Multi-scale cellular imaging of DNA double strand break repair

Live-cell and high-resolution fluorescence microscopy are powerful tools to study the organization and dynamics of DNA double-strand break repair foci and specific repair proteins in single cells. This requires specific induction of DNA double-strand breaks and fluorescent markers to follow the DNA lesions in living cells. In this review, where we focused on mammalian cell studies, we discuss different methods to induce DNA double-strand breaks, how to visualize and quantify repair foci in living cells., We describe different (live-cell) imaging modalities that can reveal details of the DNA double-strand break repair process across multiple time and spatial scales. In addition, recent developments are discussed in super-resolution imaging and single-molecule tracking, and how these technologies can be applied to elucidate details on structural compositions or dynamics of DNA double-strand break repair.</p

The art of PCR assay development: data-driven multiplexing

The present thesis describes the discovery and application of a novel methodology, named Data-Driven Multiplexing, which uses artificial intelligence and conventional molecular instruments to develop rapid, scalable and cost-effective clinical diagnostic tests. Detection of genetic material from living organisms is a biologically engineered process where organic molecules interact with each other and with chemical components to generate a meaningful signal of the presence, quantity or quality of target nucleic acids. Nucleic acid detection, such as DNA or RNA detection, identifies a specific organism based on its genetic material. In particular, DNA amplification approaches, such as for antimicrobial resistance (AMR) or COVID-19 detection, are crucial for diagnosing and managing various infectious diseases. One of the most widely used methods is Polymerase Chain Reaction (PCR), which can detect the presence of nucleic acids rapidly and accurately. The unique interaction of the genetic material and synthetic short DNA sequences called primers enable this harmonious biological process. This thesis aims to bioinformatically modulate the interaction between primers and genetic material, enhancing the diagnostic capabilities of conventional PCR instruments by applying artificial intelligence processing to the resulting signals. To achieve the goal mentioned above, experiments and data from several conventional platforms, such as real-time and digital PCR, are used in this thesis, along with state-of-the-art and innovative algorithms for classification problems and final application in real-world clinical scenarios. This work exhibits a powerful technology to optimise the use of the data, conveying the following message: the better use of the data in clinical diagnostics enables higher throughput of conventional instruments without the need for hardware modification, maintaining the standard practice workflows. In Part I, a novel method to analyse amplification data is proposed. Using a state-of-the-art digital PCR instrument and multiplex PCR assays, we demonstrate the simultaneous detection of up to nine different nucleic acids in a single-well and single-channel format. This novel concept called Amplification Curve Analysis (ACA) leverages kinetic information encoded in the amplification curve to classify the biological nature of the target of interest. This method is applied to the novel design of PCR assays for multiple detections of AMR genes and further validated with clinical samples collected at Charing Cross Hospital, London, UK. The ACA showed a high classification accuracy of 99.28% among 253 clinical isolates when multiplexing. Similar performance is also demonstrated with isothermal amplification chemistries using synthetic DNA, showing a 99.9% of classification accuracy for detecting respiratory-related infectious pathogens. In Part II, two intelligent mathematical algorithms are proposed to solve two significant challenges when developing a Data-driven multiplex PCR assay. Chapter 7 illustrates the use of filtering algorithms to remove the presence of outliers in the amplification data. This demonstrates that the information contained in the kinetics of the reaction itself provides a novel way to remove non-specific and not efficient reactions. By extracting meaningful features and adding custom selection parameters to the amplification data, we increase the machine learning classifier performance of the ACA by 20% when outliers are removed. In Chapter 8, a patented algorithm called Smart-Plexer is presented. This allows the hybrid development of multiplex PCR assays by computing the optimal single primer set combination in a multiplex assay. The algorithm's effectiveness stands in using experimental laboratory data as input, avoiding heavy computation and unreliable predictions of the sigmoidal shape of PCR curves. The output of the Smart-Plexer is an optimal assay for the simultaneous detection of seven coronavirus-related pathogens in a single well, scoring an accuracy of 98.8% in identifying the seven targets correctly among 14 clinical samples. Moreover, Chapter 9 focuses on applying novel multiplex assays in point-of-care devices and developing a new strategy for improving clinical diagnostics. In summary, inspired by the emerging requirement for more accurate, cost-effective and higher throughput diagnostics, this thesis shows that coupling artificial intelligence with assay design pipelines is crucial to address current diagnostic challenges. This requires crossing different fields, such as bioinformatics, molecular biology and data science, to develop an optimal solution and hence to maximise the value of clinical tests for nucleic acid detection, leading to more precise patient treatment and easier management of infectious control.Open Acces

Computational and experimental tools of MiRNAs in cancer

MicroRNAs (miRNAs) are short non-protein coding and single-stranded small RNA molecules with a critical role in the regulation of gene expression. These molecules are crucial regulatory elements in diverse biological processes such as apoptosis, development, and progression. miRNA genes have been associated with various human diseases, particularly cancer, and considered as a new biomarker. After the discovery of miRNAs, many researches have focused on identifying and characterizing miRNA genes in cancer. The various expression levels of miRNAs between cancer cells and normal cells are very crucial to diagnosis, prognosis, and treatment of many cancers. Many computational and experimental tools have been employed to characterize miRNAs. However, there exist some challenges in identifying miRNA using both computational and experimental tools due to miRNA features. The present review briefly introduced miRNA biology and certain computational and experimental tools for identifying and profiling miRNAs in cancer. Furthermore, we presented the advantages and challenges of these tools. © 2020, Shriaz University of Medical Sciences. All rights reserved

Telomere and Proximal Sequence Analysis Using High-Throughput Sequencing Reads

The telomere is a specialized simple sequence repeat found at the end of all linear chromosomes. It acts as a substrate for telomere binding factors that in coordination with other interacting elements form what is known as the shelterin complex to protect the end of the chromosome from the DNA damage repair machinery. The telomere shortens with each cell division, and once critically short is no longer able to perform this role. Short dysfunctional telomeres result in cellular senescence, apoptosis, or genome instability. Telomere length is regulated by many factors including cis-acting elements in the proximal sequence which is known as the subtelomere. The Riethman lab played a pivotal role in generating the reference sequence of the subtelomere in both the human and mouse genomes, providing an essential resource for this work. Short high throughput sequencing (HTS) reads generated from the simple repeat containing telomere or the segmental duplication rich subtelomere cannot be aligned to a reference genome uniquely. They are filtered and excluded from many HTS analysis methods. A ChIP-Seq analysis pipeline was developed to incorporate these multimapping reads to study DNA-protein interactions in the subtelomere. This pipeline was employed to search for factors regulating the expression TERRA, an essential long non-coding RNA, and to better characterize their transcription start sites. ChIP-seq analysis in the human subtelomere found colocalization of CTCF and Cohesin directly adjacent to the telomere and throughout the subtelomere specific repeats. Follow up functional studies showed this binding regulated TERRA transcription at these sites. Extending these analyses in the mouse genome showed very different patterns of CTCF and cohesin binding, with no evidence of binding at apparent sites of TERRA transcription. Mouse subtelomere sequence analysis showed the co-occurence of two repeats at sites of putative TERRA expression, MurSatRep1 and MMSAT4, one of which was previously shown to be expressed in lincRNAs. The Telomere Analysis from SEquencing Reads(TASER) pipeline was developed to capture telomere information from HTS data sets and used to investigate telomere changes that occur in prostate cancer. TASER analysis of 53 paired prostate tumor and normal samples revealed an overall decrease in telomere length in tumor samples relative to matched paired normal tissue, especially sequence containing the exact canonical telomere repeat. Multimapping reads contain important information, that when used properly, help elucidate understanding of telomere biology, cancer biology, and genome regulation and stability

Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms.

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms

Somatic Mutation Detection in Leukemia-Derived Circulating DNA: Utility in Monitoring Clonal Dynamics and Disease Response in Pediatric Acute Lymphoblastic Leukemia

Despite the improved outcome associated with current treatment strategies ofpediatric acute lymphoblastic leukemia (ALL), relapse still represents a major challenge. Pediatric ALL demonstrates branched evolution in response to selective pressure exerted by therapy; relapse founder clones emerge from pre-leukemic clones or minor subclones present at diagnosis. It is hence crucial to develop biomarkers capable of tracking subclones throughout therapy. Current practices for monitoring disease response in leukemia rely on the analysis of BM biopsy sample at specific time points throughout therapy. Not only the invasiveness of the BM biopsy hinders the sequential sampling, but also, the currently implied techniques are associated with a lack of sensitivity to detect subclones other than the major diagnostic clone. Somatic mutation detection in circulating-tumor DNA (Ct-DNA) offers a new venue for non-invasive studying of genetic heterogeneity and tracking clonal dynamics throughout therapy. Here, we employ targeted Next-Generation Sequencing (NGS) using a specifically designed ALL custom gene panel for Ct-DNA analysis of sequential plasma samples of 14 pediatric ALL during remission induction therapy. Utilizing 1 ml of plasma, Ct-DNA successfully captured all the clinically relevant somatic single nucleotide variants (SNVs) detected by whole exome sequencing (WES) in bone marrow (BM) biopsy samples at diagnosis. Moreover, we were able to show the ability of Ct-DNA analysis to track the change in the mutant allele fraction (MAF) across multiple time points as well as, to detect mutations in Flowcytometry (FC) MRD-negative patients. Taken together, sequential analysis of Ct-DNA in plasma demonstrates a role, as a non-invasive technique, for detecting the clonal composition as well as, tracking clonal dynamics in pediatric ALL

Toward precision medicine with nanopore technology

Currently, when patients are diagnosed with cancer, they often receive a treatment based on the type and stage of the tumor. However, different patients may respond to the same treatment differently, due to the variation in their genomic alteration profile. Thus, it is essential to understand the effect of genomic alterations on cancer drug efficiency and engineer devices to monitor these changes for therapeutic response prediction. Nanopore-based detection technology features devices containing a nanometer-scale pore embedded in a thin membrane that can be utilized for DNA sequencing, biosensing, and detection of biological or chemical modifications on single molecules. Overall, this project aims to evaluate the capability of the biological nanopore, alpha-hemolysin, as a biosensor for genetic and epigenetic biomarkers of cancer. Specifically, we utilized the nanopore to (1) study the effect of point mutations on C-kit1 G-quadruplex formation and its response to CX-5461 cancer drug; (2) evaluate the nanopore\u27s ability to detect cytosine methylation in label-dependent and label-independent manners; and (3) detect circulating-tumor DNA collected from lung cancer patients\u27 plasma for disease detection and treatment response monitoring. Compared to conventional techniques, nanopore assays offer increased flexibility and much shorter processing time

Computational methods to analyze image-based siRNA knockdown screens

Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. Thus, we performed a study to find genes that cause mitosis linked cell death upon inhibition in neuroblastoma cells. We investigated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed image-based time-lapse screening of gene knockdowns in neuroblastoma cells. We developed a classifier to classify images into cellular phenotypes, using SVM, performing manual evaluation and automatic corrections. This classifier yielded better predictions of cellular phenotypes than the standard classification protocol. We further developed an elaborated analysis pipeline based on the phenotype kinetics from the gene knockdown screening to identify genes with vital role in mitosis to identify therapeutic targets for neuroblastoma. We developed two methods (1) to generate clusters of genes with similar phenotype profiles and (2) to track the sequence of phenotype events, particularly mitosis-linked-celldeath. We identified six genes (DLGAP5, DSCC1, SMO, SNRPD1, SSBP1, and UBE2C) that cause mitosis-linked-cell-death upon knockdown in both of the neuroblastoma cell lines tested (SH-EP and SK-N-BE(2)-C). Gene expression analysis of neuroblastoma patients show that these genes are up-regulated in aggressive tumors and they show good prediction performance for overall survival. Four of these hits (DLGAP5, DSCC1, SSBP1, UBE2C) are directly involved in cell cycle and one (SMO) indirectly which is involved in cell cycle regulation. Functional association and gene-expression analysis of these hits indicated that monitoring cell cycle dynamics enabled finding promising drug targets for neuroblastoma cells. In summary, we present a bioinformatics pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells