria in response to antibiotics at sub-micrometer spatial resolution.

microbiology↗

bioRxiv2019

Arginine-enriched mixed-charge domains provide cohesion for nuclear speckle condensation

Low-complexity protein domains promote the formation of various biomolecular condensates. However, in many cases, the precise sequence features governing condensate formation and identity remain unclear. Here, we investigate the role of intrinsically disordered mixed-charge domains (MCDs) in nuclear speckle condensation. Proteins composed exclusively of arginine/aspartic-acid dipeptide repeats undergo length-dependent condensation and speckle incorporation. Substituting arginine with lysine in synthetic and natural speckle-associated MCDs abolishes these activities, identifying a key role for multivalent contacts through arginines guanidinium ion. MCDs can synergise with a speckle-associated RNA recognition motif to promote speckle specificity and residence. MCD behaviour is tuneable through net-charge: increasing negative charge abolishes condensation and speckle incorporation. By contrast, increasing positive charge through arginine leads to enhanced condensation, speckle enlargement, decreased splicing factor mobility, and defective mRNA export. Together, these results identify key sequence determinants of MCD-promoted speckle condensation, and link the speckles dynamic material properties with function in mRNA processing.

cell biology↗

bioRxiv2019

Spontaneous polyploids and antimutators compete during the evolution of mutator cells

Heterozygous mutations affecting DNA polymerase (Pol) exonuclease domains and homozygous inactivation of mismatch repair (MMR) each generate "mutator" phenotypes capable of driving tumorigenesis. Cancers with both defects exhibit an explosive increase in mutation burden that appears to reach a threshold, consistent with selection acting against further mutation accumulation. In haploid yeast, simultaneous defects in polymerase proofreading and MMR select for "antimutator" mutants that suppress the mutator phenotype. We report here that spontaneous polyploids also escape this "error-induced extinction" and routinely out-compete antimutators in evolved haploid cultures. We performed similar experiments to explore how diploid yeast adapt to the mutator phenotype. We first evolved cells with homozygous mutations affecting proofreading and MMR, which we anticipated would favor tetraploid emergence. While tetraploids arose with a low frequency, in most cultures, a single antimutator clone rose to prominence carrying biallelic mutations affecting the polymerase mutator alleles. Variation in mutation rate between subclones from the same culture suggests there exists continued selection pressure for additional antimutator alleles. We then evolved diploid yeast modeling MMR-deficient cancers with the most common heterozygous exonuclease domain mutation (POLE-P286R). Although these cells grew robustly, within 120 generations, all subclones carried truncating or nonsynonymous mutations in the POLE-P286R homologous allele (pol2-P301R) that suppressed the mutator phenotype as much as 100-fold. Independent adaptive events in the same culture were common. Our findings suggest that analogous tumor cell populations may adapt to the threat of extinction by polyclonal mutations that neutralize the POLE mutator allele and preserve intra-tumoral genetic diversity for future adaptation.

cancer biology↗

bioRxiv2019

Simultaneous mRNA and protein quantification at the single-cell level delineates trajectories of CD4+ T-cell differentiation

The transcriptomic and proteomic characterisation of CD4+ T cells at the single-cell level has been performed traditionally by two largely exclusive types of technologies: single cell RNA-sequencing (scRNA-seq) technologies and antibody-based cytometry. Here we demonstrate that the simultaneous targeted quantification of mRNA and protein expression in single-cells provides a high-resolution map of human primary CD4+ T cells, and identified precise trajectories of Th1, Th17 and regulatory T-cell (Treg) differentiation in blood and tissue. Furthermore, the sensitivity provided by this massively-parallel multi-omics approach revealed novel insight into the mechanism of expression of CD80 and CD86 on the surface of activated CD4+ Tregs and demonstrate their potential to identify recently activated T cells in circulation. This transcriptomic and proteomic hybrid technology provides a cost-effective solution to dissect the heterogeneity of immune cell populations, including more precise and detailed descriptions of the differentiation and activation of circulating and tissue-resident cells in response to therapies and in stratification of patients.

immunology↗

bioRxiv2019

Novel genetic loci affecting facial shape variation in humans

The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited despite the relevance for various fields of science and application. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching genome-wide significant association, among which 17 were previously unreported. A multi-ethnic study in additional 7,917 individuals confirmed 13 loci including 8 unreported ones. A global map of polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the face-associated genetic loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities of several face-associated DNA variants. These results substantially advance our understanding of the genetic basis underlying human facial variation and provide candidates for future in-vivo functional studies.

genomics↗

bioRxiv2019

Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge

Image features computed by specific convolutional artificial neural networks (ANNs) can be used to make state-of-the-art predictions of primate ventral stream responses to visual stimuli.\n\nHowever, in addition to selecting the specific ANN and layer that is used, the modeler makes other choices in preprocessing the stimulus image and generating brain predictions from ANN features. The effect of these choices on brain predictivity is currently underexplored.\n\nHere, we directly evaluated many of these choices by performing a grid search over network architectures, layers, image preprocessing strategies, feature pooling mechanisms, and the use of dimensionality reduction. Our goal was to identify model configurations that produce responses to visual stimuli that are most similar to the human neural representations, as measured by human fMRI and MEG responses. In total, we evaluated more than 140,338 model configurations. We found that specific configurations of CORnet-S best predicted fMRI responses in early visual cortex, and CORnet-R and SqueezeNet models best predicted fMRI responses in inferior temporal cortex. We found specific configurations of VGG-16 and CORnet-S models that best predicted the MEG responses.\n\nWe also observed that downsizing input images to ~50-75% of the input tensor size lead to better performing models compared to no downsizing (the default choice in most brain models for vision). Taken together, we present evidence that brain predictivity is sensitive not only to which ANN architecture and layer is used, but choices in image preprocessing and feature postprocessing, and these choices should be further explored.

neuroscience↗

bioRxiv2019

Transcription factor NF-κB in a basal metazoan, the sponge, has conserved and unique sequences, activities, and regulation

Biological and biochemical functions of immunity transcription factor NF-{kappa}B in basal metazoans are largely unknown. Herein, we characterize transcription factor NF-{kappa}B from the demosponge Amphimedon queenslandica (Aq), in the phylum Porifera. Structurally and phylogenetically, the Aq-NF-{kappa}B protein is most similar to NF-{kappa}B p100 and p105 among vertebrate proteins, with an N-terminal DNA-binding/dimerization domain, a C-terminal Ankyrin (ANK) repeat domain, and a DNA binding-site profile more similar to human NF-{kappa}B proteins than Rel proteins. Aq-NF-{kappa}B also resembles the mammalian NF-{kappa}B protein p100 in that C-terminal truncation results in translocation of Aq-NF-{kappa}B to the nucleus and increases its transcriptional activation activity. Overexpression of a human or sea anemone I{kappa}B kinase (IKK) can induce C-terminal processing of Aq-NF-{kappa}B in vivo, and this processing requires C-terminal serine residues in Aq-NF-{kappa}B. Unlike human NF-{kappa}B p100, however, the C-terminal sequences of Aq-NF-{kappa}B do not effectively inhibit its DNA-binding activity when expressed in human cells. Tissue of another demosponge, a black encrusting sponge, contains NF-{kappa}B site DNA-binding activity and an NF-{kappa}B protein that appears mostly processed and in the nucleus of cells. NF-{kappa}B DNA-binding activity and processing is increased by treatment of sponge tissue with LPS. By transcriptomic analysis of A. queenslandica we identified likely homologs to many upstream NF-{kappa}B pathway components. These results present a functional characterization of the most ancient metazoan NF-{kappa}B protein to date, and show that many characteristics of mammalian NF-{kappa}B are conserved in sponge NF-{kappa}B, but the mechanism by which NF-{kappa}B functions and is regulated in the sponge may be somewhat different.

cell biology↗

bioRxiv2019

Quantitation of vector uptake reveals non-Poissonian transfection dynamics in Plasmodium falciparum

The recurrent emergence of drug resistance in Plasmodium falciparum increases the urgency to genetically validate drug resistance mechanisms and identify new targets. Reverse genetics have facilitated genome-scale knockout screens in Plasmodium berghei and Toxoplasma gondii, in which pooled transfections of multiple vectors were critical to increasing scale and throughput. These approaches have not yet been implemented in P. falciparum, mainly because the extent to which pooled transfections can be performed in this species still remains unknown. Here we use next-generation sequencing to quantitate uptake of a pool of 94 barcoded vectors. The distribution of vectors in different transfections allowed us to estimate the number of barcodes and DNA molecules taken up by the parasite population. Dilution cloning showed that single transfected parasites routinely carry as many as seven episomal barcodes, revealing an intake of multiple vectors in a highly non-uniform fashion. Transfection of non-overlapping fluorescent proteins, which allowed us to follow the dynamics of the process, confirmed the tendency for parasites to take up multiple vectors from the early stages of transfection. This finding has important implications for how reverse genetics can be scaled in P. falciparum.

microbiology↗

bioRxiv2019

Genomic profiling of 553 uncharacterized neurodevelopment patients reveals a high proportion of recessive pathogenic variant carriers in an outbred population

BackgroundA substantial portion of Mendelian disease patients suffers from genetic variants that are inherited in a recessive manner. A precise understanding of pathogenic recessive variants in a population would assist in pre-screening births of such patients. However, a systematic understanding of the contribution of recessive variants to Mendelian diseases is still lacking.\n\nMethodsGenetic diagnosis and variant discovery of 553 undiagnosed Korean patients with complex neurodevelopmental problems (KND for Korean NeuroDevelopmental cohort) were performed using whole exome sequencing of patients and their parents. Pathogenic variants were selected and evaluated based on a comparison to patient symptoms and genetic properties of the variants were analyzed.\n\nResultsDisease-causing variants, including newly discovered variants, were identified in in 57.5% of the probands of the KND cohort. Of the 553 patients, 47.4% harbored variants that were previously reported as being pathogenic, and 35.1% of the previous reported pathogenic variants were inherited in a recessive manner. Genes that cause recessive disorders tend to be less constrained by loss-of-function variants and enriched in metabolic and mitochondrial pathways. This observation was applied to an estimation that approximately 1 in 17 healthy Korean individuals carry at least one of these pathogenic variants that develop severe neurodevelopmental problems in a recessive manner. Furthermore, the feasibility of these genes for carrier screening was evaluated.\n\nConclusionsWe suggest that the odds are high for healthy individuals carrying a potentially pathogenic variant, and its genetic properties. Our results will serve as a foundation for recessive variant screening to reduce occurrences of rare Mendelian disease patients. Additionally, our results highlight the utility and necessity of whole exome sequencing-based diagnostics for improving patient care in a country with a centralized medical system.

genetics↗

bioRxiv2019

Single-cell whole-genome sequencing reveals mutational landscapes of DNA mismatch repair deficiency in mouse primary fibroblasts

DNA Mismatch repair (MMR) deficiency is a major cause of hereditary non-polyposis colorectal cancer, and is also associated with increased risk of several other cancers. This is generally ascribed to the role of MMR in avoiding mutations by correcting DNA replication errors. In MMR knockout mice very high frequencies of somatic mutations, up until 100-fold of background, have been reported. However, these results have been obtained using bacterial reporter transgenes, which are not representative for the genome overall, and mutational patterns of MMR deficiency remain largely unknown. To fill this knowledge gap, we performed single-cell whole-genome sequencing of lung fibroblasts of Msh2-/- and wild-type mice. We observed a 4-fold increase of somatic single nucleotide variants (SNVs) in the fibroblasts of Msh2-/- mice compared to those of wild-type mice. The SNV signature of Msh2 deficiency was found to be driven by C>T and T>C transitions. By comparing it to human cancer signatures, we not only confirmed the inferred MMR-deficiency-related etiology of several cancer signatures but also suggested that MMR deficiency is likely the cause of a cancer signature with its etiology previously unknown. We also observed a 7-fold increase of somatic small insertions and deletions (INDELs) in the Msh2-/- mice. An elevated INDEL frequency has also been found in human MMR-related cancers. INDELs and SNVs distributed differently across genomic features in the Msh2-/- and control cells, with evidence of selection pressure and repair preference. These results provide insights into the landscape of somatic mutations in normal somatic cells caused by MMR deficiency.\n\nSignificanceOur results show that MMR deficiency in the mouse is associated with a much lower elevation of somatic mutation rates than previously reported and provides the first MMR whole-genome mutational landscapes in normal somatic cells in vivo.

genomics↗

bioRxiv2019

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

Predicting harvest timing is a key challenge to sustainably develop soft fruit farming and reduce food waste. Soft fruits are perishable, high-value and seasonal, and sales prices are typically time-sensitive. In addition, fruit harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. A novel approach for predicting soft fruit phenology and yields was developed and tested, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR). Fleeces were added to a subset of the plants to generate additional within-polytunnel variation. Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic growing conditions. After 10,000 iterations, machine learning revealed that RH was the optimal factor informing the coefficients of these curves, perhaps because it is an integrative metric of air temperature and water status. Trigonometric models transformed weather forecasts, which showed a relatively low agreement with polytunnel air temperature (R2 = 0.6) and RH (R2 = 0.5) measurements, into more accurate polytunnel-specific predictions for temperature and RH (both R2 = 0.8). We present a framework for using machine-learning techniques to calculate curve coefficients and parametrise coupled weather models which can predict fruit yields and timing to a greater degree of accuracy that previously possible. Dataloggers measuring environmental and yield data could infer model parameters using iterative training for novel fruit varieties or crop types growing in different locations without a-priori phenological information. At this stage in the development of artificial intelligence and networked microsensors, this is a step forward in generating bespoke phenological prediction models to inform and support growers.

plant biology↗

bioRxiv2019

Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of chimeric antigen receptor T cells

We propose and experimentally validate a label-free, volumetric, and automated assessment method of immunological synapse dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed approach enables automatic and quantitative spatiotemporal analyses of immunological synapse kinetics regarding morphological and biochemical parameters related to the total protein densities of immune cells, thus providing a new perspective for studies in immunology.

immunology↗