A carbon-nitrogen negative feedback loop underlies the repeated evolution of cnidarian-Symbiodiniaceae symbioses.

Symbiotic associations with Symbiodiniaceae have evolved independently across a diverse range of cnidarian taxa including reef-building corals, sea anemones, and jellyfish, yet the molecular mechanisms underlying their regulation and repeated evolution are still elusive. Here, we show that despite their independent evolution, cnidarian hosts use the same carbon-nitrogen negative feedback loop to control symbiont proliferation. Symbiont-derived photosynthates are used to assimilate nitrogenous waste via glutamine synthetase-glutamate synthase-mediated amino acid biosynthesis in a carbon-dependent manner, which regulates the availability of nitrogen to the symbionts. Using nutrient supplementation experiments, we show that the provision of additional carbohydrates significantly reduces symbiont density while ammonium promotes symbiont proliferation. High-resolution metabolic analysis confirmed that all hosts co-incorporated glucose-derived 13C and ammonium-derived 15N via glutamine synthetase-glutamate synthase-mediated amino acid biosynthesis. Our results reveal a general carbon-nitrogen negative feedback loop underlying these symbioses and provide a parsimonious explanation for their repeated evolution.

Predicting the antigenic evolution of SARS-COV-2 with deep learning.

The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.

Molecular insights into the Darwin paradox of coral reefs from the sea anemone Aiptasia.

Symbiotic cnidarians such as corals and anemones form highly productive and biodiverse coral reef ecosystems in nutrient-poor ocean environments, a phenomenon known as Darwin's paradox. Resolving this paradox requires elucidating the molecular bases of efficient nutrient distribution and recycling in the cnidarian-dinoflagellate symbiosis. Using the sea anemone Aiptasia, we show that during symbiosis, the increased availability of glucose and the presence of the algae jointly induce the coordinated up-regulation and relocalization of glucose and ammonium transporters. These molecular responses are critical to support symbiont functioning and organism-wide nitrogen assimilation through glutamine synthetase/glutamate synthase-mediated amino acid biosynthesis. Our results reveal crucial aspects of the molecular mechanisms underlying nitrogen conservation and recycling in these organisms that allow them to thrive in the nitrogen-poor ocean environments.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis.

We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream analysis. Compared with current methods, CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events simultaneously. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43 695 single cells from peripheral blood mononuclear cells.