Characterizing Hydroxyl Radical Formation from the Light-Driven Fe(II)-Peracetic Acid Reaction, a Key Process for Aerosol-Cloud Chemistry.

The reaction of peracetic acid (PAA) and Fe(II) has recently gained attention due to its utility in wastewater treatment and its role in cloud chemistry. Aerosol-cloud interactions, partly mediated by aqueous hydroxyl radical (OH) chemistry, represent one of the largest uncertainties in the climate system. Ambiguities remain regarding the sources of OH in the cloud droplets. Our research group recently proposed that the dark and light-driven reaction of Fe(II) with peracids may be a key contributor to OH formation, producing a large burst of OH when aerosol particles take up water as they grow to become cloud droplets, in which reactants are consumed within 2 min. In this work, we quantify the OH production from the reaction of Fe(II) and PAA across a range of physical and chemical conditions. We show a strong dependence of OH formation on ultraviolet (UV) wavelength, with maximum OH formation at λ = 304 ± 5 nm, and demonstrate that the OH burst phenomenon is unique to Fe(II) and peracids. Using kinetics modeling and density functional theory calculations, we suggest the reaction proceeds through the formation of an [Fe(II)-(PAA)2(H2O)2] complex, followed by the formation of a Fe(IV) complex, which can also be photoactivated to produce additional OH. Determining the characteristics of OH production from this reaction advances our knowledge of the sources of OH in cloudwater and provides a framework to optimize this reaction for OH output for wastewater treatment purposes.

Pro-inflammatory feedback loops define immune responses to pathogenic Lentivirus infection.

BACKGROUND: The Lentivirus human immunodeficiency virus (HIV) causes chronic inflammation and AIDS in humans, with variable rates of disease progression between individuals driven by both host and viral factors. Similarly, simian lentiviruses vary in their pathogenicity based on characteristics of both the host species and the virus strain, yet the immune underpinnings that drive differential Lentivirus pathogenicity remain incompletely understood. METHODS: We profile immune responses in a unique model of differential lentiviral pathogenicity where pig-tailed macaques are infected with highly genetically similar variants of SIV that differ in virulence. We apply longitudinal single-cell transcriptomics to this cohort, along with single-cell resolution cell-cell communication techniques, to understand the immune mechanisms underlying lentiviral pathogenicity. RESULTS: Compared to a minimally pathogenic lentiviral variant, infection with a highly pathogenic variant results in a more delayed, broad, and sustained activation of inflammatory pathways, including an extensive global interferon signature. Conversely, individual cells infected with highly pathogenic Lentivirus upregulated fewer interferon-stimulated genes at a lower magnitude, indicating that highly pathogenic Lentivirus has evolved to partially escape from interferon responses. Further, we identify CXCL10 and CXCL16 as important molecular drivers of inflammatory pathways specifically in response to highly pathogenic Lentivirus infection. Immune responses to highly pathogenic Lentivirus infection are characterized by amplifying regulatory circuits of pro-inflammatory cytokines with dense longitudinal connectivity. CONCLUSIONS: Our work presents a model of lentiviral pathogenicity where failures in early viral control mechanisms lead to delayed, sustained, and amplifying pro-inflammatory circuits, which in turn drives disease progression.

Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer.

Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.