Immune response genes are highly polymorphic in humans and mice, with heterogeneity amongst loci driving strain-specific host defence responses. The inadvertent retention of polymorphic loci can introduce confounding phenotypes, leading to erroneous conclusions, and impeding scientific advancement. In this study, we employ a combination of RNAseq and variant calling analyses to identify a substantial region of 129S genome, including the highly polymorphic Nlrp1 locus, proximal to Nlrp3, in one of the most commonly used mouse models of NLRP3 deficiency (Nlrp3^tm1Flv). We show that the presence of the Nlrp1^129S locus leads to an increase in NLRP1B protein expression, and a sensitising of Nlrp3^tm1Flv macrophages to NLRP1 inflammasome activation, independent of NLRP3 deficiency. Retention of 129S genome further leads to protein sequence differences and altered gene regulation across multiple cell types, including of the key tissue-resident macrophage marker, TIM4. Using alternative models of NLRP3 deficiency, including a previously undescribed conditional Nlrp3 allele enabling precise temporal and cell-type specific control over Nlrp3 deletion, we further show that NLRP3 contributes to Talabostat-driven IL-1β release. Our study also establishes a generic framework to identify functionally relevant SNPs and assess genomic contamination in transgenic mice using RNAseq data. This allows for unambiguous attribution of phenotypes to the target gene and advances the precision and reliability of research in the field of host defence responses.

免疫反应基因在人类和小鼠中高度多态性，基因座之间的异质性驱动菌株特异性宿主防御反应。无意中保留多态性基因座会引入混杂的表型，导致错误的结论，并阻碍科学进步。在这项研究中，我们采用 RNAseq 和变异检出分析的组合来识别 129S 基因组的重要区域，包括 Nlrp3 近端的高度多态性 Nlrp1 基因座，在最常用的 NLRP3 缺陷小鼠模型之一 （Nlrp3^tm1Flv） 中。我们表明，Nlrp1^129S 基因座的存在导致 NLRP1B 蛋白表达增加，以及 Nlrp3^tm1Flv 巨噬细胞对 NLRP1 炎性小体激活的敏感性，与 NLRP3 缺陷无关。保留 129S 基因组进一步导致多种细胞类型的蛋白质序列差异和基因调控改变，包括关键的组织驻留巨噬细胞标志物 TIM4。使用 NLRP3 缺陷的替代模型，包括以前未描述的条件性 Nlrp3 等位基因，能够对 Nlrp3 缺失进行精确的时间和细胞类型特异性控制，我们进一步表明 NLRP3 有助于 Talabostat 驱动的 IL-1β 释放。我们的研究还建立了一个通用框架，以识别功能相关的 SNP 并使用 RNAseq 数据评估转基因小鼠的基因组污染。这允许将表型明确归因于目标基因，并提高了宿主防御反应领域研究的精度和可靠性。