PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens.

Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model

Biosimilar drugs must closely resemble the pharmacological attributes of innovator products to ensure safety and efficacy to obtain regulatory approval. Glycosylation is one critical quality attribute that must be matched, but it is inherently difficult to control due to the complexity of its biogenesis. This usually implies that costly and time-consuming experimentation is required for clone identification and optimization of biosimilar glycosylation. Here, we describe a computational method that utilizes a Markov model of glycosylation to predict optimal glycoengineering strategies to obtain a specific glycosylation profile with desired properties. The approach uses a genetic algorithm to find the required quantities to perturb glycosylation reaction rates that lead to the best possible match with a given glycosylation profile. Furthermore, the approach can be used to identify cell lines and clones that will require minimal intervention while achieving a glycoprofile that is most similar to the desired profile. Thus, this approach can facilitate biosimilar design by providing computational glycoengineering guidelines that can be generated with a minimal time and cost

HemoCue®, an Accurate Bedside Method of Hemoglobin Measurement?

Objective. Evaluate the accuracy of this bedside method to determinehemoglobin (Hb) concentration in general surgery over a wide range of Hbvalues and to determine potential sources of error. Methods. Accuracy of Hbmeasurement using HemoCue® (AB Leo Diagnostics, Helsinborg, Sweden) wasassessed in 140 surgical blood samples using 7 HemoCue® devices incomparison with a CO-Oximeter (IL 482, Instrumentation Laboratory,Lexington, MA). To analyze potential sources of error, packed red cells andfresh frozen plasma were reconstituted to randomized Hb levels of 2-18g/dL. Results. In the surgical blood samples, the Hb concentrationdetermined by the CO-Oximeter (HbCOOX) ranged from 5.1 to 16.7 g/dL and theHb concentration measured by HemoCue® (HbHC) from 4.7 to 16.0 g/dL. Bias(HbCOOX - HbHC) between HbCOOX and HbHC was 0.6 ± 0.6 g/dL(mean ± SD) or 5.4 ± 5.0% (p < 0.001). Also in thereconstituted blood, the bias between HbCOOX and HbHC was significant (0.2± 0.3 g/dL or 2.1 ± 3.2%; p < 0.001). Themicrocuvette explained 68% of the variability between HbCOOX andHbHC. HemoCue® thus underestimates the Hb concentration by2-5% and exhibits a 8-10 times higher variability withonly 86.4% of HbHC being within ± 10% of HbCOOX.Conclusion. Although the mean bias between HbCOOX and HbHC was relativelylow, Hb measurement by HemoCue® exhibited a significant variability.Loading multiple microcuvettes and averaging the results may increase theaccuracy of Hb measurement by HemoCue

Quantification of genomic DNA repair capabilities in CHO and identification of genes impacting genomic stability

Genomic instability in CHO cells poses a challenge for biopharmaceutical production because it is associated with decline of productivity, product quality, and culture viability. Chromosome rearrangements are particularly problematic since these can decrease or eliminate transgene expression. These are caused by DNA double-strand breaks (DSBs) that are not adequately repaired by the cell, presumably due to deficiencies in DNA repair genes. In this study we have conducted a genome-wide bioinformatic analysis of single-nucleotide variants (SNVs) in DNA-repair genes in the CHO genome. We implement a reporter system in CHO cells that facilitates the quantification of the cell’s capability to repair DSBs in genomic DNA. This provides a DNA stability assessment that is superior to previous assays since these would merely read out the capability to repair artificial plasmids. By utilizing this genomic DSB repair assay, we can quantify DNA stability in standard CHO cells, various DNA repair-deficient CHO mutants, as well as in primary Chinese hamster cells. Finally, we explore how by targeting defective candidate genes from our bioinformatic analysis, this assay can be used to engineer CHO cell lines with increased genomic stability

Modulating carbohydrate–protein interactions through glycoengineering of monoclonal antibodies to impact cancer physiology

Diverse glycans on proteins help impact cell and organism physiology along with drug activity. Since many protein-based biotherapeutics are glycosylated and these glycans have biological activity, there is a desire to engineer glycosylation for recombinant protein-based biotherapeutics. Engineered glycosylation can impact the recombinant protein efficacy and also influence many cell pathways by first changing glycan-protein interactions and consequently modulating disease physiologies. However, its complexity is enormous. Due to recent advances in glycoengineering, modulating protein-glycan interactions become more amenable to therapeutic approaches. Here, we discuss how engineered glycans contribute to therapeutic monoclonal antibodies (mAbs) in the treatment of cancers, how these glycoengineered therapeutic mAbs affect the transformed phenotypes and downstream cell pathways, and how systems biology can help in the next generation mAb glycoengineering process by aiding in data analysis and guiding engineering efforts to tailor mAb glycan and ultimately drug efficacy, safety and affordability