Effects of calpain inhibitor on the apoptosis of hepatic stellate cells induced by calcium ionophore A23187.

We previously showed that changes in calcium concentrations were related to cell apoptosis in vitro. The endoplasmic reticulum (ER) is the main component of calcium storage and signal transduction, and disrupting the balance of intracellular Ca2+ can cause endoplasmic reticulum stress (ERS). In this process, the ER releases stored Ca 2+ into the cytoplasm and activates calpain-2. To further investigate the effect of calpain in hepatic stellate cells (HSCs), in the current study, we examine the effect of N-acetyl-leu-leu-norleucinal (ALLN) on apoptosis resulting from calcium ionophore A23187-induced ERS. Our findings indicate that calpain inhibition reduces calcium ionophore A23187-induced apoptosis of HSCs and decreases the expression of ER stress proteins that may be related to the calpain/caspase signaling pathway.

Serial-tilted-tapered fiber with high sensitivity for low refractive index range.

We propose an optical fiber sensor for low refractive index (RI) based on a serial-tilted-tapered fiber (STTF), which can be considered as two tightly concatenated micro Mach-Zehnder interferometers (MZIs). The STTF has a compact length of 959.8 µm, and can realize point detection and sensing in limited space. Numerical simulations reveal that a significantly strong evanescent field occurs around the STTF, making it to have the high sensitivity for surrounding RI. In the experiments, the interference dips show the nonlinear wavelength and intensity responses with increasing RI from 1.3395 to 1.3538. In the RI range of 1.3532~1.3538, the RI sensitivities reach the highest value of 2300 nm/RIU and -16183.33 dB/RIU. Moreover, the transmission spectrum of the STTF is low sensitive to temperature. These results indicate that our proposed sensor can be an appropriate candidate in most chemical and biological applications.

Enhanced redox conductivity and enriched Geobacteraceae of exoelectrogenic biofilms in response to static magnetic field.

A possible approach to enhance the performance of microbial electrochemical system such as microbial fuel cells is to increase the conductivity of catalytic biofilms and thereby the direct extracellular electron transfer within the biofilms and from the electrode. In the present study, we evaluated the impact of static low-intensity magnetic field on the anodic biofilms in microbial fuel cells (MFCs). Results demonstrated that the application of a low-intensity magnetic field (105 and 150 mT) can significantly shorten the startup time and enhance the overall performance of single-chamber MFCs in terms of current density (300%) and power density (150%). In situ conductance evaluation indicated that short-term application of magnetic field can increase biofilm conductivity, although the long-term enhancements were likely results of increased conductivity of the anodic biofilms associated with enriched population of Geobacteraceae. The peak-manner response of conductivity over gate potentials and the positive response of mature biofilm conductance to low intensity of magnetic field support the redox conduction model of the conductive exoelectrogenic biofilms.

Impact of tobramycin on the performance of microbial fuel cell.

BACKGROUND: The release of antibiotics into aquatic environments has made the treatment of wastewater containing antibiotics a world-wide public health problem. The ability of microbial fuel cells (MFCs) to harvest electricity from organic waste and renewable biomass is attracting increased interest in wastewater treatment. In this paper we investigated the bioelectrochemical response of an electroactive mixed-culture biofilm in MFC to different tobramycin concentrations. RESULTS: The electroactive biofilms showed a high degree of robustness against tobramycin at the level of µg/L. The current generation responses of the biofilms were affected by the presence of tobramycin. The inhibition ratio of the MFC increased exponentially with the tobramycin concentrations in the range of 0.1-1.9 g/L. The bacterial communities of the biofilms vary with the concentrations of tobramycin, the equilibrium of which is critical for the stability of electroactive biofilms based-MFC. CONCLUSIONS: Experimental results demonstrate that the electroactive biofilm-based MFC is robust against antibiotics at the level of µg/L, but sensitive to changes in antibiotic concentration at the level of g/L. These results could provide significant information about the effects of antibiotics on the performance MFC as a waste-treatment technology.

Microbial electrosynthesis of single cell protein and methane by coupling fast-growing Methanococcus maripaludis with microbial electrolysis cells.

Single cell protein (SCP) is a promising alternative protein source, as its production bypasses the disadvantages of animal protein production in industrial agriculture. Coupling a fast-growing hydrogen consuming organism with microbial electrolysis cells (MECs) could be a viable method for SCP production. In this study, a fast-growing and protein-rich methanogen, Methanococcus maripaludis was selected as the primary SCP source. The inoculation of M. maripaludis in MECs triggered cell synthesis with methane production. The doubling time measured was 11.2 h and the specific growth rate was 0.062 1/h. The highest SCP production rate was 13.7 mg/L/h. In the dried biomass, the weight of protein was over 60 %. Amino acid profiling of the harvested biomass demonstrated high abundance of essential amino acids. The electron flux analysis indicated that 31.3 % electrons in the electrochemical systems were directed into SCP synthesis. These results illustrated the potential for SCP production by coupling a fast-growing methanogen with MECs.

[Knockdown of calreticulin increases intracellular Ca2+ concentration and promotes apoptosis of HSC-LX2 human hepatic stellate cells].

Objective To investigate the effect of silencing calreticulin (CALR) gene on the apoptosis and intracellular Ca2+ concentration in HSC-LX2 human hepatic stellate cells. Methods Small interfering RNA (siRNA) targeting CALR was designed and transfected into HSC-LX2 cells by lipofectamine transfection, and then the cells with CALR knockdown were screened out. Apoptosis was detected by flow cytometry combined with annexin V-FITC/PI labeling. The intracellular Ca2+ concentration which was loaded with Fluo3-AM (calcium ion fluorescent probe) was observed by laser confocal microscope. The mRNA and protein levels of CALR, Bcl2 and BAX were detected by reverse-transcription PCR and Western blotting. Results Knockdown of CALR led to the increase of intracellular Ca2+ concentration, the increased apoptosis of HSC-LX2 cells, the up-regulation of BAX expression, down-regulation of Bcl2 and the obvious raise of BAX/Bcl2 ratio. Conclusion Knockdown of CALR can increase intracellular Ca2+ concentration, up-regulate the ratio of BAX/Bcl2 and promote the apoptosis of HSC-LX2 cells.

Scaling-up up-flow microbial electrolysis cells with a compact electrode configuration for continuous hydrogen production.

Maintaining high current densities is a key challenge in scaling-up microbial electrolysis cell (MEC) reactors. In this study, a novel 10 L MEC reactor with a total electrode surface area greater than 1 m2 was designed and evaluated to maximize the current density and H2 recovery. Performances of the reactor suggest that the longitudinal structure with parallel vertical orientation of the electrodes encouraged high fluid mixing and the sheet metal electrode frames provided distributed electrical connection. Results also demonstrated that the electrode pairs located next to reactor walls decreased current density, as did separating the electrodes with separators. High volumetric H2 production rate of 5.9 L/L/d was achieved at a volumetric current density of 970 A/m3 (34 A/m2). Moreover, the observed current densities of the large reactor were accurately predicted based on the internal resistance analysis of small scale MECs (0.15 L), demonstrating the scalability of the single chamber MEC design.

Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data.

Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essential yet difficult task due to the complex and unknown interactions within the system. The application of well-developed data mining technologies, such as machine learning (ML) and microbial gene sequencing techniques are promising in overcoming these challenges. In this study, we investigated the feasibility of 6 ML algorithms using genomic data and their corresponding operational parameters from 8 research groups to predict methane yield. For classification models, random forest (RF) achieved accuracies of 0.77 using operational parameters alone and 0.78 using genomic data at the bacterial phylum level alone. The combination of operational parameters and genomic data improved the prediction accuracy to 0.82 (p<0.05). For regression models, a low root mean square error of 0.04 (relative root mean square error =8.6%) was acquired by neural network using genomic data at the bacterial phylum level alone. Feature importance analysis by RF suggested that Chloroflexi, Actinobacteria, Proteobacteria, Fibrobacteres, and Spirochaeta were the top 5 most important phyla although their relative abundances were ranging only from 0.1% to 3.1%. The important features identified could provide guidance for early warning and proactive management of microbial communities. This study demonstrated the promising application of ML techniques for predicting and controlling AD performance.

CaMK II/Ca2+ dependent endoplasmic reticulum stress mediates apoptosis of hepatic stellate cells stimulated by transforming growth factor beta 1.

Previous studies by our group have demonstrated that the calcium imbalance in rat hepatic stellate cells (HSCs) can induce endoplasmic reticulum stress (ERS) and promote cell apoptosis. KN-62, an inhibitor of Calmodulin kinase II (CaMK II), can decrease the expression of CaMK II that plays a major role in regulating the steady state of intracellular Ca2+. Uridine triphosphate (UTP) plays a biological role in increasing indirectly the level of intracellular Ca2+. In the experiment, we demonstrate that KN-62 and UTP can inhibit the proliferation and promote the apoptosis in HSCs, increase the level of intracellular Ca2+ and the expression of ERS protein GRP78, and increase the apoptosis protein Caspase-12 and Bax expression, while decrease the expression of Bcl-2 protein. Our findings indicate that the CaMK II/Ca2+ signaling pathway regulates the ERS apoptosis pathway and induces HSC apoptosis.

Hydrogen production from lignocellulosic hydrolysate in an up-scaled microbial electrolysis cell with stacked bio-electrodes.

Hydrogen production from renewable resources via microbial electrolysis cells (MECs) is a promising approach for sustainable energy production. Yet high hydrogen yield from real feedstocks has not been demonstrated in up-scaled MECs. In this study, a 10-L single chamber MEC with a high electrode surface area to volume ratio (66 m2/m3) was constructed and electroactive cathodic biofilms were enriched for hydrogen evolution reaction. A high hydrogen yield of 91% was achieved using lignocellulosic hydrolysate with a hydrogen production rate of 0.71 L/L/D at an organic loading rate of 0.4 g/D. The anodic and cathodic microbial communities, with Enterococcus spp. as the known electroactive bacteria, were capable of achieving current densities of 13.7 A/m2 and 16.5 A/m2, respectively. A machine learning algorithm was used to investigate the correlation between community data and electrochemical performance, and the critical genera on determining current density were identified.

Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms.

The use of zero-valent iron (ZVI) to enhance anaerobic digestion (AD) systems is widely advocated as it improves methane production and system stability. Accurate modeling of ZVI-based AD reactor is conducive to predicting methane production potential, optimizing operational strategy, and gathering reference information for industrial design in place of time-consuming and laborious tests. In this study, three machine learning (ML) algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and deep learning (DL), were evaluated for their feasibility of predicting the performance of ZVI-based AD reactors based on the operating parameters collected in 9 published articles. XGBoost demonstrated the highest accuracy in predicting total methane production, with a root mean squared error (RMSE) of 21.09, compared to 26.03 and 27.35 of RF and DL, respectively. The accuracy represented by mean absolute percentage error also showed the same trend, with 14.26%, 15.14% and 17.82% for XGBoost, RF and DL, respectively. Through the feature importance generated by XGBoost, the parameters of total solid of feedstock (TSf), sCOD, ZVI dosage and particle size were identified as the dominant parameters that affect the methane production, with feature importance weights of 0.339, 0.238, 0.158, and 0.116, respectively. The digestion time was further introduced into the above-established model to predict the cumulative methane production. With the expansion of training dataset, DL outperformed XGBoost and RF to show the lowest RMSEs of 11.83 and 5.82 in the control and ZVI-added reactors, respectively. This study demonstrates the potential of using ML algorithms to model ZVI-based AD reactors.

Anaerobic reduction of high-polarity nitroaromatic compounds by electrochemically active bacteria: Roles of Mtr respiratory pathway, molecular polarity, mediator and membrane permeability.

Electrochemically active bacteria (EAB) are effective for the bioreduction of nitroaromatic compounds (NACs), but the exact reduction mechanisms are unclear yet. Therefore, 3-nitrobenzenesulfonate (NBS) was used to explore the biodegradation mechanism of NACs by EAB. Results show that NBS could be anaerobically degraded by Shewanella oneidensis MR-1. The generation of aminoaromatic compounds was accompanied with the NBS reduction, indicating that NBS was biodegraded via reductive approach by S. oneidensis MR-1. The impacts of NBS concentration and cell density on the NBS reduction were evaluated. The removal of NBS depends mainly on the transmembrane electron transfer of S. oneidensis MR-1. Impairment of Mtr respiratory pathway was found to mitigate the reduction of NBS, suggesting that the anaerobic biodegradation of NBS occurred extracellularly. Knocking out cymA severely impaired the extracellular reduction ability of S. oneidensis MR-1. However, the phenotype of ΔcymA mutant could be compensated by the exogenous electron mediators, implying the trans-outer membrane diffusion of mediators into the periplasmic space. This work provides a new insight into the anaerobic reduction of aromatic contaminants by EAB.

Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms.

Machine learning has emerges as a novel method for model development and has potential to be used to predict and control the performance of anaerobic digesters. In this study, several machine learning algorithms were applied in regression and classification models on digestion performance to identify determinant operational parameters and predict methane production. In the regression models, k-nearest neighbors (KNN) algorithm demonstrates optimal prediction accuracy (root mean square error = 26.6, with the dataset range of 259.0-573.8), after narrowing prediction coverage by excluding extreme outliers from the validation set. In the classification models, logistic regression multiclass algorithm yields the best prediction accuracy of 0.73. Feature importance reveals that total carbon was the determinant operational parameter. These results demonstrate the great potential of using machine learning algorithms to predict anaerobic digestion performance.

Accelerated tests for evaluating the air-cathode aging in microbial fuel cells.

Air-cathode stability is a key factor affecting the feasibility of microbial fuel cells (MFCs) in applications. However, there is no quick and effective method to evaluate the robustness and durability of the MFC air cathodes. In this study, a three-phase decrease of power density was observed in multiple MFCs that have been operated for about a year. Quantification of the contributions of cathode biofilm and salt accumulation to the current decrease suggested that the biofouling was the major contributor to the cathode aging during the first 200 days, and salt accumulation gradually outpaced biofouling afterward. An accelerated test method was then developed using fast-growing Escherichia coli, simulated soluble microbial products (SMPs), and a concentrated medium solution. Using this method, the cathode aging can be evaluated quickly within hours/days compared to over a year of operation, benefiting the development of high-performing and durable cathode materials.

Improved Simultaneous Decolorization and Power Generation in a Microbial Fuel Cell with the Sponge Anode Modified by Polyaniline and Chitosan.

In recent years, microbial fuel cell (MFC) has been regarded as a promising technology for dye wastewater treatment. Compared with traditional anaerobic reactors, MFC has better decolorization effect while producing electricity simultaneously. In this paper, a double-chamber MFC with the sponge anode modified by polyaniline and chitosan-NCNTs was employed for simultaneous azo dye decolorization and bioelectricity generation. The influence of dye concentration, co-substrate concentration, and operating temperature on the performance of MFC with the modified anodes were studied. The results showed that a high decolorization efficiency (98.41%) and maximum power density (2816.67 mW m-3) of MFC equipped with modified bioanodes were achieved due to the biocompatibility and bioelectrocatalysis of modified material. And the biomass on the modified anode's surface was increased by 1.47 times. Additionally, microbial community analysis revealed that the modification of polyaniline and chitosan-NCNTs improved the selective enrichment of specific communities and the main microorganism was the electroactive and decolorizing bacteria Enterobacter (62.84%). Therefore, the composite anode is capable of fully utilizing the synergistic role of various materials, leading to superior performance of dye decolorization in MFCs. This work provided a strategy for the research on the recovery of biomass energy and decolorization in wastewater treatment. Graphical Abstract.

Selective inhibition of methanogenesis by acetylene in single chamber microbial electrolysis cells.

Microbial electrolysis cells (MECs) for hydrogen production exhibit great advantages over many other biohydrogen production techniques in terms of versatility of substrate and hydrogen yield. However, hydrogen and acetate scavenging by methanogens puts forward a great challenge to the application of single chamber MECs when using mixed culture. In this study, we investigated the feasibility of using acetylene, a low-cost fuel and chemical building block, to selectively inhibit methanogenesis in single chamber MECs. Results demonstrate that the periodical injection of low concentration acetylene (1% and 5%) can successfully inhibit methanogenesis in MECs using both acetate and glucose as substrates. Current generation by exoelectrogens and the syntrophy between fermentative bacteria and exoelectrogens, however, were not negatively affected. Compared with the classic methanogen inhibitor, 2-Bromoethanesulfonate (BES), the low concentration acetylene demonstrates superior effectiveness in MECs. These results demonstrate the great potential of using acetylene as a cost-effective inhibitor against methanogenesis in MECs.

Linking internal resistance with design and operation decisions in microbial electrolysis cells.

The distribution of internal resistance in most microbial electrolysis cells (MECs) remains unclear, which hinders the optimization and scaling up of the technology. In this study, a method for quantifying the effects of design and operation decisions on internal resistance was applied for the first time to MECs. In typical single chamber MECs with carbon cloth electrodes, the internal resistance was distributed as follows: 210 Ω cm2 for anode, 77 Ω cm2 for cathode, and 11 Ω cm2 M for solution. While varying the spacing of the electrodes (<1 cm) had little effect on MEC performance, inducing fluid motion between the electrodes decreased the internal resistance of all MEC components: 150 Ω cm2 for anode, 47 Ω cm2 for cathode, and 5.3 Ω cm2 M for solution. Adjusting the anode to cathode surface area ratio, to balance the internal resistance distribution, resulted in a significant improvement in performance (47 A/m2 current density, 3.7 L-H2/L-liquid volume/day). These results suggest that the quantification of the internal resistance distribution enables the efficient design and operation of MECs. The parameters obtained in this study were also capable of predicting the performance of MECs from some previous studies, demonstrating the effectiveness of this method.

A 3D porous NCNT sponge anode modified with chitosan and Polyaniline for high-performance microbial fuel cell.

A microbial fuel cell (MFC) is a potential bio-electrochemical technology that utilizes microorganisms to convert chemical energy into electrical energy. The low power output of MFCs remain the bottleneck for their practical applications. In this paper, a novel, biocompatible and bioelectrocatalytic composite chitosan-nitrogen doped carbon nanotubes-polyaniline (CS-NCNT-PANI) was prepared in situ on the 3D porous NCNT/sponge and applied to an MFC anode. The PANI was grafted on the CS-NCNT backbone to synthesize the ternary composite. This bioanode not only increased the active surface area and capacity but also facilitated bacterial adhesion and enrichment of microbes. Compared with the NCNT/sponge electrode, the charge transfer impedance of the ternary composite bioanode decreased from 14.07â€¯Ω to 2.25 Ω, and the maximum power density increased from 1.4 W·m-3 to 4.2 W·m-3; meanwhile, during the chronoamperometric experiment with a charge-discharge time of 60-60 min, the cumulative charge of the composite bioanode was 18,865.8 C·m-2, which is much higher than that of the NCNT/S anode (3625.3 C·m-2). High-throughput sequencing technology revealed that the ternary composite bioanode had good biocompatibility and high diversity. Therefore, this synthesized ternary composite is a promising candidate as a capacitive and biocompatible anode material in MFC.

Urea removal coupled with enhanced electricity generation in single-chambered microbial fuel cells.

High concentration of total ammonia nitrogen (TAN) in the form of urea is known to inhibit the performance of many biological wastewater treatment processes. Microbial fuel cells (MFCs) have great potential for TAN removal due to its unique oxic/anoxic environment. In this study, we demonstrated that increased urea (TAN) concentration up to 3940 mg/L did not inhibit power output of single-chambered MFCs, but enhanced power generation by 67% and improved coulombic efficiency by 78% compared to those obtained at 80 mg/L of TAN. Over 80% of nitrogen removal was achieved at TAN concentration of 2630 mg/L. The increased nitrogen removal coupled with significantly enhanced coulombic efficiency, which was observed for the first time, indicates the possibility of a new electricity generation mechanism in MFCs: direct oxidation of ammonia for power generation. This study also demonstrates the great potential of using one MFC reactor to achieve simultaneous electricity generation and urea removal from wastewater.

Extraordinary expansion of a Sorangium cellulosum genome from an alkaline milieu.

Complex environmental conditions can significantly affect bacterial genome size by unknown mechanisms. The So0157-2 strain of Sorangium cellulosum is an alkaline-adaptive epothilone producer that grows across a wide pH range. Here, we show that the genome of this strain is 14,782,125 base pairs, 1.75-megabases larger than the largest bacterial genome from S. cellulosum reported previously. The total 11,599 coding sequences (CDSs) include massive duplications and horizontally transferred genes, regulated by lots of protein kinases, sigma factors and related transcriptional regulation co-factors, providing the So0157-2 strain abundant resources and flexibility for ecological adaptation. The comparative transcriptomics approach, which detected 90.7% of the total CDSs, not only demonstrates complex expression patterns under varying environmental conditions but also suggests an alkaline-improved pathway of the insertion and duplication, which has been genetically testified, in this strain. These results provide insights into and a paradigm for how environmental conditions can affect bacterial genome expansion.