Search

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 upregulation and re-localization of glucose and ammonium transporters. These molecular responses are critical to support symbiont functioning and organism-wide nitrogen assimilation through GS/GOGAT-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.One-sentence summaryWhole-organism nitrogen assimilation fueled by glucose from symbiotic algae enables corals to flourish in oligotrophic waters.

Symbiotic associations with Symbiodiniaceae have evolved independently across a diverse range of cnidarian taxa including reef-building corals, 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 employ the same mechanism of symbiont control in which symbiont-derived glucose is used to assimilate nitrogenous waste via amino acid biosynthesis to limit the availability of nitrogen to the symbionts. In this metabolic interaction, glucose significantly reduces symbiont density while ammonium promotes symbiont proliferation. We show that glucose-derived 13C and ammonium-derived 15N are co-incorporated into amino acids by the hosts. Metabolic differences between the hosts further suggest that corals are more susceptible to environmental stress and symbiosis breakdown due to their increased energy demands to satisfy calcification. Our results reveal the general metabolic interaction underlying these symbioses and provide a parsimonious explanation for their repeated evolution.

The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antigenic profile evolves in response to the vaccine and natural infection-derived immune pressure, resulting in immune escape and threatening public health. Exploring the possible antigenic evolutionary potentials improves public health preparedness, but it is limited by the lack of experimental assays as the sequence space is exponentially large. Here we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithm to model the viral fitness landscape and explore the antigenic evolution viain silicodirected evolution. As demonstrated by existing SARS-COV-2 variants, MLAEP can infer the order of variants along antigenic evolutionary trajectories, which is also strongly correlated with their sampling time. The novel mutations predicted by MLAEP are also found in immunocompromised covid patients and newly emerging variants, like XBB1.5. The predictions of MLAEP were validated by conducting in vitro neutralizing antibody binding assay, which demonstrated that the model-generated variants displayed significantly increased immune evasion ability compared with the controls. In sum, our approach enables profiling existing variants and forecasting prospective antigenic variants, thus may help guide the development of vaccines and increase preparedness against future variants. Our model is available athttps://mlaep.cbrc.kaust.edu.sa.

Daily concentrations of air pollution are associated with lower respiratory diseases. We investigated the short-term association of ambient air pollution with daily hospital admissions due to pneumonia among children aged 0−17 in Guangzhou city of China. Ambient air pollutants were all positively associated with children hospital admissions due to pneumonia of all ages. Significant associations were found for air pollutants except for inhalable particulate matter (PM) \(\le\)10 µm in aerodynamic diameter (PM10) in children of all ages. Increments of an interquartile range (IQR) (279.10µg/m3 and 28.42µg/m3, respectively) in the 7-day-average level of carbon monoxide (CO) and nitrogen dioxide (NO2) were associated with a 26.17% (95% confidence interval (CI) 1.40%-56.98%) and 25.09% (95%CI 0.54%-55.64%) increase in pneumonia hospitalizations for children aged 6−17, respectively. An IQR increase in CO concentrations was associated with a 15.15% (95%CI 4.34%-27.08%) increase in pneumonia hospitalizations for children aged 1−5. Daily cases of microbial detection for pneumonia were positively associated with daily NO2 concentration. The pneumonia hospitalizations due to Mycoplasma pneumonia, Flu A virus and Flu B virus, the predominant pathogenic microorganisms detected in children aged 0−5 are apparently associated with levels of PMs, CO, NO2 and O3.

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool to reveal the complex biological diversity and heterogeneity among cell populations. However, the technical noise and bias of the technology still have negative impacts on the downstream analysis. Here, we present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events. In the task, the deep learning model learns to pull together the representations of similar cells while pushing apart distinct cells, without manual labeling. 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. Further experiments to process a million-scale single-cell dataset demonstrate the scalability of CLEAR. This scalable method generates effective scRNA-seq data representation while eliminating technical noise, and it will serve as a general computational framework for single-cell data analysis.

Aiming at the characteristics that slaughterhouse wastewater contains organic pollution and ammonia nitrogen with high concentration, the combined technological treatment of AB method and internal recycle BAF are adopted. The test studies hydraulic retention time (HRT) of section A and section B of AB method and the effect of influent water loading on the COD removal, effect of the HRT of internal recycle BAF on NH 3 -N removal effect, and recovery situation after backwash of internal recycle BAF. The result shows that for section A, when HRT is 50∼55 min, and COD load is 15∼18 kg/m3·d, COD removal rate is 55∼60%; for section B, when HRT is 8∼10h, and COD load is 0.60∼0.75 kg/m3·d, COD removal rate is 75∼80%; and for internal recycle BAF, when HRT is 2.0h and dissolved oxygen (DO) is ≮2.5mg/L, NH 3 -N removal rate is 83%; and internal recycle BAF can be restored to normal levels in 3h after backwash.