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Mei, R.

Publications and source records attributed to Mei, R..

5 recordsLinked to original sources

Mass-transfer-constrained thermodynamics links fluid motion to the preferential use of hydrogen and formate in syntrophic propionate oxidation

Syntrophic propionate oxidation in methanogenic environments depends on interspecies electron transfer through hydrogen and formate, yet the physical factors governing the relative use of these carriers remain poorly understood. Here, we examined how fluid motion alters electron-transfer energetics and pathway expression in the obligate syntrophic propionate oxidizer Pelotomaculum schinkii grown in coculture with Methanospirillum hungatei. A mass-transfer-constrained thermodynamic model was used to estimate H2 and formate concentrations at the P. schinkii cell surface and calculate the corresponding Gibbs free-energy change of H2- and formate-mediated propionate oxidation under different mixing conditions and growth stages. Transcriptomic analysis was used to assess expression of electron-transfer pathways. Under unmixed conditions, formate-mediated propionate oxidation was more thermodynamically favorable than the H2-mediated pathway, consistent with highly expressed genes involved in formate production. Mixing altered coculture activity and pathway energetics. H2 was more sensitive to mixing and certain conditions shifted the energetic advantage toward H2. Expression of the major hydrogenases and formate dehydrogenases generally tracked these pathway-specific energetic changes. These results show that fluid motion reshapes the near-cell thermodynamic favorability and enables condition- and growth-stage-dependent use of alternative electron-transfer pathways. Fluid motion should therefore be considered an ecological and engineering control on syntrophic metabolism.

microbiology↗

Epidemiological evaluation and identification of the insect vector of soybean stay-green associated virus

In recent years, the emergence of soybean stay-green syndrome (SGS), also referred to as "zhengqing", in the Huang-Huai-Hai region of China has resulted in significant yield losses, with some areas experiencing a complete reduction in seed yield. SGS is a phenomenon characterized by the delayed senescence of soybean, resulting in stay-green leaves, flat pods, and stunted seed development at harvest. Our group was the first to identify a distinct geminivirus, named soybean stay-green associated geminivirus (SoSGV), as the causative agent of SGS by fulfilling Kochs postulate. To further understand the epidemiology of SoSGV, in this study, we collected 368 stay-green samples from 17 regions in 8 provinces including the Huang-Huai-Hai region and surrounding areas of China. The results showed that 228 samples tested positive for SoSGV (61.96%), and 96.93% of these positive samples showed severe pod deflation. Our epidemiological assessment reveals SGS caused by the SoSGV is prevalent in the fields, and it is undergoing geographical expansion and genetic differentiation. Additionally, we determined the other natural hosts grown in the Huang-Huai-Hai region of China. By capturing insects in the field and conducting laboratory vector transmission tests, we confirmed that the common brown leafhopper (Orosius orientalis) is the transmitting vector of SoSGV. With a better understanding of the epidemiology of SoSGV and its transmission, we can develop more effective strategies for managing and mitigating its impact on soybean yields.

microbiology↗

The "replacing surgery" of cpDNA: de novo chemical synthesis and in vivo functional testing of Chlamydomonas chloroplast genome

We have successfully designed and synthesized the 221,372-bp cpDNA SynCpV1.0 with the native cpDNA of Chlamydomonas reinhardtii as the template. Homoplasmic SynCpv1.0-harboring algal strains were obtained by biolistic transformation and selected with an ascending gradient of antibiotic pressure. Meanwhile, we were pleasantly surprised to find that SynCpV1.0 was able to re-introduce and replicate normally after the total DNA of transplastomic algal strains were transformed to Escherichia coli, it indicated that SynCpV1.0 was able to shuttle between C. reinhardtii and E. coli. Finally, we analyzed the photosynthetic properties of SynCpV1.0-harboring transplastomic strains, the results showed that they exhibited the same photosynthetic efficiency as the wild strain of C. reinhardtii CC125, and could rescue the photosynthetic defect in mutant strain of C. reinhardtii CC5168. Herein, we have performed the "replacing surgery" of cpDNA and established an ideal platform to complete multiple cycles of "Design-Build-Test" for optimizing the cpDNA of photosynthetic organisms. HighlightO_LIAn artificial cpDNA SynCpV1.0 is constructed by de novo chemical synthesis. C_LIO_LIThe "replacing surgery" of cpDNA was performed in the chloroplast of C. reinhardtii C_LIO_LIIt is found that artificial cpDNA was able to shuttle between Chlamydomonas chloroplast and E. coli. C_LIO_LIEstablish an ideal platform to complete multiple cycles of "Design-Build-Test" for optimizing the cpDNA. C_LI One-Sentence SummaryThe chloroplast genome can be replaced by a complete synthesized genome and performs the designed biological function in C. reinhardtii.

molecular biology↗

An intergeneric recombinant geminivirus causes soybean stay-green disease

Soybean is one of the most valuable legume crops in the world with high nutritional value. Recently, the outbreak of soybean stay-green syndrome has swept the soybean production in the Huang-Huai-Hai region of China, resulting in huge yield losses, which has become an epidemic and prominent problem in soybean production. However, the cause of the stay-green syndrome remains obscure. Here, we report a novel intergeneric recombinant geminivirus which causes soybean stay-green symptoms. Viral small RNA-based screening identified a new recombinant geminvirus from field soybean stay-green samples. The complete genome sequence of the virus contains 2762 nucleotide (nt) and appears to be an intergeneric recombinant virus in which protein coding for coat protein (V1) is similar to member of genus Mastrevirus, whereas proteins coding for V2, C2, C3 are most similar to those of viruses in the Maldovirus genus, and C1 and C4 are most similar to virus in genus Begomovirus. Inoculation of the infectious clone of the recombinant geminivirus through Agrobacterium rhizogenes causes typical soybean stay-green syndrome which resembles field symptoms including delayed leaf senescence, flat pods and abnormal seeds. The recombinant geminivirus can be detected in seed coat but not in cotyledon and embryo, thus failing to be transmitted by seeds. Moreover, the genome variation and epidemiological dynamic analysis were also carried out to help the continuous epidemiological surveillance of this emerging geminivirus. Collectively, this new geminivirus is tentatively named soybean stay-green associated virus (SoSGV). Our determination of the causal agent of soybean stay-green disease will bolster efforts to develop effective management strategies to control this prevalent disease in the field.

microbiology↗

Machine learning aided analyses of thousands of draft genomes reveal plant- and environment-specific features of activated sludge process

Microorganisms in activated sludge (AS) play key roles in the wastewater treatment process. However, the ecological behavior of microorganisms in AS and their differences with microorganisms in other environments have mainly been studied using 16S rRNA gene that may not truly represent their in-situ functions. Here, we present 2045 bacterial and archaeal metagenome-assembled genomes (MAGs) recovered from 1.35 Tb of metagenomic sequencing data generated from 114 AS samples of 23 full-scale wastewater treatment plants (WWTPs). The average completeness and contamination of the MAGs are 82.0% and 2.0%, respectively. We find that the AS MAGs have obviously plant-specific features and few proteins are shared by different WWTPs, especially for WWTPs located in geographically distant areas. Despite the differences, specific functional traits (e.g. functions related to aerobic metabolism, nutrient sensing/acquisition, biofilm formation, etc.) of AS MAGs could be identified by a machine learning approach, and based on these traits, AS MAGs could be differentiated from MAGs of other environments with an accuracy of 96.6%. Our work provides valuable genome resources for future investigation of the AS microbiome and also introduces a novel approach to understand the microbial ecology in different ecosystems.

microbiology↗