Enzymes present a sustainable alternative to traditional chemical industries, drug synthesis, and bioremediation applications. Because catalytic residues are the key amino acids that drive enzyme function, their accurate prediction facilitates enzyme function prediction. Sequence similarity-based approaches such as BLAST are fast but require previously annotated homologues. Machine-learning (ML) approaches aim to overcome this limitation; however, current gold-standard ML-based methods require high-quality 3D structures limiting their application to large datasets. To address these challenges, we developed Squidly, a sequence-only tool that leverages contrastive representation learning with a biology-informed, rationally designed pairing scheme to distinguish catalytic from non-catalytic residues using per-token Protein Language Model embeddings. Squidly surpasses state-of-the-art ML annotation methods in catalytic residue prediction while remaining sufficiently fast to enable wide-scale screening of databases. We ensemble Squidly with BLAST to provide an efficient tool that annotates catalytic residues with high precision and recall for both in- and out-of-distribution sequences.
While extracellular DNA (eDNA) persistence substantially influences soil microbiome investigations, its degradation kinetics remain poorly quantified. Here, we developed a primer-labeled DNA approach coupled with microcosm incubation to determine the overall and sequence-specific degradation rates of eDNA amplicon fragments across China. We observed substantial variations in the overall degradation rates of extracellular 16S rRNA gene amplicon fragments among the study sites, with degradation rate constants ranging from 0.05 to 0.16 day−1. The overall degradation rate constants showed significant correlations with soil moisture content, prokaryotic abundance, prokaryotic community profiles, and mean annual precipitation. The significant influences of moisture content on the overall degradation rates were further verified by a moisture gradient microcosm experiment. The sequence-specific degradation rate constant profiles were additionally correlated with pH, nitrogen content, and mean annual temperature. Furthermore, propidium monoazide-based exclusion of eDNA signals significantly altered soil prokaryotic abundance, richness, and prokaryotic community profiles, and the pool sizes of sequence-specific extracellular 16S rRNA gene amplicon fragments were significantly correlated with their respective degradation rates. This study developed a methodology for determining the overall and sequence-specific degradation rates of eDNA amplicon fragments, highlighting the profound influences of eDNA on soil microbial research and informing the optimization of environmental DNA technologies.
A key question in human neuroscience is to understand how individual differences in brain function relate to cognitive differences. However, the optimal condition of brain function to study between-person differences in cognition remains unclear. While many studies have developed objective biomarkers to accurately predict intelligence and general cognition, consensus on domain-specific markers has not yet emerged. Brain age has been proposed as a potential candidate, but recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting a shift toward approaches focused directly on cognitive prediction. Using a deep learning approach, we evaluated the predictive power of the functional connectome during various states (resting state, movie-watching, and n-back) on episodic memory and working memory performance. Our findings show that connectomes during tasks, especially during movie-watching, predict individual differences across cognitive domains, while resting state connectomes predict episodic memory meaningfully. Furthermore, individuals with a negative brain cognition gap (where brain predictions underestimate actual performance) exhibited lower physical activity and higher cardiovascular risk compared to those with a positive gap. This shows that knowledge of the brain cognition gap provides insights into factors contributing to cognitive resilience. Further, lower PET-derived measures of dopamine binding were linked to a greater brain cognition gap, mediated by regional functional variability. Together, our findings highlight the importance of brain state in connectome-based cognitive prediction and introduce the brain cognition gap as a potentially informative, dopamine-modulated marker of vulnerability to compromise brain function.
Social hierarchies structure groups and confer advantages on high-ranking individuals. In mice, individual position in hierarchies may emerge situationally from current group compositions, or, alternatively, may remain largely stable across groups as an internalized feature. Dominance and subordination are expressed in behaviors like tube competitions or agonistic chasing. The interaction of these behaviors in the shaping of social position in larger male mouse groups remains largely unknown. To address these questions, we developed the NoSeMaze, a semi-naturalistic, open-source, modular platform that enables automated long-term tracking of unperturbed groups. Across more than 4000 mouse-days, hierarchies derived from incidental competitions in the integrated tube tests were non-despotic, transitive, and stable even when group compositions changed. This stability supports an internalized component of competition-based social rank. Chasing was also stable across contexts. Notably, chasing was concentrated among high-ranking individuals, consistent with ongoing negotiation of social rank among individuals at the upper end of the hierarchy. The link between chasing and social rank strengthened in groups with less well-defined rank structure, where mice rely more on aggressive signaling to assert their position. Chasing and social rank were associated with certain dimensions of simultaneously measured physical and cognitive features. In summary, high-dimensional tracking with the NoSeMaze reveals that social position in mice is multifaceted and shaped by stable dimensions of individual behavior that persist across changing social contexts. The approach thus enables longitudinal modeling of individuality and social position as key resilience factors.
While aging is the greatest risk factor for the development of neurodegenerative disease, the role of aging in these diseases is poorly understood. Our previous work has shown that targeting aging pathways can be neuroprotective in animal models of neurodegenerative disease. Based on these findings, we believe that by gaining insight into the aging process that knowledge can be applied to identify novel therapeutic targets for neurodegenerative disease. To advance our understanding of aging, we used a genomics approach to identify genes regulated by multiple lifespan-extending pathways. We performed RNA sequencing on nine long-lived Caenorhabditis elegans mutants representing seven longevity pathways: insulin/IGF-1 signaling, dietary restriction, germline deficiency, impaired chemosensation, reduced translation, elevated mitochondrial ROS, and mild mitochondrial impairment. We found that most pairs of long-lived mutants exhibited a significant overlap in differentially expressed genes. Comparing gene expression across the entire panel of long-lived mutants revealed three distinct longevity groups that could be clearly distinguished by gene expression. Interestingly, two of these groups showed modulation of specific genetic pathways in opposite directions, suggesting that there are multiple alternative strategies to achieving long life. Filtering for genes similarly modulated in at least six mutants identified 196 upregulated and 62 downregulated aging genes. Upregulated genes were enriched in immunity, defense, and metabolism, while many downregulated genes impacted translation and gene expression. To assess the ability of these genes to enhance longevity individually, we knocked down the commonly upregulated genes in long-lived mutants and evaluated the resulting effect on lifespan. Using this approach, we identified several genes that affect lifespan individually. Upregulation of at least some of these genes was sufficient to enhance stress resistance and extend lifespan in wild-type worms. Overall, the shared longevity genes identified in this work offer potential targets to promote healthy aging and decrease age-onset disease.
Monkeys generalize many visual categorization rules, such as animate versus inanimate, but fail on culturally defined ones, placing their behavior closer to networks trained on images alone than to humans.
The transcription factor CHOP helps cells switch from an emergency stress response to a chronic one, where cells survive but lose some of the functions that define their identity.
Urbanization is a major global driver of biodiversity change, with species responses to urban settings ranging from avoidance to exploitation. To better understand these responses, we conducted a global analysis of urban relative affinity inferred from occurrence data across more than 30,000 animal and plant species. Our synthesis showed a consistent pattern across taxa and biogeographic regions: many species are urban avoiders, while few thrive as urban exploiters—a pattern we coin ‘species urbanness distribution’. We then assessed whether body size, an integrative ecological trait fundamental to space use, mobility, metabolism, and environmental sensitivity, showed consistent associations with urban affinity among species and across 371 taxonomic families. Analyses were conducted at the interspecific level and focused primarily on variation among taxonomic families (with an accompanying application to view results available for each family here: https://globalecologyresearchgroup.github.io/Callaghan_et_al-2026-eLife-ShinyApp/). Larger body sizes were generally associated with greater urban affinity in plants compared to animals, though these size-affinity relationships showed considerable variability among families. Our findings highlight the heterogeneous relationship between body size and urban affinity across the tree of life, underscoring the importance of tailored strategies to support urban biodiversity. This research advances ecological understanding of urban filtering and provides a framework for guiding biodiversity-sensitive urban planning amid accelerating global urbanization.
Dendritic spine dysfunction may contribute to the etiology and symptom expression of neuropsychiatric disorders. The intimate relationship between spine morphology and function suggests that decoding disease-related abnormalities from spine morphology can aid in developing synapse-targeted interventions. Here, we describe a population analysis of dendritic spine nanostructure applied to the objective grouping of multiple mouse models of neuropsychiatric disorders. This method has identified two major groups of spine phenotypes linked to schizophrenia and autism spectrum disorder (ASD). An increase in spine subpopulation with small volumes characterized the spines of schizophrenia-associated mouse models, whereas a spine subset with large volumes increased in ASD models. Schizophrenia-associated mouse models showed higher similarity in spine morphology, driven by reduced size and growth of nascent spines. The expression of Ecrg4, a gene encoding small secretory peptides, was increased in schizophrenia-associated mouse models, and functional studies confirmed its critical involvement in impaired spine dynamics and shape. These results suggest that population-level spine analysis provides rich insights into heterogeneous spine pathology, facilitating the identification of new molecular targets related to core synaptic dysfunction.
Mutations in the MECP2 gene cause the severe neurological disorder Rett syndrome. A cluster of frameshift-causing C-terminal deletions (CTDs) removes ~100 amino acids and accounts for approximately 10% of RTT-causing mutations. Their pathogenicity is unexpected because this C-terminal domain is dispensable in mice. Analysis of pathogenic and benign human MECP2 variants reveals that some individuals with apparently typical CTDs do not develop Rett syndrome, confirming that C-terminal truncations are not intrinsically pathogenic. Using human sequence data and mouse models we show that pathogenicity results from a marked reduction in MeCP2 levels and depends on the presence of a proline proline stop motif (-PPX) generated by a shift to the +2 reading frame. CTDs that shift to the +1 frame avoid this motif and are benign. Replacing the stop codon of the PPX motif with tryptophan restores MeCP2 expression and rescues RTT-like phenotypes in a CTD mouse model. An adenine base editor efficiently introduces this substitution in cultured cells. These findings define a reliable prognostic distinction between benign and pathogenic CTDs and establish a potential editing strategy for correcting disease-causing CTD mutations.
The formation of condensates by the Linker for the Activation of T-cells (LAT) is a key signal gating and amplification step in the T-cell receptor signaling pathway. LAT condensation is challenging to study in-vivo and is therefore often investigated using reconstitution experiments. While these experiments recapitulate key aspects of LAT condensation, they also exhibit some puzzling features. Here, we describe the mechanisms underlying these observations using two complementary models. First, we employ a Smoluchowski aggregation model to show that the delay time before condensation is observed arises from a low effective binding probability between LAT monomers. Second, we propose a field-theoretic model that reproduces all condensate morphologies observed in experiments, showing that they can arise from common underlying dynamics modulated by variations in experimental conditions. This result unifies different experimental observations reported previously. While this article addresses open questions regarding the formation of LAT condensates, our results also provide a common framework for understanding condensation of other multivalent membrane proteins such as EGFR, FGFR2, and nephrin.
Background: Endometrial cancer (EC) is a common gynecological malignancy with increasing incidence. While several serum biomarkers have been studied for EC, their combined prognostic value remains unclear. This study aimed to evaluate the prognostic significance of preoperative serum CA125, CA19-9, CA72-4, CEA, and AFP levels in EC patients and develop a risk score for predicting survival outcomes. Methods: A retrospective cohort study of 2,081 EC patients was conducted at Shengjing Hospital of China Medical University. Serum biomarker levels and clinicopathological data were collected. Univariate and multivariate Cox proportional hazard models were used to identify independent prognostic factors. A risk score based on CA125, CEA, and AFP levels was developed using LASSO-Cox regression, and nomograms were constructed for survival prediction. Results: Multivariate analysis identified elevated CA125 (P=0.003), AFP (P<0.0001), and CEA (P=0.014) as independent factors for overall survival (OS). These markers were also independent predictors of progression-free survival (PFS). The risk score incorporating CA125, AFP, and CEA was an independent indicator for both PFS (P<0.0001) and OS (P<0.0001). Nomograms based on the risk score and clinicopathological features demonstrated good predictive ability and calibration for survival outcomes. Conclusions: The risk score based on preoperative serum levels of CA125, CEA, and AFP is a valuable prognostic tool for predicting PFS and OS in EC patients. Nomograms incorporating this risk score accurately predict EC prognosis and may aid in clinical decision-making. Funding: This study was supported by the National Key R&D Program of China (Program Nos. 2022YFC2704400, The National Natural Science Foundation of China (No. 81872123 and 81472438); University innovation team of Liaoning Province; Special Professor of Liaoning Province 'Major Special Construction Plan' for Discipline Construction of China Medical University in 2018 (No. 3110118029); Outstanding Scientific Fund of Shengjing Hospital(No. 201601) 'Major Special Construction Plan' for Discipline Construction of China Medical University in 2018.
Tailoring malaria control interventions to regional transmission dynamics and behavioural characteristics can optimise them in resource-limited settings.
Bacterial population decline at antibiotic concentrations above the minimum inhibitory concentration (MIC) remains poorly characterized. This is because colony-forming units (CFU), the standard method to quantify inhibition, are slow, labor-intensive, and costly. Luminescence assays are widely used to quantify population dynamics at subinhibitory concentrations, yet their limitations and reliability at high concentrations remain underexplored. Here, we compared luminescence- and CFU-based rates in Escherichia coli across 20 antimicrobials. In our experiments, luminescence- and CFU-based rates did not differ significantly for half of them. For the other half, CFU-based decline rates were consistently higher. The estimates differed for two main reasons: First, because light intensity tracks biomass more closely than population size, luminescence declined more slowly than the population when bacteria filamented. Second, CFU-based estimates indicated a steeper decline when treatment reduced the number of colonies formed per plated bacterium. This can result from changes in clustering behavior, physiological changes that impair culturability, or antimicrobial carryover. Thus, the suitability of luminescence to quantify bacterial decline depends on the physiological effects of the antimicrobial and whether the quantity of interest is cell number or biomass. Within these limitations, luminescence can serve as an efficient, high-throughput alternative for quantifying bacterial dynamics at super-MIC concentrations.
