Biology subjects

Pham, D.

Publications and source records attributed to Pham, D..

25 records · Page 2Linked to original sources

bioRxiv2021

Diet-derived metabolites and mucus link the gut microbiome to fever after cytotoxic cancer treatment

Not all cancer patients with severe neutropenia develop fever, and the fecal microbiome may play a role. In neutropenic hematopoietic cell transplant patients (n=119), 63 (53%) developed a subsequent fever and had increased fecal Akkermansia muciniphila, a mucus-degrading bacteria (p=0.006, corrected for multiple comparisons). In mouse models, two therapies, irradiation and melphalan, similarly expanded A. muciniphila. Dietary restriction of unirradiated mice also expanded A. muciniphila and thinned the colonic mucus layer. Azithromycin treatment depleted A. muciniphila and preserved colonic mucus. Dietary restriction raised colonic luminal pH and reduced acetate, propionate, and butyrate. Culturing A. muciniphila with lower pH and increased propionate prevented utilization of mucin. Treating irradiated mice with azithromycin or propionate preserved the mucus layer, lessened hypothermia, and reduced inflammatory cytokines in the colon. These results suggest that diet, metabolites and colonic mucus link the microbiome to neutropenic fever, and could guide future microbiome-based preventive strategies.

microbiology↗

bioRxiv2021

A Non-redundant Role for T cell-derived IL-22 in Antibacterial Defense of Colonic Crypts

IL-22 is a key cytokine in immune defense against pathogens at barrier sites. In response to enteric attaching and effacing bacteria, IL-22 produced by type 3 innate lymphoid cells (ILC3s) is thought to be important early for induction of antimicrobial peptides (AMPs) that protect intestinal epithelial cells (IECs) in advance of T cell-derived IL-22 that arises later. Yet, the basis for a requirement for both innate and adaptive IL-22-producing immune cells in protecting the intestinal mucosa is unknown. Here, using novel mice that both report IL-22 expression and can be targeted for its lineage-specific deletion, we show that mice with deficiency of IL-22 targeted to innate immune cells, including ILC3s, have impaired STAT3 activation of surface colonic IECs colonized by bacteria early in infection. In contrast, mice with IL-22 deficiency limited to T cells have complete loss of STAT3 activation in IECs lining colonic crypts and fail to protect the crypts from bacterial invasion late despite ongoing production of IL-22 from ILC3s. T cell-derived IL-22 is required for upregulation of many host-protective genes by crypt IECs, including those encoding AMPs, neutrophil-recruiting chemokines, and mucins and mucin-related molecules, while also restricting pro-inflammatory genes downstream of IFN{gamma} and TNF signals. Thus, T cell-derived IL-22 is indispensable for antibacterial defense and damage control of intestinal crypts.

immunology↗

bioRxiv2021

A Platform for Oncogenomic Reporting and Interpretation

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we created the Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework created to facilitate the interpretation and reporting of somatic variants in cancer. PORI is unique in its integration of reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents one of the first open-source platform alternatives to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 TCGA tumours to the graph knowledge base, revealing that 88.2% have at least one potentially targetable alteration, and making available reports describing select individual samples.

bioinformatics↗

bioRxiv2019

Automated Cytogenetic Biodosimetry at Population-Scale

IntroductionThe dicentric chromosome (DC) assay accurately quantifies exposure to radiation, however manual and semi-automated assignment of DCs has limited its use for a potential large-scale radiation incident. The Automated Dicentric Chromosome Identifier and Dose Estimator Chromosome (ADCI) software automates unattended DC detection and determines radiation exposures, fulfilling IAEA criteria for triage biodosimetry. We present high performance ADCI (ADCI-HT), with the requisite throughput to stratify exposures of populations in large scale radiation events.\n\nMethodsADCI-HT streamlines dose estimation by optimal scheduling of DC detection, given that the numbers of samples and metaphase cell images in each sample vary. A supercomputer analyzes these data in parallel, with each processor handling a single image at a time. Processor resources are managed hierarchically to maximize a constant stream of sample and image analysis. Metaphase data from populations of individuals with clinically relevant radiation exposures after simulated large nuclear incidents were analyzed. Sample counts were derived from US Census data. Analysis times and exposures were quantified for 15 different scenarios.\n\nResultsProcessing of metaphase images from 1,744 samples (500 images each) used 16,384 CPUs and was completed in 1hr 11min 23sec, with radiation dose of all samples determined in 32 sec with 1,024 CPUs. Processing of 40,000 samples with varying numbers of metaphase cells, 10 different exposures from 5 different biodosimetry labs met IAEA accuracy criteria (dose estimate differences were < 0.5 Gy; median = 0.07) and was completed in ~25 hours. Population-scale metaphase image datasets within radiation contours of nuclear incidents were defined by exposure levels (either >1 Gy or >2 Gy). The time needed to analyze samples of all individuals receiving exposures from a high yield airborne nuclear device ranged from 0.6-7.4 days, depending on the population density.\n\nConclusionADCI-HT delivers timely and accurate dose estimates in a simulated population-scale radiation incident.

bioinformatics↗

bioRxiv2019

A framework for spatial normalization and voxelwise analysis of diffusion propagators in multiple MAP-MRI data sets

We describe a pipeline for constructing a study-specific template of diffusion propagators measured with mean apparent propagator (MAP) MRI that supports direct voxelwise analysis of differences between propagators across multiple data sets. The pipeline leverages the fact that MAP-MRI is a generalization of diffusion tensor imaging (DTI) and combines simple and robust processing steps from existing tensor-based image registration methods. First, we compute a DTI study template which provides the reference frame and scaling parameters needed to construct a standardized set of MAP-MRI basis functions at each voxel in template space. Next, we transform each subjects diffusion data, including diffusion weighted images (DWIs) and gradient directions, from native to template space using the corresponding tensor-based deformation fields. Finally, we fit MAP coefficients in template space to the transformed DWIs of each subject using the standardized template of MAP basis functions. The consistency of MAP basis functions across all data sets in template space allows us to: 1. compute a template of propagators by directly averaging MAP coefficients and 2. quantify voxelwise differences between co-registered propagators using the angular dissimilarity, or a probability distance metric, such as the Jensen-Shannon Divergence. We illustrate the application of this method by generating a template of MAP propagators for a cohort of healthy volunteers and show a proof-of-principle example of how this pipeline may be used to detect subtle differences between propagators in a single-subject longitudinal clinical data set. The ability to standardize and analyze multiple clinical MAP-MRI data sets could improve assessments in cross-sectional and single-subject longitudinal clinical studies seeking to detect subtle microstructural changes, such as those occurring in mild traumatic brain injury (mTBI), or during the early stages of neurodegenerative diseases, or cancer.

neuroscience↗

bioRxiv2019

TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods, yet, manual delineation remains the gold standard approach. These approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes Sorensen-Dice Similarity Coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a general additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Womens Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data, we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding is mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicate no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

neuroscience↗

bioRxiv2017

MIMoSA: A Method for Inter-Modal Segmentation Analysis

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While these lesions have been studied for over two decades using MRI technology, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WML are based on a single imaging modality, recent advances have used multimodal techniques for identifying WML. Complementary imaging modalities emphasize different tissue properties, which can help identify and characterize interrelated features of lesions. However, prior work has ignored relationships between imaging modalities, which may be informative in this clinical context. To harness the coherent changes in these measurements, we utilized inter-modal coupling regression (IMCo) to estimate the covariance structure across modalities. We then used a local logistic regression, MIMoSA, which leverages new covariance features from IMCo regression as well as the mean structure of each imaging modality in order to model the probability that any voxel is part of a lesion. Finally, we introduced a novel thresholding algorithm to fully automate the estimation of the probability maps to generate fully automated segmentations masks for 94 subjects. To evaluate the performance of the automated segmentations generated using MIMoSA we compared results with gold standard manual segmentations. We demonstrate the superiority of MIMoSA to other automated segmentation techniques by comparing it to the OASIS algorithm as well as LesionTOADS. MIMoSA resulted in statistically significant improvement in lesion segmentation.

neuroscience↗