Three-Month Excessive Body Weight Loss < 37.7% as a Predictor of Mid-term Suboptimal Outcomes Postlaparoscopic Sleeve Gastrectomy: Risk Factors and the Impact of Neutrophil-to-Lymphocyte Ratio on Adipocyte Function.

INTRODUCTION: This study aimed to evaluate the impact of achieving < 37.7% excess body-weight loss (EBWL) within 3 months of postlaparoscopic sleeve gastrectomy (LSG) on clinical outcomes and its correlation with adipocyte function. METHODS: Patients (n = 176) who underwent LSG between January 2019 and January 2023 were included. Weight loss and status of health markers were monitored postoperatively. The cohort was stratified based on EBWL < 37.7% at 3 months or not. Variables including neutrophil-to-lymphocyte ratio (NLR), insulin resistance, and comorbidities were analyzed. Omental visceral and subcutaneous adipose tissue samples were used to analyze the differences in adipocyte function by western blot. RESULTS: Patients with EBWL < 37.7% at 3 months post-LSG (suboptimal group) comprised less likelihood of achieving ≥ 50% EBWL than those who achieved ≥ 37.7% EBWL (optimal group) at 6 months (42.55% vs. 95.52% in optimal group, p < 0.001), 12 months (85.11% vs. 99.25% in optimal group, p < 0.001) and 24 months (77.14% vs. 94.74% in optimal group, p = 0.009) post-LSG. High BMI (OR = 1.222, 95% CI 1.138-1.312, p < 0.001), NLR ≥ 2.36 (OR = 2.915, 95% CI 1.257-6.670, p = 0.013), and female sex (OR = 3.243, 95% CI 1.306-8.051, p = 0.011) significantly predicted EBWL < 37.7% at 3 months post-LSG. Patients with NLR ≥ 2.36 had significantly lower adipose triglyceride lipase in omental fat (p = 0.025). CONCLUSION: EBWL < 37.7% at 3 months post-LSG is a strong predictor of subsequent suboptimal weight loss. High BMI, NLR ≥ 2.36, and female sex are risk factors in predicting EBWL < 37.7% at 3 months post-LSG. These findings may offer a reference to apply adjuvant weight loss medications to patients who are predisposed to suboptimal outcomes.

Impact of Recurrent Weight Gain Thresholds on Comorbid Conditions Progression Following Laparoscopic Sleeve Gastrectomy.

INTRODUCTION: Defining recurrent weight gain after metabolic bariatric surgery poses a significant challenge. Our study aimed to standardize recurrent weight gain measurements in patients undergoing laparoscopic sleeve gastrectomy (LSG) and ascertain its association with comorbidity progression. METHODS: We conducted a retrospective data analysis on 122 patients who underwent LSG, tracking their progress over 2-7 years. Data on weight, blood pressure measurements, and laboratory tests were collected, focusing on the postoperative period to identify nadir weight, total weight loss, and recurrent weight gain. RESULTS: Significant weight loss and comorbidity remission were noted, with diabetes, hypertension, and dyslipidemia showing substantial remission rates of 85.71%, 68.24%, and 85.37%, respectively. The median recurrent weight gain was 6.30 kg within 12 months of the nadir. Progression proportion of diabetes, hypertension, and dyslipidemia were 8.20%, 44.26%, and 40.98%, respectively. Hypertension progression was strongly associated with a recurrent weight gain ≥ 10 kg and ≥ 20% of maximum weight loss. Dyslipidemia progression was significantly correlated with recurrent weight gain ≥ 10 kg and ≥ 20% of maximum weight loss. Diabetes progression was significantly correlated with recurrent weight gain ≥ 10% of pre-surgery body weight and ≥ 25% of maximum weight loss. A ≥ 10% weight gain of maximum weight loss did not significantly impact the progression of these conditions. CONCLUSION: Recurrent weight gain ≥ 20% of maximum weight loss can be treated as a specific threshold indicating comorbidity progression post-LSG. Standardizing the measurement of recurrent weight gain can help healthcare providers to implement targeted management strategies to optimize long-term health outcomes.

Tuning single-molecule ClyA nanopore tweezers for real-time tracking of the conformational dynamics of West Nile viral NS2B/NS3 protease.

The flaviviral NS2B/NS3 protease is a conserved enzyme required for flavivirus replication. Its highly dynamic conformation poses major challenges but also offers opportunities for antiviral inhibition. Here, we established a nanopore tweezers-based platform to monitor NS2B/NS3 conformational dynamics in real-time. Molecular simulations coupled with electrophysiology revealed that the protease could be captured in the middle of the ClyA nanopore lumen, stabilized mainly by dynamic electrostatic interactions. We designed a new Salmonella typhi ClyA nanopore with enhanced nanopore/protease interaction that can resolve the open and closed states at the single-molecule level for the first time. We demonstrated that the tailored ClyA could track the conformational transitions of the West Nile NS2B/NS3 protease and unravel the conformational energy landscape of various protease constructs through population and kinetic analysis. The new ClyA-protease platform paves a way to high-throughput screening strategies for discovering new allosteric inhibitors that target the NS2B and NS3 interface.

A domain-swapped CaMKII conformation facilitates linker-mediated allosteric regulation.

Ca2+ signaling plays a key role in physiological processes such as memory formation and cardiac function. Ca2+/calmodulin-dependent protein kinase II (CaMKII) is the primary kinase that responds to Ca2+ inputs in these cells. There are four CaMKII paralogs in mammals which are alternatively spliced in the variable linker region to create upwards of 70 different variants. In this study, we systematically studied different linker regions and determined that the position of charged residues within the linker region modulates the Ca2+/CaM sensitivity of the holoenzyme. We present an X-ray crystal structure of full-length CaMKIIδ that shows a domain-swapped conformation of the subunits within the dodecameric holoenzyme. In this structure, the kinase domain of one subunit is docked onto the hub domain of a different subunit, providing an additional interface within the holoenzyme. Mutations at the equatorial and lateral interfaces revealed that the kinase-hub interaction dissociates as the hub-hub interfaces are disturbed, which led alterations in the stoichiometry of CaMKII holoenzyme and Ca2+/CaM sensitivity. Molecular dynamics simulations of linker-containing domain-swapped and non-domain-swapped CaMKIIs reveal that the domain-swapped configuration facilitates an interaction between the calmodulin binding domain and the variable linker region, such that dynamic electrostatic forces between charges on these segments can modulate the equilibrium between the compact and extended conformational states of the holoenzyme. Small angle X-ray scattering data confirms that a negatively charged linker CaMKII holoenzyme adopts a more compact conformation compared to a positively charged linker. These data support a model where patches of charged linker residues interact with the calmodulin binding domain to allosterically regulate sensitivity to Ca2+/CaM. Our findings provide a new framework for understanding CaMKII structure and allosteric regulation by the variable linker region in Ca2+-sensitive cells.

Assessing risk of recurrent small bowel obstruction after non-operative management in patients with history of intra-abdominal surgery: a population-based comprehensive analysis in Taiwan.

BACKGROUND: Despite a significant 30% ten-year readmission rate for SBO patients, investigations into recurrent risk factors after non-operative management are scarce. The study aims to generate a risk factor scoring system, the 'Small Bowel Obstruction Recurrence Score' (SBORS), predicting 6-month recurrence of small bowel obstruction (SBO) after successful non-surgical management in patients who have history of intra-abdominal surgery. METHODS: We analyzed data from patients aged ≥ 18 with a history of intra-abdominal surgery and diagnosed with SBO (ICD-9 code: 560, 568) and were successful treated non-surgically between 2004 and 2008. Participants were divided into model-derivation (80%) and validation (20%) group. RESULTS: We analyzed 23,901 patients and developed the SBORS based on factors including the length of hospital stay > 4 days, previous operations > once, hemiplegia, extra-abdominal and intra-abdominal malignancy, esophagogastric surgery and intestino-colonic surgery. Scores > 2 indicated higher rates and risks of recurrence within 6 months (12.96% vs. 7.27%, OR 1.898, p < 0.001 in model-derivation group, 12.60% vs. 7.05%, OR 1.901, p < 0.001 in validation group) with a significantly increased risk of mortality and operative events for recurrent episodes. The SBORS model demonstrated good calibration and acceptable discrimination, with an area under curve values of 0.607 and 0.599 for the score generation and validation group, respectively. CONCLUSIONS: We established the effective 'SBORS' to predict 6-month SBO recurrence risk in patients who have history of intra-abdominal surgery and have been successfully managed non-surgically for the initial obstruction event. Those with scores > 2 face higher recurrence rates and operative risks after successful non-surgical management.

Backbone interactions and secondary structures in phase separation of disordered proteins.

Intrinsically disordered proteins (IDPs) are one of the major drivers behind the formation and characteristics of biomolecular condensates. Due to their inherent flexibility, the backbones of IDPs are significantly exposed, rendering them highly influential and susceptible to biomolecular phase separation. In densely packed condensates, exposed backbones have a heightened capacity to interact with neighboring protein chains, which might lead to strong coupling between the secondary structures and phase separation and further modulate the subsequent transitions of the condensates, such as aging and fibrillization. In this mini-review, we provide an overview of backbone-mediated interactions and secondary structures within biomolecular condensates to underscore the importance of protein backbones in phase separation. We further focus on recent advances in experimental techniques and molecular dynamics simulation methods for probing and exploring the roles of backbone interactions and secondary structures in biomolecular phase separation involving IDPs.

Toward Accurate Simulation of Coupling between Protein Secondary Structure and Phase Separation.

Intrinsically disordered proteins (IDPs) frequently mediate phase separation that underlies the formation of a biomolecular condensate. Together with theory and experiment, efficient coarse-grained (CG) simulations have been instrumental in understanding the sequence-specific phase separation of IDPs. However, the widely used Cα-only models are limited in capturing the peptide nature of IDPs, particularly backbone-mediated interactions and effects of secondary structures, in phase separation. Here, we describe a hybrid resolution (HyRes) protein model toward a more accurate description of the backbone and transient secondary structures in phase separation. With an atomistic backbone and coarse-grained side chains, HyRes can semiquantitatively capture the residue helical propensity and overall chain dimension of monomeric IDPs. Using GY-23 as a model system, we show that HyRes is efficient enough for the direct simulation of spontaneous phase separation and, at the same time, appears accurate enough to resolve the effects of single His to Lys mutations. HyRes simulations also successfully predict increased ß-structure formation in the condensate, consistent with available experimental CD data. We further utilize HyRes to study the phase separation of TPD-43, where several disease-related mutants in the conserved region (CR) have been shown to affect residual helicities and modulate the phase separation propensity as measured by the saturation concentration. The simulations successfully recapitulate the effect of these mutants on the helicity and phase separation propensity of TDP-43 CR. Analyses reveal that the balance between backbone and side chain-mediated interactions, but not helicity itself, actually determines phase separation propensity. These results support that HyRes represents an effective protein model for molecular simulation of IDP phase separation and will help to elucidate the coupling between transient secondary structures and phase separation.

Biomechanical behavior of deep anterior lamellar keratoplasty: a finite element analysis.

Deep Anterior Lamellar Keratoplasty (DALK) is a surgical procedure used to restore sight and manage corneal diseases by replacing cloudy corneal tissue with allogeneic normal corneal tissue or artificial corneal material. However, the limited availability and mechanical defects of artificial corneal materials pose challenges in DALK. To predicting postoperative mechanical behavior of Deep Anterior Lamellar Keratoplasty (DALK), a three-dimensional finite element model of the postoperative DALK cornea with suture holes was developed. The postoperative corneal displacement and von Mises (VM) stress changes were also simulated under varying depths of cut (DOC: 0.16-0.26 µm), intraocular pressure (IOP: 12, 15, 18 mmHg), and central corneal thickness (CCT: 420-620 µm). The model indicated that higher IOP and CCT were associated with improved postoperative corneal stability. The postoperative corneal displacement increased from the edge to the center, while the maximum VM stress value occurs at the corneal suture hole. Corneal displacement and VM stress decrease with increasing CCT and decreasing IOP. DOC has a slight effect on corneal displacement and VM stress, with an overall positive relationship. The model has potential application in the preoperative assessment of risk in keratoplasty.

Hydrophobic gating in bundle-crossing ion channels: a case study of TRPV4.

Transmembrane ion channels frequently regulate ion permeation by forming bundle crossing of the pore-lining helices when deactivated. The resulting physical constriction is believed to serve as the de facto gate that imposes the major free energy barrier to ion permeation. Intriguingly, many ion channels also contain highly hydrophobic inner pores enclosed by bundle crossing, which can undergo spontaneous dewetting and give rise to a "vapor barrier" to block ion flow even in the absence of physical constriction. Using atomistic simulations, we show that hydrophobic gating and bundle-crossing mechanisms co-exist and complement one and another in the human TRPV4 channel. In particular, a single hydrophilic mutation in the lower pore can increase pore hydration and reduce the ion permeation free energy barrier by about half without affecting the bundle crossing. We believe that hydrophobic gating may play a key role in other bundle-crossing ion channels with hydrophobic inner pores.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels.

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Accurate Simulation of Coupling between Protein Secondary Structure and Liquid-Liquid Phase Separation.

Intrinsically disordered proteins (IDPs) frequently mediate liquid-liquid phase separation (LLPS) that underlies the formation of membraneless organelles. Together with theory and experiment, efficient coarse-grained (CG) simulations have been instrumental in understanding sequence-specific phase separation of IDPs. However, the widely-used Cα-only models are severely limited in capturing the peptide nature of IDPs, including backbone-mediated interactions and effects of secondary structures, in LLPS. Here, we describe a hybrid resolution (HyRes) protein model for accurate description of the backbone and transient secondary structures in LLPS. With an atomistic backbone and coarse-grained side chains, HyRes accurately predicts the residue helical propensity and chain dimension of monomeric IDPs. Using GY-23 as a model system, we show that HyRes is efficient enough for direct simulation of spontaneous phase separation, and at the same time accurate enough to resolve the effects of single mutations. HyRes simulations also successfully predict increased beta-sheet formation in the condensate, consistent with available experimental data. We further utilize HyRes to study the phase separation of TPD-43, where several disease-related mutants in the conserved region (CR) have been shown to affect residual helicities and modulate LLPS propensity. The simulations successfully recapitulate the effect of these mutants on the helicity and LLPS propensity of TDP-43 CR. Analyses reveal that the balance between backbone and sidechain-mediated interactions, but not helicity itself, actually determines LLPS propensity. We believe that the HyRes model represents an important advance in the molecular simulation of LLPS and will help elucidate the coupling between IDP transient secondary structures and phase separation.

Efficacy and safety of an extended-release sebacoyl dinalbuphine ester for laparoscopic cholecystectomy: A randomized controlled trial.

BACKGROUND: A long-acting κreceptor agonist parenteral analgesic may theoretically improve acute pain and reduce incidence of chronic postsurgical pain (CPSP) after laparoscopic cholecystectomy with minimal drug-related side effects of the traditional µreceptor opioids. METHODS: Eighty adult patients undergoing elective laparoscopic cholecystectomy were randomly assigned to receive single intramuscular injection of an extended-release sebacoyl dinalbuphine ester (SDE, Naldebain 150 mg; n = 40) or placebo (n = 40) after anesthesia induction. Standard multimodal analgesia (MMA) was administered for postoperative pain control. The primary endpoint was pain intensity within 7 days after surgery. The secondary endpoints were incidence CPSP at 3 months and adverse reactions up to 7 days after surgery. RESULTS: The highest visual analogue scale (VAS) and area under the curve of VAS 0 to 48 hours after operation were not different between the two groups and a similar proportion of patients requested rescue parenteral analgesics. Average pain intensities were also not different at 72 hours and 7 days after surgery. Incidence of CPSP was 22.5% and 13.1% in patients who received placebo and SDE treatment, respectively (P = .379). Significantly higher incidence of drug-related adverse events, including dizziness, nausea and injection site reactions, were recorded in the SDE group. CONCLUSION: A single dose of extended-release analgesic SDE given intraoperatively did not provide sufficient add-on effect for acute and chronic pain management after laparoscopic cholecystectomies in patients who received standard postoperative MMA. Intramuscular injection of 150 mg SDE in patients with average body mass causes adverse events that could have been overlooked. More clinical studies are warranted to determine the target populations who may benefit from SDE injections for improvement of acute and chronic postsurgical pain management.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels.

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ΔV 1/2 , with a RMSE ∼ 32 mV and correlation coefficient of R ∼ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V 1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ΔV 1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction. Author Summary: Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.

Predicting in-hospital mortality risk for perforated peptic ulcer surgery: the PPUMS scoring system and the benefit of laparoscopic surgery: a population-based study.

BACKGROUND: The major treatment for perforated peptic ulcers (PPU) is surgery. It remains unclear which patient may not get benefit from surgery due to comorbidity. This study aimed to generate a scoring system by predicting mortality for patients with PPU who received non-operative management (NOM) and surgical treatment. METHOD: We extracted the admission data of adult (≥ 18 years) patients with PPU disease from the NHIRD database. We randomly divided patients into 80% model derivation and 20% validation cohorts. Multivariate analysis with a logistic regression model was applied to generate the scoring system, PPUMS. We then apply the scoring system to the validation group. RESULT: The PPUMS score ranged from 0 to 8 points, composite with age (< 45: 0 points, 45-65: 1 point, 65-80: 2 points, > 80: 3 points), and five comorbidities (congestive heart failure, severe liver disease, renal disease, history of malignancy, and obesity: 1 point each). The areas under ROC curve were 0.785 and 0.787 in the derivation and validation groups. The in-hospital mortality rates in the derivation group were 0.6% (0 points), 3.4% (1 point), 9.0% (2 points), 19.0% (3 points), 30.2% (4 points), and 45.9% when PPUMS > 4 point. Patients with PPUMS > 4 had a similar in-hospital mortality risk between the surgery group [laparotomy: odds ratio (OR) = 0.729, p = 0.320, laparoscopy: OR = 0.772, p = 0.697] and the non-surgery group. We identified similar results in the validation group. CONCLUSION: PPUMS scoring system effectively predicts in-hospital mortality for perforated peptic ulcer patients. It factors in age and specific comorbidities is highly predictive and well-calibrated with a reliable AUC of 0.785-0.787. Surgery, no matter laparotomy or laparoscope, significantly reduced mortality for scores < = 4. However, patients with a score > 4 did not show this difference, calling for tailored approaches to treatment based on risk assessment. Further prospective validation is suggested.

Machine Learning Generation of Dynamic Protein Conformational Ensembles.

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

Synergistic and Antagonistic Antiproliferative Effects of Ribociclib (Lee011) and Irinotecan (SN38) on Colorectal Cancer Cells.

BACKGROUND/AIM: Colorectal cancer (CRC) is one of the most common malignancies and cause of cancer-related deaths worldwide. The combination of chemotherapeutics working with different mechanisms enhances the therapeutic effects and delays the development of resistance. This study investigated the anticancer effect of the combination of ribociclib (LEE011) and irinotecan (SN38) on CRC cells. MATERIALS AND METHODS: HT-29 and SW480 cells were treated with LEE011, SN38, or the combination of LEE011 and SN38. Cell viability and cell cycle distribution were analyzed. The expression of cell cycle- and apoptosis-related proteins was determined using western blot. RESULTS: The combination of LEE011 and SN38 elicited a synergistic antiproliferative effect on HT-29 (PIK3CAP449T mutation) cells, and an antagonistic antiproliferative effect on SW480 (KRASG12V mutation) cells. LEE011 inhibited retinoblastoma protein (Rb) phosphorylation and led to G1 arrest in HT-29 and SW480 cells. SN38 treatment caused a significant increase in the phosphorylation levels of Rb, cyclin B1, and CDC2 in SW480 cells and induced S phase arrest. Furthermore, SN38 treatment increased the phosphorylation levels of p53 and activated caspase-3 and caspase-8 in HT-29 and SW480 cells. LEE011-induced G1 arrest contributed to its synergistic antiproliferative effect with SN38 in HT-29 cells through the down-regulation of the phosphorylation of Rb. In addition, it elicited an antagonistic effect with SN38 in SW480 cells by changing the phosphorylation levels of Rb and activating caspase-8. CONCLUSION: The effects of the combination of LEE011 and conventional chemotherapy drugs on CRC depend on the chemotherapy drug and the specific gene mutation harbored by tumor cells.

Coupled binding and folding of disordered SPIN N-terminal region in myeloperoxidase inhibition.

Gram-positive pathogenic bacteria Staphylococcus express and secret staphylococcal peroxidase inhibitor (SPIN) proteins to help evade neutrophil-mediated immunity by inhibiting the activity of the main oxidative-defense player myeloperoxidase (MPO) enzyme. SPIN contains a structured 3-helix bundle C-terminal domain, which can specifically bind to MPO with high affinity, and an intrinsically disordered N-terminal domain (NTD), which folds into a structured ß-hairpin and inserts itself into the active site of MPO for inhibition. Mechanistic insights of the coupled folding and binding process are needed in order to better understand how residual structures and/or conformational flexibility of NTD contribute to the different strengths of inhibition of SPIN homologs. In this work, we applied atomistic molecular dynamics simulations on two SPIN homologs, from S. aureus and S. delphini, respectively, which share high sequence identity and similarity, to explore the possible mechanistic basis for their different inhibition efficacies on human MPO. Direct simulations of the unfolding and unbinding processes at 450 K reveal that these two SPIN/MPO complexes systems follow surprisingly different mechanisms of coupled binding and folding. While coupled binding and folding of SPIN-aureus NTD is highly cooperative, SPIN-delphini NTD appears to mainly utilize a conformational selection-like mechanism. These observations are in contrast to an overwhelming prevalence of induced folding-like mechanisms for intrinsically disordered proteins that fold into helical structures upon binding. Further simulations of unbound SPIN NTDs at room temperature reveal that SPIN-delphini NTD has a much stronger propensity of forming ß-hairpin like structures, consistent with its preference to fold and then bind. These may help explain why the inhibition strength is not well correlated with binding affinity for different SPIN homologs. Altogether, our work establishes the relationship between the residual conformational stability of SPIN-NTD and their inhibitory function, which can help us develop new strategies towards treating Staphylococcal infections.

Inner pore hydration free energy controls the activation of big potassium channels.

Hydrophobic gating is an emerging mechanism in regulation of protein ion channels where the pore remains physically open but becomes dewetted to block ion permeation. Atomistic molecular dynamics simulations have played a crucial role in understanding hydrophobic gating by providing the molecular details to complement mutagenesis and structural studies. However, existing studies rely on direct simulations and do not quantitatively describe how the sequence and structural changes may control the delicate liquid-vapor equilibrium of confined water in the pore of the channel protein. To address this limitation, we explore two enhanced sampling methods, namely metadynamics and umbrella sampling, to derive free-energy profiles of pore hydration in both the closed and open states of big potassium (BK) channels, which are important in cardiovascular and neural systems. It was found that metadynamics required substantially longer sampling times and struggled to generate stably converged free-energy profiles due to the slow dynamics of cooperative pore water diffusion even in the barrierless limit. Using umbrella sampling, well-converged free-energy profiles can be readily generated for the wild-type BK channels as well as three mutants with pore-lining mutations experimentally known to dramatically perturb the channel gating voltage. The results show that the free energy of pore hydration faithfully reports the gating voltage of the channel, providing further support for hydrophobic gating in BK channels. Free-energy analysis of pore hydration should provide a powerful approach for quantitative studies of how protein sequence, structure, solution conditions, and/or drug binding may modulate hydrophobic gating in ion channels.

Re-Balancing Replica Exchange with Solute Tempering for Sampling Dynamic Protein Conformations.

Replica exchange with solute tempering (REST) is a highly effective variant of replica exchange for enhanced sampling in explicit solvent simulations of biomolecules. By scaling the Hamiltonian for a selected "solute" region of the system, REST effectively applies tempering only to the degrees of freedom of interest but not the rest of the system ("solvent"), allowing fewer replicas for covering the same temperature range. A key consideration of REST is how the solute-solvent interactions are scaled together with the solute-solute interactions. Here, we critically evaluate the performance of the latest REST2 protocol for sampling large-scale conformation fluctuations of intrinsically disordered proteins (IDPs). The results show that REST2 promotes artificial protein conformational collapse at high effective temperatures, which seems to be a designed feature originally to promote the sampling of reversible folding of small proteins. The collapse is particularly severe with larger IDPs, leading to replica segregation in the effective temperature space and hindering effective sampling of large-scale conformational changes. We propose that the scaling of the solute-solvent interactions can be treated as free parameters in REST, which can be tuned to control the solute conformational properties (e.g., chain expansion) at different effective temperatures and achieve more effective sampling. To this end, we derive a new REST3 protocol, where the strengths of the solute-solvent van der Waals interactions are recalibrated to reproduce the levels of protein chain expansion at high effective temperatures. The efficiency of REST3 is examined using two IDPs with nontrivial local and long-range structural features, including the p53 N-terminal domain and the kinase inducible transactivation domain of transcription factor CREB. The results suggest that REST3 leads to a much more efficient temperature random walk and improved sampling efficiency, which also further reduces the number of replicas required. Nonetheless, our analysis also reveals significant challenges of relying on tempering alone for sampling large-scale conformational fluctuations of disordered proteins. It is likely that more efficient sampling protocols will require incorporating more sophisticated Hamiltonian replica exchange schemes in addition to tempering.

Incidence Trend of Follicular Lymphoma in Taiwan Compared to Japan and Korea, 2001-2019.

A continuous increase in follicular lymphoma has been observed in Taiwan, Japan, and South Korea over the last few decades. This study aimed to evaluate the difference in incidence trends of follicular lymphoma in Taiwan, Japan, and South Korea between 2001 and 2019. The data for the Taiwanese populations was obtained from the Taiwan Cancer Registry Database, and those for the Japanese and Korean population were retrieved from the Japan National Cancer Registry and some additional reports, both of which included population-based cancer registry data, from Japan and Korea. Follicular lymphoma accounted for 4231 cases from 2002-2019 in Taiwan, 3744 cases from 2001-2008 and 49,731 cases from 2014-2019 in Japan; and 1365 cases from 2001-2012 and 1244 cases from 2011-2016 in South Korea. The annual percentage change for each time period was 3.49% (95% confidence interval: 2.75-4.24%) in Taiwan, 12.66% (95% confidence interval [CI]: 9.59-15.81%) and 4.95% (95% CI: 2.14-7.84%) in Japan, and 5.72% (95% CI: 2.79-8.73%) and 7.93% (95% CI: -1.63-18.42%) in South Korea. Our study confirms that the increasing trends of follicular lymphoma incidence in Taiwan and Japan have been remarkable in recent years, especially the rapid increase in Japan between 2014 and 2019; however, there was no significant in-crease from 2011 to 2015 in South Korea.