Stem cell culture engineering - process scale up and beyond

Advances in stem cell research and recent work on clinical trials employing stem cells have heightened the prospect of stem cell applications in regenerative medicine. The eventual clinical application of stem cells will require transforming cell production from laboratory practices to robust processes. Most stem cell applications will require extensive ex vivo handling of cells, from isolation, cultivation, and directed differentiation to product cell separation, cell derivation, and final formulation. Some applications require large quantities of cells in each defined batch for clinical use in multiple patients; others may be for autologous use and require only small-scale operations. All share a common requirement: the production must be robust and generate cell products of consistent quality. Unlike the established manufacturing process of recombinant protein biologics, stem cell applications will likely see greater variability in their cell source and more fluctuations in product quality. Nevertheless, in devising stem cell-based bioprocesses, much insight could be gained from the manufacturing of biological materials, including recombinant proteins and anti-viral vaccines. The key to process robustness is thus not only the control of traditional process chemical and physical variables, but also the sustenance of cells in the desired potency or differentiation state through controlling non-traditional variables, such as signaling pathway modulators.     

Sex-specific activation of cell death signalling pathways in cerebellar granule neurons exposed to oxygen glucose deprivation followed by reoxygenation

Neuronal death pathways following hypoxia–ischaemia are sexually dimorphic, but the underlying mechanisms are unclear. We examined cell death mechanisms during OGD (oxygen-glucose deprivation) followed by Reox (reoxygenation) in segregated male (XY) and female (XX) mouse primary CGNs (cerebellar granule neurons) that are WT (wild-type) or Parp-1 [poly(ADP-ribose) polymerase 1] KO (knockout). Exposure of CGNs to OGD (1.5 h)/Reox (7 h) caused cell death in XY and XX neurons, but cell death during Reox was greater in XX neurons. ATP levels were significantly lower after OGD/Reox in WT-XX neurons than in XY neurons; this difference was eliminated in Parp-1 KO-XX neurons. AIF (apoptosis-inducing factor) was released from mitochondria and translocated to the nucleus by 1 h exclusively in WT-XY neurons. In contrast, there was a release of Cyt C (cytochrome C) from mitochondria in WT-XX and Parp-1 KO neurons of both sexes; delayed activation of caspase 3 was observed in the same three groups. Thus deletion of Parp-1 shunted cell death towards caspase 3-dependent apoptosis. Delayed activation of caspase 8 was also observed in all groups after OGD/Reox, but was much greater in XX neurons, and caspase 8 translocated to the nucleus in XX neurons only. Caspase 8 activation may contribute to increased XX neuronal death during Reox, via caspase 3 activation. Thus, OGD/Reox induces death of XY neurons via a PARP-1-AIF-dependent mechanism, but blockade of PARP-1-AIF pathway shifts neuronal death towards a caspase-dependent mechanism. In XX neurons, OGD/Reox caused prolonged depletion of ATP and delayed activation of caspase 8 and caspase 3, culminating in greater cell death during Reox.     

An alkali-thermotolerant extracellular protease from a newly isolated Streptomyces sp. DP2

Of six alkalitolerant, extracellular protease producing bacterial strains isolated, DP2 displayed maximum activity. This organism was designated as Streptomyces sp. DP2 and identified as Streptomyces ambofaciens. Maximum protease yield was observed after 48 hours of submerged fermentation using various carbon and nitrogen sources. Fructose was found to be the best substrate for protease production, followed by maltose, lactose and wheat bran. Mustard cake is reported for the first time as the most ideal nitrogen source although soybean meal also gave comparable yield. The protease produced by Streptomyces sp. DP2 exhibited extensive activity over a broad pH range (4–12) with maximum activity at pH 8, and was active over a broad range of elevated temperatures (50–100°C), and possessed thermostability at 60–90°C for up to 1 hour. Enzyme activity was reduced by EDTA (25%), SDS (16%), and PMSF (6%). This novel alkaline protease has both alkali- and thermostability that may have industrial significance.     

A novel dimer-tetramer transition captured by the crystal structure of the HIV-1 Nef

HIV-1 Nef modulates disease progression through interactions with over 30 host proteins. Individual chains fold into membrane-interacting N-terminal and C-terminal core (Nef_core) domains respectively. Nef exists as small oligomers near membranes and associates into higher oligomers such as tetramers or hexadecamers in the cytoplasm. Earlier structures of the Nef_core in apo and complexed forms with the Fyn-kinase SH3 domain revealed dimeric association details and the role of the conserved PXXP recognition motif (residues 72–78) of Nef in SH3-domain interactions. The crystal structure of the tetrameric Nef reported here corresponds to the elusive cytoplasmic stage. Comparative analyses show that subunits of Nef_core dimers (open conformation) swing out with a relative displacement of ∼22 Å and rotation of ∼174° to form the ‘closed’ tetrameric structure. The changes to the association are around Asp125, a conserved residue important for viral replication and the important XR motif (residues 107–108). The tetramer associates through C4 symmetry instead of the 222 symmetry expected when two dimers associate together. This novel dimer-tetramer transition agrees with earlier solution studies including small angle X-ray scattering, analytical ultracentrifugation, dynamic laser light scattering and our glutaraldehyde cross-linking experiments. Comparisons with the Nef_core—Fyn-SH3 domain complexes reveal that the PXXP motif that interacts with the SH3-domain in the dimeric form is sterically occluded in the tetramer. However the 151–180 loop that is distal to the PXXP motif and contains several protein interaction motifs remains accessible. The results suggest how changes to the oligomeric state of Nef can help it distinguish between protein partners.     

Identifying hosts of families of viruses: a machine learning approach

Identifying emerging viral pathogens and characterizing their transmission is essential to developing effective public health measures in response to an epidemic. Phylogenetics, though currently the most popular tool used to characterize the likely host of a virus, can be ambiguous when studying species very distant to known species and when there is very little reliable sequence information available in the early stages of the outbreak of disease. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome.     

Multiple analytical approaches reveal distinct gene-environment interactions in smokers and non smokers in lung cancer

Complex disease such as cancer results from interactions of multiple genetic and environmental factors. Studying these factors singularly cannot explain the underlying pathogenetic mechanism of the disease. Multi-analytical approach, including logistic regression (LR), classification and regression tree (CART) and multifactor dimensionality reduction (MDR), was applied in 188 lung cancer cases and 290 controls to explore high order interactions among xenobiotic metabolizing genes and environmental risk factors. Smoking was identified as the predominant risk factor by all three analytical approaches. Individually, CYP1A1*2A polymorphism was significantly associated with increased lung cancer risk (OR = 1.69;95%CI = 1.11–2.59,p = 0.01), whereas EPHX1 Tyr113His and SULT1A1 Arg213His conferred reduced risk (OR = 0.40;95%CI = 0.25–0.65,p<0.001 and OR = 0.51;95%CI = 0.33–0.78,p = 0.002 respectively). In smokers, EPHX1 Tyr113His and SULT1A1 Arg213His polymorphisms reduced the risk of lung cancer, whereas CYP1A1*2A, CYP1A1*2C and GSTP1 Ile105Val imparted increased risk in non-smokers only. While exploring non-linear interactions through CART analysis, smokers carrying the combination of EPHX1 113TC (Tyr/His), SULT1A1 213GG (Arg/Arg) or AA (His/His) and GSTM1 null genotypes showed the highest risk for lung cancer (OR = 3.73;95%CI = 1.33–10.55,p = 0.006), whereas combined effect of CYP1A1*2A 6235CC or TC, SULT1A1 213GG (Arg/Arg) and betel quid chewing showed maximum risk in non-smokers (OR = 2.93;95%CI = 1.15–7.51,p = 0.01). MDR analysis identified two distinct predictor models for the risk of lung cancer in smokers (tobacco chewing, EPHX1 Tyr113His, and SULT1A1 Arg213His) and non-smokers (CYP1A1*2A, GSTP1 Ile105Val and SULT1A1 Arg213His) with testing balance accuracy (TBA) of 0.6436 and 0.6677 respectively. Interaction entropy interpretations of MDR results showed non-additive interactions of tobacco chewing with SULT1A1 Arg213His and EPHX1 Tyr113His in smokers and SULT1A1 Arg213His with GSTP1 Ile105Val and CYP1A1*2C in nonsmokers. These results identified distinct gene-gene and gene environment interactions in smokers and non-smokers, which confirms the importance of multifactorial interaction in risk assessment of lung cancer.     

Homeostatic regulation of marginal zone B cells by invariant natural killer T cells

Marginal zone B cells (MZB) mount a rapid antibody response, potently activate naïve T cells, and are enriched in autoreactive B cells. MZBs express high levels of CD1d, the restriction element for invariant natural killer T cells (iNKT). Here, we examined the effect of iNKT cells on MZB cell activation and numbers in vitro and in vivo in normal and autoimmune mice. Results show that iNKT cells activate MZBs, but restrict their numbers in vitro and in vivo in normal BALB/c and C57/BL6 mice. iNKT cells do so by increasing the activation-induced cell death and curtailing proliferation of MZB cells, whereas they promote the proliferation of follicular B cells. Sorted iNKT cells can directly execute this function, without help from other immune cells. Such MZB regulation by iNKTs is mediated, at least in part, via CD1d on B cells in a contact-dependent manner, whereas iNKT-induced proliferation of follicular B cells occurs in a contact- and CD1d-independent manner. Finally, we show that iNKT cells reduce ‘autoreactive’ MZB cells in an anti-DNA transgenic model, and limit MZB cell numbers in autoimmune-prone (NZB×NZW)F1 and non-obese diabetic mice, suggesting a potentially new mechanism whereby iNKT cells might regulate pathologic autoimmunity. Differential regulation of follicular B cells versus potentially autoreactive MZBs by iNKT cells has important implications for autoimmune diseases as well as for conditions that require a rapid innate B cell response.     

The wiring economy principle: connectivity determines anatomy in the human brain

Minimization of the wiring cost of white matter fibers in the human brain appears to be an organizational principle. We investigate this aspect in the human brain using whole brain connectivity networks extracted from high resolution diffusion MRI data of 14 normal volunteers. We specifically address the question of whether brain anatomy determines its connectivity or vice versa. Unlike previous studies we use weighted networks, where connections between cortical nodes are real-valued rather than binary off-on connections. In one set of analyses we found that the connectivity structure of the brain has near optimal wiring cost compared to random networks with the same number of edges, degree distribution and edge weight distribution. A specifically designed minimization routine could not find cheaper wiring without significantly degrading network performance. In another set of analyses we kept the observed brain network topology and connectivity but allowed nodes to freely move on a 3D manifold topologically identical to the brain. An efficient minimization routine was written to find the lowest wiring cost configuration. We found that beginning from any random configuration, the nodes invariably arrange themselves in a configuration with a striking resemblance to the brain. This confirms the widely held but poorly tested claim that wiring economy is a driving principle of the brain. Intriguingly, our results also suggest that the brain mainly optimizes for the most desirable network connectivity, and the observed brain anatomy is merely a result of this optimization.