ORCID Profile
0000-0001-9537-060X
Current Organisations
University Hospital of Basel
,
University Children’s Hospital Basel: Basel, BS, CH
,
Sportsclinic Zurich
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University of Melbourne
,
University of Cambridge
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Publisher: Oxford University Press (OUP)
Date: 05-2019
Publisher: Oxford University Press (OUP)
Date: 10-04-2018
Publisher: Cold Spring Harbor Laboratory
Date: 04-05-2023
DOI: 10.1101/2023.05.04.539481
Abstract: 2. The dominant paradigm for analysing genetic variation relies on a central idea: all genomes in a species can be described as minor differences from a single reference genome. However, this approach can be problematic or inadequate for bacteria, where there can be significant sequence ergence within a species. Reference graphs are an emerging solution to the reference bias issues implicit in the “single-reference” model. Such a graph represents variation at multiple scales within a population – e.g., nucleotide- and locus-level. The genetic causes of drug resistance in bacteria have proven comparatively easy to decode compared with studies of human diseases. For ex le, it is possible to predict resistance to numerous anti-tuberculosis drugs by simply testing for the presence of a list of single nucleotide polymorphisms and insertion/deletions, commonly referred to as a catalogue. We developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph, a process that can be replicated for other species. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44,709 and 138 publicly available Illumina and Nanopore s les with associated phenotypes. We find DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe on Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA – and suggest mutations that may warrant reclassification as associated with resistance. 3. Mycobacterium tuberculosis is the bacterium responsible for tuberculosis (TB). TB is one of the leading causes of death worldwide before the coronavirus pandemic it was the leading cause of death from a single pathogen. Drug-resistant TB incidence has recently increased, making the detection of resistance even more vital. In this study, we develop a new software tool to predict drug resistance from whole-genome sequence data of the pathogen using new reference graph models to represent a reference genome. We evaluate it on M. tuberculosis against existing tools for resistance prediction and show improved performance. Using our method, we discover new resistance-associated variations and discuss reclassification of a selection of existing mutations. As such, this work contributes to TB drug resistance diagnostic efforts. In addition, the method could be applied to any bacterial species, so is of interest to anyone working on antimicrobial resistance. 4. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files . The software method presented in this work, DrPRG, is freely available from GitHub under an MIT license at bhall88/drprg . We used commit 9492f25 for all results via a Singularity[1] container from the URI docker://quay.io/mbhall88/drprg:9492f25 . All code used to generate results for this study are available on GitHub at bhall88/drprg-paper . All data used in this work are freely available from the SRA/ENA/DRA and a copy of the datasheet with all associated phenotype information can be downloaded from the archived repository at 0.5281/zenodo.7819984 or found in the previously mentioned GitHub repository. The Mycobacterium tuberculosis index used in this work is available to download through DrPRG via the command drprg index --download mtb@20230308 or from GitHub at bhall88/drprg-index .
Publisher: Elsevier BV
Date: 02-2023
Publisher: MDPI AG
Date: 21-06-2022
DOI: 10.3390/IJMS23136900
Abstract: Nasal chondrocytes (NCs) have a higher and more reproducible chondrogenic capacity than articular chondrocytes, and the engineered cartilage tissue they generate in vitro has been demonstrated to be safe in clinical applications. Here, we aimed at determining the feasibility for a single-stage application of NCs for cartilage regeneration under minimally invasive settings. In particular, we assessed whether NCs isolated using a short collagenase digestion protocol retain their potential to proliferate and chondro-differentiate within an injectable, swiftly cross-linked and matrix-metalloproteinase (MMP)-degradable polyethylene glycol (PEG) gel enriched with human platelet lysate (hPL). NC-hPL-PEG gels were additionally tested for their capacity to generate cartilage tissue in vivo and to integrate into cartilage/bone compartments of human osteochondral plugs upon ectopic subcutaneous implantation into nude mice. NCs isolated with a rapid protocol and embedded in PEG gels with hPL at low cell density were capable of efficiently proliferating and of generating tissue rich in glycosaminoglycans and collagen II. NC-hPL-PEG gels developed into hyaline-like cartilage tissues upon ectopic in vivo implantation and integrated with surrounding native cartilage and bone tissues. The delivery of NCs in PEG gels containing hPL is a feasible strategy for cartilage repair and now requires further validation in orthotopic in vivo models.
Publisher: Cold Spring Harbor Laboratory
Date: 23-08-2017
DOI: 10.1101/179531
Abstract: Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling: directly translating the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4000 reads, we show that our model provides state-of-the-art basecalling accuracy even on previously unseen species. Chiron achieves basecalling speeds of over 2000 bases per second using desktop computer graphics processing units.
Publisher: Microbiology Society
Date: 08-08-2023
Abstract: Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore s les with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis – including deletion of genes katG and pncA – and suggest mutations that may warrant reclassification as associated with resistance.
Publisher: Cold Spring Harbor Laboratory
Date: 08-03-2022
DOI: 10.1101/2022.03.04.22271870
Abstract: Mycobacterium tuberculosis whole-genome sequencing (WGS) using Illumina technology has been widely adopted for genotypic drug susceptibility testing (DST) and outbreak investigation. Oxford Nanopore Technologies is reported to have higher error rates but has not been thoroughly evaluated for these applications. We analyse 151 isolates from Madagascar, South Africa and England with phenotypic DST and matched Illumina and Nanopore data. Using PacBio assemblies, we select Nanopore filters for BCFtools (software) detection of single nucleotide polymorphisms (SNPs). We compare transmission clusters identified by Nanopore and the United Kingdom Health Security Agency Illumina pipeline (COMPASS). We compare Illumina and Nanopore WGS-based DST predictions using Mykrobe (software). Nanopore/BCFtools identifies SNPs with median precision/recall of 99·5/90·2% compared with 99·6/91·9% for Illumina/COMPASS. Using a threshold of 12 SNPs for putative transmission clusters, Illumina identifies 98 isolates as unrelated and 53 as belonging to 19 distinct clusters (size range 2-7). Nanopore reproduces this distribution with addition of 5 singleton isolates to distinct clusters and merging of two cluster pairs. Illumina-based clusters are also replicated using a 5 SNP threshold. Clustering accuracy is maintained using mixed Illumina/Nanopore datasets. Genotyping resistance variants is highly concordant, with 0(4) discordant SNPs (indels) across 151 isolates genotyped at (60,000) SNPs (indels). Illumina and Nanopore sequence data provide comparable cluster-identification and DST results. Academy for Medical Sciences (SGL018\\110), Oxford Wellcome Institutional Strategic Support Fund (ISSF TT17 4). Swiss South Africa Joint Research Award (Swiss national science Foundation and South African national research foundation). Two key types of information can be obtained from laboratory testing of M. tuberculosis isolates to help directly guide public health interventions: drug susceptibility testing (DST) to guide therapy, and bacterial typing to enrich understanding of the epidemiology and guide interventions to mitigate transmission. DST is typically performed by the “gold standard” culture-based phenotyping method or nucleic acid lification assays targeting specific resistance-conferring mutations. Studies over the last 7 years have shown that prediction of susceptibility profile using Illumina-technology genome sequence data is possible, and can be automated. In a key publication, the CRyPTIC consortium and UK 100,000 Genomes project evaluated the method on over 10,000 genomes including prospectively s led isolates and showed that for first-line tuberculosis (TB) drugs (isoniazid, rif icin, ethambutol, pyrazinamide) a pan-susceptibility profile is accurate enough to be used clinically. The genetic basis of resistance remains imperfectly understood for second-line TB drugs, in particular for new and repurposed drugs (bedaquiline, clofazimine, delamanid, linezolid). Prior work in the field of genotypic DST was heavily based on Illumina technology, which provides short (70-300 base pair) sequence reads of very high quality. Many different softwares (e.g. TBProfiler, Mykrobe, MTBseq, kvarq) have been designed for sequence analysis and genotypic DST. However, the increasingly used Nanopore sequencing platforms yield very different data with much longer sequence reads (frequently over 1kb) and higher error rates including systematic biases. To date, very limited evaluation of Nanopore-based drug susceptibility prediction has been performed using the only two compatible tools (Mykrobe (n=5 independent s les), TBProfiler (n=3 independent s les)). Molecular typing of M. tuberculosis allows lineage identification and detection of putative transmission clusters. In the last decade, multiple M. tuberculosis molecular epidemiology studies have shown how genomic information can complement traditional epidemiology in identifying person-to-person transmission clusters with a high level of resolution. Typically, the number of single nucleotide polymorphism (SNP) disagreements between genomes, or SNP distance, is calculated and single-linkage clustering is performed for genomes falling within retrospectively established transmission thresholds of either 5 or 12 SNPs. Just as with DST, these thresholds were established with Illumina sequencing data. The increased error rate in Nanopore sequencing is believed to lead to inflated SNP distances if standard genome analysis tools are used. Prior to this study it was unknown what impact on isolate-clustering this would incur. Full-scale adoption of genomic sequencing in tuberculosis reference laboratories has so far taken place in a limited number of settings - England, the Netherlands, and New York State - all using Illumina-based sequencing data. Building on current evidence, specific WHO technical guidance and ersification and democratisation of technology, sequencing is expected to be increasingly used in tuberculosis control globally. For the first time, our study offers 4 key deliverables intended to inform adoption of Nanopore technology as an alternative, or a complement, to Illumina. First: a systematic head-to-head comparison of Nanopore and Illumina data for M. tuberculosis drug susceptibility profiling and isolate clustering, including quantitative metrics for cluster precision and recall. Second: an assessment of the impact of mixed Illumina and Nanopore data on clustering which represents an increasingly common challenge. Third: an open-source software pipeline allowing research and reference laboratories to replicate our analytical approach. Fourth: a publicly available curated test set of 151 isolates, including matched Illumina and Nanopore sequence data, and (for a subset of seven isolates) high-quality PacBio assemblies, for method development and validation. Catalogues of drug resistance conferring mutations will keep improving, especially for new and repurposed drugs. Our data confirms that Illumina and Nanopore sequencing technologies can be used to identify those mutations equally accurately in M. tuberculosis . Bacterial molecular typing is constantly shown to support the understanding of disease transmission and tuberculosis control in new settings. The bioinformatics tools and filters we have developed, assessed, and made publicly available allow the use of Nanopore or mixed-technology data to appropriately cluster genetically related isolates. We provide a measure of the expected level of over-clustering associated with Nanopore technology. This study confirms that Illumina and Nanopore sequence data provide comparable DST results and isolate cluster-identification.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 09-2021
DOI: 10.1126/SCITRANSLMED.AAZ4499
Abstract: Nasal chondrocyte–based grafts modulate osteoarthritis-like conditions in animal models and improve life quality in patients with knee osteoarthritis.
Publisher: Springer Science and Business Media LLC
Date: 20-01-2022
DOI: 10.1186/S12885-021-09160-1
Abstract: Circulating cell-free DNA (cfDNA) in the plasma of cancer patients contains cell-free tumour DNA (ctDNA) derived from tumour cells and it has been widely recognized as a non-invasive source of tumour DNA for diagnosis and prognosis of cancer. Molecular profiling of ctDNA is often performed using targeted sequencing or low-coverage whole genome sequencing (WGS) to identify tumour specific somatic mutations or somatic copy number aberrations (sCNAs). However, these approaches cannot efficiently detect all tumour-derived genomic changes in ctDNA. We performed WGS analysis of cfDNA from 4 breast cancer patients and 2 patients with benign tumours. We sequenced matched germline DNA for all 6 patients and tumour s les from the breast cancer patients. All s les were sequenced on Illumina HiSeqXTen sequencing platform and achieved approximately 30x, 60x and 100x coverage on germline, tumour and plasma DNA s les, respectively. The mutational burden of the plasma s les (1.44 somatic mutations/Mb of genome) was higher than the matched tumour s les. However, 90% of high confidence somatic cfDNA variants were not detected in matched tumour s les and were found to comprise two background plasma mutational signatures. In contrast, cfDNA from the di-nucleosome fraction (300 bp–350 bp) had much higher proportion (30%) of variants shared with tumour. Despite high coverage sequencing we were unable to detect sCNAs in plasma s les. Deep sequencing analysis of plasma s les revealed higher fraction of unique somatic mutations in plasma s les, which were not detected in matched tumour s les. Sequencing of di-nucleosome bound cfDNA fragments may increase recovery of tumour mutations from plasma.
Publisher: Elsevier BV
Date: 09-2022
Location: Switzerland
Location: United Kingdom of Great Britain and Northern Ireland
Location: Australia
No related grants have been discovered for Marcus Mumme.