An antibody that prevents serpin polymerisation acts by inducing a novel allosteric behavior

Serpins are important regulators of proteolytic pathways with an antiprotease activity that involves a conformational transition from a metastable to a hyperstable state. Certain mutations permit the transition to occur in the absence of a protease; when associated with an intermolecular interaction, this yields linear polymers of hyperstable serpin molecules, which accumulate at the site of synthesis. This is the basis of many pathologies termed the serpinopathies. We have previously identified a monoclonal antibody (mAb4B12) that, in single-chain form, blocks α1-Antitrypsin (α1-AT) polymerisation in cells. Here, we describe the structural basis for this activity. The mAb4B12 epitope was found to encompass residues Glu32, Glu39 and His43 on helix A and Leu306 on helix I. This is not a region typically associated with the serpin mechanism of conformational change, and correspondingly the epitope was present in all tested structural forms of the protein. Antibody binding rendered β-sheet A - on the opposite face of the molecule - more liable to adopt an 'open' state, mediated by changes distal to the breach region and proximal to helix F. The allosteric propagation of induced changes through the molecule was evidenced by an increased rate of peptide incorporation and destabilisation of a preformed serpin-enzyme complex following mAb4B12 binding. These data suggest that prematurely shifting the β-sheet A equilibrium towards the 'open' state out of sequence with other changes suppresses polymer formation. This work identifies a region potentially exploitable for a rational design of ligands that is able to dynamically influence α1-AT polymerisation

A systematic CRISPR screen defines mutational mechanisms underpinning signatures caused by replication errors and endogenous DNA damage.

Mutational signatures are imprints of pathophysiological processes arising through tumorigenesis. We generated isogenic CRISPR-Cas9 knockouts (Δ) of 43 genes in human induced pluripotent stem cells, cultured them in the absence of added DNA damage, and performed whole-genome sequencing of 173 subclones. ΔOGG1, ΔUNG, ΔEXO1, ΔRNF168, ΔMLH1, ΔMSH2, ΔMSH6, ΔPMS1, and ΔPMS2 produced marked mutational signatures indicative of being critical mitigators of endogenous DNA modifications. Detailed analyses revealed mutational mechanistic insights, including how 8-oxo-dG elimination is sequence-context-specific while uracil clearance is sequence-context-independent. Mismatch repair (MMR) deficiency signatures are engendered by oxidative damage (C>A transversions), differential misincorporation by replicative polymerases (T>C and C>T transitions), and we propose a 'reverse template slippage' model for T>A transversions. ΔMLH1, ΔMSH6, and ΔMSH2 signatures were similar to each other but distinct from ΔPMS2. Finally, we developed a classifier, MMRDetect, where application to 7,695 WGS cancers showed enhanced detection of MMR-deficient tumors, with implications for responsiveness to immunotherapies

Low-input library preparation methods for single molecule sequencing

Over the past two decades, high-throughput DNA sequencing has revolutionized our understanding of epigenetics. The development of quantitative assays that use sequencing to measure chromatin accessibility and methylation state have provided key insights into oncogenesis and cancer metastasis. Recently developed 3rd generation technologies offer significant improvements over current sequencing platforms, including kilobase-scale read lengths and native detection of epigenetic marks on single molecules. Profiling techniques built on these platforms can therefore capture the epigenetic states of single chromatin fibers with unprecedented resolution. However, the inherently higher input requirements for these assays and sequencing platforms have limited their general applicability in medical settings, where sample material is constrained. In this dissertation, we address this problem by developing a new generalizable strategy for preparing native 3rd generation sequencing libraries from low-input samples using a hyperactive transposase. In Chapter 1, we motivate and contextualize this work by discussing how the epigenome is dysregulated in cancer progression and the current tools we use to study it. Then, in Chapter 2 we discuss how our transposase-mediated method enables the study of chromatin fibers in a range of clinically relevant samples including cancer cell lines and patient derived xenograft models. In Chapter 3, we present further methodological improvements that enable direct library preparation from cells and nuclei, collectively lowering input requirements ~20X and making native single molecule profiling studies competitive with existing epigenomic assays. Finally, we conclude in Chapter 4 by considering how our method is a general tool for using 3rd generation sequencing as a read out for biological assays. We demonstrate this for two specific cases: high-depth profiling of targeted genomic regions and resolving chromatin states in single cells

A sense-antisense RNA interaction promotes breast cancer metastasis via regulation of NQO1 expression

Antisense RNAs are ubiquitous in human cells, yet their role is largely unexplored. Here we profiled antisense RNAs in the MDA-MB-231 breast cancer cell line and its highly lung metastatic derivative. We identified one antisense RNA that drives cancer progression by upregulating the redox enzyme NADPH quinone dehydrogenase 1 (NQO1), and named it NQO1-AS. Knockdown of either NQO1 or NQO1-AS reduced lung colonization in a mouse model, and investigation into the role of NQO1 indicated that it is broadly protective against oxidative damage and ferroptosis. Breast cancer cells in the lung are dependent on this pathway, and this dependence can be exploited therapeutically by inducing ferroptosis while inhibiting NQO1. Together, our findings establish a role for NQO1-AS in the progression of breast cancer by regulating its sense mRNA post-transcriptionally. Because breast cancer predominantly affects females, the disease models used in this study are of female origin and the results are primarily applicable to females

A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies

Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods to 3,107 whole-genome-sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight interorgan variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, but is also essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses

Substitution mutational signatures in whole-genome-sequenced cancers in the UK population.

Whole-genome sequencing (WGS) permits comprehensive cancer genome analyses, revealing mutational signatures, imprints of DNA damage and repair processes that have arisen in each patient's cancer. We performed mutational signature analyses on 12,222 WGS tumor-normal matched pairs, from patients recruited via the UK National Health Service. We contrasted our results to two independent cancer WGS datasets, the International Cancer Genome Consortium (ICGC) and Hartwig Foundation, involving 18,640 WGS cancers in total. Our analyses add 40 single and 18 double substitution signatures to the current mutational signature tally. Critically, we show for each organ, that cancers have a limited number of 'common' signatures and a long tail of 'rare' signatures. We provide a practical solution for utilizing this concept of common versus rare signatures in future analyses