Mycobacterium tuberculosis (Mtb) depends on the pathogen’s ability to adapt to the variable immune pressures exerted by the host. Understanding this interplay has proven difficult, largely because experimentally tractable animal models do not recapitulate the heterogeneity of tuberculosis disease. We leveraged the genetically diverse Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb mutants to create a resource for associating bacterial genetic requirements with host genetics and immunity. We report that CC strains vary dramatically in their susceptibility to infection and produce qualitatively distinct immune states. Global analysis of Mtb transposon mutant fitness (TnSeq) across the CC panel revealed that many virulence pathways are only required in specific host microenvironments, identifying a large fraction of the pathogen’s genome that has been maintained to ensure fitness in a diverse population. Both immunological and bacterial traits can be associated with genetic variants distributed across the mouse genome, making the CC a unique population for identifying specific host-pathogen genetic interactions that influence pathogenesis.The outcome of an encounter with  Mycobacterium tuberculosis (Mtb) produces heterogeneous outcomes that are influenced by genetic and phenotypic variation in both the host and the pathogen. Classic human genetic studies show that host variation influences immunity to tuberculosis (TB)  . Likewise traits . The role traits . Evidence traits , includie traits . HoweverMtb infection , revealing a critical role of Th1 immunity. Mice lacking factors necessary for the production of Th1 cells or the protective cytokine interferon gamma (IFNγ) are profoundly susceptible to nfection . Defectsnfection , demonstnfection .Mtb in genetically diverse populations is more heterogeneous than any single small animal model  and Diversity Outbred (DO) mouse populations are new mammalian resources that more accurately represent the genetic and phenotypic heterogeneity observed in outbred populations . These mMtb transposon mutants to determine whether the CC population could be used to characterize the interactions between host and pathogen. Using over 60 diverse mouse strains, we report that the CC panel encompasses a broad spectrum of TB susceptibility and immune phenotypes. By leveraging high-resolution bacterial phenotyping known as ‘Transposon Sequencing’ (TnSeq), we quantified the relative fitness of a saturated library of Mtb mutants across the CC panel and specific immunological mouse knockout strains. We report that approximately three times more bacterial genes contribute to fitness in the diverse panel than in any single mouse strain, defining a large fraction of the bacterial genome that is dedicated to adapting to distinct immune states. Association of both host immunological phenotypes and bacterial fitness traits with Quantitative Trait Loci (QTL) demonstrated the presence of discrete Host-Interacting-with Pathogen QTL (HipQTL) that represent inter-species genetic interactions that influence the pathogenesis of this infection. Together, these observations support the CC population as a tractable model of host diversity that greatly expands the spectrum of immunological and pathological states that can be modeled in the mouse.We combined the natural but reproducible host variation of the CC panel with a comprehensive library of Mtb. To enable bacterial transposon sequencing (TnSeq) studies downstream, the animals were infected via the intravenous (IV) route with a saturated library of Mtb transposon mutants (infectious dose of 105 CFU), which in sum produce an infection that is similar to the wild-type parental strain (r = 0.88) and revealed a wide variation in bacterial burden across the panel (9 bacteria in their lungs (r = 0.43)  . Thus, tr = 0.79), CCL3 , G-CSF (r = 0.78), and CXCL1 ), and more general proinflammatory cytokines (IL-6 (r = 0.80) and IL-1α (r = 0.76)) . In general, correlations between these metrics indicated that systemic disease was associated with bacterial replication and inflammation . Lung CF= 0.76)) . These f= 0.76)) .2) of these immunological and disease traits. The percent of the variation attributed to genotype ranged from 56%–87% , several strains failed to conform to this relationship . The dotionship . In partR < 0.3). Despite the clear protective role for IFNγ , CXCL9 (MIG), and CCL5 (RANTES)  and aerosol (R = 0.92) routes. IFNγ levels were variable across the strains (R = −0.22), concordant with findings from the CC screen (R = −0.21). Altogether, this survey of TB-related traits demonstrated a broad range of susceptibility and the presence of qualitatively distinct and genetically determined disease states.To assess the reproducibility of these findings in an aerosol infection model, we tested four CC genotypes that were susceptible by IV infection, including CC027, CC032, CC037, and CC042. We infected groups of 4–6 mice per genotype with H37Rv strain via low-dose aerosol infection (~100 CFU), including B6 mice as resistant controls. At 4-weeks post infection, we quantified lung CFU, lung cytokine abundance and weight loss as measurements of TB disease. Compared to the resistant B6 mice, the selected CC strains demonstrated higher bacterial burden in the lung  and sign strains  and did TipQTL), which were associated with TB disease or cytokine traits, were identified and numbered in accordance with previously reported TipQTL , one of its QTL fell within the Tip5 bacterial burden interval on chromosome 2   . These o2 intercross approach to independently assess the importance of the lung CFU QTL on chromosomes 7 and 15  were genotyped . At 1 month post infection, lung CFU was quantified, and we conducted QTL mapping in R/qtl2 (Tip8 (peak position Chr7:28.6 Mb), thus validating this locus as a main driver of bacterial burden. In this reduced complexity cross, we did not observe a QTL on chromosome 15 (Tip10). This may be due to the B6 haplotype at this locus in CC030, which did not represent the strongest phenotypic contrast to CAST. Additionally in the mapping validation study, we identified a new resistance (low lung CFU) locus on chromosome 8 , driven by the CC029 cross partner with the CAST haplotype. This QTL was not present in the original CC screen, probably due to the low representation of the CAST haplotype at that marker in the CC cohort tested. Altogether, this intercross strategy validated Tip8 as a strong predictor of lung CFU, though rigorous validation of Tip10 may require a more optimal pairing of parental strains.A number of factors can limit the statistical significance of QTL identified in the CC population, including small effect sizes, limited genotype availability, and the genetic complexity of the trait. We took an Fectively . The F2 enotyped  and infen R/qtl2  to identMtb mutants after selection in each CC host genotype. To serve as benchmarks of known immunological lesions, we also performed TnSeq in B6 mice that were lacking the mediators of Th1 immunity, lymphocytes (Rag2-/-) and IFNγ (Ifng-/-), or were lacking the immunoregulatory mediators that control disease by inhibiting inflammation, nitric oxide synthase (Nos2-/-) (Cybb-/-)  were sequenced, capturing 60 distinct mouse genotypes  as a high-resolution phenotyping approach to estimate the relative abundance of individual Nos2-/-)  or the NCybb-/-) . The relby TnSeq . A totalenotypes .Mtb genes that are required for growth or survival of Mtb in B6 mice, based on significant underrepresentation of the corresponding mutant after four weeks of in vivo selection. Eighty-seven percent of these genes overlapped with a similar previous analysis in BALB/c mice , siderophore-mediated iron acquisition (mbtA), phosphate transport (pstC2), glycerol catabolism (glpK), and RNA processing (rnaseJ) were generated and tagged with a unique molecular barcode. These mutants were combined with a barcoded wild-type parental strain, and the resulting ‘mini-pool’ was subjected to in vivo selection after IV infection of a sub-panel of mouse strains, as in the original screen. The relative abundance of each mutant was determined by sequencing the amplified barcodes and data from all reliably detected strains is shown in . This study also allowed us to estimate the sensitivity of the TnSeq method, which could detect even the 30% fitness defect of the ∆glpK strain between the B6 and CC018 animals  (irtAB) (mce4) (glnQ and rv2563)  ; for the (irtAB) , cholest) (mce4) , glutami rv2563)  and for  rv2563) . Despite or Nos2 , consist or Nos2 , which ise lines . In bothMtb genes previously thought to be dispensable in the mouse model and were only necessary in CC strains. For example, the alkyl hydroperoxide reductase, AhpC has been proposed to function with the adjacently encoded peroxiredoxin, AhpD and is critical for detoxifying reactive nitrogen intermediates in vitro  are implicated in both fatty acid uptake and modifying host cell membranes but are dispensable for replication in B6 mice  and the espACD operon (rv3616c-rv3614c). Similarly, other modules consisted of genes responsible for ESX5 secretion (Module 7), mycobactin synthesis (Module 4), the Mce1 and Mce4 lipid importers (Modules 5 and 16), phthiocerol dimycocerosate synthesis , PDIM transport (Module 16), and phosphate uptake (Module 14). The 20 genes assigned to Module six included two components of an important oxidative stress resistance complex (sseA and rv3005c) and were highly enriched for mutants predicted to be involved in this same process via genetic interaction mapping  in at least two independent comparisons . We thensigC, was found in Module one along with a non-ribosomal peptide synthetic operon. Previous genome-wide ChIP-seq and overexpression screens support a role for SigC in regulating this operon  and ESX1, consistent with previous TnSeq studies in susceptible Nos2-/- and C3HeB/FeJ mice  were distributed across the mouse genome  and eccD1 (Hip22), that explain at least a portion of the observed variable abundance of these Mtb mutants (Mtb virulence pathways (mbt), or Module 16 . In all three cases, a single mutant from the module was independently associated with a QTL at the same position as the module eigentrait , and all genes in the corresponding network cluster . Specifically, we found no statistical support for differentiating these QTL based on position (p = 0.93) (mbt mutants (Mtb’s environment and has opposing effects of the requirement for mycobactin synthesis and ESX1 secretion. The relationship between this variant and the mce4-associated QTL (Hip43) was less clear, as the statistical support for independent QTL was weak  and the effects of founder haplotypes were similar but not identical   and the  mutants . We concdentical . Some of1 or mbt . Based oTipQTL overlapped with HipQTL (Tip5/Hip4 on chromosome two and Tip9/Hip35 on chromosome 17), suggesting specific interactions between bacterial fitness and immunity. However, most Tip- and HipQTL were distinct, indicating that the fitness of sensitized bacterial mutants can be used to detect genetic variants that subtly influence the bacterial environment but not overtly alter disease. We chose to further investigate whether HipQTL might alter overall bacterial disease using the most significant HipQTL on chromosome 10 (Hip42). We found that the founder haplotypes associated with extreme trait values at this QTL could differentiate CC strains with significantly altered total bacterial burden, and the NOD and WSB haplotypes were associated with higher bacterial numbers , and the Tip8 locus which we validated by intercross. For each QTL region, we identified genes that belonged to a differentially expressed transcriptional module in mouse lungs following Mtb infection  encodes a lysosomal cysteine proteinase and has previously been associated with TB disease risk in humans (Tip8), which was driven solely by the genetically divergent CAST founder haplotype, contained over 50 genes (mbt mutants (Hip42) had a complex causal haplotype pattern (AJ/B6/NZO vs. 129/CAST/PWK vs. NOD/WSB) suggesting multiple variants might be impacting common genes. Within this interval, we identified 13 genes expressed in response to Mtb infection, three of which had SNPs fully or partially consistent with at least one of the identified causal haplotype groups  or Taconic (RagN12) and bred at UMASS. Male mice from 52 CC strains were purchased from the UNC Systems Genetics Core Facility (SGCF) between July 2013 and August 2014. The 52 CC strains used in this study include: CC001/Unc, CC002/Unc, CC003/Unc, CC004/TauUnc, CC005/TauUnc, CC006/TauUnc, CC007/Unc, CC008/GeniUnc, CC009/Unc, CC010/GeniUnc, CC011/Unc, CC012/GeniUnc, CC013/GeniUnc, CC015/Unc, CC016/GeniUnc, CC017/Unc, CC018/Unc, CC019/TauUnc, CC021/Unc, CC022/GeniUnc, CC023/GeniUnc, CC024/GeniUnc, CC025/GeniUnc, CC027/GeniUnc, CC028/GeniUnc, CC029/Unc, CC030/GeniUnc, CC031/GeniUnc, CC032/GeniUnc, CC033/GeniUnc, CC034/Unc, CC035/Unc, CC037/TauUnc, CC038/GeniUnc, CC039/Unc, CC040/TauUnc, CC041/TauUnc, CC042/GeniUnc, CC043/GeniUnc, CC044/Unc, CC045/GeniUnc, CC046/Unc, CC047/Unc, CC051/TauUnc, CC055/TauUnc, CC056/GeniUnc, CC057/Unc, CC059/TauUnc, CC060/Unc, CC061/GeniUnc, CC065/Unc, CC068/TauUnc. More information regarding the CC strains can be found at http://csbio.unc.edu/CCstatus/index.py?run=AvailableLines.information.Male and female Collaborative Cross parental strains  and single gene immunological knockout mice were purchased from The Jackson Laboratory (2 mice were generated in the FPMV lab at UNC by crossing CC030 and CC029 mice (purchased from the SGCF in 2016) to generate F1s (both CC030 dam by CC029 sires as well as CC029 dam by CC030 sires). The resulting F1s were subsequently intercrossed to generate 251 F2 mice with all possible grandparental combinations. Male and female F2 mice were shipped to UMASS for Mtb infections.CC030 x CC029 FMtb between 8 and 12 weeks of age. Male mice were used for initial large CC screen; male and female mice were used for F2 validation cohort.All mice were housed in a specific pathogen-free facility under standard conditions . Mice were infected with M. tuberculosis strains (H37Rv background) were grown in Middlebrook 7H9 medium containing oleic acid-albumin-dextrose-catalase (OADC), 0.2% glycerol, and 0.05% Tween 80 to log-phase with shaking (200 rpm) at 37 °C. Hygromycin (50 µg/ml) or kanamycin (20 µg/ml) was added when necessary. The TnSeq library consisting of Himar1 transposon mutants was described previously  containing 0.05% Tween 80, and sonicated before diluting to desired concentration (see below).All eviously . The ∆biescribed . For pooescribed , which i6 CFU of a saturated library of Himar1 transposon mutants . Mice were infected over three infection batches, as denoted in 6 CFU per mouse was plated on 7H10 agar with 20 µg/mL kanamycin. After three weeks of growth, colonies were harvested by scraping and genomic DNA was extracted. The relative abundance of each transposon mutant was estimated as described . CFU was determined by dilution plating on 7H10 agar with 20 ug/mL kanamycin or 50 µg/mL hygromycin as required.Chromosomal equivalent (CEQ) was enumerated according to previously published protocol . Cytokin6 CFU/mouse resulting in an infectious dose (D1 CFU) of 1 × 105 in the spleen). At indicated time points, approximately 10,000 CFU from the spleen homogenate of each mouse was plated on 7H10 agar. Genomic DNA was extracted for sequencing as described previously  were infected with a pool of deletion mutants at equal ratios via the intravenous route  were infected via IV route with an infectious dose of 105 CFU of TnSeq library (as described above), to replicate the original CC infection experimental conditions. Mice were sacrificed at 1 month post-infection and bacterial burden was quantified by plating for CFU (as described above).For CC030 x CC029 F2 transformed and quantile normalized. The resulting phenotype values were used for both WGCNA and QTL mapping.TnSeq libraries were prepared and counts of each transposon mutant were estimated as described . NCBI Re2 fold change (LFC) in counts between all possible pairs of mouse genotypes were performed by resampling .Weighted gene correlation network analysis (WGCNA) was applied to categorize the 679 significantly varying genes into 20 internally-correlated modules . Moduleshclust in R (with the ‘Ward.D2’ distance metric). Then, for each module of genes, the LFCs in each cluster of mice were pooled and compared to all the other mice using a t-test, identifying modules with a mean LFC in a specific mouse cluster that is significantly higher or lower than the average across all the other mice. The resulting p-values over all combinations of gene modules and mouse clusters were adjusted using Benjamini-Hochberg for an overall FDR < 0.05.In order to perform association analysis between modules of genes and clusters of mice , the michclust in R package heatmaply; traits scaled as per default function) and dendrogram nodes colored by 3 k-means. Correlation between disease-related TB traits for both IV and aerosol validation experiments was determined by Pearson’s correlation and visualized using corrplot (ordered by hclust) (2) of the immunological and TB disease-related traits was calculated by estimating the percent of variation attributed to between strain differences relative to within strain noise as previously described (Appendix1)   and 2. Hpendix1) . This is1s and F2 mice from the CC030xCC029 cross were genotyped on the MiniMUGA array  and formatted for R/qtl2 (version 0.20)  . QTL mapFor QTL mapping in the CC panel, the Most Recent Common Ancestor  36-stateFor CC QTL analysis, genotype and phenotype data were imported into R (version 3.6.1) and reformatted for R/qtl2 (version 0.20) . IndividTip5, Hip42) or were validated by intercross (Tip8). For each QTL interval , we identified mouse genes that were in differentially expressed modules between infected lungs of resistant and susceptible mouse strains , Smith et al. have used collaborative cross mice to better understand host susceptibility to TB. Those papers used aerosol infection.In this work, the authors use intravenous infections so as to be able to test a library of bacterial mutants that the lab has previously constructed with the view of identifying the comprehensive set of virulence genes required for infection. the goal of this work would be to understand better the differences in individuals who are more or less resistant to TB and how the pathogen's wider gene set allows it to infect a greater proportion of people successfully.In two previous papers , Smith et al. have used collaborative cross mice to better understand host susceptibility to TB. Those papers used aerosol infection.In this work, the authors use intravenous infections so as to be able to test a library of bacterial mutants that the lab has previously constructed. The ability to use large numbers of bacteria delivered intravenously should theoretically avoid the bottlenecks resulting from aerosol infection. However, possibly because of the small numbers of mice used from each genotype , the data are noisy. This is obvious even in Figure 1A where bacterial burdens in only 15/59 groups are statistically different from the B6 mouse used, using relatively nonstringent statistical analyses. This most likely represents experimental variability, which disadvantages the rest of the work and its conclusions.The problem becomes obvious early in the paper when they look at the correlation between lung and spleen bacterial burdens. An R2 of 0.43 is not a moderate correlation but a poor correlation. Either there is an interesting reason for this or more likely it simply reflects small numbers. The correlations between bacterial burdens and cytokines/chemokines are also week to nonexistent. Here too, the lung – spleen disparity is a problem. For instance, IL-1b and TNFa are weakly correlated for lung burdens and not at all for spleen. For IFNγ, which the authors make a point of detailing, the data do not support their statements. There is no correlation even if the two or three high (#) strains are excluded. This makes the TipQTL analysis of genetic variants that control TB immunophenotypes also suspect and the statistics are weak. The argument that Tip8 is a strong predictor of lower bacterial burden is also not convincing based on Figure 3B. First, there is strong overlap in the bacterial CFU between samples 2 and 3, one of which has the Tip8 CAST and the other which does not. Second, the last group which also contains the CAST at Tip8 looks not different from the third one. What would be the reason for that? Overall, these data are not statistically robust. This looks like noise.It is also not clear these new findings relate to their previous ones in Smith et al. 2019 mBio. In the current paper, the authors follow up by infecting a B6/CAST cross by the aerosol route to conclude that the bacterial control of CAST mice is independent of IFNγ-secreting cells. In the previous paper, they perform a different cross, CC001/CC042 to also dissociate bacterial control from IFNγ production. How do the current findings relate to the previous ones? The previous ones are far from understood and it feels as if the additional data are more confusing than clarifying.On this weak framework, the authors try to determine if the CC mice can reveal the extended requirement for pathogen genes that are not reflected in single mutant strains. This work begins in Figure 4.  Beyond this, I have several questions:1) The authors state that while the pathogen gene sets were different from mouse strain to mouse strain, the number of genes required in each strain was roughly the same. What is their explanation/model for this constant number, given a 3-log difference in bacterial burdens between the strains reflecting enormous differences in susceptibility? How could this model be tested? This is an important point.The authors report a common set of ~ 130 genes. Are there other common subgroups defined by common pathogen gene sets? These types of analyses are essential to both validating the system and in so doing may give biological information. What about the additional genes in the CC subset over the immunodeficient KO set? What can be learned from them?And beyond this, I do not follow their further analyses and am just not convinced that they are meaningful. There seems to be a lot of statistical massaging with seemingly erudite analyses but with the small number of mice used per genotype, the large error and the glossing over of problems, it is hard to see how this paper represents a significant advance.I hope that the paper is being co-reviewed by individuals expert in mouse genetics and in statistics or in both. If not, this will be essential.Reviewer #2:M. tuberculosis (Mtb) infection. The hope is to better recapitulate the diversity of outcomes of Mtb infection seen in humans, a current major limitation of the dominant B6 mouse model. While some important human Mtb infection outcomes  are apparently not captured by the CC mice, there is nevertheless an impressive variation in infection outcomes in the CC panel . There is also large variation in host responses. For example, CAST mice control infection but are found to lack an IFNγ response thought previously to be critical for Mtb control based on studies in B6 (but known to be similarly absent from some human controllers). The authors also infect the CC mice with pools of transposon mutants, allowing identification of specific host genotypes that confer fitness effects on specific bacterial mutants. The resulting analyses identify loci that affect quantitative immunological phenotypes (TipQTL) or fitness of bacterial mutants (HipQTL). No causative genetic variants underlying these QTL are validated by independently generated mutation, so in some sense, the "discoveries" here are limited. However, the impact of this work is likely to be great as a resource that can be mined by investigators in the field to identify specific mouse strains in which, for example, a specific bacterial mutant has a phenotype, or a particular host response is observed, greatly facilitating followup work.This work takes advantage of the genetic diversity of a panel of mice  mice) to identify mice that produce heterogeneous outcomes after Given the lack of a major new biological insight or mechanism, perhaps this should be considered as a "Tools and Resources" paper (https://reviewer.elifesciences.org/author-guide/types) rather than a regular research paper.The use of a high-dose IV model of infection, while necessary for TnSeq experiments, may limit the relevance of the observations to pulmonary TB. This concern is somewhat ameliorated by the fact that the susceptibility of the parent strains largely recapitulates the expected phenotype based on previous aerosol infections. However, for a few key CC mice with interesting phenotypes  it would be nice to validate in a replication experiment and that the phenotype is consistently seen even with aerosol infections. The experiment in Figure 3 could potentially serve as such validation, but it is not clear whether this experiment was using IV or aerosol infection (please specify this clearly in the legend and text regardless).Line 143: please specify in the text the dose (CFU) of bacteria that were delivered IV. This is important information and should be in the main text.Line 159: "Thus, the CC panel encompasses a much greater quantitative range of susceptibility than standard inbred lines." – this is not obviously true. The range of CFU budens seen from NOD to WSB appears to capture most of the range of the CC panel. Perhaps change the wording here?Line 208: is it really "none", as in not a single IFNγ+ T cell? The bar graph suggests that there is some, so perhaps revise this sentence.Line 251: "mice that contained CAST at Tip8, Tip10 or both loci had reduced CFU burden". It appears from the data in Figure 3 that in fact the only statistically significant difference is for the Tip8 locus, and that the presence of the CAST Tip10 allele abolishes this difference. If that is correct, the manuscript needs to be clear that in fact the Tip10 locus did not validate .Line 274: "Consistent with our previous work, we identified 234 Mtb genes that are required for growth or survival in Mtb in B6 mice". In fact, how consistent are the data? Of the 234 genes identified, how many were previously identified? How many of the previously identified genes were in the set of 234? Please specify so that the readers can get a sense of the experiment-to-experiment variability of the approach.Why would more Mtb genes be essential in Rag1-/- mice as compared to WT mice? This seems unexpected to me and perhaps deserves some comment? What are these genes – could this be discussed at least a little? Line 340 mentions that ESX genes are among those preferentially required in Rag1-/- . Could the authors elaborate a bit more on what they think is going on here?Line 281: "As more CC strains, and presumably more distinct immune states, were included in the analysis, the cumulative number of genes necessary for growth in these animals also increased". It is possible that repeating the experiment with more and more B6 animals would also increase the cumulative number of genes necessary? In other words, might the sensitivity of the TnSeq method be what is limiting here as opposed to host genetic diversity? I presume the authors have a good argument against this concern; it would be helpful for readers to hear it.Line 295: "the resulting mini-pool was subjected to in vivo selection in the same manner as the TnSeq study" – does this mean that the minipool was used to infect ALL the same mouse strains  as in Figure 4A? This isn't clear. A related concern regarding Figure 4C: why are selected mouse strains shown in this figure? Are they the strains that showed the biggest signal in the TnSeq experiment? The worry is they were cherry picked. This could be explained better.Line 298: "relative abundance predicted by TnSeq was reproduced with deletion mutants" – this is difficult to evaluate because the fitness of each of the mutants in each of the strains in Figure 4A is not stated. I realize the full dataset of all the TnSeq data is available as a spreadsheet but it would be nice if the reader didn't have to dig through that. Ideally it would be great to create a searchable website in which investigators could enter their gene of interest and the strains in which the mutant shows a phenotype (and the magnitude of the phenotype) could be displayed.Line 312: again, it is not clear what the TnSeq predicted. Please state that result here too.Line 435: which HipQTL are associated with both ahpC and eccD1? Please specify. I don't see this information in the cited figure either . Please make sure this is correct.Reviewer #3:M. tuberculosis genetic requirements with host genetics and immunity. The authors report several new findings. For example, they report that several Mtb virulence pathways become unmasked only under specific host microenvironments. They also found that a number of genotypes were able to control bacterial replication but had very low levels of IFNγ. Also, dependent on the genetic composition of the host, related disease traits such as lung bacterial burden and weight loss could be dissociated. Another significant outcome was identifying candidate genes underlying the QTLs and showing that the QTLs were generally distinct from CC loci that control immunity to other pathogens.In this elegant and expansive study, the authors have studied bacterial adaptive response in the context of variable immune pressure exerted by diverse hosts. They used the Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb transposon mutants to uncover Overall, I find this to be a complete and thorough study.Reviewer #4:I reviewed this study only to evaluate the robustness of the results and the statistical evaluations used in the study. No attention was paid to novelty or scientific interest. Some of the statistical approaches used by the authors were beyond my expertise and require further analysis by a professional statistician to which I lay no claim to being. This study used multiple simultaneous comparisons of both animal strains and bacterial strains, with what appears to be generally quite small sample size, non-replicated experiments. Data files that are presented either omit entirely some experiments or provide only summary data of mean values without indicating numbers of subjects, measures of variability such as standard deviation; no information on the results of replicate studies, if any were performed, are in these data files. Therefore I find it impossible to determine if many of the "significant" associations are robust. I give some examples below:1) Data for Figure 1. The data set for figure 1 only summary data. There is no indication of the number of animals studied in each group and no indication of standard deviation. Also, there is no indication in the data set or in the text if any of the experimental results for 1A to 1D were based on replicate studies. The underlying data for figures E to H are not available in the figure 1 data file; the text gives the range of subject sizes and states that two independent experiments were performed, but it is unclear if the data presented in the figures represents both experiments, just one experiment, or if the data presented includes both experiments whether the results were simply combined or if there was some normalization performed. The statistical analysis method does not say whether the data were log-transformed before analysis. All of the author's conclusions may be absolutely correct but it is difficult to know this without more detailed information.2) Data for Figure 4. The data set for this figure only includes the underlying data for 4A. No underlying data are given for 4C, D and E. No indication is given of the number of repeated experiments, and if these were performed what the results were. No method is given for the statistical analysis used in 4D. The authors conclude that there are significant differences between mouse strains for several Mtb strains, but give no information on the numbers of mice studied, variation within groups, repeat experiments, etc; also no statistical analysis is performed. Again, the author's conclusions may be absolutely correct but it is difficult to know this without seeing the underlying data.While the authors may believe that their answers to the transparent reporting form were complete and accurate, based on what I have found I'm not certain that this is correct.Reviewer #5:Sample size and reproducibility. The groups were small , which could result in a high variance per group. It would be important to evaluate how reproducible are the phenotypic measurements per strain. The authors mention reproducibility per genotype (Page 8 line 177), but they do not clearly show it. Perhaps a dimension reduction approach (PCA or UMAP) could be used to evaluate the distance between replicates. As specific examples: How many animals were used per group in outlier strains ? What was the variance for the y-axis measurements? Could variance explain the outlier nature of the strain?QTL analysis. The CC QTL analysis faces similar challenges to genetic studies in humans. Overlooking complex population structure can lead to inflated false positives but can also prevent harvesting the full potential of the samples. Limitations of the QTL analysis are briefly introduced (Page 11 line 241) but the explanation in methods is convoluted and difficult to follow. The study could benefit from using their SNPs data for genome-wide association testing using all strains correcting by population structure. This strategy was shown to be robust when performed to a study of similar framework using different inbred strains and their crosses (PMID 32365090). This method could address sample size and QTL as the animals would be treated as unique samples (not per group) correcting by population structure (similarities between samples in the dataset). If correct, this would be a more straightforward genome-wide analysis.Detailed comments:In methods, it is stated that TipQTL significance was assessed using permutations. However, in the Results section and in table 1 it is not clearly stated if the p values are raw or corrected via the permutation.Homo sapiens is now widely accepted, perhaps 2000-6000 years. Has co-evolution been demonstrated or is this hypothesized? Same point with the role of genetic variation presented as established.Line 63: Co-existence of Mtb and When describing correlations, suggest a standard terminology: Strong, Moderate, Weak. Highly correlated is of uncertain meaning. On line 182 the R2 was 0.57 but the text said correlated. Some use the following: 0-0.19 = very weak, 0.2-0.39 = weak, 0.40-0.59 = moderate, 0.6-0.79 = strong, 0.8-1 = very strong correlationLine 151. What does it mean when the susceptibility is largely consistent with other studies. Can the authors spell out the inconsistencies somehow?Line 220. What is reasonable statistical confidence?-/-, IFN-/-, Nos2-/- and CYbb-/- mice produced x essential genes, and that the CC raised it from x to y. The paragraph tells us the 234 genes were identified in Bl6 and that the plateau was ~750. How far from 234 to 750 was reached with the 4 specified knockout mice?Line 272. At some point, it would be useful to read in the text that the combined studies of WT Bl6, RAGLine 321. I did not understand what was complex about the kinetics. Perhaps this can be clarified?eLife. Your article has been reviewed by 5 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Bavesh Kana as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Host-pathogen genetic interactions underlie tuberculosis susceptibility" for consideration by Reviewers and I concur that your manuscript provides value to the field as a "Tools and Resource" paper for generating new hypotheses. The genotypic diversity explored in the study, together with an assessment of disease pathology/outcomes, provides a useful framework to pose important questions that are currently topical for tuberculosis. That said, it would be important to frame the study as a resource rather than trying to probe specific questions with the current dataset, which can be limited by the depth of data available currently in the work. I encourage you to consider carefully how best to present this to the field in a manner that acknowledges the limitations and but also creates the space for innovative and explorative future work.To facilitate this, please address the following concerns:1. For a paper reporting a potentially useful resource, it is imperative that all data be publicly available for further interrogation and exploration. Reviewers expressed concern that this is not the case with your submission. For the GEO link, data appear available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164156. However, for the GitHub link, the Smith repository was not located. There was only one repository (Bellerose): https://github.com/sassettilab?tab=repositories. Please address this.2. In lines 145-147, it is stated: Groups of 3-6 male mice per genotype were infected and TB disease-related traits were quantified at one-month post-infection. Data from all surviving animals are provided in Figure 1-sourcedata1. However, the source data file indicates that the N is 1-3 for most of the data, except for two strains, Bl6 and CC010, which are reported to have 6 mice. There are asterisks for significance in only in a minority of the bars (~15) in Figure 1A. What about the remainder? What n's did they have? Too few to determine significance? This should be clarified.3. Explain why an R value of 0.43 would be considered a moderate correlation, when elsewhere you suggest that there could be biological differences that may make this correlation weak. Similarly, it is often the case that in inflammatory processes, TNF and IFNγ track together, even if made by different cells, which is not even strictly the case. These are two examples of continued disagreement. Please reconsider carefully how you present these data.4. Line 184-188. The "significant" associations described in this section are for non-replicate studies with N=2 . Perhaps reconsider the use of "significant" in this context. In Line 253, this experiment used a large N and hence likely produced robust results.5. Figure 1 A. Giving results of an ANOVA with post-hoc analysis comparison of means to a single mouse strain (C57/BL6) is difficult to interpret because of the potential bias from post-hoc decisions about which strain serves as a comparator. For example, using AJ as a comparator shows no significant differences with other strains. For this reason, excluding this analysis is warranted.6. Line 199: "A number of genotypes were able to control bacterial replication yet had very low levels of IFNΓ " -- for the benefit of the reader, please specify which genotypes fall into this category. Ideally this would be specifically mentioned in the text and in addition the genotypes of interest would be labelled in the figure. Similarly for Figure 1C, it would be helpful if the significant "outlier" genotypes were labelled in the figure so the reader can know which these are.7. Line 487: "Two TipQTL overlapped with HipQTL …". Please specify in the text which overlapping QTL are being referred to here. It seems evident from the figure but specifying would assist the reader here.8. Some of the type on the figures is really tiny, e.g., 1A, 1B, most of figure 4, parts of figure 6, etc. Enlarge the panels with the tiniest type so that they are more readable.Reviewer #1:The main criticism of the initial submission was the small sample size per CC genotype. Admittedly, and as expected, a number of animals within the susceptible CC genotype died during the study. For this reason, 23 out of the 60 CC genotypes had less than three mice per group, including 3 out of the 4 top CC strains . In this revised version of the manuscript, the authors addressed the criticism by replicating the difference in lung CFU, weight, and selected cytokines between B6 and the top four CC strains. Moreover, based on the response to reviewer 1 they have indicated robust literature supporting that sample size per group did not impact the statistical power of their analyses. As far as I am concerned, they have addressed most of the reviewer's comments properly. Some of the analyses suggested in the first round of review were not formally evaluated. However, given that the paper was now submitted as a "Tools and Resources" it should be possible for independent groups to explore the data produced by Smith, C. et al. in details or using different analytical approaches. I have not further commentsReviewer #2:Overall I remain enthusiastic about this manuscript. It has been a longstanding question whether the diversity of immune responses seen in humans could be modeled in mice and this manuscript represents the most substantial (by far) attempt to address this question. Although some key human phenotypes  do not appear to be recapitulated in the CC mouse panel, there are definitely several interesting phenotypes reported that are not seen in the typically used B6 strain. For example, the identification of strains of mice that control TB despite low levels of IFNγ production is an interesting phenotype that may phenocopy the "resister" phenotype observed in some humans.I think the quality of the work is overall high and that, given experimental constraints, sufficient effort was expended to assure the rigor and reproducibility of the datasets.Originally my main concern was that the paper reports little new in the way of biological insight -- for example, the host genes underlying the TipQTL or HipQTL were not identified, and the underlying mechanisms responsible for the observed immune phenotypes were not dissected. Despite this, I think the manuscript is an important contribution if published as a "Tools and Resource" paper, which is an idea the authors appear to have embraced in their response to reviews. In particular, identification of CC strains which give phenotypes for hundreds of specific bacterial mutants (that give no phenotype in the standard B6 strain) is likely to be extremely helpful to investigators in the field.1) Please confirm that this manuscript is now submitted as a "Tools and Resource" paper.2) Line 199: "A number of genotypes were able to control bacterial replication yet had very low levels of [IFNγ] " -- for the benefit of the reader, please specify which genotypes fall into this category. Ideally this would be specifically mentioned in the text and in addition the genotypes of interest would be labelled in the figure. Similarly for Figure 1C, it would be helpful if the significant "outlier" genotypes were labelled in the figure so the reader can know which these are.3) Line 487: "Two TipQTL overlapped with HipQTL …". Please specify in the text which overlapping QTL are being referred to here. I think it is evident from the figure but I would be reassured I am interpreting the figure correctly if you could be specific here.4) Some of the type on the figures is absurdly tiny, e.g., 1A, 1B, most of figure 4, parts of figure 6, etc. I would suggest enlarging the panels with the tiniest type so that they are more readable.Reviewer #4:The sole purpose of my review was to determine if the described experiments were robust, in terms of sample sizes and replication.The data provided in the manuscript are not robust, with a few exceptions. The experiment studying 60 different mouse strains analyzed for 21 different outcomes  utilized a single experiment with mouse strain numbers ranging from 1 to 6 (not 3 to 6 as specified in the manuscript), where the N was 1 for one mouse strain, 2 for 22 strains, 3 for 35 strains and 6 for 2 strains. No replicate studies were performed for the entire group. The chances of analytic variability and assignment of significance to interactions between mouse strain and measured outcome are far too great to regard these data as anything other than hypothesis generating data requiring confirmation. The authors say that the experiment represented in figures 1E-H validate their global conclusions for this data set, whereas they really only validate results for five different mouse strains studied for four different outcomes, two of which are highly correlated (lung cfu and weight loss). This validation study was not repeated, to demonstrate reproducibility, which would bolster those results.There continues to be a lack of raw data availability supporting some of the various experiments presented in the manuscript. The Github file specified in the manuscript is either not yet posted or not available publicly (and therefore not to me). The data underlying the experiments presented in figure 1 is available only in summary form  but not for each mouse studied. Neither the raw nor summary data for figure 4D are not found in any of the data files, and specifically not in the Figure 4 supplemental data 2 file.Specific comments1) 184-188. The "significant" associations described in this section are for non-replicate studies with N=2 .2) 253. This experiment used a large N and hence likely produced robust results.3) Figure 1 A. Giving results of ANOVA with post-hoc analysis comparison of means to results to a single mouse strain (C57/BL6) is difficult to interpret because of the potential bias from post-hoc decisions about which strain serves as a comparator. For example, using AJ as a comparator shows no significant differences with other strains. For this reason, leaving out that analysis is warranted.4) Figure 4 C data. Multiple comparisons are performed without apparent adjustment for such, for small N (3 to 5) non-replicate studies. Were these comparisons preplanned?5) Figure 4D data. No raw data are available.Reviewer #5:1. In lines 145-147, the authors state: Groups of 3-6 male mice per genotype were infected and TB disease-related traits were quantified at one-month post-infection. Data from all surviving animals are provided in Figure1-sourcedata1.I am puzzled by the 3-6 mice reported because my reading of the source data file is that the N is 1-3 except for two strains at most, Bl6 and CC010 which are reported to have 6 mice.There are asterisks for significance in only in a minority of the bars (~15) in Figure 1A. What about the remainder? What n's did they have? Too few to determine significance?2. It is puzzling why the authors would continue to defend their characterization of an R value of 0.43 as a moderate correlation, when they themselves point out that there could be biological differences that may make this correlation weak. Similarly, it is often the case that in inflammatory processes, TNF and IFNγ track together, even if made by different cells, which is not even strictly the case. These are two examples of continued disagreement.3. The authors seem to be interested in converting this paper to a Tool/Resource. However, the narrative remains the same as when they wrote it as a Research Paper. For the paper to be considered as a Tool/Resource, the paper needs to be written as such, backing off from strong claims and rewritten as a hypothesis generating screen. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1:In two previous papers , Smith et al. have used collaborative cross mice to better understand host susceptibility to TB. Those papers used aerosol infection.In this work, the authors use intravenous infections so as to be able to test a library of bacterial mutants that the lab has previously constructed with the view of identifying the comprehensive set of virulence genes required for infection. The goal of this work would be to understand better the differences in individuals who are more or less resistant to TB and how the pathogen's wider gene set allows it to infect a greater proportion of people successfully.In this work, the authors use intravenous infections so as to be able to test a library of bacterial mutants that the lab has previously constructed. The ability to use large numbers of bacteria delivered intravenously should theoretically avoid the bottlenecks resulting from aerosol infection. However, possibly because of the small numbers of mice used from each genotype , the data are noisy.While this characterization of our group size is correct, we edited the text to make it clear that groups of 3-5 mice per genotype were infected (Line 145). Of the 60 strains, a small number of animals within susceptible CC genotype groups died during the study and were not phenotyped. Data from all surviving animals is provided in Figure1-SourceData1.This is obvious even in Figure 1A where bacterial burdens in only 15/59 groups are statistically different from the B6 mouse used, using relatively nonstringent statistical analyses. This most likely represents experimental variability, which disadvantages the rest of the work and its conclusions.We disagree that our data are “noisy”. In general, our ability to detect statistically significant differences in lung CFU between genotypes that vary by ~1 log is comparable to many other studies. Regardless, this is not a point to belabor, as this critique seems to reflect expectations that our study was never designed to fulfill. A classical “mouse study” is powered to detect even small differences in pairwise comparisons between groups. Our study was not designed for the pairwise comparison of genotypes. Instead, this is primarily a genetic mapping study, where the statistical power is derived from the inclusion of multiple genotypes that share a trait-associated haplotype (seven on average in our population). The proper estimate of “noise” in this type of study is based on the heritability of the variation (i.e. the proportion of the trait variation that can be attributed to genetics). Heritability estimates were provided in Figure1-FigureSupp3 of the original submission and show that greater than 80% of the variation in lung CFU and CEQ are due to genetic variation – not “noise”. We also note that the majority of similar genetic mapping studies include only a single animal per genotype. Our strategy to include multiple animals per genotype improves trait value estimation and the overall rigor and reproducibility of our work, relative to almost any other comparable published genetic study. This design is based on systematic studies by members of our group showing that the inclusion of additional CC strains improved mapping power more than additional replicates  and that individual outlier CC strains were identifiable and statistically significant using as few as 2 mice per genotype. Using only 2 mice per strain, CC studies are powered equivalently to a ~400 outbred mouse study per trait . Thus, far from being a limitation of our work, the inclusion of 3-5 animals per genotype makes our study among the most rigorous to date.The problem becomes obvious early in the paper when they look at the correlation between lung and spleen bacterial burdens. An R2 of 0.43 is not a moderate correlation but a poor correlation. Either there is an interesting reason for this or more likely it simply reflects small numbers. The correlations between bacterial burdens and cytokines/chemokines are also week to nonexistent. Here too, the lung – spleen disparity is a problem. For instance, IL-1b and TNFa are weakly correlated for lung burdens and not at all for spleen.This comment appears to be based on the reviewer’s own expectations for correlated traits. We both fundamentally disagree with these expectations, and suggest that the reviewer did not consider correlated traits for which there is a much more solid rationale. For example, the reviewer ignores the strong correlation between CFU and CEQ in both lung and spleen . Based on the criteria suggested by Reviewer 5, these represent “very strong” correlations that support the accuracy of our bacterial burden estimates and our heritability measures. Thus, the more modest correlation between lung and spleen burdens that the reviewer criticizes can be attributed to an important biological difference that is completely consistent with our current understanding of the differential importance of specific immune mechanisms in lung and spleen ). Similarly, it is unclear why the reviewer expects IL1-b and TNFa to correlate, as they are produced by different cells as a result of different signaling pathways. Much more reasonable expectations would be a correlation between IL-1a and IL-1b (r=0.84), or between the monocyte/granulocyte chemoattractants, MIP-1b, MIP-1a, MCP1, G-CSF, MIP-2, CXCL1 (r=0.89-0.95). These very strong correlations support the accuracy of our cytokine measurements. We ask the reviewer to reconsider these data with an open mind. If they still concerned, we also provide additional aerosol validation data in the new Figure 1 E-H.In sum, we strongly disagree with this critique of our experimental design, group sizes, and data quality. However, we concede that our study was not designed for robust pairwise comparisons between strains and agree that additional experimental validation/replication strengthens the work. As a result, we have extensively revised Figure 1 to remove conclusions based on pairwise comparisons. These data have been replaced with a simple replication study, which demonstrates the reproducibility of our data in an aerosol infection model. We hope the reviewer agrees that this improves the rigor of the work, and supports subsequent analyses of the dataset.For IFNγ, which the authors make a point of detailing, the data do not support their statements. There is no correlation even if the two or three high (#) strains are excluded.This represents a misunderstanding, as the text clearly stated that IFNγ and CFU were not correlated. Regardless, these data have been removed to simplify the conclusions of Figure 1.This makes the TipQTL analysis of genetic variants that control TB immunophenotypes also suspect and the statistics are weak.We again note that QTL mapping is not based on simple pairwise comparisons but draws its statistical power from the presence of multiple strains that share a haplotype at a trait associated locus . The majority of previous QTL mapping studies use single animals per genotype, and our inclusion of multiple animals per genotype significantly increases the accuracy of our trait value estimations. Finally, this version of the manuscript, like the first, includes a heritability estimate for every trait. The heritability of most traits is >75%, and we are completely transparent in reporting the traits that are more prone to technical variation, which represent cytokines that are produced at relatively low levels. The text referring to this Figure1-FigureSupp3 table has been edited to accentuate its importance.The argument that Tip8 is a strong predictor of lower bacterial burden is also not convincing based on Figure 3B. First, there is strong overlap in the bacterial CFU between samples 2 and 3, one of which has the Tip8 CAST and the other which does not. Second, the last group which also contains the CAST at Tip8 looks not different from the third one. What would be the reason for that? Overall, these data are not statistically robust. This looks like noise.2 offspring of genetically diverse CC strains. The observed variation in susceptibility between individuals in this population is not “noise”, but the result of genetic variation. This is both expected and necessary. In no situation would it be reasonable to expect that the distributions of CFU values between these populations would not “overlap”. Instead, we asked if the effect of the Tip8 locus could be discerned, even in the presence of other genetic variants segregating in the CC. The statistically significant difference identified between these populations represents rigorous support for the Tip8 QTL, akin to a replication cohort in a human GWAS study. To take this one step further, we have since genotyped a total of 261 F2 intercross mice that we infected with Mtb and quantified lung burden. We conducted QTL mapping and validated the chromosome 7 locus as a genome-wide significant QTL underlying lung CFU. We present this further validation in the new Figure 3C and text. We note that this kind of rigorous independent validation of a QTL is almost never performed before publication.The previous Figure 3B compares the FIt is also not clear these new findings relate to their previous ones in Smith et al. 2019 mBio. In the current paper, the authors follow up by infecting a B6/CAST cross by the aerosol route to conclude that the bacterial control of CAST mice is independent of IFNγ-secreting cells. In the previous paper, they perform a different cross, CC001/CC042 to also dissociate bacterial control from IFNγ production. How do the current findings relate to the previous ones? The previous ones are far from understood and it feels as if the additional data are more confusing than clarifying.2 offspring of genetically diverse CC strains. The observed variation in susceptibility between individuals in this population is not “noise”, but the result of genetic variation. This is both expected and necessary. In no situation would it be reasonable to expect that the distributions of CFU values between these populations would not “overlap”. Instead, we asked if the effect of the Tip8 locus could be discerned, even in the presence of other genetic variants segregating in the CC. The statistically significant difference identified between these populations represents rigorous support for the Tip8 QTL, akin to a replication cohort in a human GWAS study. To take this one step further, we have since genotyped a total of 261 F2 intercross mice that we infected with Mtb and quantified lung burden. We conducted QTL mapping and validated the chromosome 7 locus as a genome-wide significant QTL underlying lung CFU. We present this further validation in the new Figure 3C and text. We note that this kind of rigorous independent validation of a QTL is almost never performed before publication.The previous Figure 3B compares the FOn this weak framework, the authors try to determine if the CC mice can reveal the extended requirement for pathogen genes that are not reflected in single mutant strains. This work begins in Figure 4. We thank the reviewer for noting this mistake. It has been fixed .Beyond this, I have several questions:1) The authors state that while the pathogen gene sets were different from mouse strain to mouse strain, the number of genes required in each strain was roughly the same. What is their explanation/model for this constant number, given a 3-log difference in bacterial burdens between the strains reflecting enormous differences in susceptibility? How could this model be tested? This is an important point.This is a very interesting effect that has been noted in previous publications. While it is not unreasonable to expect more genes to be necessary for fitness in stressful situations, this expectation is not supported by experimental data. Previous TnSeq studies find that while different gene sets are necessary for optimal growth/survival in resistant and susceptible animals, the size of these gene sets are roughly similar . This observation is consistent with in vitro studies, where similarly sized gene sets are necessary for growth in different media conditions . One must conclude that while some genes become more important in resistant animals, others become less so. While it is difficult to speculate further, we have added this concept to the discussion of the revised manuscript (lines 592-599).The authors report a common set of ~ 130 genes. Are there other common subgroups defined by common pathogen gene sets? These types of analyses are essential to both validating the system and in so doing may give biological information. What about the additional genes in the CC subset over the immunodeficient KO set? What can be learned from them?Identifying “common subgroups defined by pathogen gene sets” is the sole purpose of Figure 5 and the corresponding 600 words of text (lines 422-481). Validation of the system is confirmed by the identification of known functional pathways in TnSeq-derived gene modules , and the association of these modules with mouse subgroups . “Additional genes in the CC subset over the immunodeficient KO set” are presented in Figure 5B, along with functional annotation. While we appreciate that this comment was meant as a constructive suggestion, we do not know how to respond, except to note that this analysis was performed and included.And beyond this, I do not follow their further analyses and am just not convinced that they are meaningful. There seems to be a lot of statistical massaging with seemingly erudite analyses but with the small number of mice used per genotype, the large error and the glossing over of problems, it is hard to see how this paper represents a significant advance.I hope that the paper is being co-reviewed by individuals expert in mouse genetics and in statistics or in both. If not, this will be essential.We can only hope the reviewer will reconsider their expectations. Our study was never designed to rely on single pairwise comparisons between genotypes, as a typical “mouse study” would. Instead, our study exceeds the rigor of many comparable genetic mapping studies by including multiple animals per genotype and including independent validation of an important QTL . Our “statistical messaging and seemingly erudite analyses” represent rigorous analytical strategies that are appropriate for our study design, and as cited, represent standards in the field . Our mouse group size was entirely appropriate for our goals. We demonstrate that our data are not prone to “large error”. The implication that we are “glossing over problems” is unwarranted and vague. We trust that the reviewer will defer to other referees with expertise in mouse genetics and statistics to critique these aspects of the work.Reviewer #2:This work takes advantage of the genetic diversity of a panel of mice  mice) to identify mice that produce heterogeneous outcomes after M. tuberculosis (Mtb) infection. The hope is to better recapitulate the diversity of outcomes of Mtb infection seen in humans, a current major limitation of the dominant B6 mouse model. While some important human Mtb infection outcomes  are apparently not captured by the CC mice, there is nevertheless an impressive variation in infection outcomes in the CC panel . There is also large variation in host responses. For example, CAST mice control infection but are found to lack an IFNγ response thought previously to be critical for Mtb control based on studies in B6 (but known to be similarly absent from some human controllers). The authors also infect the CC mice with pools of transposon mutants, allowing identification of specific host genotypes that confer fitness effects on specific bacterial mutants. The resulting analyses identify loci that affect quantitative immunological phenotypes (TipQTL) or fitness of bacterial mutants (HipQTL). No causative genetic variants underlying these QTL are validated by independently generated mutation, so in some sense, the "discoveries" here are limited. However, the impact of this work is likely to be great as a resource that can be mined by investigators in the field to identify specific mouse strains in which, for example, a specific bacterial mutant has a phenotype, or a particular host response is observed, greatly facilitating followup work.Given the lack of a major new biological insight or mechanism, perhaps this should be considered as a "Tools and Resources" paper (https://reviewer.elifesciences.org/author-guide/types) rather than a regular research paper.This is a great idea. We agree completely and have submitted the revised version as a “Tools and Resources” paper.The use of a high-dose IV model of infection, while necessary for TnSeq experiments, may limit the relevance of the observations to pulmonary TB. This concern is somewhat ameliorated by the fact that the susceptibility of the parent strains largely recapitulates the expected phenotype based on previous aerosol infections. However, for a few key CC mice with interesting phenotypes  it would be nice to validate in a replication experiment and that the phenotype is consistently seen even with aerosol infections. The experiment in Figure 3 could potentially serve as such validation, but it is not clear whether this experiment was using IV or aerosol infection (please specify this clearly in the legend and text regardless).In response to this comment, as well as those of Reviewer 1, we have extensively revised Figure 1 to focus on experimental replication in an aerosol model and increase the value of the larger dataset. The new validation data using an aerosol infection model are presented in Figure 1 E-H. The legend to Figure 3 was edited as requested.Line 143: please specify in the text the dose (CFU) of bacteria that were delivered IV. This is important information and should be in the main text.This has been added .Line 159: "Thus, the CC panel encompasses a much greater quantitative range of susceptibility than standard inbred lines." – this is not obviously true. The range of CFU budens seen from NOD to WSB appears to capture most of the range of the CC panel. Perhaps change the wording here?Absolutely correct. The text has been edited (lines 151-155).Line 208: is it really "none", as in not a single IFNγ+ T cell? The bar graph suggests that there is some, so perhaps revise this sentence.These data were removed in response to Reviewer 1.Line 251: "mice that contained CAST at Tip8, Tip10 or both loci had reduced CFU burden". It appears from the data in Figure 3 that in fact the only statistically significant difference is for the Tip8 locus, and that the presence of the CAST Tip10 allele abolishes this difference. If that is correct, the manuscript needs to be clear that in fact the Tip10 locus did not validate .This was a very helpful comment. To build on this validation study of lung CFU, we have now generated a total of 251 F2 intercross mice between informative CC strains, genotyped, infected with Mtb, quantified lung burden at one-month post infection, and performed genome-wide QTL mapping. In this rigorous validation study, identified Tip8 as significantly associated with lung CFU, thus validating the Chr7 locus as the main QTL. We expand discussion of Tip10 as suggested (lines 255-271).Line 274: "Consistent with our previous work, we identified 234 Mtb genes that are required for growth or survival in Mtb in B6 mice". In fact, how consistent are the data? Of the 234 genes identified, how many were previously identified? How many of the previously identified genes were in the set of 234? Please specify so that the readers can get a sense of the experiment-to-experiment variability of the approach.The most comparable previous dataset was generated in BALB/c mice. The text now reports that 87% of the genes we report in B6 were also found in BALB/c, and nearly 100% of these genes were consistently identified in the larger CC panel (lines 290-296). These observations support both the sensitivity and specificity of our TnSeq analysis.Why would more Mtb genes be essential in Rag1-/- mice as compared to WT mice? This seems unexpected to me and perhaps deserves some comment? What are these genes – could this be discussed at least a little? Line 340 mentions that ESX genes are among those preferentially required in Rag1-/- . Could the authors elaborate a bit more on what they think is going on here?Reviewer 1 also requested additional discussion of gene set sizes and functional insights. While we hesitate to speculate in too much detail, additional details are now included in the discussion (lines 592-599).Line 281: "As more CC strains, and presumably more distinct immune states, were included in the analysis, the cumulative number of genes necessary for growth in these animals also increased". It is possible that repeating the experiment with more and more B6 animals would also increase the cumulative number of genes necessary? In other words, might the sensitivity of the TnSeq method be what is limiting here as opposed to host genetic diversity? I presume the authors have a good argument against this concern; it would be helpful for readers to hear it.This was a very helpful suggestion. The revised manuscript includes a study verifying that the inclusion of additional B6 libraries does not substantially increase the estimate of genes necessary for growth. For this study, we randomly paired six replicate TnSeq libraries from 4-week post infection B6 mice to identify genes required for growth in each of the three runs vs cumulatively. The process was repeated 10 times with the results shown in Figure4-FigureSupplement 1 and show that a single “run”, which is equivalent to the data generated from each genotype in our larger study, was sufficient to identify the majority of genes, and adding additional datasets did not substantially increase the cumulative gene count.Line 295: "the resulting mini-pool was subjected to in vivo selection in the same manner as the TnSeq study" – does this mean that the minipool was used to infect ALL the same mouse strains  as in Figure 4A? This isn't clear. A related concern regarding Figure 4C: why are selected mouse strains shown in this figure? Are they the strains that showed the biggest signal in the TnSeq experiment? The worry is they were cherry picked. This could be explained better.A small focused pool of bacterial mutants was used to infect a subset of CC mice, which were chosen based on TnSeq data. Thus, this experiment was designed to test a number of individual observations from the TnSeq dataset, and these are shown in the figure. We have added additional explanation of our rationale (lines 309-319). Importantly, we note that, “data from all reliably-detected strains is shown in Figure 4C” to alleviate the reviewer’s concern that the presented data were “cherry picked”.Line 298: "relative abundance predicted by TnSeq was reproduced with deletion mutants" – this is difficult to evaluate because the fitness of each of the mutants in each of the strains in Figure 4A is not stated. I realize the full dataset of all the TnSeq data is available as a spreadsheet but it would be nice if the reader didn't have to dig through that. Ideally it would be great to create a searchable website in which investigators could enter their gene of interest and the strains in which the mutant shows a phenotype (and the magnitude of the phenotype) could be displayed.Both the TnSeq data and the corresponding data from the minipool infection is provided in Figure 4C, so it should not be necessary for the reader to refer to the supplementary tables. The suggested searchable database including these data and many other publicly available datasets has been constructed, and will be reported in a separate publication.Line 312: again, it is not clear what the TnSeq predicted. Please state that result here too.Done (line 342-344).Line 435: which HipQTL are associated with both ahpC and eccD1? Please specify. I don't see this information in the cited figure either . Please make sure this is correct.Done (line 457-458).Reviewer #4:I reviewed this study only to evaluate the robustness of the results and the statistical evaluations used in the study. No attention was paid to novelty or scientific interest. Some of the statistical approaches used by the authors were beyond my expertise and require further analysis by a professional statistician to which I lay no claim to being. This study used multiple simultaneous comparisons of both animal strains and bacterial strains, with what appears to be generally quite small sample size, non-replicated experiments.The revised manuscript contains independent validation of the susceptibility and cytokine production traits in Figure 1 , independent validation of an important QTL in Figure 3 , and independent validation of the TnSeq predictions in Figure 4 . In all cases, our larger datasets have proven to be robust and reproducible.Data files that are presented either omit entirely some experiments or provide only summary data of mean values without indicating numbers of subjects, measures of variability such as standard deviation; no information on the results of replicate studies, if any were performed, are in these data files. Therefore I find it impossible to determine if many of the "significant" associations are robust.Several of the reviewer’s specific examples were quite helpful, and we respond to each below.1) Data for Figure 1. The data set for figure 1 only summary data. There is no indication of the number of animals studied in each group and no indication of standard deviation. Also, there is no indication in the data set or in the text if any of the experimental results for 1A to 1D were based on replicate studies. The underlying data for figures E to H are not available in the figure 1 data file; the text gives the range of subject sizes and states that two independent experiments were performed, but it is unclear if the data presented in the figures represents both experiments, just one experiment, or if the data presented includes both experiments whether the results were simply combined or if there was some normalization performed. The statistical analysis method does not say whether the data were log-transformed before analysis. All of the author's conclusions may be absolutely correct but it is difficult to know this without more detailed information.The reviewer highlights a significant oversight – the lack of details in our supplementary table. This has been remedied. We now provide the group size, mean, and standard deviation for each measurement. We have deposited all raw data in our Sassetti Lab GitHub repository, which is publicly available. The other comments refer to data that has been removed in response to Reviewer 1.2) Data for Figure 4. The data set for this figure only includes the underlying data for 4A. No underlying data are given for 4C, D and E. No indication is given of the number of repeated experiments, and if these were performed what the results were. No method is given for the statistical analysis used in 4D. The authors conclude that there are significant differences between mouse strains for several Mtb strains, but give no information on the numbers of mice studied, variation within groups, repeat experiments, etc; also no statistical analysis is performed. Again, the author's conclusions may be absolutely correct but it is difficult to know this without seeing the underlying data.The underlying data for 4A was all included in the previous submission . This is also the source data for Figure 4E as mentioned in the figure legend (line 967). For 4C, all mouse numbers were in the legend (line 955) and we additionally include source data file of counts for each Mtb mutant in individual mice . For 4D, the number of mice were in the legend (line 960) and we have added additional text to specify the statistical testing conducted between the lung burden of B6 and CAST mice at each specified timepoint (lines 960-961).While the authors may believe that their answers to the transparent reporting form were complete and accurate, based on what I have found I'm not certain that this is correct.We verify that the revised manuscript complies with the transparent reporting requirements.Reviewer #5:Sample size and reproducibility. The groups were small , which could result in a high variance per group. It would be important to evaluate how reproducible are the phenotypic measurements per strain. The authors mention reproducibility per genotype (Page 8 line 177), but they do not clearly show it.The reviewer refers to our heritability analysis which was provided in Figure1-FigureSupp3. Heritability is the fraction of variation attributable to between strain differences relative to within strain noise. This is explicitly: SS(strain)/SS in an ANOVA table  is SS(strain)+SS(error)) . Our heritability analysis shows that >75% of variation could be attributed to genetic differences for the majority of traits. In the revised manuscript, we also provide p-values of each trait showing that genotype has a significant effect on all traits.In addition, the revised manuscript includes group size, mean, and standard deviation for each trait and genotype in Figure1-SourceData1, validation of susceptibility and cytokine abundance traits in Figure 1 , validation of an important QTL in Figure 3C , and validation of TnSeq measurements in Figure 4 . This provides a transparent assessment of technical variability, and demonstrates the reproducibility of our dataset.Perhaps a dimension reduction approach (PCA or UMAP) could be used to evaluate the distance between replicates.This is a very interesting idea, and we appreciate the suggestion. However, it is difficult to envision how PCA or UMAP could produce a quantitative assessment of genotype-dependent variation that is more robust than the heritability analysis we provide in Table S2.As specific examples: How many animals were used per group in outlier strains ? What was the variance for the y-axis measurements? Could variance explain the outlier nature of the strain?Outliers were identified by linear regression using studentized residuals, which accounts for intra-genotype variation. This is now explained more clearly in the main text (lines 179-183) and figure legend.QTL analysis. The CC QTL analysis faces similar challenges to genetic studies in humans. Overlooking complex population structure can lead to inflated false positives but can also prevent harvesting the full potential of the samples. Limitations of the QTL analysis are briefly introduced (Page 11 line 241) but the explanation in methods is convoluted and difficult to follow. The study could benefit from using their SNPs data for genome-wide association testing using all strains correcting by population structure. This strategy was shown to be robust when performed to a study of similar framework using different inbred strains and their crosses (PMID 32365090). This method could address sample size and QTL as the animals would be treated as unique samples (not per group) correcting by population structure (similarities between samples in the dataset). If correct, this would be a more straightforward genome-wide analysis.We appreciate the reviewer’s constructive suggestions. While the reviewer is entirely correct that in certain situations , direct SNP-based approaches can allow for improved resolution of causal variants, we would argue that in many cases these SNP approaches would in fact further cloud the analyses.The field standard for CC studies  has been the haplotype-based genetic mapping which also includes a kinship  correction . This is largely due to the fact that , in a finite population such as the CC, the strain distribution patterns of single SNPs can lead to false associations (due to long-range linkage disequilibrium). In the cited case, a simple monogenic trait (the white spot) was assessed, and the problem should only be exacerbated for multi-genic traits).Furthermore, we have strong evidence presented here that several of our discovered QTL are due to multiple alleles . Members of our team have shown this causally before . As any given SNP cannot fully capture 3 (or more) states, the field has acknowledged that haplotype mapping is the state of the art.We agree that in the future, with designs like the DO studies , or F2 crosses , will facilitate improved SNP  mapping and we are excited to begin anticipating these.Detailed comments:In methods, it is stated that TipQTL significance was assessed using permutations. However, in the Results section and in table 1 it is not clearly stated if the p values are raw or corrected via the permutation.This has been corrected.Homo sapiens is now widely accepted, perhaps 2000-6000 years. Has co-evolution been demonstrated or is this hypothesized? Same point with the role of genetic variation presented as established.Line 63: Co-existence of Mtb and Additional citations have been added (lines 63-64).When describing correlations, suggest a standard terminology: Strong, Moderate, Weak. Highly correlated is of uncertain meaning. On line 182 the R2 was 0.57 but the text said correlated. Some use the following: 0-0.19 = very weak, 0.2-0.39 = weak, 0.40-0.59 = moderate, 0.6-0.79 = strong, 0.8-1 = very strong correlationThis is an excellent idea, and we edited the text accordingly.Line 151. What does it mean when the susceptibility is largely consistent with other studies. Can the authors spell out the inconsistencies somehow?This text was edited to be more explicit (lines 151-155).Line 220. What is reasonable statistical confidence?The text was edited to remove this confusing reference.-/-, IFN-/-, Nos2-/- and CYbb-/- mice produced x essential genes, and that the CC raised it from x to y. The paragraph tells us the 234 genes were identified in Bl6 and that the plateau was ~750. How far from 234 to 750 was reached with the 4 specified knockout mice?Line 272. At some point, it would be useful to read in the text that the combined studies of WT Bl6, RAGThe requested statement was added (lines 302-306).Line 321. I did not understand what was complex about the kinetics. Perhaps this can be clarified?This text was clarified (Lines 342-344).References:Aylor, D. L., W. Valdar, W. Foulds-Mathes, R. J. Buus, R. A. Verdugo, R. S. Baric, M. T. Ferris, J. A. Frelinger, M. Heise, M. B. Frieman, L. E. Gralinski, T. A. Bell, J. D. Didion, K. Hua, D. L. Nehrenberg, C. L. Powell, J. Steigerwalt, Y. Xie, S. N. Kelada, F. S. Collins, I. V. Yang, D. A. Schwartz, L. A. Branstetter, E. J. Chesler, D. R. Miller, J. Spence, E. Y. Liu, L. McMillan, A. Sarkar, J. Wang, W. Wang, Q. Zhang, K. W. Broman, R. Korstanje, C. Durrant, R. Mott, F. A. Iraqi, D. Pomp, D. Threadgill, F. P. de Villena and G. A. Churchill (2011). "Genetic analysis of complex traits in the emerging Collaborative Cross." Genome Res 21(8): 1213-1222.Broman, K. W., D. M. Gatti, P. Simecek, N. A. Furlotte, P. Prins, Ś. Sen, B. S. Yandell and G. A. Churchill (2019). 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Johnson, T. E. Johnson, G. Kempermann, C. Kendziorski, M. Kotb, R. F. Kooy, B. Llamas, F. Lammert, J. M. Lassalle, P. R. Lowenstein, L. Lu, A. Lusis, K. F. Manly, R. Marcucio, D. Matthews, J. F. Medrano, D. R. Miller, G. Mittleman, B. A. Mock, J. S. Mogil, X. Montagutelli, G. Morahan, D. G. Morris, R. Mott, J. H. Nadeau, H. Nagase, R. S. Nowakowski, B. F. O'Hara, A. V. Osadchuk, G. P. Page, B. Paigen, K. Paigen, A. A. Palmer, H. J. Pan, L. Peltonen-Palotie, J. Peirce, D. Pomp, M. Pravenec, D. R. Prows, Z. Qi, R. H. Reeves, J. Roder, G. D. Rosen, E. E. Schadt, L. C. Schalkwyk, Z. Seltzer, K. Shimomura, S. Shou, M. J. Sillanpää, L. D. Siracusa, H. W. Snoeck, J. L. Spearow, K. Svenson, L. M. Tarantino, D. Threadgill, L. A. Toth, W. Valdar, F. P. de Villena, C. Warden, S. Whatley, R. W. Williams, T. Wiltshire, N. Yi, D. Zhang, M. Zhang and F. Zou (2004). "The Collaborative Cross, a community resource for the genetic analysis of complex traits." Nat Genet 36(11): 1133-1137.Churchill, G. A., D. M. Gatti, S. C. Munger and K. L. Svenson (2012). "The Diversity Outbred mouse population." Mamm Genome 23(9-10): 713-718.Ferris, M. T., D. L. Aylor, D. Bottomly, A. C. Whitmore, L. D. Aicher, T. A. Bell, B. Bradel-Tretheway, J. T. Bryan, R. J. Buus, L. E. Gralinski, B. L. Haagmans, L. McMillan, D. R. Miller, E. Rosenzweig, W. Valdar, J. Wang, G. A. Churchill, D. W. Threadgill, S. K. McWeeney, M. G. Katze, F. Pardo-Manuel de Villena, R. S. Baric and M. T. Heise (2013). "Modeling host genetic regulation of influenza pathogenesis in the collaborative cross." PLoS Pathog 9(2): e1003196.Graham, J. B., J. L. Swarts, M. Mooney, G. Choonoo, S. Jeng, D. R. Miller, M. T. Ferris, S. McWeeney and J. M. Lund (2017). "Extensive Homeostatic T Cell Phenotypic Variation within the Collaborative Cross." Cell Rep 21(8): 2313-2325.Gralinski, L. E., M. T. Ferris, D. L. Aylor, A. C. Whitmore, R. Green, M. B. Frieman, D. Deming, V. D. Menachery, D. R. Miller, R. J. Buus, T. A. Bell, G. A. Churchill, D. W. Threadgill, M. G. Katze, L. McMillan, W. Valdar, M. T. Heise, F. Pardo-Manuel de Villena and R. S. Baric (2015). "Genome Wide Identification of SARS-CoV Susceptibility Loci Using the Collaborative Cross." PLoS Genet 11(10): e1005504.Keele, G. R., W. L. Crouse, S. N. P. Kelada and W. Valdar (2019). "Determinants of QTL Mapping Power in the Realized Collaborative Cross." G3 (Bethesda) 9(5): 1707-1727.Keele, G. R., T. Zhang, D. T. Pham, M. Vincent, T. A. Bell, P. Hock, G. D. Shaw, J. A. Paulo, S. C. Munger, F. Pardo-Manuel de Villena, M. T. Ferris, S. P. Gygi and G. A. Churchill (2021). "Regulation of protein abundance in genetically diverse mouse populations." Cell Genomics: 100003.Pseudomonas aeruginosa Infection in Human Lung Disease." mBio 11(2).Lorè, N. I., B. Sipione, G. He, L. J. Strug, H. J. Atamni, A. Dorman, R. Mott, F. A. Iraqi and A. Bragonzi (2020). "Collaborative Cross Mice Yield Genetic Modifiers for Mycobacterium tuberculosis Conditionally Essential Metabolic Pathways." mSystems 4(4).Minato, Y., D. M. Gohl, J. M. Thiede, J. M. Chacon, W. R. Harcombe, F. Maruyama and A. D. Baughn (2019). "Genomewide Assessment of Mishra, B. B., R. R. Lovewell, A. J. Olive, G. Zhang, W. Wang, E. Eugenin, C. M. Smith, J. Y. Phuah, J. E. Long, M. L. Dubuke, S. G. Palace, J. D. Goguen, R. E. Baker, S. Nambi, R. Mishra, M. G. Booty, C. E. Baer, S. A. Shaffer, V. Dartois, B. A. McCormick, X. Chen and C. M. Sassetti (2017). "Nitric oxide prevents a pathogen-permissive granulocytic inflammation during tuberculosis." Nat Microbiol 2: 17072.Noll, K. E., A. C. Whitmore, A. West, M. K. McCarthy, C. R. Morrison, K. S. Plante, B. K. Hampton, H. Kollmus, C. Pilzner, S. R. Leist, L. E. Gralinski, V. D. Menachery, A. Schäfer, D. Miller, G. Shaw, M. Mooney, S. McWeeney, F. Pardo-Manuel de Villena, K. Schughart, T. E. Morrison, R. S. Baric, M. T. Ferris and M. T. Heise (2020). "Complex Genetic Architecture Underlies Regulation of Influenza-A-Virus-Specific Antibody Responses in the Collaborative Cross." Cell Rep 31(4): 107587.Mycobacterium tuberculosis Infection and Must Be Actively Repressed by PD-1 to Prevent Lethal Disease." PLoS Pathog 12(5): e1005667.Sakai, S., K. D. Kauffman, M. A. Sallin, A. H. Sharpe, H. A. Young, V. V. Ganusov and D. L. Barber (2016). "CD4 T Cell-Derived IFN-γ Plays a Minimal Role in Control of Pulmonary Smith, C. M., M. K. Proulx, R. Lai, M. C. Kiritsy, T. A. Bell, P. Hock, F. Pardo-Manuel de Villena, M. T. Ferris, R. E. Baker, S. M. Behar and C. M. Sassetti (2019). "Functionally Overlapping Variants Control Tuberculosis Susceptibility in Collaborative Cross Mice." mBio 10(6).[Editors’ note: what follows is the authors’ response to the second round of review.]Reviewers and I concur that your manuscript provides value to the field as a "Tools and Resource" paper for generating new hypotheses. The genotypic diversity explored in the study, together with an assessment of disease pathology/outcomes, provides a useful framework to pose important questions that are currently topical for tuberculosis. That said, it would be important to frame the study as a resource rather than trying to probe specific questions with the current dataset, which can be limited by the depth of data available currently in the work. I encourage you to consider carefully how best to present this to the field in a manner that acknowledges the limitations and but also creates the space for innovative and explorative future work.We thank the reviewers and editor for review of our work. To facilitate the paper as a useful “Tools and Resources” paper, we have added text throughout the manuscript highlighting the specific Tools and Resources aspects of each section, detailed the limitations and discussed further avenues for follow-up and new lines of enquiry this work has opened.To facilitate this, please address the following concerns:1. For a paper reporting a potentially useful resource, it is imperative that all data be publicly available for further interrogation and exploration. Reviewers expressed concern that this is not the case with your submission. For the GEO link, data appear available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164156. However, for the GitHub link, the Smith repository was not located. There was only one repository (Bellerose): https://github.com/sassettilab?tab=repositories. Please address this.This GitHub repo has been made public.2. In lines 145-147, it is stated: Groups of 3-6 male mice per genotype were infected and TB disease-related traits were quantified at one-month post-infection. Data from all surviving animals are provided in Figure 1-sourcedata1. However, the source data file indicates that the N is 1-3 for most of the data, except for two strains, Bl6 and CC010, which are reported to have 6 mice. There are asterisks for significance in only in a minority of the bars (~15) in Figure 1A. What about the remainder? What n's did they have? Too few to determine significance? This should be clarified.We clarified this after previous review to indicate that while at least 3 mice of each genotype was infected, some individual mice within a group died early and hence were not phenotyped. To make this even more explicit, we have added a column into Figure1-SourceData1_CCTB_Phenotypes to state “N of mice infected” and have changed the “N” column to “N of surviving phenotyped animals”.3. Explain why an R value of 0.43 would be considered a moderate correlation, when elsewhere you suggest that there could be biological differences that may make this correlation weak. Similarly, it is often the case that in inflammatory processes, TNF and IFNγ track together, even if made by different cells, which is not even strictly the case. These are two examples of continued disagreement. Please reconsider carefully how you present these data.This standardized nomenclature is defined in the text and was used in response to the previous review. Since the interpretation of this terminology is subjective, we use a systematic nomenclature.4. Line 184-188. The "significant" associations described in this section are for non-replicate studies with N=2 . Perhaps reconsider the use of "significant" in this context. In Line 253, this experiment used a large N and hence likely produced robust results.The word “significant” has been removed.5. Figure 1 A. Giving results of an ANOVA with post-hoc analysis comparison of means to a single mouse strain (C57/BL6) is difficult to interpret because of the potential bias from post-hoc decisions about which strain serves as a comparator. For example, using AJ as a comparator shows no significant differences with other strains. For this reason, excluding this analysis is warranted.C57BL/6J was used as the comparator as that is the standard mouse strain used in the TB field. As a Tools and Resources paper, comparing this new mouse panel to the accepted/standard mouse strain C57BL/6J is the comparisons most researchers immediately would look for (and have enquired about from the preprint) and thus would be most beneficial for researchers in the field. A sentence has been added to figure legend 1 to explicitly point researchers to the most useful comparisons.6. Line 199: "A number of genotypes were able to control bacterial replication yet had very low levels of IFNΓ " -- for the benefit of the reader, please specify which genotypes fall into this category. Ideally this would be specifically mentioned in the text and in addition the genotypes of interest would be labelled in the figure. Similarly for Figure 1C, it would be helpful if the significant "outlier" genotypes were labelled in the figure so the reader can know which these are.These genotypes have been specified again in the text and figure legend.7. Line 487: “Two TipQTL overlapped with HipQTL …”. Please specify in the text which overlapping QTL are being referred to here. It seems evident from the figure but specifying would assist the reader here.This has been specified in the text (line 492 and highlighted in the marked up manuscript).8. Some of the type on the figures is really tiny, e.g., 1A, 1B, most of figure 4, parts of figure 6, etc. Enlarge the panels with the tiniest type so that they are more readable.All figures have been enlarged to the maximum size permittable.