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Summary: Farnesylation is a lipid post-translational modification of proteins crucial for protein membrane anchoring and cellular signaling. Farnesyltransferase inhibitors (FTIs), such as tipifarnib, are being tested in cancer therapy. However, the full impact of FTIs on farnesylation substrates remains poorly understood, thus limiting their use in precision medicine. In this study, we performed a global proteomics analysis to investigate farnesylation and the effects of tipifarnib in lung cancer cell lines. Using metabolic labeling and mass spectrometry, we identified farnesylated proteins and mapped their subcellular localization. We also analyzed tipifarnib-dependent protein relocalization and proteome-wide changes. Key findings include the potential therapeutic value of FTIs for NRAS-mutated melanoma and GNAQ/GNA11-mutated uveal melanoma by inhibiting INPP5A farnesylation. Additionally, we identified a synergistic drug combination involving tipifarnib and a ferroptosis inducer and discovered PTP4A1 as a regulator of interferon signaling. Our data, covering 15,080 proteins, offer valuable insights for future studies of farnesylation and FTIs.

Understanding the growth pathway of faceted alloy nanoparticles at the atomic level is crucial to morphology control and property tuning, but remains a challenge. Here, the authors reveal the particle growth and facet formation mechanisms of octahedral Pt3Ni nanoparticles using multiple cutting-edge in situ techniques.

During the COVID-19 pandemic, many countries adopted Digital Contact Tracing (DCT) technology to control infections. However, the widely-used Bluetooth Low Energy (BLE)-based DCT requires both the infected individual and the contact to have the application activated to detect exposure. Forcing citizens to install the DCT application could compromise their privacy. Therefore, to make DCT a truly usable tool, it is crucial to develop a DCT system that possesses high sensitivity, without depending on the application usage rate.The Computation of Infection Risk via Confidential Locational Entries (CIRCLE) is a DCT method that utilizes connection logs from mobile phone base stations, theoretically offering much higher sensitivity than BLE-based DCT. However, its real performance has not been proven, and thus, this paper estimates the sensitivity and specificity of both BLE-based DCT and CIRCLE in a comparative setting. The estimation combines simulated movement patterns of residents with real-world data from app usage in Japan, utilizing both simulation and numerical modeling, with missing data supplemented through sensitivity analysis.The sensitivity of BLE-based DCT is severely limited by the application’s usage rate, with an estimated baseline of just 10.9%, and even under highly optimistic assumptions, it only reaches 27.0%. In contrast, CIRCLE demonstrated a significantly higher sensitivity of 85.6%, greatly surpassing BLE-based DCT. The specificity of CIRCLE, though, decreased as the number of infected individuals increased, dropping to less than half of BLE-based DCT’s specificity during widespread infection. The BLE-based DCT used during the pandemic suffers from low sensitivity. While CIRCLE has specificity challenges, it provides exceptionally high sensitivity. Integrating these methods could redefine the design of digital contact tracing, leading to better utility for future infection control.

Efficient killing of nemotodes by Stropharia rugosoannulata Farlow ex Murrill cultures are observed. It is shown that the nematode-attacking activity of this fungus is carried out by these spiny acanthocytes and that mechanical force is an important factor in the process and the growth and nematode-attacking activity of the fungus in soil are also determined. Results suggest that acanthocytes are functional in soil.

Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot’s operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology.

An enzymatic approach to label peptide N-termini with isotope-coded affinity tags is presented. This method exploits the high activity of trypsin for peptide synthesis in organic solvents. A cosubstrate containing a stable isotope-coded Arg residue and a biotin tag was synthesized. When the cosubstrate was incubated with tryptic peptides and trypsin in ethanol solution, the stable isotope-coded affinity tag was specifically coupled onto the N-termini of peptides via the formation of new peptide bonds. The labeled peptides were specifically enriched by avidin affinity chromatography and then were submitted to liquid chromatography-tandem mass spectrometry (LC/MS/MS) for quantification. This enrichment step effectively reduced the interference by unlabeled peptides. The excellent performance of this approach was demonstrated by labeling standard peptides as well as a mouse liver digest. In addition to one amino acid residue, a few dipeptide tags were also introduced to the N-termini of peptides successfully by this enzymatic approach. It was found that the identifications for samples labeled with these tags were highly complementary. Coupling a short sequence tag onto peptides could be an effective approach to improve the coverage for proteome analysis. [ABSTRACT FROM AUTHOR]

Due to its constitutive activity and ubiquitous distribution, CK2 is the most pleiotropic kinase among the individual members of the protein kinase superfamily. Identification of CK2 substrates is vital to decipher its role in biological processes. However, only a limited number of CK2 substrates were identified so far. In this study, we developed an integrated phosphoproteomics workflow to identify the CK2 substrates in large scale. First, in vitro kinase reactions with immobilized proteomes were combined with quantitative phosphoproteomics to identify in vitro CK2 phosphorylation sites, which leaded to identification of 988 sites from 581 protein substrates. To reduce false positives, we proposed an approach by comparing these in vitro sites with the public databases that collect in vivo phosphorylation sites. After the removal of the sites that were excluded in the databases, 605 high confident CK2 sites corresponding to 356 proteins were retained. The CK2 substrates identified in this study were based on the discovery mode, in which an unbiased overview of CK2 substrates was provided. Our result revealed that CK2 substrates were significantly enriched in the spliceosomal proteins, indicating CK2 might regulate the functions of spliceosome. [ABSTRACT FROM AUTHOR]