Iksoo Chang

Locomotion is a fundamental behavior of Caenorhabditis elegans (C. elegans). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans. Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans, and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.

X-linked inhibitor of apoptosis protein (XIAP) deficiency causes refractory inflammatory bowel disease. The XIAP protein plays a pivotal role in the pro-inflammatory response through the nucleotide-binding oligomerization domain-containing signaling pathway that is important in mucosal homeostasis. We analyzed the molecular mechanism of non-synonymous pathogenic variants (PVs) of XIAP BIR2 domain. We generated N-terminally green fluorescent protein-tagged XIAP constructs of representative non-synonymous PVs. Co-immunoprecipitation and fluorescence cross-correlation spectroscopy showed that wild-type XIAP and RIP2 preferentially interacted in live cells, whereas all non-synonymous PV XIAPs failed to interact properly with RIP2. Structural analysis showed that various structural changes by mutations, such as hydrophobic core collapse, Zn-finger loss, and spatial rearrangement, destabilized the two loop structures (174–182 and 205–215) that critically interact with RIP2. Subsequently, it caused a failure of RIP2 ubiquitination and loss of protein deficiency by the auto-ubiquitination of all XIAP mutants. These findings could enhance our understanding of the role of XIAP mutations in XIAP-deficient inflammatory bowel disease and may benefit future therapeutic strategies.

Locomotion is a fundamental behavior of Caenorhabditis elegans ( C. elegans ). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans . Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans , and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.

Background: Alzheimer’s disease affects profoundly the quality of human behavior and cognition. The very broad distribution of its severity across various human subjects requires the quantitative diagnose of Alzheimer’s disease beyond the conventional tripartite classification of cohorts such as cognitively normal (CN), mild cognitive impairment (MCI), Alzheimer’s disease (AD). The unfolding of such broad distributions by the quantitative and continuous degree of AD severity is necessary for the precise diagnose in the cross-sectional study of different stages in AD. Results: We conducted the massive reanalysis on MRI images of 761 human brains based on the accumulated bigdata of Alzheimer’s Disease Neuroimaging Initiative. The score matrix of cortical thickness profile at cortex points of subjects was constructed by statistically learning the cortical thickness data of 761 human brains. We also developed a new and simple algebraic predictor which provides the quantitative and continuous degree of AD severity of subjects along the scale from 0 for fully CN to 1 for fully AD state. The mathematical measure of a new predictor for the degree of AD severity is presented based on a covariance correlation matrix of cortical thickness profile between human subjects. One can remove the uncertainty in the determination of different stages in AD by the quantitative degree of AD severity and thus go far beyond the tripartite classification of cohorts. Conclusions: We unfold the nature of broad distribution of AD severity of subjects even within a given cohort by the scale from 0 for fully CN to 1 for fully AD state. The quantitative and continuous degree of AD severity developed in this study would be a good practical measure for diagnosing the different stages in AD severity.

Background: Alzheimer disease(AD) affects profoundly the quality of human life. Quantifying the severity degree of AD for an individual person is critical for the early diagnose and prescription for delaying the further dementia progression. However, the quantitative diagnose for human subjects of the mild cognitively impairment (MCI) or AD with the different degree of dementia severity is still a difficult task due to both the very broad distribution of dementia severities and the lack of good quantitative determinant to assess it.Methods: We performed the bigdata analysis of cortical thickness of 1516 human brain images. Instead of dealing with cortical thickness at all 327,684 vertices on the whole cortex of a human brain from MRI, we extracted the essential cortical thickness data at a few hundred vertices to distinguish efficiently AD, MCI, and cognitively normal (CN) cohorts each other. We learned a statistical score matrix and a covariance correlation matrix of cortical thickness profile between human subjects as a set of classifier and predictor for diagnosing the dementia states of AD.Results: The subjects with AD were recognized with more than 91% accuracy in self-recognition test, and the independent validation subjects with AD were predicted correctly with more than 82% accuracy in stratified 3-fold cross validation test. Given a new person for diagnosing and provided with the covariance correlation matrix developed in this study as a predictor, more importantly one could estimate the personalized severity degree of dementia in terms of a quantitative value, ranging from 0 for the basin of CN state to 1 for the basin of AD state. Also, one could sort out the broad spectrum of the severity of dementia for MCI subjects in that whether they are prone to CN or how much they are progressed toward AD.Conclusions: Here, a new approach of algebraic bigdata analysis is presented for the quantitative assessment on both the severity degree of AD and the systematic classification of three cohorts based on cortical thickness data of 1516 human brain images from MRI. This new approach can facilitate the better diagnose of AD with the different degree of dementia.

Alzheimer disease(AD) affects profoundly the quality of human life. Quantifying the severity degree of Alzheimer dementia for an individual person is critical for the early diagnose and prescription for delaying the further dementia progression. However, the quantitative diagnose for human subjects of the mild cognitively impairment(MCI) or AD with the different degree of dementia severity is a difficult task due to both the very broad distribution of dementia severities and the lack of good quantitative determinant to assess it. Here, based on cortical thickness profiles of human brains measured by magnetic resonance experiment, a new algebraic approach is presented for the personalized quantification of the severity degree of AD, ranging from 0 for the basin of cognitively normal state to 1 for AD state. Now one can unfold the broad distribution of dementia severities and corresponding cortex regions of human brains with MCI or AD by the different severity degree.

A conformational change from normal prion protein(PrP^C) to abnormal prion protein(PrP^SC) induces fatal neurodegenerative diseases. Acidic pH is well-known factors involved in the conformational change. Because the protonation of H187 is strongly linked to the change in PrP stability, we examined the charged residues R156, E196, and D202 around H187. Interestingly, there have been reports on pathological mutants, such as H187R, E196A, and D202N. In this study, we focused on how an acidic pH and pathological mutants disrupt this electrostatic network and how this broken network destabilizes PrP structure. To do so, we performed a temperature-based replica-exchange molecular dynamics (T-REMD) simulation using a cumulative 252 μs simulation time. We measured the distance between amino acids comprising four salt bridges (R156–E196/D202 and H187–E196/D202). Our results showed that the spatial configuration of the electrostatic network was significantly altered by an acidic pH and mutations. The structural alteration in the electrostatic network increased the RMSF value around the first helix (H1). Thus, the structural stability of H1, which is anchored to the H2–H3 bundle, was decreased. It induces separation of R156 from the electrostatic network. Analysis of the anchoring energy also shows that two salt-bridges (R156-E196/D202) are critical for PrP stability.

Understanding the gating mechanism of ion channel proteins is key to understanding the regulation of cell signaling through these channels. Channel opening and closing are regulated by diverse environmental factors that include temperature, electrical voltage across the channel, and proton concentration. Low permeability in voltage-gated potassium ion channels (Kv) is intimately correlated with the prolonged action potential duration observed in many acidosis diseases. The Kv channels consist of voltage-sensing domains (S1–S4 helices) and central pore domains (S5–S6 helices) that include a selectivity filter and water-filled cavity. The voltage-sensing domain is responsible for the voltage-gating of Kv channels. While the low permeability of Kv channels to potassium ion is highly correlated with the cellular proton concentration, it is unclear how an intracellular acidic condition drives their closure, which may indicate an additional pH-dependent gating mechanism of the Kv family. Here, we show that two residues E327 and H418 in the proximity of the water cavity of Kv1.2 play crucial roles as a pH switch. In addition, we present a structural and molecular concept of the pH-dependent gating of Kv1.2 in atomic detail, showing that the protonation of E327 and H418 disrupts the electrostatic balance around the S6 helices, which leads to a straightening transition in the shape of their axes and causes dewetting of the water-filled cavity and closure of the channel. Our work offers a conceptual advancement to the regulation of the pH-dependent gating of various voltage-gated ion channels and their related biological functions. Author Summary: The acid sensing ion channels are a biological machinery for maintaining the cell functional under the acidic or basic cellular environment. Understanding the pH-dependent gating mechanism of such channels provides the structural insight to design the molecular strategy in regulating the acidosis. Here, we studied the voltage-gated potassium ion channel Kv1.2 which senses not only the electrical voltage across the channels but also the cellular acidity. We uncovered that two key residues E327 and H418 in the pore domain of Kv1.2 channel play a role as pH-switch in that their protonation control the gating of the pore in Kv1.2 channel. It offered a molecular insight how the acidity reduces the ion permeability in voltage-gated potassium channels.