Biology subjects

Schmidt, J. A.

Publications and source records attributed to Schmidt, J. A..

4 recordsLinked to original sources

bioRxiv2025

Single Cell Resolution Tracking of Cutaneous T-Cell Lymphoma Reveals Clonal Evolution in Disease Progression

Cutaneous T-cell lymphoma (CTCL) remains a challenging disease due to its significant heterogeneity, therapy resistance, and relentless progression. Multi-omics technologies offer the potential to provide uniquely precise views of disease progression and response to therapy. We present here a comprehensive multi-omics view of CTCL clonal evolution, incorporating exome, whole genome, epigenome, bulk-, single cell (sc) VDJ-, and scRNA-sequencing of 114 clinically annotated serial skin, peripheral blood, and lymph node samples from 35 CTCL patients. We leveraged this extensive dataset to define the molecular underpinnings of CTCL progression in individual patients at single cell resolution with the goal of identifying clinically useful biomarkers and therapeutic targets. Our studies identified a large number of recurrent progression-associated clonal genomic alterations; we highlight mutation of CCR4, PI3K signaling, and PD-1 checkpoint pathways as evasion tactics deployed by malignant T cells. We also identified a gain of function mutation in STAT3 (D661Y) and demonstrated by CUT&RUN-seq that it enhances binding to transcription start sites of genes in Rho GTPase pathways, which we previously reported to have activated chromatin and increased expression in HDACi-resistant CTCL. These data provide further support for a previously unrecognized role for Rho GTPase pathway dysregulation in CTCL progression. A striking number of progression-associated mutations occurred in chromatin methylation modifiers, including EZH2, suggesting that EZH1/2 inhibition may also benefit patients with CTCL. Knowledge of these molecular changes should be leveraged for improved disease monitoring, biomarker-informed clinical trial design, and new therapeutic strategies in this challenging and incurable cancer.

cancer biology↗

bioRxiv2023

Vertical transmission of African-lineage Zika virus through the fetal membranes in a rhesus Macaque (Macaca mulatta) model

Zika virus (ZIKV) can be transmitted vertically from mother to fetus during pregnancy, resulting in a range of outcomes, including severe birth defects and fetal/infant death. Potential pathways of vertical transmission in utero have been proposed but remain undefined. Identifying the timing and routes of vertical transmission of ZIKV may help us identify when interventions would be most effective. Furthermore, understanding what barriers ZIKV overcomes to effect vertical transmission may help improve models for evaluating infection by other pathogens during pregnancy. To determine the pathways of vertical transmission, we inoculated 12 pregnant rhesus macaques with an African-lineage ZIKV at gestational day 30 (term is 165 days). Eight pregnancies were surgically terminated at either seven or 14 days post-maternal infection. Maternal-fetal interface and fetal tissues and fluids were collected and evaluated with RT-qPCR, in situ hybridization for ZIKV RNA, immunohistochemistry, and plaque assays. Four additional pregnant macaques were inoculated and terminally perfused with 4% paraformaldehyde at three, six, nine, or ten days post-maternal inoculation. For these four cases, the entire fixed pregnant uterus was evaluated with in situ hybridization for ZIKV RNA. We determined that ZIKV can reach the MFI by six days post-infection and infect the fetus by ten days. Infection of the chorionic membrane and the extraembryonic coelomic fluid preceded infection of the fetus and the mesenchymal tissue of the placental villi. We did not find evidence to support a transplacental route of ZIKV vertical transmission via infection of syncytiotrophoblasts or villous cytotrophoblasts. The pattern of infection observed in the maternal-fetal interface provides evidence of vertical ZIKV transmission through the fetal membranes. Authors SummaryZika virus (ZIKV) can be vertically transmitted from mother to fetus during pregnancy resulting in adverse pregnancy outcomes. For vertical transmission to occur, ZIKV must overcome the barriers of the maternal-fetal interface, yet the exact pathway ZIKV takes remains undefined. The maternal-fetal interface consists of the maternal decidua, the placenta, and the fetal membranes. ZIKV could reach the fetus through the placenta if it can infect the layer of cells that are directly exposed to maternal blood. ZIKV could also reach the fetus by infecting the decidua and then the adjacent fetal membranes. To determine the pathways of ZIKV vertical transmission, we infected pregnant macaques and evaluated ZIKV burden in the maternal-fetal interface and fetus shortly after maternal infection. The pattern of infection observed suggests that ZIKV vertically transmits through the fetal membranes, not the placenta. This finding is significant because it challenges the assumption that vertical transmission occurs exclusively across the placenta. By including the fetal membranes in our models of vertical transmission, we can more accurately determine which pathogens can be vertically transmitted. Ultimately, this study demonstrates that fetal membranes are an essential barrier to pathogens that warrant further investigation.

pathology↗

bioRxiv2021

A new pipeline for the normalization and pooling of metabolomics data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through PC-PR2 analysis; (iii) application of linear mixed models to remove unwanted variability, including samples originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

bioinformatics↗

bioRxiv2020

hcga: Highly Comparative Graph Analysis for network phenotyping

AO_SCPLOWBSTRACTC_SCPLOWNetworks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how HCGA can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. HCGA is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical research to quantitatively analyse and draw insights from network data.

systems biology↗