Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning

A combination of omics-based analyses to elucidate the effect of NaCl concentrations on the metabolites and microbial dynamics during the ripening fermentation of Pixian-Douban

The study investigated the effect of different sodium chloride (NaCl) concentrations (10%, 15%, and 20%) on the ripening fermentation of Pixian-Douban, a traditional fermented condiment. The results showed that NaCl affected the dynamics of physicochemical parameters, volatile components, fatty acids, amino metabolites, organic acids, and microbial composition, and their dynamic modes were different. After 253 days fermentation, the 10% NaCl Pixian-Douban had significantly (p < 0.05) higher levels of total organic acids (20,308.25 mg/kg), amino metabolites (28,144.96 mg/kg), and volatiles (3.36 mg/kg) compared to 15% and 20% NaCl Pixian-Douban. Notably, the possible health risk associated with high concentration of biogenic amines in 10% NaCl Pixian-Douban is of concern. Moreover, correlation analyses indicated that the effect of NaCl on the quality of Pixian-Douban may be mainly related to bacteria. This study deepens the knowledge about the role of NaCl in ripening fermentation of Pixian-Douban and contributes to develop low-NaCl Pixian-Douban product.

Unveiling the unique role of iron in the metabolism of methanogens: A review

Methanogens are the main participants in the carbon cycle, catalyzing five methanogenic pathways. Methanogens utilize different iron-containing functional enzymes in different methanogenic processes. Iron is a vital element in methanogens, which can serve as a carrier or reactant in electron transfer. Therefore, iron plays an important role in the growth and metabolism of methanogens. In this paper, we cast light on the types and functions of iron-containing functional enzymes involved in different methanogenic pathways, and the roles iron play in energy/substance metabolism of methanogenesis. Furthermore, this review provides certain guiding significance for lowering CH4 emissions, boosting the carbon sink capacity of ecosystems and promoting green and low-carbon development in the future.

Alternating polarity as a novel strategy for building synthetic microbial communities capable of robust Electro-Methanogenesis

Electro-methanogenic microbial communities can produce biogas with high efficiency and have attracted extensive research interest. In this study an alternating polarity strategy was developed to build electro-methanogenic communities. In two-chamber bioelectrochemical systems amended with activated carbon, the electrode potential was alternated between +0.8 V and -0.4 V vs. standard hydrogen electrode every three days. Cumulative biogas production under alternating polarity increased from 45 L/L/kg-activated carbon after start-up to 125 L/L/kg after the 4th enrichment, significantly higher than that under intermittent cathode (-0.4 V/open circuit), continuous cathode (-0.4 V), and open circuit. The communities assembled under alternating polarity were electroactive and structurally different from those assembled under other conditions. One Methanobacterium population and two Geobacter populations were consistently abundant and active in the communities. Their 16S rRNA was up-regulated by electrode potentials. Bayesian networks inferred close associations between these populations. Overall, electro-methanogenic communities have been successfully assembled with alternating polarity.

Validation of 16S rRNA gene sequencing and metagenomics for evaluating microbial immigration in a methanogenic bioreactor

To quantitatively evaluate the impact of microbial immigration from an upstream community on the microbial assembly of a downstream community, an ecological genomics (ecogenomics)-based mass balance (EGMB) model coupled with 16S rRNA gene sequencing was previously developed. In this study, a mock community was used to further validate the EGMB models and demonstrate the feasibility of using metagenome-based EGMB model to reveal both microbial activity and function. The mock community consisting of Aeromonas, Escherichia, and Pseudomonas was fed into a lab-scale methanogenic bioreactor together with dissolved organic substrate. Using qPCR, 16S rRNA gene, 16S rRNA gene copy number normalization (GCN), and metagenome, results showed highly comparable community profiles in the feed. In the bioreactor, Aeromonas and Pseudomonas exhibited negative growth rates throughout the experiment by all approaches. Escherichia's growth rate was negative by most biomarkers but was slightly positive by 16S rRNA gene. Still, all approaches showed a decreasing trend toward negative in the growth rate of Escherichia as reactor operation time increased. Uncultivated populations of phyla Desulfobacterota, Chloroflexi, Actinobacteriota, and Spirochaetota were observed to increase in abundance, suggesting their contribution in degrading the feed biomass. Based on metabolic reconstruction of metagenomes, these populations possessed functions of hydrolysis, fermentation, fatty acid degradation, or acetate oxidation. Overall results supported the application of both 16S rRNA gene- and metagenome-based EGMB models to measure the growth rate of microbes in the bioreactor, and the latter had advantage in providing insights into the microbial functions of uncultivated populations.

Roles of zero-valent iron in anaerobic digestion: Mechanisms, advances and perspectives

With the rapid growth of population and urbanization, more and more bio-wastes have been produced. Considering organics contained in bio-wastes, to recover resource from bio-wastes is of great significance, which can not only achieve the resource recycle, but also protect the environment. Anaerobic digestion (AD) has been proved as one of the most promising strategies to recover bio-energy from bio-wastes, as well as to realize the reduction of bio-wastes. However, the conventional interspecies electron transfer is sensitive to environmental shocks, such as high ammonia, organic pollutants, metal ions, etc., which lead to instability or failure of AD. The recent findings have proved that the introduction of zero-valent iron (ZVI) in AD system can significantly enhance methane production from bio-wastes. This review systematically highlighted the recent advances on the roles of ZVI in AD, including underlying mechanisms of ZVI on AD, performance enhancement of AD contributed by ZVI, and impact factors of AD regulated by ZVI. Furthermore, current limitations and outlooks have been analyzed and concluded. The roles of ZVI on underlying mechanisms in AD include regulating reaction conditions, electron transfer mode and function of microbial communities. The addition of ZVI in AD can not only enhance bio-energy recovery and toxic contaminants removal from bio-wastes, but also have the potential to buffer adverse effect caused by inhibitors. Moreover, the electron transfer modes induced by ZVI include both interspecies hydrogen transfer and direct interspecies electron transfer pathways. How to comprehensively evaluate the effects of ZVI on AD and further improve the roles of ZVI in AD is urgently needed for practical application of ZVI in AD. This review aims to provide some references for the introduction of ZVI in AD for enhancing bio-energy recovery from bio-wastes.

Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects

Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.