NLRP1 is an inflammasome forming pattern recognition receptor (PRR). When activated by pathogen- and damage- associated molecular patterns (PAMPS/DAMPS), NLRP1 inflammasome formation leads to inflammation through the production of proinflammatory cytokines IL-18 and IL-1β. As with other inflammasome forming NLR family members, NLRP1 also regulates cell death processes, termed pyroptosis. The domain structure of NLRP1 differs between mice and humans, making it possible for the function of the inflammasome to differ between species and adds complexity to the study of this NLR family member. In humans, mutations in both coding and non-coding regions of the NLRP1 gene are linked to a variety of diseases. Likewise, interruption of NLRP1 inhibitors or changes in the prevalence of NLRP1 activators can also impact disease pathobiology. Adding to its complexity, the NLRP1 inflammasome plays a dichotomous role in human diseases, functioning to either attenuate or augment miscellaneous biological processes in a tissue specific manner. For example, NLRP1 plays a protective role in the gastrointestinal tract by modulating the microbiome composition; however, it augments neurological disorders, cardio-pulmonary diseases, and cancer through promoting inflammation. Thus, it is critical that the role of NLRP1 in each of these disease processes be robustly defined. In this review, we summarize the current research landscape to provide a better understanding of the mechanisms associated with NLRP1 function and dysfunction in human disease pathobiology. We propose that a better understanding of these mechanisms will ultimately result in improved insight into immune system dysfunction and therapeutic strategies targeting inflammasome function in multiple human diseases.

Brucella abortus is a zoonotic pathogen that causes brucellosis. Because of Brucella’s unique LPS layer and intracellular localization predominately within macrophages, it can often evade immune detection. However, pattern recognition receptors are capable of sensing Brucella pathogen-associated molecular patterns (PAMPS). For example, NOD-like receptors (NLRs) can form a multi-protein inflammasome complex to attenuate Brucella pathogenesis. The inflammasome activates IL-1β and IL-18 to drive immune cell recruitment. Alternatively, inflammasome activation also initiates inflammatory cell death, termed pyroptosis, which augments bacteria clearance. In this report, we assess canonical and non-canonical inflammasome activation following B. abortus infection. We conducted in vivo studies using Asc^−/− mice and observed decreased mouse survival, immune cell recruitment, and increased bacteria load. We also conducted studies with Caspase-11^−/− mice and did not observe any significant impact on B. abortus pathogenesis. Through mechanistic studies using Asc^−/− macrophages, our data suggests that the protective role of ASC may result from the induction of pyroptosis through a gasdermin D-dependent mechanism in macrophages. Additionally, we show that the recognition of Brucella is facilitated by sensing the PAMP gDNA rather than the less immunogenic LPS. Together, these results refine our understanding of the role that inflammasome activation and pyroptosis plays during brucellosis.

The innate immune system plays a key role in modulating host immune defense during bacterial disease. Upon sensing pathogen-associated molecular patterns (PAMPs), the multi-protein complex known as the inflammasome serves a protective role against bacteria burden through facilitating pathogen clearance and bacteria lysis. This can occur through two mechanisms: (1) the cleavage of pro-inflammatory cytokines IL-1β/IL-18 and (2) the initiation of inflammatory cell death termed pyroptosis. In recent literature, AIM2-like Receptor (ALR) and Nod-like Receptor (NLR) inflammasome activation has been implicated in host protection following recognition of bacterial DNA. Here, we review current literature synthesizing mechanisms of DNA recognition by inflammasomes during bacterial respiratory disease. This process can occur through direct sensing of DNA or indirectly by sensing pathogen-associated intracellular changes. Additionally, DNA recognition may be assisted through inflammasome–inflammasome interactions, specifically non-canonical inflammasome activation of NLRP3, and crosstalk with the interferon-inducible DNA sensors Stimulator of Interferon Genes (STING) and Z-DNA Binding Protein-1 (ZBP1). Ultimately, bacterial DNA sensing by inflammasomes is highly protective during respiratory disease, emphasizing the importance of inflammasome involvement in the respiratory tract.

Breast cancer is a devastating malignancy, accounting for 40,000 female deaths and 30% of new female cancer diagnoses in the United States in 2019 alone. The leading cause of breast cancer related deaths is the metastatic burden. Therefore, preclinical models for breast cancer need to analyze metastatic burden to be clinically relevant. The 4T1 breast cancer model provides a spontaneously-metastasizing, quantifiable mouse model for stage IV human breast cancer. However, most 4T1 protocols quantify the metastatic burden by manually counting stained colonies on tissue culture plates. While this is sufficient for tissues with lower metastatic burden, human error in manual counting causes inconsistent and variable results when plates are confluent and difficult to count. This method offers a computer-based solution to human counting error. Here, we evaluate the protocol using the lung, a highly metastatic tissue in the 4T1 model. Images of methylene blue-stained plates are acquired and uploaded for analysis in Fiji-ImageJ. Fiji-ImageJ then determines the percentage of the selected area of the image that is blue, representing the percentage of the plate with metastatic burden. This computer-based approach offers more consistent and expeditious results than manual counting or histopathological evaluation for highly metastatic tissues. The consistency of Fiji-ImageJ results depends on the quality of the image. Slight variations in results between images can occur, thus it is recommended that multiple images are taken and results averaged. Despite its minimal limitations, this method is an improvement to quantifying metastatic burden in the lung by offering consistent and rapid results.