Biology subjects

Kim, E.

Publications and source records attributed to Kim, E..

187 records · Page 11Linked to original sources

bioRxiv2018

Lrfn2-mutant mice display suppressed synaptic plasticity and inhibitory synapse development and abnormal social communication and startle response

SALM1, also known as LRFN2, is a PSD-95-interacting synaptic adhesion molecule implicated in the regulation of NMDA receptor (NMDAR) clustering largely based on in vitro data, although its in vivo functions remain unclear. Here, we found that mice lacking SALM1/LRFN2 (Lrfn2-/- mice) show a normal density of excitatory synapses but altered excitatory synaptic function, including enhanced NMDAR-dependent synaptic transmission but suppressed NMDAR-dependent synaptic plasticity in the hippocampal CA1 region. Unexpectedly, SALM1 expression is detected in both glutamatergic and GABAergic neurons, and Lrfn2-/- CA1 pyramidal neurons show decreases in the density of inhibitory synapses and frequency of spontaneous inhibitory synaptic transmission. Behaviorally, ultrasonic vocalization was suppressed in Lrfn2-/- pups separated from their mothers, and acoustic startle was enhanced, but locomotion, anxiety-like behavior, social interaction, repetitive behaviors, and learning and memory were largely normal in adult Lrfn2-/- mice. These results suggest that SALM1/LRFN2 regulates excitatory synapse function, inhibitory synapse development, and social communication and startle behaviors in mice.\n\nSignificance StatementSynaptic adhesion molecules regulate synapse development and function, which govern neural circuit and brain functions. The SALM/LRFN family of synaptic adhesion proteins consists of five known members whose in vivo functions are largely unknown. Here we characterized mice lacking SALM1/LRFN2 (SALM1 knockout) known to associate with NMDA receptors and found that these mice showed altered NMDA receptor-dependent synaptic transmission and plasticity, as expected, but unexpectedly also exhibited suppressed inhibitory synapse development and synaptic transmission. Behaviorally, SALM1 knockout pups showed suppressed ultrasonic vocalization upon separation from their mothers, and SALM1 knockout adults showed enhanced responses to loud acoustic stimuli. These results suggest that SALM1/LRFN2 regulates excitatory synapse function, inhibitory synapse development, social communication, and acoustic startle behavior.

neuroscience↗

bioRxiv2018

Galleria mellonella as an Insect Model for P. destructans, the Cause of White-Nose Syndrome in Bats

Pseudogymnoascus destructans is the fungal pathogen responsible for White-nose Syndrome (WNS), a disease that has killed millions of bats in North America over the last decade. A major obstacle to research on P. destructans has been the lack of a tractable infection model for monitoring virulence. Here, we establish a high-throughput model of infection using larvae of Galleria mellonella, an invertebrate used to study host-pathogen interactions for a wide range of microbial species. We demonstrate that P. destructans can kill G. mellonella larvae in an inoculum-dependent manner when infected larvae are housed at 13{degrees}C or 18{degrees}C. Larval killing is an active process, as heat-killed P. destructans spores caused significantly decreased levels of larval death compared to live spores. We also show that fungal spores that were germinated prior to inoculation were able to kill larvae 3-4 times faster than non-germinated spores. Lastly, we identified chemical inhibitors of P. destructans and used G. mellonella to evaluate these inhibitors for their ability to reduce virulence. We demonstrate that two chemicals, trifluoperazine and amphotericin B, can effectively block larval killing by P. destructans and thereby establish that this infection model can be used to screen biocontrol agents against this fungal pathogen.

microbiology↗

bioRxiv2017

Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata

De novo resistance is a major issue for the use of targeted anticancer drugs in the clinic. By integrating experimental data we have created a hybrid neural network/agent-based model to simulate the evolution and spread of resistance to the drug Pramlintide in cutaneous squamous cell carcinoma. Our model can eventually be used to predict patient responses to the drug and thus enable clinicians to make decisions regarding personalized, precision treatment regimes for patients.

cancer biology↗

bioRxiv2017

Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning

Cell protrusion plays important roles in cell migration by pushing plasma membrane forward. Cryptic lamellipodia induce the protrusion of submarginal cells in collective cell migration where cells are attached and move together. Although computational image analysis of cell protrusion has been done extensively, the study on protrusion activities of cryptic lamellipodia is limited due to difficulties in image segmentation. This study seeks to aid in the computational analysis of submarginal cell protrusion in collective cell migration by using deep learning to detect the cryptic lamellipodial edges from fluorescence time-lapse movies. Due to the noisy features within overlapping cells, the conventional image analysis algorithms such as Canny edge detector and intensity thresholding are limited. By combining Canny edge detector, Convolutional Neural Networks (CNNs), and local intensity thresholding, we were able to detect cryptic lamellipodial edges of submarginal cells with high accuracy from the fluorescence time-lapse movies of PtK1 cells stained with a plasma membrane marker. We used relatively small effort to prepare the training set to train the CNN to detect the cryptical lamellipodial edges in fluorescence time-lapse movies. This work demonstrates that deep learning can be combined with the conventional image analysis algorithms to facilitate the computational analysis of highly complex time-lapse movies of collective cell migration.

bioinformatics↗

bioRxiv2017

Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

cancer biology↗

bioRxiv2016

Genetic and transgenic reagents for Drosophila simulans, D.mauritiana, D. yakuba, D. santomea and D. virilis

Species of the Drosophila melanogaster species subgroup, including the species D. simulans, D. mauritiana, D. yakuba, and D. santomea, have long served as model systems for studying evolution. Studies in these species have been limited, however, by a paucity of genetic and transgenic reagents. Here we describe a collection of transgenic and genetic strains generated to facilitate genetic studies within and between these species. We have generated many strains of each species containing mapped piggyBac transposons including an enhanced yellow fluorescent protein gene expressed in the eyes and a phiC31 attP site-specific integration site. We have tested a subset of these lines for integration efficiency and reporter gene expression levels. We have also generated a smaller collection of other lines expressing other genetically encoded fluorescent molecules in the eyes and a number of other transgenic reagents that will be useful for functional studies in these species. In addition, we have mapped the insertion locations of 58 transposable elements in D. virilis that will be useful for genetic mapping studies.

genetics↗

bioRxiv2016

Harnessing the lymphocyte meta-phenotype to optimize adoptive cell therapy

There is an urgent need for reliable effective therapy for patients with metastatic sarcoma. Approaches that manipulate the immune system have shown promise for patients with advanced, widely disseminated malignancies. One of these approaches is adoptive cell therapy (ACT), where tumor-infiltrating lymphocytes (TIL) are isolated from the tumor, expanded ex vivo, and then transferred back to the patient. This approach has shown great promise in melanoma, leading to an objective response in approximately half of treated patients [14]. Standard protocols involve characterization of TIL populations with respect to adaptive CD4+ and CD8+ T-lymphocytes, but neglect the possible role of the innate lymphoid repertoire. Due to toxicity and the high cost associated with ACT, the IFN-{gamma} release assay is currently used as a proxy to identify suitable TIL isolates for ACT. Efforts in TIL-ACT for sarcoma, which are pre-clinical and pioneered at Moffitt Cancer Center, have shown that only a minority of the TIL cultures show tumor specific activity in ex vivo IFN-{gamma} assays. Surprisingly, internal melanoma trial data reveal a lack of correlation between IFN-{gamma} assay and clinical outcomes, highlighting the need for a more reliable proxy. We hypothesize the existence of a predictable TIL meta-phenotype that leads to optimal tumor response. Here, we describe preliminary efforts to integrate prospective and existing patient data with mathematical models to optimize the TIL meta-phenotype prior to re-injection.

cancer biology↗