Philippa Kaur

Conceptualization of organismal to tumor biogeography through clone phylogeniesWe first map organismal biogeographic concepts and models to the process of migration and colonization of cancer cells during metastasis. Tumors are populations consisting of a diversity of cancer cells with different genetic profiles that may represent different lineages in the clone phylogeny. We use the example in Fig. 1, which contains a phylogeny of 17 clones found in one primary tumor (P) and four metastases (M1–M4). Events occurring along a branch in a phylogeny are anagenetic events, which include diversification, extinction, and expansion12,14. In organismal evolutionary biology, anagenetic events are not directly observed except through the fossil record. However, one can map the collection of genetic variants that likely arose on individual lineage in a phylogeny. In many cancers, sequencing of temporally sampled biopsies’ can directly reveal anagenetic events similar to the sequencing of ancient DNA in paleogenomics.The other types of evolutionary events in the phylogeny are cladogenetic, including genetic divergences and dispersals (Fig. 1). Genetic differences observed among species and populations are the key to detect cladogenetic events reconstructed in molecular phylogenies of living descendants. In cancer, temporal sampling of biopsies can reveal cladogenetic events that produced extinct descendants.In biogeography, genetic divergence results in the diversification of lineages within an area. Sometimes, the term duplication is used, but we avoid its further use because of the confusion it may cause in evolutionary genomics. Divergence events are also observed in a clone phylogeny, particularly when clone lineages diverge from each other within a tumor or across tumors. The exact opposite of genetic diversification can also be observed when lineages partially or fully disappear from the phylogeny. Extinction can occur due to random chance, selection, or environmental pressures. Even though extinction is rarely discussed in tumor clone phylogenetics, it happens frequently.Phylogenies also reveal movements of lineages between locations (geographic areas or body parts) when the locations of individual cells, species, or populations are known5,6,7,8,9,15. When lineages accumulate genetic differences along a branch in the phylogeny, and the evolved lineages migrate to a new area, we observe an expansion event. Expansions differ from dispersals in such that the growth of a population occurs in the same place. This movement of cells of a clone from one location to another, where they would potentially form a metastasis, results in the dispersal of  these cells of that clone to additional areas, which is modeled by a dispersal rate (d) in organismal biogeography. When a clone genetically diverges following its migration, then a distant dispersal event is said to have occurred. Similarly, when a clone diverges from the rest of the clones within a tumor and disperses to another tumor, we have observed an expansion event. Thus, clone phylogenies can give insights into the origin and trajectory of cancer cells between tumors.When a clone is no longer present at a location, it is extinct at that location. Extinctions are modeled by an extinction rate (e) in biogeographic models. As a result of extinction, the range of descendent clones on a phylogeny can be smaller than the ancestors. Biogeography models also have a parameter (J) to consider founder events that establish new populations from a few individuals. In phylogenies, founder events can be detected if only one or a few cells are found to have moved from one location to another to start diversifying in a new area. Both distant dispersal and founder events may result in forming a new colony of cells, i.e., a new metastasis in the case of cancer cell migrations. The primary distinction between dispersal and founder events is the relative number of migrating cells. Founder events are due to one or a few cells, whereas dispersal events involve a larger number of migrating cells. Founder events are expected to be more common in tumor evolution because metastases are thought to be formed by the spread of only one or a few cancer cells. These biogeographic events have been mathematically modeled and implemented in various approaches to infer species migration events12, which are directly applicable in the inference of cancer cell migrations between tumors.Model fitsWe began by analyzing the statistical fits of six biogeographic models (Table 1) to 80 computer-simulated tumor evolutionary datasets. Simulations enable us to assess the performance of computational approaches and reveal potential caveats associated with their use because the ground truth is known. These datasets were simulated using four main clone migration schemes defined by the different number of migrating clones (1–3), the small and large number of tumor areas (5–7 tumors, m5 datasets; 8–11 tumors, m8 datasets), and the different types of source areas of migration (primary or metastasis). The following seeding scenarios reflect this complexity of the clone migration schemes: monoclonal single-source seeding (mS), polyclonal single-source seeding (pS), polyclonal multisource seeding (pM), and polyclonal reseeding (pR) (see “Methods” section).Table 1 Phylogenetic and biogeographic events considered in seven biogeographic models used for analysis.Full size tableWe considered biogeographic models that weigh genetic divergence, dispersal/expansion, and extinction events differently (Table 1). We also explored the provision of including founder events in our models on the accuracy of detecting clone migrations. The parameterization of the aforementioned events results in models with two free parameters, i.e., dispersal rate (d) and extinction rate (e), and models with three free parameters by adding the founder-event speciation (J); see “Methods” section for more details.Overall, we tested six biogeographic models for their fit to the tumor data, three models with two free parameters and three others with three free parameters. BAYAREALIKE, DEC, and DIVALIKE models have two parameters each. They are nested within their respective models that add the founder effect, resulting in a model with three free parameters (hereinafter +J models). We used the BioGeoBEARS software for all model fit analyses. In data analysis, we first inferred phylogeny of cancer cell populations (clone phylogeny) using an existing method16, followed by the use of BioGeoBEARS to infer the clone migration history in which the clone phylogeny is used along with the location of tumor sites in which each clone is observed (Fig. 2). BioGeoBEARS estimates the probabilities of annotating internal nodes with tumor locations. These annotations are then used to derive cancer cell migration paths when two adjacent nodes are annotated with different tumor locations. In these analyses, we assumed the correct clone phylogeny because our focus was not assessing the impact of errors in a phylogeny on the accuracy of clone migration inferences. We also compared the accuracy of migration histories reconstructed using biogeographic models in BioGeoBEARS with those obtained from the approaches that do not model biogeographic processes (BBM9, MACHINA5, and PathFinder7).Figure 2Data analysis pipeline using BioGeoBEARS in R14 to infer clonal migration histories.Full size imageWe first conducted Likelihood Ratio Tests (LRTs) to examine the improvement offered by considering founder events in modeling tumor migrations. In this case, the fit of the BAYAREALIKE, DEC, and DIVALIKE models was compared to their +J counterparts, respectively. The null hypothesis was rejected for more than 50% of the datasets (BAYAREALIKE: 71.25%, DEC: 60%, and DIVALIKE: 53.75%; P  More

The Fur-regulated T6SS1 plays an important role in iron acquisition in C. necator
To explore the function of T6SS1 (Reut_A1713 to Reut_A1733) in C. necator (Fig. S1A), we analyzed the T6SS1 promoter and identified a Fur binding site (AGAAATA) upstream of gene reut_A1733. This Fur binding site was highly similar to the Fur-box reported in E. coli [38], with a probability score of 2.25 (out of a maximum score = 2.45) (Fig. S1B), which was calculated by applying the position weight matrix to a sequence [39]. Incubation of the T6SS1 promoter probe with purified Fur protein led to decreased mobility of the probe in the electrophoretic mobility shift assay, suggesting a direct interaction between Fur and the T6SS1 promoter (Fig. 1A). To further determine the function of Fur on the expression of T6SS1, a single-copy PT6SS1::lacZ fusion reporter was introduced into the chromosomes of C. necator wild-type (WT), Δfur deletion mutant, and the Δfur(fur) complementary strain. Compared to WT, the PT6SS1::lacZ promoter activity was significantly increased in the Δfur mutant (about 2.2-fold), and this increase could be restored by introducing the complementary plasmid pBBR1MCS-5-fur (Fig. 1B). Similar results were obtained by analyzing the expression of T6SS1 core component genes (hcp1, clpV1, vgrG1, and tssM1) with qRT-PCR (Fig. S1C). These results demonstrate that the expression of T6SS1 in C. necator is directly repressed by Fur, the master regulator of genes involved in iron homeostasis in many prokaryotes [40, 41].Fig. 1: Regulation of T6SS1 expression by Fur.A The interactions between His6-Fur and the T6SS1 promoter examined by EMSA. Increasing amounts of Fur (0, 0.03, 0.06, 0.13, 0.25, and 1.0 μM) and 10 nM DNA fragments were used in the assay. A 500 bp unrelated DNA fragment (Control A) and 1 µM BSA (Control B) were included in the assay as negative controls. B Fur represses the expression of T6SS1. β-galactosidase activities of T6SS1 promoter from chromosomal lacZ fusions in relevant C. necator strains were measured. C Iron uptake requires T6SS1. Stationary-phase C. necator strains were washed twice with M9 medium. Iron associated with indicated bacterial cells were measured with ICP-MS. The vector corresponds to the plasmid pBBR1MCS-5 (B) and pBBR1MCS-2 (C), respectively. Data are represented as mean values ± SD of three biological replicates, each with three technical replicates. **p  More

AffiliationsAnimal Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsAbigail Muscat, Evelien M. de Olde, Raimon Ripoll-Bosch & Imke J. M. de BoerFarming Systems Ecology group, Wageningen University & Research, Wageningen, The NetherlandsHannah H. E. Van ZantenPublic Administration and Policy group, Wageningen University & Research, Wageningen, The NetherlandsTamara A. P. Metze & Catrien J. A. M. TermeerPlant Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsMartin K. van IttersumAuthorsAbigail MuscatEvelien M. de OldeRaimon Ripoll-BoschHannah H. E. Van ZantenTamara A. P. MetzeCatrien J. A. M. TermeerMartin K. van IttersumImke J. M. de BoerCorresponding authorCorrespondence to
Imke J. M. de Boer. More

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