Biology subjects

Kim, B. G.

Publications and source records attributed to Kim, B. G..

2 recordsLinked to original sources

bioRxiv2021

Oncomodulin derived from regeneration-associated macrophages in dorsal root ganglia promotes axon regeneration in the spinal cord

Preconditioning nerve injury drives pro-regenerative perineuronal macrophage activation in dorsal root ganglia (DRG). The present study reports that oncomodulin (ONCM) is produced from the regeneration-associated macrophages (RAMs) and strongly influences regeneration of DRG sensory axons. Preconditioning injury upregulated ONCM in DRG macrophages in a CCR2 dependent manner. ONCM in macrophages was necessary to produce RAMs in the in vitro model of neuron-macrophage interaction and played an essential role in for preconditioning or CCL2-induced neurite outgrowth. ONCM potently increased neurite outgrowth in cultured DRG neurons by activating a distinct gene set, particularly neuropeptide-related genes. Increasing extracellularly secreted ONCM in DRGs sufficiently enhanced capacity of neurite outgrowth. To achieve sustained ONCM activity in vivo, recombinant ONCM was encapsulated by a reducible epsilon-poly(L-lysine)-nanogel (REPL-NG) system based on electrostatic interaction. Localized injection of REPL-NG/ONCM complex into DRGs achieved a remarkable long-range axonal regeneration beyond spinal cord lesion, surpassing the extent of the preconditioning effects.

neuroscience↗

bioRxiv2021

Analysis of B-cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

Analyzing B cell receptor (BCR) repertoires is immensely useful in evaluating ones immunological status. Conventionally, repertoire analysis methods have focused on comprehensive assessments of clonal compositions, including V(D)J segment usage, nucleotide insertions/deletions, and amino acid distributions. Here, we introduce a novel computational approach that applies deep-learning-based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that this new approach enables us to not only accurately cluster BCR repertoires of coronavirus disease 2019 (COVID-19) patients and healthy subjects but also efficiently track minute changes in immune status over time as patients undergo treatment. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved a mean accuracy rate of over 87% given a repertoire of CDR3 sequences.

bioinformatics↗