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Distribution, frequency, and functional implications of mutations during laboratory evolution of obligate syntrophy
We evaluated whether the selection of mutations in the same genes (i.e., “parallel evolution” [17]) had contributed to improvements in syntrophic growth of Dv and Mm across independent evolution lines, all of which started with the same ancestral clone of each organism. The goal of this analysis was to focus on generalized strategies for adaptation to syntrophy, irrespective of the culturing condition so we investigated parallelism across both U and H lines. Based on the number of mutations (normalized to gene length and genome size) in Dv and Mm across 13 evolved lines (six lines designated U for “uniform” conditions with continuous shaking and seven H lines for “heterogenous” conditions without shaking), we calculated a G-score [18] (“goodness-of-fit”, see “Methods” section [18]) to assess if the observed parallel evolution rate was higher than expected by chance. The “observed G-score” was calculated as the sum of G-scores for all genes in the genome of each organism; mean and standard deviation of “expected G-scores” were calculated through 1000 simulations of randomizing locations of observed numbers of mutations across the genome of each organism. The observed total G-score for Dv (1092.617) and Mm (805.02) was significantly larger than the expected mean G-score (Dv: 798.19 ± 14.99, Z = 19.63 and Mm: 564.83 ± 15.95, Z = 15.06), demonstrating significant parallel evolution across lines.
With the exception of five high G-score genes (DVU0597, DVU1862, DVU0436, DVU0013, and DVU2394), which were mutated during long term salt adaptation of Dv [19], mutations in other high G-score genes appeared to be putatively specific to syntrophic interactions. Altogether, 24 genes in Dv and 16 genes in Mm associated with core processes had accumulated function modulating mutations across at least 2 or more evolution lines (Fig. 2 and Supplementary Table S1). Signal transduction and regulatory gene mutations (seven in Dv and six in Mm) represented 19.9% and 27.2% of all mutations in Dv and Mm, respectively, similar to long term laboratory evolution of E. coli [18], potentially because their influence on the functions of many genes [20, 21]. We also observed missense and nonsense mutations in outer membrane and transport functions (four genes in Dv and three genes in Mm). For example, the highest G-score gene in Dv, DVU0799—an abundant outer membrane porin for the uptake of sulfate and other solutes in low-sulfate conditions [22], was mutated early across all lines, with at least two missense mutations in UE3 (S223Y) and UA3 (T242P). Notably, the regulator of the archaellum operon (MMP1718) had the highest G-score with frameshift (11 lines) and nonsynonymous coding (2 lines) mutations [23]. Similarly, two motility-associated genes of Dv (DVU1862 and DVU3227) also accumulated frameshift, nonsense and nonsynonymous mutations across 4 H and 3 U lines. Together, these observations were consistent with other laboratory evolution experiments performed in liquid media [24], suggesting that retaining motility has a fitness cost during syntrophy [25, 26].
Fig. 2: Frequency and location of high G-score mutations in Dv (A) and Mm (B) across 13 independent evolution lines.

SnpEff predicted impact of mutations* are indicated as moderate (orange circles) or high (red circles) with the frequency of mutations indicated by node size. Expected number of mutations for each gene was calculated based on the gene length and the total number of mutations in a given evolution line. Genes with parallel changes were ranked by calculating a G (goodness of fit) score between observed and expected values and indicated inside each panel. Mutations for each gene are plotted along their genomic coordinates (vertical axes) across 13 evolution lines (horizontal axes). Total number of mutations for a given gene is shown as horizontal bar plots. [*HIGH impact mutations: gain or loss of start and stop codons and frameshift mutations; MODERATE impact mutations: codon deletion, nonsynonymous in coding sequence, change or insertion of codon; low impact mutations: synonymous coding and nonsynonymous start codon].

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Consistent with our previous observation that obligate mutual interdependence drove the erosion of metabolic independence of Dv [5, 27], mutations in SR genes were among the top contributors to the total G-score in Dv (DVU2776 (74.7), DVU1295 (46.5), DVU0846 (42.9), and DVU0847 (22.3)). However, it was intriguing that DVU2776 (DsrC), which catalyzes the conversion of sulfite to sulfide, the final step in SR, accumulated function modulating but not loss-of-function mutations across six lines. The functional impact of these mutations is not clear but it is possible that these changes might alter previously suggested alternative roles for this gene, including electron confurcation for the oxidation of lactate [28], sulfite reduction, 2-thiouridine biosynthesis and possibly gene regulation [29].
Analysis of temporal appearance and combinations of mutations across evolution lines
Growth characteristics of all evolution lines improved by the 300th generation [4], and in some lines even before the appearance of SR− mutations, indicating that mutations in other genes had also contributed to improvements in syntrophy. Each evolution line had at least 8 and up to 13 out of 24 high G-score mutations in Dv, while Mm had mutations in at least 5 and up to 10 out of 16 high G-score genes. We interrogated the temporal order in which high G-score mutations were selected and the combinations in which they co-existed in each evolution line to uncover evidence for epistatic interactions in improving obligate syntrophy. Indeed, missense mutations in DsrC (DVU2776) were fixed simultaneously with the appearance of loss of function mutations in one of two sigma 54 type regulators (DVU2894, DVU2394) in lines HA2, and UR1 (P = 5.40 × 10−5). In rare instances, we also observed that some high G-score mutations co-occurred across evolution lines, e.g., two U- and one H-line consistently showed for at least two time points a mutation in DVU1283 (GalU) coexisting with mutations in DVU2394 (P = 5.04 × 10−3). More commonly, the combinations of high G-score gene mutations varied across multiple lines. In fact, no two lines possessed identical combination of high G-score gene mutations (Fig. 3A, B), and many high-frequency mutations were uniquely present or absent in different lines (Fig. 3C, D).
Fig. 3: Frequency and time of appearance of mutations through 1 K generations of laboratory evolution lines of Dv and Mm cocultures.

The heat maps display frequency of mutations in genes (rows) in Dv (A) and Mm (B) in each evolution line, ordered from early to later generations (horizontal axis). High G-score genes are shown in red font and their G-score rank is shown to the left in gray shaded box, also in red font. Bar plots above heat maps indicate total number of mutations in each generation and the color indicates impact of mutation. Use “Frequency”, “Generations”, and “Mutation impact” key below the heat maps for interpretation. Mutations that were unique to each evolution line is shown in (C, D) for Dv and Mm, respectively. E The heatmap illustrates a selective sweep across both organisms in line HS3.

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Mutations in high G-score genes appeared consistently in all evolution lines (P 80% EPD-03 vs, More

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In most of the forest soil samples used in this survey, specimens belonging to the genus Hantzschia are quite common. Based molecular as well as on light microscopy (LM) and scanning electron microscopy (SEM) observations of 25 strains, seven different taxa were recognized. Figure 1 contains the locations of the strain’s habitats. In anticipation of the nomenclatural consequences, we are using the new names already here but will describe them formally later.
Figure 1

Map with the habitat locations of the studied strains.

Full size image

Molecular data
The obtained phylogenetic tree for representatives of the different strains Hantzschia contains several large clades, some of which are monophyletic, while others contain several different species names (Fig. 2). In the analyzed tree, the largest clade is represented by different strains of H. amphioxys, the structure of which is described in the corresponding molecular analysis section. At the same time, the most significant is that in the same clade there is strain H. amphioxys D27_008, which has been designated as epitype20. One of the largest is the clade with H. abundans, which, in addition to our strains, and some that have already been published, includes the group of strains referred to as “Hantzschia sp. 3” (Sterre6)e, (Sterre6)f from Souffreau et al.16. We propose to refer to all of these strains as H. abundans. The next clade consists of the new species of H. attractiva and three strains of Hantzschia sp. 2 (Mo1)a, (Mo1)e, (Mo1)m from Souffreau et al.16, the latter we propose to merge into the new species named H. pseudomongolica, which is sister to H. attractiva. Given the topology of the tree and the morphological features of the representatives, we can conclude that there is a close relation between H. abundans and H. attractiva plus H. pseudomongolica. A separate group consists of two clades with sufficient statistical support (likelihood bootstrap, LB 76; posterior probability, PP 100), one of which is represented by two strains of H. parva, and the other with strains of H. cf. amphioxys (Sterre1)f, (Sterre1)h. Another large clade represents a set of strains of Hantzschia sp. 1 and Hantzschia sp. 2 (Mo1)h, (Mo1)l from Souffreau et al.16, among which there are both large cells (86–89 µm length) and smaller ones (37–39 µm length); strains also differ by striation – from 18–20 striae in 10 μm (strain (Mo1)h) to 21–22 in 10 μm (strain (Ban1)h). It is possible that Hantzschia sp. 1 and Hantzschia sp. 2 (Mo1)h, (Mo1)l may be several closely related species. Besides the large clades, there are a number of separate branches in the tree, representing separate strains: Hantzschia sp. 1 (Ban1)d, and the new species H. belgica (H. cf. amphioxys (Sterre3)a from Souffreaua et al.16) and H. stepposa. Interesting is the position of the H. abundans (Tor3)c strain, which is very distant from other representatives of H. abundans and probably is a cryptic taxon, whose taxonomic status needs to be revised.
Figure 2

Bayesian tree for representatives of the different strains Hantzschia, from an alignment with 40 sequences and 1785 characters (partial rbcL gene and 28S rDNA fragments). Type strains indicated in bold. The epitype of Hantzschia amphioxys is underlined. Values above the horizontal lines (on the left of slash) are bootstrap support from RAxML analyses ( More

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The two proteomics methods used, 2D-DIGE and iTRAQ LC–MS/MS are complementary and both rely on LC–MS/MS for protein identification. 2D-DIGE is a top-down approach, quantifying the differentially expressed proteins at the protein level before identifying the protein by LC–MS/MS, while the iTRAQ method is a bottom-up approach, where the whole proteome is first digested with trypsin, the generated peptides are separated by chromatography and identified and measured by mass spectrometry. Mass spectrometry has become the primary method to analyze proteomes, benefitting from genomic sequences and bioinformatics tools that can translate the sequences into predicted proteins. There are excellent reviews of proteomics methods and how they may be used across species8,9.
In total, 19 of the 26 proteins identified using the 2D-DIGE method were also identified by iTRAQ (Supplementary Table 1) which showed that these findings were replicated using two complementary experimental methods. In the 2D-DIGE method, most of the proteins were found in multiple spots, suggesting that they were differentially modified. 2D-DIGE can separate proteins based on a single charge difference. Some of the spots contained multiple proteins so it was difficult to determine the fold change of each of the proteins in these spots. For example, protein C4A was identified in 7 different spots, likely representing multiple isoforms. We were not able to corroborate the different post-translational modifications (PTMs) with iTRAQ, as the experiment was not designed to look for PTMs, only total protein quantitation. A drawback of 2D-DIGE is that keratin introduced into the sample from reagents at the time of electrophoresis or through the multiple steps required for protein extraction is also seen in the gels10,11,12. It is unlikely that the keratins were from the serum samples, as blood was collected directly into vacuum tubes. Because of the issue of keratin contamination, the 2D-DIGE method is considered more qualitative in its determination and thus in this study, iTRAQ data were the primary basis for quantitation.
Pathway analysis detects groups of proteins that are linked in pathways that may be related to disease processes. We used Pathway Studio using subnetwork enrichment analysis to determine disease pathways potentially in place for the red tide and IRL manatees. The Pathway Studio database is constructed from relationships detected between proteins and diseases from articles present in Pubmed but is heavily directed towards human and rodent proteomes. To be able to use this tool, we assigned human homologs to the identified manatee proteins, assuming that based on their sequence homology the proteins would function in a similar way. There are many studies that suggest this assumption has merit, for example Nonaka and Kimura have examined the evolution of the complement system and found clear indications of homology among vertebrates13.
The top 20 pathways for the red tide group (Table 3) and the IRL group (Table 4) show the diverse set of molecular pathways that may be affected by the exposures. Many of the same pathways appeared for both groups including thrombophilia, inflammation, wounds and injuries, acute phase reaction and amyloidosis. Thrombophilia was the most upregulated pathway for the IRL group (p-value 1.10E-19) and the second most upregulated pathway for the red tide group (p-value 4.1E-19). Thrombophilia, a condition in which blood clots occur in the absence of injury, happens when clotting factors become unbalanced. We obtained proteomics information on 12 of the proteins in this pathway, with some moving in opposing directions. The dysregulated proteins that were increased for both red tide and the IRL groups were SERPIN D1 (Serpin family member D 1), CRP (C-reactive protein), and PLAT (plasminogen activator) and the ones that were decreased in both groups, were SERPIN C1 (Serpin family member C 1), F5 (coagulation factor 5), and ALB (albumin). One protein, AGT (angiotensinogen), was upregulated in the red tide group but downregulated in the IRL. HRG (histidine rich glycoprotein), PROS1 (Protein S), C4BPA (complement component 4 binding protein alpha, and F2 (coagulation factor 2, also known as prothrombin) were downregulated in the red tide group but upregulated in the IRL group. The disparate regulation of proteins in this pathway suggests that clotting was among the pathways disrupted in the affected manatees. Red tide exposed manatees often present with hemorrhagic issues in their intestines, lungs and the brain (14), suggesting that downregulation of coagulation factors may be responsible for this clinical evaluation. Interestingly HRG was upregulated in the IRL by 1.34-fold and downregulated in the red tide group by 0.56-fold, making this protein a good biomarker to distinguish the two events.
Table 3 Subnetwork enrichment pathways for serum proteins obtained from manatees exposed to red tide.
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Table 4 Subnetwork enrichment pathways for serum proteins obtained from manatees sampled in the IRL.
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Among the manatees in the red tide group, inflammation was ranked 3rd (p-value  More