Biology subjects

Han, E.

Publications and source records attributed to Han, E..

27 records · Page 2Linked to original sources

bioRxiv2023

Data-driven optimization yielded a highly-efficient CRISPR/Cas9 system for gene editing in Arabidopsis

The CRISPR/Cas9 technology is widely used for plant gene editing. In Arabidopsis, the early CRISPR/Cas9 systems using constitutive promoters to drive Cas9 expression usually have low heritable mutation efficiencies in the T1 generation. Germ cell-specific or cell division-specific promoters, like EC1.2en-EC1.1p fusion, YAO and CDC45, have been used as alternatives to direct Cas9 expression, but the efficiency of getting nonchimeric mutations is still not high. To further improve gene editing efficiency, we used gene co-expression network analysis to identify NUC1 as a candidate promoter for driving Cas9 expression. NUC1 has expression patterns similar to YAO, but expresses at a much higher level. We constructed a CRISPR/Cas9 system pHY07 that uses the NUC1 promoter to drive Cas9 and carries the mCherry protein as a fluorescent marker for selecting transgenic seeds. Using this system to edit the GL2 gene, we obtained apparently nonchimeric mutations in 55% of the T1 transgenic plants. Among the Cas9-free T2 plants 99% harbored mutations that are stably inherited from the previous generation. Therefore, our system exhibited extremely high editing efficiency, and through fluorescent screening of transgenic seeds, it become easy to obtain Cas9-free and stable genetic mutants in the T2 generation.

plant biology↗

bioRxiv2022

R-BIND 2.0: An Updated Database of Bioactive RNA-Targeting Small Molecules and Associated RNA Secondary Structures

Discoveries of RNA roles in cellular physiology and pathology are raising the need for new tools that modulate the structure and function of these biomolecules, and small molecules are proving useful. In 2017, we curated the RNA-targeted BIoactive ligaNd Database (R-BIND) and discovered distinguishing physicochemical properties of RNA-targeting ligands, leading us to propose the existence of an "RNA-privileged" chemical space. Biennial updates of the database and the establishment of a website platform (rbind.chem.duke.edu) have provided new insights and tools to design small molecules based on the analyzed physicochemical and spatial properties. In this report and R-BIND 2.0 update, we refined the curation approach and ligand classification system as well as conducted analyses of RNA structure elements for the first time to identify new targeting strategies. Specifically, we curated and analyzed RNA target structural motifs to determine properties of small molecules that may confer selectivity for distinct RNA secondary and tertiary structures. Additionally, we collected sequences of target structures and incorporated an RNA Structure Search algorithm into the website that outputs small molecules targeting similar motifs without a priori secondary structure knowledge. Cheminformatic analyses revealed that, despite the 50% increase in small molecule library size, the distinguishing properties of R-BIND ligands remained significantly different to that of proteins and are therefore still relevant to RNA-targeted probe discovery. Combined, we expect these novel insights and website features to enable rational design of RNA-targeted ligands and to serve as a resource and inspiration for a variety of scientists interested in RNA targeting.

biochemistry↗

bioRxiv2022

Exploitation of neighbouring subsoil for nutrient acquisition under annual-perennial strip intercropping systems

Little is known of how the deep root systems of perennial crops contribute to deeper and better resource use when intercropped with annuals in arable fields. Therefore, we aimed at measuring the capacity of perennial deep roots, alfalfa (Medicago sativa L.) and curly dock (Rumex crispus L.) to access the nutrient source located under the neighboring annuals at 1.0 and 2.5 m of soil depth. Alfalfa and curly dock were able to access the tracer-labelled source placed at a distance under the annual crop strips. As a result, the reliance on deeper soil layer for nutrient uptake under intercroppings became greater compared with sole-croppings. Combination of an annual cereal (winter rye) and a perennial legume (alfalfa) with contrasting root systems exhibited higher resource complementarity compared with intercroppings having similar root systems or absence of legumes. Our results demonstrated that the deep-rooted perennials when intercropped with annuals can induce vertical niche complementarity, especially at deeper soil layers. This was assumed to be due to the vertically stratified root activity between the crop components, however, the magnitude of the effects depended on choice of crop combinations, and on types of tracers. Future studies should include estimates such as relative yield total and land equivalent ratio to quantitatively determine the effects of resource acquisition under annual-perennial intercropping in arable fields.

plant biology↗

bioRxiv2021

Pseudomonas aeruginosa differentiates substrate rigidity using retraction of type IV pili

The ability of eukaryotic cells to differentiate substrate stiffness is fundamental for many processes such as the development of stem cells into mature tissue. Here, we establish that bacteria feel their microenvironment in a similar manner. We show that Pseudomonas aeruginosa actively probes and measures substrate stiffness using type IV pili (TFP). The activity of the major virulence factor regulator Vfr is peaked with stiffness in a physiologically important range between 0.1 kPa (mucus) and 1000 kPa (cartilage). The local concentration of PilA at the base of dynamic TFP changes during extension and retraction in a surface dependent manner due to slow PilA diffusion in the cell membrane. Traction force measurements reveal that TFP retraction deforms even stiff substrates. Modeling of the measured substrate deformation and optical tweezers experiments suggest that TFP adhere at the tip only. Informed by these experimental results, we developed a model that describes substrate stiffness dependent dynamics of the polar PilA concentration which are quantitatively consistent with the transcriptional response to stiffness. Manipulating the ATPase activity of the TFP motors changes the TFP extension and retraction velocities and consequently the PilA concentration dynamics in a manner that is predictive of the experimental stiffness response. This work points to the use of a competition between PilA diffusion and TFP extension-retraction as a molecular shear rheometer. Our results highlight that stiffness sensing is a conserved property between the kingdoms of life.

microbiology↗

bioRxiv2020

Digging roots is easier with AI

The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

plant biology↗

bioRxiv2020

Astroglial Calcium Signaling Encodes Sleep Need in Drosophila

Sleep is under homeostatic control, whereby increasing wakefulness generates sleep need and triggers sleep drive. However, the molecular and cellular pathways by which sleep need is encoded are poorly understood. In addition, the mechanisms underlying both how and when sleep need is transformed to sleep drive are unknown. Here, using ex vivo and in vivo imaging, we show in Drosophila that astroglial Ca2+ signaling increases with sleep need. We demonstrate that this signaling is dependent on a specific L-type Ca2+ channel and is required for homeostatic sleep rebound. Thermogenetically increasing Ca2+ in astrocytes induces persistent sleep behavior, and we exploit this phenotype to conduct a genetic screen for genes required for the homeostatic regulation of sleep. From this large-scale screen, we identify TyrRII, a monoaminergic receptor required in astrocytes for sleep homeostasis. TyrRII levels rise following sleep deprivation in a Ca2+-dependent manner, promoting further increases in astrocytic Ca2+ and resulting in a positive-feedback loop. These data suggest that TyrRII acts as a gate to enable the transformation of sleep need to sleep drive at the appropriate time. Moreover, our findings suggest that astrocytes then transmit this sleep need to the R5 sleep drive circuit, by upregulation and release of the interleukin-1 analog Spatzle. These findings define astroglial Ca2+ signaling mechanisms encoding sleep need and reveal dynamic properties of the sleep homeostatic control system.

neuroscience↗

bioRxiv2020

RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semi-automatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.

plant biology↗

bioRxiv2019

Gene modules associated with human diseases revealed by network analysis

Despite many genes associated with human diseases have been identified, disease mechanisms often remain elusive due to the lack of understanding how disease genes are connected functionally at pathways level. Within biological networks, disease genes likely map to modules whose identification facilitates etiology studies but remains challenging. We describe a systematic approach to identify disease-associated gene modules. A gene co-expression network based on the graphical Gaussian model (GGM) was constructed using the GTEx dataset and assembled into 652 gene modules. Screening these modules identified those with disease genes enrichment for obesity, cardiomyopathy, hypertension, and autism, which illuminated the molecular pathways underlying their pathogenesis. Using mammalian phenotypes derived from mouse models, potential disease candidate genes were identified from these modules. Also analyzed were epilepsy, schizophrenia, bipolar disorder, and depressive disorder, revealing shared and distinct disease modules among brain disorders. Thus, disease genes converge on modules within our GGM gene co-expression network, which provides a general framework to dissect genetic architecture of human diseases.

systems biology↗

bioRxiv2018

Ex vivo dynamics of human glioblastoma cells in a microvasculature-on-a-chip system correlates with tumor heterogeneity and subtypes

The perivascular niche (PVN) plays an essential role in brain tumor stem-like cell (BTSC) fate control, tumor invasion, and therapeutic resistance. Herein we report on the use of a microvasculature-on-a-chip system as a PVN model to evaluate the dynamics of BTSCs ex vivo from 10 glioblastoma patients. We observed that BTSCs preferentially localize in the perivascular zone. Live cell tracking revealed that the cells residing in the vicinity of microvessels had the lowest motility, while a fraction of cells on the microvessels unexpectedly possessed the highest motility and migrated over the longest distance. These results indicate that the perivascular zone is a niche for BTSCs, while the microvascular tracks are also a path for long-distance tumor cell migration and invasion. Additionally, the degree of co-localization between tumor cells and microvessels varied significantly across patients. To validate the results from our microvasculature-on-a-chip system, we used single-cell transcriptome sequencing (10 patients and 21,750 single cells in total) to identify the subtype of each tumor cell. The co-localization coefficient was found to correlate positively with proneural (stem-like) or mesenchymal (invasive) but not classical (proliferative) tumor cells. Furthermore, we found that a gene signature profile including PDGFRA correlated strongly with the \"homing\" of brain tumor cells to the PVN. Our findings demonstrated that ex vivo dynamics of human brain tumor cells in a microvasculature-on-a-chip model can recapitulate in vivo tumor cell dynamics, heterogeneity, and subtypes, representing a new route to the study of human tumor cell biology and uncover patient-specific tumor cell functions.

bioengineering↗