Biology subjects

West, J. J.

Publications and source records attributed to West, J. J..

4 recordsLinked to original sources

bioRxiv2023

Serine synthesis pathway upregulated by E-cadherin is essential for the proliferation and metastasis of breast cancers

The loss of E-cadherin (E-cad), an epithelial cell adhesion molecule, has been implicated in the epithelial-mesenchymal transition (EMT), promoting invasion and migration of cancer cells and, consequently, metastasis. However, recent studies have demonstrated that E-cad supports the survival and proliferation of metastatic cancer cells, suggesting that our understanding of E-cad in metastasis is far from comprehensive. Here, we report that E-cad upregulates the de novo serine synthesis pathway (SSP) in breast cancer cells. The SSP provides metabolic precursors for biosynthesis and resistance to oxidative stress, critically beneficial for E-cad-positive breast cancer cells to achieve faster tumor growth and more metastases. Inhibition of PHGDH, a rate-limiting enzyme in the SSP, significantly and specifically hampered the proliferation of E-cad-positive breast cancer cells and rendered them vulnerable to oxidative stress, inhibiting their metastatic potential. Our findings reveal that E-cad adhesion molecule significantly reprograms cellular metabolism, promoting tumor growth and metastasis of breast cancers.

cancer biology↗

bioRxiv2020

The Arf-GEF Steppke promotes F-actin accumulation, cell protrusions and tissue sealingduring Drosophila dorsal closure

Cytohesin Arf-GEFs promote actin polymerization and protrusions of cultured cells, whereas the Drosophila cytohesin, Steppke, antagonizes actomyosin networks in several developmental contexts. To reconcile these findings, we analyzed epidermal leading edge actin networks during Drosophila embryo dorsal closure. Here, Steppke is required for F-actin of the actomyosin cable and for actin-based protrusions. steppke mutant defects in the leading edge actin networks are associated with improper sealing of the dorsal midline, but are distinguishable from effects of myosin mis-regulation. Steppke localizes to leading edge cell-cell junctions with accumulations of the F-actin regulator Enabled emanating from either side. Enabled requires Steppke for full leading edge recruitment, and genetic interaction shows the proteins cooperate for dorsal closure. Steppke over-expression induces ectopic, actin-rich, lamellar cell protrusions, an effect dependent on the Arf-GEF activity and PH domain of Steppke, but independent of Steppke recruitment to myosin-rich AJs via its coiled-coil domain. Thus, Steppke promotes actin polymerization and cell protrusions, effects that occur in conjunction with Steppkes previously reported regulation of myosin contractility during dorsal closure.

developmental biology↗

bioRxiv2020

An integrated model system to gain mechanistic insights into biofilm formation and antimicrobial resistance development in Pseudomonas aeruginosa MPAO1

Pseudomonas aeruginosa MPAO1 is the parental strain of the widely utilized transposon mutant collection for this important clinical pathogen. Here, we validate a model system to identify genes involved in biofilm growth and antibiotic resistance. Our model employs a genomics-driven workflow to assemble the complete MPAO1 genome, identify unique and conserved genes by comparative genomics with the PAO1 reference strain and missed genes by proteogenomics. Among over 200 unique MPAO1 genes, we identified six general essential genes that were overlooked when mapping public Tn-seq datasets against PAO1, including an antitoxin. Genomic data were integrated with phenotypic data from an experimental workflow using a user-friendly, soft lithography-based microfluidic flow chamber for biofilm growth. Experiments conducted across three laboratories delivered reproducible data on P. aeruginosa biofilms and validated both known and novel genes involved in biofilm growth and antibiotic resistance identified in screens of the mutant collection. Differential protein expression data from planktonic cells versus biofilm confirmed upregulation of candidates known to affect biofilm formation, of structural and secreted proteins of type six secretion systems, and provided proteogenomic evidence for some missed MPAO1 genes. This integrated, broadly applicable model promises to improve the mechanistic understanding of biofilm formation, antimicrobial tolerance and resistance evolution.

systems biology↗

bioRxiv2019

Mapping biology from mouse to man using transfer learning

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt from these experiments will transfer to the human. Yet, it is commonly the case that biology does not transfer effectively, often for unknown reasons. Despite its importance to translational research this transfer process is not currently rigorously quantified. Here, we show that transfer learning - the branch of machine learning that concerns passing information from one domain to another - can be used to efficiently map biology from mouse to man, using the bone marrow (BM) as a representative example of a complex tissue. We first trained an artificial neural network (ANN) to accurately recognize various different cell types in mouse BM using data obtained from single-cell RNA-sequencing (scRNA-Seq) experiments. We found that this ANN, trained exclusively on mouse data, was able to identify individual human cells obtained from comparable scRNA-Seq experiments of human BM with 83% overall accuracy. However, while some human cell types were easily identified, others were not, indicating important differences in biology. To obtain a more accurate map of the human BM we then retrained the mouse ANN using scRNA-Seq data from a limited sample of human BM cells. Typically, less than 10 human cells of a given type were needed to accurately learn its representation in the updated model. In some cases, human cell identities could be inferred directly from the mouse ANN without retraining, via a process of biologically-guided zero-shot learning. These results show how machine learning can be used to reconstruct complex biology from limited data and have broad implications for biomedical research.

systems biology↗