Research on the effects of ferric salt added into aeration tank for phosphorus removal on biochemical system

In this paper, two iron salts, ferrous chloride (FeCl2) and ferric chloride (FeCl3), are directly added into an aeration tank for phosphorus removal, and their effects on the biochemical system are studied; the water quality parameters such as pH and alkalinity are also investigated. The extent of influence of the added iron salts on the pH and alkalinity of aerated solutions is demonstrated to be FeCl3 > FeCl2. When the dosage of iron ions is 20 mg/L, the decrease in pH and alkalinity caused by FeCl3 is 0.5 and 65 mg/L, which is higher than FeCl2 by 2% and 26%. The initial phosphorus removal effect of FeCl2 is worse than that of FeCl3, but after continued aeration and oxidation, the phosphorus removal effect of FeCl2 can be improved; however, the final phosphorus removal effect is basically the same as that of FeCl3 added directly. The results show that FeCl2 is preferred when iron salt is added directly into the aeration tank to remove phosphorus. The proposed scheme can reduce the effect of iron salts on the alkalinity of the biochemical system on the premise of ensuring the phosphorus removal effect of the system, and is conducive to ensuring the stable operation of the biochemical system.

Multi-site phosphorylation of yeast Mif2/CENP-C promotes inner kinetochore assembly

Kinetochores control eukaryotic chromosome segregation by connecting chromosomal centromeres to spindle microtubules. Duplication of centromeric DNA necessitates kinetochore disassembly and subsequent reassembly on the nascent sisters. To search for a regulatory mechanism that controls the earliest steps of kinetochore assembly, we studied Mif2/CENP-C, an essential basal component. We found that Polo-like kinase (Cdc5) and Dbf4-dependent kinase (DDK) phosphorylate the conserved PEST region of Mif2/CENP-C and that this phosphorylation directs inner kinetochore assembly. Mif2 phosphorylation promotes kinetochore assembly in a reconstituted biochemical system, and it strengthens Mif2 localization at centromeres in cells. Disrupting one or more phosphorylation sites in the Mif2-PEST region progressively impairs cellular fitness and sensitizes cells to microtubule poisons. The most severe Mif2-PEST mutations are lethal in cells lacking otherwise non-essential Ctf19 complex factors. These data suggest that multi-site phosphorylation of Mif2/CENP-C is a robust switch that controls inner kinetochore assembly, ensuring accurate chromosome segregation.

Modeling the Performance of Full-Scale Anaerobic Biochemical System Treating Deinking Pulp Wastewater Based on Modified Anaerobic Digestion Model No. 1

The deinking pulp (DIP) is a main resource for paper making, and the wastewater from DIP process needs to be treated. Anaerobic biochemical technique has been widely applied in DIP wastewater treatment, due to the remarkable capability in reducing high chemical oxygen demand (COD). In this study, a mathematical simulation model was established to investigate the performance of a full-scale anaerobic biochemical system for treating DIP wastewater. The model was based on Anaerobic Digestion Model No. 1 (ADM1), which was modified according to the specific anaerobic digestion process for DIP wastewater treatment. The hydrodynamic behavior of a full-scale anaerobic biochemical system was considered in this model. The characteristics of the influent DIP wastewater were assessed, and then, the substrate COD proportion was divided successfully for the necessity of ADM1 applying. The Monte Carlo technique was implemented to distinguish the most sensitive parameters that influenced the model output indicators comprising effluent COD and biogas production. The sensitive parameters were estimated and optimized. The optimized value of k_m_pro is 12.02, K_S_pro is 0.35, k_m_ac is 4.26, K_S_ac is 0.26, k_m_h2 is 16.62, and K_S_h2 is 3.21 × 10–5. The model was calibrated with 150 days operation values measured in the field. The subsequent 100 days on-site values were used to validate the model, and the results obtained by the simulations were in good agreement. This study provides a meaningful and theoretical model guidance for full-scale wastewater anaerobic biochemical treatment simulation.

NusG links transcription and translation in Escherichia coli extracts

In bacteria, transcription is coupled to, and can be regulated by, translation. Although recent structural studies suggest that the N-utilization substance G (NusG) transcription factor can serve as a direct, physical link between the transcribing RNA polymerase (RNAP) and the lead ribosome, mechanistic studies investigating the potential role of NusG in mediating transcription-translation coupling are lacking. Here, we report development of a cellular extract- and reporter gene-based, in vitro biochemical system that supports transcription-translation coupling as well as the use of this system to study the role of NusG in coupling. Our findings show that NusG is required for coupling and that the enhanced gene expression that results from coupling is dependent on the ability of NusG to directly interact with the lead ribosome. Moreover, we provide strong evidence that NusG-dependent coupling enhances gene expression through a mechanism in which the lead ribosome that is tethered to the RNAP by NusG suppresses spontaneous backtracking of the RNAP on its DNA template that would otherwise inhibit transcription.

Speciation and Antibiotic Susceptibilities of Coagulase Negative Staphylococci Isolated from Ocular Infections

Coagulase-negative staphylococci (CoNS) are frequently occurring ocular opportunistic pathogens that are not easily identifiable to the species level. The goal of this study was to speciate CoNS and document antibiotic susceptibilities from cases of endophthalmitis (n = 50), keratitis (n = 50), and conjunctivitis/blepharitis (n = 50) for empiric therapy. All 150 isolates of CoNS were speciated using (1) API Staph (biochemical system), (2) Biolog GEN III Microplates (phenotypic substrate system), and (3) DNA sequencing of the sodA gene. Disk diffusion antibiotic susceptibilities for topical and intravitreal treatment were determined based on serum standards. CoNS identification to the species level by all three methods indicated that S. epidermidis was the predominant species of CoNS isolated from cases of endophthalmitis (84–90%), keratitis (80–86%), and conjunctivitis/blepharitis (62–68%). Identifications indicated different distributions of CoNS species among endophthalmitis (6), keratitis (10), and conjunctivitis/blepharitis (13). Antibiotic susceptibility profiles support empiric treatment of endophthalmitis with vancomycin, and keratitis treatment with cefazolin or vancomycin. There was no clear antibiotic choice for conjunctivitis/blepharitis. S. epidermidis was the most frequently found CoNS ocular pathogen, and infection by other CoNS appears to be less specific and random. Antibiotic resistance does not appear to be a serious problem associated with CoNS.

Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power

Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biochemical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape(GPL) measurements into a low-dimensional feature-space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, including deep neural networks. LANTERN furthermore extracts useful insights into the landscape including its inherent dimensionality, a latent space of additive mutational effects, and novel metrics of landscape structure. LANTERN facilitates straightforward discovery of fundamental mechanisms in GPLs, while also reliably extrapolating to unexplored regions of genotypic-space.

NO● Represses the Oxygenation of Arachidonoyl PE by 15LOX/PEBP1: Mechanism and Role in Ferroptosis

We recently discovered an anti-ferroptotic mechanism inherent to M1 macrophages whereby high levels of NO● suppressed ferroptosis via inhibition of hydroperoxy-eicosatetraenoyl-phosphatidylethanolamine (HpETE-PE) production by 15-lipoxygenase (15LOX) complexed with PE-binding protein 1 (PEBP1). However, the mechanism of NO● interference with 15LOX/PEBP1 activity remained unclear. Here, we use a biochemical model of recombinant 15LOX-2 complexed with PEBP1, LC-MS redox lipidomics, and structure-based modeling and simulations to uncover the mechanism through which NO● suppresses ETE-PE oxidation. Our study reveals that O2 and NO● use the same entry pores and channels connecting to 15LOX-2 catalytic site, resulting in a competition for the catalytic site. We identified residues that direct O2 and NO● to the catalytic site, as well as those stabilizing the esterified ETE-PE phospholipid tail. The functional significance of these residues is supported by in silico saturation mutagenesis. We detected nitrosylated PE species in a biochemical system consisting of 15LOX-2/PEBP1 and NO● donor and in RAW264.7 M2 macrophages treated with ferroptosis-inducer RSL3 in the presence of NO●, in further support of the ability of NO● to diffuse to, and react at, the 15LOX-2 catalytic site. The results provide first insights into the molecular mechanism of repression of the ferroptotic Hp-ETE-PE production by NO●.

Identifiability of stochastically modelled reaction networks

Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous-time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data are obtained via stochastic simulations.

Compartor: a toolbox for the automatic generation of moment equations for dynamic compartment populations

Summary Many biochemical processes in living organisms take place inside compartments that can interact with each other and remodel over time. In a recent work, we have shown how the stochastic dynamics of a compartmentalized biochemical system can be effectively studied using moment equations. With this technique, the time evolution of a compartment population is summarized using a finite number of ordinary differential equations, which can be analyzed very efficiently. However, the derivation of moment equations by hand can become time-consuming for systems comprising multiple reactants and interactions. Here we present Compartor, a toolbox that automatically generates the moment equations associated with a user-defined compartmentalized system. Through the moment equation method, Compartor renders the analysis of stochastic population models accessible to a broader scientific community. Availability and implementation Compartor is provided as a Python package and is available at https://pypi.org/project/compartor/. Source code and usage tutorials for Compartor are available at https://github.com/zechnerlab/Compartor.