Staphylococcus aureus proteases trigger eosinophil-mediated skin inflammation.

Staphylococcus aureus skin colonization and eosinophil infiltration are associated with many inflammatory skin disorders, including atopic dermatitis, bullous pemphigoid, Netherton's syndrome, and prurigo nodularis. However, whether there is a relationship between S. aureus and eosinophils and how this interaction influences skin inflammation is largely undefined. We show in a preclinical mouse model that S. aureus epicutaneous exposure induced eosinophil-recruiting chemokines and eosinophil infiltration into the skin. Remarkably, we found that eosinophils had a comparable contribution to the skin inflammation as T cells, in a manner dependent on eosinophil-derived IL-17A and IL-17F production. Importantly, IL-36R signaling induced CCL7-mediated eosinophil recruitment to the inflamed skin. Last, S. aureus proteases induced IL-36α expression in keratinocytes, which promoted infiltration of IL-17-producing eosinophils. Collectively, we uncovered a mechanism for S. aureus proteases to trigger eosinophil-mediated skin inflammation, which has implications in the pathogenesis of inflammatory skin diseases.

Frontotemporal lobar degeneration targets brain regions linked to expression of recently evolved genes.

In frontotemporal lobar degeneration (FTLD), pathological protein aggregation in specific brain regions is associated with declines in human-specialized social-emotional and language functions. In most patients, disease protein aggregates contain either TDP-43 (FTLD-TDP) or tau (FTLD-tau). Here, we explored whether FTLD-associated regional degeneration patterns relate to regional gene expression of human accelerated regions (HARs), conserved sequences that have undergone positive selection during recent human evolution. To this end, we used structural neuroimaging from patients with FTLD and human brain regional transcriptomic data from controls to identify genes expressed in FTLD-targeted brain regions. We then integrated primate comparative genomic data to test our hypothesis that FTLD targets brain regions linked to expression levels of recently evolved genes. In addition, we asked whether genes whose expression correlates with FTLD atrophy are enriched for genes that undergo cryptic splicing when TDP-43 function is impaired. We found that FTLD-TDP and FTLD-tau subtypes target brain regions with overlapping and distinct gene expression correlates, highlighting many genes linked to neuromodulatory functions. FTLD atrophy-correlated genes were strongly enriched for HARs. Atrophy-correlated genes in FTLD-TDP showed greater overlap with TDP-43 cryptic splicing genes and genes with more numerous TDP-43 binding sites compared with atrophy-correlated genes in FTLD-tau. Cryptic splicing genes were enriched for HAR genes, and vice versa, but this effect was due to the confounding influence of gene length. Analyses performed at the individual-patient level revealed that the expression of HAR genes and cryptically spliced genes within putative regions of disease onset differed across FTLD-TDP subtypes.

Building an ab initio solvated DNA model using Euclidean neural networks.

Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent molecules. We present a machine-learned electron density model based on a Euclidean neural network framework that includes a built-in understanding of equivariance to model explicitly solvated double-stranded DNA. By training the machine learning model using molecular fragments that sample the key DNA and solvent interactions, we show that the model predicts electron densities of arbitrary systems of solvated DNA accurately, resolves polarization effects that are neglected by classical force fields, and captures the physics of the DNA-solvent interaction at the ab initio level.

Data-driven fine-grained region discovery in the mouse brain with transformers.

Technologies such as spatial transcriptomics offer unique opportunities to define the spatial organization of the mouse brain. We developed an unsupervised training scheme and novel transformer-based deep learning architecture to detect spatial domains across the whole mouse brain using spatial transcriptomics data. Our model learns local representations of molecular and cellular statistical patterns which can be clustered to identify spatial domains within the brain from coarse to fine-grained. Discovered domains are spatially regular, even with several hundreds of spatial clusters. They are also consistent with existing anatomical ontologies such as the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv3) and can be visually interpreted at the cell type or transcript level. We demonstrate our method can be used to identify previously uncatalogued subregions, such as in the midbrain, where we uncover gradients of inhibitory neuron complexity and abundance. Notably, these subregions cannot be discovered using other methods. We apply our method to a separate multi-animal whole-brain spatial transcriptomic dataset and show that our method can also robustly integrate spatial domains across animals.

Metabolic Reprogramming of Tumor-Associated Macrophages Using Glutamine Antagonist JHU083 Drives Tumor Immunity in Myeloid-Rich Prostate and Bladder Cancers.

Glutamine metabolism in tumor microenvironments critically regulates antitumor immunity. Using the glutamine-antagonist prodrug JHU083, we report potent tumor growth inhibition in urologic tumors by JHU083-reprogrammed tumor-associated macrophages (TAMs) and tumor-infiltrating monocytes. We show JHU083-mediated glutamine antagonism in tumor microenvironments induced by TNF, proinflammatory, and mTORC1 signaling in intratumoral TAM clusters. JHU083-reprogrammed TAMs also exhibited increased tumor cell phagocytosis and diminished proangiogenic capacities. In vivo inhibition of TAM glutamine consumption resulted in increased glycolysis, a broken tricarboxylic acid (TCA) cycle, and purine metabolism disruption. Although the antitumor effect of glutamine antagonism on tumor-infiltrating T cells was moderate, JHU083 promoted a stem cell-like phenotype in CD8+ T cells and decreased the abundance of regulatory T cells. Finally, JHU083 caused a global shutdown in glutamine-utilizing metabolic pathways in tumor cells, leading to reduced HIF-1α, c-MYC phosphorylation, and induction of tumor cell apoptosis, all key antitumor features. Altogether, our findings demonstrate that targeting glutamine with JHU083 led to suppressed tumor growth as well as reprogramming of immunosuppressive TAMs within prostate and bladder tumors that promoted antitumor immune responses. JHU083 can offer an effective therapeutic benefit for tumor types that are enriched in immunosuppressive TAMs.

Functional network collapse in neurodegenerative disease.

Cognitive and behavioral deficits in Alzheimer's disease (AD) and frontotemporal dementia (FTD) result from brain atrophy and altered functional connectivity. However, it is unclear how atrophy relates to functional connectivity disruptions across dementia subtypes and stages. We addressed this question using structural and functional MRI from 221 patients with AD (n=82), behavioral variant FTD (n=41), corticobasal syndrome (n=27), nonfluent (n=34) and semantic (n=37) variant primary progressive aphasia, and 100 cognitively normal individuals. Using partial least squares regression, we identified three principal structure-function components. The first component showed overall atrophy correlating with primary cortical hypo-connectivity and subcortical/association cortical hyper-connectivity. Components two and three linked focal syndrome-specific atrophy to peri-lesional hypo-connectivity and distal hyper-connectivity. Structural and functional component scores predicted global and domain-specific cognitive deficits. Anatomically, functional connectivity changes reflected alterations in specific brain activity gradients. Eigenmode analysis identified temporal phase and amplitude collapse as an explanation for atrophy-driven functional connectivity changes.