Integrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences

Detecting selection in B cell immunoglobulin (Ig) sequences is critical to understanding affinity maturation, and can provide insights into antigen-driven selection in normal and pathologic immune responses. The most common sequence-based methods for detecting selection analyze the ratio of replacement (R) and silent (S) mutations using a binomial statistical analysis. However, these approaches have been criticized for low sensitivity. An alternative method is based on the analysis of lineage trees constructed from sets of clonally-related Ig sequences. Several tree shape measures have been proposed as indicators of selection that can be statistically compared across cohorts. However, we show that tree shape analysis is confounded by underlying experimental factors that are difficult to control for in practice, including the sequencing depth and number of generations in each clone. Thus, though lineage tree shapes may reflect selection, their analysis alone is an unreliable measure of in vivo selection. To usefully capture the information provided by lineage trees, we propose a new method that applies the binomial statistical method to mutations identified based on lineage tree structure. This hybrid method is able to detect selection with increased sensitivity in both simulated and experimental data sets. We anticipate that this approach will be especially useful in the analysis of large-scale Ig sequencing data sets generated by high-throughput sequencing technologies

Financing repurposed drugs for rare diseases: a case study of Unravel Biosciences

Abstract Background We consider two key challenges that early-stage biotechnology firms face in developing a sustainable financing strategy and a sustainable business model: developing a valuation model for drug compounds, and choosing an appropriate operating model and corporate structure. We use the specific example of Unravel Biosciences—a therapeutics platform company that identifies novel drug targets through off-target mechanisms of existing drugs and then develops optimized new molecules—throughout the paper and explore a specific scenario of drug repurposing for rare genetic diseases. Results The first challenge consists of producing a realistic financial valuation of a potential rare disease repurposed drug compound, in this case targeting Rett syndrome. More generally, we develop a framework to value a portfolio of pairwise correlated rare disease compounds in early-stage development and quantify its risk profile. We estimate the probability of a negative return to be $$80.8\%$$ 80.8 % for a single compound and $$56.1\%$$ 56.1 % for a portfolio of 8 drugs. The probability of selling the project at a loss decreases from $$79.2\%$$ 79.2 % (phase 3) for a single compound to $$55.4\%$$ 55.4 % (phase 3) for the 8-drug portfolio. For the second challenge, we find that the choice of operating model and corporate structure is crucial for early-stage biotech startups and illustrate this point with three concrete examples. Conclusions Repurposing existing compounds offers important advantages that could help early-stage biotech startups better align their business and financing issues with their scientific and medical objectives, enter a space that is not occupied by large pharmaceutical companies, and accelerate the validation of their drug development platform

Scalable Device for Automated Microbial Electroporation in a Digital Microfluidic Platform

Electrowetting-on-dielectric (EWD) digital microfluidic laboratory-on-a-chip platforms demonstrate excellent performance in automating labor-intensive protocols. When coupled with an on-chip electroporation capability, these systems hold promise for streamlining cumbersome processes such as multiplex automated genome engineering (MAGE). We integrated a single Ti:Au electroporation electrode into an otherwise standard parallel-plate EWD geometry to enable high-efficiency transformation of Escherichia coli with reporter plasmid DNA in a 200 nL droplet. Test devices exhibited robust operation with more than 10 transformation experiments performed per device without cross-contamination or failure. Despite intrinsic electric-field nonuniformity present in the EP/EWD device, the peak on-chip transformation efficiency was measured to be 8.6 ± 1.0 × 10⁸ cfu·μg^–1 for an average applied electric field strength of 2.25 ± 0.50 kV·mm^–1. Cell survival and transformation fractions at this electroporation pulse strength were found to be 1.5 ± 0.3 and 2.3 ± 0.1%, respectively. Our work expands the EWD toolkit to include on-chip microbial electroporation and opens the possibility of scaling advanced genome engineering methods, like MAGE, into the submicroliter regime