Biology subjects

Bowen, D.

Publications and source records attributed to Bowen, D..

3 recordsLinked to original sources

bioRxiv2022

Safety assessment of insecticidal proteins: A case study utilizing His-tagged protein to determine the safety of Mpp75Aa1.1, a new ETX_MTX2 protein that controls western corn rootworm

The recently discovered insecticidal protein Mpp75Aa1.1 from Brevibacillus laterosporus is one member of the ETX_MTX family of beta-pore forming proteins ({beta}-PFPs) expressed in genetically modified (GM) maize to control western corn rootworm (WCR; Diabrotica virgifera virgifera LeConte). In this paper, bioinformatic analysis establishes that although Mpp75Aa1.1 shares varying degrees of similarity to members of the ETX_MTX2 protein family, it is unlikely to contribute to any allergenic, toxic, or otherwise adverse biological effects. The safety of Mpp75Aa1.1 is further supported by a weight of evidence including evaluation of history of safe use (HOSU) of ETX_MTX2 proteins. Assessments using either purified Mpp75Aa1.1 protein or a poly-histidine-tagged (His-tagged) variant of the Mpp75Aa1.1 protein demonstrate that both forms of the protein are heat labile at temperatures at or above 55 {degrees}C, degraded by gastrointestinal proteases within 0.5 min, and have no adverse effects in acute mouse oral toxicity studies at a dose level of 1920 or 2120 mg/kg body weight. Results also indicate that the domain-based protein characterization should be considered as part of the weight of evidence for the safe consumption of food or feed. Furthermore, these results support the use of His-tagged proteins as suitable surrogates for assessing the safety of their non-tagged parent proteins.

pharmacology and toxicology↗

bioRxiv2020

The self-peptide repertoire plays a critical role in transplant tolerance induction

While direct allorecognition underpins both solid organ allograft rejection and tolerance induction, the specific molecular targets of most directly-alloreactive CD8+ T cells have not been defined. In this study, we used a combination of genetically-engineered MHC class I (MHC I) constructs, mice with a hepatocyte-specific mutation in the class I antigen-presentation pathway and immunopeptidomic analysis to provide definitive evidence for the contribution of the peptide cargo of allogeneic MHC I molecules to transplant tolerance induction. We established a systematic approach for the discovery of directly-recognised pMHC epitopes, and identified 17 strongly immunogenic H-2Kb-associated peptides recognised by CD8+ T cells from B10.BR (H-2k) mice, 13 of which were also recognised by BALB/c (H-2d) mice. As few as five different tetramers used together were able to identify a high proportion of alloreactive T cells within a polyclonal population, suggesting that there are immunodominant allogeneic MHC-peptide complexes that can account for a large component of the alloresponse.

immunology↗

bioRxiv2018

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology↗