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Research Article
21 September 2021

Prevalence and Specificity of Chemoreceptor Profiles in Plant-Associated Bacteria

Authors: Claudia Sanchis-López https://orcid.org/0000-0002-8206-1565, Jean Paul Cerna-Vargas https://orcid.org/0000-0001-5489-0385, Saray Santamaría-Hernando https://orcid.org/0000-0001-6763-3839, Cayo Ramos https://orcid.org/0000-0002-2362-5041, Tino Krell https://orcid.org/0000-0002-9040-3166, Pablo Rodríguez-Palenzuela https://orcid.org/0000-0002-4963-9177, Emilia López-Solanilla https://orcid.org/0000-0002-8578-7433, Jaime Huerta-Cepas https://orcid.org/0000-0003-4195-5025 [email protected], José J. Rodríguez-Herva https://orcid.org/0000-0001-9962-2261 [email protected]Authors Info & Affiliations
https://doi.org/10.1128/msystems.00951-21
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ABSTRACT

Chemosensory pathways are among the most abundant prokaryotic signal transduction systems, allowing bacteria to sense and respond to environmental stimuli. Signaling is typically initiated by the binding of specific molecules to the ligand binding domain (LBD) of chemoreceptor proteins (CRs). Although CRs play a central role in plant-microbiome interactions such as colonization and infection, little is known about their phylogenetic and ecological specificity. Here, we analyzed 82,277 CR sequences from 11,806 representative microbial species covering the whole prokaryotic phylogeny, and we classified them according to their LBD type using a de novo homology clustering method. Through phylogenomic analysis, we identified hundreds of LBDs that are found predominantly in plant-associated bacteria, including several LBDs specific to phytopathogens and plant symbionts. Functional annotation of our catalogue showed that many of the LBD clusters identified might constitute unknown types of LBDs. Moreover, we found that the taxonomic distribution of most LBD types that are specific to plant-associated bacteria is only partially explained by phylogeny, suggesting that lifestyle and niche adaptation are important factors in their selection. Finally, our results show that the profile of LBD types in a given genome is related to the lifestyle specialization, with plant symbionts and phytopathogens showing the highest number of niche-specific LBDs. The LBD catalogue and information on how to profile novel genomes are available at https://github.com/compgenomicslab/CRs.
IMPORTANCE Considering the enormous variety of LBDs at sensor proteins, an important question resides in establishing the forces that have driven their evolution and selection. We present here the first clear demonstration that environmental factors play an important role in the selection and evolution of LBDs. We were able to demonstrate the existence of LBD families that are highly enriched in plant-associated bacteria but show a wide phylogenetic spread. These findings offer a number of research opportunities in the field of single transduction, such as the exploration of similar relationships in chemoreceptors of bacteria with a different lifestyle, like those inhabiting or infecting the human intestine. Similarly, our results raise the question whether similar LBD types might be shared by members of different sensor protein families. Lastly, we provide a comprehensive catalogue of CRs classified by their LBD region that includes a large number of putative new LBD types.

INTRODUCTION

To ensure cell survival, bacteria have to adapt to changing environmental conditions (1). For this, bacterial cells are equipped with an array of different signal transduction systems that sense different environmental stimuli, such as osmolarity, oxygen tension, temperature, pH, light, nutrients, toxins, and other chemicals (2). Chemosensory pathways represent one of the primary bacterial signal transduction mechanisms, and more than half of all the bacterial genomes contain signaling genes (3). Most chemosensory pathways appear to mediate chemotaxis (3), whereas others have been associated with type IV pilus-based motility (4) or alternative cellular functions such as the control of second messenger levels (4, 5).
In a canonical chemosensory pathway, signals are perceived by binding specific molecules to the ligand binding domain (LBD) of chemoreceptors (CRs), which modulates the activity of the CheA autokinase and the subsequent transphosphorylation to the CheY response regulator. In canonical CRs, the extracytosolic LBD is flanked by two transmembrane (TM) regions, a cytosolic HAMP domain, and a signaling domain (MCPsignal). While the CR signaling domain (MCPsignal) is highly conserved, LBDs are rapidly evolving domains (6), which reflects the wide variety of chemoeffectors to be sensed. To date, more than 80 different LBD families have been identified (7, 8), and new types of LBDs continue to be discovered (9). The thermodynamic parameters for ligand binding to the individual CRs are very similar to those for binding to specific LBDs (10, 11), supporting the idea that the molecular determinants for signal recognition by CRs are located in the LBD. Further evidence of this came from the construction of chimeric receptors recombining LBDs with other signaling domains (e.g., autokinase domains), where the LBD was proved to define the function of the chimera (12, 13). Thus, while the conserved MCPsignal domain can be used to identify CRs, their LBDs allow them to be classified on the basis of their function (7, 8).
On the other hand, there is evidence suggesting that the genomic repertory of CRs is related to bacterial lifestyle (14, 15). For instance, it has been shown that plant-associated bacteria (PAB) possess a particularly large number of CRs (8, 16), indicating that chemosensory signaling is indeed an important requisite for plant-bacterium interactions. This is of particular relevance for plant pathogens and symbionts, for which it has been shown that flagellum-mediated chemotaxis is required for optimal virulence or symbiosis establishment (17–25). Plants represent complex habitats for colonization by different kinds of microorganisms, and PAB species can colonize the plant rhizosphere, phyllosphere, or endosphere (26). Motile sensory behavior has been shown to play a key role in the establishment of plant-microbe interactions, since bacteria that can sense and rapidly navigate toward niches optimal for growth and survival will have a clear competitive advantage (27–29). These considerations are valid for both pathogenic and nonpathogenic relationships between microorganisms and plants (8, 16). Similarly, microbial inhabitants of the phyllosphere, comprising the aerial part of plants, have to deal with the challenges of life on leaf surfaces, where flagellar motility confers advantages in terms of epiphytic fitness (30). The epiphytic lifestyle also represents the initial stage of foliar colonization by many bacterial phytopathogens, preceding entry into the leaf apoplast via wounds or natural plant openings (e.g., stomata) (30). However, despite their biological significance, the function and cognate signal have been determined for only a limited number of CRs from PAB, and very little information exists on their phylogenetic and ecological specificity.
In order to study those LBD types most tightly coupled to the plant-associated lifestyle, here we comprehensively identified the CR genes in all known bacterial lineages and classified them according to their LBDs, with a particular focus on the LBD types linked to a plant-associated lifestyle. As such, we employed a novel de novo methodology to extract putative LBD regions from all CR sequences and group them into homology-based clusters (i.e., putative LBD types). This analysis allowed us to identify hundreds of LBD types highly specific for PAB species, many of them unknown. We further found that the taxonomic distribution of the majority of PAB-specific LBD clusters is only partially explained by phylogeny, suggesting that niche and host adaptation might have played relevant roles for their selection. Together, these results form a solid basis for the design of experiments aimed at identifying CRs that are essential for plant-microbe interactions and virulence.

RESULTS

Towards a global catalogue of chemoreceptors in plant-associated bacteria.

In order to maximize the coverage of our analysis, we first built a comprehensive catalogue of CRs detected across the entire prokaryotic phylogeny (Fig. 1). Species genomes were retrieved from the proGenomes v2 databases (31). Unlike the NCBI Taxonomy database, which is not an authoritative source for nomenclature or classification (32), proGenomes2 data do not rely on taxonomic names to identify species. Instead, each species-representative genome in proGenomes is delineated based on the evolutionary distances calculated between universally conserved genes present in nearly all organisms (32, 33). To establish links between CRs and the plant-associated lifestyle, we compiled three manually curated databases of PAB (see Materials and Methods): (i) PAB-broad, a reference database of 960 organisms found in multiple plant environments including leaves, roots, and rhizospheric soil; (ii) PAB-phyto, a subset database of 119 species including only known phytopathogens; and (iii) PAB-symb, which groups 192 plant symbionts. Using HMM-based searches, we then mined all the sequences containing the MCPsignal domain in the 11,806 species-representative genomes from the proGenomes database, compiling a global catalogue of 82,277 CR sequences from 5,546 genomes (see Data Set S3 in the supplemental material). This confirms the broad distribution of CRs, with 47% of the representative genomes containing at least one chemotactic receptor.
FIG 1
FIG 1 Schematic view of the bioinformatics pipeline used to identify CRs that are potentially relevant for plant association. From a set of 11,806 representative prokaryotic genomes, 82,277 protein sequences were mined using HMM-based searches against the MCPsignal Pfam domain (PF00015). CR topology was analyzed by predicting transmembrane regions (TMs) and Pfam domains. Based on the topological analysis, LBD regions were predicted and a set of 72,480 LBD sequences was obtained. Clustering of LBDs based on sequence homology (20% minimum sequence identity with at least 50% sequence coverage) resulted in 5,149 clusters or subfamilies of LBDs, of which 1,842 contained a single sequence. To study a possible link between the LBD profiles and plant-associated lifestyle, a manually curated subset of 960 representative species of plant-associated bacteria (PAB) was generated, including phytopathogen (119) and symbiont (192) subsets. The determination of the proportion of PAB LBDs present in each cluster allowed us to assign the degree of plant specificity (DPS) value for each LBD subfamily. Subsequent analysis of high-DPS clusters identified LBD clusters that are potentially important for bacterium-plant associations. Furthermore, the validation of the high-DPS clusters as good ecological indicators was corroborated by measuring their phylogenetic signal. A detailed step-by-step description of the process can be found in Materials and Methods.
PAB species possessed almost twice as many CRs per genome (22.86) as those species not classified as plant associated (12.94), with the subset of phytopathogens showing the highest number (27.29). No CRs were predicted in 178 out of the 960 PAB genomes, indicating that more than 81% of PABs possess at least one CR gene, a percentage largely superior to the bacterial average (47%). From all species considered in this study, 36 PAB genomes stood out by their high content of CRs (Table 1), most notably the following: (i) 14 genomes from the Pseudomonas genus (49 to 60 CRs), including the well-known plant pathogens P. syringae and P. savastanoi (34), and (ii) 9 genomes from the Herbaspirillum genus (52 to 67 CRs), a group of betaproteobacteria that endophytically colonize gramineous species, thereby promoting their growth (35).
TABLE 1
TABLE 1 List of PAB with the highest number of predicted CRs
TaxIdBiosampleRepresentative speciesNo. of CRsPAB-phytoaPAB-symbb
1078773SAMN04334956Herbaspirillum rubrisubalbicans M167  
1144319SAMN00839627Herbaspirillum sp. CF44467  
964SAMN03779333Herbaspirillum seropedicae65  
193SAMN02982994Azospirillum lipoferum65  
286727SAMN02982917Azospirillum oryzae64  
346179SAMN03785417Herbaspirillum rhizosphaerae62  
864073SAMN02471292Herbaspirillum frisingense GSF3062  
237610SAMN05860868Pseudomonas psychrotolerans60  
288000SAMN02598359Bradyrhizobium sp. BTAi160 S
92645SAMN06130964Herbaspirillum frisingense59  
1175306SAMN02469572Herbaspirillum sp. GW10359  
1121033SAMN02440867Azospirillum halopraeferens DSM 367558  
169679SAMN05170519Clostridium saccharobutylicum58  
29438SAMN03837775Pseudomonas savastanoi57P 
1262470SAMN03010392Herbaspirillum hiltneri N355  
582667SAMN05192568Methylobacterium pseudosasicola54  
50340SAMN05216581Pseudomonas fuscovaginae54  
1001585SAMN02603190Pseudomonas mendocina NK-0154  
1749078SAMN04216969Pseudomonas sp. EpS/L2553  
1190415SAMN05216593Pseudomonas asturiensis53  
50340SAMN03100370Pseudomonas fuscovaginae53  
129140SAMN03976254Pseudomonas syringae pv. tagetis52P 
294SAMN04992557Pseudomonas fluorescens52  
1855289SAMN05216319Duganella sp. CF40252  
1144342SAMN00839653Herbaspirillum sp. YR52252  
47885SAMN03365871Pseudomonas oryzihabitans51  
205918SAMN02604347Pseudomonas syringae pv. syringae B728a51P 
1907416SAMN05880558Aeromonas sp. RU39B51  
693986SAMN03075686Methylobacterium oryzae CBMB2050  
1736267SAMN04151647Pseudomonas sp. Leaf12750  
114615SAMEA3138227Bradyrhizobium sp. ORS 27850 S
1028989SAMD00019511Pseudomonas sp. StFLB20950  
80867SAMN04009978Acidovorax avenae50P 
1122963SAMN02440654Pleomorphomonas oryzae DSM 1630050  
223283SAMN02604017Pseudomonas syringae pv. tomato DC300049P 
1245469SAMD00061052Bradyrhizobium oligotrophicum S5849 S
a
P, phytopathogen.
b
S, plant symbiont.

Classifying chemoreceptors according to their ligand binding domain.

As the ecological relevance of CRs is mostly defined by their LBD region, we explored whether sequence segments corresponding to the LBD, rather than the full-length CR sequences, were related to a plant-associated lifestyle. To maximize the number of LBD sequences included in our analysis and not limit this to known LBD types from the Pfam database (7), we inferred LBDs based on the domain architecture of each CR. First, we extracted LBD sequences from the whole set of 82,277 CRs. Next, and given the high variability in the domains that could be considered LBDs, we identified putative LBDs using three different strategies: (i) detecting sequence regions matching any known domain other than the MCPsignal or HAMP, (ii) locating sequence regions flanked by two TM regions, and (iii) taking domains between the N-terminus and a single TM region. In total, we retrieved 72,480 putative LBD sequences, which could be fitted into three main groups based on their length (Fig. 2). The first group includes LBDs with a size between 60 and 110 amino acids, containing 21% of all the LBDs detected. The most abundant LBD family within this size range was PAS_3. The second group, comprising LBDs from 130 to 200 amino acids, contained over 45% of all LBDs and included 4HB_MCP_1 as the predominant family. The third group, comprising LBD lengths between 220 and 299 amino acids, covers 26% of all LBDs and has dCache_1 as the most abundant LBD family. Only 8% of all the LBDs detected fell outside these three size ranges, and the three most abundant LBDs were 4HB_MCP_1 (17.6%), dCache_1 (15.5%), and PAS_3 (9.2%).
FIG 2
FIG 2 Length distribution of the LBDs. The analysis was conducted on 72,480 LBDs, and the predominant LBD types within each of the main peaks are indicated. Only LBDs shorter than 500 amino acids (aa) are represented.
We next investigated whether LBDs could be classified into broader sequence homology clusters, each representing a group of LBD sequences sharing a common evolutionary origin. Using relaxed homology thresholds (E value ≤10−3, 50% coverage, 20% amino acid identity), we grouped all 72,480 LBD sequences into 5,149 family clusters (Data Set S4), of which 3,307 contain more than 1 sequence. This de novo clustering approach might not be adequate for a detailed functional characterization of LBDs, as single residue changes have been shown to modify LBD ligand affinities (36–38). Nevertheless, each of our LBD clusters could be interpreted as an independent LBD type, with implicit levels of functional and ecological conservation. In fact, our approach consistently recovered all known LBD types and distributed them into 2,068 compact clusters where 90% of their members belonged to the same Pfam domain family (Table S1). Moreover, our clustering strategy allowed us to split large LBD families into finely grained subcategories (Fig. 3). For example, despite 4HB_MCP and Cache-like being present at similar levels in the initial CR sequence database, the number of derived clusters differs significantly, namely, 20.9% for 4HB_MCP compared to 8.3% for Cache-like. In the case of 4HB_MCP_1, the 10,034 sequences group into 856 different clusters compared to the 283 clusters for the 9,162 dCache_1 sequences, indicating higher sequence conservation in the latter. The situation is even more drastic in the case of PAS_3 LBDs, where 2,675 sequences group into just 21 clusters (Table S1), indicating a very low degree of diversity.
FIG 3
FIG 3 Visual representation of the abundance of the LBD families. The outer donut of the chart represents the distribution of each LBD type and its relative abundance (in percentage of sequences), and the number of clusters with at least 90% of their sequences sharing the same LBD type, as defined by the Pfam signature. The LBDs are sorted according to the number of clusters within each LBD type. The inner donut of the chart represents all the clusters included within each LBD category, indicating the number of sequences contained in each subfamily. All singletons are merged in the last section of each LBD type (e.g., LBDs classified as “Unknown” have 1,242 singletons, that is, clusters containing only one sequence). “Mixed clusters” are those that do not reach the 90% threshold of sequences with the same Pfam model per cluster. “Low-abundance LBDs” include those LBD types that group into fewer than 12 “compact clusters.”
Notably, an important fraction (45%) of the LBD clusters inferred could not be confidently associated with any previous family of Pfam domains, since more than 90% of their LBD sequences did not match to any known domain signature, suggesting the existence of a large number of unknown LBD types.

Identifying PAB-specific ligand binding domains.

To identify LBD families specific to a plant-associated lifestyle, we analyzed each LBD cluster and calculated the corresponding percentage of PAB species therein, which we referred to as the degree of plant specificity (DPS; see Materials and Methods). For each LBD cluster, we calculated three DPS values, based on three databases of PAB species: (i) DPS-broad, calculated based on the PAB-broad reference database; (ii) DPS-phyto, based on the PAB-phyto subset; and (iii) DPS-symb, using the PAB-symb subgroup as a reference. In all cases, the DPS values ranged from 0% (no LBD family observed in the corresponding PAB database) to 100% (the LBD cluster includes only species from a given reference PAB database). From the 3,307 LBD nonsingleton clusters, we identified 419 and 139 clusters with a DPS-broad score of ≥50%, and ≥80%, respectively. Similarly, many LBD clusters showed high specificity in the stricter PAB reference databases (Data Set S5).
To further validate our findings, we cross-linked our predictions with experimental data from previous studies (39–43). In particular, we found that CRs with increased expression in planta, and particularly those required for full bacterial virulence, belonged to high-DPS clusters (Table 2). This list includes CRs that are upregulated in Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14, two soft-rot bacterial strains (39); Dickeya dianthicola RNS04.9, which grows on macerated potato tubers (40); and Xanthomonas fragariae, which grows on strawberry leaves (41). Similarly, we found several CRs with very high DPS values (80%) that were shown to be relevant in Xanthomonas citri virulence (42) or required for fitness of Pseudomonas savastanoi pv. savastanoi in olive knots (43). Taken together, these data support the validity of our approach to identify CRs that are relevant for a plant-associated lifestyle.
TABLE 2
TABLE 2 CRs predicted to be involved in plant-bacterium interactions
CR gene ID (in the original source)LBD typeDPS (%)DPS-phyto (%)DPS-symb (%)Cluster no.Amino acid identity (%) to representative LBDs from the databaseBacterial species and strainReference
ABF-0014824TarH1001000932100Dickeya dadantii 393739
ABF-0015168TarH68.0940.430179100Dickeya dadantii 393739
ABF-0016115HBM63.6448.480409100Dickeya dadantii 393739
ABF-0016585TarH51.0914.600.7342100Dickeya dadantii 393739
ABF-0017097Unknown80.0080.000838100Dickeya dadantii 393739
ABF-00176744HB_MCP_110087.500630100Dickeya dadantii 393739
ABF-0019851TarH61.2242.860123100Dickeya dadantii 393739
ABF-0019855TarH61.2242.860123100Dickeya dadantii 393739
ABF-0020431sCache_234.5512.730233100Dickeya dadantii 393739
DDI_0843dCache_138.6110.131.2751100Dickeya dianthicola RNS04.940
DDI_0932sCache_234.5512.730233100Dickeya dianthicola RNS04.940
DDI_1647TarH61.2242.86012388.97Dickeya dianthicola RNS04.940
DDI_1649TarH61.2242.860123100Dickeya dianthicola RNS04.940
DDI_2258HBM1001000792100Dickeya dianthicola RNS04.940
DDI_40924HB_MCP_110087.500630100Dickeya dianthicola RNS04.940
ADT-0000027HBM63.6448.48040996.03Pectobacterium carotovorum WPP1439
ADT-0000661sCache_238.4612.82016099.30Pectobacterium carotovorum WPP1439
ADT-0001320TarH51.0914.600.734298.84Pectobacterium carotovorum WPP1439
ADT-0001602TarH56.9929.03011694.15Pectobacterium carotovorum WPP1439
ADT-0001887TarH61.2242.86012397.59Pectobacterium carotovorum WPP1439
ADT-0002104TarH1001000932100Pectobacterium carotovorum WPP1439
ADT-0003152TarH68.0940.43017991.15Pectobacterium carotovorum WPP1439
ADT-00032454HB_MCP_110087.50063097.40Pectobacterium carotovorum WPP1439
ADT-0003418Unknown80.0080.00083895.60Pectobacterium carotovorum WPP1439
PSA3335_17610Unknown87.5050.000835100Pseudomonas savastanoi NCPPB333543
XAC1892Unknown100100084686.77Xanthomonas citri subsp. citri XHG342
XAC24484HB_MCP_139.2314.6207798.88Xanthomonas citri subsp. citri XHG342
NBC2815_010244HB_MCP_192.0084.000549100Xanthomonas fragariae IPO 348541
NBC2815_020054HB_MCP_160.5342.110353100Xanthomonas fragariae IPO 348541
NBC2815_020084HB_MCP_188.4688.460273100Xanthomonas fragariae IPO 348541
NBC2815_020094HB_MCP_182.1475.000340100Xanthomonas fragariae IPO 348541
Interestingly, we also found that many PAB-specific clusters (41.75%) are formed by proteins of unknown LBD type, suggesting the presence of a significant number of uncharacterized LBD types. Excluding unknown LBD families, the most common domains among high-DPS clusters are 4HB_MCP_1 (26%), TarH (4.5%), and HBM (4%) (Table 3). It is remarkable that the three domain families form four-helix bundle structures (37, 38). The case of the HBM and TarH domains is particularly interesting, as the majority of sequences that belonged to these categories concentrated in very few high-DPS clusters: 57.0% (516/906) of all HBM sequences are grouped into 23 high-DPS clusters, and 36.7% (1,042/2,840) of all TarH sequences are grouped into 26 high-DPS clusters. This indicates a strong association of the TarH and HBM domains with the plant-associated lifestyle. In contrast, despite being the second most abundant LBD in bacteria (Table S2), the dCache_1 domain was not very abundant in PAB.
TABLE 3
TABLE 3 Distribution of LBD types among clusters with high DPS (≥50%)
LBD typeNo. of clusters% of clusters over totalaNo. of LBD sequences with the indicated domainbAvg no. of LBD sequences per cluster
Unknown24341.752,0538.45
4HB_MCP_115125.952,64217.50
TarH264.471,04240.08
HBM233.9551622.43
CHASE391.5514215.78
PilZ71.20395.57
PAS_961.0371.17
sCache_240.6924561.25
NIT40.697919.75
Protoglobin30.5215351
PAS_830.5293
PAS_330.528227.33
dCache_130.5215150.33
Cache_3-Cache_230.527926.33
PAS_420.3442
CHASE420.3431.50
Usher10.1711
Tox-URI210.1711
SURF110.1711
SOR_SNZ10.1711
sCache_3_310.1722
Porin_410.1711
Peripla_BP_510.1711
PAS_710.171919
PapC_N10.1711
Glyco_hydro_2_N10.1711
Glyco_hydro_10610.1711
FHIPEP10.1711
DUF407710.1755
dCache_310.177171
CBS10.1722
Asparaginase10.1711
ABC_tran10.1711
5TM-5TMR_LYT10.172020
Total510 7,377 
a
Percentages are calculated over the total number of LBD clusters with at least 90% of their sequences sharing the same LBD type. These total clusters comprise more than 88% of the total number of clusters in this work.
b
Sum of the total number of sequences sharing the same domain type found in the indicated clusters.

Phylogenetic versus ecological signal in PAB-specific ligand binding domains.

Intrigued by the potential ecological significance of PAB-specific LBD clusters, we further tested whether their taxonomic distribution is due to the phylogenetic signal of the underlying species, or if it might be driven by additional ecological factors. To address this issue, we reconstructed the complete phylogeny of the 11,806 species considered here (see Materials and Methods) and used it to assess the taxonomic distribution of each individual LBD cluster. Using the δ-approach (44), we found that the majority (75.7%) of plant-associated LBD types (DPS ≥50%) did not follow the expected phylogenetic signal. In contrast, the taxonomic distribution of most PAB-specific LBDs was scattered over the global bacterial phylogeny (Fig. 4). This observation was consistent for the three PAB reference databases considered in this study, using stricter DPS cutoffs, and even when the species lacking CR genes were excluded from the analysis (Fig. S1).
FIG 4
FIG 4 Phylogenetic signal detection in plant-associated LBD clusters. (A) Proportion of the significant phylogenetic signal among LBD clusters enriched in PAB-broad, PAB-phyto, and PAB-symb species within two thresholds (≥50%, ≥80%). The significance through a P value test with 100 iterations, P value ≥0.05 rejects the null hypothesis of a phylogenetic signal (see Materials and Methods). (B) LBD cluster 549 (in red) distributed according to the chemotactic species phylogeny (5,763 representative species). The green leaves of the tree represent the PAB species. DPS, δ, and P values for this LBD cluster are represented in the lower left box. (C) Phylogeny representation of LBD cluster 549, containing 27 LBD sequences distributed across 4 orders (numbered 1 to 4 in the tree). The domain architecture prediction is shown for each of the CRs.
Overall, the lack of phylogenetic signal for most of the LBD clusters, together with the fact that the LBDs tested are enriched in PAB species, suggests that the evolution of the sensory machinery of bacterial species might be at least partially driven by ecological pressures. This should allow the use of particular LBD clusters, even if functionally undefined, as lifestyle biomarkers. This issue is best illustrated by the LBD cluster 549 (Fig. 4B and C), which contains 27 CRs from broadly distributed bacterial families and orders, while retaining a high plant-association signal (DPS-broad >80%).

LBD profiles per genome.

To investigate whether the profile of LBD clusters per genome could be informative about the plant-associated bacterial lifestyle, we studied the full repertoire of CRs among different PAB species. The genomes from the PAB species not only contained more CRs than those of non-PAB species, but also, many of their CRs could be considered highly specific to plant-related environments. In fact, assessing the LBD profiles per genome showed that microorganisms with a pronounced plant-associated lifestyle (i.e., PAB-symb and PAB-phyto) harbor more specific CRs than other PAB species (Fig. 5). On average, 28% and 20% of plant-symbiont and plant-phytopathogen CRs, respectively, are highly specific (DPS-broad >80%). In contrast, other PAB with a less pronounced plant-associated lifestyle, like nonsymbiont and nonphytopathogen plant-associated species, contained significantly fewer specific CRs (6%) (Fig. 5). Taken together, this information reinforces the idea that the repertoire of CRs has been partially shaped by niche adaptation, with more specialized adaptations leading to more specific CRs.
FIG 5
FIG 5 Distribution of the high-plant-specificity LBDs in the PAB species profiles per genome. (A) Calculation of the proportion of high-DPS-broad (≥80%) LBDs in the total number of LBDs present in each species. The graph illustrates the distribution of the number of species according to the proportional ranges, plotting the species count as PAB-symb (yellow), PAB-phyto (brown), and the rest of PAB (green). (B) Absolute number of LBDs with a DPS-broad value of ≥80% in each PAB genome. The graph illustrates the distribution of the number of species as an absolute count of high-DPS LBD ranges. The species count is plotted as PAB-symb (yellow), PAB-phyto (brown), and the rest of PAB (green).

DISCUSSION

In the present study, we carried out a comprehensive phylogenomic analysis of the full repertoire of CRs from a wide collection of microbial genomes, classifying them according to their LBDs. To maximize the representativeness of our study, we used more than 82,000 species-level CR sequences from 11,000 species-representative genomes, significantly expanding the scope of previous works (7, 15, 45), in terms of both the number of sequences examined and the phylogenetic coverage. To achieve this, we developed a novel method to extract LBDs and classified them based on a de novo homology-based clustering approach, departing from the traditional classification of CRs centered around their general protein topology (15, 45–47) or on known LBD domain searches (7). This approach allowed us to identify many new potential LBD types, suggesting that the chemosensing landscape remains largely unexplored. Additionally, we believe that our strategy delineating large LBD families into finely grained subcategories could provide further information (Fig. 3). Moreover, by classifying CRs based on their putative LBD type, for the first time we were able to quantify to what extent the chemosensory activity of PAB is linked to lifestyle.
Considering the enormous variety of LBDs at sensor proteins, establishing the forces that have driven their evolution is an important question that was never specifically addressed. To our knowledge, we present here the first clear demonstration showing that environmental factors play an important role in the selection and evolution of LBDs. We found that the specificity of LBDs to a plant-associated lifestyle could not be explained by just a phylogenetic signal, since the taxonomic distribution of most PAB-specific LBD types was scattered over the microbial phylogeny, which at times covered different orders and phyla. This indicates that the selection of the certain CRs might indeed be guided by ecological factors, opening the possibility of identifying lifestyle biomarkers.
We also found that bacterial species more tightly associated with plant environments (such as plant symbionts and phytopathogens) tend to have stronger lifestyle specificity signals in their CR repertory. For instance, plant symbionts had the largest number of PAB-specific LBDs per genome, followed by phytopathogens, with both showing significantly higher ratios than generic soil microbiota. It appears likely that even stronger links between the chemosensory capabilities of bacteria and their lifestyle will be detected in the future as more data become available on new organisms (e.g., via metagenomics sequencing) and on their niche adaptation (i.e., plant-tissue specificity).
These findings thus offer a number of research opportunities in the field of signal transduction. First, it can be explored whether similar relationships can be observed in CRs of bacteria with a different lifestyle, such as for example those that inhabit or infect the human intestine. Another interesting issue that needs to be addressed is the question whether similar LBD types are shared by members of different sensor protein families. Major families of these receptors are sensor histidine kinases; chemoreceptors; adenylate, diadenylate, and diguanylate cyclases; and certain cAMP, c-di-AMP, and c-di-GMP phosphodiesterases, as well as Ser/Thr/Tyr protein kinases and phosphoprotein phosphatases (48). As the different sensor proteins of a given strain are exposed to the same signals, it appears plausible that the same LBD types might be present in members of different sensor protein families. Several examples have been reported in this direction, such as the specific sensing of nitrate by PilJ-type LBDs of the NarQ-type sensor kinases (49), the McpN chemoreceptor (50), and the PAS domain, universally found in different signal transduction systems (48). It would be of interest to estimate the global occurrence of such cases.
Overall, we believe that our study provides a comprehensive resource for future studies on bacterial chemoreception and that it sets the basis for the identification of novel CRs relevant for bacterium-plant interactions.

MATERIALS AND METHODS

Chemoreceptor (CR) sequence retrieval.

From the genomes of 11,806 representative species in the proGenomes2 database (31), 82,277 CR sequences were obtained. The representative species in proGenomes2 are the result of a phylogeny-based classification of all RefSeq (51) genomes, where species delineation is based on a systematic phylogenetic threshold (i.e., <95% divergence in 40 universal marker genes) rather than relying on the NCBI taxonomic names. Although this might lead to inconsistencies with the current NCBI Taxonomy names for strains and species, it better represents the genomic definition of species, as well as providing a standardized classification system (33, 52). To identify CRs in our set of representative genomes, all the sequences matching the MCPsignal Pfam domain signature (PF00015) were retrieved using HMMER 3.1b2 (53), Pfam-A 31.0 (54), and the specific gathering threshold provided for the MCPsignal HMM Pfam model. Multiple hits were resolved by retaining the match with the highest bit score. In analogy to previous studies (7, 55, 56), the presence of an MCPsignal domain in the sequence was the only criterion used for CR identification.

Ligand binding domain (LBD) extraction.

For each CR sequence, transmembrane regions (TMs) were predicted using TMHMM2 (57). The position of the TM region(s) was used to infer the putative extracellular LBD regions, which were subsequently annotated using the Pfam domain database. When no significant Pfam matches were found, LBD sequences were labeled as “unknown.” Two different topologies of extracellular LBDs were considered: (i) sequence regions flanked by two TM regions and (ii) sequence regions located between one TM and the N-terminal sequence. In both cases, sequences shorter than 30 amino acids were discarded. Intracellular LBD regions, as well as potentially overlooked extracellular LBDs (e.g., due to undetected TMs), were inferred based on the detection of Pfam domains other than the MCPsignal and HAMP domains. Pfam mappings were performed using HMMER (53) searches as implemented in eggNOG-mapper v.2.0.5 (53, 58). When more than two domains mapped to the same region, the best hit was selected. The final data set contained 72,480 LBD sequences.

Clustering of LBD sequences.

De novo homology-based clustering of the 72,480 LBD sequences was inferred using MMseqs2 (59) with an E value threshold of 0.01, 20% minimum identity, and 50% minimum query coverage. These parameters were chosen to maximize remote homology detection and to infer LBD clusters with broad phylogenetic divergence (i.e., distant homologues) while still grouping sequences with a common evolutionary origin. The MMseqs2 command used was “mmseqs cluster -c 0.2 –min-seq-id 0.5 –cov-mode 2”.

Construction of the databases for plant-associated bacteria (PAB).

A curated list of PAB was manually curated from the 11,806 representative species. As a first filter, we used the habitat information (i.e., “host plant-associated” label) provided by proGenomes2, which is based on the PATRIC database (31). The resulting list was reviewed manually to exclude uncertain or incorrectly annotated entries by checking their metadata and associated literature. Additionally, we included other known plant-associated species on the list that were missed by the PATRIC database but that were considered PAB based on published data. In total, we identified 960 reference species (PAB-broad) that could be considered related to the plant environment. From this list, we extracted two subdatabases (see Data Set S1 in the supplemental material): phytopathogens (PAB-phyto, 119 members) and plant symbionts (PAB-symb, 192 members).

Degree of plant specificity (DPS).

A specificity score for plant association was calculated for each LBD type based on the proportion of PAB species present in each LBD cluster. We calculated three score values, which we refer to as the degree of plant specificity (DPS), depending on the PAB reference database used: DPS-broad, the proportion of PAB-broad species in each LBD cluster; DPS-phyto, the proportion of PAB-phyto species; and DPS-symb, the proportion of PAB-symb species.

Phylogenetic tree reconstruction and visualization.

Multiple sequence alignments were built for each cluster using Clustal Omega v1.2.4 (60), and phylogeny was inferred by IQ-Tree v1.6.12 using the default parameters (61). The trees were further analyzed and visualized using ETE3 v3.0 (62), with custom Python scripts integrating the annotations of each sequence for its taxonomy, domain architecture, sequence alignment, and plant-specificity prediction (DPS).

Phylogenetic signal tests.

The phylogenetic signal tests were performed using the δ-approach (44), a phylogenetic analogue of the Shannon entropy that measures the degree of phylogenetic signal between a categorical trait (trait vector) and a phylogeny (metric-tree). We used the δ-approach to specifically test the null hypothesis that a given taxonomic distribution of an LBD follows the phylogenetic signal of the underlying species, which provided us with a P value for each LBD cluster. We applied 100 iterations per test and set the P value threshold at 0.05.
The species phylogeny used as a reference in all the tests was reconstructed using the ETE3 (62) supermatrix-based workflow and a concatenated alignment of 40 universal marker genes (63) extracted from the 11,806 species-representative genomes using the FetchMG tool (64). Multiple sequence alignments were inferred using Clustal Omega v1.2.4 (60), and phylogenetic reconstruction was performed with FastTree v2.1 (65). Moreover, an alternative species phylogeny including only genomes with at least one CR was reconstructed using the same methodology. As the δ-statistic has poor sensitivity in detecting the phylogenetic signal for small taxon sample sizes (<20 taxa), LBD clusters mapping to reference phylogenetic tree nodes smaller than 20 leaves were discarded from the analysis (Data Set S2).

ACKNOWLEDGMENTS

This research has been supported by grants PGC2018-098073-A-I00 MCIU/AEI/FEDER, UE (to J.H.-C.), BIO2016-76779-P (to T.K.), AGL2017-82492-C2-1-R (to C.R.), and RTI2018-095222-B-I00 (to E.L.-S.) from the Ministerio de Ciencia, Innovación y Universidades, Spain, as well as grant P18-FR-1621 (to T.K.) from the Junta de Andalucía. C.S.-L. was supported by the FPU program (FPU19/06635, MICINN-Spain), and J.P.C.-V. by the FPI program (BES-2016-076452, MINECO-Spain).

Supplemental Material

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mSystems
Volume 6 • Number 5 • 26 October 2021
eLocator: 10.1128/msystems.00951-21
Editor: Marnix Medema, Wageningen University
PubMed: 34546073

Copyright

© 2021 Sanchis-López et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

History

Received: 12 August 2021
Accepted: 2 September 2021
Published online: 21 September 2021

Keywords

  1. MCP
  2. chemoreceptor
  3. chemotaxis
  4. methyl-accepting chemotaxis protein
  5. plant-associated bacteria
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Contributors

Authors

Claudia Sanchis-López https://orcid.org/0000-0002-8206-1565
Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
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Jean Paul Cerna-Vargas https://orcid.org/0000-0001-5489-0385
Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
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Saray Santamaría-Hernando https://orcid.org/0000-0001-6763-3839
Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
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Cayo Ramos https://orcid.org/0000-0002-2362-5041
Área de Genética, Facultad de Ciencias, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, Universidad de Málaga-Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), Málaga, Spain
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Tino Krell https://orcid.org/0000-0002-9040-3166
Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Granada, Spain
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Pablo Rodríguez-Palenzuela https://orcid.org/0000-0002-4963-9177
Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
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Emilia López-Solanilla https://orcid.org/0000-0002-8578-7433
Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
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Jaime Huerta-Cepas https://orcid.org/0000-0003-4195-5025 [email protected]
Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
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José J. Rodríguez-Herva https://orcid.org/0000-0001-9962-2261 [email protected]
Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
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Editor

Marnix Medema
Editor
Wageningen University

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Citations

Citation
Sanchis-López C, Cerna-Vargas JP, Santamaría-Hernando S, Ramos C, Krell T, Rodríguez-Palenzuela P,López-Solanilla E,Huerta-Cepas J, Rodríguez-Herva JJ, 2021. Prevalence and Specificity of Chemoreceptor Profiles in Plant-Associated Bacteria. mSystems 6:10.1128/msystems.00951-21.
https://doi.org/10.1128/msystems.00951-21

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