DHX9 SUMOylation is required for the suppression of R-loop-associated genome instability.

RNA helicase DHX9 is essential for genome stability by resolving aberrant R-loops. However, its regulatory mechanisms remain unclear. Here we show that SUMOylation at lysine 120 (K120) is crucial for DHX9 function. Preventing SUMOylation at K120 leads to R-loop dysregulation, increased DNA damage, and cell death. Cells expressing DHX9 K120R mutant which cannot be SUMOylated are more sensitive to genotoxic agents and this sensitivity is mitigated by RNase H overexpression. Unlike the mutant, wild-type DHX9 interacts with R-loop-associated proteins such as PARP1 and DDX21 via SUMO-interacting motifs. Fusion of SUMO2 to the DHX9 K120R mutant enhances its association with these proteins, reduces R-loop accumulation, and alleviates survival defects of DHX9 K120R. Our findings highlight the critical role of DHX9 SUMOylation in maintaining genome stability by regulating protein interactions necessary for R-loop balance.

Investigation of Immunogenicity Assessment of Biosimilar Monoclonal Antibodies in the United States.

Immunogenicity is critical for biologics. However, reference biologics labeling documents do not necessarily mention immunogenicity impact, rendering the development of biosimilars more challenging. We aimed to investigate the comparative assessment of immunogenicity profiles between biosimilars and their respective reference biologics in the review reports of the biosimilar monoclonal antibody applications approved by the Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA) as of March 13, 2022, covering 22 applications approved between April 5, 2016, and December 17, 2021. The maximum differences in anti-drug antibody (ADA) and neutralizing antibody (NAb) incidences between biosimilars and reference products mostly fell within ± 15% (-13.6% to 12%) and ± 20% (-17.4% to 17.1%, except extreme values of -23.4% and 66.7%), respectively. In comparison with antineoplastic agents, more immunosuppressants had ADA-positive (11/11, 100.0% vs. 8/10, 80.0%)/NAb-positive (11/11, 100.0% vs. 3/10, 30.0%) subjects, and the distribution of the aforementioned incidence differences was wider. The investigated biosimilars with available data for analysis demonstrated a high degree of consistency with their reference products in terms of the impact on pharmacokinetic parameters. No increase in immunogenicity was found in available switching studies. Most (16/22, 72.7%) biosimilars were issued post-marketing requirements that were not directly related to immunogenicity concerns. The FDA considered the totality of evidence assessing clinical consequences of immunogenicity differences, if any. Additional information on titers and subgroup analysis may be warranted to elucidate the critical attributes of immunogenicity impact and to aid in forming cost-effective strategies for biosimilar development.

Effectiveness of Tai Chi on fibromyalgia patients: A meta-analysis of randomized controlled trials.

OBJECTIVE: To identify empirical evidence on the effectiveness of Tai Chi in treating fibromyalgia (FM). METHOD: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to compare the effectiveness of Tai Chi and standard care or conventional therapeutic exercise in patients with FM. PubMed, Medline, and Physiotherapy Evidence Database were searched for relevant studies published before May 2019. Treatment effectiveness was evaluated using the fibromyalgia impact questionnaire (FIQ), and the total score, pain score, sleep quality index, fatigue, depression, and quality of life were assessing among the patients. RESULTS: Six RCTs with 657 patients were included. Results of our meta-analysis indicated that Tai Chi exerts significant positive effects on reducing the total FIQ score at 12-16 weeks (standard mean difference [SMD]: -0.61; 95% confidence interval [CI]: -0.90 to -0.31) and pain score (SMD: -0.88; 95% CI: -1.58 to -0.18), improving sleep quality (SMD: -0.57; 95% CI: -0.86 to -0.28), relieving fatigue (SMD: -0.92; 95% CI: -1.81 to -0.04), alleviating depression (SMD: -0.49; 95% CI: -0.97 to -0.01), and enhancing quality of life physically (SMD: 6.21; 95% CI: 3.18-9.24) and psychologically (SMD: 5.15; 95% CI: 1.50-8.81). CONCLUSION: Tai Chi exerts significantly greater effects on patients with FM than standard care; therefore, we suggest that Tai Chi can be used as an alternative treatment. However, more large-scale, high-quality, and multicenter trials are required to provide stronger evidence on the effectiveness of Tai Chi, as an alternative to aerobic exercise, compared with conventional therapeutic exercise.

Learning the Inverse Dynamics of Robotic Manipulators in Structured Reproducing Kernel Hilbert Space.

We investigate the modeling of inverse dynamics without prior kinematic information for holonomic rigid-body robots. Despite success in compensating robot dynamics and friction, general inverse dynamics models are nontrivial. Rigid-body models are restrictive or inefficient; learning-based models are generalizable yet require large training data. The structured kernels address the dilemma by embedding the robot dynamics in reproducing kernel Hilbert space. The proposed kernels autonomously converge to rigid-body models but require fewer samples; with a semi-parametric framework that incorporates additional parametric basis for friction, the structured kernels can efficiently model general rigid-body robots. We tested the proposed scheme in simulations and experiments; the models that consider the structure of function space are more accurate.

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.