Biology subjects

Jafari, F.

Publications and source records attributed to Jafari, F..

2 recordsLinked to original sources

bioRxiv2023

A Simple Bias Reduction Algorithm for RNA Sequencing Datasets

RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used in the clinical diagnostic space for cancer patients. While the information gained is intended to impact treatment decisions, numerous technical and quality issues remain. This includes inaccuracies in the dissemination of gene-gene relationships. For such reasons, clinical decisions are still mostly driven by DNA biomarkers, such as gene mutations or fusions. In this study, we aimed to correct for systemic bias based on RNA-sequencing platforms in order to improve our understanding of the gene-gene relationships. To do so, we examined standard pre-processed RNA-seq datasets obtained from three studies conducted by two consortium efforts including The Cancer Genome Atlas (TCGA) and Stand Up 2 Cancer (SU2C). We particularly examined the TCGA Bladder Cancer (n = 408) and Prostate Cancer (n = 498) studies as well as the SU2C Prostate Cancer study (n = 208). Using various statistical tests, we detected expression-level dependent, per-sample biases in all datasets. Using simulations, we show that these biases corrupt the results of t-tests designed to identify expression level differences between subpopulations. Importantly, these biases introduce large errors into estimates of gene-gene correlations. To mitigate these biases, we introduce Local Leveling as a novel mathematical approach that transforms count level data and corrects these observed biases. Local Leveling specifically corrects for the bias due to the inherent differential detection of transcripts that is driven by differential expression levels. Based on standard forms of count data (Raw counts, transcripts per million, fragments per kilobase of exon per million), we demonstrate that local leveling effectively removes the observed per-sample biases, and improves the accuracy in simulated statistical tests. Importantly, this led to systemic changes of gene-gene relationships when examining the correlation of key oncogenes, such as the Androgen Receptor, with all other detectable genes. Altogether, Local Leveling improves our capacity towards understanding gene-gene relationships, which may lead to novel ways to utilize the information derived from clinical tests.

genomics↗

bioRxiv2019

Implementation of Design of Experiments (DOE) for Optimization of Feeding Strategy and Glyco-Engineering of Trastuzumab Biosimilar

Fed-batch cell culture is the most commonly used process for antibody production in biopharmaceutical industries. Basal media, feed, feeding strategy and glycan structures are always among the most important concerns during process development and optimization. In this study, first, a traditional screening study was performed to identify the top media/feed combinations by evaluating the cell culture performance including cell growth and protein titre. Optimization of the process was also performed using response surface methodology in order to find the most optimum feeding strategy and glucose set point regarding final titre of the recombinant monoclonal antibody being produced in Chinese hamster ovary cell line. The focus of this study is not only on titre, but also on product quality and comparability especially protein glycosylation. The prediction model of product titre as a function of feeding percentage and glucose set point was successfully applied for the second set of experiments that was performed for glycan improvement. Statistical design of experiments was applied to determine the most important factors and their effects on galactosylated and afucosylated glycans. Uridine, manganese, galactose and fucosyltransferase inhibitor were chosen to evaluate if their presence can affect glycans and to obtain their best combination for fed-batch culture supplementation. We determined that 2.5 % daily feeding combined with maintaining the glucose set point on 2.5{+/-}0.2 g/L could achieve final titre of 2.5{+/-} 0.1 g/L. Galactosylation of antibody was increased about 25% using MnCl2 and galactose while afucosylation was increased about 8% in presence of fucosyltransferase inhibitor. Galactose and Mn2+ led to a shift from G0F to G1F and presence of Fucosyltransferase inhibitor caused to an increase in G0 compared to its absence. These results demonstrated that supplementation of culture with all these components can provide exact control of antibody galactosylation and fucosylation with minimal impact on culture characteristics and product quality attributes. Subsequently, validation experiments were also carried out in 5L STR bioreactors which showed that similar results could be achieved in bioreactors compared to shake flasks regarding both titre and quality.

bioengineering↗