Biology subjects

Lewis, J. S.

Publications and source records attributed to Lewis, J. S..

25 records · Page 2Linked to original sources

bioRxiv2019

Targeted brain tumor radiotherapy using an Auger emitter

Glioblastoma multiforme is a highly aggressive form of brain cancer whose location, tendency to infiltrate healthy surrounding tissue, and heterogeneity significantly limit survival, with scant progress having been made in recent decades. 123I-MAPi (Iodine-123 Meitner-Auger PARP1 inhibitor) is a precise therapeutic tool composed of a PARP1 inhibitor radiolabeled with an Auger- and gamma-emitting iodine isotope. Here, the PARP inhibitor, which binds to the DNA repair enzyme PARP1, specifically targets cancer cells, sparing healthy tissue, and carries a radioactive payload within reach of the cancer cells DNA. The high relative biological efficacy of Auger electrons within their short range of action is leveraged to inflict DNA damage and cell death with high precision. The gamma ray emission of 123I-MAPi allows for the imaging of tumor progression and therapy response, and for patient dosimetry calculation. Here we demonstrated the efficacy and specificity of this small molecule radiotheranostic in a complex preclinical model. In vitro and in vivo studies demonstrate high tumor uptake and a prolonged survival in mice treated with 123I-MAPi when compared to vehicle controls. Different methods of drug delivery were investigated to develop this technology for clinical applications, including convection enhanced delivery (CED) and intrathecal injection. Taken together, these results represent the first full characterization of an Auger-emitting PARP inhibitor, demonstrate a survival benefit in mouse models of GBM, and confirm the high potential of 123I-MAPi for clinical translation.\n\nOne Sentence SummaryA novel PARP1-targeted Auger radiotherapeutic shows translational potential as a theranostic tool for imaging and killing cancer cells, resulting in tumor delineation and prolonged survival in a glioblastoma model.

cancer biology↗

bioRxiv2019

Single-molecule imaging of eukaryotic replisomes reveals compositional plasticity

Structural and biochemical studies have revealed the basic principles of how the replisome duplicates genomic DNA, but little is known about its dynamics during DNA replication. We reconstitute the 34 proteins needed to form the S. cerevisiae replisome and show how changing local concentrations of the key DNA polymerases tunes the ability of the complex to efficiently recycle these proteins or to dynamically exchange them. Particularly, we demonstrate redundancy of the Pol DNA polymerase activity in replication and show that Pol primase and the lagging-strand Pol {delta} can be re-used within the replisome to support the synthesis of large numbers of Okazaki fragments. This unexpected malleability of the replisome might allow it to deal with barriers and resource challenges during replication of large genomes.

biophysics↗

bioRxiv2018

Recycling of single-stranded DNA-binding protein by the bacterial replisome.

Single-stranded DNA-binding proteins (SSBs) support DNA replication by protecting single-stranded DNA from nucleolytic attack, preventing intra-strand pairing events, and playing many other regulatory roles within the replisome. Recent developments in single-molecule approaches have led to a revised picture of the replisome that is much more complex in how it retains or recycles protein components. Here we visualise how an in vitro reconstituted E. coli replisome recruits SSB by relying on two different molecular mechanisms. Not only does it recruit new SSB molecules from solution to coat newly formed single-stranded DNA on the lagging strand, but it also internally recycles SSB from one Okazaki fragment to the next. We show that this internal transfer mechanism is balanced against recruitment from solution in a manner that is concentration dependent. By visualising SSB dynamics in live cells, we show that both internal transfer and external exchange mechanisms are physiologically relevant.

biophysics↗

bioRxiv2018

A Primase-Induced Conformational Switch Controls the Stability of the Bacterial Replisome

Recent studies of bacterial DNA replication have led to a picture of the replisome as an entity that freely exchanges DNA polymerases and displays intermittent coupling between the helicase and polymerase(s). Challenging the textbook model of the polymerase holoenzyme acting as a stable complex coordinating the replisome, these observations suggest a role of the helicase as the central organizing hub. We show here that the molecular origin of this newly-found plasticity lies in the >400-fold increase in strength of the interaction between the polymerase holoenzyme and the replicative helicase upon association of the primase with the replisome. By combining in vitro ensemble-averaged and single-molecule assays, we demonstrate that this conformational switch operates during replication and promotes recruitment of multiple holoenzymes at the fork. Our observations provide a molecular mechanism for polymerase exchange and offer a revised model for the replication reaction that emphasizes its stochasticity.

biochemistry↗

bioRxiv2018

Nuclease dead Cas9 is a programmable roadblock for DNA replication

DNA replication occurs on chromosomal DNA while processes such as DNA repair, recombination and transcription continue. However, we have limited experimental tools to study the consequences of collisions between DNA-bound molecular machines. Here, we repurpose a catalytically inactivated Cas9 (dCas9) construct fused to the photo-stable dL5 protein fluoromodule as a novel, targetable protein-DNA roadblock for studying replication fork arrest at the single-molecule level in vitro as well as in vivo. We find that the specifically bound dCas9-guideRNA complex arrests viral, bacterial and eukaryotic replication forks in vitro.

biochemistry↗

bioRxiv2018

Spatial and temporal organization of RecA in the Escherichia coli DNA-damage response

The RecA protein orchestrates the cellular response to DNA damage via its multiple roles in the bacterial SOS response. Lack of tools that provide unambiguous access to the various RecA states within the cell have prevented understanding of the spatial and temporal changes in RecA structure/function that underlie control of the damage response. Here, we develop a monomeric C-terminal fragment of the {lambda} repressor as a novel fluorescent probe that specifically interacts with RecA filaments on single-stranded DNA (RecA*). Single-molecule imaging techniques in live cells demonstrate that RecA is largely sequestered in storage structures during normal metabolism. Upon DNA damage, the storage structures dissolve and the cytosolic pool of RecA rapidly nucleates to form early SOS-signaling complexes, maturing into DNA-bound RecA bundles at later time points. Both before and after SOS induction, RecA* largely appears at locations distal from replisomes. Upon completion of repair, RecA storage structures reform.

biophysics↗

bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

ecology↗