Ranran Huang

Oxygenic and anoxygenic photosynthesis have long been considered defining traits of cyanobacteria. However, whether the important cyanobacterial genus Synechococcus is capable of anoxygenic photosynthesis remains unconfirmed. Here, we report that Synechococcus sp. PCC 7002 is capable of anoxygenic photosynthesis when sulfide (H₂S) is supplied as the sole electron donor. Combining the targeted deletion of the sulfide: quinone oxidoreductase gene (Δsqr) with 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) mediated the inhibition of photosystem II. We demonstrated that SQR-mediated H₂S oxidation sustains light-dependent CO₂ fixation in the absence of O₂ evolution. Our genome-wide transcriptomic profiling further revealed that polysulfide (H₂S_n) and hydrogen peroxide (H₂O₂) function as distinct signaling molecules in oxygenic and anoxygenic photosynthesis, modulating central carbon and energy metabolism. In central carbon metabolism, H₂S_n markedly upregulates the expression of key genes, including psbA, petC, rbcL, and rbcS, whereas H₂O₂ downregulates these genes. Within energy metabolism, both molecules converge on oxidative phosphorylation by upregulating genes encoding NADH dehydrogenase and ATP synthase. Furthermore, H₂Sₙ treatment uniquely induces sulfur-assimilation and ROS-detoxifying enzymes, conferring a markedly higher tolerance than H₂O₂. These findings provide direct evidence of anoxygenic photosynthesis in the genus Synechococcus and uncover a dual regulatory network that allows Synechococcus sp. PCC 7002 to balance redox homeostasis under fluctuating oxic/anoxic conditions.

The background perturbations in hypersonic flows have a significant impact on boundary layer transitions. As one of the primary approaches of fluid dynamic studies, the traditional noisy wind tunnels are dominated by the acoustic disturbances radiated from hypersonic turbulent boundary layer on the nozzle. A large number of investigations have focused on the overall characteristics of acoustic radiation. However, few have looked into the variation of the characteristics with the frequency and scale of disturbances. Therefore, the propagation characteristics of acoustic wave varying with frequency is thoroughly investigated in Mach-6 hypersonic flow in this study by experimental and numerical methods. The experiment of the two hot-wire probes indicated that the convection velocity of low-frequency acoustic disturbances is ∼60%–75% of the freestream speed and tends to increase with frequency. Direct numerical simulation showed that the primary radiation angle determined by low-frequency disturbances is about 30°. The correlation analysis showed that the convection velocities normalized by the freestream velocity for high-frequency disturbances are about 83.3%, resulting in a radiation angle approaching 90°. Meanwhile, measurements by focused laser differential interferometer also revealed that the high-frequency disturbances had a preferred gradient direction of 0°, indicating a radiation angle of 90°. In general, the current study indicates that the radiation angles and convection velocities of disturbances increase from 30° to 90° and from 67% to 83.3%, respectively, as the frequency increases.

Marine microorganisms are increasingly recognized as primary producers of marine secondary metabolites, drawing growing research interest. Many of these organisms are unculturable, posing challenges for study. Metagenomic techniques enable research on these unculturable microorganisms, identifying various biosynthetic gene clusters (BGCs) related to marine microbial secondary metabolites, thereby unveiling their secrets. This review comprehensively analyses metagenomic methods used in discovering marine microbial secondary metabolites, highlighting tools commonly employed in BGC identification, and discussing the potential and challenges in this field. It emphasizes the key role of metagenomics in unveiling secondary metabolites, particularly in marine sponges and tunicates. The review also explores current limitations in studying these metabolites through metagenomics, noting how long‐read sequencing technologies and the evolution of computational biology tools offer more possibilities for BGC discovery. Furthermore, the development of synthetic biology allows experimental validation of computationally identified BGCs, showcasing the vast potential of metagenomics in mining marine microbial secondary metabolites.

A two-point cylindrical-focused laser differential interferometer (2P-CFLDI) system and a conventional Z-type Schlieren were used to measure the hypersonic turbulent boundary layer on a flat plate at Mach number Ma = 6 and Reynolds number Re = 1.08 × 10⁶ m⁻¹. The boundary layer thickness at the measurement location and the noise radiation angle were obtained by post-processing the Schlieren image. The 2P-CFLDI data underwent cross-correlation analysis to calculate the mean convective velocities at different heights and compared with previous experimental and numerical results. The experimentally measured mean convective velocities agree with the trend of available DNS and experimental results. The mean convective velocity near the wall is significantly larger than the local mean velocity and is the main noise source region. Further filtering treatment shows that the convective velocity of the disturbed structure decreases gradually with the increase in the disturbance scale. The differences between convective velocities at different scales are significantly larger outside the boundary layer than inside the boundary layer, which is in agreement with the findings of the previous hot wire experiments. Near the wall, large-scale disturbances mainly determine the localized mean convective velocity, which are the main source of noise radiation for the hypersonic turbulent boundary layer.

Flow similarity is one of the most desirable features for turbulence under different conditions. For hypersonic boundary layers, different wall temperatures are known to change the turbulence evolution by influencing the intercomponent energy transfer, while the flow similarity has received less attention. Based on direct numerical simulations, this work investigates the spectral distribution of velocity components and pressure–strain correlations for hypersonic boundary layers at Mach 6 and wall-to-recovery temperature ratios equal to 0.3, 0.5, 0.76, and 1. The wall-normal evolution of turbulent structures is compared by the spectra peak scale λSP, wall-normal location ySP, and the vertical profiles of peak scale λP. For all cases, the peak scale profiles exhibit linear increases with a height of 0.1≤y/δ≤0.25. The linear growth of the turbulence scale in the logarithmic region indicates the existence of self-similar structures, and the agreement of peak scales in the outer scaling suggests the flow similarity under different wall temperatures. Likewise, the streamwise pressure–strain term has the peak scale profile linearly dependent on the height for the same vertical range 0.1≤y/δ≤0.25 with velocity components, while the wall-normal and spanwise terms exhibit both linear and constant vertical dependence depending on the wall temperatures. With the increase in wall temperature, the peak scale exhibits broader constant vertical dependence and narrower linear vertical dependence. The cospectral analysis further confirms that the pressure–strain correlations are maximized at different spanwise-vertical aspect ratios of turbulence structures.

Selective RNA processing and stabilization (SRPS) facilitates the differential expression of multiple genes in polycistronic operons. However, how the coordinated actions of SRPS‐related enzymes affect stoichiometric regulation remains unclear. In the present study, the first genome‐wide targetome analysis is reported of these enzymes in Escherichia coli, at a single‐nucleotide resolution. A strictly linear relationship is observed between the RNA pyrophosphohydrolase processing ratio and scores assigned to the first three nucleotides of the primary transcript. Stem‐loops associated with PNPase targetomes exhibit a folding free energy that is negatively correlated with the termination ratio of PNPase at the 3′ end. More than one‐tenth of the RNase E processing sites in the 5′‐untranslated regions（UTR） form different stem‐loops that affect ribosome‐binding and translation efficiency. The effectiveness of the SRPS elements is validated using a dual‐fluorescence reporter system. The findings highlight a multi‐layer and quantitative regulatory method for optimizing the stoichiometric expression of genes in bacteria and promoting the application of SRPS in synthetic biology.

Genome mining of metagenomic data has become the preferred method for the bioprospecting of novel compounds by cataloguing secondary metabolite potential. However, the accurate detection of BGCs requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until recently with new long-read technologies.

Introduction: In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters.Methods: In order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder, respectively. EVMP also provides optional data augmentation, which generates multiple copies of the data by selecting different base promoters for the same synthetic promoter.Results: In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% (R²). Data augmentation based on multiple base promoters further improved the model performance by 17.95% (MAE) and 7.25% (R²) compared with non-EVMP SOTA record.Discussion: In further study, extended vision (or k-mer) is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. The source code is publicly available on GitHub: https://github.com/Tiny-Snow/EVMP.