Modulator Of Activity Research Articles

AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs

BackgroundMulti-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments.ResultsWe present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heterogeneous networks integrated with multi-omic data in a highly generalizable framework, in contrast to existing methods, which are mostly appropriate for at most two specific omic types. It is powered by Random Walk with Restart for multiplex-heterogeneous networks, with additional capabilities including degree bias adjustment and biased random walk for multi-objective module identification. AMEND was applied to two real-world multi-omic datasets: renal cell carcinoma data from The cancer genome atlas and an O-GlcNAc Transferase knockout study. Additional analyses investigate the performance of various subroutines of AMEND on tasks of node ranking and degree bias adjustment.ConclusionsWhile the analysis of multi-omic datasets in a network context is poised to provide deeper understanding of health and disease, new methods are required to fully take advantage of this increasingly complex data. The current study combines several network analysis techniques into a single versatile method for analyzing biological networks with multi-omic data that can be applied in many diverse scenarios. Software is freely available in the R programming language at https://github.com/samboyd0/AMEND.

ShapeBand: Design of a Shape-Changing Wristband with Soft Materials and Physiological Sensors for Anxiety Regulation

We introduce ShapeBand, a new shape-changing wristband designed for exploring multisensory and interactive anxiety regulation with soft materials and physiological sensing. Our approach takes a core principle of self-help psychotherapeutic intervention, aiming to help users to recognize anxiety triggers and engage in regulation with attentional distraction. We conducted user-centered design activities to iteratively refine our design requirements and delve into users’ rich experiences, preferences, and feelings. With ShapeBand, we explored bidirectional and dynamic interaction flow in anxiety regulation and subjective factors influencing its use. Our findings suggest that integrating both active and passive modulations can significantly enhance user engagement for effective anxiety intervention. Further, different interactions, characterized by dynamic alterations in bubbles and water flow in the ShapeBand, can provide users with a gamified experience and convey more potent effects. This study provides valuable insights into the future design of tangible anxiety regulation interfaces that can be tailored to subjective feelings and individual needs.

Remodeling of Embryo Architecture in Response to Vanadium and Increased Temperatures: From Morphometric to Molecular Changes

The study of ecotoxicity induced by vanadium (V) represents an area of increasing interest due to the growing use of V in both the industrial and pharmaceutical areas. This leads to its introduction into water environments, marking a developing problem, especially since rising global temperatures appear to intensify its toxic properties. Cytotoxicological approaches carried out on whole marine embryos represent a valid research tool since they grow directly in contact with the pollutants and are equipped with highly responsive cells to stressors. Here, we discuss the detrimental impact on Paracentrotus lividus sea urchin embryos resulting from the combination of V and higher temperatures, reflecting the effects of climate variation. The results demonstrate the remodeling of embryonic architecture at the morphometric level, revealing developmental delays and anomalies. These malformations involve variations in the total skeletal mass due to the almost total absence of the skeleton, with the exception of small calcareous aggregates. Furthermore, both a modulation in total tissue remodeling enzymatic activities and a variation in the amount of three MMP-like gelatinases (MMP-2, -9, and -14) were observed. This research demonstrates that climate change significantly increases the harmful effects of V, emphasizing the necessity for comprehensive toxicity assessments in environmental evaluations.

Investigating the Efficacy of the Web-Based Common Elements Toolbox (COMET) Single-Session Interventions in Improving UK University Student Well-Being: Randomized Controlled Trial.

Mental health problems in university students are associated with many negative outcomes, yet there is a gap between need and timely access to help. Single-session interventions (SSIs) are designed to be scalable and accessible, delivering core evidence-based intervention components within a one-off encounter. COMET (Common Elements Toolbox) is an online self-help SSI that includes behavioral activation, cognitive restructuring, gratitude, and self-compassion. COMET has previously been evaluated in India, Kenya, and the United States with promising results. This study tests the acceptability, appropriateness, perceived utility, and efficacy of COMET among UK university students during the peripandemic period. We conducted a randomized controlled trial evaluating the efficacy of COMET compared with a control group, with 2- and 4-week follow-ups. Outcome variables were subjective well-being, depression severity, anxiety severity, positive affect, negative affect, and perceived stress. We also measured intervention satisfaction immediately after completion of COMET. All UK university students with access to the internet were eligible to participate and were informed of the study online. The data were analyzed using linear mixed models and reported in accordance with the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist. Of the 831 people screened, 468 participants were randomized to a condition, 407 completed the postintervention survey, 147 returned the 2-week follow-up survey, 118 returned the 4-week follow-up survey, and 89 returned both. Of the 239 randomized, 212 completed COMET. Significant between-group differences in favor of the COMET intervention were observed at 2-week follow-ups for subjective well-being (Warwick-Edinburgh Mental Well-Being Scale; mean difference [MD] 1.39, 95% CI 0.19-2.61; P=.03), depression severity (9-item Patient Health Questionnaire; MD -1.31, 95% CI -2.51 to -0.12; P=.03), and perceived stress (4-item Perceived Stress Scale; MD -1.33, 95% CI -2.10 to -0.57; P<.001). Overall, participants were satisfied with COMET, with the majority endorsing the intervention and its modules as acceptable, appropriate, and exhibiting high utility. The self-compassion module was most often reported as the participants' favorite module and the behavioral activation module was their least favorite. Qualitative analysis revealed that participants found COMET generally accessible, but too long, and experienced immediate and long-term beneficial effects. This study demonstrated high engagement with the COMET intervention, along with preliminary short-term efficacy. Almost all participants completed the intervention, but study attrition was high. Participant feedback indicated a high level of overall satisfaction with the intervention, with perceived accessibility, immediate benefits, and potential long-term impact being notable findings. These findings support the potential value of COMET as a mental health intervention and highlight important areas for further improvement. ClinicalTrials.gov NCT05718141; https://clinicaltrials.gov/ct2/show/NCT05718141.

Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch

This study investigates the operational efficiency of the lab-scale oxidation ditch (OD) functioning in simultaneous nitrification and denitrification modes, focusing on forecasting biochemical oxygen demand (BOD5) concentrations over a five-day horizon. This forecasting capability aims to optimize the operational regime of aeration tanks by adjusting the specific load on organic pollutants through active sludge dosage modulation. A comprehensive statistical analysis was conducted to identify trends and seasonality alongside significant correlations between the forecasted values and various time lags. A total of 20 time lags and the “month” feature were selected as significant predictors. These models employed include Multi-head Attention Gated Recurrent Unit (MAGRU), long short-term memory (LSTM), Autoregressive Integrated Moving Average–Long Short-Term Memory (ARIMA–LSTM), and Prophet and gradient boosting models: CatBoost and XGBoost. Evaluation metrics (Mean Squared Error (MSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Coefficient of Determination (R2)) indicated similar performance across models, with ARIMA–LSTM yielding the best results. This architecture effectively captures short-term trends associated with the variability of incoming wastewater. The SMAPE score of 1.052% on test data demonstrates the model’s accuracy and highlights the potential of integrating artificial neural networks (ANN) and machine learning (ML) with mechanistic models for optimizing wastewater treatment processes. However, residual analysis revealed systematic overestimation, necessitating further exploration of significant predictors across various datasets to enhance forecasting quality.

Harnessing the power of proteins in modulation of miRNAs for targeting Iron deficiency Anemia: Opinion for future implications and strategies

Iron Deficiency Anemia (IDA) remains a pervasive global health challenge, disproportionately affecting vulnerable populations such as women and children. This review explores the cutting-edge interplay between microRNAs (miRNAs) and proteins in erythropoiesis, highlighting novel therapeutic strategies for IDA. Emerging evidence underscores the pivotal role of miRNAs—such as miR-15a, miR-24, miR-150, and miR-223—in regulating erythropoiesis, with dysregulation linked to hematologic and systemic diseases. Proteins, acting as modulators of miRNA activity, present innovative pathways for intervention by influencing erythropoiesis at multiple stages, from stem cell proliferation to red blood cell maturation. Our synthesis highlights key molecular mechanisms: miR-15a suppresses erythropoiesis by inhibiting c-Myb, miR-24 impairs heme biosynthesis through ALK4 regulation, while miR-150 and miR-223 modulate critical hematopoietic pathways affecting cell differentiation and apoptosis. These miRNA-protein interactions suggest targeted therapies such as protein-based miRNA modulators could optimize erythropoiesis, advancing IDA management. Additionally, the review emphasizes the potential of leveraging protein-miRNA interactions for precision medicine, especially in resource-limited settings where anemia’s burden is profound. By bridging current knowledge gaps, our proposed strategies offer personalized and scalable therapeutic solutions. This comprehensive perspective lays the groundwork for future interventions addressing one of the world’s most widespread public health crises.

Flavonoids as Potential Modulators of Pancreatic Lipase Catalytic Activity

Background/Objectives: Obesity has reached pandemic proportions, with predictions suggesting that, by 2030, over 1.5 billion people will be affected. Pancreatic lipase (PL), the enzyme primarily responsible for the absorption of dietary lipids, presents a potential target for obesity management. However, while porcine pancreatic lipase (PPL) is commonly used as the enzyme source for screening potential inhibitors, its effect on human pancreatic lipase (HPL) is rarely reported. This work aimed to screen the inhibitory effects of a library of flavonoids with different functional groups on the activity of PL from the human pancreas (triacylglycerol acyl hydrolase, EC 3.1.1.3) and compare it to the effects of the porcine pancreas (type II, EC 3.1.1.3), establishing, whenever possible, a structure–activity relationship. Methods: The inhibitory effects of a library of 48 flavonoids with different hydroxy, glycosyl, rutinosyl, galloyl, and extended alkyl groups were evaluated against PPL and HPL. The kinetic parameters and inhibitory mechanisms of the most active flavonoids were determined, and in silico docking studies of the more potent flavonoids were also performed, using the active site of HPL. Results/Conclusions: Variations in enzyme catalytic activity were observed depending on the source of the enzyme. The inhibitory effect was particularly influenced by the presence of extended alkyl groups at the C-3 of the C-ring and the C2=C3 double bond of the C-ring and the presence of a pyrogallol group at the C-2′, C-3′ and C-4′ of the B-ring. Docking results showed a strong correlation between docking scores and observed inhibitory activities, highlighting the critical role of specific substituents on the flavonoid backbone in enhancing detailed interaction dynamics with key amino acids. Compounds 28, 29, and 30, with alkyl groups, showed the highest docking scores, interacting with residues HIS151, PHE215, ARG256, and HIS263. Further analysis also revealed that specific substituents improved pocket occupancy and formed additional interactions with residues TYR114, PRO180, ILE209, and PHE215, which are crucial for inhibition. These binding characteristics closely mimic those observed with orlistat, reinforcing their mechanistic similarities in inhibiting HPL and validating their inhibitory activities.

Demystifying the Antidepressant Mechanism of Action of Stinels, a Novel Class of Neuroplastogens: Positive Allosteric Modulators of the NMDA Receptor

Plastogens are a class of therapeutics that function by rapidly promoting changes in neuroplasticity. A notable example, ketamine, is receiving great attention due to its combined rapid and long-term antidepressant effects. Ketamine is an N-methyl-D-aspartate receptor (NMDAR) antagonist, and, in addition to its therapeutic activity, it is associated with psychotomimetic and dissociative side effects. Stinels—rapastinel, apimostinel, and zelquistinel—are also plastogens not only with rapid and long-term antidepressant effects but also with improved safety and tolerability profiles compared to ketamine. Previous descriptions of the mechanism by which stinels modulate NMDAR activity have been inconsistent and, at times, contradictory. The purpose of this review is to clarify the mechanism of action and contextualize stinels within a broader class of NMDAR-targeting therapeutics. In this review, we present the rationale behind targeting NMDARs for treatment-resistant depression and other psychiatric conditions, describe the various mechanisms by which NMDAR activity is regulated by different classes of therapeutics, and present evidence for the stinel mechanism. In contrast with previous descriptions of glycine-like NMDAR partial agonists, we define stinels as positive allosteric modulators of NMDAR activity with a novel regulatory binding site.

Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base.

Large-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers. Tumor proteins having significant pan-cancer associations with muscle were referenced against secretome proteins secreted to blood from the Human Protein Atlas, then verified as increased in paired tumor vs. normal tissues in pan-cancer manner. This workflow revealed seven secreted proteins from cancers afflicting kidneys, head and neck, lungs and pancreas that classified as protein-binding activity modulator, extracellular matrix protein or intercellular signaling molecule. Concordance of these biomarkers with validated molecular signatures of cachexia and senescence supported relevance to muscle and cachexia disease biology, and high tumor expression of the biomarker set associated with lower overall survival. In this article, we discuss avenues by which skeletal muscle and cachexia may be regulated by these candidate pan-cancer proteins secreted to blood, and conceptualize a strategy that considers them collectively as a biomarker signature with potential for refinement by data analytics and radiogenomics for predictive testing of future risk in a non-invasive, blood-based panel amenable to broad uptake and early management.

Research on Small-Target Detection of Flax Pests and Diseases in Natural Environment by Integrating Similarity-Aware Activation Module and Bidirectional Feature Pyramid Network Module Features

In the detection of the pests and diseases of flax, early wilt disease is elusive, yellow leaf disease symptoms are easily confusing, and pest detection is hampered by issues such as diversity in species, difficulty in detection, and technological bottlenecks, posing significant challenges to detection efforts. To address these issues, this paper proposes a flax pest and disease detection method based on an improved YOLOv8n model. To enhance the detection accuracy and generalization capability of the model, this paper first employs the Albumentations library for data augmentation, which strengthens the model’s adaptability to complex environments by enriching the diversity of training samples. Secondly, in terms of model architecture, a Bidirectional Feature Pyramid Network (BiFPN) module is introduced to replace the original feature extraction network. Through bidirectional multi-scale feature fusion, the model’s ability to distinguish pests and diseases with similar features and large scale differences is effectively improved. Meanwhile, the integration of the SimAM attention mechanism enables the model to learn information from three-dimensional channels, enhancing its perception of pest and disease features. Additionally, this paper adopts the EIOU loss function to further optimize the model’s bounding box regression, reducing the distortion of bounding boxes caused by high sample variability. The experimental results demonstrate that the improved model achieves a significant detection performance on the flax pest and disease dataset, with notable improvements in the detection accuracy and mean average precision compared to the original YOLOv8n model. Finally, this paper proposes a YOLOv8n model with a four-headed detection design, which significantly enhances the detection capability for small targets such as pests and diseases with a size of 4 × 4 pixels or larger by introducing new detection heads and optimizing feature extraction. This method not only improves the detection accuracy for flax pests and diseases but also maintains a high computational efficiency, providing effective technical support for the rapid and precise detection of flax pests and diseases and possessing an important practical application value.

All-optical perception based on partially coherent optical neural networks.

In the field of image processing, optical neural networks offer advantages such as high speed, high throughput, and low energy consumption. However, most existing coherent optical neural networks (CONN) rely on coherent light sources to establish transmission models. The use of laser inputs and electro-optic modulation devices at the front end of these neural networks diminishes their computational capability and energy efficiency, thereby limiting their practical applications in object detection tasks. This paper proposes a partially coherent optical neural network (PCONN) transmission model based on mutual intensity modulation. This model does not depend on coherent light source inputs or active electro-optic modulation devices, allowing it to directly compute and infer using natural light after simple filtering, thus achieving full optical perception from light signal acquisition to computation and inference. Simulation results indicate that the model achieves a highest classification accuracy of 96.80% and 86.77% on the MNIST and Fashion-MNIST datasets, respectively. In a binary classification simulation test based on the ISDD segmentation dataset, the model attained an accuracy of 94.69%. It is estimated that this system's computational inference speed for object detection tasks is 100 times faster than that of traditional CONN, with energy efficiency approximately 50 times greater. In summary, our proposed PCONN model addresses the limitations of conventional optical neural networks in coherent light environments and is anticipated to find applications in practical object detection scenarios.

Hypothyroidism Alters Uterine Kisspeptin System and Activity Modulators in Cyclic Rats.

Hypothyroidism causes ovarian dysfunction and infertility in women and animals and impairs the hypothalamic expression of kisspeptin (Kp). However, kisspeptin is also expressed in the genital system, and the lack of the Kp receptor (Kiss1r) in the uterus is linked to reduced implantation rates. This study investigated the impact of hypothyroidism on the uterine expression of Kp and Kiss1r in female rats throughout the estrous cycle and the associated changes in uterine activity modulators. Hypothyroidism was induced through daily administration of propylthiouracil (PTU) over a period of 14 days. Plasma levels of LH, E2, and P4, cyclicity, body and uterine weight, uterine histomorphometry, and the gene and/or protein expression of Kiss1, Kiss1r, estrogen receptor α (ERα), progesterone receptor (PR), and thyroid hormone receptor α (TRα) were assessed. Additionally, proliferative activity (CDC-47) and the gene expression of uterine receptivity mediators (SMO, WNT4, BMP2, HAND2, MUC1, and LIF) were evaluated. Hypothyroidism prolonged the diestrus and increased progesterone levels during this phase, while decreasing luteinizing hormone and estradiol on proestrus. In the uterus, hypothyroidism reduced Kp immunostaining on diestrus and KISS1R mRNA levels on proestrus. These changes were accompanied by reduced endometrial glands, reduced uterine proliferative activity, and reduced ERα gene and protein expression. Additionally, hypothyroidism led to reduced uterine gene expression of LIF, BMP2, WNT4, and HAND2. On the other hand, thyroid hypofunction increased uterine PR and TRα immunostaining, while it reduced PGR gene expression on diestrus. These findings demonstrate that hypothyroidism reduces the expression of Kiss1/Kiss1r system in the uterus, which is associated with disrupted uterine estrogen and progesterone signaling and reduced expression of uterine receptivity mediators across the rat estrous cycle.

Girolline is a sequence context-selective modulator of eIF5A activity

Natural products have a long history of providing probes into protein biosynthesis, with many of these compounds serving as therapeutics. The marine natural product girolline has been described as an inhibitor of protein synthesis. Its precise mechanism of action, however, has remained unknown. The data we present here suggests that girolline is a sequence-selective modulator of translation factor eIF5A. Girolline interferes with ribosome-eIF5A interaction and induces ribosome stalling where translational progress is impeded, including on AAA-encoded lysine. Our data furthermore indicate that eIF5A plays a physiological role in ribosome-associated quality control and in maintaining the efficiency of translational progress. Girolline helped to deepen our understanding of the interplay between protein production and quality control in a physiological setting and offers a potent chemical tool to selectively modulate gene expression.

Characterization and modulation of human insulin degrading enzyme conformational dynamics to control enzyme activity.

Insulin degrading enzyme (IDE) is a dimeric 110 kDa M16A zinc metalloprotease that degrades amyloidogenic peptides diverse in shape and sequence, including insulin, amylin, and amyloid-β, to prevent toxic amyloid fibril formation. IDE has a hollow catalytic chamber formed by four homologous subdomains organized into two ~55 kDa N- and C- domains (IDE-N and IDE-C, respectively), in which peptides bind, unfold, and are repositioned for proteolysis. IDE is known to transition between a closed state, poised for catalysis, and an open state, able to release cleavage products and bind new substrate. Here, we present five cryoEM structures of the IDE dimer at 3.0-4.1 Å resolution, obtained in the presence of a sub-saturating concentration of insulin. Analysis of the heterogeneity within the particle populations comprising these structures combined with all-atom molecular dynamics (MD) simulations permitted a comprehensive characterization of IDE conformational dynamics. Our analysis identified the structural basis and key residues for these dynamics that were not revealed by IDE static structures. Notably arginine-668 serves as a molecular latch mediating the open-close transition and facilitates key protein motions through charge-swapping interactions at the IDE-N/C interface. Our size-exclusion chromatography-coupled small-angle X-ray scattering and enzymatic assays of an arginine-668 to alanine mutant indicate a profound alteration of conformational dynamics and catalytic activity. Taken together, this work highlights the power of integrating experimental and computational methodologies to understand protein dynamics, offers the molecular basis of unfoldase activity of IDE, and provides a new path forward towards the development of substrate-specific modulators of IDE activity.

FOXP2-immunoreactive corticothalamic neurons in neocortical layers 6a and 6b are tightly regulated by neuromodulatory systems.

The FOXP2/Foxp2 gene, linked to fine motor control in vertebrates, is a potential candidate gene thought to play a prominent role in human language production. It is expressed specifically in a subset of corticothalamic (CT) pyramidal cells (PCs) in layer 6 (L6) of the neocortex. These L6 FOXP2+ PCs project exclusively to the thalamus, with L6a PCs targeting first-order or both first- and higher-order thalamic nuclei, whereas L6b PCs connect only to higher-order nuclei. Synaptic connections established by both L6a and L6b FOXP2+ PCs have low release probabilities and respond strongly to acetylcholine (ACh), triggering action potential (AP) trains. Notably, L6b FOXP2- PCs are more sensitive to ACh than L6a, and L6b FOXP2+ PCs also react robustly to dopamine. Thus, FOXP2 labels L6a and L6b CT PCs, which are precisely regulated by neuromodulators, highlighting their roles as potent modulators of thalamic activity.

Biologically-targeted discovery-replication scan identifies G×G interaction in relation to risk of Barrett's esophagus and esophageal adenocarcinoma.

Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (A) known/plausible links to BE/EAC pathogenesis (n=493) or (B) prior evidence of biological interactions (n=4,196). ∼75 x 106 SNP×SNP interactions were screened via hierarchical group lasso (glinternet) using BEACON GWAS data. The top ∼2000 interactions retained in each scan were prioritized using P values from single logistic models. Identical scans were repeated among males only (78%), with two independent GWAS datasets used for replication. In overall and male-specific primary replications, 11 of 187 and 20 of 191 interactions satisfied P<0.05, respectively. The strongest evidence for secondary replication was for rs17744726×rs3217992 among males, with consistent directionality across all cohorts (Pmeta=2.19×10-8); rs3217992 "T" was associated with reduced risk only in individuals homozygous for rs17744726 "G". Rs3217992 maps to the CDKN2B 3'UTR and reportedly disrupts microRNA-mediated repression. Rs17744726 maps to an intronic enhancer region in BLK. Through in-silico prioritization and experimental validation, we identified a nearby proxy variant (rs4841556) as a functional modulator of enhancer activity. Enhancer-gene mapping and eQTLs implicated BLK and FAM167A as targets. The first systematic G×G investigation in BE/EAC, this study uncovers differential risk associations for CDKN2B variation by BLK genotype, suggesting novel biological dependency between two risk loci encoding key mediators of tumor suppression and inflammation.

Functionally active modulators targeting the LRRK2 WD40 repeat domain identified by FRASE-bot in CACHE Challenge #1.

Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenges emerged as real-life stress tests for computational hit-finding strategies. In CACHE Challenge #1, 23 participants contributed their original workflows to identify small-molecule ligands for the WD40 repeat (WDR) of LRRK2, a promising Parkinson's target. We applied the FRASE-based hit-finding robot (FRASE-bot), a platform for interaction-based screening allowing a drastic reduction of the explorable chemical space and a concurrent detection of putative ligand-binding sites. In two screening rounds, 84 compounds were procured for experimental testing and 8 were confirmed to bind LRRK2-WDR with dissociation constants (K d) ranging from 3 to 41 μM. To investigate the functional effect of WDR ligands, they were tested for their ability to modify the LRRK2 activity markers in HEK293T cells. Two compounds showed statistically significant increases in the kinase activity of WT LRRK2, and two compounds affected the conformation and kinase activity of major LRRK2 mutants.

Truncated TrkB: The predominant TrkB Isoform in Nociceptors.

Truncated TrkB (TrkBT1), traditionally considered a dominant-negative regulator of full-length TrkB (TrkBTK+), remains poorly understood in peripheral sensory neurons, particularly nociceptors. Furthermore, sensory neuronal TrkB expression and function has been traditionally associated with non-nociceptive neurons, particularly Aδ low-threshold mechanoreceptors. This study challenges prevailing assumptions by demonstrating that TrkBT1 is the predominant TrkB isoform expressed in sensory neurons and plays a functional role in modulating neuronal activity. We demonstrate that TrkBT1 is the predominant isoform expressed in nociceptors, identified by markers such as TRPV1, TRPA1, TRPM8 and 5HT3A, as well as non-nociceptors, while the full-length isoform (TrkBTK+) is restricted to non-nociceptive subpopulation. Functionally, we show that acute application of BDNF induces modest calcium influx in nociceptors and prolonged BDNF exposure significantly potentiates capsaicin-induced calcium influx, an effect blocked by the TrkB-specific antagonist ANA12. Additionally, BDNF also promotes the survival of both nociceptive and non-nociceptive neurons in culture, an effect dependent on TrkBT1 activity. Our data also reveal that ANA12 inhibits BDNF-mediated neuronal sensitization and survival in a concentration-dependent manner, implicating distinct TrkBT1 signaling pathways in these processes. Collectively, our findings redefine TrkBT1 as a functional modulator of nociceptor activity rather than a passive regulator of full-length TrkB. By uncovering its dual roles in nociceptor sensitization and survival, this study provides new insights into the molecular mechanisms of BDNF/TrkB signaling in pain. Future work evaluating the role of TrkBT1 in sensory biology could offer new perspectives on how this receptor contributes to neuronal function and plasticity during chronic pain conditions.

Multiple Binding Modes of Inhibitors to Human Carbonic Anhydrases: An Update on the Design of Isoform-Specific Modulators of Activity.

Human carbonic anhydrases (hCAs) are widespread zinc enzymes that catalyze the hydration of CO2 to bicarbonate and a proton. Currently, 15 isoforms have been identified, of which only 12 are catalytically active. Given their involvement in numerous physiological and pathological processes, hCAs are recognized therapeutic targets for the development of inhibitors with biomedical applications. However, despite massive development efforts, very few of the presently available hCA inhibitors show selectivity for a specific isoform. X-ray crystallography is a very useful tool for the rational drug design of enzyme inhibitors. In 2012 we published in Chemical Reviews a highly cited review on hCA family (Alterio, V. et al. Chem Rev. 2012, 112, 4421-4468), analyzing about 300 crystallographic structures of hCA/inhibitor complexes and describing the different CA inhibition mechanisms existing up to that date. However, in the period 2012-2023, almost 700 new hCA/inhibitor complex structures have been deposited in the PDB and a large number of new inhibitor classes have been discovered. Based on these considerations, the aim of this Review is to give a comprehensive update of the structural aspects of hCA/inhibitor interactions covering the period 2012-2023 and to recapitulate how this information can be used for the rational design of more selective versions of such inhibitors.

Distinct mechanisms drive divergent phenotypes in hypertrophic and dilated cardiomyopathy associated TPM1 variants.

Hypertrophic and dilated cardiomyopathies (HCM and DCM, respectively) are inherited disorders that may be caused by mutations to the same sarcomeric protein but have completely different clinical phenotypes. The precise mechanisms by which point mutations within the same gene bring about phenotypic diversity remain unclear. Our objective has been to develop a mechanistic explanation of diverging phenotypes in two TPM1 mutations, E62Q (HCM) and E54K (DCM). Drawing on data from the literature and experiments with stem cell-derived cardiomyocytes expressing the TPM1 mutations of interest, we constructed computational simulations that provide plausible explanations of the distinct muscle contractility caused by each variant. In E62Q, increased calcium sensitivity and hypercontractility was explained most accurately by a reduction in effective molecular stiffness of tropomyosin and alterations in its interactions with the actin thin filament that favor the 'closed' regulatory state. By contrast, the E54K mutation appeared to act via long-range allosteric interactions to increase the association rate of the C-terminal troponin I mobile domain to tropomyosin/actin. These mutation-linked molecular events produced diverging alterations in gene expression that can be observed in human engineered heart tissues. Modulators of myosin activity confirmed our proposed mechanisms by rescuing normal contractile behavior in accordance with predictions.