BIO regulates the ex vivo expansion and function of hematopoietic stem cells by inhibiting GSK-3β

Hematopoietic stem cells (HSCs) have been applied in clinic settings for treating hematologic diseases, including leukemic disorders, immune deficiencies, and hemoglobinopathies. Umbilical cord blood(UCB) is an important source of HSCs. However, the low frequency of HSCs per unit of UCB remains a big hurdle to their wider applications. Wnt/β-catenin pathway plays important roles in the self-renewal of HSCs in vivo, but the roles of Wnt/β-catenin signaling on ex vivo expansion of HSCs remains controversial. GSK3β is the major regulator of Wnt pathway. Here, we evaluate the effects of 6-bromoindirubin-3’-oxime (BIO), a GSK3β inhibitor, on ex vivo expansion characteristics and regenerative potential of (UCB)-derived CD34+ cells. Please click Additional Files below to see the full abstract

Implications of MDCK cell heterogeneity in cell-based influenza vaccine production

Influenza is a global public health issue that causes serious illness with high mortality rate. Currently, Madin-Darby canine kidney (MDCK) cell culture-based influenza vaccine production moving up to the front as an inexorable trend for the substitution of egg-based vaccine production, owing to its high degree of flexibility and scalability. However, MDCK cells are a continuous cell line and comprise a heterogeneous pool of non-clonal cells that differ in morphological as well as functional features in influenza virus production. The impurity of cell population may lead to fugacious tendency in virus production, and long-term culture may bring potential risk of unstable viral production or vaccine quality as cells in MDCK subclonal population may encounter unexpected manifestation of chromosomal rearrangement, loss of the virus susceptibility, or reduction of the virus partials packaging capability during the culture. Although many details of the influenza virus life cycle have already been unraveled, little is known about the ability of subclones in virus infection, intracellular replication, and virus release during viral vaccine production process. With the widely utilizing of omics-based approaches and progressively accumulating of omics database, transcriptome profile analysis will be a powerful strategy to explore the mechanism of cell heterogeneity, providing great significance for the development of robust virus producing cell line and robust virus production process. This work aims to explore a deeper understanding on the MDCK cell heterogeneity used in influenza virus production. For this purpose, a MDCK cell line that has been extensively used in industrial production was subcloned and examined for the influenza virus productivity. The virus productivity spread over a wide range of more than 300-fold among different clones, which revealed large variations in their ability to produce progeny viruses. The high and low producer as well as parent cell population were expanded to explore the intracellular virus propagation process, and the expression levels of all the annotated genes were quantified across the different subclones using RNA-seq. The RT-qPCR results showed that the influenza virus RNA synthesis and virus release differed dramatically among subclones during a synchronized single-cycle infection. Pathway analysis performed on the genes indicated that most of the genes are not differentially expressed, but a few key cellular metabolic pathways are differentially expressed among the subclones, especially the genes related to the virus infection, replication and release. These results spurs further hypothesis to improve our mechanistic understanding of cell line stability and virus propagation process, which will have significant impact on rationalizing cell line development of viral vaccine producing mammalian cells

Steady-state edge burst: From free-particle systems to interaction-induced phenomena

The interplay between the non-Hermitian skin effect and the imaginary gap of lossy lattices results in the edge burst, a boundary-induced dynamical phenomenon in which an exceptionally large portion of particle loss occurs at the edge. Here, we find that this intriguing non-Hermitian dynamical phenomenon can be exactly mapped into the steady-state density distribution of a corresponding open quantum system. Consequently, the bulk-edge scaling relation of loss probability in the edge burst maps to that of steady-state density. Furthermore, we introduce a many-body open-system model in which the two-body loss generates an interaction-induced non-Hermitian skin effect. Using the positive-$P$ method, we demonstrate the validity of the scaling relation for steady-state correlators. These results provide a unique perspective on the interaction-induced many-body non-Hermitian skin effect. Our predictions are testable in state-of-the-art experimental platforms.Comment: 13 pages, 6 figure

Efficient influenza vaccine manufacturing: Single MDCK suspension cells in chemically defined medium

Facing the constant global high demand for influenza vaccines, improving production capacity is most important. For influenza vaccine production, cell culture-based processes have advantages regarding flexibility, efficiency, and safety in comparison with the traditional egg-based processes. To avoid problems related to microcarrier-based approaches and serum containing media, growth of suspension cells in chemically-defined media is favoured. In addition, such a process has advantages regarding the improvement of virus titers, the scale-up of the production process, and overall productivity in up- and downstream processing. In this study, a previously developed MDCK suspension cell line [1] was cultivated in an in-house chemically defined medium to evaluate cell growth and virus production. For the purpose of process intensification, virus adaptation and infection strategies were investigated to achieve high cell densities and to maximize virus titers. Therefore, an adapted influenza virus strain (A/PR/8/34 H1N1 RK1) was generated by a series of virus passages with low multiplicity of infection (MOI). Virus infections were carried out by supplementing 100% of fresh medium, infecting cells with a MOI of 10-3, and with trypsin addition at 72 h of cell cultivations in shake flasks and bioreactors. For scale-up, MDCK cells were cultivated in a DASGIP bioreactor system, optimizing stirring speed, time of infection, pH and DO levels both for cell growth and virus infection. Cell count, viability, main extracellular metabolites, and virus titers were measured to compare productivity between shake flasks and bioreactors. In batch culture (shake flasks and bioreactors), single MDCK cells were grown to maximum cell densities of 1.2 x107 cells/ml with cell viabilities exceeding 98% at high cell specific growth rates of 0.036 h-1. Virus adaptation to the MDCK suspension cell line led to a fast infection and stable virus titers over time. Regarding process optimization, optimal pH (cell growth: 7.00, infection: 7.20), DO (40%) and agitation speed (80 rpm) were chosen for influenza A virus production in three parallel bioreactors. Cell densities of 1.0 x107 cells/ml were achieved at time of infection (72 h) before performing a dilution step. Post infection, similar virus infection dynamics were observed in shake flasks and bioreactors. For both cultivation systems maximal HA titers of 3.6 log10(HAU/100µl) were achieved without reduction of cell-specific virus titer (12,000 virions/cell). Overall, a highly efficient and scalable upstream process was realized by cultivation of MDCK suspension cells as single cells in chemically defined medium. This is a strong basis for a promising application in large-scale influenza vaccine manufacturing and potential process intensification towards high cell density virus production. [1] Huang D. et al., PloS One 10 (2015): e0141686. doi: 10.1371/journal.pone.014168

Learning to Search for Job Shop Scheduling via Deep Reinforcement Learning

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modeling partial solutions at each construction step. This paper proposes a novel DRL-based method to learn improvement heuristics for JSSP, where graph representation is employed to encode complete solutions. We design a Graph Neural Network based representation scheme, consisting of two modules to effectively capture the information of dynamic topology and different types of nodes in graphs encountered during the improvement process. To speed up solution evaluation during improvement, we design a novel message-passing mechanism that can evaluate multiple solutions simultaneously. Extensive experiments on classic benchmarks show that the improvement policy learned by our method outperforms state-of-the-art DRL-based methods by a large margin

Highly efficient influenza virus production: A MDCK-based high-cell-density process

Seasonal vaccination campaigns for influenza in developed and developing countries create a massive demand for 500 million (2015) vaccine doses every year [1]. Besides egg-based vaccine manufacturing, production platforms based on animal cell culture increasingly contribute to this overall growing market. In order to intensify cell culture-based influenza virus production, high-cell-density (HCD) cultivation of suspension cells can be applied to improve virus titer, process productivity and production costs [2]. For process optimization and evaluation of HCD conditions, cells cultivated using semi-perfusion approaches in small shakers can be used as a scale-down model for bioreactors operating in full perfusion mode [3]. In this study, a previously developed MDCK suspension cell line [4] was adapted to a new serum free medium [5] to facilitate higher growth rate, cell density and virus titer both in batch and in HCD. Therefore, MDCK cells cultivated in Smif-8 medium were slowly adapted to a new cultivation medium (Xeno™) by stepwise increasing the Xeno content. Fully adapted cells were cultivated in shaker flasks to evaluate the performance of influenza A virus production in batch and HCD. Cell densities exceeding 2∙107 cells/mL were achieved in shakers using semi-perfusion, where cell free medium was manually replaced with fresh medium. Volume and time interval of media replacement were chosen to achieve a constant cell-specific perfusion rate of 2.5 pL/(cell h). Cell cultures were infected with influenza virus (A/PR/8/34 H1N1 RKI) with trypsin addition. Cell count, viability, main metabolites and virus titer (HA-assay & TCID50) were monitored pre and post infection. Medium adaptation resulted in a MDCK suspension cell line with morphological, growth, and metabolic characteristics different from parental cells. Cells fully adapted to Xeno medium were growing to higher cell densities (1.4∙107 vs 6∙106 cells/mL) with higher specific growth rate (µmax: 0.036 vs 0.026 1/h), cells were bigger (15-16 vs 13-14 µm) and grew without aggregate formation. Due to higher cell densities at time of infection, virus titers up to 3.6 log10(HAU/100µL) were reached. In semi-perfusion, adapted MDCK cells were grown up to 6∙107 cells/mL, however, maximum virus titer and productivity were observed with 4∙107 cells/mL. In multiple harvests, very high virus titer exceeding 4 log10(HAU/100µL) and up to 9∙109 virions/mL (TCID50) were measured, which corresponded to an accumulated titer of 4.5 log10(HAU/100µL). Cell-specific virus titer was similar or higher compared to the reference batch infections, depending on perfusion and infection strategy. Overall, results in this semi-perfusion scale-down model for influenza A virus production suggest a highly efficient and productive upstream process for influenza virus production, with an up to six-fold improved space time yield compared to batch mode. [1] Palache A. et al., Vaccine 35 (2017): 4681–4686. doi: 10.1016/j.vaccine.2017.07.053 [2] Genzel Y. et al., Vaccine 32 (2014): 2770–2781. doi: 10.1016/j.vaccine.2014.02.016 [3] Vázquez-Ramírez D. et al., Vaccine (2018): article in press. doi: 10.1016/j.vaccine.2017.10.112 [4] Lohr V. et al., Vaccine 28 (2010): 6256–6264. doi: 10.1016/j.vaccine.2010.07.004 [5] Xeno™-S001S MDCK Cell Serum Free Medium (#FG0100402), Bioengine, Shanghai, Chin

The theoretical direct-band-gap optical gain of Germanium nanowires

We calculate the electronic structures of Germanium nanowires by taking the effective-mass theory. The electron and hole states at the G-valley are studied via the eight-band k.p theory. For the [111] L-valley, we expand the envelope wave function using Bessel functions to calculate the energies of the electron states for the first time. The results show that the energy dispersion curves of electron states at the L-valley are almost parabolic irrespective of the radius of Germanium nanowires. Based on the electronic structures, the density of states of Germanium nanowires are also obtained, and we find that the conduction band density of states mostly come from the electron states at the L-valley because of the eight equivalent degenerate L points in Germanium. Furthermore, the optical gain spectra of Germanium nanowires are investigated. The calculations show that there are no optical gain along z direction even though the injected carrier density is 4x1019 cm-3 when the doping concentration is zero, and a remarkable optical gain can be obtained when the injected carrier density is close to 1x1020 cm-3, since a large amount of electrons will prefer to occupy the low-energy L-valley. In this case, the negative optical gain will be encountered considering free-carrier absorption loss as the increase of the diameter. We also investigate the optical gain along z direction as functions of the doping concentration and injected carrier density for the doped Germanium nanowires. When taking into account free-carrier absorption loss, the calculated results show that a positive net peak gain is most likely to occur in the heavily doped nanowires with smaller diameters. Our theoretical studies are valuable in providing a guidance for the applications of Germanium nanowires in the field of microelectronics and optoelectronics