Biology subjects

Guthridge, J. M.

Publications and source records attributed to Guthridge, J. M..

4 recordsLinked to original sources

bioRxiv2025

A population-scale atlas of blood and tissue in lupus nephritis

One Sentence SummaryA single-cell atlas of paired blood and tissue samples from Lupus Nephritis patients and healthy controls identified stromal and immune populations within renal tissue, including the scar-associated macrophage populations, which correlate with and may drive renal disease activity. Lupus nephritis (LN), a severe manifestation of Systemic Lupus Erythematosus (SLE), is a heterogeneous disease driven by diverse immune and tissue cell types. We obtained 538K single-cell and 140K single-nuclear profiles from kidney biopsies of 155 LN patients and 30 pre-implantation transplant biopsy controls, along with 325K single-cell blood profiles overlapping many of these patients. We identified key tissue cell types and cell states, and immune cell states; we were able to determine cell states that were tissue specific, and those that were present in the blood. We observed that LN pathological features are significantly associated with cell states using differential gene expression and Covarying Neighborhood Analysis (CNA). These analyses revealed broad changes in cell states associated with irreversible chronic tissue damage. After controlling for the effects of ongoing tissue damage, we observed that expansion of key glomerular and Scar Associated Macrophages (SAMs) populations tracked with increasing inflammatory disease activity. SAMs appear to drive LN fibrosis and, in active disease, infiltrate the glomeruli more than other myeloid cells. These observations strongly support that therapeutic targeting of myeloid populations may offer an as-of-yet unproven strategy to prevent renal inflammation and ongoing kidney damage in LN.

molecular biology↗

bioRxiv2023

Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease with currently no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in At-Risk populations prior to clinical disease onset is crucial to establishing effective prevention strategies. Here, we applied mass cytometry to deeply characterize the immunophenotypes in blood from At-Risk individuals identified through the presence of serum antibodies to citrullinated protein antigens (ACPA) and/or first-degree relative (FDR) status (n=52), as compared to established RA (n=67), and healthy controls (n=48). We identified significant cell expansions in At-Risk individuals compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5low naive B cell population was expanded in ACPA-positive FDRs. Further, we developed an "RA immunophenotype score" classification method based on the degree of enrichment of cell states relevant to established RA patients. This score significantly distinguished At-Risk individuals from controls. In all, we systematically identified activated lymphocyte phenotypes in At-Risk individuals, along with immunophenotypic differences among both ACPA+ and ACPA-FDR At-Risk subpopulations. Our classification model provides a promising approach for understanding RA pathogenesis with the goal to further improve prevention strategies and identify novel therapeutic targets.

genomics↗

bioRxiv2022

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human systems own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

immunology↗

bioRxiv2022

Genetic load in incomplete lupus erythematosus

Incomplete lupus erythematosus (ILE) patients have lupus features but insufficient criteria for systemic lupus erythematosus (SLE) classification. Some ILE patients transition to SLE, but most avoid major organ involvement. This study tested whether the milder disease course in ILE is influenced by reduced SLE-risk allele genetic load. We calculated the genetic load based on 99 SLE-associated risk alleles in European American SLE patients (>4 ACR-1997 criteria, n=171), ILE patients (3 ACR-1997 criteria; n=174), a subset of ILE patients not meeting SLICC classification (ILESLICC, n=119), and healthy controls (n=133). ILE and SLE patients had significantly greater SLE-risk allele genetic load compared to healthy controls, while ILESLICC patients had a trend toward an increased genetic load, although not statistically significant. However, the genetic load did not differ between ILE and SLE. In conclusion, ILE and SLE patients have comparable genetic loads of SLE risk loci, suggesting similar genetic predisposition between these conditions. Phenotypic differences between SLE and ILE may instead be influenced by ILE-specific variants and gene-environment interactions.

immunology↗