Maxwell Lewis Neal

Peroxisomes are versatile organelles mediating energy homeostasis and redox balance. While peroxisome dysfunction is linked to numerous diseases, the mechanisms regulating peroxisome dynamics during cellular stress remain elusive. Using yeast, we show that proteotoxic stress, including loss of endoplasmic reticulum (ER) or cytosolic chaperone function, impaired ER protein translocation, disrupted N-linked glycosylation, or reductive stress, triggers peroxisome proliferation. This occurs through increased de novo biogenesis from the ER, as well as growth and division, rather than impaired pexophagy. Peroxisome biogenesis is essential for cellular recovery from proteotoxic stress. Through comprehensive testing of major signaling pathways, we determine this response to be mediated by activation of the heat shock response and inhibition of Target of Rapamycin (TOR) signaling. Notably, the effects of proteotoxic stress and TOR inhibition on peroxisomes are also observed in human fibroblasts. Our findings reveal a critical and conserved role of peroxisomes in cellular response to proteotoxic stress.

The mechanisms that underlie protective immunity to Mycobacterium tuberculosis (Mtb) remain incompletely defined. To identify immune correlates associated with protection, we performed single-cell RNA sequencing of lung immune cells after aerosol Mtb infection of naïve mice and mice with contained Mtb infection (CoMtb), a model of naturally acquired resistance, across multiple time points, mouse strains, and Mtb strains. Across conditions, protection was associated with distinct temporal patterns of immune activation, cell recruitment, and resolution. Early after challenge, CoMtb mice exhibited rapid, transient recruitment and activation of CD4⁺ T cells, macrophages, NK, and NKT cells, accompanied by short-lived bursts of type I and II interferon signaling, increased oxidative phosphorylation, and enhanced chemokine-mediated cell–cell communication. In contrast, primary infection elicited delayed but sustained interferon and neutrophil responses and higher bacterial burdens. These data indicate that protection involves dynamically coordinated immune pathways rather than the magnitude of any single response. Transcriptional features of CoMtb overlapped with those observed in nonhuman primates following intravenous BCG vaccination, including enrichment of activated tissue-resident CD4⁺ T cells and innate effector populations. Together, these findings support a model in which effective immunity to Mtb depends on the timing and coordination of immune activation, providing a framework for vaccine strategies that reproduce protective lung immune dynamics.

Pulmonary Mycobacterium tuberculosis (Mtb) infection results in a variety of heterogeneous lesion structures, from necrotic granulomas to alveolitis, but the mechanisms regulating their development remain unclear. Using a mouse model of concomitant immunity and subsequent aerosol infection, we demonstrate that counter regulation between neutrophils and CD4 T cells occurs very early during infection and governs these distinct pathologies. In primary Mtb infection, a dysregulated feed-forward circuit of neutrophil recruitment occurs, in which neutrophils hinder CD4 T cell interactions with infected macrophages, cause granuloma necrosis, and establish a replicative niche that drives a two-log increase in lung bacterial burden. Conversely, the rapid recruitment and activation of T cells due to concomitant immunity promotes local macrophage activation and dampens detrimental neutrophil responses. Together, these studies uncover fundamental determinants of tuberculosis lung pathology, which have important implications for new strategies to prevent or treat tuberculosis.

The malaria parasite has a complex lifecycle involving various host cell environments in both human and mosquito hosts. The parasite must tightly regulate gene expression at each stage in order to adapt to its current environment while continuing development. However, it is challenging to study gene function and regulation of essential genes across the parasite’s multi-host lifecycle. Thus, we adapted a recently developed a single-plasmid dimerizable Cre recombinase system for rapamycin-controllable expression of Cas9, allowing for conditional introduction of mutations. We explored rates of gene deletion using varying repair template lengths, showing functionality of donor templates under 250bp for homology-directed repair. As a proof of concept, we conditionally disrupted two uncharacterized genes in blood and gametocyte stages, identifying new stage-specific phenotypes. Importance: As progress towards eliminating malaria has stalled, there is a pressing need for new antimalarials and vaccines. Genes essential to multiple stages of development represent ideal candidates for both antimalarials and vaccines. However, much of the parasite genome remains uncharacterized. Conditional gene perturbation approaches are needed in order to study gene function across the lifecycle. Currently available tools are limited in their ability to perturb genes at the scale required for large screens. We describe a tool that allows for conditional introduction of desired mutations by controlling Cas9 with the DiCre-loxP system. We demonstrate the accessibility of this approach by designing gRNA-donor pairs that can be commercially synthesized. This toolkit provides a scalable system for identifying new drug and vaccine candidates targeting multiple stages of the parasite lifecycle

We introduce DeleteomeTools, an R package that leverages the Deleteome compendium of yeast single-gene deletion transcriptomes to predict gene function. Primarily, the package provides functions for identifying similarities between the transcriptomic signatures of deletion strains, thereby associating genes of interest with others that may be functionally related. We describe how our software predicted a novel relationship between the yeast nucleoporin Nup170 and the Ctf18-RFC complex, which was confirmed experimentally, revealing a previously unknown link between nuclear pore complexes and the DNA replication machinery. To assess the package’s broader predictive capabilities, we performed a systematic evaluation that tested how well it predicted Gene Ontology (GO) annotations already applied to the subset of genes deleted in Deleteome strains. We show that our package predicted a majority of reported GO:biological process annotations with semantic similarities ranging from moderate to identical. We also discuss how our strategy for quantifying similarity between deletion strains, which relies on differential expression signatures, differs from other approaches that use global expression profiles, and why it has the potential to identify functional relationships that might otherwise go undetected.

Peroxisomes are versatile organelles mediating energy homeostasis and redox balance. While peroxisome dysfunction is linked to numerous diseases, the molecular mechanisms and signaling pathways regulating peroxisomes during cellular stress remain elusive. Using yeast, we show that perturbations disrupting protein homeostasis including loss of ER or cytosolic chaperone function, impairments in ER protein translocation, blocking ER N-glycosylation, or reductive stress, cause peroxisome proliferation. This proliferation is driven by increased de novo biogenesis from the ER as well as increased fission of pre-existing peroxisomes, rather than impaired pexophagy. Notably, peroxisome biogenesis is essential for cellular recovery from proteotoxic stress. Through comprehensive testing of major signaling pathways, we determine this response to be mediated by activation of the heat shock response and inhibition of Target of Rapamycin (TOR) signaling. Finally, the effects of proteotoxic stress and TOR inhibition on peroxisomes are also captured in human fibroblasts. Overall, our findings reveal a critical and conserved role of peroxisomes in cellular response to proteotoxic stress.

perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell.

perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually-quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is available at https://github.com/AitchisonLab/perox-per-cell

We introduce DeleteomeTools, an R package that leverages the Deleteome compendium of yeast single-gene deletion transcriptomes to predict gene function. Primarily, the package provides functions for identifying similarities between the transcriptomic signatures of deletion strains, thereby associating genes of interest with others that may be functionally related. We describe how our software predicted a novel relationship between the yeast nucleoporin Nup170 and the Ctf18-RFC complex, which was confirmed experimentally, revealing a previously unknown link between nuclear pore complexes and the DNA replication machinery. To assess the package’s broader predictive capabilities, we performed a systematic evaluation that tested how well it predicted Gene Ontology (GO) annotations already applied to the subset of genes deleted in Deleteome strains. We show that our package predicted a majority of reported GO: biological process annotations with semantic similarities ranging from moderate to identical. We also discuss how our strategy for quantifying similarity between deletion strains, which relies on differential expression signatures, differs from other approaches that use global expression profiles and why it has the potential to identify functional relationships that might otherwise go undetected.

Traditional antiviral therapies often have limited effectiveness due to toxicity and the emergence of drug resistance. Host-based antivirals are an alternative, but can cause nonspecific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR knockout (KO) screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting antiviral SL drug targets. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Our comparison of SARS-CoV-2 and influenza infection data revealed potential broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.