Biology subjects

Grant, N.

Publications and source records attributed to Grant, N..

5 recordsLinked to original sources

bioRxiv2026

Biologic therapy is associated with selective changes in airway eosinophil subpopulations in severe asthma

RationaleEosinophilic airway inflammation is common in severe asthma and strongly associated with symptoms, exacerbations, and impaired lung function. Although type 2 (T2)-targeted biologics improve outcomes and reduce eosinophils, many patients experience residual symptoms and exacerbations. Emerging evidence suggests that these biologics may differentially affect specific airway eosinophil subpopulations, representing a potential mechanism of suboptimal treatment response. ObjectiveDetermine the effect of biologic treatment on eosinophil subpopulations in adults with severe asthma using in-depth immune profiling with mass cytometry (CyTOF). MethodsFifty adults with severe asthma (28 biologic-naive, 22 on stable-dose biologic therapy for [≥]6 months) underwent clinical phenotyping, spirometry, blood sampling, and sputum induction. Twenty-nine sputum samples passed quality control thresholds and were profiled by CyTOF. Manually gated sputum eosinophils were clustered using FlowSOM to identify eosinophil subpopulations, and cluster abundances and marker expression were compared across treatment groups. Measurements and Main ResultsCyTOF revealed treatment-associated shifts in circulating immune cells (lower CD4+ T cells and B cells, higher monocytes) and lower sputum CD8+ T cells. Unsupervised clustering of sputum eosinophils identified eight distinct subpopulations, and selective depletion of Cluster 6 was noted in biologic-treated participants (biologic-naive vs anti-TSLP logFC -4.98, p=0.003; biologic-naive vs anti-IL5 logFC -6.89, p=0.01). Higher Cluster 6 proportion correlated with worse ACT scores (rho = -0.44, p = 0.02) and lung function (FEV1 % predicted: rho = -0.47, p < 0.01; FEV1/FVC: rho = -0.40, p = 0.03). Functionally, Cluster 6 displayed enriched trafficking/activation markers (CCR3/Eotaxin-1, CD69, CD80, CRTH2) and non-T2 inflammatory mediators (TNF, IL-8, TLR7). ConclusionBiologic therapy in severe asthma was associated with selective depletion of a highly activated sputum eosinophil subpopulation with capability to drive both T2 and non-T2 inflammatory pathways. This cluster correlated with worse asthma control and lung function, indicating it may be a biologically important driver of persistent disease and potential biomarker to more accurately predict treatment response.

immunology↗

bioRxiv2026

Evolutionary Integration of a Foreign Aromatic Catabolic Pathway Drives Metabolic Trade-offs in Methylobacterium extorquens PA1

Bacteria often acquire novel metabolic functions through horizontal gene transfer, allowing them to utilize new carbon sources. However, to benefit from these new pathways, they must be integrated with the hosts native metabolism. In nature, this process is fine-tuned via selection, enabling bacteria to exploit new niches. Alternate routes for pathway integration might yield distinct patterns of trade-offs, leading to differentiation within the adapting population. Understanding the metabolic mechanisms underlying these trade-offs provides insight into what maintains metabolic diversity in the environment. Lignin, a complex aromatic biopolymer, serves as an ideal substrate for exploring these questions, as monomers require complex metabolic pathways to break aromatic rings and be fed directly into central metabolism. In this study, we used the phyllosphere bacterium Methylobacterium extorquens PA1 to examine how a previously engineered catabolic gene cluster enabling lignin monomer utilization integrates with central metabolism. To this end, we experimentally evolved strains on lignin monomers vanillate (VA) and protocatechuate (PCA). Whole-genome sequencing revealed substrate-specific mutations that collectively reprogram stress responses and carbon storage regulation. These mutations resulted in myriad metabolic trade-offs: VA adapted strains showed diminished growth on PCA, PCA adapted strains largely cross-adapted to VA, and both VA and PCA evolution decreased growth rate on non-aromatic native substrates like succinate. These findings illuminate how evolution optimizes catabolic flux and resource allocation to efficiently integrate a foreign pathway following horizontal gene transfer, and the pleiotropic effects of such optimization. ImportanceMicrobial use of aromatics derived from lignin, whether in natural ecosystems or in biomass conversion, has been a major research focus. While aromatic pathways are well-known to have broad substrate specificity, pulling in aromatic molecules at various levels of conversion, it is unclear how much evolutionary constraint there is upon adaptation to each of these steps. By experimentally evolving Methylobacterium extorquens carrying a foreign lignin-derived aromatic catabolic pathway, we demonstrate that adaptation involves not just increased pathway flux but also regulatory rewiring and reallocation of resources, especially through PHB metabolism and the TCA cycle. The results reveal substrate-specific trade-offs and cross-adaptation, illustrating how new functions reshape fitness landscapes. These insights on metabolic innovation, pleiotropy, and diversification following horizontal gene transfer have broad implications for microbial evolution and metabolic engineering.

microbiology↗

bioRxiv2025

Single-cell RNA Sequencing Analysis of Sputum Cell Transcriptomes Reveals Pathways and Communication Networks That Contribute to the Pathogenesis of Asthma

BackgroundAsthma is driven by complex interactions amongst structural airway cells, cells of the immune system, and the environmental. While sputum cell characterization has been instrumental in studying asthma pathogenesis and refining treatment strategies, the nuances of cellular transcriptomes and intercellular communication in asthmatic sputum remain poorly understood. MethodsWe employed single-cell RNA sequencing to analyze cells isolated form the sputum from 16 asthma patients and 8 non-asthmatic controls. Cell identities were established using curated marker genes and SingleR annotation. We compared cell-specific gene expression and communication networks between asthmatic and control groups, correlating findings with distinct pathways that were dysregulated in asthma. Findings37,565 cellular transcriptomes were captured and analyzed. 15 distinct cell populations were identified, including various macrophages, monocytes, dendritic cells, and lymphocytes, along with rare cell types such as mast cells, innate lymphoid cells, bronchial epithelial cells, and eosinophils. Intercellular communication analysis indicated heightened signaling activity in asthma compared to controls, particularly in CD4+ T cells and dendritic cells which exhibited the most significant increases in RNA expression of outgoing signaling molecules. Notably, the ADAM12-SDC4 and CCL22-CCR4 ligand-receptor pathways demonstrated the strongest shifts between asthma and control subjects, particularly between dendritic cells and CD4 lymphocytes. InterpretationSC RNA seq profiling the asthma cellular transcriptome analysis of sputum highlights both innate and adaptive immune mechanisms that are significantly amplified in asthma. The elevated expression of ADAM12-SCD4 and CCL22-CC4 point to their critical role in asthma pathogenesis, suggesting potential avenues for targeted therapies and improved management of this chronic condition. Research in contextO_ST_ABSEvidence Before This StudyC_ST_ABSAsthma is a chronic inflammatory disease of the airways driven by intricate interactions between airway structural and immune cells. Previous transcriptomic studies have focused on bulk RNA samples from the airway, leaving significant gaps in our understanding of the cellular dynamics that characterize the disease. Added Value of This StudyThis study pioneers the use of single-cell RNA sequencing on sputum samples from patients with asthma, revealing a detailed landscape of cell phenotypes and dynamic communication patterns that distinguish asthmatic individuals from those without the disease. Notably, heightened intercellular communication was observed in asthma, particularly between CD4+ T cells and dendritic cells, confirming that there is a robust network of interactions between immune and structural cells. The notable increase of ADAM12-CCR4 communication from dendritic cells to other cell populations further emphasizes the dysregulation present in asthma. Implications of All Available EvidenceOur transcriptomic profiling illuminates distinct and amplified communication pathways involving CD4+ T cells and dendritic cells, aligning with established paradigms of both adaptive and innate immune responses in asthma pathogenesis. The identification of ADAM12 and CCR4 pathway dysregulation adds a critical layer to our understanding of the molecular mechanisms underpinning asthma, paving the way for potential therapeutic targets and personalized treatment strategies. Single cell profiling of the sputum has the capacity to characterize the breadth of cellular phenotypes, their functional status, and the communication in the airway at a level not previously attainable.

genomics↗

bioRxiv2020

Divergent evolution of mutation rates and biases in the long-term evolution experiment with Escherichia coli

All organisms encode enzymes that replicate, maintain, pack, recombine, and repair their genetic material. For this reason, mutation rates and biases also evolve by mutation, variation, and natural selection. By examining metagenomic time series of the Lenski long-term evolution experiment (LTEE) with Escherichia coli (Good, et al. 2017), we find that local mutation rate variation has evolved during the LTEE. Each LTEE population has evolved idiosyncratic differences in their rates of point mutations, indels, and mobile element insertions, due to the fixation of various hypermutator and antimutator alleles. One LTEE population, called Ara+3, shows a strong, symmetric wave pattern in its density of point mutations, radiating from the origin of replication. This pattern is largely missing from the other LTEE populations, most of which evolved missense, indel, or structural mutations in topA, fis, and dusB-- loci that all affect DNA topology. The distribution of mutations in those genes over time suggests epistasis and historical contingency in the evolution of DNA topology, which may have in turn affected local mutation rates. Overall, the replicate populations of the LTEE have largely diverged in their mutation rates and biases, even though they have adapted to identical abiotic conditions.

evolutionary biology↗

bioRxiv2019

Approaches for integrating heterogeneous RNA-seq data reveals cross-talk between microbes and genes in asthmatic patients

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNAseq) can be used on sputum, but it can be challenging to interpret because sputum contains a complex and heterogeneous mixture of human cells and exogenous (microbial) material. In this study, we developed a methodology that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. We use this to relate bulk RNAseq data from 115 asthmatic patients with clinical information, microscope images, and single-cell profiles. First, we mapped sputum RNAseq to human and exogenous sources. Next, we decomposed the human reads into cell-expression signatures and fractions of these in each sample; we validated the decomposition using targeted single-cell RNAseq and microscopy. We observed enrichment of immune-system cells (neutrophils, eosinophils, and mast cells) in severe asthmatics. Second, we inferred microbial abundances from the exogenous reads and then associated these with clinical variables -- e.g., Haemophilus was associated with increased white blood cell count and Candida, with worse lung function. Third, we applied a generative model, Latent Dirichlet allocation (LDA), to identify patterns of gene expression and microbial abundances and relate them to clinical data. Based on this, we developed a method called LDA-link that connects microbes to genes using reduced-dimensionality LDA topics. We found a number of known connections, e.g. between Haemophilus and the gene IL1B, which is highly expressed by mast cells. In addition, we identified novel connections, including Candida and the calcium-signaling gene CACNA1E, which is highly expressed by eosinophils. These results speak to the mechanism by which gene-microbe interactions contribute to asthma and define a strategy for making inferences in heterogeneous and noisy RNAseq datasets.

genomics↗