Redesigning the stereospecificity of tyrosyl-tRNA synthetase

D-Amino acids are largely excluded from protein synthesis, yet they are of great interest in biotechnology. Unnatural amino acids have been introduced into proteins using engineered aminoacyl-tRNA synthetases (aaRSs), and this strategy might be applicable to D-amino acids. Several aaRSs can aminoacylate their tRNA with a D-amino acid; of these, tyrosyl-tRNA synthetase (TyrRS) has the weakest stereospecificity. We use computational protein design to suggest active site mutations in Escherichia coli TyrRS that could increase its D-Tyr binding further, relative to L-Tyr. The mutations selected all modify one or more sidechain charges in the Tyr binding pocket. We test their effect by probing the aminoacyl-adenylation reaction through pyrophosphate exchange experiments. We also perform extensive alchemical free energy simulations to obtain L-Tyr/D-Tyr binding free energy differences. Agreement with experiment is good, validating the structural models and detailed thermodynamic predictions the simulations provide. The TyrRS stereospecificity proves hard to engineer through charge-altering mutations in the first and second coordination shells of the Tyr ammonium group. Of six mutants tested, two are active towards D-Tyr; one of these has an inverted stereospecificity, with a large preference for D-Tyr. However, its activity is low. Evidently, the TyrRS stereospecificity is robust towards charge rearrangements near the ligand. Future design may have to consider more distant and/or electrically neutral target mutations, and possibly design for binding of the transition state, whose structure however can only be modeled.

Smad7 in the hippocampus contributes to memory impairment in aged mice after anesthesia and surgery

BACKGROUND

Postoperative cognitive dysfunction (POCD) is a common neurological complication following anesthesia and surgery. Increasing evidence has demonstrated that neuroinflammation caused by systemic inflammatory responses during the perioperative period is a key factor in the occurrence of POCD. In addition, SMAD family member 7 (Smad7) has been confirmed to play vital roles in the pathogenesis and treatment of inflammatory diseases, such as inflammatory bowel disease. However, whether Smad7 participates in the regulatory process of neuroinflammation and apoptosis in the development of POCD is still unknown.

METHODS

In this study, a POCD mouse model was constructed by unilateral nephrectomy under anesthesia, and cognitive function was assessed using the fear conditioning test and open field test. The expression of Smad7 at the mRNA and protein levels in the hippocampus 3 days after surgery was examined by qRT-PCR, western blot and immunofluorescence assays. Furthermore, to identify whether the elevation of Smad7 in the hippocampus after unilateral nephrectomy contributes to cognitive impairment, the expression of Smad7 in the hippocampal CA1 region was downregulated by crossing Smad7fl/fl conditional mutant mice and CaMKIIα-Cre line T29-1 transgenic mice or stereotaxic injection of shRNA-Smad7. Inflammation and apoptosis in the hippocampus were assessed by measuring the mRNA levels of typical inflammatory cytokines, including TNF-α, IL-1β, IL-6, CCL2, CXCL1, and CXCL2, and the protein levels of apoptotic proteins, including Bax and Bcl2. In addition, apoptosis in the hippocampus postoperation was investigated by a terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining assay. Finally, western blotting was used to explore how Smad7 mediates inflammation and apoptosis postoperation.

RESULTS

The results unequivocally revealed that elevated Smad7 in the hippocampal CA1 region significantly inhibited TGF-β signal transduction by blocking Smad2/3 phosphorylation, which enhanced neuroinflammation and apoptosis in the hippocampus and further led to learning and memory impairment after surgery.

CONCLUSIONS

Our results revealed that Smad7 contributes to cognitive impairment after surgery by enhancing neuroinflammation and apoptosis in the hippocampus and might serve as a promising therapeutic target for the treatment of memory impairment after anesthesia surgery.

Let-7b-TLR7 Signaling Axis Contributes to the Anesthesia/Surgery-Induced Cognitive Impairment

Perioperative neurocognitive disorders (PNDs) are severe and common neurological complications among elderly patients following anesthesia and surgery. As the first line of defense of the innate immune system, Toll-like receptors (TLRs) have been found to be involved in the occurrence of neurodegenerative diseases in recent years. However, the role of TLR7 in the pathology and development of PNDs remains largely unclear. In our current study, we hypothesized that increased microRNA let-7b (let-7b) during anesthesia and surgical operation would activate TLR7 signaling pathways and mediate PNDs. Using a mouse model of PNDs, 18-20 months wild-type (WT) mice were undergoing unilateral nephrectomy, and increased TLR7 and let-7b expression levels were found in the surgery group compared with the Sham group. Of note, increased TLR7 was found to be co-localized with let-7b in the hippocampal area CA1 in the PNDs model. In addition, TLR7 and let-7b inhibition could improve hippocampus-dependent memory and attenuate the production of inflammatory cytokines. Together, our results indicated that TLR7 activation and up-regulation might be triggered by increased let-7b under stressful conditions and initiated the downstream inflammatory signaling, playing a substantial role in the development of PNDs.

Identification of potential key circular RNAs related to cognitive impairment after chronic constriction injury of the sciatic nerve

Chronic neuropathic pain is commonly accompanied by cognitive impairment. However, the underlying mechanism in the occurrence of cognitive deficits under constant nociceptive irritation remains elusive. Herein, we established a chronic neuropathic pain model by chronic constriction injury (CCI) of the unilateral sciatic nerve in rats. Behavioral tests indicated that CCI rats with long-term nociceptive threshold decline developed significant dysfunction of working memory and recognitive memory starting at 14 days and lasting for at least 21 days. Afterward, circRNA expression profiles in the hippocampus of CCI and sham rats were analyzed via high-throughput sequencing to explore the potential key factors associated with cognitive impairment induced by ongoing nociception, which showed 76 differentially expressed circRNAs, 39 upregulated and 37 downregulated, in the CCI group. These differentially expressed circRNA host genes were validated to be primarily associated with inflammation and apoptotic signaling pathways according to GO/KEGG analysis and the circRNA-miRNA-mRNA network, which was also confirmed through the analysis of neuroinflammation and neuronal apoptosis. Consequently, we assumed that enhanced neuroinflammation and neuronal apoptosis might act as potential regulators of cognitive impairment induced by chronic neuropathic pain. The identification of the regulatory mechanism would provide promising clinical biomarkers or therapeutic targets in the diagnostic prediction and intervention treatment of memory deficits under neuropathic pain conditions.

An intelligent garment for long COVID-19 real-time monitoring

As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and supervised learning-based medical image analysis have proven to be useful. Current research on these two technical roadmaps has various drawbacks, despite their respective benefits. Wearable systems allow only the real-time monitoring of physiological parameters (heart rate, temperature, blood oxygen saturation, or SpO2). Therefore, they are unable to conduct in-depth investigations or differentiate COVID-19 from other illnesses that share similar symptoms. Medical image analysis using supervised learning-based models can be used to conduct in-depth analyses and provide precise diagnostic decision support. However, these methods are rarely used for real-time monitoring. In this regard, we present an intelligent garment combining the precision of supervised learning-based models with real-time monitoring capabilities of wearable systems. Given the relevance of electrocardiogram (ECG) signals to long COVID-19 symptom severity, an explainable data fusion strategy based on multiple machine learning models uses heart rate, temperature, SpO2, and ECG signal analysis to accurately assess the patient's health status. Experiments show that the proposed intelligent garment achieves an accuracy of 97.5 %, outperforming most of the existing wearable systems. Furthermore, it was confirmed that the two physiological indicators most significantly affected by the presence of long COVID-19 were SpO2 and the ST intervals of ECG signals.

Poly(I:C) attenuates myocardial ischemia/reperfusion injury by restoring autophagic function

Polyinosinic-polycytidylic acid (poly(I:C)) is the agonist of Toll-like receptor 3 (TLR3), which participates in innate immune responses under the condition of myocardial ischemia/reperfusion injury (MIRI). It has been shown that poly(I:C) exhibited cardioprotective activities through the PI3K/Akt pathway, which is the main signal transduction pathway during autophagy. However, the precise mechanism by whether poly(I:C) regulates autophagy remains poorly understood. Thus, this study was designed to investigate the therapeutic effect of poly(I:C) against MIRI and the underlying pathway connection with autophagy. We demonstrated that 1.25 and 5 mg/kg poly(I:C) preconditioning significantly reduced myocardial infarct size and cardiac dysfunction. Moreover, poly(I:C) significantly promoted cell survival by restoring autophagy flux and then regulating it to an adequate level Increased autophagy protein Beclin1 and LC3II together with p62 degradation after additional chloroquine. In addition, mRFP-GFP-LC3 adenoviruses exhibited autophagy activity in neonatal rat cardiac myocytes (NRCMs). Mechanistically, poly(I:C) activated the PI3K/AKT/mTOR pathway to induce autophagy, which was abolished by LY294002 (PI3K antagonist), rapamycin (autophagy activator and mTOR inhibitor), or 3-methyladenine (autophagy inhibitor), suggesting either inhibition of the PI3K/Akt/mTOR pathway or autophagy activity interrupt the beneficial effect of poly(I:C) preconditioning. In conclusion, poly(I:C) promotes cardiomyocyte survival from ischemia/reperfusion injury by regulating autophagy via the PI3K/Akt/mTOR pathway.

Extracellular RNAs-TLR3 signaling contributes to cognitive impairment after chronic neuropathic pain in mice

Chronic pain is often associated with cognitive decline, which could influence the quality of the patient's life. Recent studies have suggested that Toll-like receptor 3 (TLR3) is crucial for memory and learning. Nonetheless, the contribution of TLR3 to the pathogenesis of cognitive decline after chronic pain remains unclear. The level of TLR3 in hippocampal neurons increased in the chronic constriction injury (CCI) group than in the sham group in this study. Importantly, compared to the wild-type (WT) mice, TLR3 knockout (KO) mice and TLR3-specific neuronal knockdown mice both displayed improved cognitive function, reduced levels of inflammatory cytokines and neuronal apoptosis and attenuated injury to hippocampal neuroplasticity. Notably, extracellular RNAs (exRNAs), specifically double-stranded RNAs (dsRNAs), were increased in the sciatic nerve, serum, and hippocampus after CCI. The co-localization of dsRNA with TLR3 was also increased in hippocampal neurons. And the administration of poly (I:C), a dsRNA analog, elevated the levels of dsRNAs and TLR3 in the hippocampus, exacerbating hippocampus-dependent memory. In additon, the dsRNA/TLR3 inhibitor improved cognitive function after CCI. Together, our findings suggested that exRNAs, particularly dsRNAs, that were present in the condition of chronic neuropathic pain, activated TLR3, initiated downstream inflammatory and apoptotic signaling, caused damage to synaptic plasticity, and contributed to the etiology of cognitive impairment after chronic neuropathic pain.

MMSG-DTA: A Multimodal, Multiscale Model Based on Sequence and Graph Modalities for Drug-Target Affinity Prediction

Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets. Despite its importance, existing methodologies often face significant limitations in capturing comprehensive global features from molecular graphs, which are essential for accurately characterizing drug properties. Furthermore, protein feature extraction is predominantly restricted to 1D amino acid sequences, which fail to adequately represent the spatial structures and complex functional regions of proteins. These shortcomings impede the development of models capable of fully elucidating the mechanisms underlying drug-target interactions. To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. The model combines graph neural networks with Transformers to effectively capture both local node-level features and global structural features of molecular graphs. Additionally, a graph-based modality is employed to improve the extraction of protein features from amino acid sequences. To further enhance the model's performance, an attention-based feature fusion module is incorporated to integrate diverse feature types, thereby strengthening its representation capacity and robustness. We evaluated MMSG-DTA on three public benchmark data sets─Davis, KIBA, and Metz─and the experimental results demonstrate that the proposed model outperforms several state-of-the-art methods in DTA prediction. These findings highlight the effectiveness of MMSG-DTA in advancing the accuracy and robustness of drug-target interaction modeling.