Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit

Early Changes in Tumor Perfusion from T1-Weighted Dynamic Contrast-Enhanced MRI following Neural Stem Cell-Mediated Therapy of Recurrent High-Grade Glioma Correlate with Overall Survival

Background. The aim of this study was to correlate T1-weighted dynamic contrast-enhanced MRI- (DCE-MRI-) derived perfusion parameters with overall survival of recurrent high-grade glioma patients who received neural stem cell- (NSC-) mediated enzyme/prodrug gene therapy. Methods. A total of 12 patients were included in this retrospective study. All patients were enrolled in a first-in-human study (NCT01172964) of NSC-mediated therapy for recurrent high-grade glioma. DCE-MRI data from all patients were collected and analyzed at three time points: MRI#1—day 1 postsurgery/treatment, MRI#2— day 7 ± 3 posttreatment, and MRI#3—one-month follow-up. Plasma volume (Vp), permeability (Ktr), and leakage (λtr) perfusion parameters were calculated by fitting a pharmacokinetic model to the DCE-MRI data. The contrast-enhancing (CE) volume was measured from the last dynamic phase acquired in the DCE sequence. Perfusion parameters and CE at each MRI time point were recorded along with their relative change between MRI#2 and MRI#3 (Δ32). Cox regression was used to analyze patient survival. Results. At MRI#1 and at MRI#3, none of the parameters showed a significant correlation with overall survival (OS). However, at MRI#2, CE and λtr were significantly associated with OS (p<0.05). The relative λtr and Vp from timepoint 2 to timepoint 3 (Δ32λtr and Δ32Vp) were each associated with a higher hazard ratio (p<0.05). All parameters were highly correlated, resulting in a multivariate model for OS including only CE at MRI#2 and Δ32Vp, with an R2 of 0.89. Conclusion. The change in perfusion parameter values from 1 week to 1 month following NSC-mediated therapy combined with contrast-enhancing volume may be a useful biomarker to predict overall survival in patients with recurrent high-grade glioma

Characterization of biotechnologically relevant extracellular lipase produced by Aspergillus terreus NCFT 4269.10

Abstract Enzyme production by Aspergillus terreus NCFT 4269.10 was studied under liquid static surface and solid-state fermentation using mustard oil cake as a substrate. The maximum lipase biosynthesis was observed after incubation at 30 °C for 96 h. Among the domestic oils tested, the maximum lipase biosynthesis was achieved using palm oil. The crude lipase was purified 2.56-fold to electrophoretic homogeneity, with a yield of 8.44%, and the protein had a molecular weight of 46.3 kDa as determined by SDS-PAGE. Enzyme characterization confirmed that the purified lipase was most active at pH 6.0, temperature of 50 °C, and substrate concentration of 1.5%. The enzyme was thermostable at 60 °C for 1 h, and the optimum enzyme&#8211;substrate reaction time was 30 min. Sodium dodecyl sulfate and commercial detergents did not significantly affect lipase activity during 30-min incubation at 30 °C. Among the metal ions tested, the maximum lipase activity was attained in the presence of Zn2+, followed by Mg2+ and Fe2+. Lipase activity was not significantly affected in the presence of ethylenediaminetetraacetic acid, sodium lauryl sulfate and Triton X-100. Phenylmethylsulfonyl fluoride (1 mM) and the reducing, &#946;-mercaptoethanol significantly inhibited lipase activity. The remarkable stability in the presence of detergents, additives, inhibitors and metal ions makes this lipase unique and a potential candidate for significant biotechnological exploitation

Characterization of purified α-amylase produced by Aspergillus terreus NCFT 4269.10 using pearl millet as substrate ABOUT THE AUTHORS

Abstract: α-amylase was produced by Aspergillus terreus NCFT 4269.10 using both liquid static surface (LSSF) and solid-state fermentation using pearl millet residues as substrate. The maximum production of α-amylase was noticed at 30°C incubated for 96h. The crude α-amylase was purified to electrophoretic homogeneity and characterized. Characterization of amylase confirmed that the purified α-amylase was found to be most stable at pH 5.0, 60°C temperature, and a substrate concentration of 1.25%. The enzyme was active for 40 min at 70°C with an optimum enzyme-substrate reaction time of 60 min. Amylase was compatible with all detergents tested having highest activity with Surf excel followed by Henko and Ariel. SDS and Tween 20 reduced the activity. Among the metal ions tested, the maximum α-amylase activity was attained in the presence of Ca 2+ , followed by Mg 2+ and Mn 2+ . The activity of α-amylase was not considerably affected in the presence of ethylenediaminetetraacetic acid and Triton X-100. Amylase activity was accelerated in the presence of sodium lauryl sulfate and phenylmethylsulfonyl fluoride did not significantly (or slightly) affect the activity and stability. Tween 20, urea (5%), and the reducing agent, β-mercaptoethanol significantly inhibited the activity of α-amylase. Owin

Comparative analysis, distribution, and characterization of microsatellites in Orf virus genome

Abstract Genome-wide in-silico identification of microsatellites or simple sequence repeats (SSRs) in the Orf virus (ORFV), the causative agent of contagious ecthyma has been carried out to investigate the type, distribution and its potential role in the genome evolution. We have investigated eleven ORFV strains, which resulted in the presence of 1,036–1,181 microsatellites per strain. The further screening revealed the presence of 83–107 compound SSRs (cSSRs) per genome. Our analysis indicates the dinucleotide (76.9%) repeats to be the most abundant, followed by trinucleotide (17.7%), mononucleotide (4.9%), tetranucleotide (0.4%) and hexanucleotide (0.2%) repeats. The Relative Abundance (RA) and Relative Density (RD) of these SSRs varied between 7.6–8.4 and 53.0–59.5 bp/kb, respectively. While in the case of cSSRs, the RA and RD ranged from 0.6–0.8 and 12.1–17.0 bp/kb, respectively. Regression analysis of all parameters like the incident of SSRs, RA, and RD significantly correlated with the GC content. But in a case of genome size, except incident SSRs, all other parameters were non-significantly correlated. Nearly all cSSRs were composed of two microsatellites, which showed no biasedness to a particular motif. Motif duplication pattern, such as, (C)-x-(C), (TG)-x-(TG), (AT)-x-(AT), (TC)- x-(TC) and self-complementary motifs, such as (GC)-x-(CG), (TC)-x-(AG), (GT)-x-(CA) and (TC)-x-(AG) were observed in the cSSRs. Finally, in-silico polymorphism was assessed, followed by in-vitro validation using PCR analysis and sequencing. The thirteen polymorphic SSR markers developed in this study were further characterized by mapping with the sequence present in the database. The results of the present study indicate that these SSRs could be a useful tool for identification, analysis of genetic diversity, and understanding the evolutionary status of the virus

TMIC-19. USING QUANTITATIVE MR IMAGING TO RELATE GBM MASS EFFECT TO PERFUSION AND DIFFUSION CHARACTERISTICS OF THE TUMOR MICRO-ENVIRONMENT

Biomechanical forces are known to affect tumor growth and evolution [1]. Likewise, tumor growth drives physical changes in the micro-environment that affect tissue solid and fluid mechanics. Tumor mass effect, resulting from rapid tumor cell proliferation, has been shown to be prognostic for poor outcome in glioblastoma (GBM) patients and to be associated with the expression of gene signatures consistent with proliferative growth phenotype [2]. Similarly, elevated interstitial fluid flow (IFF) has been shown to drive GBM invasion [3]. This study investigates the relationship between tumor mass effect, diffusion, perfusion and IFF in GBM using anatomical (pre- and post-contrast T1 weighted, T2/FLAIR) and quantitative MR imaging (Dynamic Contrast Enhanced (DCE) MRI, and Diffusion Weighted Imaging (DWI)). We use data from 39 patients from the Ivy Glioblastoma Atlas Project (Ivy GAP)[4] which provides matched imaging, ISH, RNA, gene expression and clinical data over the course of treatment. We analyze pre-operative anatomic imaging data to determine the tumor-induced mass effect in each patient using quantitative measures such as ‘Lateral ventricle displacement’ [2]. Perfusion and diffusion measures are derived from pre-operative DCE and DWI imaging. Additionally, we estimate IFF velocities in the tumor region using DCE imaging data in combination with a computational model of fluid flow [5]. References: [1] R.K. Jain et al. Annu. Rev. Biomed. Eng., 2014, 16, 321–346. [2] T.C. Steed et al. Scientific Reports, 2018, 8, 2827. [3] K.M. Kingsmore et al. Integr. Biol., 2016, 8 1246-1260 [4] N. Shah et al. Data from Ivy GAP. The Cancer Imaging Archive 2016. [5] K.M. Kingsmore et al. APL Bioengineering, 2018, 2, 031905