Biology subjects

Johnson, R. P.

Publications and source records attributed to Johnson, R. P..

4 recordsLinked to original sources

bioRxiv2026

Interleukin-6 restricts pre-thymic T cell lineage commitment of progenitors driving loss of SIV control

Effective T cell reconstitution in people living with HIV is central to durable immune control and cure strategies. Sustained thymic output underpins T cell recovery and requires continuous seeding by T cell-committed progenitors originating in the bone marrow (BM). Using the SIV/rhesus macaque model, we identified a thymus-seeding progenitor (TSP; CD4-CD8-CD34CD38-CD7) in BM declining rapidly following SIV infection. This loss closely associated with reduction in T cell lineage committed differentiation of BM-derived hematopoietic stem and progenitor cells (HSPCs). Importantly, both the decline in TSPs and the impairment of pre-thymic T cell potential were strongly associated with early loss of viral control, independent of peripheral T cell dynamics. Plasma interleukin-6 (IL-6) levels robustly predicted the magnitude of TSP loss and the restriction of T cell-biased HSPC differentiation. Integrated transcriptomic and proteomic analyses revealed inflammatory imprinting of HSPCs characterized by activation of the IL-6-JAK-STAT axis, inflammasome engagement, and coordinated suppression of key T cell specification factors, including RUNX1, FYN, and ZAP70. In a nonanimal model of thymopoiesis, IL-6 exposure of rhesus macaque and human HSPCs inhibited their transition from DN1 (CD38-) to DN2 (CD38) TSP states, indicating an early block in T cell lineage commitment. Conversely, ex vivo IL-6 receptor blockade restored thymocyte differentiation to levels comparable to untreated controls. Collectively, these findings demonstrate that pathogenic inflammation restricts pre-thymic T cell development early after infection, directly contributing to loss of viral control. These findings have important implications for understanding the mediators of anti-viral T cell immunity and HIV cure.

immunology↗

bioRxiv2025

A thymus-independent artificial organoid system supports complete thymopoiesis from Rhesus macaque-derived hematopoietic stem and progenitor cells

Thymic output has been extensively studied. While advanced ex-vivo T cell generative approaches exist for mouse and human models, such advancements for nonhuman primate model are lacking. We report the establishment of a rhesus macaque-specific artificial thymic organoid (Rh-ATO) system enabling robust ex vivo T cell generation from CD34 hematopoietic stem and progenitor cells (HSPCs). A continuum of distinct thymopoietic stages were recorded - robust T cell specification of HSPCs resembling thymus seeding progenitors, emergence of CD4+CD3-immature single positive, CD4+CD8+ double positive thymocytes, and finally, generation of CD4 and CD8 single-positive T cell subsets expressing CD38, consistent with recent thymic emigrant phenotype. These events closely mirrored Bonafide thymopoietic stages observed in the thymus. T cells expressed TCRs and exhibited polyfunctional cytokine expression. Thus, we report first demonstration of an off the shelf NHP-specific 3D system recapitulating thymopoiesis, providing a translational platform for modeling T cell development, therapeutic strategies, and immunopathogenesis.

immunology↗

bioRxiv2022

Modulation of type I interferon responses potently inhibits SARS-CoV-2 replication and inflammation in rhesus macaques

Type-I interferons (IFN-I) are critical mediators of innate control of viral infections, but also drive recruitment of inflammatory cells to sites of infection, a key feature of severe COVID-19. Here, and for the first time, IFN-I signaling was modulated in rhesus macaques (RMs) prior to and during acute SARS-CoV-2 infection using a mutated IFN2 (IFN-modulator; IFNmod), which has previously been shown to reduce the binding and signaling of endogenous IFN-I. In SARS-CoV-2-infected RMs, IFNmod reduced both antiviral and inflammatory ISGs. Notably, IFNmod treatment resulted in a potent reduction in (i) SARS-CoV-2 viral load in Bronchoalveolar lavage (BAL), upper airways, lung, and hilar lymph nodes; (ii) inflammatory cytokines, chemokines, and CD163+MRC1-inflammatory macrophages in BAL; and (iii) expression of Siglec-1, which enhances SARS-CoV-2 infection and predicts disease severity, on circulating monocytes. In the lung, IFNmod also reduced pathogenesis and attenuated pathways of inflammasome activation and stress response during acute SARS-CoV-2 infection. This study, using an intervention targeting both IFN- and IFN-{beta} pathways, shows that excessive inflammation driven by type 1 IFN critically contributes to SARS-CoV-2 pathogenesis in RMs, and demonstrates the potential of IFNmod to limit viral replication, SARS-CoV-2 induced inflammation, and COVID-19 severity.

immunology↗

bioRxiv2020

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel

BioHansel performs high-resolution genotyping of bacterial isolates by identifying phylogenetically informative single nucleotide polymorphisms (SNPs), also known as canonical SNPs, in whole genome sequencing (WGS) data. The application uses a fast k-mer matching algorithm to map pathogen WGS data to canonical SNPs contained in hierarchically structured schemas and assigns genotypes based on the detected SNP profile. Using modest computing resources, BioHansel efficiently types isolates from raw sequence reads or assembled contigs in a matter of seconds, making it attractive for use by public health, food safety, environmental, and agricultural authorities that wish to apply WGS methodologies for their surveillance, diagnostics, and research programs. BioHansel currently provides canonical SNP genotyping schemas for four prevalent Salmonella serovars--Typhi, Typhimurium, Enteritidis and Heidelberg--as well as a schema for Mycobacterium tuberculosis. Users can also supply their own schemas for genotyping other organisms. BioHansels quality assurance system assesses the validity of the genotyping results and can identify low quality data, contaminated datasets, and misidentified organisms. BioHansel is targeted to support surveillance, source attribution, risk assessment, diagnostics, and rapid screening for public health purposes, such as product recalls. BioHansel is an open source application with packages available for PyPI, Conda, and the Galaxy workflow manager. In summary, BioHansel performs efficient, rapid, accurate, and high-resolution classification of bacterial genomes from sequence reads or assembled contigs on standard computing hardware. BioHansel is suitable for use as a general research tool as well as in fully operationalized WGS workflows at the front lines of infectious disease surveillance, diagnostics, and outbreak investigation and response. Impact statementPublic health, food safety, environmental, and agricultural authorities are currently engaged in a global effort to incorporate whole genome sequencing technologies into their infectious disease research, surveillance, and outbreak investigation programs. Its widespread adoption, however, has been impeded by two major obstacles: the need for high performance computing to generate results and the expert knowledge required to interpret and communicate those results. BioHansel addresses these limitations by rapidly genotyping pathogens from whole genome sequence data in an accurate, simple, familiar, and easily sharable manner using standard computing resources. BioHansel provides a compact and readily interpretable genotype based on canonical SNP genotyping schemas. BioHansels genotyping nomenclature encodes the pathogens position in its population structure, which simplifies and facilitates its comparison with actively circulating strains and historical strains. The genotyping information provided by BioHansel can identify points of intervention to prevent the spread of pathogenic bacteria, screen for the presence of priority pathogens, and perform source attribution and risk assessment. Thus, BioHansel serves as a readily accessible and powerful WGS method, implementable on a laptop, for genotyping pathogens to detect, monitor, and control the emergence and spread of infectious disease through surveillance, screening, diagnostics, and outbreak investigation and response activities. Data summaryBioHansel is a Python 3 application available as PyPI, Conda Galaxy Tool Shed packages. It is an open source application distributed under the Apache License, Version 2.0. Source code is available at https://github.com/phac-nml/biohansel. The BioHansel user guide is available at https://bio-hansel.readthedocs.io/en/readthedocs/. Supplementary Materials are available at https://github.com/phac-nml/biohansel-manuscript-supplementary-data. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

bioinformatics↗