Temporal genome-wide fitness analysis of Mycobacterium marinum during infection reveals the genetic requirement for virulence and survival in amoebae and microglial cells

ABSTRACT

Tuberculosis remains the most pervasive infectious disease and the recent emergence of drug-resistant strains emphasizes the need for more efficient drug treatments. A key feature of pathogenesis, conserved between the human pathogen Mycobacterium tuberculosis and the model pathogen Mycobacterium marinum, is the metabolic switch to lipid catabolism and altered expression of virulence genes at different stages of infection. This study aims to identify genes involved in sustaining viable intracellular infection. We applied transposon sequencing (Tn-Seq) to M. marinum, an unbiased genome-wide strategy combining saturation insertional mutagenesis and high-throughput sequencing. This approach allowed us to identify the localization and relative abundance of insertions in pools of transposon mutants. Gene essentiality and fitness cost of mutations were quantitatively compared between in vitro growth and different stages of infection in two evolutionary distinct phagocytes, the amoeba Dictyostelium discoideum and the murine BV2 microglial cells. In the M. marinum genome, 57% of TA sites were disrupted and 568 genes (10.2%) were essential, which is comparable to previous Tn-Seq studies on M. tuberculosis and M. bovis. Major pathways involved in the survival of M. marinum during infection of D. discoideum are related to DNA damage repair, lipid and vitamin metabolism, the type VII secretion system (T7SS) ESX-1, and the Mce1 lipid transport system. These pathways, except Mce1 and some glycolytic enzymes, were similarly affected in BV2 cells. These differences suggest subtly distinct nutrient availability or requirement in different host cells despite the known predominant use of lipids in both amoeba and microglial cells.

IMPORTANCE

The emergence of biochemically and genetically tractable host model organisms for infection studies holds the promise to accelerate the pace of discoveries related to the evolution of innate immunity and the dissection of conserved mechanisms of cell-autonomous defenses. Here, we have used the genetically and biochemically tractable infection model system Dictyostelium discoideum/Mycobacterium marinum to apply a genome-wide transposon-sequencing experimental strategy to reveal comprehensively which mutations confer a fitness advantage or disadvantage during infection and compare these to a similar experiment performed using the murine microglial BV2 cells as host for M. marinum to identify conservation of virulence pathways between hosts.

INTRODUCTION

Mycobacterium marinum is the causative agent of a tuberculosis-like disease in poikilotherms and is also an opportunistic human pathogen, where the infection is limited to the skin and extremities due to growth restriction at about 30°C (1). In contrast to Mycobacterium tuberculosis, which has evolved as a strict human pathogen, M. marinum remains a generalist and has the capacity to affect a wide range of animal hosts and protozoa. Although M. marinum has a larger genome than M. tuberculosis, they are phylogenetically closely related and the processes accompanying infection at a cellular level are similar (2, 3).

Mycobacteria possess a remarkably elaborate cell wall to protect themselves against environmental stresses and chemicals (4, 5), and components of the cell wall are well conserved between M. tuberculosis and M. marinum. The most remarkable is a specific hydrophobic outer layer, called the “mycomembrane,” composed of several lipids including mycolic acids (MA) and the complex and branched phthiocerol dimycocerosates (PDIMs) (6, 7). The mycobacterial cell wall can be considered a “lipidic virulence factor” and is essential to establish the infection and also to persist in a non-replicative state during dormancy. The stages of infection of M. tuberculosis and M. marinum in a variety of evolutionary distant phagocytes are extremely well conserved, and a single cycle of infection lasts about 48 hours (illustrated in Fig. 1A). After entry by phagocytosis in animal phagocytes of the innate immune system (8) or in amoebae (3, 9), secretion of virulence factors, and especially of EsxA, via the ESX-1 secretion system inflicts membrane damage to the Mycobacteria-containing Vacuole (MCV) (10–12), which are repaired by a combination of cytosolic machinery. After cycles of damage and repair, the mycobacteria reach the cytosol where they continue to proliferate until they disseminate via lytic and non-lytic mechanisms (13–16).

Fig 1.

Description of the selection, library coverage, and sample selection. (A) M. marinum infection course in D. discoideum over 48 hours. M. marinum is phagocytosed by D. discoideum and rapidly manipulates its phagocytic pathway by blocking the phagolysosomal fusion to reside within a replicative niche (MCV). M. marinum proliferates within its MCV which finally breaks and releases mycobacteria to the cytosol where the bacteria continue to grow. (B) Workflow of M. marinum library selection during infection in D. discoideum. M marinum stably expressing m-Cherry was thawed and grown overnight. A sample was collected which constituted the “input sample” or inoculum, the remaining culture was used to infect D. discoideum AX2 (Ka) and samples were taken over a time course of 24 and 48 hpi. M. marinum was recollected from the 48 hpi samples and used to infect again D. discoideum. After another 48 hpi, samples were collected which, for simplicity, are called 2 × 48 hpi samples. (C) Histogram generated from the Hidden Markov Model (HMM) analysis using TRANSIT, representing the essentiality of the 27 samples of M. marinum mutant libraries. The calls for different regions are divided into five categories: (1) essential regions (ES) which are mostly devoid of insertions (2), non-essential genes (NE) containing read-counts around the global mean (3), growth-defect genes (GD), and (4) growth-advantage genes (GA) which represent genes with significantly increased or decreased normalized read counts, respectively (5), genes without any TA site. (D) Identification of low-permissive TA sites in the M. marinum genome was performed for each of the 102,057 TA sites. We determined the surrounding ±3 bp sequence and checked whether it matches with the pattern “SGNTANCS” which has been reported to be “low-permissive” to transposon insertions. The histogram reports gene essentiality in dependence on the number of low-permissive TA sites. The same categories as for panel C were applied here. (E) Principal components 1 and 2 of normalized insertion counts, colored by conditions: Inoculum (n = 7), 24 hpi (n = 6), 48 hpi (n = 8), and 2 × 48 hpi (n = 6). Below the axis label, the percentage of variance captured by the respective principal component is denoted.

Intracellular mycobacteria use host lipids as a carbon and energy source during active growth and dormancy (17, 18). M. tuberculosis, in particular, has the unique ability to use cholesterol and fatty acids as sole carbon sources in vivo and in vitro (19). It has been demonstrated that M. marinum can use fatty acids derived from triacylglycerols in lipid droplets as well as from host membrane phospholipids for its metabolism and storage (20). To access the host lipids required for its survival, intracellular mycobacteria need specific transporters such as those of the MCE family that are conserved among many bacteria species including mycobacteria. Of the four mce operons present in the genome of mycobacteria (mce1-4), two have been characterized in vitro and in vivo as important for the uptake of cholesterol (mce4) and fatty acids (mce1) (21).

The social amoeba, Dictyostelium discoideum is an alternative phagocytic host model to study interactions with environmental and pathogenic bacteria (9, 22). The phagocytic pathway is highly conserved and represents a major restriction point for bacteria in both macrophages and amoebae. Nevertheless, M. marinum can survive inside D. discoideum in a similar way as M. tuberculosis in macrophages (Fig. 1A) (3, 22, 23). D. discoideum has emerged as a powerful genetically and biochemically tractable model to study processes of M. marinum infection, and in particular has recently been used to demonstrate the role of the ESCRT and autophagy machinery in MCV membrane repair (24, 25), the role of metal poisoning in bacterial growth restriction (26, 27) as well as to identify anti-infective compounds (28, 29). Therefore, we use D. discoideum and M. marinum as a model system to study cell-autonomous defense mechanisms that are relevant to the pathogenesis of tuberculosis (3, 8, 9).

Although the D. discoideum host system has been exploited mainly with the use of targeted, candidate-based approaches, to investigate interactions with pathogenic bacteria (23, 30), it is also highly suitable to more systematic approaches such as proteomic profiling of the MCV (31), or transcriptomics survey of pathogen and host by dual RNA-Seq (32).

Transposon mutagenesis sequencing (Tn-Seq) is a method used to identify essential or relevant genes in bacterial survival or growth under specific conditions or selections. The principle is based on identifying the location and the frequency of the transposon insertions in a library of mutants by sequencing the transposon-genome insertion sites. The Mariner transposon, used in the present study, inserts at every TA dinucleotide sequence throughout the genome. The depth of sequencing allows the quantification of the relative frequency of individual mutants in the pool before and after selection (33). The Tn-Seq method has been performed with many environmental and pathogenic bacteria (34–37), including M. tuberculosis (38–41), before being adapted to other mycobacteria (42–44). To date, only one Tn-Seq study performed in M. marinum using the E11 strain has been published (45). However, comparative genome analysis of M. marinum strains isolated from diseased fish or human patients suggests that the E11 strain and the M strain likely belong to two different clusters (46) and might have different host ranges. For example, M. marinum M is virulent for all the model hosts studied so far, from amoebae to flies, fish, and mice (47, 48). On the contrary, the E11 strain is avirulent for D. discoideum (45).

While several screens have been employed to identify genes that are important for mycobacteria survival in vitro and in vivo, in the present study, we used the M. marinum strain M, isolated from a human patient, and commonly used as a model for M. tuberculosis pathogenesis (3, 8, 22). We identified essential genes and performed a comparison with previously published sets of essential genes from M. marinum strain E11, M. tuberculosis, and M. bovis. We then examined the fitness impact of genome-wide insertion mutations at various stages of infection in both phagocytes, the amoeba D. discoideum and murine BV2 microglial cells, providing invaluable insights into the temporal genetic requirements for M. marinum to establish an infection.

RESULTS AND DISCUSSION

Selection process and distribution of the transposon insertions

The M. marinum strain M (hereafter described as M. marinum) library was generated with the phage MycoMarT7 vector carrying a Himar1 transposon that inserts at the majority of TA dinucleotides in the genome, as previously described for M. bovis and M. tuberculosis (41, 42). For each infection, a frozen aliquot of the M. marinum library was grown in 7H9 to generate the reference input sample (hereafter called “Inoculum”), and the transposon insertion sites were sequenced. The selection was performed in two consecutive rounds of D. discoideum infection. As illustrated in Fig. 1A, a full infection cycle, from uptake, to MCV genesis and breakage, to dissemination takes 48 hours (3). To sample the early and late stages of a 48-hour infection cycle (Fig. 1A), M. marinum was collected from populations of infected D. discoideum at 24 hpi and 48 hpi. To increase the impact of the selection, we performed an iteration of a full infection cycle. For this, the 48 hpi sample was grown in 7H9 overnight and used as an inoculum (hereafter called “Inoculum_bis”) for a second round of infection resulting in the “2 × 48 hpi” condition (Fig. 1B).

The genome of M. marinum has a length of 6,660,144 bp with one chromosome of 6,636,827 bp and one plasmid of 23,317 bp (2). The genome features 102,057 TA sites, theoretically allowing for an insertion every 65 bp on average, with 82.6% (84,295 sites) found within annotated genes and 17.4% (17,762 sites) in intergenic regions. Based on the sequencing of seven inoculum samples, we observed insertion in 57% (58,137 sites) of the TA sites (criteria: average normalized read count ≥1) (Fig. 1C; Table S1 at https://tinyurl.com/5dbxww5w). Recently, the presence of low-permissive sequences with consensus “(GC)GNTANC(GC)” has been highlighted in the M. tuberculosis genome (49). In the M. marinum genome, we found 9,370 TA sites (9.2% of the total) embedded in a low-permissive sequence. In agreement with that prediction, only 711 (7.6%) of these sites had an insertion. We then investigated the impact of these low-permissive TA sites on the gene essentiality status identified by TRANSIT (Fig. 1D; Table S1 at https://tinyurl.com/5dbxww5w). To retain a maximum of information, a combination of the two available annotations for the genome of M. marinum strain M (NC_010612.1 and CP000854.1) was used in addition to the plasmid found in the M strain pMM23 (NC_010604.1), resulting in a total number of 5,480 annotated genes. Because our primary aim was to measure the fitness impact of mutations, we concentrated on insertions that disrupt coding sequences, as they are expected to lead to loss of function. In addition, this approach allows us to directly compare our results with previously reported data on M. marinum strain E11, M. tuberculosis, and M. bovis, where authors also focused on insertions in coding regions. All the TA sites are listed in Table S1 (https://tinyurl.com/5dbxww5w) and those mapping to coding sequences are shown in File S1 (https://tinyurl.com/5dbxww5w).

We found that, among the 651 essential genes identified in the inoculum with MMAR codes (Fig. S2), 56 of them have more than 25% low-permissive TA sites, raising the possibility of false positives. Within the 568 genes comprised in the essential core (see definition below), 192 genes exhibit a low-permissive TA site percentage of ≤5%, while six genes have a percentage of ≥50%. This indicates that our collection of core essential genes remains relatively unaffected by low-permissive TA sites (Table S2). Please note that the 84,495 sites mapping to genes include 84,295 unique TA sites and 200 TA sites that map to overlapping gene predictions (https://www.ncbi.nlm.nih.gov/genome/annotation_prok/) and are thus counted twice in the gene-wise summary in Table S2 compared to Table S1. Principal component analysis (PCA) of all 33 samples from each pool (Inoculum n = 7; 24 hpi n = 6; 48 hpi n = 8; Inoculum_bis n = 6; and 2 × 48 hpi n = 6) revealed excellent grouping by condition, except for the 48 hpi and Inoculum_bis samples, which show a major overlap (Fig. S1A). Removal of the Inoculum_bis samples did not impact significantly the PCA analysis of the other samples (Fig. 1E), and therefore this sample group was excluded from further TnSeq analysis. The remaining 27 samples were included in the subsequent analyses. In addition, we estimate that the additional power raised by comparing all conditions at once instead of having to integrate two separate comparisons (each to its inoculum) far outweighs the small potential bias introduced.

Identification of the core of essential genes and comparison to other mycobacteria

The data were analyzed using TRANSIT, which is software that automates the analysis of Himar1 Tn-Seq data sets (50). TRANSIT uses a Bayesian method and a hidden Markov model (HMM) which is implemented as a 4-state model assigning the genes into four categories: essential regions (ES) which are mostly devoid of insertions, non-essential genes (NE) containing read-counts around the global mean, growth-defect genes (GD), and growth-advantage genes (GA) which represent genes with significantly increased or decreased normalized read-counts, respectively.

TRANSIT identified 651, 635, 630, and 704 essential genes in the inoculum, 24 hpi, 48 hpi, and 2 × 48 hpi groups, respectively. In all, 568 genes (10.2%) were essential in all conditions and this intersection was deemed to be the “essential core” (Fig. 2A; Table S2). In principle, insertion mutants in genes that are essential for growth in non-selective broth (a total of 651) should be absent from the starting pool (the Inoculum) and, therefore, should also not appear at any later stage of any selection, including after infection of a phagocyte. Indeed, a few such mutants are detected, for example, 12 genes in the inoculum are essential but ceased to be defined as such during any stage of infection. We consider that the detection of a low number of essential genes not shared between all conditions (between 1 and 20) reflects the noise level of the strategy and analysis. By contrast, the 42 essential genes that are specific for the 2 × 48 hpi condition are discussed below. Therefore, we are convinced that our operational definition of the essential core greatly helps to reduce the number of false positives, thus allowing us to concentrate on truly essential genes.

Fig 2.

Identification of the “essential core” of M. marinum in D. discoideum. (A) Essential genes for each condition were determined with TRANSIT. The Venn diagram shows the intersection of all four conditions, that is, the “essential core” containing 568 genes. (B) Comparison of the “essential core” of M. marinum M strain with the M. marinum E11 strain, the Mtb H37Rv strain, and the M. bovis strain. Comparisons were performed by considering only M. marinum genes from the MMAR nomenclature and with at least one orthologous gene in the compared strain. (C) KEGG terms enriched in the essential core are sorted as a dotplot. Using R and the package clusterProfiler terms were filtered by P-value < 0.05. The P-value is color coded, and the number of enriched genes found in the respective term is coded by dot size. GeneRatio equals the number of differentially expressed genes against the number of genes associated with a GO term in the M. marinum genome.

Then, we compared the essential core of M. marinum to the sets of essential genes previously identified for other mycobacteria species, the M. marinum strain E11 (45), the M. tuberculosis H37Rv (40), and M. bovis (42) (Fig. 2B). First, we identified M. marinum strain M orthologs of ES genes reported in the three studies, and then we determined the intersection between these genes and our essential core. This interspecies intersection of essential genes was 37.3% (N = 237) with M. marinum E11 (45), 51.4% (N = 311) with M. tuberculosis H37Rv (40), and 27.7% (N = 161) with M. bovis (42). Furthermore, 96 orthologous genes were common between the “essential core” identified here and the three studies on M. marinum E11, M. tuberculosis H37Rv, and M. bovis. This interspecies gene set includes genes involved in fundamental cellular processes such as DNA replication (GyrA, GyrB, DnaG) or ATP synthase genes, and nucleotide biosynthesis enzymes such as pyrimidine synthase and genes involved in the de novo purine synthesis (purH) (Fig. 2B; Table S2).

The M. marinum essential core was further characterized using a KEGG enrichment analysis to pinpoint overrepresented functional categories. Functional groups with a P-value < 0.05 are illustrated in a dotplot (Fig. 2C) and an enrichment map (Fig. S2). The size and color of the dots in the dotplot represented the number of genes in the KEGG term and their significance, respectively (Fig. 2C; Table S4). The major categories primarily encompassed intrinsic metabolic pathways essential for bacterial survival both in vivo and/or in vitro. Notably, the indispensability of DNA replication machinery was underscored, with significant enrichment of genes related to replication fork formation and movement (such as dnaB, encoding DNA helicase, dnaG, encoding DNA primase, and dnaE1, encoding DNA polymerase III). Genes involved in the repair of lagging strand Okazaki fragments (e.g., polA, encoding DNA polymerase I, and ligA, encoding DNA ligase) were also found to be essential.

Furthermore, essential functional groups encompassed genes related to cell wall synthesis, including those within the peptidoglycan biosynthesis pathway, such as murB and ddlA, encoding D-alanine D-alanine ligase. In addition, pathways for the biosynthesis of valine, leucine, isoleucine, and lysine were enriched, featuring genes like ilvB1 (MMAR_1720) and lysA. These two genes are known to be essential in M. tuberculosis and mutations in these genes are only obtained when the medium is supplemented with the respective amino acids (51, 52). Importantly, a set of 42 essential genes specific to 2 × 48 hpi was identified. Among these, icl1 (MMAR_0792), encoding the enzyme isocitrate lyase involved in the TCA cycle, was notable. Indeed, disruption of this gene has been linked to impaired chronic-phase persistence of M. tuberculosis in mice (53). In addition, whiB1 (MMAR_1338), a transcriptional regulator belonging to the WhiB family, was found to be essential at 2 × 48 hpi. WhiB1 is known to play a role in cell growth and the initiation of dormancy in response to nitric oxide (NO) (54). Although D. discoideum does not produce NO to combat bacterial infections, WhiB1 might sense and respond to more general redox stress in this infection model. Furthermore, two genes involved in dihydrofolate biosynthesis folB (MMAR_5110) and folP1 (MMAR_5111) were identified, with the latter being essential for de novo synthesis of folate. Inhibition of its enzymatic activity results in the depletion of the folate pool, leading to growth inhibition and bacterial death (55).

Hierarchical clustering of time-series

We wanted to gain a deeper understanding of insertion frequencies resolved by time and infection stages. We calculated the fold change of Tn insertions per gene for each time point using the Inoculum as the reference. This analysis generated profiles for all mutated genes, and the complete list of profiles is available in Table S2 and File S1 (https://tinyurl.com/5dbxww5w). Then, we applied hierarchical clustering to the temporal profiles (see Material and Methods) and classified them into nine clusters with distinct patterns. The clusters contained between 387 (cluster 9) and 681 (cluster 1) genes (Fig. 3A). To delve deeper into the biological functions characterizing each cluster, we conducted a GO term enrichment analysis for each of them. Most clusters did not exhibit significantly enriched functional groups with the specified cutoff values (P-value ≤ 0.01) for this analysis, except for clusters 2, 7, and 9 which are represented as enrichment maps (Fig. 3B through D). Cluster 2 consists of profiles of mutants that display a slight decrease in their representation in the pool during infection, while cluster 7 groups mutants with a more drastic decrease, particularly at 2 × 48 hpi. By contrast, cluster 9 contains mutants that continuously increase in abundance in the pools (Fig. 3A). Clusters 2 and 7 are primarily enriched in genes associated with metabolic processes. Cluster 2 is enriched in pathways related to lipid biosynthesis, including triacylglycerol (TAG) and glycerolipid biosynthetic pathways, and diacylglycerol O-acyltransferase activity (Fig. 3B; Fig. S3A). TAGs have been linked to the persistence of M. tuberculosis, serving as a major energy source for slow-metabolizing populations (56). Within cluster 2, we identified MMAR_1519, the homolog of tgs1 in M. tuberculosis, mutations in tgs1 in M. tuberculosis significantly reduce TAG accumulation (57). Cluster 7 encompasses genes associated with various metabolic processes, including peptidoglycan and PDIM synthesis, fatty acid import (mce1 operon), amino acid metabolism (glutamine), and vitamin biosynthesis (biotin, cobalamin, and folate) (Fig. 3C; Fig. S3B). In addition, genes involved in DNA repair are prominent in this cluster, indicating that the bacterium needs to repair DNA damage during infection in D. discoideum. Counterintuitively, cluster 9 also contained a subset of genes involved in damage DNA binding, possibly indicating that the response to DNA damage is complex and some mutations increase fitness during infection (Fig. 3D; Fig. S3C).

Fig 3.

Hierarchical clustering of log₂ fold changes in D. discoideum. The relative abundance of transposon insertions at various infection time points was compared to the initial inoculum. From these binary comparisons, a log₂fc and a P-value were computed. (A) Genes with at least 5 TA sites and a normalized read count >1 in any of the samples were selected. The Manhattan distance between each pair of genes across the different time points was computed and a hierarchical clustering algorithm with ward.D linkage was run. The resulting clustering tree was cut at a height of 35, and 9 clusters containing between 383 (cluster 8 and 9) and 669 (cluster 1) genes were retained. (B, C, D) Enriched GO terms in clusters 2, 7, and 9 are depicted as an enrichment map. Using R and the package clusterProfiler terms were filtered by P-value < 0.01. Edges between nodes represent overlap between the connected terms and the number of enriched genes found in the respective term are coded by dot size.

Fitness cost of mutants during D. discoideum infection

To identify mutations that play a role during infection, we compared the mutant abundance (measured by the number of unique Tn-Seq reads per gene) at different infection time points (24 hpi, 48 hpi) and after the second iteration of selection (2 × 48 hpi) with the Inoculum pool. We used the HMM resampling option in the TRANSIT software and considered mutants to be significantly enriched or depleted in a condition if the log₂ ratio was ≥0.585 or ≤−0.585, with a P-value of ≤0.05 (Fig. S4E through G; Table S3).

Mutations with no impact on fitness in a given condition have a log₂fc close to zero. A negative log₂fc represents gene disruptions resulting in a fitness disadvantage (FD), while a positive log₂fc indicates gene disruptions leading to a fitness advantage (FA). The results are visualized in volcano plots, displaying the binary logarithmic fold change (log₂fc) and a negative decadic logarithm of P-values for each pairwise comparison with the Inoculum (Fig. S4). Overall, we found uneven distributions, with more mutations causing an FD at 24 hpi (Fig. S4E) and 2 × 48 hpi (Fig. S4G), while at 48 hpi (Fig. S4F), the effect of mutations was more evenly spread. Specifically, at 24 hpi, mutations in 70 genes had a significant impact, all leading to an FD. At 48 hpi, 74 mutations had a significant impact, with 45 leading to an FD and 29 to an FA. Finally, at 2 × 48 hpi, the highest number of mutations had a significant impact, with 360 conferring an FD and only 15 leading to an FA.

To examine functional relationships among the enriched and depleted M. marinum mutants at different time points, we used the STRING database, which consolidates data from various sources, including physical and functional associations between proteins (58). M. marinum genes were mapped to their M. tuberculosis orthologs prior to conducting the analysis. A color code was used to indicate the log₂fc, ranging from dark blue (mutations causing FD) to dark red (mutations causing FA). The different conditions were represented as circles around the gene names, with the inner circle corresponding to 24 hpi, the middle layer to 48 hpi, and the outer layer to 2 × 48 hpi (Fig. 4). The analysis confirmed that most mutations predominantly lead to a FD, including mutants in ESX-1-mediated secretion, the OppABCD periplasmic peptide transport system, in iron homeostasis, DNA repair, cobalamin, and biotin metabolism. A few scattered mutations led to an FA, such as in relA or Rv3816c.

Fig 4.

Protein interactions of FA/FD genes across conditions. The figure displays a protein-protein interaction network of Mtb orthologs of FA or FD genes, retrieved from the STRING database (genes filtered for absolute log₂fc ≥ 0.585, P-value ≤ 0.05, projected on the full STRING network, confidence cutoff of 0.8, singletons were omitted, query organism: Mycobacterium tuberculosis H37Rv). Color coding was used to identify the effect of the mutation ranging from dark blue (mutations leading to FD) to dark red (mutations leading to FA). The color scale in the layered rings represents from inside to outside the log₂fc of 24, 48, and 2 × 48 hpi, respectively, compared to the Inoculum.

ESX-1 subunits were identified, forming a high-confidence interactome, including MycP1 (a protease involved in regulating ESX-1 secretion and virulence) and EccCb1 (a member of the FtsK/SpoIIIE-like ATPase family responsible for transporting proteins across the mycobacterial membrane). As visualized by the color-coded rings, most mutations show a monotonous temporal profile. However, some mutations exhibit an alternating phenotype, as seen in glpK (FD at 24 hpi and 48 hpi, FA at 2 × 48 hpi), which has been linked to phase variation associated with changes in physiology and drug tolerance during M. tuberculosis infection (59). The opposite phenotype was observed for uvrC (FA at 24 hpi and 48 hpi, FD at 2 × 48 hpi) a gene involved in the nucleotide excision repair pathway (NER) (Fig. 4).

A GO enrichment analysis was performed to uncover specific pathway signatures and determine a temporal trend in the fitness cost of disrupted genes. We submitted significant genes (P-value ≤ 0.1, absolute log₂fc ≥ 0.585) to the enrichment analysis, combining 24 hpi FA and FD genes (Fig. S4A) and 48 hpi FA and FD genes (Fig. S4B; Table S4). We also submitted FA and FD genes separately to the enrichment analysis, maintaining a P-value of ≤0.1 for FA genes (Fig. S4C) and setting a cutoff of P-value ≤ 0.01 for FD genes (Table 1; Table S4). Different thresholding was motivated by a more comprehensive visualization, applying a laxer cutoff for categories for early time points and a stricter cutoff for 2 × 48 hpi. The enriched pathways were mainly associated with metabolic processes, cell wall biogenesis, cell cycle, and protein export. The 24 hpi timepoint showed the most limited number of enriched pathways and included the menaquinone pathway (part of the electron transport chain) and the cell division pathway, with genes involved in chromosome structure and partitioning. At the end of one or two rounds of selection (48 and 2 × 48 hpi), coinciding with M. marinum cytosolic proliferation, a higher number of functional groups emerged. Among the most enriched pathways common to the 48 and 2 × 48 hpi, we found the pyridoxine (vitamin B6) and the biotin biosynthesis pathways, which support the biosynthesis of methionine, glycine, serine, pantothenate, purines, and thymidine (60). At 2 × 48 hpi, which included the highest number of differentially transposon-mutated genes in the FD category, a cluster of genes related to the SOS response and DNA damage was identified. Many helicases (RecG, MMAR_1358) and the well-studied recA, a gene involved in the regulation of nucleotide excision repair and the induction of the SOS response were present, confirming the DNA damage response detected in cluster 7 (Fig. 3C) (61). This reiterates that D. discoideum infection results in significant DNA damage in the bacterium and consequently the repair is necessary for intracellular fitness. Interestingly, the fatty acid biosynthesis pathway and related pathways, as shown in the enrichment map, were identified at 2 × 48 hpi in both FA and FD categories (Fig. S4C and D). This indicates the vital role of fatty acid metabolism in mycobacterial survival in the host, consistent with the waxy mycomembrane importance in virulence. Among the genes in these functional groups, we found the polyketide synthase pks15/1 involved in phenolic glycolipid (PGL) synthesis. In addition, the presence of processes linked to fatty acid biosynthesis enriched in both FA and FD categories indicates that the requirements of these metabolic pathways are finely tuned during infection as opposed to growth in vitro.

The esx-1 operon and genes implicated in vitamin and lipid metabolism are required for M. marinum survival in D. discoideum

The general pattern highlights that the majority of gene insertions had no impact on intracellular fitness and were considered neutral (NE), with only a few leading to fitness advantages (FA) or disadvantages (FD) in intracellular growth (Fig. 5A through C; Table S3). To visualize the chromosomal distribution of fitness-altering insertions during D. discoideum infection and pinpoint potential “hot spots” of significant mutations, we plotted the log₂fc of all genes along their genomic positions. Genes were color-coded based on their P-value (P-value ≤ 0.1 or P-value ≤ 0.01) at 24 hpi (Fig. 5A), 48 hpi (Fig. 5B), and 2 × 48 hpi (Fig. 5C; Table S3).

Fig 5.

Fitness cost of M. marinum genes in D. discoideum over time and with respect to genome position. The log₂fc was obtained by comparing the time points 24 hpi, 48 hpi, and 2 × 48 hpi to the Inoculum. Genes are represented by dots which are color-coded according to the P-value associated with the log₂fc. Red: <0.01, purple: between 0.01 and 0.1, gray: >0.1. Plotting the log₂fc in dependence of genomic position (x-axis) reveals the fitness impact of operons in each condition: (A) 24 hpi, (B) 48 hpi, and (C) 2 × 48 hpi. Striking loci and operons are highlighted by labels and circles, such as the esx-1 and mce1 operons or genes associated with biotin metabolism.

At 24 hpi, most of the mutations were found in genes of unknown function annotated as conserved or hypothetical proteins. The gene pckA (MMAR_0451) encoding phosphoenolpyruvate carboxykinase (PEPCK) and catalyzing the first committed step in gluconeogenesis and the esx-1 operon (10 genes between MMAR_5440 to MMAR 5449) involved in MCV escape stand out with a relatively high, negative log₂fc and high significance (P-value 0–0.01). At 48 hpi, in addition to the continued depletion of mutants in pckA and the esx-1 operon, we observed an increased FD associated with mutations in genes typically required by mycobacteria that are consuming lipids. These included isocitrate lyase icl2 (MMAR_0158) which is involved in the glyoxylate shunt and the methylcitrate cycle (62), pckA, korAB that functions as an alternative α-ketoglutarate dehydrogenase pathway (63), and succinate dehydrogenase (sdhACD) that is required for the further oxidation of the succinate produced by korAB, the glyoxylate shunt, the methylcitrate cycle, and the methyl malonyl pathway. Interestingly, prpCD, encoding methylcitrate synthase and methylcitrate dehydratase, are not required for D.discoideum infection but the bacterium does need mutAB encoded methylmalonyl-CoA mutase implying bypass of the propionate half of the methylcitrate cycle using the methylmalonyl pathways. This activity requires biotin and cobalamin; thus, this is one mechanistic reason for the essentiality of biotin and cobalamin synthesis for intracellular infection (Fig. 4; Table S3). A further prominent attenuating mutation was observed in cpsA (MMAR_4966), which encodes a LytR-CpsA-Psr (LCP) domain-containing protein necessary for M. tuberculosis to evade killing by NADPH oxidase and LC3-associated phagocytosis (64). Interestingly, some mutations at this time point conferred a fitness advantage. For instance, insertions in the genes encoding the ABC transporter pabC (MMAR_4873) and proC (MMAR_0826), as well as fbiB (MMAR_1280), functions of which are not fully elucidated.

At 2 × 48 hpi, mutations in icl2, pckA, and the esx-1 operon further reduced the fitness compared with 48 hpi. The number of genes in the esx-1 operon with mutations exhibiting significant FD increased from 10 genes at 48 hpi to 16 at 2 × 48 hpi. Some other well-known virulence factors were implicated at various stages of infection (Table S2), including the heparin-binding hemagglutinin (MMAR_0800: hbha), responsible for bacterial adhesion to non-phagocytic host cells in vertebrate infection, and a subunit of the T7SS ESX-5 (MMAR_2680), responsible for transporting cell envelope proteins. Mutants in various genes implicated in lipid metabolism (MMAR_1778: tesA, MMAR_2124: relA, MMAR_4966: cpsA, MMAR_0258: fadD10, MMAR_0319: fadD7, MMAR_4864: mshD), in the whiB7 transcriptional regulatory factor (MMAR_1365), and pknG, encoding a serine/threonine protein kinase crucial for M. tuberculosis viability in vitro and infection models (65) were also depleted.

Furthermore, mutations in genes involved in vitamin metabolism, such as biotin (MMAR_2383 to MMAR_2387) or cobalamin (MMAR_1883 to MMAR_1885), also led to a strong FD. In terms of mutations resulting in an FA, we observed a depletion of mutants in gene encoding a nitrate response regulator (MMAR_4782: NarL), a glycerol kinase, as discussed above (MMAR_5208: glpK), an acetyltransferase (MMAR_5379), and several hypothetical secreted or regulatory proteins (MMAR_1939, MMAR_1165) (Table S3). The glpK phenotype likely results from the change of carbon source between the Middlebrook media and the intracellular niche.

Fitness cost of mutations in the mce1 and mce4 operons during D. discoideum infection

In bacteria, functionally related genes are often organized into operons, ensuring the coordinated expression of all constituent genes and the proper stoichiometry of the corresponding proteins and enzymes. As mentioned earlier, we observed pathways linked to lipid biosynthetic processes in both FA- and FD-enriched GO terms (Fig. S4C and D). This raised the question of whether other operons, as organizational units, exhibit similar trends in terms of fitness cost (FA or FD). We investigated the impact of mutations in key mycobacterial operons, including the canonical virulence locus esx-1 (Fig. 6A), the two lipid transporter operons mce1 and mce4 (Fig. 6B), and genes associated with PDIM and PGL biosynthesis (Fig. 6C) (7, 66). The analysis of the esx-1 operon and its associated genes revealed that most mutations led to FD, underscoring the indispensable role of this secretion system for M. marinum growth during infection. It is noteworthy that the fitness deficit conferred by most of the esx-1 mutations increased during the course of infection and upon iteration of the selection, with a 16-fold difference observed for mutation in genes encoding EsxA (ESAT-6) and EsxB (CFP10) at 2 × 48 hpi (Fig. 6A; Table S3).

Fig 6.

Fitness cost over time for selected genes and operons of M. marinum in D. discoideum. The color-coded log₂fc in dependence on conditions reveals fitness costs within operons. Operons and associated genes were selected. (A) esx-1 operon system, (B) mce1 and mce4 operon systems, (C) genes associated with phthiocerol dimycocerosates (PDIM) and phenolic glycolipids (PGL) synthesis. The labels include the M. marinum MMAR nomenclature, in brackets the Mtb ortholog (as indicated on mycobrowser.epfl.ch, with “-” indicating the absence of an Mtb ortholog), and the associated function.

During infection, mycobacterial survival heavily relies on importing lipids from the host via Mce transporters to support central metabolism and cell-wall biosynthesis (60). Heatmaps showcasing the fitness trends of mutations in the mce1 operon, responsible for fatty-acid transport, and the mce4 operon, responsible for (chole)sterol transport, are shown in Fig. 6B. Both Mce systems consist each of two putative permeases (mce1: MMAR_0410, MMAR_0411 – mce4: MMAR_4989, MMAR_4988) and six cell-wall-associated proteins (mce1: MMAR_0412 to MMAR_0417 - mce4: MMAR_4982 to MMAR_4987). Genes encoding for accessory proteins such as LucA (MMAR_5242/Rv3723), Mce-associated membrane proteins (MAMs), an orphaned MAM (OMAM), and mceG (MMAR_0439/Rv0199), encoding a putative ATPase, play crucial roles in stabilizing and facilitating the function of both Mce transporters (Fig. 6B) (19, 67, 68) (Fig. 6B). Note that only the mce1 operon has a regulator encoded by mce1R (MMAR_0408).

Our data revealed that all mutations in genes belonging to the mce1 operon led to FD, beginning at 48 hpi and reaching their highest negative fold change at 2 × 48 hpi. The only exceptions were Mce1R (MMAR_0408) and Fad5 (MMAR_0409), which exhibited a neutral phenotype. Most importantly, mutations in the gene encoding the ATPase MceG, predicted to interact with all the Mce transporters and to be the only cytoplasmic protein, led to an FD (69). These results suggest that mutations in the mce1 operon and accessory genes primarily affect M. marinum growth and survival during the late cytosolic stage of infection in D. discoideum (Fig. 6B). However, mutations in the mce4 operon displayed irregular phenotypes, with some mutations causing a neutral effect or slight FD. This finding is intriguing, as mutations in the mce4 operon, previously described as detrimental to M. tuberculosis survival in the host, appear to have a relatively minor impact on the bacterium during infection in D. discoideum. Interestingly, mutations in two genes encoding OMAMs, omamA and omamB, resulted in a progressive FD between 24 and 2 × 48 hpi (Fig. 6B), likely because of their function in Mce1-mediated transport.

The pathway that leads to PDIM and PGL synthesis comprises common steps leading to mycocerosic acid (PGL&PDIM, Fig. 6C), followed by two specific branches (PDIM or PGL). PDIMs play a pivotal role in bacterial entry into the macrophage, the regulation of phagosome acidification, phagosomal escape, and the utilization of host cholesterol as a carbon source (6, 70). On the other hand, PGLs are polyketide-derived virulence factors capable of limiting the capacity of activated macrophages to induce nitric oxide synthase (iNOS) and generate NO upon mycobacterial infection (71). In contrast to the mce1 and mce4 operons, almost all mutations in genes common to the biosynthesis of PGL and PDIM biosynthesis exhibited a fitness defect at 48 hpi and 2 × 48 hpi (Fig. 6C), supporting the notion that both may play a pivotal role at late stages of infection (10, 11). A closer examination of PDIM and PGL pathways indicated a fitness defect for PDIM mutants, while mutations in genes specific to PGL biosynthesis (including the operon MMAR_1759 to MMAR_1762) led to an intracellular fitness advantage phenotype. This counterintuitive positive impact of loss of PGL synthesis might be resulting from an indirect gain of function due to substrate rerouting to the PDIM pathway upon interruption of the PGL pathway, increasing total PDIM synthesis, boosting virulence and thus effectively leading to an apparent fitness advantage of these transposon mutants.

Comparative Tn-Seq shows common fitness impact and essentiality in amoebae and murine microglial host cells during M. marinum infection

To provide a comprehensive comparison and highlight evolutionary conservation, we conducted Tn-Seq experiments with M. marinum during infection of BV2 murine microglial cells. A single time point of 48 hpi was selected because, as in D. discoideum, a complete infection cycle in these phagocytes follows the same stages and also lasts approximately 48 hours (see Fig. 1A) (13, 29, 72). In total, seven samples were included in the analysis: four from the inoculum and three at 48 hpi (Table S2). Subsequently, a PCA was computed and plotted to observe the data distribution, revealing distinct clustering of the inoculum and 48 hpi samples (Fig. S5).

To identify mutations impacting intracellular bacterial growth during BV2 infection, we applied the TRANSIT resampling method to compare read counts at 48 hpi to those of the inoculum, analogous to the procedure used in the D. discoideum experiments. An examination of all genes on a volcano plot (Fig. 7A) clearly illustrated that there were more mutations leading to an FD compared to those leading to an FA. This trend was consistent with the results obtained for the 2 × 48 hpi D. discoideum infection (Fig. S4G). Notably, the mutation with the highest FD (a 32-fold change) was found in a gene coding for glucose-6-phosphate isomerase Pgi (MMAR_4557), while the mutation with the highest FA (a 10-fold increase) was found in the aspartate decarboxylase PanD (MMAR_5104). PanD is essential for the biosynthesis of coenzyme A and is the putative target of the first-line M. tuberculosis antibiotic, pyrazinamide. The apparent intracellular growth advantage of panD mutation in BV2 microglial cells may indicate the increased availability of pantothenate or β-alanine in these cells relative to the microbiological medium.

Fig 7.

Fitness cost over time of M. marinum in BV2 microglial cells. (A) Volcano plot of 48 hpi versus the inoculum. The x-axis shows the log₂fc and the y-axis the −log₁₀ of the associated P-values. Thresholds used to filter genes for enrichment analysis are depicted as thick black lines: log₂fc ≥ 0.585 and ≤0.585 (corresponding to 1.5-fold change) and P-value ≤ 0.1. For visualization, P-values < 0.0001 were capped at 0.0001. (B, C) Thresholded genes at 48 hpi were submitted to enrichment analysis using R and the package clusterProfiler. Terms were filtered by P-value < 0.01. In (C), edges between nodes represent the overlap between the connected terms, enriched genes found in the respective terms are coded by dot size. GeneRatio equals the number of differentially expressed genes against the number of genes associated with a GO term in the M. marinum genome. (D) Fitness cost at 48 hpi in BV2 microglial cells and with respect to genome position. Genes are represented by dots which are color-coded according to the P-value associated with the log₂fc. Red: <0.01, purple: between 0.01 and 0.1, gray: >0.1. Plotting the log₂fc in dependence on genomic position (x-axis) reveals the fitness impact of operons at 48 hpi.

Next, a GO enrichment analysis was performed on differentially represented mutations at 48 hpi, using P-value ≤ 0.1 and absolute log₂fc ≥ 0.585. Enriched terms were then filtered by P-value < 0.01 (Fig. 7B and C). Similar functional groups were enriched during BV2 infection compared to D. discoideum, with a prominent focus on vitamin biosynthetic processes such as for biotin and cobalamin (Table S4). The enrichment map also revealed an rRNA methylation cluster, hinting at ribosomal heterogeneity and altered translation during infection (Fig. 7B and C). Certain functional groups enriched after the selection in amoebae, such as genes from the TCA cycle and lipid catabolic processes, were not evident in BV2 cells indicating metabolic differences of bacteria internalized in amoeba and microglial cells (Table S4).

As in the D. discoideum infection, the log₂fc values were plotted along the chromosome to identify spatial relationships of intracellular fitness genes, with significance coded by P-value ≤ 0.1 or P-value ≤ 0.01 (Fig. 7D). These plots revealed no major patterns associated with genomic position although they highlighted the importance of the region encoding ESX-1. Among the mutations leading to FD, we identified genes like icl2, pckA, and whiB7, associated with lipid metabolism as well as cell wall biosynthesis (MMAR_1778: tesA), and survival during infection (MMAR_0713: pknG), and the esx-1 operon, all of which play a crucial role in the bacterium’s survival in both hosts (see “BV2” column in Fig. 6A). However, some mutations leading to an FD were specifically identified during BV2 infection, such as MMAR_0644, MMAR_1138, MMAR_2991, MMAR_3347, and MMAR_4937. Their functions remain undescribed, except for glucose-6-phosphate isomerase (MMAR_4557: pgi), which plays a central role in glycolysis and gluconeogenesis and its disruption results in glucose auxotrophy (73), and trehalose-6-phosphate synthetase (MMAR_4978: otsA), which is required for optimal growth in vitro and survival in a mouse M. tuberculosis infection model (74). As in D. discoideum infections, mutations in the mce4 operon and genes encoding accessory proteins do not lead to FD or FA (Fig. 6B), while most mutations in the mce1 operon led to opposite phenotypes, an FD in the amoeba and an FA in BV2 (though not reaching statistical significance) (Table S3).

Conclusions

The combination of transposon insertion mutagenesis and deep sequencing allows for quantifying insertion mutants in a pool, aiding in the identification of essential genes and those linked to selectable phenotypes (37). In this study, we employed Tn-Seq to assess mutation fitness costs in M. marinum, aiming to understand host-pathogen interactions in D. discoideum and BV2 microglial cells. Our findings revealed that 57% of TA sites in the M. marinum genome were disrupted, with 9.2% at low-permissive insertion sites, consistent with previous Tn-Seq studies in pathogenic mycobacteria (44, 75, 76).

Specifically, our results were similar to Tn-Seq studies in M. tuberculosis, where 42%–64% of TA sites and 9% of low-permissive sites were disrupted (49). Our study identified 568 essential genes (10.2%), comparable to previous Tn-Seq studies in M. tuberculosis (40, 77). Unlike an earlier Tn-Seq study on M. marinum E11, which identified only 6% of essential genes (45), the discrepancy might be due to genomic diversity as M. marinum strains were classified into two different clusters “M” and “Aronson” types (78). Among the essential genes identified in M. marinum M, 237 orthologs were found in M. marinum E11 (37.3%) (45), 311 orthologs in M. tuberculosis (44.4%) (40), and 161 orthologs in M. bovis (42). The larger overlap of M. marinum with M. tuberculosis H37Rv compared to the E11 strain of the same species might be partly due to M. marinum M strain and M. tuberculosis H37Rv being able to infect humans, whereas M. marinum strain E11 has more environmental hosts. Our study identified 153 essential genes specific to the M. marinum M strain. This specificity could result from variations in sequencing depth or species-specific genomic differences. Our study, along with others, demonstrates that Tn-Seq is a robust technique for identifying pathways affecting bacterial fitness during infection. However, pooled strain studies are susceptible to trans-complementation or “helper effects,” particularly in clumpy mycobacteria, where co-infection of host cells can potentially lead to false-positive or false-negative fitness impact results.

The importance of vitamin metabolism for M. marinum during D. discoideum infection was highly evident in our experiments. Biotin, in particular, emerged as essential for M. marinum survival in both cell types, leading to a pronounced fitness deficit when mutations occurred in genes such as MutA/B and BioA/B/F1/D. Biotin is an indispensable cofactor for biotin-dependent enzymes involved in the catabolism of fatty acids via MutA/B in the TCA cycle (79). Interestingly, M. tuberculosis cannot scavenge sufficient biotin from its host and relies on de novo synthesis through genes like bioA and bioD. This suggests that biotin may be universally required for M. marinum during eukaryotic cell infection, in line with previous studies indicating the role of biotin in establishing and maintaining chronic infections in murine tuberculosis models (80). Carbon metabolism, including glycolysis and gluconeogenesis, also emerged as essential pathways for M. marinum survival. Mutations in genes related to these pathways, such as pckA (encoding the first enzyme in gluconeogenesis), were detrimental during infection and essential for establishing and maintaining the infection (81). Furthermore, the data underscored the significance of the methylcitrate cycle in mycobacteria infection, as mutations in icl also resulted in a fitness deficit during D. discoideum infection (53). This is again indicative that the intracellular bacterium is utilizing lipids as a nutritional source. Altogether, these findings validate the robustness of the Tn-Seq method in M. marinum for highlighting important pathways involved in bacterial survival during infection. Furthermore, these results demonstrate the conservation of virulence strategies employed by M. marinum to infect amoebae and mammalian cells.

However, some differences were observed between the D. discoideum and BV2 models. For instance, genes like HBHA, linked to adherence and lipid inclusion formation, were required only for D. discoideum infection (82, 83). Similarly, mutations in genes encoding enzymes involved in mycobactin production, a siderophore produced by the bacteria to scavenge iron, only led to an FD during D. discoideum infection, although they are known to affect bacterial virulence in human hosts, too (84, 85). Differences between the two host phagocytes included the observation that mutation of the RelA GTP pyrophosphokinase, which controls the stringent response, was only found to be attenuating in BV2 cells. This may reflect a fundamental difference in growth control required for survival in the different host cells. These potential host specificities might be attributed to differences in selection timing, the temperature during infections, and/or distinct host defense or pathogen virulence strategies.

Our Tn-Seq data demonstrate that mutations occurring in many sites of the mce1 operon in the D. discoideum model resulted in an FD, whereas mutations in the same operon had a neutral effect during BV2 microglial cell infection (Fig. 6B). Moreover, mutations in genes encoding proteins involved in the stability of both the Mce1 and Mce4 complexes, such as the accessory OmamA protein (68) and the ATP-ase MceG, lead to an FD in D. discoideum only, while mutations in LucA had neutral effect in both phagocytic models (67). These results support the hypothesis outlined in the literature that mutations in the mce1 operon impact fatty acid acquisition and result in reduced intracellular growth of M. tuberculosis, although a direct link between lipid acquisition and intracellular growth is not completely understood. One possible explanation for the variation in fitness phenotypes between these phagocytes might lie in the differences in lipid composition between D. discoideum and BV2 microglial cells. M. marinum’s ability to infect a wide range of hosts allows it to adapt its lipid metabolism based on the lipids available in the infected environment. To investigate this hypothesis, further investigations are required to understand the impact of mce1 mutations during the course of infection.

Our data (Table S3) align closely with the results of numerous targeted and genome-wide mutagenesis studies in M. marinum, particularly those investigating fitness costs during infection in both amoebal and animal phagocytes. Our Tn-Seq data clearly indicate an FD as a result of mutations in the esx-1 locus, consistent with reports that the RD1 and DCE mutants are attenuated for intracellular growth in both D. discoideum and macrophages (86, 87). Another study examining the role of TesA in cell-wall-associated lipid synthesis and intracellular growth found that a tesA mutant is attenuated in both D. discoideum and zebrafish embryos (88), in line with the present genome-wide analysis.

At 2 × 48 hpi, mutations in genes encoding some putative effectors of the esx-5 operon, including esxM, led to an FD, which correlates with previous publications showing that an esxM knockout is attenuated during infection (89). Similarly, mutations in iron acquisition genes, mbtB and irtB, resulted in a fitness defect, which corroborates reports for ΔmbtB and ΔmbtB-ΔirtAB strains being impaired for growth in various phagocytes, including D. discoideum (84). It is worth noting, however, that some mutations did not impact infection, including in the zinc efflux gene ctpC, while others in genes encoding PIP phosphatases (PtpA, PtpB, and SapM) led to an FA, whereas recent publications have shown that CtpC (27) and PIP phosphatases (90) are required for intracellular growth of M. marinum in D. discoideum.

In conclusion, this study presents a successful Tn-Seq analysis performed in M. marinum M, providing a robust resource to explore the fitness costs of gene disruption during infection. We have identified genes and pathways that contribute to M. marinum infectivity, which can be further investigated in future studies. Collectively, this study opens the door for the use of Tn-Seq in rapidly screening and identifying genes involved in M. marinum fitness across various host model systems, offering diverse insights into the evolution of pathogenicity in mycobacteria.

MATERIALS AND METHODS

Bacterial strains, plasmids, and culture methods

Bacterial strains used in this study are listed in Table 1. M. marinum M strain was grown in Middlebrook 7H9 (Difco) supplemented with 10% OADC (Becton Dickinson), 0.2% glycerol (Biosciences), and 0.05% Tween 80 (Sigma) at 32°C in shaking culture at 150 rpm in the presence of 5 mm glass beads to prevent aggregation. The axenic D. discoideum Ax2(Ka) was cultured at 22°C in Hl5c medium (Formedium) supplemented with 100 U/mL of penicillin and 100 µg/mL of streptomycin (Invitrogen). BV2 cells were grown at 37°C in Dulbecco’s modified Eagle’s medium (DMEM) high glucose supplemented with 6% of heated inactivated fetal bovine serum (FBS) and 100 U/mL of penicillin in a 5% CO₂ environment.

TABLE 1.

Thresholding of the input for pathway analyses^a

Host organism	Condition	FA & FD	FA	FD	Thresholds
D. discoideum	24 hpi	97	7	90	abs(log2fc) ≥ 0.585; P-value ≤ 0.1
	48 hpi	97	38	59	abs(log2fc) ≥ 0.585; P-value ≤ 0.1
	2 × 48 hpi	268	–	–	abs(log2fc) ≥ 0.585; FA & FD: P-value ≤ 0.01
	–	–	19	–	abs(log2fc) ≥ 0.585; FA: P-value ≤ 0.1
	–	–	–	261	abs(log2fc) ≥ 0.585; FD: P-value ≤ 0.01
BV2	48 hpi	183	12	171	abs(log2fc) ≥ 0.585; P-value ≤ 0.1

^a –, not applicable.

Selection of transposon M. marinum mutants in D. discoideum and BV2

A transposon mutant library was generated essentially as described previously (41, 42). Briefly, M. marinum was grown in 300 mL to the mid-log phase at 30°C. The bacteria were centrifuged at 4,000 rpm and washed with pre-warmed MP buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 10 mM MgSO₄, and 2 mM CaCl₂). The cells were resuspended in a total volume of 10 mL MP buffer and 5 mL of fMycoMar phage stock at approximately 1 × 10¹¹ cfu/mL was added and incubated for 6 hours at 32°C. The transduction mix was centrifuged and washed in 7H9 with 0.05% Tween 80 and plated on 14 × 15 cm petri dishes containing 7H11/OADC with kanamycin at 25 µg/mL and grown at 30°C for 7 days before harvesting into 10% glycerol and freezing at −80°C. Dilutions of the transduction were simultaneously plated for enumeration. For library selection, the M. marinum-Tn library inoculum was spinoculated on 5 × 10⁶ adherent D. discoideum cells at a multiplicity of infection (MOI) of 5 to minimize any helper effect by having 1 to 3 bacteria per cell. After washing off non-phagocytosed bacteria, the infected cells were incubated at 25˚C in a filtered Hl5c medium containing 5 µg/mL of streptomycin and 5 U/mL of penicillin to avoid extracellular growth. At either 24 or 48 hpi D. discoideum were scraped and lysed with Triton 0.1%, intracellular bacteria were collected and plated on ten 15 cm 7H10 plates with 25 µg/mL kanamycin, 10% OADC, 0.2% glycerol, and 0.05% Tween-80. The same incubation time was applied after each collected time point. The same procedure was used for the second round of selection but the inoculum used for this step was the frozen aliquot of M. marinum collected at 48 hpi after the first round.

BV2 cells infection assay

BV2 cells were cultured in DMEM medium supplemented with FCS in 10  cm petri dishes at 37°C. The day prior to infection, cells were trypsinized, and passaged to reach 60–70% confluency on the day of the experiment. For library selection, the M. marinum-Tn library inoculum was spinoculated on 5 × 10⁶ adherent BV2 cells at a multiplicity of infection (MOI) of 3 to minimize any helper effect. Mycobacteria were centrifuged at RT at 500 × g for 10  min onto pre-plated BV2 cells to promote efficient and synchronous uptake. After 20 min of phagocytosis, extracellular bacteria were washed and cells were incubated for 48 hours under 5% of CO₂ at 32°C. At 48 hpi, BV2 cells were scraped and lysed with Triton 0.1%, intracellular bacteria were collected and plated on ten 15 cm 7H10 plates with 25 µg/mL kanamycin, 10% OADC, 0.2% glycerol, and 0.05% Tween-80.

Construction of gDNA libraries of M. marinum mutant pools

M. marinum gDNA was extracted following an adapted protocol from Belisle and Sonnenberg (91). Briefly, M. marinum mutant pools were centrifuged and resuspended in breaking buffer (50 mM Tris, 10 mM EDTA, and 100 mM NaCl, pH8). Mechanical lysis was performed with the Fastprep homogenizer for 30 s, this step was repeated three times. The upper phase was chemically lysed with RNase (200 µg/mL) and lysozyme (100 µg/mL) 1 hour at 37°C. Then, 0.1 volume of SDS 10% and 0.01 volume of proteinase K (100 µg/mL) were added for 1 h at 55°C. Extraction steps were realized with an equal volume of phenol:chloroform:isopropanol 25:24:1 for 30 min followed by chloroform:isopropanol for 5 min. Precipitation was performed with 3 M sodium acetate pH 5.2 and 1 volume of isopropanol 100%, gDNA was pelleted and washed with ethanol 70%. The extracted gDNA was quantified using Qubit (Invitrogen) and its quality was checked with the TapeStation system (Agilent).

A 2 µg aliquot of gDNA in Tris buffer (10 mM Tris, 0.1 mM EDTA, pH 8) was sheared in a Covaris S2 machine following these parameters: intensity 4, duty cycle 10%, cycles 200 and time 65 s. After purification with AMPure XP purification kit (Agencourt), 1,000 ng of sheared DNA fragment ends were repaired, blunt ended and a d-A tail added following NEBNextEnd/d A-Tailing module protocol. Specific adapters 1 and 2 described in Table 2 were self-hybridized and then ligated to the “A” tail ends by T4 ligase (Promega). After purification with AMPure XP purification kit (Agencourt), 40 ng of libraries was amplified by PCR using KAPA polymerase with the following parameters: 95°C 3 min; 98°C 20 s, 60°C 15 s, 72°C 15 s (repeated for 25 cycles); 72°C 1 min for elongation. Transposon junctions were amplified with the IS6 primer: 5′ CAAGCAGAAGACGGCATACGA 3′ and specific primers MarA to MarP described in Table 3. PCR products were purified with the AMPure XP purification kit and 16-plexed samples were pooled in approximately equimolar amounts of 5 nM and run in single and double index reads on a Hiseq 4000 (Illumina).

TABLE 2.

Bacterial strains used in the study

Strains	Comments	Source or reference
M. marinum strain M	WT strain	TMC1218
M. marinum strain M	KanR	This study

TABLE 3.

Primers used in the study for M. marinum mutant pools amplification

Name	Sequences (5′–3′)
Adapt1	c*a*a*g*cAGAAGACGGCATACGAGATNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCg*a*t*c*t
Adapt2	g*a*t*c*gGAAg*a*g*c*a–P
IS6	CAAGCAGAAGACGGCATACGA
MarA	AATGATACGGCGACCACCGAGATCTACACATCACGACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGGGACTTATCAGCCAACC
MarB	AATGATACGGCGACCACCGAGATCTACACCGATGTACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGGGGACTTATCAGCCAACC
MarC	AATGATACGGCGACCACCGAGATCTACACTTAGGCACACTCTTTCCCTACACGACGCTCTTCCGATCTGATACGGGGACTTATCAGCCAACC
MarD	AATGATACGGCGACCACCGAGATCTACACTGACCAACACTCTTTCCCTACACGACGCTCTTCCGATCTTATCTACGGGGACTTATCAGCCAACC
MarE	AATGATACGGCGACCACCGAGATCTACACACAGTGACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGGGACTTATCAGCCAACC
MarF	AATGATACGGCGACCACCGAGATCTACACGCCAATACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGGGGACTTATCAGCCAACC
MarG	AATGATACGGCGACCACCGAGATCTACACCAGATCACACTCTTTCCCTACACGACGCTCTTCCGATCTGATACGGGGACTTATCAGCCAACC
MarH	AATGATACGGCGACCACCGAGATCTACACACTTGAACACTCTTTCCCTACACGACGCTCTTCCGATCTTATCTACGGGGACTTATCAGCCAACC
MarI	AATGATACGGCGACCACCGAGATCTACACGATCAGACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGGGACTTATCAGCCAACC
MarJ	AATGATACGGCGACCACCGAGATCTACACTAGCTTACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGGGGACTTATCAGCCAACC
MarK	AATGATACGGCGACCACCGAGATCTACACGGCTACACACTCTTTCCCTACACGACGCTCTTCCGATCTGATACGGGGACTTATCAGCCAACC
MarL	AATGATACGGCGACCACCGAGATCTACACCTTGTAACACTCTTTCCCTACACGACGCTCTTCCGATCTTATCTACGGGGACTTATCAGCCAACC
MarM	AATGATACGGCGACCACCGAGATCTACACAGTCAAACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGGGACTTATCAGCCAACC
MarN	AATGATACGGCGACCACCGAGATCTACACAGTTCCACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGGGGACTTATCAGCCAACC
MarO	AATGATACGGCGACCACCGAGATCTACACATGTCAACACTCTTTCCCTACACGACGCTCTTCCGATCTGATACGGGGACTTATCAGCCAACC
MarP	AATGATACGGCGACCACCGAGATCTACACCCGTCCACACTCTTTCCCTACACGACGCTCTTCCGATCTTATCTACGGGGACTTATCAGCCAACC

Transposon sequence analysis

After demultiplexing the sequence data with the P5-index reads, the first step was pre-processing of the obtained raw sequences in the fastq format. The raw sequence files were first checked using the FASTQC tool which allows detection of low-quality samples and outliers via sequence quality plots. For each read, the read end nucleotides with a FASTQ quality Phred score “#” were removed, and the whole read was disposed of if the number of “#” exceeded 50 bp of half read length. Also, non-transposon reads were filtered out by keeping only the reads containing the sequence “TGTTA” at the beginning of the read. After these pre-processing steps, reads were mapped on the reference genome, which consists of the sequence of M. marinum M genome (NC_010612.1) and M. marinum M plasmid pMM23 (NC_010604.1) using Bowtie2. Only alignments with mapping quality >30 were kept for the downstream analysis which consisted of counting transposon insertions from alignment SAM files. Alignments having both the same insertion site and P7-indexed read were considered artefactual amplicons generated during the PCR amplification and were subsequently removed prior to counting of transposon insertions for each TA site. To detect and flag low-permissive sites, we determined the surrounding ±3 bp sequence and checked whether it matches with the pattern “SGNTANCS,” which has been associated with decreased transposon insertion (49) (see Table S1 at https://tinyurl.com/5dbxww5w).

The well-documented open-source software TRANSIT (50) was used for essentiality analysis and conditional essentiality analysis. For the former, the hidden Markov model (HMM) method was used with default parameters. The method uses an HMM to classify both, TA sites and corresponding genes into essential, non-essential, growth defects or growth advantages. NC_010612.1 and CP000854.1 were both used as gene annotations, plus the plasmid M plasmid pMM23 NC_010604.1. The TRANSIT input and output are documented in Tables S1 (https://tinyurl.com/5dbxww5w) and S2. TRANSIT outputs summary files on the TA site level (Table S1) and the gene annotation site level (Table S2). On the TA site level, only unique TA sites are presented, whereas in the gene summary file, TA sites in regions where two gene annotations overlap are presented for both of the overlapping genes. This method allows us to obtain gene essentiality calls for each condition, including the Inoculum, separately. Genes that were essential in the Inoculum but not during later time points were considered an artifact and were subsequently accounted for by limiting the essential core to the intersection of essential genes among all conditions. Furthermore, one gene was removed, due to a lack of MMAR ID. The essential core from this study was compared with genes being identified as essential in three other studies. Only orthologous genes were considered (E11 orthologs given by Weerdenburg et al. (45), H37Rv orthologs given by mycobrowser.epfl.ch, release 3, 5 June 2018) and only M. marinum M strain genes with a MMAR ID were used. This was done using R 4.2.1.

For conditional essentiality analysis, the resampling method of TRANSIT was used with the mean over non-zero sites (nzMean), effectively excluding low-permissive sites. Prior to that, the samples were normalized to contain 10⁶ reads per sample. The TRANSIT resampling method allows us to compare a test condition to a reference condition by subtracting the mean read count in the test condition from the mean read count in the reference condition, to obtain a difference of means. A null distribution was calculated by permutating genes among sites and samples (“pooled” option) and used to obtain an associated P-value. The corresponding TRANSIT output is documented in Table S3.

M. marinum cluster analysis for the evolution of the GD/GA scores

The gene clusters analysis was performed with the R programming language, using the content of Table S2 as input. Genes with at least 5 TA sites and a normalized read count >1 in any of the samples were selected. For each gene, we computed its profile across time points by considering the average of the normalized gene count for each condition. The Manhattan distance between each pair of gene profile was computed and a hierarchical clustering algorithm with ward.D linkage was run. The resulting clustering tree was cut at a height of 35, allowing us to retain nine clusters (Table S3).

Gene ontology analysis

Before functional group analysis via KEGG and GO enrichment, genes were thresholded as follows: fitness advantage (FA), log₂fc ≥ 0.585, fitness disadvantage (FD), log₂fc ≤ −0.585, FA and FD, abs(log₂fc) ≥0.585. Genes were additionally thresholded by P-value as follows: 24 hpi and 48 hpi, P-value ≤ 0.1, 2 × 48 hpi, P-value ≤ 0.01. Different significance thresholding was motivated by better visualization of enriched terms.

Prior to submission to the enricher function or enrichKEGG function of the R package clusterProfiler, only genes with a MMAR ID were used and mapped to UniProt AC using the mycobrowser annotation (release 3, 28 March 2018) and the UniProt annotation (queried for “Mycobacterium marinum”) (92). GO and KEGG term analyses were done with R (version 4.2.1) and the package clusterProfiler (version 4.4.4, 10.1016/j.xinn.2021.100141), respectively. For KEGG analysis, the KEGG annotation (release 103.1, 1 September 2022) (93) version was used, for GO terms, the QuickGO annotation for Mycobacterium marinum (version from 19.03.2021) was used (94). GO term names were extracted using the R package GO.db version 3.15.0. Group size was limited between 2 and 200 genes and only filtered by P-value < 0.01 for GO term analysis and P-value < 0.05 for KEGG analysis. For GO term analysis, all three aspects of the ontology were used: biological processes, molecular function, and cellular component.

STRING database analysis

The analysis was performed using Cytoscape 3.9.1 (95) with Omics Visualizer 1.3.0 (58) and the STRING database version 11.5 (96). First, log₂fc and P-values were taken by comparing 24, 48, and 2 × 48 hpi to the inoculum via the TRANSIT resampling methods, as described above. Genes were filtered for absolute log₂fc ≥ 0.585, P-value ≤ 0.05 in any of the three time points, and converted to each respective ortholog in Mycobacterium tuberculosis, for which the full STRING network was subsequently retrieved. The network was cut at a confidence cutoff of 0.8 and singletons were omitted. The color scale in the layered rings represents from inside to outside the log₂fc of 24, 48, and 2 × 48 hpi, respectively, compared to the inoculum and was chosen to be capped at an absolute value of 4, for better visibility of colors.

DATA AVAILABILITY

The HiSeq data sets are available in the Sequence Read Archive (SRA) repository, Bioproject accession number PRJNA967609. The supplemental tables and files are available in the Gene Expression Omnibus (GEO) repository, accession number GSE249096.

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/msystems.01326-23.

Legends. msystems.01326-23-s0001.docx.

Supplemental figure legends.

Figure S1. msystems.01326-23-s0002.pdf.

PCA of all experimental conditions in D. discoideum and library saturation.

Figure S2. msystems.01326-23-s0003.pdf.

Differential representation of M. marinum mutants

Figure S3. msystems.01326-23-s0004.pdf.

Hierarchical clustering of log2 fold changes in D. discoideum.

Figure S4. msystems.01326-23-s0005.pdf.

Fitness advantage and fitness disadvantage over the infection time course in D. discoideum.

Figure S5. msystems.01326-23-s0006.pdf.

Clustering of differentially affected genes of M. marinum during infection of BV2 microglial cells.

Table S2. msystems.01326-23-s0007.xlsx.

Gene counts for D. discoideum and BV2 experiments.

Table S3. msystems.01326-23-s0008.xlsx.

Conditional essentiality analysis for D. discoideum and BV2 experiments.

Table S4. msystems.01326-23-s0009.xlsx.

Enrichment analyses for D. discoideum and BV2 experiments.

OPEN PEER REVIEW. reviewer-comments.pdf.

An accounting of the reviewer comments and feedback.

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