Maher, E. | Search bioRxiv

bioRxiv2025

Yeast MoClo secretion and surface display toolkit 2.0: improvements and applications for analysis of protein-protein interactions and whole-cell biocatalysis

Saccharomyces cerevisiae is an invaluable model organism for both fundamental biological research and biotechnological applications including recombinant protein production as well as protein and metabolic engineering. We previously developed a modular cloning (MoClo) based toolkit for S. cerevisiae that facilitates rapid optimization of signal peptides and anchor proteins for efficient secretion and/or surface display of heterologous proteins of interest. Here we describe further improvements and applications of this yeast secretion and display (YSD) toolkit. New parts encoding anchor proteins based on N-terminal fusion to a truncated Aga1 and C-terminal fusion to Aga2, each with three possible epitope tag options, are described. We also added parts that facilitate high throughput detection of secreted proteins of interest through GFP fluorescence complementation and parts encoding "secretion boosting" yeast proteins, whose overexpression has previously been reported to enhance secretion of heterologous proteins. In addition, two surface display applications of the toolkit are showcased. We demonstrate that yeast surface display of an anti-GFP nanobody allows cost-effective evaluation of the interactions of GFP-tagged proteins of interest, either by flow cytometry or yeast-based co-immunoprecipitation. In addition, using yeast cells as whole-cell catalysts, we show that co-display of the poly(ethylene terephthalate) (PET) degrading enzyme leaf-branch compost cutinase with hydrophobin1 enhances the breakdown of PET plastic, while triple co-display of these proteins with MHETase causes complete conversion of the intermediary monohydroxyLethyl-terephthalate (MHET) to terephthalic acid. The diverse applications described herein demonstrate the broad applications of the updated MoClo YSD toolkit 2.0 in both synthetic biology and other research fields.

synthetic biology↗

bioRxiv2019

Classification of Brain Tumor IDH Status using MRI and Deep Learning

Isocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose a novel automated pipeline for predicting IDH status noninvasively using deep learning and T2-weighted (T2w) MR images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MRI and genomic data were obtained from The Cancer Imaging Archive dataset (TCIA) for 260 subjects (120 High grade and 140 Low grade gliomas). A fully automated 2D densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects, using 5-fold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated and IDH wild-type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

neuroscience↗