Biology subjects

Hwang, W.

Publications and source records attributed to Hwang, W..

4 recordsLinked to original sources

bioRxiv2018

Whole-Genome Genomics Correlates of Response To Anti-PD1 Therapy in Relapsed/Refractory Natural Killer/T Cell Lymphoma

AbstractThis study aims to identify recurrent genetic alterations in relapsed or refractory (RR) natural-killer/T-cell lymphoma (NKTL) patients who have achieved complete response (CR) with programmed cell death 1 (PD-1) blockade therapy. Seven of the eleven patients treated with pembrolizumab achieved CR while the remaining four had progressive disease (PD). Using whole genome sequencing (WGS), we found recurrent clonal structural rearrangements (SR) of the PD-L1 gene in four of the seven (57%) CR patients pretreated tumors. These PD-L1 SRs consist of inter-chromosomal translocations, tandem duplication and micro-inversion that disrupted the suppressive function of PD-L1 3UTR. Interestingly, recurrent JAK3-activating (p.A573V) mutations were also validated in two CR patients tumors that did not harbor the PD-L1 SR. Importantly, these mutations were absent in the four PD cases. With immunohistochemistry (IHC), PD-L1 positivity could not discriminate patients who archived CR (range: 6%-100%) from patients who had PD (range: 35%-90%). PD-1 blockade with pembrolizumab is a potent strategy for RR NKTL patients and genomic screening could potentially accompany PD-L1 IHC positivity to better select patients for anti-PD-1 therapy.

genomics↗

bioRxiv2017

Energetic costs, precision, and efficiency of a biological motor in cargo transport

Molecular motors play key roles in organizing the interior of cells. An efficient motor in cargo transport would travel with a high speed and a minimal error in transport time (or distance) while consuming minimal amount of energy. The travel distance and its variance of motor are, however, physically constrained by energy consumption, the principle of which has recently been formulated into the thermodynamic uncertainty relation. Here, we reinterpret the uncertainty measure ([Q]) defined in the thermodynamic uncertainty relation such that a motor efficient in cargo transport is characterized with a small [Q]. Analyses on the motility data from several types of molecular motors show that [Q] is a nonmonotic function of ATP concentration and load (f). For kinesin-1, [Q] is locally minimized at [ATP] {approx} 200 M and f {approx} 4 pN. Remarkably, for the mutant with a longer neck-linker this local minimum vanishes, and the energetic cost to achieve the same precision as the wild-type increases significantly, which underscores the importance of molecular structure in transport properties. For the biological motors studied here, their value of [Q] is semi-optimized under the cellular condition ([ATP] {approx} 1 mM, f = 0 - 1 pN). We find that among the motors, kinesin-1 at single molecule level is the most efficient in cargo transport.

biophysics↗

bioRxiv2016

Quantifying the Heat Dissipation from Molecular Motor’s Transport Properties in Nonequilibrium Steady States

Theoretical analysis, which maps single molecule time trajectories of a molecular motor onto unicyclic Markov processes, allows us to evaluate the heat dissipated from the motor and to elucidate its dependence on the mean velocity and diffusivity. Unlike passive Brownian particles in equilibrium, the velocity and diffusion constant of molecular motors are closely inter-related to each other. In particular, our study makes it clear that the increase of diffusivity with the heat production is a natural outcome of active particles, which is reminiscent of the recent experimental premise that the diffusion of an exothermic enzyme is enhanced by the heat released from its own catalytic turnover. Compared with freely diffusing exothermic enzymes, kinesin-1 whose dynamics is confined on one-dimensional tracks is highly efficient in transforming conformational fluctuations into a locally directed motion, thus displaying a significantly higher enhancement in diffusivity with its turnover rate. Putting molecular motors and freely diffusing enzymes on an equal footing, our study offers thermodynamic basis to understand the heat enhanced self-diffusion of exothermic enzymes.

biophysics↗

bioRxiv2016

Decoding Single Molecule Time Traces with Dynamic Disorder

Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm - Variational Bayes-double chain Markov model (VB-DCMM) - to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+ solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.\n\nAuthor SummaryWe have developed a new algorithm to better decode single molecule data with dynamic disorder. Our new algorithm, which represents a substantial improvement over other methodologies, can detect the presence of dynamic disorder in each trajectory and quantify the kinetic characteristics of underlying energy landscape. As a model system, we applied our algorithm to the single molecule FRET time traces of H-DNA. While duplex-triplex transitions of H-DNA are conventionally interpreted in terms of two-state kinetics, slowly varying dynamic patterns corresponding to hidden internal states can also be identified from the individual time traces. Our algorithm reveals that at least 4 distinct internal states are required to correctly interpret the data.

biophysics↗