Differential metaproteomics of bacteria grown in vitro and in planta reveals functions used during growth on maize roots

Differential metaproteomics of bacteria grown in vitro and in planta reveals functions used during growth on maize roots | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Differential metaproteomics of bacteria grown in vitro and in planta reveals functions used during growth on maize roots View ORCID Profile Anna-Katharina Garrell , View ORCID Profile John Cheadle , View ORCID Profile Nathan Crook , View ORCID Profile Gaurav Pal , View ORCID Profile Alecia N. Septer , View ORCID Profile Maggie R. Wagner , View ORCID Profile Ashley E. Beck , View ORCID Profile Manuel Kleiner doi: https://doi.org/10.1101/2025.06.02.657423 Anna-Katharina Garrell 1 Department of Plant and Microbial Biology, North Carolina State University , Raleigh, NC, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anna-Katharina Garrell John Cheadle 2 Department of Chemical and Biomolecular Engineering, North Carolina State University , Raleigh, NC, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John Cheadle Nathan Crook 2 Department of Chemical and Biomolecular Engineering, North Carolina State University , Raleigh, NC, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nathan Crook Gaurav Pal 1 Department of Plant and Microbial Biology, North Carolina State University , Raleigh, NC, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gaurav Pal Alecia N. Septer 3 Department of Earth, Marine and Environmental Sciences, University of North Carolina at Chapel Hill , Chapel Hill, NC, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alecia N. Septer Maggie R. Wagner 4 Department of Ecology and Evolutionary Biology, Kansas Biological Survey and Center for Ecological Research, University of Kansas , Lawrence, KS, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maggie R. Wagner Ashley E. Beck 5 Department of Biological and Environmental Sciences, Carroll College , Helena, MT, United States 6 Department of Biological and Agricultural Engineering, North Carolina State University , Raleigh, NC, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ashley E. Beck Manuel Kleiner 1 Department of Plant and Microbial Biology, North Carolina State University , Raleigh, NC, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manuel Kleiner For correspondence: manuel_kleiner{at}ncsu.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Microbes are ubiquitous in the rhizosphere and play crucial roles in plant health, yet the metabolisms and physiologies of individual species in planta remain poorly understood. In this study, we examined microbial gene expression in response to the maize root environment for seven bacterial species originally isolated from maize roots. We grew each species individually, both in vitro in a minimal medium and in planta , and used differential metaproteomics to identify functions upregulated specifically when bacteria are grown on maize roots. We identified between 1,500 and 2,100 proteins from each species, with approximately 30-70% of these proteins being differentially abundant between the two conditions. While we found that transporter proteins were upregulated in all species in planta , all other differentially abundant functions varied greatly between species, suggesting niche specialization in root-associated microbes. Indeed, in vitro assays confirmed that Curtobacterium pusillum likely degrades plant hemicellulose, Enterobacter ludwigii may benefit the plant by phosphate solubilization, and Herbaspirillum robiniae colonizes maize roots more effectively when both of its Type VI Secretion Systems are functional. Together, our findings highlight both conserved and species-specific bacterial strategies for growth in the root environment and lay a foundation for future work investigating the mechanisms underlying plant-microbiota interactions. Introduction Plant-associated bacteria represent an important component of agricultural ecosystems, as they are ubiquitous and have been shown to support plant health and growth 1 – 4 . As agricultural production faces increasing challenges from climate change, there is a vested interest in harnessing beneficial bacteria to improve crop resilience and productivity. While the application of microbial inoculants has emerged as a promising strategy, realizing their full potential requires a deeper understanding of microbial colonization, function, and adaptation in plant environments. As such, there is an increasing push to not only identify which microbes are present and their functional potential, but to also understand which microbial functions are actively expressed in their respective environments. This will ultimately lead to a better understanding of the mechanisms underlying community assembly, microbial colonization and persistence on plant roots, and beneficial plant-microbe interactions. In an effort to identify bacterial functions that affect plant health, recent studies have used transposon mutagenesis and various ‘omics approaches to probe plant-microbe interactions 5 – 14 . These studies have revealed that phosphate uptake, nitrogen metabolism, biopolymer export, and amino acid transport and metabolism, among many others, may contribute to microbial colonization and survival in the root environment. While these studies provided insights into functions of relevance for plant-associated microorganisms, our understanding of which functions are important for plant-associated as compared to free-living growth is limited. Additionally, while hundreds of microbial species are known to colonize plants and the rhizosphere 15 – 18 , gene expression in planta has been measured for very few; thus, our understanding of which functions are of general relevance for plant-associated growth, versus which functions are specific to particular microbial taxa, is limited. To address these knowledge gaps, we studied gene expression of seven microbial species grown individually in vitro and in planta using metaproteomics. Maize is the second most widely grown crop in the world serving as a main source for human food, livestock feed, and biofuel 19 . Maize is also a key model system for plant genetics 20 , 21 , and more recently, for plant-microbe interactions 22 – 24 . In 2017, Niu et al. developed a simplified and representative synthetic community (SynCom) for maize containing seven bacterial species isolated from maize roots: Brucella pituitosa , Chryseobacterium indologenes , Curtobacterium pusillum , Enterobacter ludwigii , Herbaspirillum robiniae , Pseudomonas putida , and Stenotrophomonas maltophilia . 24 These isolates were found to consistently colonize maize roots for up to 15 days and have since been used to investigate microbe-dependent heterosis 23 and metabolic microbe-microbe interactions 25 . Additional tools and methods have also been developed for this community 26 – 28 , making it an excellent model for studying plant-microbe interactions. Metaproteomics allows for the identification and quantification of thousands of proteins in host-associated microbes, providing insight into the functions driving microbial phenotypes and interactions 29 – 31 . With the development and advancements made in the field of metaproteomics over the last two decades, our ability to identify the proteins responsible for phenotypes in complex systems has improved dramatically and deepened our understanding of microbe-microbe and microbe-plant interactions 12 , 32 – 34 . Differential metaproteomics, in particular, allows us to probe how changes in the biotic and abiotic environment affect gene expression of both microbes and hosts. Dissecting the functions underlying interactions between plant roots and individual bacterial species is essential to understanding the specific traits that enable root colonization and persistence. Therefore, we focused on single-species interactions with maize roots by growing seven maize-root bacterial isolates individually in vitro in a defined, minimal medium and in planta on sterile maize roots 24 . We used metaproteomics to identify bacterial proteins that were differentially abundant between the two conditions, allowing us to determine metabolic pathways and physiological functions that are upregulated during growth in planta and thus potentially important for growth in or on roots. We then used in vitro assays to further investigate hemicellulose degradation in C. pusillum and H. robiniae and phosphorus solubilization in all seven species, and generated functional knockouts of the Type VI Secretion System in H. robiniae to investigate its role in plant-microbe interactions. Results and Discussion Hundreds to more than a thousand proteins were differentially abundant in response to growth on maize roots To determine bacterial genes relevant for growth on maize roots, we grew seven bacterial species individually on sterile maize roots ( in planta ) and in a defined, minimal medium ( in vitro ). We used LC-MS/MS-based metaproteomics to quantify relative protein abundances in each condition. This differential metaproteomics approach allowed us to identify bacterial genes that are upregulated in response to the maize root environment. Using this method, we identified between 1,507 and 2,159 bacterial proteins for each of the seven species ( Table 1 ). Approximately 30-70% of these proteins were differentially abundant (Student’s T-Test, Benjamini-Hochberg FDR q < 0.1) between conditions, indicating environment-specific metabolic and physiological responses in each of the species. View this table: View inline View popup Download powerpoint Table 1. Overview of total detected and differentially abundant proteins (Student’s T-Test, Benjamini-Hochberg correction, q < 0.1) in seven bacterial species grown in vitro and in planta . To identify the functional roles of the differentially abundant proteins, we annotated these proteins, assigning a total of 28 broad categories and 134 sub-categories (Supplementary Data 1). It is important to note that included in the broad categories is a category called “Unknown Function”. This category contained proteins for which we attempted to assign a function but were unable to find a confident match. Of the ∼4,000 differentially abundant proteins we were able to annotate, most belonged to three primary functional categories: transport, gene expression, and carbon metabolism ( Fig. 1A ). The extent and condition in which these metabolic and physiological functions were differentially expressed varied for each species ( Fig. 1B ). For example, during growth in planta , carbon metabolism-associated protein abundances increased overall in CPU, ELU, HRO, and PPU, but decreased in BPI, CIN, and SMA. Similarly, adhesion- and motility-associated proteins increased in CPU and HRO in planta , but decreased in CIN, SMA, PPU, ELU, and BPI. Secretion system-associated protein abundances varied between species in that their in planta abundances generally increased in HRO and decreased in ELU, PPU, and SMA, but remained unchanged in CIN, CPU, and BPI. Interestingly, transport-associated proteins increased in all species when grown in planta . Download figure Open in new tab Figure 1. Protein abundances and differential abundance varied greatly between species when grown in planta . A) Number of proteins whose abundances significantly differed between in planta and in vitro growth. Broad functional categories are listed on the y-axis and individual species are listed on the x-axis (3-5 biological replicates) (Student’s T-Test, Benjamini-Hochberg adjusted p-value ( q ) < 0.1). B) Averages of log 2 fold changes of all bacterial proteins whose abundances significantly differed between in vitro and in planta conditions (Student’s T-Test, Benjamini-Hochberg adjusted p-value ( q ) < 0.1), categorized by broad function. Fold change greater than 0 indicates a higher abundance in planta , and a fold change less than 0 indicates a higher abundance in vitro . Although the broad functional categories provide an initial overview of functions that are most responsive to in planta growth, the more detailed functional categories provide a much clearer picture of the specific metabolism and physiology of each of the seven species during in planta growth ( Fig. 2 ). For example, within the “Carbon Metabolism” broad category, there are 29 detailed categories, which include pathways such as arabinose metabolism, inositol metabolism, and polysaccharide degradation. Here, it becomes evident that specific carbon metabolism pathways were more abundant in planta in each of the seven species ( Fig. 2 ); this contrasts with the broad-level analysis of carbon metabolism-associated proteins where increased in planta abundances were detected only in CPU, ELU, HRO, and PPU ( Fig. 1B ). Similarly, in the broad-level analysis, HRO was the only species with increased in planta expression of secretion systems ( Fig. 1B ). However, by examining individual secretion systems, we found that CIN also increased expression of Type IX Secretion System (T9SS) and gliding motility proteins, and ELU and SMA increased expression of Type II Secretion System (T2SS) proteins ( Fig. 2 ). Download figure Open in new tab Figure 2. Differential expression of specific functional categories varied greatly between species in response to in planta growth. Averages of log 2 fold changes of bacterial proteins whose abundances significantly differed between in vitro and in planta conditions (Student’s T-Test, Benjamini-Hochberg adjusted p-value ( q ) < 0.1), categorized by detailed and broad function. Fold change greater than 0 indicates a higher abundance in planta , and a fold change less than 0 indicates a higher abundance in vitro . We therefore focused our analysis and follow-up work on detailed functional categories, with a particular focus on functions that were upregulated in planta . Below, we discuss results from proteins whose functions could potentially mediate (1) physical plant-bacterial interactions (Carbon Metabolism, Secretion Systems, Adhesion and Motility), (2) bacterial provisions to the plant (Phosphorus Metabolism), (3) general bacterial response systems (Transporters), and (4) proteins whose functions could be newly identified as plant-associated (Unknown Functions). Carbon metabolism pathways showed significant shifts in expression in planta Curtobacterium pusillum (CPU) and Herbaspirillum robiniae (HRO) hemicellulose degradation enzymes were more abundant in planta and are involved in growth on hemicellulose in vitro We found that hemicellulose degradation enzymes increased in abundance in CPU and HRO in planta ( Fig. 3A ). Hemicelluloses are structural components of plant cell walls and include a range of polysaccharides, including xyloglucans, xylans, mannans, and glucomannans, among others 53 . In CPU, α-L-arabinofuranosidase abundance was significantly higher in planta than in vitro ( Fig. 3A ). This enzyme cleaves arabinose sidechains from arabinoxylans, which are a common form of hemicellulose. Xylose isomerase, xylulokinase, arabinose isomerase, ribulose-phosphate-3-epimerase, and ribose-5-phosphate isomerase also had higher abundances in planta than in vitro , indicating that arabinose and xylose from arabinoxylan were further metabolized via the pentose phosphate pathway. Download figure Open in new tab Figure 3. Hemicellulose-degrading enzymes in HRO and CPU were in higher abundance in planta and were expressed when grown on hemicellulose in vitro . A) Centered log-ratio transformed abundances of hemicellulose degradation-associated proteins that were significantly more abundant in planta in CPU and HRO (Student’s T-test, Benjamini-Hochberg adjusted p-value ( q ) < 0.1). White blocks indicate proteins that were either not identified or not significantly differentially abundant. B) OD600 values of CPU and HRO after 0, 8, and 24 hours of growth in minimal media with different primary carbon sources. C) Average relative abundances (% normalized spectral abundance factor (NSAF)) of hemicellulose degradation-associated proteins when CPU was grown in minimal medium with glucose and malate or xylan. D) Average relative abundances (%NSAF) of hemicellulose degradation-associated proteins when HRO was grown in minimal medium with glucose and malate, no carbon source, or xylan. The two xylose-1-dehydrogenases indicated are homologs. Asterisks indicate a Benjamini-Hochberg corrected p < 0.05 (Student’s T-test). Error bars indicate standard deviation. In HRO, the abundance of a putative xylanase, which hydrolyzes xylan chains to xylose monomers, was significantly higher in planta than in vitro . Xylose transporter substrate- and ATP-binding proteins were additionally more abundant in planta , as well as xylose-1-dehydrogenase, which is involved in downstream xylose degradation. These indicate that HRO may also degrade hemicellulose. To test whether CPU and HRO were capable of growing on hemicellulose, we performed an in vitro growth assay in which xylan was the primary carbon source. We grew CPU and HRO in three different versions of the defined, minimal medium: with glucose and malate as the primary carbon sources, with xylan as the primary carbon source, and without any primary carbon source. Amino acids were also present in the medium (Supplemental Materials and Methods). We then used proteomics to determine whether the hemicellulose-degrading enzymes detected in planta were also present at increased levels when CPU and HRO were grown on xylan in vitro . We found that CPU grew in the minimal medium when xylan was the primary carbon source and showed minimal growth when grown with no primary carbon source ( Fig. 3B ) (the growth that was seen without a primary carbon source was likely due to the amino acids present in the medium). Given the limited growth in the medium without a primary carbon source, we were unable to collect cells for proteomic analysis for this condition. However, we were able to compare abundances of hemicellulose proteins when CPU was grown on xylan and when it was grown on glucose and malate. We found increased abundances of five of the six putative hemicellulose catabolism proteins we had identified in planta when grown on xylan compared to when grown on glucose and malate ( Fig. 3C ). These proteins included ɑ-L-arabinofuranosidase, arabinose/xylose ABC transport ATP-binding protein, arabinose isomerase, xylose isomerase, and xylulokinase, further supporting the hypothesis that CPU degrades hemicellulose in the maize root environment. Altogether, our results showing that CPU hemicellulose degradation enzymes were more abundant in planta , that CPU was able to grow on hemicellulose as a primary carbon source in vitro , and that the same hemicellulose degradation enzymes were more abundant when grown on hemicellulose suggest that CPU does indeed degrade hemicellulose in the maize rhizosphere. However, the exact location of hemicellulose degradation is unclear. It has previously been reported that cell wall degradation can contribute to bacterial colonization of plant tissues 54 , 55 , which could suggest that CPU may colonize the endosphere. However, given that hemicelluloses are also available in the rhizosphere via sloughed off root cells 56 , 57 and plant exudates 58 , 59 , CPU may also scavenge hemicellulose in the rhizosphere. We additionally found that HRO grew equally well in the minimal medium with xylan as the primary carbon source and in the minimal medium without a primary carbon source ( Fig. 3B ), likely due to the presence of amino acids in the medium. It is therefore difficult to conclude whether HRO catabolizes xylan in vitro . However, when comparing proteins expressed in the presence of xylan to those expressed with no carbon source or with glucose and malate, we did find increased abundances of some of the hemicellulose catabolism enzymes that had been in higher abundance in planta , including xylose transporters and xylose-1-dehydrogenase ( Fig. 3D ). Notably, abundance of the putative xylanase did not change when xylan was present, indicating that it may not truly have xylanase activity. This highlights the importance of experimentally confirming enzyme activity, rather than relying solely on annotation tools. Fatty acids may be used as a carbon source by Curtobacterium pusillum (CPU) and Herbaspirillum robiniae (HRO) in planta We found that proteins from the fatty acid β-oxidation cycle were significantly more abundant when CPU and HRO were grown in planta versus in vitro ( Fig. 4A ) (with the exception of acetyl-CoA C-acyltransferase, which, while not statistically significant, was also more abundant in HRO in planta ). This pathway is used in fatty acid catabolism, oxidizing fatty acids to acetyl-CoA, which can then be fed into the TCA cycle. Furthermore, the glyoxylate cycle, which is an anaplerotic pathway that funnels acetyl-CoA from β-oxidation into central carbon metabolism, was increased in HRO in planta : isocitrate lyase, which catalyzes the first step of the glyoxylate cycle, was significantly more abundant in planta than in vitro , and malate synthase, which catalyzes the second step of the glyoxylate shunt, though not statistically significant, was increased in planta ( Fig. 4B ) 60 . Download figure Open in new tab Figure 4. Fatty acid catabolism enzymes had higher abundances when C. pusillum and H. robiniae were grown in planta than in vitro . A) Abundances (% NSAF) of fatty acid β-oxidation cycle enzymes, with enzyme homologs indicated by Roman numerals. i ) CPU_079236941.1, ii ) CPU_079238824.1, iii ) HRO_079214242.1, iv ) HRO_079218280.1, v ) CPU_079238788.1, vi ) CPU_099051966.1, vii ) HRO_079217199.1, viii ) HRO_079218538.1, ix ) CPU_079235882.1, x ) HRO_079218255.1, xi ) HRO_079217198.1, xii ) HRO_079217197.1. Note that the enzymes listed as 3-hydroxyacyl-CoA dehydrogenase and enoyl-CoA hydratase for CPU (CPU_079235882.1) in Fig. 4A are the same enzyme; this enzyme showed homology to FadB, which contains two active sites: one 3-hydroxyacyl-CoA dehydrogenase domain and one enoyl-CoA hydratase domain, enabling it to perform both reactions 63 . Error bars indicate standard deviation. B) Abundances (%NSAF) of glyoxylate cycle enzymes. Asterisks indicate a Benjamini-Hochberg corrected p < 0.1 (Student’s T-test) when comparing CLR-transformed abundance values of proteins between in vitro and in planta conditions. Error bars indicate standard deviation. Taken altogether, our data suggest that fatty acids are a potential carbon source for HRO and CPU in planta . Similar findings have been reported by Hemmerle et al. 12 , who found an increase in Rhizobium β-oxidation cycle enzymes during co-inoculation with another bacterial species in the Arabidopsis phyllosphere, and by Van Dijk and Nelson 61 , who suggested that competition for plant-derived fatty acids plays a role in biological control of Pythium by Enterobacter in cotton seedlings. Zhalnina et al. 62 additionally found that plant-derived fatty acids were depleted when bacteria were grown in Avena root exudates. However, there is much yet unknown about microbial consumption of fatty acids, including fatty acid type (e.g. length, saturation) and source (e.g. exudates, host cell membranes), particularly in the rhizosphere. This highlights a gap in our understanding of microbial physiology in planta , as it is possible that fatty acids are an important carbon source for plant-associated bacteria. Phosphate solubilization enzyme abundance in planta varied between species When ELU was grown on the maize root, we found that phosphorus metabolism-related proteins were found in higher abundance than when grown in vitro ( Fig. 1B ) In particular, we observed a higher abundance of alkaline phosphatase in planta ( Fig. 5A ). Alkaline phosphatases are involved in liberating phosphate from organic compounds and polyphosphates, allowing organisms to scavenge phosphate from the environment when free phosphate is limited 64 . The higher abundance of this enzyme in ELU in planta could therefore suggest a phosphate limitation in the root environment. Interestingly, however, we observed the opposite trend in CIN and SMA wherein their alkaline phosphatases were actually less abundant in planta than in vitro . Since all plants were watered with the same amount and concentration of Murashige-Skoog medium, these contrasting patterns point to species-specific regulation of phosphate solubilization. All three alkaline phosphatases contained predicted signal peptides and cleavage sites (SignalP 65 ), indicating that they are secreted and likely function extracellularly. Download figure Open in new tab Figure 5. Enterobacter ludwigii (ELU) encoded alkaline phosphatase was more abundant in planta and ELU is capable of phosphate solubilization in vitro . A) Relative abundance (% NSAF) of significantly differentially abundant alkaline phosphatases in ELU, CIN, and SMA when grown in vitro and in planta (Student’s T-Test; Benjamini-Hochberg corrected p < 0.1). Error bars indicate standard deviation. B) Zone of clearance (cm), indicative of phosphate solubilization, when each species was grown on Pikovskaya agar after 5 days. Error bars indicate standard deviation. Previous studies have shown that Enterobacter species can solubilize phosphate 66 and increase maize phosphorus content 67 . We confirmed that ELU AA4 is also capable of phosphate solubilization in vitro on Pikovskaya agar ( Fig. 5B ). This raises the possibility that the increased abundance of alkaline phosphatase in planta contributes to the solubilization of phosphate, potentially increasing its availability for uptake by the maize root. We also measured the phosphate solubilization capability of the other six species on Pikovskaya agar ( Fig. 5B ), and surprisingly found that CIN and SMA did not solubilize phosphate in this assay. However, it has previously been shown that some bacteria are capable of phosphate solubilization in liquid media, but not agar 68 , which may explain this observation. Bacterial secretion systems were upregulated in planta Both Type VI Secretion Systems in Herbaspirillum robiniae (HRO) are involved in colonization and growth on maize roots Of the seven bacterial species investigated in this study, five encode genes for Type VI Secretion Systems (T6SSs), with PPU, CIN, and SMA each encoding one T6SS, and ELU and HRO each encoding two T6SSs. While most of the differentially abundant T6SS proteins in CIN, ELU, PPU, and SMA were found in lower abundances in planta compared to in vitro , proteins found in both HRO T6SS gene clusters were significantly more abundant in planta ( Fig. 6A , 6B). T6SSs are protein secretion systems commonly found in Gram-negative bacteria that have been shown to play important roles in mediating host-microbe and microbe-microbe interactions 69 – 74 . In plant-associated bacteria, the T6SS is found in both pathogenic and beneficial taxa and can play roles in plant disease, plant colonization, and interactions with other microbes in the environment. 72 – 75 The higher abundance of T6SS proteins when HRO was grown in planta suggests that they may play an important role in interactions between HRO and the maize root. Download figure Open in new tab Figure 6. Secretion system expression during in planta growth. A) Average log(2) fold change (calculated with centered-log ratio transformed abundances) of differentially abundant T6SS proteins in each species for which T6SS proteins were detected (n = 3-5). A log(2) fold change greater than 0 indicates a higher abundance in planta , while a log(2) fold change less than 0 indicates a higher abundance in vitro . B) Abundances (% NSAF) of detected HRO T6SS proteins (n = 3-4). Asterisks indicate a Benjamini-Hochberg corrected p < 0.1 (Student’s T-test) when comparing CLR-transformed abundance values of proteins between in vitro and in planta conditions. C) Mean colony forming units (CFU) per gram of root fresh weight (FW) of HRO T6SS mutants when grown on maize roots for 14 days (n = 8-9). Asterisk indicates a p-value < 0.05 (Student’s T-test) when comparing each knockout mutant to the wildtype (HRO). D) Log(2) fold change (calculated with centered-log ratio transformed abundance) of differentially abundant T9SS and gliding motility proteins in CIN. A log(2) fold change greater than 0 indicates a higher abundance in planta , while a log(2) fold change less than 0 indicates a higher abundance in vitro . Asterisks indicate a Benjamini-Hochberg corrected p < 0.1 (Student’s T-test) when comparing CLR-transformed abundance values of proteins between in vitro and in planta conditions. Error bars in panels B and C indicate standard deviations. In order to investigate the in planta role of the HRO T6SSs, we generated three T6SS knockout mutants by disrupting the tssF gene in each (single knockouts) and both (double knockout) of the gene clusters, as disruption of tssF has previously been shown to reduce T6SS function 69 , 76 . This resulted in the following mutants: ΔtssF1 , ΔtssF2 , and ΔtssF1ΔtssF2 . After confirming that tssF disruption had no sizable effect on in vitro growth (Fig. S1), we inoculated each of these mutants and the wildtype strain individually on sterile maize seedlings and grew the plants for two weeks to determine whether functional loss of the T6SS would result in a reduction in colonization. While we saw some reduction in colonization by the single tssF mutants compared to the wildtype, the strongest effect was seen in the double mutant, ΔtssF1ΔtssF2 , with an approximately 3-fold reduction in abundance ( p < 0.05) ( Fig. 6C ). This indicates that the use of both T6SSs has an additive effect on colonization and growth of HRO on the maize root. The increased abundance of T6SS proteins in HRO in planta , along with the reduced colonization by the T6SS mutants, suggests that these secretion systems contribute to HRO colonization and persistence on maize roots. This is consistent with previous studies that have shown that T6SSs contribute to colonization and persistence by both pathogenic and non-pathogenic plant-associated bacteria 77 , 78 . In the context of direct plant-microbe interactions, T6SSs have been implicated in diverse in planta functions, including biofilm formation, siderophore production, and metal acquisition 77 , 79 , 80 . T6SSs are also known to be involved in interbacterial competition and killing 72 , 73 , although this role is less relevant in this study, as each species was inoculated on sterile seedlings individually. Further work is needed to identify the specific proteins secreted by the HRO T6SSs in planta and to understand how they influence root colonization and persistence and, potentially, interactions with other microbes under more complex conditions. Type IX Secretion System and gliding motility are tightly associated with one another, but are found to be expressed at different levels in planta in Chryseobacterium indologenes (CIN) Using T9GPred, NCBI PGAP, and UniProt, we identified 29 genes encoding proteins essential for the T9SS and gliding motility, as well as accessory and secreted proteins, in CIN 49 . We detected 25 of these proteins in the CIN proteomes and found that 12 were significantly differentially abundant either in vitro or in planta ( Fig. 6D ). The T9SS is involved in secretion of a large diversity of proteins, including those involved in functions such as polysaccharide degradation, motility, and adhesion, among others 81 , 82 . It is additionally tightly associated with gliding motility, in that T9SS function is often essential for gliding motility, and a number of gliding motility proteins are important for stabilization of the T9SS structure 81 . While the role of the T9SS in plant-associated microbes is still largely unknown, there is evidence that it may play a role in colonization of seeds and plant roots 83 . Interestingly, all T9SS proteins were found in lower abundance in planta than in vitro ( Fig. 6D ), while of the five of the eight predicted T9SS-secreted proteins were significantly more abundant in planta . Previous studies have shown that T9SS substrates accumulate intracellularly or within the periplasm when T9SS components were knocked out, suggesting that T9SS substrate gene expression is not dependent on T9SS component expression 84 – 86 , which could help explain the pattern seen in this study. We used InterProScan to try to identify protein domains that may indicate function of these potentially secreted proteins, but were unsuccessful. Taken altogether, these findings indicate that the predicted T9SS-secreted proteins may play a role independent of the T9SS machinery and/or may be secreted through an alternative pathway, leaving the role of the T9SS in CIN growth on maize roots unclear. Transporter proteins were more abundant in all seven bacterial species when grown in planta Transporter proteins were significantly more abundant in planta than in vitro across all seven species studied ( Fig. 1B , Fig. 2 ). The expression patterns of these transporters can provide useful insight into the physiologies and substrate preferences of bacteria, as well as nutrient availability in a given environment. In total, we annotated 570 differentially abundant transporter proteins across all seven species and categorized them into 21 different functional categories (Supplementary Data 2). Among these, we found that three transporter types were particularly abundant, each comprising more than 1% of the proteome in at least one species: amino acid transporters, porins, and sugar transporters ( Fig. 7A ). Miscellaneous transporters also composed a sizable portion of the proteome in BPI, CIN, and SMA. However, the proteins that were annotated as belonging to “Miscellaneous Transport” were largely transporter components for which we were unable to predict a substrate, and thus, we do not discuss these in depth. Download figure Open in new tab Figure 7. Proteins from four transporter categories composed at least 1% of the proteome in at least one of the bacterial species studied. Chemotaxis and motility proteins were differentially abundant in H. robiniae and E. ludwigii . A) Summed abundance (% NSAF) of significantly (Student’s T-Test; Benjamini-Hochberg corrected p < 0.1) differentially abundant transporter proteins across all seven bacterial species. B) Log(2) fold change (of CLR-transformed abundance values) of significantly differentially abundant chemotaxis and flagellar proteins in H. robiniae and E. ludwigii . A log(2) fold change greater than 0 indicates a higher abundance in planta , while a log(2) fold change less than 0 indicates a higher abundance in vitro (Student’s T-Test; Benjamini-Hochberg corrected p < 0.1). Amino Acid Abundances of amino acid transporter proteins varied across each of the seven species: differentially expressed amino acid transporter proteins in BPI, ELU, and HRO were particularly abundant and high in number, while CIN, CPU, PPU, and SMA had markedly less abundant transporter proteins ( Fig. 7A ). In ELU, high-affinity arginine, branched-chain amino acid, cystine, glutamine, and histidine transporter proteins (both ATP-binding and substrate-binding) were significantly more abundant in planta than in vitro (Supplementary Data 2). This effect could be due to the fact that the minimal medium used in the in vitro condition included arginine, leucine, isoleucine, cystine, and glutamine at concentrations of 200-350 µmol/L, while it is estimated that the concentrations of individual amino acids in the rhizosphere are approximately 100-1000x lower 87 . Surprisingly, it was difficult to make observations regarding amino acid uptake in HRO and BPI in the root environment, as transporter components were not consistently differentially abundant in the same condition. For example, among BPI branched-chain amino acid ABC transporter proteins, three substrate-binding proteins and one permease were more abundant in planta , while one substrate-binding protein and two ATP-binding proteins were more abundant in vitro . These results may be confounded by the fact that it can be difficult to assign transporter substrates by the ATP-binding subunit, as the ATP-binding subunit does not bind directly to the transporter substrate. Thus, it remains unclear which amino acids HRO and BPI may be scavenging in the plant environment. Porins Porins were significantly more abundant in planta across multiple species, particularly BPI, ELU, HRO, and SMA, suggesting an important role in bacterial survival in the maize rhizosphere. Porins are outer membrane proteins that generally facilitate passive diffusion of small molecules across the cell membrane, although it has been suggested that they can be cation- or anion-specific under certain conditions 88 . In plant-associated bacteria, porins have been associated with nutrient uptake, biofilm formation, antibiotic sensitivity, and stress resistance 88 – 90 . While similarly high abundances of bacterial porins have been found in the phyllosphere and rhizosphere 13 , 91 , 92 , the lack of porin specificity and broad range of potential functions currently makes it difficult to draw any conclusions as to the role of these porins in BPI, ELU, HRO, and SMA in planta . Sugar Of the 109 differentially abundant sugar transport-related proteins, 95 were significantly more abundant in planta . These proteins covered a variety of transporter families and/or mechanisms, including MFS, ABC, Sus, and PTS transport mechanisms. While we were unable to identify the specific substrates of all transporters, we did find a number of 5-carbon, 6-carbon, disaccharide, and polysaccharide transporters, indicating that these species may consume a large diversity of substrates. Notably, all of the differentially abundant sugar transporters identified in CIN belong to the starch utilization system (Sus), which is widespread in the phylum Bacteroidetes. We identified three SusD homologs that were upregulated in vitro and four that were upregulated in planta , as well as one SusE/F homolog upregulated in vitro . SusDEF proteins are outer membrane-bound starch-binding proteins that have been shown to be heavily involved in a cell’s ability to grow on different starches. 93 We additionally identified two SusC homologs upregulated in vitro and four upregulated in planta . These are TonB-dependent transporters responsible for the translocation of maltooligosaccharides into the periplasm. 93 These results suggest that sugar uptake is a key component of bacterial adaptation to the maize root environment, with multiple species exhibiting increased expression of transporters for a wide range of carbon sources. The differential expression of SusCDEF components in CIN further supports the idea that root-associated bacteria dynamically regulate starch utilization in response to environmental conditions. Chemotaxis and motility protein abundances were highly species-specific in planta Two distinct patterns of chemotaxis and motility gene expression were present between HRO and ELU. In HRO, eight chemotaxis-related proteins were differentially abundant, with seven significantly more abundant in planta ( Fig. 7B ). Additionally, all eight differentially abundant flagellar proteins were significantly more abundant in planta , suggesting that HRO enhances motility in response to specific environmental cues in the root environment. In contrast, ELU showed the opposite pattern: 14 of the 15 differentially abundant chemotaxis proteins were significantly more abundant in vitro , as were six of the seven differentially abundant flagellar proteins ( Fig. 7B ). This may indicate a shift from a motile lifestyle in vitro to a less motile lifestyle in planta . While many of the classic chemotaxis signaling cascade pathway proteins were present in HRO and ELU, such as CheA, CheV, CheW, CheR, and CheZ, there were also a number of methyl-accepting chemotaxis proteins and receptors whose ligands are unknown. As such, it is difficult to predict the chemoattractants to which these microbes may respond. Chemotaxis and motility have often been associated with increased plant colonization capability of bacteria 94 – 96 . However, it has been suggested that some bacteria require chemotaxis and motility functions specifically during early colonization, but then form biofilms upon colonization 95 . Given that the plants in this experiment were grown for two weeks, we would not have detected functions relevant to early colonization, but rather those involved in continued persistence in the root environment. It is therefore possible that chemotaxis and motility are still involved in ELU colonization of maize roots, but not highly expressed once colonization has been established. While we did not identify biofilm formation-related proteins in ELU, it is still possible that ELU forms a biofilm in planta , given that other Enterobacter species have been shown to form biofilms 97 , 98 . Thus, while chemotaxis and motility are commonly associated with enhanced plant-microbe interactions, the contrasting patterns of chemotaxis and motility gene expression between HRO and ELU suggest that their roles in root-associated bacteria are highly species-specific and potentially context-dependent. Proteins of unknown function made up a large proportion of the differentially expressed proteins in each of the seven species It is important to note that despite extensive manual and automated curation efforts, 2,679 of the 6,487 differentially abundant proteins could not be confidently annotated and/or categorized. These proteins were either assigned “Unknown Function” or were not annotated at all. This indicates that there remain a large number of microbial functions that may be highly relevant for growth and colonization of maize root that are yet undiscovered. This highlights that abundant and strongly differentially abundant proteins with unknown functions should be prioritized as targets for biochemical and molecular functional characterization, as these may drive novel phenotypes. Conclusions In this study, we used differential metaproteomics to investigate how seven maize root-associated bacterial species alter their gene expression when colonizing and persisting on the maize root. While the differential metaproteomic approach used in this study has uncovered many microbial functions related to growth in the maize rhizosphere, we recognize that there are limitations to our approach. Firstly, we studied plant-microbe interactions with mono-colonization experiments; in nature, microbe-plant interactions occur in complex environments with many other organisms at play, and it has become increasingly clear that microbe-microbe interactions can have significant impacts on bacterial gene expression 12 , 73 , 99 , 100 . Based on this, we expect that microbe-microbe interactions within the plant context will have strong impacts on microbial in planta gene expression, and such gene expression responses are not captured in our study. Secondly, while we carefully curated our protein annotations by using multiple databases and tools, we recognize that homology-based protein annotations are not always correct 101 , 102 . We successfully substantiated several functions that were differentially abundant by using in vitro assays, proteomics, and knockout mutants. While the task of confirming all the functions that we found to change in planta is much too large for a single study, our results indicate that proteomic changes are a good indicator of changes in microbial functions. Thirdly, we were unable to confidently annotate a sizable portion of the proteins we identified. Thus, it seems likely that many functions related to maize colonization and growth remain undetected in these species. Indeed, it has been suggested that 40-60% of genes cannot be confidently assigned functions 103 , which has prompted work in recent years to develop high-throughput computational and experimental methods to assign functions to unknown proteins 103 – 105 . Plant-associated bacteria are not exempt from this problem that faces all microbiologists, and must be included in future annotation efforts. Finally, we acknowledge that some observed differences in gene expression may be due to growth in a liquid medium versus a solid medium (maize root in a clay-sand mixture). However, we did design the minimal medium used in the in vitro condition to simulate the nutrient conditions of the root environment by using Murashige-Skoog as the base medium (this was also used to water the plants in the in planta treatments) and by using carbon sources readily found in maize root exudates 106 . Moreover, several of the responses we observed - such as those related to plant polysaccharide degradation, phosphate solubilization, and Type VI Secretion Systems - are consistent with known mechanisms of plant-microbe interactions, and were supported experimentally. Thus, while we cannot fully exclude environmental effects unrelated to the plant, we are confident that many of the observed proteomic shifts reflect responses to growth on the maize root. By investigating gene expression in single bacterial species grown in vitro and in planta , we were able to identify specific physiological and metabolic traits that enable colonization and growth in the maize root environment. These results provide several avenues for future research into microbe-host, microbe-microbe, and microbiota-host interactions. Our findings on the role of HRO T6SSs in rhizosphere growth raise further questions regarding the function of each T6SS and how they may interact with one another and with the plant host to increase colonization. Furthermore, given that T6SSs have frequently been associated with microbe-microbe interactions in various plant systems 72 , 73 , 107 , understanding whether and how the T6SSs in HRO play a role in microbe-microbe interactions in the maize root environment would provide insights into the dynamics of microbial interactions within microbiota in the plant host context. Along these lines, as mentioned above, it is important to remember that host-microbe interaction studies must be scaled up in complexity in order to draw meaningful conclusions about host-microbe interactions in situ . Given that each of these bacteria belong to the same SynCom, we suggest that the next step would be to repeat a similar experiment in which bacterial functions are compared between mono-inoculation and co-inoculation with the entire SynCom to begin understanding how the dynamics and interactions within microbiota may impact and/or alter microbe-host interactions. Ultimately, it will be essential to study such plant-microbe interactions in increasingly complex communities to better understand the dynamics of interactions between different members in situ . Methods Culturing and inoculation of seven bacterial species for in vitro and in planta characterization We used the isolates Stenotrophomonas maltophilia AA1 (SMA; DSM 114483), Brucella pituitosa AA2 (BPI; DSM 114565), Curtobacterium pusillum AA3 (CPU; DSM 114566), Enterobacter ludwigii AA4 (ELU; previously E. cloacae ; DSM 114484), Chryseobacterium indologenes AA5 (CIN; DSM 114485), Herbaspirillum robiniae AA6 (HRO; DSM 114508), and Pseudomonas putida AA7 (PPU; DSM 114486) published by Niu et al. 24 . We cultured all isolates as described by Salvato et al. 27 . Briefly, we streaked bacterial species from glycerol stocks on selective 0.1x tryptic soy agar (TSA) plates and incubated at 30°C for 48 hours as described by Niu et al . 35 . An individual colony of each species was inoculated into 5 mL of tryptic soy broth (TSB) and shaken at 30°C for 8 hours. 1 mL of the culture was inoculated into 100 mL of TSB and grown in an incubated shaker at 180 rpm at 30°C overnight. 40 mL of each overnight culture was pelleted by centrifugation at 8000 xg for 8 minutes, re-suspended in 30 mL of phosphate-buffered saline (PBS, VWR International), and pelleted again. The final pellets were re-suspended in 30 mL of PBS buffer. For the in vitro characterization portion of the experiment, we inoculated 1 mL of each washed and re-suspended overnight culture into 50 mL of a minimal medium that was developed specifically for this experiment. We designed this minimal medium to consist of a base of 0.5x Murashige-Skoog (MS; Research Products International) augmented with glucose, malate, amino acids, and vitamins in order to grow the seven species in an environment as similar as possible to the in planta environment (in which 0.5x Murashige-Skoog was used to inoculate the maize seeds; see below) (SI Materials and Methods; Tables S2 and S3). We inoculated four replicate cultures (n = 4) for each species. We incubated cultures at 30°C in a shaker at 180 rpm, and OD600 was measured immediately after inoculation and after 2, 3, 4, 5, and 6 hours. When the cultures were at approximately mid-log phase, 2 mL of each culture was centrifuged at 8000 xg for 8 minutes, and each pellet was frozen at -80°C until protein extraction. For the in planta characterization portion of the experiment, we diluted the remainder of each washed and re-suspended overnight culture from above to 10 7 cells/mL, which was determined by OD600 using an OD-to-CFU standard curve, as described in Salvato et al. 27 . 10 mL of the diluted cell suspension was added to 1 L of 0.5x Murashige-Skoog (final concentration of 10 5 cells/mL). We added Zea mays cv. Sugar Bun seeds (untreated; Johnny’s Selected Seeds) to a beaker with dH 2 O, removing any kernels that floated to the top. For seed sterilization, we followed a protocol described in Parnell et al. and Wagner et al. 23 , 36 Briefly, we immersed seeds in 70% ethanol for 3 minutes, followed by 2% sodium hypochlorite for 3 minutes. Seeds were then rinsed five times with sterile dH 2 O. 150 µL was taken from the last rinse and spread onto 0.1x TSA plates, which were incubated for 24 hours at 30°C to check for sterility. Sterile seeds were deposited individually in a sterile WhirlPak bag (Nasco; SKU B01450) containing 50 mL of a sterile 1:1 clay (Pro’s Choice Rapid Dry, OIL-DRI)/sand (Multi-Purpose sand, Sakrete) mixture. An additional 50 mL of sterile 1:1 clay sand mixture were added on top of the seed. Seeds were watered with 65 mL (6.5 x 10 6 cells) of the cell suspensions prepared above. Bags were sealed with AeraSeal (Millipore Sigma), and placed in a growth chamber at constant 25°C, with a cycle of 16 hours of fluorescent light and 8 hours of darkness for 14 days. For HRO, SMA, PPU, CIN, and CPU, we inoculated 6 bags, each containing one seed, to account for any seeds that did not grow or germinate, with the goal of obtaining 4-5 biological replicates. For BPI and ELU, we were not able to extract a sufficient amount of bacterial protein from a single plant, so we pooled two plants for each replicate. We therefore planted 20 bags, again to account for any seeds that did not grow or germinate, with the goal of having 10 plants that could be pooled for 5 replicates. Collection of bacterial cells from maize roots We harvested bacteria from maize roots using a method based on the protocol described in Salvato et al. 27 . After 14 days, plants were gently pulled from the WhirlPak bags. Roots were gently rinsed with dH 2 O to remove adhering clay/sand and the primary roots were cut from plants, weighed, and cut roughly into 1 cm pieces. Root fragments were placed in 1 mL sterile PBS with six sterile 3 mm glass beads, then vortexed 3 times for 1 minute, with 10 seconds dwell time between each vortex. The resulting slurry was transferred to new centrifuge tubes and centrifuged at 15,000 xg for 7 minutes. Supernatant was removed and pellets were frozen at -80°C until protein extraction. Protein extraction and peptide preparation Bacterial pellets were removed from -80°C and thawed at room temperature. SDT lysis buffer (4% [wt/vol] SDS, 100 mM Tris-HCl, pH 7.6, 0.1 M DTT) was added to the pellets at a ∼10:1 ratio (e.g. 500 µL SDT for a 50 µL pellet). Suspensions were heated for 5-10 minutes at 95°C in a heat block. The Gram-positive species, CPU, was sonicated prior to heating in SDT using a Qsonica Q700 with three cycles of 30 seconds at 10% amplitude on and 1 minute off in order to disrupt the cell wall. Samples were then centrifuged for 5 minutes at 21,000 xg to remove cell debris. We prepared peptides from the resulting protein extracts for CIN, CPU, HRO, SMA, and PPU using a filter-aided sample preparation (FASP) protocol adapted from Wisniewski et al . 37 , 38 Briefly, we mixed 60 µL of the protein extracts with 400 µL of UA solution (8 M urea in 0.1 M Tris/HCl, pH 8.5), loaded the samples onto 10 kDa MWCO centrifugal filters (VWR International), and centrifuged at 14,000 xg for 20 minutes. This step was repeated once. An additional 200 µL UA was added to the filter and centrifuged at 14,000 xg for 20 minutes. We then added 100 µL of IAA (0.05 M iodoacetamide in UA solution) and incubated the samples at room temperature for 20 minutes. Filter units were centrifuged at 14,000 xg for 30 minutes. We added 100 µL of UA to the filter units and centrifuged at 14,000 xg for 20 minutes. This step was repeated twice. We added 100 µL ABC (50 mM ammonium bicarbonate) to the filter units and centrifuged at 14,000 xg for 30 minutes. This step was repeated twice. Filters were transferred to new collection tubes. We tryptically digested the proteins by adding 0.5-1 µg trypsin in ABC and incubated at 37°C for 16 hours in a wet chamber. Filters were centrifuged at 14,000 xg for 20 minutes to elute peptides, then 50 µL of 0.5 M NaCl was added before centrifuging at 14,000 xg for 20 minutes. We had difficulty attaining sufficiently high quality chromatograms for ELU and BPI using the FASP protocol and thus used a suspension trapping (S-Trap) sample preparation protocol with a few modifications to prepare peptides from these species 39 . Briefly, 140 µL of cell lysate was reduced by adding 6.4 µL of 500 mM DTT and incubated at 95°C for 10 minutes. Samples were cooled to room temperature for 10 minutes. We alkylated samples by adding 12.7 µL of 500 mM IAA before incubating at room temperature in the dark for 30 minutes. Samples were then acidified with 16 µL 12% phosphoric acid. 1,050 µL (6 times the total volume of the sample) of 100 mM triethylammonium bicarbonate buffer (TEAB) in 90% methanol was added to the samples, and samples were loaded onto the S-Trap column in volumes of 600 µL. Loaded S-Trap columns were then centrifuged at 4,000 xg for 30 seconds and flow-through was discarded. Columns were washed three times with 400 µL of the TEAB/methanol solution and centrifuged at 4,000 xg for 30 seconds; flow-through was discarded. Tryptic digestion of proteins was performed by adding 0.8 µg trypsin in 50 mM TEAB in water to samples; samples were incubated for 16 hours at 37°C. Resulting peptides were eluted by first adding 80 µL of 50 mM TEAB in water, then 80 µL of 0.2% formic acid, then 80 µL of 0.2% formic acid in 50% acetonitrile, with a 1 minute centrifugation at 4000 xg between each step. We used the Pierce microBCA assay (Thermo Fisher Scientific) to determine peptide concentrations. LC-MS/MS HRO, SMA, PPU, CIN, and CPU samples were analyzed by one-dimensional LC-MS/MS as described by Mordant and Kleiner, and samples were blocked and randomized according to the method published by Oberg and Vitek 38 , 40 . “For each sample, [1200] ng of tryptic peptides were loaded with an UltiMate 3000 RSLCnano liquid chromatograph (Thermo Fisher Scientific) in loading solvent A (2% acetonitrile, 0.05% trifluoroacetic acid) onto a 5-mm, 30-μm-inner diameter C18 Acclaim PepMap100 precolumn and desalted (Thermo Fisher Scientific). Peptides were then separated on a 75-cm × 75-μm analytical EASY-Spray column packed with PepMap RSLC C18, 2-μm material (Thermo Fisher Scientific) heated to 60°C via the integrated column heater at a flow rate of 300 nL min −1 using a 140 min gradient going from 95% buffer A (0.1% formic acid) to 31% buffer B (0.1% formic acid, 80% acetonitrile) in 102 min, then to 50% B in 18 min, to 99% B in 1 min, and ending with 99% B. Carryover was reduced by wash runs (injection of 20 μL acetonitrile with 99% eluent buffer B) between samples. “The analytical column was connected to a Q Exactive HF hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) via an EASY-Spray source. Eluting peptides were ionized via electrospray ionization (ESI). MS1 spectra were acquired by performing a full MS scan at a resolution of 60,000 on a 380 to 1,600 m/z window. MS2 spectra were acquired using a data-dependent approach by selecting for fragmentation the 15 most abundant ions from the precursor MS1 spectra. A normalized collision energy of 25 was applied in the high cell density (HCD) cell to generate the peptide fragments for MS2 spectra. Other settings of the data-dependent acquisition included a maximum injection time of 100 ms, a dynamic exclusion of 25 s, and exclusion of ions of +1 charge state from fragmentation. About 60,000 MS/MS spectra were acquired per sample.” 38 ELU and BPI samples were analyzed using a similar method to that above, but with a few modifications. For each sample, 1000 ng of tryptic peptides were loaded using the method and liquid chromatograph described above. The analytical column was connected via an EASY-Spray source to an Exploris 480 hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific). Peptides were separated on the analytical column using the same 140 min gradient as above. MS1 spectra were obtained with a full MS scan at a resolution of 60,000 on a 380 to 1,600 m/z window. MS2 spectra were generated by choosing the 15 most abundant peptides from the precursor MS1 spectra for fragmentation. Fragmentation was performed in the ion routing multipole with a normalized collision energy of 27%. For MS2 we used a maximum injection time of 50 ms, a dynamic exclusion of 25 s, and exclusion of ions of +1 charge state from fragmentation. Roughly 100,000 MS/MS spectra were acquired per sample. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE 41 partner repository with the dataset identifier PXD064252. Protein databases We constructed protein sequence databases for protein identification by combining the protein sequences from each bacterial species with the Zea mays protein sequences (we used protein sequences from cv. B73, because cv. Sugar Bun has not yet been sequenced), and the cRAP database containing common laboratory contaminants ( https://www.thegpm.org/crap/index.html ). Bacterial species protein sequences were downloaded from NCBI ( https://www.ncbi.nlm.nih.gov/bioproject?term=PRJNA357031 ) and Z. mays protein sequences were downloaded from UniProt ( https://www.uniprot.org/proteomes/UP000007305 ). Given the redundancy in the Z. mays genome, we clustered its protein sequences using CD-HIT with a 99% similarity threshold 42 . The databases are available in PRIDE (PXD064252). Protein identification and data processing We searched raw MS spectra against the databases described above using the method described in Blakeley-Ruiz et al. 43 . Briefly, we used the run calibration, SEQUEST HT and percolator nodes in Proteome Discoverer 2.3 (Thermo Fisher Scientific). We used the following search settings: trypsin (full), 2 missed cleavages, 10 ppm precursor mass tolerance, 0.1 Da fragment mass tolerance. We included the following dynamic modifications: oxidation on M (+15.995 Da), deamidation on N, Q, R (0.984 Da), and acetyl on the protein N terminus (+42.011 Da). We also included the static modification carbamidomethyl on C (+57.021 Da). We filtered for proteins that were identified with a false discovery rate (FDR) less than 5% and were classified as a Master Protein by Proteome Discoverer. We additionally filtered for proteins that had at least one peptide-spectrum match (PSM) in at least 75% of replicates in at least one condition. We additionally filtered out all Z. mays and cRAP proteins, as we were only interested in bacterial proteins for this study. For imputation of missing values in the dataset, we added 1 to all values. We used centered log-ratio transformation prior to statistical analysis. We used the Student’s T-test corrected with the Benjamini-Hochberg false discovery rate (q < 0.1) for multiple hypothesis testing to determine whether protein abundances were significantly different between the in planta and in vitro conditions. Protein annotation Given the general lack of reliability of the functional annotations of proteins in the databases, we used a multipronged approach to assign functional annotations to the proteins that showed differential abundances between the two conditions 44 . We first used Mantis to automatically assign functions to all differentially abundant proteins 45 . We then manually compared annotations to those generated by NCBI PGAP that were associated with the protein sequences downloaded from NCBI. We additionally used SwissProt, DBCan3, SecRet6, T9GPred, and TCDB to fill in gaps and/or to confirm ambiguous annotations 46 – 50 . The dataset was too large to manually curate all annotations, but we were able to use this method to assign functions to ∼72% (3,997 proteins) of the differentially abundant proteins. It is important to note that included in the broad categories is a category called “Unknown Function”. This category contained proteins for which we attempted to assign a function but were unable to find a confident match. We then assigned broad and detailed functional categories to the manually annotated, differentially abundant proteins based on the KEGG and MetaCyc pathway databases 51 , 52 . In total, we assigned 28 broad and 134 detailed functional categories (Supplementary Data 1). Protein annotations and our ontologies can be found in Supplementary Data 2. Hemicellulose degradation assay To determine whether HRO and CPU are capable of degrading hemicellulose in vitro , we conducted a hemicellulose degradation assay. These species were chosen for the assay due to the observation that hemicellulose degradation proteins were differentially abundant in planta . Recipes for the growth media used in this assay can be found in SI Materials and Methods. CPU and HRO were cultured and overnight cultures were washed as described above. 100 µL of washed cells were inoculated in 5 mL of each liquid media and placed in a shaker at 30°C at 180 rpm. OD600 was measured at 8 hours and 24 hours. Cultures were pelleted after 24 hours and pellets were frozen at - 80°C before proteins were extracted and peptides prepared using S-Trap. TssF Knockout mutant generation and growth curves To generate the tssF mutants, we constructed two integrative vectors using the pMRMTK-clo3’ plasmid backbone (Addgene plasmid # 203924; http://n2t.net/addgene:203924 ; RRID:Addgene_203924) generated in a previous study. 28 The tssF1 integrative vector (pJC110) was assembled from the backbone, a chloramphenicol resistance ( cm r ) cassette, and 1 kb genomic regions flanking the tssF1 site in Herbaspirillum robiniae AA6 (HRO). The tssF2 integrative vector (pJC134) was similarly constructed using a kanamycin resistance ( kan r ) cassette and the 1 kb tssF2 -flanking regions. DNA fragments were amplified by PCR using the appropriate primers (Table S1), followed by DpnI digestion, gel extraction and purification. Gibson assembly for each integrative vector was performed using the NEBuilder® HiFi DNA Assembly Master Mix (Cat #: E2621L) following the protocol for a 4-part assembly. Initial transformations were performed in NEB® 5-alpha Competent Escherichia coli (High Efficiency) (Catalog #: C2987H), and constructs were verified by Plasmidsaurus using Oxford Nanopore Technology with custom analysis and annotation. To generate the single knockout mutants, electrocompetent HRO cells were prepared (as previously described in Van Schaik et al. ) and 50 µL of the cell suspension was transformed with 1192 ng of pJC110 (resulting in a Δ tssF1::cm r knockout mutant) or 724 ng of pJC134 (resulting in a Δ tssF2 :: kan r knockout mutant). To generate the double knockout mutant, HRO Δ tssF1::cm r was transformed with484 ng of pJC134, resulting in a Δ tssF1::cm r , Δ tssF2 :: kan r mutant 28 . Transformants were plated on the appropriate selective media (LB + Cm or LB + Kan). As pJC110 contained an AmpR cassette, the Δ tssF1::cm r transformants were plated on LB + Amp to counterselect against ampicillin resistance. Integration of antibiotic resistance cassettes to replace tssF1 and tssF2 was confirmed by PCR and amplicon sequencing. To determine whether the mutants showed any general loss in fitness compared to the wildtype, we conducted growth curves of each mutant and wildtype HRO over 24 hours. Cells were plated from frozen stock onto full strength TSA containing the appropriate antibiotics for each mutant. Three colonies were chosen as biological replicates and grown in TSB containing the appropriate antibiotics for each mutant overnight in a 30°C shaking incubator at 250 rpm. Cell cultures were diluted to an OD600 of 0.01, and the 3 biological replicates for each condition were aliquoted into a 96-well plate with TSB along with an uninoculated control. Absorbance at 600 nm was measured over a 24-hour period using the Tecan Sunrise microplate reader with continuous shaking (250 rpm) at 30°C. Growth curve graphs were generated in GraphPad Prism 10. Plant colonization experiment with tssF knockout mutants Seeds for the plant colonization experiment were pre-germinated 2 days before experiment set up. We sterilized and rinsed Zea mays cv. Sugar Bun seeds (untreated; Johnny’s Selected Seeds) using the method described above for in planta characterization. We placed strips of autoclaved germination paper (Fisher Scientific Cat. No. NC1466201) into the bottoms of sterile 24-well plate wells, then placed a sterilized seed into each well. We then added 200 µL sterile dH 2 O to each seed and covered the plates. Plates were incubated at 30°C in the dark for 48 hours. We inoculated the wildtype HRO strain and its mutants on their respective selective plates (SI Materials and Methods) and incubated them at 30°C for 48 hours. An individual colony of each species was inoculated into 5 mL of TSB with 50 µg/mL kanamycin (Fisher Scientific CAS No. 25389-94-0) and/or chloramphenicol (Millipore Sigma CAS No. 56-76-7) for the knockout mutants and shaken at 180 rpm at 30°C for 8 hours. 500 µL of the culture was inoculated into 50 mL of tryptic soy broth (with 50 µg/mL kanamycin and/or chloramphenicol for the knockout mutants) and grown in an incubated shaker at 180 rpm at 30°C overnight. Cells were collected, washed, and diluted in the same manner as described in the culturing and inoculation section above. The seedlings that were germinated in 24-well plates were planted and inoculated in sterile WhirlPak bags following the protocol described above. Plants were placed in a growth chamber on 12 hour cycles of fluorescent light and dark at 27°C and 23°C, respectively, for 14 days. 10 bags, each containing one seed, were inoculated for each of the conditions. Roots were collected, rinsed, and cut following the method described above. 0.3 g of root fragments were placed into 1 mL sterile PBS with six sterile 3 mm glass beads, then vortexed 3 times for 1 minute, with 10 seconds dwell time between each vortex. Following the protocol outlined by Niu and Kolter, 20 µL of the slurry was then serially diluted in a 96-well plate for 10 -1 to 10 -8 dilutions 35 . 10 µL of each dilution was then spotted on a selective plate using a multichannel pipette, and plates were tilted to spread the drops. Plates were incubated at 30°C for 36 hours, before colony forming units were counted. Phosphate solubilization assay We streaked bacterial species from glycerol stocks on their respective selective agar plates and incubated the plates at 30°C for three days. A loopful of bacterial growth was streaked in a single line on Pikovskaya agar plates (HiMedia) and the plates were incubated at 27°C for five days. We then measured the zone of clearance from the edge of the streak to the edge of the cleared zone. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The mass spectrometry proteomics data and the databases used for protein identification have been deposited to the ProteomeXchange Consortium via the PRIDE 41 partner repository with the dataset identifier PXD064252. [Reviewer access: Token: ZaIwMhjZOFR9 ; Username: reviewer_pxd064252{at}ebi.ac.uk ; Password: fmeqkkPGIpA7]. The seven bacterial species used in this study are available from DSMZ with the accession numbers 114483, 114565, 114566, 114484, 114485, 114508, 114486. Competing interests The authors declare that they have no competing interests. Funding This work was funded by the USDA National Institute of Food and Agriculture under awards 2021-67013-34537 (AEB and MK) and 2022-67013-36672 (MK and MRW), the Novo Nordisk Foundation InROOT project (NNF19SA0059362) (MK), and the National Science Foundation (NC, MK, and MRW) (IOS-2120593 and IOS-2421771). Authors’ contributions A.G.: Experimental design, data collection, data processing, data analysis and interpretation, writing the manuscript J.C.: T6SS mutant design and construction, T6SS mutant growth curve N.C.: T6SS mutant design G.P.: Phosphate solubilization assay A.N.S.: Conceptual and experimental design of T6SS follow-up experiment, editing M.R.W.: Conceptualization of the study, editing A.E.B.: Experimental design, editing M.K.: Conceptualization of the study, experimental design, mentoring in data analysis and interpretation, writing, editing All authors read and approved the final manuscript. Acknowledgements We made all LC-MS/MS measurements in the Molecular Education, Technology, and Research Innovation Centre (METRIC) at NC State University. We thank the staff of the NC State University Phytotron for the use of their facilities. We thank Dr. Alfredo Blakeley-Ruiz for his guidance on protein annotation and all Kleiner lab members for discussions on methods, results, and data analysis. Funder Information Declared National Institute of Food and Agriculture , 2021-67013-34537 , 2022-67013-36672 Novo Nordisk Foundation , NNF19SA0059362 National Science Foundation , IOS-2120593 , IOS-2421771 References 1. ↵ Panke-Buisse , K. , Poole , A. C. , Goodrich , J. K. , Ley , R. E. & Kao-Kniffin , J . Selection on soil microbiomes reveals reproducible impacts on plant function . ISME J . 9 , 980 – 989 ( 2015 ). OpenUrl CrossRef PubMed 2. Poudel , M. et al. The Role of Plant-Associated Bacteria, Fungi, and Viruses in Drought Stress Mitigation . Front. Microbiol . 12 , ( 2021 ). 3. 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The Impact of Type VI Secretion System, Bacteriocins and Antibiotics on Bacterial Competition of Pectobacterium carotovorum subsp. brasiliense and the Regulation of Carbapenem Biosynthesis by Iron and the Ferric-Uptake Regulator . Front. Microbiol. 10 , ( 2019 ). View the discussion thread. Back to top Previous Next Posted June 02, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Differential metaproteomics of bacteria grown in vitro and in planta reveals functions used during growth on maize roots Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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