A New Fluidized Bed Bioreactor Based on Diversion-Type Microcapsule Suspension for Bioartificial Liver Systems.

A fluidized bed bioreactor containing encapsulated hepatocytes may be a valuable alternative to a hollow fiber bioreactor for achieving the improved mass transfer and scale-up potential necessary for clinical use. However, a conventional fluidized bed bioreactor (FBB) operating under high perfusion velocity is incapable of providing the desired performance due to the resulting damage to cell-containing microcapsules and large void volume. In this study, we developed a novel diversion-type microcapsule-suspension fluidized bed bioreactor (DMFBB). The void volume in the bioreactor and stability of alginate/chitosan microcapsules were investigated under different flow rates. Cell viability, synthesis and metabolism functions, and expression of metabolizing enzymes at transcriptional levels in an encapsulated hepatocyte line (C3A cells) were determined. The void volume was significantly less in the novel bioreactor than in the conventional FBB. In addition, the microcapsules were less damaged in the DMFBB during the fluidization process as reflected by the results for microcapsule retention rates, swelling, and breakage. Encapsulated C3A cells exhibited greater viability and CYP1A2 and CYP3A4 activity in the DMFBB than in the FBB, although the increases in albumin and urea synthesis were less prominent. The transcription levels of several CYP450-related genes and an albumin-related gene were dramatically greater in cells in the DMFBB than in those in the FBB. Taken together, our results suggest that the DMFBB is a promising alternative for the design of a bioartificial liver system based on a fluidized bed bioreactor with encapsulated hepatocytes for treating patients with acute hepatic failure or other severe liver diseases

Transcriptome Analysis of Porcine PBMCs Reveals the Immune Cascade Response and Gene Ontology Terms Related to Cell Death and Fibrosis in the Progression of Liver Failure

Background. The key gene sets involved in the progression of acute liver failure (ALF), which has a high mortality rate, remain unclear. This study aims to gain a deeper understanding of the transcriptional response of peripheral blood mononuclear cells (PBMCs) following ALF. Methods. ALF was induced by D-galactosamine (D-gal) in a porcine model. PBMCs were separated at time zero (baseline group), 36 h (failure group), and 60 h (dying group) after D-gal injection. Transcriptional profiling was performed using RNA sequencing and analysed using DAVID bioinformatics resources. Results. Compared with the baseline group, 816 and 1,845 differentially expressed genes (DEGs) were identified in the failure and dying groups, respectively. A total of five and two gene ontology (GO) term clusters were enriched in 107 GO terms in the failure group and 154 GO terms in the dying group. These GO clusters were primarily immune-related, including genes regulating the inflammasome complex and toll-like receptor signalling pathways. Specifically, GO terms related to cell death, including apoptosis, pyroptosis, and autophagy, and those related to fibrosis, coagulation dysfunction, and hepatic encephalopathy were enriched. Seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cytokine-cytokine receptor interaction, hematopoietic cell lineage, lysosome, rheumatoid arthritis, malaria, and phagosome and pertussis pathways were mapped for DEGs in the failure group. All of these seven KEGG pathways were involved in the 19 KEGG pathways mapped in the dying group. Conclusion. We found that the dramatic PBMC transcriptome changes triggered by ALF progression was predominantly related to immune responses. The enriched GO terms related to cell death, fibrosis, and so on, as indicated by PBMC transcriptome analysis, seem to be useful in elucidating potential key gene sets in the progression of ALF. A better understanding of these gene sets might be of preventive or therapeutic interest

Targeted Metabolomics Analysis of Bile Acids in Patients with Idiosyncratic Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is rare but clinically important due to a high rate of mortality. However, specific biomarkers for diagnosing and predicting the severity and prognosis of DILI are lacking. Here, we used targeted metabolomics to identify and quantify specific types of bile acids that can predict the severity of DILI. A total of 161 DILI patients were enrolled in this prospective cohort study, as well as 31 health controls. A targeted metabolomics method was used to identify 24 types of bile acids. DILI patients were divided into mild, moderate, and severe groups according to disease severity. A multivariate analysis was performed to identify characteristic bile acids. Then the patients were divided into severe and non-severe groups, and logistic regression was used to identify bile acids that could predict DILI severity. Among the enrolled DILI patients, 32 were in the mild group, 90 were in the moderate group, and 39 were in the severe group. Orthogonal partial least squares-discriminant analysis (OPLS-DA) modeling clearly discriminated among the different groups. Among the four groups, glycochenodeoxycholate (GCDCA), taurochenodeoxycholate (TCDCA), deoxycholic acid (DCA), Nor Cholic acid (NorCA), glycocholic acid (GCA), and taurocholic acid (TCA) showed significant differences in concentration between at least two groups. NorCA, GCDCA, and TCDCA were all independent risk factors that differentiated severe DILI patients from the other groups. The area under the receiver operating characteristic curve (AUROC) of GCDCA, TCDCA, and NorCA was 0.856, 0.792, and 0.753, respectively. Together, these three bile acids had an AUROC of 0.895 for predicting severe DILI patients. DILI patients with different disease severities have specific bile acid metabolomics. NorCA, GCDTA, and TCDCA were independent risk factors for differentiating severe DILI patients from less-severe patients and have the potential to predict DILI severity

Effects of fluidization on empty microcapsule integrity within the bioreactors.

<p>(A) Microcapsule retention rates of the DMFBB and FBB operated at 90 and 150 ml/min. (B) Swelling rates (%) of microcapsules in the DMFBB and FBB operated at 90 and 150ml/min. (C) Percentages of broken microcapsules in the DMFBB and FBB operated at 90 and 150 ml/min. The following results were obtained: (A): When the DMFBB was operated at 90 ml/min, the rate of microcapsule retention was 99.8% compared to 91.74% in the FBB at day 1 (p = 0.0025), 98.08% compared to 90.3% at day 2 (p = 0.0022), and 96.55% compared to 87.68% at day 3 (p = 0.0024).When the DMFBB was operated at 150 ml/min, the rate of microcapsule retention was 99.78% compared to 92.14% in the FBB at day 1 (p = 0.0051), 97.49% compared to 87.98% at day 2 (p = 0.0014), and 94.71% compared to 84.95% at day 3 (p = 0.0008). (B): When the DMFBB was operated at 90 ml/min, the swelling rate (%) of microcapsules was 7.7% compared to 15.81% in the FBB at day 1 (P = 0.0176), 11.59% compared to 19.64% at day 2 (p = 0.0027), and 12.8% compared to 34.81% at day 3 (p = 0.0005). When the DMFBB was operated at 150 ml/min, the swelling rate of microcapsules was 5.49% compared to 23.49% in the FBB at day 1 (p = 0.0328), 14.35% compared to 27.59% at day 2 (p = 0.0241), and 30.11% compared to 36.66% at day 3 (p = 0.3258). (C): When the DMFBB was operated at 90 ml/min, the percentage of broken microcapsules in the DMFBB was 1.6% compared to 3.2% in the FBB at day 1 (p = 0.0095), 4.8% compared to 7.4% at day 2 (p = 0.0117), and 6.4% compared to 11.6% at day 3 (p = 0.0017). When DMFBB was operated at 150 ml/min, the percentage of broken microcapsules in the DMFBB was 1.9% compared to 4.2% in the FBB at day 1 (p = 0.0042), 5.6% compared to 9.8% at day 2 (p = 0.0114), and 7.4% compared to 11.8% at day 3 (p = 0.0031).</p

Primers Used in the Real-time Quantitative PCR Analyses.

<p>Primers Used in the Real-time Quantitative PCR Analyses.</p

Two types of bioreactors and experimental setup for fluidization.

<p>(A) Schematic of the diversion-type microcapsule suspension fluidized bed bioreactor (DMFBB). (1) inlet; (2) bottom cap; (3) pool of incoming buffer; (4) turbine guide vanes; (5) 300 mesh membrane filters; 6) cylindrical bioreactor containing the microcapsules; (7) end cap; and (8) outlet. The dimensions associated with the turbine guide vanes were: height of the turbine = 15 mm; thickness of each turbine blade = 10 mm, blade inlet angle = 90°, blade outlet (outer edge) angle = 26°, blade outlet (inner edge) = 71°, screw pitch = 70 mm; number turbine vane rotations = 0.207, and diameter of fixed middle axis = 7 mm. (B) Schematic of the traditional fluidized bed bioreactor (FBB). (1) inlet; (2) bottom top; (3) pool of incoming buffer; (4) 300 mesh membrane filters; (5) cylindrical bioreactor containing the microcapsules; (6) end cap; and (7) outlet. (C) The experimental set up for dynamic culture of C3A cells within the fluidized bed bioreactor. C3A cells were encapsulated in alginate/chitosan microspheres with a diameter of 800 μm.</p

Fluidization performance of two bioreactors.

<p>(A) Fluidization performance of the FBB at 90 ml/min. (B) Fluidization performance of the FBB at 150 ml/min. (C) Fluidization performance of the DMFBB at 90 ml/min. (D) Fluidization performance of the DMFBB at 150 ml/min. At the beginning, the red indicates fixed microcapsules at the bottom of the reactor, and the blue indicates fluidized flow (DMEM). As the flow rate was increased, microcapsules in the center at the bottom of the FBB were forced upwards to the top of the bioreactor, leading to weak fluidization. Conversely, the microcapsules in the DMFBB were gradually mixed with the flowing medium, and eventually, dynamic and balanced fluidization was established. In the images, the colors from red to yellow or green represented different microcapsule densities under different conditions. (E) Fluidization in DMFBB and FBB in terms of bed expansion (h/h0) as the perfusion flow rate was increased from 0 to150 ml/min.</p

Normalized gene expression of CYP450 and phase II enzymes in C3A cells after 72 h of culture in fluidized and static conditions.

<p>All data were normalized to the activity of C3A cells cultured in static conditions.</p

Galphai1 and Galphai3 regulate macrophage polarization by forming a complex containing CD14 and Gab1

Heterotrimeric G proteins have been implicated in Toll-like receptor 4 (TLR4) signaling in macrophages and endothelial cells. However, whether guanine nucleotide-binding protein G(i) subunit alpha-1 and alpha-3 (Gαi1/3) are required for LPS responses remains unclear, and if so, the underlying mechanisms need to be studied. In this study, we demonstrated that, in response to LPS, Gαi1/3 form complexes containing the pattern recognition receptor (PRR) CD14 and growth factor receptor binding 2 (Grb2)-associated binding protein (Gab1), which are required for activation of PI3K-Akt signaling. Gαi1/3 deficiency decreased LPS-induced TLR4 endocytosis, which was associated with decreased phosphorylation of IFN regulatory factor 3 (IRF3). Gαi1/3 knockdown in bone marrow-derived macrophage cells (Gαi1/3 KD BMDMs) exhibited an M2-like phenotype with significantly suppressed production of TNF-α, IL-6, IL-12, and NO in response to LPS. The altered polarization coincidedwith decreased Akt activation. Further, Gαi1/3 deficiency caused LPS tolerance in mice. In vitro studies revealed that, in LPS-tolerant macrophages, Gαi1/3 were down-regulated partially by the proteasome pathway. Collectively, the present findings demonstrated that Gαi1/3 can interact with CD14/Gab1, which modulates macrophage polarization in vitro and in vivo.Fil: Li, Xiaolin. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Wang, Duowei. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Chen, Zen. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Lu, Ermei. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Wang, Zhuo. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Duan, Jingjing. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Tian, Wei. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Wang, Yun. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: You, Linjun. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Zou, Yulian. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Cheng, Yan. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Zhu, Qingyi. Jiangsu Province Hospital of Traditional Chinese Medicine. Departament of Urology; ChinaFil: Wan, Xiaojian. Second Military Medical University. Department of Anesthesiology and Intensive Care Medicine, Changhai Hospita; ChinaFil: Xia, Tao. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Birnbaumer, Lutz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. National Institute of Environmental Health Sciences. Laboratory of Neurobiology, ; Estados UnidosFil: Yang, Yong. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; Chin