Biology subjects

Ritchie, M.

Publications and source records attributed to Ritchie, M..

10 recordsLinked to original sources

bioRxiv2026

ARCHER-LD: Rapid Long-Range Linkage Disequilibrium Calculations at Biobank Scale using GPU Acceleration

Linkage disequilibrium (LD) information from one's own dataset is considered optimal for downstream analyses such as statistical fine-mapping. However, the computational complexity ({approx}N2 / 2 computations for N variants) lead most studies to use external reference panels, such as from 1000 Genomes. To capture LD across all variant pairs in biobank-scale whole-genome sequencing (WGS) datasets with hundreds of millions of variants, new computational strategies are essential. We present a novel approach that uses multi-GPU distributed computing to compute R2 for every variant pair in a dataset. On chromosome 22 (1.8 million variants) of the 30x WGS 1000 Genomes dataset, our method using eight consumer-level 12GB GPUs (NVIDIA RTX 2080TIs) takes <10 minutes, while the same calculation with PLINK using a high-end 64-threaded CPU (Intel Xeon Gold 6338) takes >80 minutes, corresponding to a {approx}8x speedup. We further demonstrate true biobank-scale performance in the Penn Medicine Biobank (PMBB; 57,170 samples), computing chromosome 1 LD ({approx}1.38 million variants) in 49 minutes versus 22.7 hours for PLINK, a {approx}28x speedup. With this method, we successfully computed on the aforementioned 30x WGS 1000 Genomes dataset ({approx}120 million variants and {approx}2500 samples) the entire LD matrix (>1e16, or 10 quadrillion elements) in under 6 hours using 512 NVIDIA 40GB A100 GPUs on the Department of Energy Argonne Leadership Computing Facility Polaris Supercomputer. We make this tool, coded in Python using CuPy, publicly available. Using this tool, researchers can leverage the full extent of their genomic data without relying on external LD reference panels and acquire more accurate, population-specific findings, particularly for groups underrepresented in existing databases.

bioinformatics↗

bioRxiv2024

Concomitant suppression of COX-1 and COX-2 is insufficient to induce enteropathy associated with chronic NSAID use

Nonsteroidal anti-inflammatory drugs (NSAIDs) are the most widely used medications for the management of chronic pain; however, they are associated with numerous gastrointestinal (GI) adverse events. Although many mechanisms have been suggested, NSAID-induced enteropathy has been thought to be primarily due to inhibition of both cyclooxygenases (COX) -1 and -2, which results in suppression of prostaglandin synthesis. Yet surprisingly, we found that concomitant postnatal deletion of Cox-1 and -2 over 10 months failed to cause intestinal injury in mice unless they were treated with naproxen or its structural analog, phenylpropionic acid, which is not a COX inhibitor. Cox double knockout mice exhibit a distinct gut microbiome composition and cohousing them with controls rescues their dysbiosis and delays the onset of NSAID-induced GI bleeding. In both the UK Biobank and All of Us human cohorts, coadministration of antibiotics with NSAIDs is associated with an increased frequency of GI bleeding. These results show that prostaglandin suppression plays a trivial role in NSAID-induced enteropathy. However, Cox deletion causes dysbiosis of the gut microbiome that amplifies the enteropathic response to NSAIDs.

pharmacology and toxicology↗

bioRxiv2024

Genomic analyses in Drosophila do not support the classic allopatric model of speciation.

The allopatric model of speciation has dominated our understanding of speciation biology and biogeography since the Modern Synthesis. It is uncontroversial because reproductive isolation may readily emerge as a by-product of evolutionary divergence during allopatry unopposed by gene flow. Recent genomic studies have found that gene flow between species is common, but whether allopatric speciation is common has rarely been systematically tested across a continuum of closely-related species. Here, we fit a range of demographic models of evolutionary divergence to whole-genome sequence data from 93 pairs of Drosophila species to infer speciation histories and levels of post-divergence gene flow. We find that speciation with gene flow is common, even between currently allopatric pairs of species. Estimates of historical gene flow are not predicted by current range overlap. Whilst evidence for secondary contact is generally limited, a few sympatric pairs showed strong support for a secondary contact model. Our analyses suggest that most speciation processes involve some long-term gene flow, perhaps due to repeated cycles of allopatry and contact, without requiring an extensive allopatric phase.

evolutionary biology↗

bioRxiv2024

Jchain-DTR Mice Allow for Diphtheria Toxin-Mediated Depletion ofAntibody-Secreting Cells and Evaluation of Their Differentiation Kinetics

Antibody-secreting cells (ASCs) are generated following B cell activation and constitutively secrete antibodies. As such, ASCs play key roles in humoral immunity, including responses to pathogens, vaccination, and homeostatic clearance of cell debris. Therefore, understanding basic tenents of ASC biology such as their differentiation kinetics following B cell stimulation is of importance. We developed a mouse model which expresses simian heparin-binding EGF-like growth factor (HBEGF) [diphtheria toxin receptor (DTR)] under the control of the endogenous Jchain locus (J-DTR). ASCs from J-DTR mice expressed high levels of cell surface DTR and were acutely depleted following diphtheria toxin treatment. Furthermore, proof-of-principle experiments demonstrated the ability to use J-DTR mice to track ASC reconstitution following depletion in the spleen, bone marrow and thymus which represent sites of ASC generation and/or retention. Overall, J-DTR mice provide a new and highly effective genetic tool allowing for the study of ASC biology in a wide range of potential applications.

immunology↗

bioRxiv2022

Pharmaco-Pheno-Multiomic Integration Reveals Biomarker Profiles and Therapeutic Response Prediction Models in Leukemia and Ovarian Cancer

Despite considerable progress made in improving therapeutic strategies, the overall survival for patients diagnosed with various cancer types remains low. Further, patients often cycle through multiple therapeutic options before finding an effective regimen for the specific malignancy being treated. A focus on building enhanced computational models, which prioritize therapeutic regimens based on a tumors complete molecular profile, will improve the patient experience and augment initial outcomes. In this study, we present an integrative analysis of multiple omic datasets coupled with phenotypic and therapeutic response profiles of Cytarabine from a cohort of primary AML tumors, and Olaparib from a cohort of Patient-Derived Xenograft (PDX) models of ovarian cancer. These analyses, termed Pharmaco-Pheno-Multiomic (PPMO) Integration, established novel complex biomarker profiles that were used to accurately predict prospective therapeutic response profiles in cohorts of newly profiled AML and ovarian tumors. Results from the computational analyses also provide new insights into disease etiology and the mechanisms of therapeutic resistance. Collectively, this study provides proof-of-concept in the use of PPMO to establish highly accurate predictive models of therapeutic response, and the power of leveraging this method to unveil cancer disease mechanisms.

cancer biology↗

bioRxiv2021

CD36 family members are TCR-independent ligands for CD1 antigen-presentingmolecules

CD1c presents lipid-based antigens to CD1c-restricted T cells which are thought to be a major component of the human T cell pool. The study of CD1c-restricted T cells, however, is hampered by the presence of an abundantly expressed CD1c-binding partner on blood cells distinct to the T cell receptor (TCR), confounding analysis of TCR-mediated CD1c tetramer staining. Here, we identify the CD36 family (CD36, CD36-L1 and CD36-L2) as novel ligands for CD1c, CD1b and CD1d proteins, and show that CD36 is the receptor responsible for non-TCR-mediated CD1c tetramer staining of blood cells. Moreover, CD36-blockade enables tetramer-based identification of CD1c-restricted T cells and clarifies identification of CD1b- and CD1d-restricted T cells. We use this technique to characterise CD1c-restricted T cells ex vivo and show diverse phenotypic features, TCR repertoire and antigen-specific subsets. Accordingly, this work will enable further studies into the biology of CD1 and human CD1-restricted T cells. One Sentence SummaryCD1 molecules bind CD36 family members and blockade of this interaction facilitates the study of CD1-restricted T cells.

immunology↗

bioRxiv2018

Using long-read sequencing to detect imprinted DNA methylation

Systematic variation in the methylation of cytosines at CpG sites plays a critical role in early development of humans and other mammals. Of particular interest are regions of differential methylation between parental alleles, as these often dictate monoallelic gene expression, resulting in parent of origin specific control of the embryonic transcriptome and subsequent development, in a phenomenon known as genomic imprinting. Using long-read nanopore sequencing we show that, with an average genomic coverage of approximately ten, it is possible to determine both the level of methylation of CpG sites and the haplotype from which each read arises. The long-read property is exploited to characterise, using novel methods, both methylation and haplotype for reads that have reduced basecalling precision compared to Sanger sequencing. We validate the analysis both through comparison of nanopore-derived methylation patterns with those from Reduced Representation Bisulfite Sequencing data and through comparison with previously reported data. Our analysis successfully identifies known imprinting control regions as well as some novel differentially methylated regions which, due to their proximity to hitherto unknown monoallelically expressed genes, may represent new imprinting control regions.

genomics↗

bioRxiv2018

SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs

Conventional single cell RNA-seq methods are destructive, such that a given cell cannot also then be tested for fate and function, without a time machine. Here, we develop a clonal method SIS-seq, whereby single cells are allowed to divide, and progeny cells are assayed separately in SISter conditions; some for fate, others by RNA-seq. By cross-correlating progenitor gene expression with mature cell fate within a clone, and doing this for many clones, we can identify the earliest gene expression signatures of dendritic cell subset development. SIS-seq could be used to study other populations harboring clonal heterogeneity, including stem, reprogrammed and cancer cells to reveal the transcriptional origins of fate decisions.

systems biology↗

bioRxiv2018

Long-range chromatin interactions on the inactive X and at Hox clusters are regulated by the non-canonical SMC protein Smchd1.

The regulation of higher order chromatin structure is complex and dynamic; however we do not yet understand the full suite of mechanisms governing architecture. Here we reveal the non-canonical SMC protein Smchd1 as a novel regulator of long-range chromatin interactions, and add it to the canon of epigenetic proteins required for Hox gene regulation. The effect of losing Smchd1-dependent chromatin interactions has varying outcomes dependent on chromatin context. At autosomal targets transcriptionally sensitive to Smchd1 deletion, we find increased short-range interactions and ectopic enhancer activation. By contrast, the inactive X chromosome is transcriptionally refractive to Smchd1 ablation, despite chromosome-wide increases in short-range interactions. There we observe spreading of H3K27me3 domains into regions not normally decorated by this mark. Together these data suggest Smchd1 has the capacity to insulate the chromatin, thereby limiting access to other chromatin modifying proteins.

molecular biology↗

bioRxiv2017

Glimma: interactive graphics for gene expression analysis

MotivationSummary graphics for RNA-sequencing and microarray gene expression analyses may contain upwards of tens of thousands of points. Details about certain genes or samples of interest are easily obscured in such dense summary displays. Incorporating interactivity into summary plots would enable additional information to be displayed on demand and facilitate intuitive data exploration.\n\nResultsThe open-source Glimma package creates interactive graphics for exploring gene expression analysis with a few simple R commands. It extends popular plots found in the limma package, such as multi-dimensional scaling plots and mean-difference plots, to allow individual data points to be queried and additional annotation information to be displayed upon hovering or selecting particular points. It also offers links between plots so that more information can be revealed on demand. Glimma is widely applicable, supporting data analyses from a number of well established Bioconductor workflows (limma, edgeR and DESeq2) and uses D3/JavaScript to produce HTML pages with interactive displays that enable more effective data exploration by end-users. Results from Glimma can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.\n\nAvailability and ImplementationThe Glimma R package is available from http://bioconductor.org/packages/devel/bioc/html/Glimma.html.

bioinformatics↗