Biology subjects

Kaufman, A.

Publications and source records attributed to Kaufman, A..

3 recordsLinked to original sources

bioRxiv2021

Resurrecting essential amino acid biosynthesis in a mammalian cell

Major genomic deletions in independent eukaryotic lineages have led to repeated ancestral loss of biosynthesis pathways for nine of the twenty canonical amino acids1. While the evolutionary forces driving these polyphyletic deletion events are not well understood, the consequence is that extant metazoans are unable to produce nine essential amino acids (EAAs). Previous studies have highlighted that EAA biosynthesis tends to be more energetically costly2,3, raising the possibility that these pathways were lost from organisms with access to abundant EAAs in the environment4,5. It is unclear whether present-day metazoans can reaccept these pathways to resurrect biosynthetic capabilities that were lost long ago or whether evolution has rendered EAA pathways incompatible with metazoan metabolism. Here, we report progress on a large-scale synthetic genomics effort to reestablish EAA biosynthetic functionality in a mammalian cell. We designed codon-optimized biosynthesis pathways based on genes mined from Escherichia coli. These pathways were de novo synthesized in 3 kilobase chunks, assembled in yeasto and genomically integrated into a Chinese Hamster Ovary (CHO) cell line. One synthetic pathway produced valine at a sufficient level for cell viability and proliferation, and thus represents a successful example of metazoan EAA biosynthesis restoration. This prototrophic CHO line grows in valine-free medium, and metabolomics using labeled precursors verified de novo biosynthesis of valine. RNA-seq profiling of the valine prototrophic CHO line showed that the synthetic pathway minimally disrupted the cellular transcriptome. Furthermore, valine prototrophic cells exhibited transcriptional signatures associated with rescue from nutritional starvation. This work demonstrates that mammalian metabolism is amenable to restoration of ancient core pathways, thus paving a path for genome-scale efforts to synthetically restore metabolic functions to the metazoan lineage.

synthetic biology↗

bioRxiv2021

DeepLIIF: Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Quantification

Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as DeepLIIF, we present a single-step solution to stain deconvolution/separation, cell segmentation, and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunofluorescence (mpIF) staining of the same slides, we segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. Moreover, a new nuclear-envelop stain, LAP2beta, with high (>95%) cell coverage is introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. By simultaneously translating input IHC images to clean/separated mpIF channels and performing cell segmentation/classification, we show that our model trained on clean IHC Ki67 data can generalize to more noisy and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10, and TP53. We thoroughly evaluate our method on publicly available benchmark datasets as well as against pathologists semi-quantitative scoring. The code, the pre-trained models, along with easy-to-run containerized docker files as well as Google CoLab project are available at https://github.com/nadeemlab/deepliif.

bioinformatics↗

bioRxiv2019

Scalable and cost-effective ribonuclease-based rRNA depletion for bacterial transcriptomics

Bacterial RNA sequencing (RNA-seq) is a powerful approach for quantitatively delineating the global transcriptional profiles of microbes in order to gain deeper understanding of their physiology and function. Cost-effective bacterial RNA-seq requires efficient physical removal of ribosomal RNA (rRNA), which otherwise dominates transcriptomic reads. However, current methods to effectively deplete rRNA of diverse non-model bacterial species are lacking. Here, we describe a probe and ribonuclease based strategy for bacterial rRNA removal. We implemented the method using either chemically synthesized oligonucleotides or amplicon-based single-stranded DNA probes and validated the technique on three novel gut microbiota isolates from three distinct phyla. We further showed that different probe sets can be used on closely related species. We provide a detailed methods protocol, probe sets for >5,000 common microbes from RefSeq, and an online tool to generate custom probe libraries. This approach lays the groundwork for large-scale and cost-effective bacterial transcriptomics studies.

genomics↗